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tests/micropython/opt_level.py
sebi5361/micropython
181
12788851
<reponame>sebi5361/micropython import micropython as micropython # check we can get and set the level micropython.opt_level(0) print(micropython.opt_level()) micropython.opt_level(1) print(micropython.opt_level()) # check that the optimisation levels actually differ micropython.opt_level(0) exec('print(__debug__)') micropython.opt_level(1) exec('print(__debug__)') exec('assert 0')
2.453125
2
60145395-perspective-transform/perspective_transform.py
nathancy/stackoverflow
3
12788852
<gh_stars>1-10 from imutils.perspective import four_point_transform import cv2 import numpy # Load image, grayscale, Gaussian blur, Otsu's threshold image = cv2.imread("1.jpg") gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) blur = cv2.GaussianBlur(gray, (5,5), 0) thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1] # Find contours and sort for largest contour cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if len(cnts) == 2 else cnts[1] cnts = sorted(cnts, key=cv2.contourArea, reverse=True) displayCnt = None for c in cnts: # Perform contour approximation peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.02 * peri, True) if len(approx) == 4: displayCnt = approx break # Obtain birds' eye view of image warped = four_point_transform(image, displayCnt.reshape(4, 2)) cv2.imshow("thresh", thresh) cv2.imshow("warped", warped) cv2.imshow("image", image) cv2.imwrite("thresh.png", thresh) cv2.imwrite("warped.png", warped) cv2.imwrite("image.png", image) cv2.waitKey()
2.515625
3
count_sort.py
gmolnar1/Find-My-Game
3
12788853
import math import os import sys import pprint def count_sort_func(data,maxdata,index): maxdata +=1 count_list = [0]*(maxdata) count_dict = data for n in data: count_list[n[index]] +=1 i = 0 count = 0 for n in range(len(count_list)): print(n) while(count_list[n]>0): for vals in count_dict: j = 0 if vals[index]==n: #pprint.pprint(vals) pprint.pprint(count_dict) print("---------------------------------") data[i] = vals count+=1 print(count) count_dict.pop(j) break j+=1 #data[i] = n i+=1 print("Hi") count_list[n] -= 1 #pprint(list(data)) return data
3.40625
3
nesta/core/routines/datasets/crunchbase/crunchbase_root_task.py
anniyanvr/nesta
13
12788854
''' Root task (Crunchbase) ======================== Luigi routine to collect all data from the Crunchbase data dump and load it to MySQL. ''' import luigi import datetime import logging from nesta.core.routines.datasets.crunchbase.crunchbase_parent_id_collect_task import ParentIdCollectTask from nesta.core.routines.datasets.crunchbase.crunchbase_geocode_task import CBGeocodeBatchTask from nesta.core.luigihacks.misctools import find_filepath_from_pathstub as f3p from nesta.core.orms.crunchbase_orm import Base from nesta.core.orms.orm_utils import get_class_by_tablename class RootTask(luigi.WrapperTask): '''A dummy root task, which collects the database configurations and executes the central task. Args: date (datetime): Date used to label the outputs db_config_path (str): Path to the MySQL database configuration production (bool): Flag indicating whether running in testing mode (False, default), or production mode (True). ''' date = luigi.DateParameter(default=datetime.date.today()) production = luigi.BoolParameter(default=False) insert_batch_size = luigi.IntParameter(default=500) db_config_path = luigi.Parameter(default=f3p("mysqldb.config")) db_config_env = luigi.Parameter(default="MYSQLDB") def requires(self): '''Collects the database configurations and executes the central task.''' _routine_id = "{}-{}".format(self.date, self.production) logging.getLogger().setLevel(logging.INFO) yield ParentIdCollectTask(date=self.date, _routine_id=_routine_id, test=not self.production, insert_batch_size=self.insert_batch_size, db_config_path=self.db_config_path, db_config_env=self.db_config_env) geocode_kwargs = dict(date=self.date, _routine_id=_routine_id, test=not self.production, db_config_env="MYSQLDB", insert_batch_size=self.insert_batch_size, env_files=[f3p("nesta"), f3p("config/mysqldb.config"), f3p("config/crunchbase.config")], job_def="py37_amzn2", job_queue="HighPriority", region_name="eu-west-2", poll_time=10, memory=4096, max_live_jobs=2) for tablename in ['organizations', 'funding_rounds', 'investors', 'people', 'ipos']: _class = get_class_by_tablename(Base, f'crunchbase_{tablename}') yield CBGeocodeBatchTask(city_col=_class.city, country_col=_class.country, location_key_col=_class.location_id, job_name=f"Crunchbase-{tablename}-{_routine_id}", **geocode_kwargs)
2.265625
2
lake/attributes/control_signal_attr.py
StanfordAHA/Lake
11
12788855
<reponame>StanfordAHA/Lake import kratos as kts from enum import Enum class ControlSignalAttr(kts.Attribute): def __init__(self, is_control=False, ignore=False, doc_string=""): super().__init__() self.value = "control_signal" self.is_control = is_control self.ignore = ignore self.documentation = doc_string def set_documentation(self, new_doc): self.documentation = new_doc def get_documentation(self): return self.documentation def get_control(self): return self.is_control def get_ignore(self): return self.ignore
2.5
2
pandaf/000/mc.py
cpausmit/Kraken
0
12788856
import FWCore.ParameterSet.Config as cms import FWCore.ParameterSet.VarParsing as VarParsing import re import os process = cms.Process("PandaNtupler") cmssw_base = os.environ['CMSSW_BASE'] options = VarParsing.VarParsing ('analysis') options.register('isData', False, VarParsing.VarParsing.multiplicity.singleton, VarParsing.VarParsing.varType.bool, "True if running on Data, False if running on MC") options.register('isSignal', True, VarParsing.VarParsing.multiplicity.singleton, VarParsing.VarParsing.varType.bool, "True if running on MC signal samples") options.parseArguments() isData = options.isData process.load("FWCore.MessageService.MessageLogger_cfi") process.MessageLogger.cerr.FwkReport.reportEvery = 5000 process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) ) # ---- define the input file -------------------------------------------- process.source = cms.Source( "PoolSource", fileNames = cms.untracked.vstring('XX-LFN-XX') ) # ---- define the output file ------------------------------------------- process.TFileService = cms.Service( "TFileService", closeFileFast = cms.untracked.bool(True), fileName = cms.string("kraken-output-file-tmp_000.root"), ) ##----------------GLOBAL TAG --------------------------- # used by photon id and jets process.load("Configuration.Geometry.GeometryIdeal_cff") process.load('Configuration.StandardSequences.Services_cff') process.load("Configuration.StandardSequences.MagneticField_cff") process.load('Configuration.StandardSequences.FrontierConditions_GlobalTag_condDBv2_cff') if (isData): process.GlobalTag.globaltag = '80X_dataRun2_2016SeptRepro_v3' else: process.GlobalTag.globaltag = '80X_mcRun2_asymptotic_2016_TrancheIV_v6' ### LOAD DATABASE from CondCore.DBCommon.CondDBSetup_cfi import * ######## LUMI MASK if isData and False: import FWCore.PythonUtilities.LumiList as LumiList process.source.lumisToProcess = LumiList.LumiList(filename = '/afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions16/13TeV/ReReco/Final/Cert_271036-284044_13TeV_23Sep2016ReReco_Collisions16_JSON.txt').getVLuminosityBlockRange() print "Using local JSON" ### LOAD CONFIGURATION process.load('PandaProd.Filter.infoProducerSequence_cff') process.load('PandaProd.Filter.MonoXFilterSequence_cff') process.load('PandaProd.Ntupler.PandaProd_cfi') ### ##ISO process.load("RecoEgamma/PhotonIdentification/PhotonIDValueMapProducer_cfi") process.load("RecoEgamma/ElectronIdentification/ElectronIDValueMapProducer_cfi") process.PandaNtupler.isData = isData process.triggerFilterSequence = cms.Sequence() # let's turn this off for now if options.isSignal: process.PandaNtupler.nSystWeight = -1 #-----------------------JES/JER---------------------------------- from CondCore.DBCommon.CondDBSetup_cfi import * if isData: jeclabel = 'Summer16_23Sep2016AllV3_DATA' else: jeclabel = 'Summer16_23Sep2016V3_MC' process.jec = cms.ESSource("PoolDBESSource", DBParameters = cms.PSet(messageLevel = cms.untracked.int32(0)), timetype = cms.string('runnumber'), toGet = cms.VPSet( cms.PSet(record = cms.string('JetCorrectionsRecord'), tag = cms.string('JetCorrectorParametersCollection_'+jeclabel+'_AK4PFPuppi'), label = cms.untracked.string('AK4PFPuppi') ), cms.PSet(record = cms.string('JetCorrectionsRecord'), tag = cms.string('JetCorrectorParametersCollection_'+jeclabel+'_AK8PFPuppi'), label = cms.untracked.string('AK8PFPuppi') ), cms.PSet(record = cms.string('JetCorrectionsRecord'), tag = cms.string('JetCorrectorParametersCollection_'+jeclabel+'_AK4PFchs'), label = cms.untracked.string('AK4PFchs') ), cms.PSet(record = cms.string('JetCorrectionsRecord'), tag = cms.string('JetCorrectorParametersCollection_'+jeclabel+'_AK8PFchs'), label = cms.untracked.string('AK8PFchs') ), ), ) process.jec.connect = cms.string('sqlite:jec/%s.db'%jeclabel) process.es_prefer_jec = cms.ESPrefer('PoolDBESSource', 'jec') if isData: jerlabel = 'Spring16_25nsV6_DATA' else: jerlabel = 'Spring16_25nsV6_MC' process.jer = cms.ESSource("PoolDBESSource", DBParameters = cms.PSet(messageLevel = cms.untracked.int32(0)), toGet = cms.VPSet( cms.PSet(record = cms.string('JetResolutionRcd'), tag = cms.string('JR_%s_PtResolution_AK4PFchs'%jerlabel), label = cms.untracked.string('AK4PFchs_pt'), ), cms.PSet(record = cms.string('JetResolutionRcd'), tag = cms.string('JR_%s_PhiResolution_AK4PFchs'%jerlabel), label = cms.untracked.string('AK4PFchs_phi'), ), cms.PSet(record = cms.string('JetResolutionScaleFactorRcd'), tag = cms.string('JR_%s_SF_AK4PFchs'%jerlabel), label = cms.untracked.string('AK4PFchs'), ), cms.PSet(record = cms.string('JetResolutionRcd'), tag = cms.string('JR_%s_PtResolution_AK4PFPuppi'%jerlabel), label = cms.untracked.string('AK4PFPuppi_pt'), ), cms.PSet(record = cms.string('JetResolutionRcd'), tag = cms.string('JR_%s_PhiResolution_AK4PFPuppi'%jerlabel), label = cms.untracked.string('AK4PFPuppi_phi'), ), cms.PSet(record = cms.string('JetResolutionScaleFactorRcd'), tag = cms.string('JR_%s_SF_AK4PFPuppi'%jerlabel), label = cms.untracked.string('AK4PFPuppi'), ), ) ) process.jer.connect = cms.string('sqlite:jer/%s.db'%jerlabel) process.es_prefer_jer = cms.ESPrefer('PoolDBESSource', 'jer') #-----------------------ELECTRON ID------------------------------- from PandaProd.Ntupler.egammavid_cfi import * initEGammaVID(process,options) #### RECOMPUTE JEC From GT ### from PhysicsTools.PatAlgos.tools.jetTools import updateJetCollection jecLevels= ['L1FastJet', 'L2Relative', 'L3Absolute'] if options.isData: jecLevels.append('L2L3Residual') updateJetCollection( process, jetSource = process.PandaNtupler.chsAK4, labelName = 'UpdatedJEC', jetCorrections = ('AK4PFchs', cms.vstring(jecLevels), 'None') ) process.PandaNtupler.chsAK4=cms.InputTag('updatedPatJetsUpdatedJEC') # replace CHS with updated JEC-corrected process.jecSequence = cms.Sequence( process.patJetCorrFactorsUpdatedJEC* process.updatedPatJetsUpdatedJEC) ########### MET Filter ################ process.load('RecoMET.METFilters.BadPFMuonFilter_cfi') process.BadPFMuonFilter.muons = cms.InputTag("slimmedMuons") process.BadPFMuonFilter.PFCandidates = cms.InputTag("packedPFCandidates") process.BadPFMuonFilter.taggingMode = cms.bool(True) process.load('RecoMET.METFilters.BadChargedCandidateFilter_cfi') process.BadChargedCandidateFilter.muons = cms.InputTag("slimmedMuons") process.BadChargedCandidateFilter.PFCandidates = cms.InputTag("packedPFCandidates") process.BadChargedCandidateFilter.taggingMode = cms.bool(True) process.metfilterSequence = cms.Sequence(process.BadPFMuonFilter *process.BadChargedCandidateFilter) if not options.isData: process.PandaNtupler.metfilter = cms.InputTag('TriggerResults','','PAT') ############ RECOMPUTE PUPPI/MET ####################### from PhysicsTools.PatUtils.tools.runMETCorrectionsAndUncertainties import runMetCorAndUncFromMiniAOD runMetCorAndUncFromMiniAOD(process, ## PF MET isData=isData) process.PandaNtupler.pfmet = cms.InputTag('slimmedMETs','','PandaNtupler') process.MonoXFilter.met = cms.InputTag('slimmedMETs','','PandaNtupler') from PhysicsTools.PatAlgos.slimming.puppiForMET_cff import makePuppiesFromMiniAOD makePuppiesFromMiniAOD(process,True) process.puppi.useExistingWeights = False # I still don't trust miniaod... process.puppiNoLep.useExistingWeights = False runMetCorAndUncFromMiniAOD(process, ## Puppi MET isData=options.isData, metType="Puppi", pfCandColl=cms.InputTag("puppiForMET"), recoMetFromPFCs=True, jetFlavor="AK4PFPuppi", postfix="Puppi") process.puppiForMET.photonId = process.PandaNtupler.phoLooseIdMap process.PandaNtupler.puppimet = cms.InputTag('slimmedMETsPuppi','','PandaNtupler') process.MonoXFilter.puppimet = cms.InputTag('slimmedMETsPuppi','','PandaNtupler') ############ RUN CLUSTERING ########################## process.jetSequence = cms.Sequence() # btag and patify puppi AK4 jets from RecoJets.JetProducers.ak4GenJets_cfi import ak4GenJets from PhysicsTools.PatAlgos.tools.pfTools import * if not isData: process.packedGenParticlesForJetsNoNu = cms.EDFilter("CandPtrSelector", src = cms.InputTag("packedGenParticles"), cut = cms.string("abs(pdgId) != 12 && abs(pdgId) != 14 && abs(pdgId) != 16") ) process.ak4GenJetsNoNu = ak4GenJets.clone(src = 'packedGenParticlesForJetsNoNu') process.jetSequence += process.packedGenParticlesForJetsNoNu process.jetSequence += process.ak4GenJetsNoNu # btag and patify jets for access later addJetCollection( process, labelName = 'PFAK4Puppi', jetSource=cms.InputTag('ak4PFJetsPuppi'), # this is constructed in runMetCorAndUncFromMiniAOD algo='AK4', rParam=0.4, pfCandidates = cms.InputTag("puppiForMET"), pvSource = cms.InputTag('offlineSlimmedPrimaryVertices'), svSource = cms.InputTag('slimmedSecondaryVertices'), muSource = cms.InputTag('slimmedMuons'), elSource = cms.InputTag('slimmedElectrons'), btagInfos = ['pfImpactParameterTagInfos','pfInclusiveSecondaryVertexFinderTagInfos'], btagDiscriminators = ['pfCombinedInclusiveSecondaryVertexV2BJetTags'], genJetCollection = cms.InputTag('ak4GenJetsNoNu'), genParticles = cms.InputTag('prunedGenParticles'), getJetMCFlavour = False, # jet flavor disabled ) if not isData: process.jetSequence += process.patJetPartonMatchPFAK4Puppi process.jetSequence += process.patJetGenJetMatchPFAK4Puppi process.jetSequence += process.pfImpactParameterTagInfosPFAK4Puppi process.jetSequence += process.pfInclusiveSecondaryVertexFinderTagInfosPFAK4Puppi process.jetSequence += process.pfCombinedInclusiveSecondaryVertexV2BJetTagsPFAK4Puppi process.jetSequence += process.patJetsPFAK4Puppi ##################### FAT JETS ############################# from PandaProd.Ntupler.makeFatJets_cff import initFatJets, makeFatJets fatjetInitSequence = initFatJets(process,isData) process.jetSequence += fatjetInitSequence if process.PandaNtupler.doCHSAK8: ak8CHSSequence = makeFatJets(process, isData=isData, pfCandidates='pfCHS', algoLabel='AK', jetRadius=0.8) process.jetSequence += ak8CHSSequence if process.PandaNtupler.doPuppiAK8: ak8PuppiSequence = makeFatJets(process, isData=isData, pfCandidates='puppi', algoLabel='AK', jetRadius=0.8) process.jetSequence += ak8PuppiSequence if process.PandaNtupler.doCHSCA15: ca15CHSSequence = makeFatJets(process, isData=isData, pfCandidates='pfCHS', algoLabel='CA', jetRadius=1.5) process.jetSequence += ca15CHSSequence if process.PandaNtupler.doPuppiCA15: ca15PuppiSequence = makeFatJets(process, isData=isData, pfCandidates='puppi', algoLabel='CA', jetRadius=1.5) process.jetSequence += ca15PuppiSequence if not isData: process.ak4GenJetsYesNu = ak4GenJets.clone(src = 'packedGenParticles') process.jetSequence += process.ak4GenJetsYesNu ############################### process.p = cms.Path( process.infoProducerSequence * process.triggerFilterSequence * process.jecSequence * process.egmGsfElectronIDSequence * process.egmPhotonIDSequence * process.photonIDValueMapProducer * # iso map for photons process.electronIDValueMapProducer * # iso map for photons process.fullPatMetSequence * # pf MET process.puppiMETSequence * # builds all the puppi collections process.egmPhotonIDSequence * # baseline photon ID for puppi correction process.fullPatMetSequencePuppi * # puppi MET process.monoXFilterSequence * # filter process.jetSequence * # patify ak4puppi and do all fatjet stuff process.metfilterSequence * process.PandaNtupler )
1.726563
2
src/server/blueprints/security.py
1064CBread/1064Chat
2
12788857
from flask import Blueprint, Flask, render_template, request blueprint = Blueprint(__name__, __name__, url_prefix='/auth') @blueprint.route('/login', methods=['GET', 'POST']) def login(): if request.method != 'POST': return render_template("login_start.jinja") print(request.form) return 'You "logged" in. EMAIL: ' + request.form['email'] + '; PASS: ' + request.form['password'] @blueprint.route('/register', methods=['GET', 'POST']) def register(): if request.method != 'POST': return render_template("register_start.jinja") return 'You "register" an account. EMAIL: ' + request.form['email'] + '; PASS: ' + request.form['password'] def registerself(app: Flask, prefix=''): app.register_blueprint(blueprint, url_prefix=prefix + blueprint.url_prefix)
2.71875
3
ch04/ch04_04/ch04_04.py
z2x3c4v5bz/pybook_yehnan
0
12788858
if __name__ == '__main__': s = 'abcde' print(s[::-1]) print(s[-1::-1]) print(s[-1:0:-1]) # failed ''' edcba edcba edcb '''
2.640625
3
pvlibs/data_import/__init__.py
bfw930/pvlibs
0
12788859
<filename>pvlibs/data_import/__init__.py<gh_stars>0 ''' Imports ''' # core import protocols from .core import parse_data_file
1.1875
1
utils.py
ondrejba/monte_carlo
14
12788860
def update_mean(value, mean, count): """ Update value of a streaming mean. :param value: New value. :param mean: Mean value. :param count: Number of values averaged. :return: """ return (value - mean) / (count + 1)
3.265625
3
src/6/reading_nested_and_variable_sized_binary_structures/writepolys.py
tuanavu/python-gitbook
14
12788861
import struct import itertools polys = [ [ (1.0, 2.5), (3.5, 4.0), (2.5, 1.5) ], [ (7.0, 1.2), (5.1, 3.0), (0.5, 7.5), (0.8, 9.0) ], [ (3.4, 6.3), (1.2, 0.5), (4.6, 9.2) ], ] def write_polys(filename, polys): # Determine bounding box flattened = list(itertools.chain(*polys)) min_x = min(x for x, y in flattened) max_x = max(x for x, y in flattened) min_y = min(y for x, y in flattened) max_y = max(y for x, y in flattened) with open(filename, 'wb') as f: f.write(struct.pack('<iddddi', 0x1234, min_x, min_y, max_x, max_y, len(polys))) for poly in polys: size = len(poly) * struct.calcsize('<dd') f.write(struct.pack('<i', size+4)) for pt in poly: f.write(struct.pack('<dd', *pt)) # Call it with our polygon data write_polys('polys.bin', polys)
2.484375
2
src/TRAIN_MODELS/CLASSIFICATION/classificationModel_functions.py
CaT-zTools/Deep-CaT-z-software
0
12788862
<gh_stars>0 # -*- coding: utf-8 -*- """ @author: DeepCaT_Z """ #%% ############################################ ######### IMPORTS: DO NOT TOUCH ############## ################################################ import torch from torch import nn from sklearn.metrics import f1_score from torch import optim from time import time import numpy as np from skimage.io import imsave, imread from skimage.transform import resize from skimage.morphology import erosion, disk from torch.utils.data import Dataset from torchvision import transforms from config_Classification_DeepCaT_Z import * import os #%% ############################### UTILS FOR DATASET CONSTRUCTION ############ # Data augmentation operations class BrightnessTransform: def __call__(self, sample): brightness = np.random.rand()*0.3-0.15 if 'X' in sample: sample['X'] = np.clip(sample['X'] + brightness, 0, 1) if 'F' in sample: sample['F'][0] = np.clip(sample['F'][0] + brightness, 0, 1) return sample class FlipTransform: def __call__(self, sample): if np.random.rand() > 0.5: if 'X' in sample: sample['X'] = np.flip(sample['X'], 2) if 'S' in sample: sample['S'] = np.flip(sample['S'], 1) return sample class Rot90Transform: def __call__(self, sample): k = np.random.randint(0, 4) if 'X' in sample: sample['X'] = np.rot90(sample['X'], k, (1, 2)) if 'S' in sample: sample['S'] = np.rot90(sample['S'], k, (0, 1)) return sample class CropTransform: def __call__(self, sample): dx = np.random.randint(0, IMG_LARGE_SIZE-IMG_SIZE) dy = np.random.randint(0, IMG_LARGE_SIZE-IMG_SIZE) if 'X' in sample: sample['X'] = sample['X'][:, dy:dy+IMG_SIZE, dx:dx+IMG_SIZE] if 'S' in sample: sample['S'] = sample['S'][dy:dy+IMG_SIZE, dx:dx+IMG_SIZE] return sample class ShiftTransform: def __call__(self, sample): dx = np.random.randint(0, IMG_LARGE_SIZE-IMG_SIZE) dy = np.random.randint(0, IMG_LARGE_SIZE-IMG_SIZE) if dx and dy: xdir = np.random.randint(0, 2) ydir = np.random.randint(0, 2) if 'X' in sample: X = np.zeros_like(sample['X']) if xdir == 0 and ydir == 0: X[:, dy:, dx:] = sample['X'][:, :-dy, :-dx] elif xdir == 1 and ydir == 0: X[:, dy:, :-dx] = sample['X'][:, :-dy, dx:] elif xdir == 0 and ydir == 1: X[:, :-dy, dx:] = sample['X'][:, dy:, :-dx] else: X[:, :-dy, :-dx] = sample['X'][:, dy:, dx:] sample['X'] = X if 'S' in sample: S = np.zeros_like(sample['S']) if xdir == 0 and ydir == 0: S[dy:, dx:] = sample['S'][:, :-dy, :-dx] elif xdir == 1 and ydir == 0: S[dy:, :-dx] = sample['S'][:, :-dy, dx:] elif xdir == 0 and ydir == 1: S[:-dy, dx:] = sample['S'][:, dy:, :-dx] else: S[:-dy, :-dx] = sample['S'][:, dy:, dx:] sample['S'] = S return sample # Functions for loading the dataset + apply augmentation operations while training class MiceDataset(Dataset): def __init__(self, dirname, fold, steps, with_augment, with_sampling): self.dirname = dirname assert os.path.exists(f'{self.dirname}_{IMG_SIZE}'), f'{self.dirname}_{IMG_SIZE} does not exist' assert os.path.exists(f'{self.dirname}_{IMG_LARGE_SIZE}'), f'{self.dirname}_{IMG_LARGE_SIZE} does not exist' self.fold = fold self.transform_big = None self.transform_small = None self.steps = steps if with_augment: self.transform_big = transforms.Compose([ CropTransform(), BrightnessTransform(), Rot90Transform(), FlipTransform(), ]) self.transform_small = transforms.Compose([ BrightnessTransform(), Rot90Transform(), FlipTransform(), ]) files = sorted(os.listdir(f'{dirname}_{IMG_SIZE}/{fold}/frames')) # structure files in contiguous video sequences videos = [] for fname in files: if len(videos) != 0 and int(fname.split('_')[1]) == int(videos[-1][-1].split('_')[1])+2: videos[-1].append(fname[:-4]) else: videos.append([fname[:-4]]) videos = [[video[i+step] for step in steps] for video in videos for i in range(np.max(np.abs(self.steps)), len(video))] # oversample based on activities if with_sampling: A = np.array([np.loadtxt(f'{dirname}_{IMG_SIZE}/{fold}/labels/{video[-1]}.txt', np.int32) for video in videos]) - 1 # repeat all classes until they equalize the majority class reps = np.round(np.max(np.bincount(A)) / np.bincount(A)).astype(int) for video, a in zip(videos.copy(), A): videos += [video] * (reps[a]-1) self.videos = videos def __len__(self): return len(self.videos) def __getitem__(self, i): # sometimes we use the smaller or the bigger images transform = self.transform_small imgsize = IMG_SIZE if self.transform_big: if np.random.rand() < 0.5: imgsize = IMG_LARGE_SIZE transform = self.transform_big video = self.videos[i] sample = {} sample['X'] = np.array([(imread(f'{self.dirname}_{imgsize}/{self.fold}/frames/{fname}.png', True)[..., np.newaxis]/255).astype(np.float32) for fname in video]) fname = video[-1] sample['A'] = np.array(int(open(f'{self.dirname}_{imgsize}/{self.fold}/labels/{fname}.txt').read()) - 1, np.int64) if transform: sample = transform(sample) # swap color axis: numpy (HWC), but torch (CHW) sample['X'] = sample['X'].transpose((0, 3, 1, 2)) # numpy array -> torch tensor for k in sample: sample[k] = torch.from_numpy(np.ascontiguousarray(sample[k])) return sample #%% ############################### UTILS FOR NETWORKS ######################## class Reshape(nn.Module): def __init__(self, *args): super().__init__() self.shape = args def forward(self, x): return (x.contiguous()).view(self.shape) class SliceRNN(nn.Module): def forward(self, x): output, hn = x return output[:, -1] #%% ############################### CLASSIFICATION NETWORK #################### class Model_X_A(nn.Module): def __init__(self, timesteps): super().__init__() # 128 => 64, 8, 8 (4096) # 256 => 16, 16, 16 (4096) # 512 => 4, 32, 32 (4096) last_kernel = {32: 1024, 64: 256, 128: 64, 256: 16, 512: 4} self.net = nn.Sequential( Reshape(-1, CHANNELS_SIZE, IMG_SIZE, IMG_SIZE), nn.Conv2d(CHANNELS_SIZE, 64, 3, 2, padding=1), nn.ReLU(), nn.Conv2d(64, 64, 3, 2, padding=1), nn.ReLU(), nn.Conv2d(64, 64, 3, 2, padding=1), nn.ReLU(), nn.Conv2d(64, last_kernel[IMG_SIZE], 3, 2, padding=1), nn.ReLU(), Reshape(-1, timesteps, 64*8*8), nn.Dropout(), nn.RNN(64*8*8, 128, batch_first= True), SliceRNN(), nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, NCLASSES), ) def forward(self, x): return (self.net(x),) #%% ############################### LOSSES DEFINITION ######################## def ce(preds, labels): return nn.CrossEntropyLoss()(preds, labels[:, 0]) def mse(preds, labels): return torch.mean((preds - labels)**2) def acc(preds, labels): return torch.sum(preds.argmax(1) == labels[:, 0]) / len(labels) def bacc(preds, labels): labels = labels[:, 0] preds = preds.argmax(1) return torch.mean(torch.tensor([ torch.sum((preds == k) & (labels == k)) / torch.sum(labels == k) for k in torch.unique(labels)])) def mae(preds, labels): return torch.mean(torch.abs(preds - labels)) def dice(preds, labels): smooth = 1. num = 2 * (preds * labels).sum() den = preds.sum() + labels.sum() return 1 - (((num+smooth) / (den+smooth)) / len(labels)) def bce_dice(preds, labels): return nn.BCELoss()(preds, labels) + dice(preds, labels) def f1_k(k): def f(preds, labels): labels = (labels[:, 0] == k).numpy().astype(int) preds = (preds.argmax(1) == k).numpy().astype(int) return f1_score(labels, preds) f.__name__ = f'f1-{k}' return f #%% ######################## TRAINING METHODS ################################ def predict(model, device, ds): Y = [] preds = [] with torch.no_grad(): model.eval() for batch in ds: X = [batch['X'].to(device)] Y.append([batch['A']]) ps = model(*X) preds.append([p.cpu() for p in ps]) return [torch.cat([o[0] for o in preds])], [torch.cat([o[0] for o in Y])] def compute_metrics(fold, predictions, labels, metrics): ret = {} with torch.no_grad(): for m in metrics['A']: ret[f'{fold}_A_{m.__name__}'] = m(predictions[0], labels[0]) return ret def train(model, device, epochs, tr, ts, metrics, losses, losses_weights, lr): history = [] optimizer = optim.Adam(model.parameters(), lr) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience = 5) for epoch in range(epochs): print(f'Epoch {epoch+1}/{epochs}') tic = time() # Train train_loss, valid_loss = 0.0, 0.0 model.train() train_labels = [] train_preds = [] for bi, batch in enumerate(tr): X = [batch['X'].to(device)] Y = [batch['A'].to(device)] if bi == 0: X[0].requires_grad = True optimizer.zero_grad() preds = model(*X) loss = 0 loss += losses['A'](preds[0], Y[0]) * losses_weights['A'] train_loss += loss / len(tr) loss.backward() optimizer.step() train_labels.append([y.cpu() for y in Y]) train_preds.append([p.cpu() for p in preds]) # Calculate and save gradients to folder "grads" # if bi == 0: # save gradients relative to X # x = X[0] # for xi in range(x.shape[0]): # xx = x[xi].detach().cpu().numpy() # xx = np.concatenate(list(xx), 2) # concat horizontally # gg = x.grad[xi].cpu().numpy() # gg = np.abs(gg) # normalize gradients # gg = (1 - (gg-gg.min()) / (gg.max()-gg.min())) # # gg = dilation(gg, disk(2)) # gg = np.concatenate(list(gg), 2) # concat horizontally # gg = erosion(gg[0, :, :], disk(1)) # gg = np.expand_dims(gg, axis=0) # xx = np.transpose(xx, (1, 2, 0)) * 0.4 # gg = np.transpose(gg, (1, 2, 0)) * 0.6 # fig = np.concatenate((xx+gg, xx+0.6, xx+gg), 2) # fig = (fig*255).astype(np.uint8) # imsave(f'grads\\grads-epoch{epoch}-image{xi}.png', fig[:, :, :3]) train_labels = [torch.cat([o[0] for o in train_labels])] train_preds = [torch.cat([o[0] for o in train_preds])] # Evaluate avg_metrics = dict( train_loss=train_loss.cpu().detach(), **compute_metrics('train', train_preds, train_labels, metrics), **compute_metrics('test', *predict(model, device, ts), metrics), ) toc = time() print('- %ds - %s' % (toc-tic, ' - '.join(f'%s: %f' % t for t in avg_metrics.items()))) history.append(avg_metrics) return history
2.078125
2
HW1_P4.py
ZhaoQii/Naive-Bayesian-Classifier-in-Spam-Detection
0
12788863
<reponame>ZhaoQii/Naive-Bayesian-Classifier-in-Spam-Detection # -*- coding: utf-8 -*- """ Created on Sun Sep 26 15:14:51 2017 @author: <NAME> """ from matplotlib import style import matplotlib.pyplot as plt import scipy as sp import pandas as pd import numpy as np import os os.chdir('/Users/ap/Dropbox/2017FALL/EECS E6720BayesianModelforML/HW1/EECS6720-hw1-data') xtrain = pd.read_csv('X_train.csv', header = None) ytrain = pd.read_csv('label_train.csv', header = None) xtest = pd.read_csv('X_test.csv', header = None) ytest = pd.read_csv('label_test.csv', header = None) # set all hyperparameters a = 1 b = 1 e = 1 f = 1 N = ytrain.shape[0] N0 = np.sum(ytrain[0] == 0) N1 = np.sum(ytrain[0] == 1) ystar1_giveny = (e + np.sum(ytrain == 1)) / (N + e + f) ystar0_giveny = (f + np.sum(ytrain == 0)) / (N + e + f) col_sum0 = np.sum(xtrain.loc[ytrain.index[ytrain[0] == 0].tolist()], 0) col_sum1 = np.sum(xtrain.loc[ytrain.index[ytrain[0] == 1].tolist()], 0) def cal_log_negbin(x, alpha, beta): log_p = sp.special.gammaln(x + alpha) - sp.special.gammaln(alpha) - \ sp.special.gammaln(x + 1) + np.log((beta / (beta + 1)) ** alpha) + \ np.log((1 / (beta + 1)) ** x) return(log_p) def pred_1prob(xstar): log_p = 0 for i, v in xstar.iteritems(): temp = cal_log_negbin(v, a + col_sum1[i], b + N1) log_p = log_p + temp p = np.exp(log_p)*ystar1_giveny return(p) def pred_0prob(xstar): log_p = 0 for i, v in xstar.iteritems(): temp = cal_log_negbin(v, a + col_sum0[i], b + N0) log_p = log_p + temp p = np.exp(log_p)*ystar0_giveny return(p) test0 = xtest.apply(pred_0prob, axis = 1) test1 = xtest.apply(pred_1prob, axis = 1) test0 = np.asarray(test0[0].tolist()) test1 = np.asarray(test1[0].tolist()) # There are some emails can not be determined. The reason is the number of several variables # of them are larger enough to let log(predict_probability) = -inf meaning predict_probability # is just 0 for both y = 1 or y = 0 num_undetermined = np.count_nonzero(np.where((test1 == 0) & (test0 == 0))) pred_y = 1 * (test0 < test1) # If we can not decide them, we tend to regard them are spam since too much same words in them. pred_y[np.where((test1 == 0) & (test0 == 0))] = 1 v11 = np.sum([a and b for a, b in zip(ytest[0].values == 0, pred_y == 0)]) v12 = np.sum([a and b for a, b in zip(ytest[0].values == 0, pred_y == 1)]) v21 = np.sum([a and b for a, b in zip(ytest[0].values == 1, pred_y == 0)]) v22 = np.sum([a and b for a, b in zip(ytest[0].values == 1, pred_y == 1)]) table = pd.DataFrame({'real_notspam':[v11, v12], 'real_spam':[v21, v22]}, index = ['predict_notspam', 'predict_spam']) #### (c) #### # Pick three mislabeled emails firstly mis3 = ytest.index[ytest[0] != pred_y].tolist() mis3 = mis3[:3] temp = test0 + test1 # temp is the sum of both probabilities temp[np.where(temp == 0)] = np.float('nan') # Normalize the probabilities, if temo = 0, set it to nan test0 = np.divide(test0, temp) test1 = np.divide(test1, temp) # E(lambda1) = E(E(lambda1|xi:yi=1)), where lambda1|xi:yi=1 is the posterior of # The lambda1 given the data, specificly, Gamma(1+sum(xi:yi=1), 1+N1), same for lambda0 Elambda1 = (col_sum1 + 1) / (N1 + 1) Elambda0 = (col_sum0 + 1) / (N0 + 1) with open('README.txt', 'r') as file: xnames = file.read().split('\n') def make_plots(index): style.use('ggplot') plt.xticks(range(53), xnames, rotation = 'vertical') plt.plot(xtest.loc[index], 'b-', label = 'Features') plt.plot(Elambda0, 'r-', label = r'$E(\vec{\lambda_{0}})$') plt.plot(Elambda1, 'g-', label = r'$E(\vec{\lambda_{1}})$') plt.legend(loc='best') plt.title('The Features of {}th Sample VS '.format(index + 1) + \ r'$E(\vec{\lambda_{1}})$' + '&' r'$E(\vec{\lambda_{0}})$') plt.show() # Predictive Probability def get_pred_prob(index): print('P({}th Email is not Spam) ='.format(index + 1), test0[index]) print('P({}th Email is Spam) ='.format(index + 1), test1[index]) for each in mis3: make_plots(each) get_pred_prob(each) #### (d) #### cloest3 = abs(test0 - test1).argsort()[0:3] for each in cloest3: make_plots(each) get_pred_prob(each)
2.421875
2
checkout/migrations/0013_auto_20211024_1504.py
kevin-ci/janeric2
1
12788864
<reponame>kevin-ci/janeric2<filename>checkout/migrations/0013_auto_20211024_1504.py # Generated by Django 3.1.5 on 2021-10-24 15:04 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('checkout', '0012_auto_20211024_1453'), ] operations = [ migrations.AlterModelOptions( name='productshippingdata', options={'ordering': ['product__active', 'product__category__division', 'product__category__name', 'product__product_family__name', 'product__product_size__name'], 'verbose_name_plural': 'Product Shipping Data'}, ), ]
1.5625
2
joke teller.py
naitikvaru/python_joke_teller
1
12788865
<reponame>naitikvaru/python_joke_teller<gh_stars>1-10 import pyjokes print(pyjokes.get_joke())
1.59375
2
vergeml/sources/mnist.py
ss18/vergeml
324
12788866
<gh_stars>100-1000 from vergeml.img import INPUT_PATTERNS, open_image, fixext, ImageType from vergeml.io import source, SourcePlugin, Sample from vergeml.data import Labels from vergeml.utils import VergeMLError from vergeml.sources.labeled_image import LabeledImageSource import random import numpy as np from PIL import Image import os.path import json from operator import methodcaller import io from typing import List import gzip import hashlib _FILES = ("train-images-idx3-ubyte.gz", "train-labels-idx1-ubyte.gz", "t10k-images-idx3-ubyte.gz", "t10k-labels-idx1-ubyte.gz") _MNIST_LABELS = ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9") _FASHION_MNIST_LABELS = ("tshirt_top", "trouser", "pullover", "dress", "coat", "sandal", "shirt", "sneaker", "sag", "ankle_boot") # we use the md5 to check for fashion mnist, so we can provide the labels # automatically _MD5_FASHION = "8d4fb7e6c68d591d4c3dfef9ec88bf0d" def _md5(fname): hash_md5 = hashlib.md5() with open(fname, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): hash_md5.update(chunk) return hash_md5.hexdigest() @source('image', descr="Load images in MNIST format.") class InputMnist(SourcePlugin): data = None def num_samples(self, split: str) -> int: return len(self.data[split]) def read_sample(self, split: str, index: int): return self.data[split][index] def _check_files(self): self.data = dict(train=[], val=[], test=[]) samples_dir = self.config["samples_dir"] files = [os.path.join(samples_dir, file) for file in _FILES] for path in files: if not os.path.exists(path): raise VergeMLError("File not found in samples_dir: {}".format( os.path.basename(path))) if _md5(files[0]) == _MD5_FASHION: self.meta['labels'] = _FASHION_MNIST_LABELS else: self.meta['labels'] = _MNIST_LABELS # preload for split, images, labels in (('train', files[0], files[1]), ('test', files[2], files[3])): with gzip.open(images) as f: # First 16 bytes are magic_number, n_imgs, n_rows, n_cols pixels = np.frombuffer(f.read(), 'B', offset=16) pixels = pixels.reshape(-1, 28, 28) with gzip.open(labels) as f: # First 8 bytes are magic_number, n_labels integer_labels = np.frombuffer(f.read(), 'B', offset=8) n_cols = integer_labels.max() + 1 for ix, imagearr in enumerate(pixels): label = integer_labels[ix] onehot = np.zeros((n_cols), dtype='float32') onehot[label] = 1.0 self.data[split].append((Image.fromarray(imagearr), onehot, dict(labels=self.meta['labels'], filename=images, split=split, types=('pil', 'labels')))) if split == 'train': n = self.config['val_num'] if self.config['val_perc'] is not None: n = int(len(self.data['train']) * self.config['val_perc'] // 100) if n is not None: if n > len(self.data['train']): raise VergeMLError("number of test samples is greater than number of available samples.") rng = random.Random(self.config['random_seed']) count = len(self.data[split]) indices = rng.sample(range(count), count) self.data['val'] = [self.data['train'][i] for i in indices[:n]] self.data['train'] = [self.data['train'][i] for i in indices[n:]] else: if self.config['test_num']: if self.config['test_num'] > len(self.data['test']): raise VergeMLError("number of test samples is greater than number of available samples.") rng = random.Random(self.config['random_seed']) indices = rng.sample(range(len(self.data[split])), len(pixels)) self.data['test'] = [self.data['test'][i] for i in indices[:n]] plugin = InputMnist
2.140625
2
spellChecker/spellCheckerApp/apps.py
spell-checkers/web-spell-checker
0
12788867
from django.apps import AppConfig class SpellcheckerappConfig(AppConfig): name = 'spellCheckerApp'
1.070313
1
src/RIOT/tests/gnrc_sock_ip/tests/01-run.py
ARte-team/ARte
2
12788868
<gh_stars>1-10 #!/usr/bin/env python3 # Copyright (C) 2016 <NAME> <<EMAIL>> # # This file is subject to the terms and conditions of the GNU Lesser # General Public License v2.1. See the file LICENSE in the top level # directory for more details. import sys from testrunner import run def testfunc(child): child.expect_exact(u"Calling test_sock_ip_create__EAFNOSUPPORT()") child.expect_exact(u"Calling test_sock_ip_create__EINVAL_addr()") child.expect_exact(u"Calling test_sock_ip_create__EINVAL_netif()") child.expect_exact(u"Calling test_sock_ip_create__no_endpoints()") child.expect_exact(u"Calling test_sock_ip_create__only_local()") child.expect_exact(u"Calling test_sock_ip_create__only_local_reuse_ep()") child.expect_exact(u"Calling test_sock_ip_create__only_remote()") child.expect_exact(u"Calling test_sock_ip_create__full()") child.expect_exact(u"Calling test_sock_ip_recv__EADDRNOTAVAIL()") child.expect_exact(u"Calling test_sock_ip_recv__ENOBUFS()") child.expect_exact(u"Calling test_sock_ip_recv__EPROTO()") child.expect_exact(u"Calling test_sock_ip_recv__ETIMEDOUT()") child.expect_exact(u" * Calling sock_ip_recv()") child.expect(r" \* \(timed out with timeout \d+\)") child.expect_exact(u"Calling test_sock_ip_recv__socketed()") child.expect_exact(u"Calling test_sock_ip_recv__socketed_with_remote()") child.expect_exact(u"Calling test_sock_ip_recv__unsocketed()") child.expect_exact(u"Calling test_sock_ip_recv__unsocketed_with_remote()") child.expect_exact(u"Calling test_sock_ip_recv__with_timeout()") child.expect_exact(u"Calling test_sock_ip_send__EAFNOSUPPORT()") child.expect_exact(u"Calling test_sock_ip_send__EINVAL_addr()") child.expect_exact(u"Calling test_sock_ip_send__EINVAL_netif()") child.expect_exact(u"Calling test_sock_ip_send__ENOTCONN()") child.expect_exact(u"Calling test_sock_ip_send__socketed_no_local_no_netif()") child.expect_exact(u"Calling test_sock_ip_send__socketed_no_netif()") child.expect_exact(u"Calling test_sock_ip_send__socketed_no_local()") child.expect_exact(u"Calling test_sock_ip_send__socketed()") child.expect_exact(u"Calling test_sock_ip_send__socketed_other_remote()") child.expect_exact(u"Calling test_sock_ip_send__unsocketed_no_local_no_netif()") child.expect_exact(u"Calling test_sock_ip_send__unsocketed_no_netif()") child.expect_exact(u"Calling test_sock_ip_send__unsocketed_no_local()") child.expect_exact(u"Calling test_sock_ip_send__unsocketed()") child.expect_exact(u"Calling test_sock_ip_send__no_sock_no_netif()") child.expect_exact(u"Calling test_sock_ip_send__no_sock()") child.expect_exact(u"ALL TESTS SUCCESSFUL") if __name__ == "__main__": sys.exit(run(testfunc))
1.984375
2
loldib/getratings/models/NA/na_kalista/__init__.py
koliupy/loldib
0
12788869
<reponame>koliupy/loldib<filename>loldib/getratings/models/NA/na_kalista/__init__.py from .na_kalista_top import * from .na_kalista_jng import * from .na_kalista_mid import * from .na_kalista_bot import * from .na_kalista_sup import *
1.078125
1
mne/tests/test_coreg.py
jdammers/mne-python
0
12788870
from glob import glob import os import os.path as op from shutil import copyfile from nose.tools import assert_raises import numpy as np from numpy.testing import assert_array_almost_equal import mne from mne.datasets import testing from mne.transforms import (Transform, apply_trans, rotation, translation, scaling) from mne.coreg import (fit_matched_points, create_default_subject, scale_mri, _is_mri_subject, scale_labels, scale_source_space, coregister_fiducials) from mne.io.constants import FIFF from mne.utils import _TempDir, run_tests_if_main from mne.source_space import write_source_spaces from functools import reduce def test_coregister_fiducials(): """Test coreg.coregister_fiducials()""" # prepare head and MRI fiducials trans = Transform('head', 'mri', rotation(.4, .1, 0).dot(translation(.1, -.1, .1))) coords_orig = np.array([[-0.08061612, -0.02908875, -0.04131077], [0.00146763, 0.08506715, -0.03483611], [0.08436285, -0.02850276, -0.04127743]]) coords_trans = apply_trans(trans, coords_orig) def make_dig(coords, cf): return ({'coord_frame': cf, 'ident': 1, 'kind': 1, 'r': coords[0]}, {'coord_frame': cf, 'ident': 2, 'kind': 1, 'r': coords[1]}, {'coord_frame': cf, 'ident': 3, 'kind': 1, 'r': coords[2]}) mri_fiducials = make_dig(coords_trans, FIFF.FIFFV_COORD_MRI) info = {'dig': make_dig(coords_orig, FIFF.FIFFV_COORD_HEAD)} # test coregister_fiducials() trans_est = coregister_fiducials(info, mri_fiducials) assert trans_est.from_str == trans.from_str assert trans_est.to_str == trans.to_str assert_array_almost_equal(trans_est['trans'], trans['trans']) @testing.requires_testing_data def test_scale_mri(): """Test creating fsaverage and scaling it.""" # create fsaverage using the testing "fsaverage" instead of the FreeSurfer # one tempdir = _TempDir() fake_home = testing.data_path() create_default_subject(subjects_dir=tempdir, fs_home=fake_home, verbose=True) assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed" fid_path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif') os.remove(fid_path) create_default_subject(update=True, subjects_dir=tempdir, fs_home=fake_home) assert op.exists(fid_path), "Updating fsaverage" # copy MRI file from sample data (shouldn't matter that it's incorrect, # so here choose a small one) path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri', 'T1.mgz') path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') copyfile(path_from, path_to) # remove redundant label files label_temp = op.join(tempdir, 'fsaverage', 'label', '*.label') label_paths = glob(label_temp) for label_path in label_paths[1:]: os.remove(label_path) # create source space print('Creating surface source space') path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif') src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir, add_dist=False) write_source_spaces(path % 'ico-0', src) mri = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') print('Creating volume source space') vsrc = mne.setup_volume_source_space( 'fsaverage', pos=50, mri=mri, subjects_dir=tempdir, add_interpolator=False) write_source_spaces(path % 'vol-50', vsrc) # scale fsaverage os.environ['_MNE_FEW_SURFACES'] = 'true' scale = np.array([1, .2, .8]) scale_mri('fsaverage', 'flachkopf', scale, True, subjects_dir=tempdir, verbose='debug') del os.environ['_MNE_FEW_SURFACES'] assert _is_mri_subject('flachkopf', tempdir), "Scaling fsaverage failed" spath = op.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif') assert op.exists(spath % 'ico-0'), "Source space ico-0 was not scaled" assert os.path.isfile(os.path.join(tempdir, 'flachkopf', 'surf', 'lh.sphere.reg')) vsrc_s = mne.read_source_spaces(spath % 'vol-50') pt = np.array([0.12, 0.41, -0.22]) assert_array_almost_equal(apply_trans(vsrc_s[0]['src_mri_t'], pt * scale), apply_trans(vsrc[0]['src_mri_t'], pt)) scale_labels('flachkopf', subjects_dir=tempdir) # add distances to source space mne.add_source_space_distances(src) src.save(path % 'ico-0', overwrite=True) # scale with distances os.remove(spath % 'ico-0') scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir) ssrc = mne.read_source_spaces(spath % 'ico-0') assert ssrc[0]['dist'] is not None def test_fit_matched_points(): """Test fit_matched_points: fitting two matching sets of points""" tgt_pts = np.random.RandomState(42).uniform(size=(6, 3)) # rotation only trans = rotation(2, 6, 3) src_pts = apply_trans(trans, tgt_pts) trans_est = fit_matched_points(src_pts, tgt_pts, translate=False, out='trans') est_pts = apply_trans(trans_est, src_pts) assert_array_almost_equal(tgt_pts, est_pts, 2, "fit_matched_points with " "rotation") # rotation & translation trans = np.dot(translation(2, -6, 3), rotation(2, 6, 3)) src_pts = apply_trans(trans, tgt_pts) trans_est = fit_matched_points(src_pts, tgt_pts, out='trans') est_pts = apply_trans(trans_est, src_pts) assert_array_almost_equal(tgt_pts, est_pts, 2, "fit_matched_points with " "rotation and translation.") # rotation & translation & scaling trans = reduce(np.dot, (translation(2, -6, 3), rotation(1.5, .3, 1.4), scaling(.5, .5, .5))) src_pts = apply_trans(trans, tgt_pts) trans_est = fit_matched_points(src_pts, tgt_pts, scale=1, out='trans') est_pts = apply_trans(trans_est, src_pts) assert_array_almost_equal(tgt_pts, est_pts, 2, "fit_matched_points with " "rotation, translation and scaling.") # test exceeding tolerance tgt_pts[0, :] += 20 assert_raises(RuntimeError, fit_matched_points, tgt_pts, src_pts, tol=10) run_tests_if_main()
1.90625
2
surgame/src/particles.py
anokata/pythonPetProjects
3
12788871
import util import pygame import math import images class Particles(util.Block): lifetime = 100 def __init__(self, x, y, n=10, lifetime=10, imgname=images.particleDefault): super().__init__(x, y, imgname) self.particles_xyd = list() # координаты и скорости частицы (x, y, dx, dy) spd = 2 self.lifetime = lifetime self.n = n for i in range(n): p = {'x': x, 'y': y} p['dx'] = spd * (math.cos(i*2*math.pi/n)) p['dy'] = spd * (math.sin(i*2*math.pi/n)) self.particles_xyd.append(p) def step(self): self.lifetime -= 1 if self.lifetime: for p in self.particles_xyd: p['x'] += p['dx'] p['y'] += p['dy'] return True else: return False def draw(self, cam, screen): if self.lifetime: for p in self.particles_xyd: super().draw(p['x'], p['y'], cam, screen)
3.109375
3
tests/test_maphash.py
MoonVision/maphash
3
12788872
<filename>tests/test_maphash.py import json from pathlib import Path from maphash import maphash def test_hashes_int(): assert ( maphash(1) == "67b176705b46206614219f47a05aee7ae6a3edbe850bbbe214c536b989aea4d2" ) def test_hashes_float(): assert ( maphash(0.1) == "75bd59c8426679f3ef7b3a37184ee08e2e5ecee840e330acf6782d77cf2a2d1b" ) def test_hashes_str(): assert ( maphash("string") == "00ff5fd099f3820fa1196c77d97331caaec09301635641a113b1b81d268b26df" ) def test_hashes_dict(): assert ( maphash(dict()) == "840eb7aa2a9935de63366bacbe9d97e978a859e93dc792a0334de60ed52f8e99" ) def test_hashes_list(): assert ( maphash(list()) == "ca4510738395af1429224dd785675309c344b2b549632e20275c69b15ed1d210" ) def test_hashes_none(): assert ( maphash(None) == "3ea445410f608e6453cdcb7dbe42d57a89aca018993d7e87da85993cbccc6308" ) def test_hashes_complex_document(): with Path("tests/json-schema-v7.json").open() as fp: doc = json.load(fp) assert ( maphash(doc) == "37385bcbbdf1ea13531c53db2784b26ca7248c283b432c382c12e6f1e65d0249" ) def test_different_key_order_same_hash(): assert maphash(dict(a=1, b=2, c=3)) == maphash(dict(b=2, c=3, a=1)) def test_additional_entry_different_hash(): d1 = dict(d=4) d2 = d1.copy() d2["e"] = 5 assert maphash(d1) != maphash(d2) def test_different_list_item_order_different_hash(): assert maphash([1, 2, 3]) != maphash([2, 3, 1])
2.78125
3
frozenweb/__init__.py
rufrozen/frozenweb
0
12788873
<reponame>rufrozen/frozenweb from .config import Config from .builder import Builder from .server import Server
1.0625
1
promise-types/http/http_promise_type.py
olehermanse/cfengine_basics
0
12788874
"""HTTP module for CFEngine""" import os import urllib import urllib.request import ssl import json from cfengine import PromiseModule, ValidationError, Result _SUPPORTED_METHODS = {"GET", "POST", "PUT", "DELETE", "PATCH"} class HTTPPromiseModule(PromiseModule): def __init__(self, *args, **kwargs): super().__init__("http_promise_module", "1.0.0", *args, **kwargs) def validate_promise(self, promiser, attributes): if "url" in attributes: url = attributes["url"] if type(url) != str: raise ValidationError("'url' must be a string") if not url.startswith(("https://", "http://")): raise ValidationError("Only HTTP(S) requests are supported") if "method" in attributes: method = attributes["method"] if type(method) != str: raise ValidationError("'method' must be a string") if method not in _SUPPORTED_METHODS: raise ValidationError("'method' must be one of %s" % ", ".join(_SUPPORTED_METHODS)) if "headers" in attributes: headers = attributes["headers"] headers_type = type(headers) if headers_type == str: headers_lines = headers.splitlines() if any(line.count(":") != 1 for line in headers_lines): raise ValidationError("'headers' must be string with 'name: value' pairs on separate lines") elif headers_type == list: if any(line.count(":") != 1 for line in headers): raise ValidationError("'headers' must be a list of 'name: value' pairs") elif headers_type == dict: # nothing to check for dict? pass else: raise ValidationError("'headers' must be a string, an slist or a data container" + " value with 'name: value' pairs") if "payload" in attributes: payload = attributes["payload"] if type(payload) not in (str, dict): raise ValidationError("'payload' must be a string or a data container value") if type(payload) == str and payload.startswith("@") and not os.path.isabs(payload[1:]): raise ValidationError("File-based payload must be an absolute path") if "file" in attributes: file_ = attributes["file"] if type(file_) != str or not os.path.isabs(file_): raise ValidationError("'file' must be an absolute path to a file") if "insecure" in attributes: insecure = attributes["insecure"] if type(insecure) != str or insecure not in ("true", "True", "false", "False"): raise ValidationError("'insecure' must be either \"true\" or \"false\"") def evaluate_promise(self, promiser, attributes): url = attributes.get("url", promiser) method = attributes.get("method", "GET") headers = attributes.get("headers", dict()) payload = attributes.get("payload") target = attributes.get("file") insecure = attributes.get("insecure", False) canonical_promiser = promiser.translate(str.maketrans({char: "_" for char in ("@", "/", ":", "?", "&", "%")})) if headers and type(headers) != dict: if type(headers) == str: headers = {key: value for key, value in (line.split(":") for line in headers.splitlines())} elif type(headers) == list: headers = {key: value for key, value in (line.split(":") for line in headers)} if payload: if type(payload) == dict: try: payload = json.dumps(payload) except TypeError: self.log_error("Failed to convert 'payload' to text representation for request '%s'" % url) return (Result.NOT_KEPT, ["%s_%s_request_failed" % (canonical_promiser, method), "%s_%s_payload_failed" % (canonical_promiser, method), "%s_%s_payload_conversion_failed" % (canonical_promiser, method)]) if "Content-Type" not in headers: headers["Content-Type"] = "application/json" elif payload.startswith("@"): path = payload[1:] try: # Closed automatically when this variable gets out of # scope. Thank you, Python! payload = open(path, "rb") except OSError as e: self.log_error("Failed to open payload file '%s' for request '%s': %s" % (path, url, e)) return (Result.NOT_KEPT, ["%s_%s_request_failed" % (canonical_promiser, method), "%s_%s_payload_failed" % (canonical_promiser, method), "%s_%s_payload_file_failed" % (canonical_promiser, method)]) if "Content-Lenght" not in headers: headers["Content-Length"] = os.path.getsize(path) # must be 'None' or bytes or file object if type(payload) == str: payload = payload.encode("utf-8") request = urllib.request.Request(url=url, data=payload, method=method, headers=headers) SSL_context = None if insecure: # convert to a boolean insecure = (insecure.lower() == "true") if insecure: SSL_context = ssl.SSLContext() SSL_context.verify_method = ssl.CERT_NONE try: if target: # TODO: create directories with open(target, "wb") as target_file: with urllib.request.urlopen(request, context=SSL_context) as url_req: if not (200 <= url_req.status <= 300): self.log_error("Request for '%s' failed with code %d" % (url, url_req.status)) return (Result.NOT_KEPT, ["%s_%s_request_failed" % (canonical_promiser, method)]) # TODO: log progress when url_req.headers["Content-length"] > REPORTING_THRESHOLD done = False while not done: data = url_req.read(512 * 1024) target_file.write(data) done = bool(data) else: with urllib.request.urlopen(request, context=SSL_context) as url_req: if not (200 <= url_req.status <= 300): self.log_error("Request for '%s' failed with code %d" % (url, url_req.status)) return (Result.NOT_KEPT, ["%s_%s_request_failed" % (canonical_promiser, method)]) done = False while not done: data = url_req.read(512 * 1024) done = bool(data) except urllib.error.URLError as e: self.log_error("Failed to request '%s': %s" % (url, e)) return (Result.NOT_KEPT, ["%s_%s_request_failed" % (canonical_promiser, method)]) except OSError as e: self.log_error("Failed to store '%s' response to '%s': %s" % (url, target, e)) return (Result.NOT_KEPT, ["%s_%s_request_failed" % (canonical_promiser, method), "%s_%s_file_failed" % (canonical_promiser, method)]) if target: self.log_info("Saved request response from '%s' to '%s'" % (url, target)) else: self.log_info("Successfully executed%s request to '%s'" % ((" " + method if method else ""), url)) return (Result.REPAIRED, ["%s_%s_request_done" % (canonical_promiser, method)]) if __name__ == "__main__": HTTPPromiseModule().start()
2.734375
3
preprocessing/get_industry_sector.py
marwage/stock_prediction
0
12788875
<reponame>marwage/stock_prediction import logging import os import pandas as pd import sys from pymongo import MongoClient def get(output_path: str): client = MongoClient() info_db = client["companyinfodb"] collection_names = info_db.list_collection_names() industries = set() sectors = set() for company in collection_names: info = info_db[company].find_one({}) industries.add(info["Industry"]) sectors.add(info["Sector"]) print(industries) print(sectors) industries_list = [] industries_values = [] for i, industry in enumerate(industries): industries_list.append(industry) industries_values.append(float(i + 1)) data_frame = pd.DataFrame({"industry": industries_list, "value": industries_values}) file_name = "industries.csv" data_frame.to_csv(os.path.join(output_path, file_name), index=False) sector_list = [] sector_values = [] for i, sector in enumerate(sectors): sector_list.append(sector) sector_values.append(float(i + 1)) data_frame = pd.DataFrame({"sector": sector_list, "value": sector_values}) file_name = "sectors.csv" data_frame.to_csv(os.path.join(output_path, file_name), index=False) def main(): output_path = os.path.join(".", "data") os.makedirs(output_path, exist_ok=True) log_path = os.path.join(".", "log/get_industry_sector.log") os.makedirs(os.path.dirname(log_path), exist_ok=True) logging.basicConfig( filename=log_path, level=logging.INFO, format="%(asctime)s:%(levelname)s:%(message)s" ) get(output_path) if __name__ == "__main__": main()
2.59375
3
main_website/migrations/0004_auto_20201228_1649.py
kiza054/woodhall-website
2
12788876
<reponame>kiza054/woodhall-website # Generated by Django 3.1.4 on 2020-12-28 16:49 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('main_website', '0003_auto_20201228_1456'), ] operations = [ migrations.CreateModel( name='Event', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=200)), ('description', models.TextField()), ('start_time', models.DateTimeField()), ('end_time', models.DateTimeField()), ], ), migrations.AlterModelOptions( name='waitinglist', options={'verbose_name': 'Waiting List Entry', 'verbose_name_plural': 'Waiting List Entries'}, ), migrations.AlterField( model_name='waitinglist', name='parent_carer_email', field=models.EmailField(max_length=254, verbose_name='email of parent/carer'), ), ]
1.898438
2
src/genie/libs/parser/iosxr/tests/test_show_ethernet_yang.py
nujo/genieparser
4
12788877
<filename>src/genie/libs/parser/iosxr/tests/test_show_ethernet_yang.py<gh_stars>1-10 import re import unittest from unittest.mock import Mock import xml.etree.ElementTree as ET from pyats.topology import Device from genie.ops.base import Context from genie.metaparser.util.exceptions import SchemaEmptyParserError from genie.libs.parser.iosxr.show_ethernet import ShowEthernetTrunkDetail, \ ShowEthernetTags class test_show_ethernet_tags_yang(unittest.TestCase): device = Device(name='aDevice') device1 = Device(name='bDevice') empty_output = {'execute.return_value': ''} golden_parsed_output = {'interface': {'GigabitEthernet0/0/0/0': {'sub_interface': {'GigabitEthernet0/0/0/0.501': {'vlan_id': {'2': {'inner_encapsulation_type': 'dot1q', 'inner_encapsulation_vlan_id': '5', 'mtu': '1522', 'outer_encapsulation_type': 'dot1q'}}}}}}} class etree_holder(): def __init__(self): self.data = ET.fromstring(''' <data> <interfaces xmlns="http://cisco.com/ns/yang/Cisco-IOS-XR-pfi-im-cmd-oper"> <interface-xr> <interface> <interface-name>GigabitEthernet0/0/0/0</interface-name> <interface-handle>GigabitEthernet0/0/0/0</interface-handle> <interface-type>IFT_GETHERNET</interface-type> <hardware-type-string>GigabitEthernet</hardware-type-string> <state>im-state-up</state> <line-state>im-state-up</line-state> <encapsulation>ether</encapsulation> <encapsulation-type-string>ARPA</encapsulation-type-string> <mtu>1514</mtu> <is-l2-transport-enabled>false</is-l2-transport-enabled> <state-transition-count>1</state-transition-count> <last-state-transition-time>1100222</last-state-transition-time> <is-dampening-enabled>false</is-dampening-enabled> <speed>1000000</speed> <duplexity>im-attr-duplex-full</duplexity> <media-type>im-attr-media-other</media-type> <link-type>im-attr-link-type-force</link-type> <in-flow-control>im-attr-flow-control-off</in-flow-control> <out-flow-control>im-attr-flow-control-off</out-flow-control> <mac-address> <address>52:54:00:ff:50:0e</address> </mac-address> <burned-in-address> <address>52:54:00:ff:50:0e</address> </burned-in-address> <carrier-delay> <carrier-delay-up>10</carrier-delay-up> <carrier-delay-down>0</carrier-delay-down> </carrier-delay> <bandwidth>1000000</bandwidth> <max-bandwidth>1000000</max-bandwidth> <is-l2-looped>false</is-l2-looped> <description></description> <data-rates> <input-data-rate>0</input-data-rate> <input-packet-rate>0</input-packet-rate> <output-data-rate>0</output-data-rate> <output-packet-rate>0</output-packet-rate> <peak-input-data-rate>0</peak-input-data-rate> <peak-input-packet-rate>0</peak-input-packet-rate> <peak-output-data-rate>0</peak-output-data-rate> <peak-output-packet-rate>0</peak-output-packet-rate> <bandwidth>1000000</bandwidth> <load-interval>9</load-interval> <output-load>0</output-load> <input-load>0</input-load> <reliability>255</reliability> </data-rates> <interface-statistics> <stats-type>full</stats-type> <full-interface-stats> <packets-received>0</packets-received> <bytes-received>0</bytes-received> <packets-sent>0</packets-sent> <bytes-sent>0</bytes-sent> <multicast-packets-received>0</multicast-packets-received> <broadcast-packets-received>0</broadcast-packets-received> <multicast-packets-sent>0</multicast-packets-sent> <broadcast-packets-sent>0</broadcast-packets-sent> <output-drops>0</output-drops> <output-queue-drops>0</output-queue-drops> <input-drops>0</input-drops> <input-queue-drops>0</input-queue-drops> <runt-packets-received>0</runt-packets-received> <giant-packets-received>0</giant-packets-received> <throttled-packets-received>0</throttled-packets-received> <parity-packets-received>0</parity-packets-received> <unknown-protocol-packets-received>0</unknown-protocol-packets-received> <input-errors>0</input-errors> <crc-errors>0</crc-errors> <input-overruns>0</input-overruns> <framing-errors-received>0</framing-errors-received> <input-ignored-packets>0</input-ignored-packets> <input-aborts>0</input-aborts> <output-errors>0</output-errors> <output-underruns>0</output-underruns> <output-buffer-failures>0</output-buffer-failures> <output-buffers-swapped-out>0</output-buffers-swapped-out> <applique>0</applique> <resets>0</resets> <carrier-transitions>1</carrier-transitions> <availability-flag>0</availability-flag> <last-data-time>1490888108</last-data-time> <seconds-since-last-clear-counters>0</seconds-since-last-clear-counters> <last-discontinuity-time>1489787869</last-discontinuity-time> <seconds-since-packet-received>4294967295</seconds-since-packet-received> <seconds-since-packet-sent>4294967295</seconds-since-packet-sent> </full-interface-stats> </interface-statistics> <if-index>0</if-index> </interface> <interface> <interface-name>GigabitEthernet0/0/0/0.501</interface-name> <interface-handle>GigabitEthernet0/0/0/0.501</interface-handle> <interface-type>IFT_VLAN_SUBIF</interface-type> <hardware-type-string>VLAN sub-interface(s)</hardware-type-string> <state>im-state-up</state> <line-state>im-state-up</line-state> <encapsulation>dot1q</encapsulation> <encapsulation-type-string>802.1Q</encapsulation-type-string> <mtu>1522</mtu> <is-l2-transport-enabled>false</is-l2-transport-enabled> <state-transition-count>1</state-transition-count> <last-state-transition-time>1100222</last-state-transition-time> <is-dampening-enabled>false</is-dampening-enabled> <speed>1000000</speed> <mac-address> <address>52:54:00:ff:50:0e</address> </mac-address> <carrier-delay> <carrier-delay-up>0</carrier-delay-up> <carrier-delay-down>0</carrier-delay-down> </carrier-delay> <bandwidth>1000000</bandwidth> <max-bandwidth>1000000</max-bandwidth> <is-l2-looped>false</is-l2-looped> <parent-interface-name>GigabitEthernet0/0/0/0</parent-interface-name> <description></description> <encapsulation-information> <encapsulation-type>vlan</encapsulation-type> <dot1q-information> <encapsulation-details> <vlan-encapsulation>qinq</vlan-encapsulation> <stack> <outer-tag>2</outer-tag> <second-tag>5</second-tag> </stack> </encapsulation-details> </dot1q-information> </encapsulation-information> <data-rates> <input-data-rate>0</input-data-rate> <input-packet-rate>0</input-packet-rate> <output-data-rate>0</output-data-rate> <output-packet-rate>0</output-packet-rate> <peak-input-data-rate>0</peak-input-data-rate> <peak-input-packet-rate>0</peak-input-packet-rate> <peak-output-data-rate>0</peak-output-data-rate> <peak-output-packet-rate>0</peak-output-packet-rate> <bandwidth>1000000</bandwidth> <load-interval>9</load-interval> <output-load>0</output-load> <input-load>0</input-load> <reliability>255</reliability> </data-rates> <interface-statistics> <stats-type>full</stats-type> <full-interface-stats> <packets-received>0</packets-received> <bytes-received>0</bytes-received> <packets-sent>0</packets-sent> <bytes-sent>0</bytes-sent> <multicast-packets-received>0</multicast-packets-received> <broadcast-packets-received>0</broadcast-packets-received> <multicast-packets-sent>0</multicast-packets-sent> 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<seconds-since-packet-received>4294967295</seconds-since-packet-received> <seconds-since-packet-sent>4294967295</seconds-since-packet-sent> </full-interface-stats> </interface-statistics> <if-index>0</if-index> </interface> <interface> <interface-name>GigabitEthernet0/0/0/2</interface-name> <interface-handle>GigabitEthernet0/0/0/2</interface-handle> <interface-type>IFT_GETHERNET</interface-type> <hardware-type-string>GigabitEthernet</hardware-type-string> <state>im-state-admin-down</state> <line-state>im-state-admin-down</line-state> <encapsulation>ether</encapsulation> <encapsulation-type-string>ARPA</encapsulation-type-string> <mtu>1514</mtu> <is-l2-transport-enabled>false</is-l2-transport-enabled> <state-transition-count>0</state-transition-count> <last-state-transition-time>1100377</last-state-transition-time> <is-dampening-enabled>false</is-dampening-enabled> <speed>1000000</speed> <duplexity>im-attr-duplex-full</duplexity> <media-type>im-attr-media-other</media-type> <link-type>im-attr-link-type-force</link-type> <in-flow-control>im-attr-flow-control-off</in-flow-control> <out-flow-control>im-attr-flow-control-off</out-flow-control> <mac-address> <address>52:54:00:ff:40:4e</address> </mac-address> <burned-in-address> <address>52:54:00:ff:40:4e</address> </burned-in-address> <carrier-delay> <carrier-delay-up>10</carrier-delay-up> <carrier-delay-down>0</carrier-delay-down> </carrier-delay> <bandwidth>1000000</bandwidth> <max-bandwidth>1000000</max-bandwidth> <is-l2-looped>false</is-l2-looped> <description></description> <data-rates> <input-data-rate>0</input-data-rate> <input-packet-rate>0</input-packet-rate> <output-data-rate>0</output-data-rate> <output-packet-rate>0</output-packet-rate> <peak-input-data-rate>0</peak-input-data-rate> <peak-input-packet-rate>0</peak-input-packet-rate> <peak-output-data-rate>0</peak-output-data-rate> <peak-output-packet-rate>0</peak-output-packet-rate> <bandwidth>1000000</bandwidth> <load-interval>9</load-interval> <output-load>0</output-load> <input-load>0</input-load> <reliability>255</reliability> </data-rates> <interface-statistics> <stats-type>full</stats-type> <full-interface-stats> <packets-received>0</packets-received> <bytes-received>0</bytes-received> <packets-sent>0</packets-sent> <bytes-sent>0</bytes-sent> <multicast-packets-received>0</multicast-packets-received> <broadcast-packets-received>0</broadcast-packets-received> <multicast-packets-sent>0</multicast-packets-sent> <broadcast-packets-sent>0</broadcast-packets-sent> <output-drops>0</output-drops> <output-queue-drops>0</output-queue-drops> <input-drops>0</input-drops> <input-queue-drops>0</input-queue-drops> <runt-packets-received>0</runt-packets-received> <giant-packets-received>0</giant-packets-received> <throttled-packets-received>0</throttled-packets-received> <parity-packets-received>0</parity-packets-received> <unknown-protocol-packets-received>0</unknown-protocol-packets-received> <input-errors>0</input-errors> 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<interface-handle>GigabitEthernet0/0/0/3</interface-handle> <interface-type>IFT_GETHERNET</interface-type> <hardware-type-string>GigabitEthernet</hardware-type-string> <state>im-state-admin-down</state> <line-state>im-state-admin-down</line-state> <encapsulation>ether</encapsulation> <encapsulation-type-string>ARPA</encapsulation-type-string> <mtu>1514</mtu> <is-l2-transport-enabled>false</is-l2-transport-enabled> <state-transition-count>0</state-transition-count> <last-state-transition-time>1100377</last-state-transition-time> <is-dampening-enabled>false</is-dampening-enabled> <speed>1000000</speed> <duplexity>im-attr-duplex-full</duplexity> <media-type>im-attr-media-other</media-type> <link-type>im-attr-link-type-force</link-type> <in-flow-control>im-attr-flow-control-off</in-flow-control> <out-flow-control>im-attr-flow-control-off</out-flow-control> <mac-address> <address>52:54:00:ff:f9:5e</address> </mac-address> <burned-in-address> <address>52:54:00:ff:f9:5e</address> </burned-in-address> <carrier-delay> <carrier-delay-up>10</carrier-delay-up> <carrier-delay-down>0</carrier-delay-down> </carrier-delay> <bandwidth>1000000</bandwidth> <max-bandwidth>1000000</max-bandwidth> <is-l2-looped>false</is-l2-looped> <description></description> <data-rates> <input-data-rate>0</input-data-rate> <input-packet-rate>0</input-packet-rate> <output-data-rate>0</output-data-rate> <output-packet-rate>0</output-packet-rate> <peak-input-data-rate>0</peak-input-data-rate> <peak-input-packet-rate>0</peak-input-packet-rate> <peak-output-data-rate>0</peak-output-data-rate> <peak-output-packet-rate>0</peak-output-packet-rate> <bandwidth>1000000</bandwidth> <load-interval>9</load-interval> <output-load>0</output-load> <input-load>0</input-load> <reliability>255</reliability> </data-rates> <interface-statistics> <stats-type>full</stats-type> <full-interface-stats> <packets-received>0</packets-received> <bytes-received>0</bytes-received> <packets-sent>0</packets-sent> <bytes-sent>0</bytes-sent> <multicast-packets-received>0</multicast-packets-received> <broadcast-packets-received>0</broadcast-packets-received> <multicast-packets-sent>0</multicast-packets-sent> <broadcast-packets-sent>0</broadcast-packets-sent> <output-drops>0</output-drops> <output-queue-drops>0</output-queue-drops> <input-drops>0</input-drops> <input-queue-drops>0</input-queue-drops> <runt-packets-received>0</runt-packets-received> <giant-packets-received>0</giant-packets-received> <throttled-packets-received>0</throttled-packets-received> <parity-packets-received>0</parity-packets-received> <unknown-protocol-packets-received>0</unknown-protocol-packets-received> <input-errors>0</input-errors> <crc-errors>0</crc-errors> <input-overruns>0</input-overruns> <framing-errors-received>0</framing-errors-received> <input-ignored-packets>0</input-ignored-packets> <input-aborts>0</input-aborts> <output-errors>0</output-errors> <output-underruns>0</output-underruns> <output-buffer-failures>0</output-buffer-failures> <output-buffers-swapped-out>0</output-buffers-swapped-out> <applique>0</applique> <resets>0</resets> <carrier-transitions>0</carrier-transitions> <availability-flag>0</availability-flag> <last-data-time>1490888108</last-data-time> <seconds-since-last-clear-counters>0</seconds-since-last-clear-counters> <last-discontinuity-time>1489787869</last-discontinuity-time> <seconds-since-packet-received>4294967295</seconds-since-packet-received> <seconds-since-packet-sent>4294967295</seconds-since-packet-sent> </full-interface-stats> </interface-statistics> <if-index>0</if-index> </interface> <interface> <interface-name>GigabitEthernet0/0/0/4</interface-name> <interface-handle>GigabitEthernet0/0/0/4</interface-handle> <interface-type>IFT_GETHERNET</interface-type> <hardware-type-string>GigabitEthernet</hardware-type-string> <state>im-state-admin-down</state> <line-state>im-state-admin-down</line-state> <encapsulation>ether</encapsulation> <encapsulation-type-string>ARPA</encapsulation-type-string> <mtu>1514</mtu> <is-l2-transport-enabled>false</is-l2-transport-enabled> <state-transition-count>0</state-transition-count> <last-state-transition-time>1100377</last-state-transition-time> <is-dampening-enabled>false</is-dampening-enabled> <speed>1000000</speed> <duplexity>im-attr-duplex-full</duplexity> <media-type>im-attr-media-other</media-type> <link-type>im-attr-link-type-force</link-type> <in-flow-control>im-attr-flow-control-off</in-flow-control> <out-flow-control>im-attr-flow-control-off</out-flow-control> <mac-address> <address>52:54:00:ff:23:6b</address> </mac-address> <burned-in-address> <address>52:54:00:ff:23:6b</address> </burned-in-address> <carrier-delay> <carrier-delay-up>10</carrier-delay-up> <carrier-delay-down>0</carrier-delay-down> </carrier-delay> <bandwidth>1000000</bandwidth> <max-bandwidth>1000000</max-bandwidth> <is-l2-looped>false</is-l2-looped> <description></description> <data-rates> 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<seconds-since-last-clear-counters>0</seconds-since-last-clear-counters> <last-discontinuity-time>1489787869</last-discontinuity-time> <seconds-since-packet-received>4294967295</seconds-since-packet-received> <seconds-since-packet-sent>4294967295</seconds-since-packet-sent> </full-interface-stats> </interface-statistics> <if-index>0</if-index> </interface> <interface> <interface-name>GigabitEthernet0/0/0/5</interface-name> <interface-handle>GigabitEthernet0/0/0/5</interface-handle> <interface-type>IFT_GETHERNET</interface-type> <hardware-type-string>GigabitEthernet</hardware-type-string> <state>im-state-admin-down</state> <line-state>im-state-admin-down</line-state> <encapsulation>ether</encapsulation> <encapsulation-type-string>ARPA</encapsulation-type-string> <mtu>1514</mtu> <is-l2-transport-enabled>false</is-l2-transport-enabled> <state-transition-count>0</state-transition-count> <last-state-transition-time>1100377</last-state-transition-time> <is-dampening-enabled>false</is-dampening-enabled> <speed>1000000</speed> <duplexity>im-attr-duplex-full</duplexity> <media-type>im-attr-media-other</media-type> <link-type>im-attr-link-type-force</link-type> <in-flow-control>im-attr-flow-control-off</in-flow-control> <out-flow-control>im-attr-flow-control-off</out-flow-control> <mac-address> <address>52:54:00:ff:f5:54</address> </mac-address> <burned-in-address> <address>52:54:00:ff:f5:54</address> </burned-in-address> <carrier-delay> <carrier-delay-up>10</carrier-delay-up> <carrier-delay-down>0</carrier-delay-down> </carrier-delay> <bandwidth>1000000</bandwidth> <max-bandwidth>1000000</max-bandwidth> <is-l2-looped>false</is-l2-looped> <description></description> <data-rates> <input-data-rate>0</input-data-rate> <input-packet-rate>0</input-packet-rate> <output-data-rate>0</output-data-rate> <output-packet-rate>0</output-packet-rate> <peak-input-data-rate>0</peak-input-data-rate> <peak-input-packet-rate>0</peak-input-packet-rate> <peak-output-data-rate>0</peak-output-data-rate> <peak-output-packet-rate>0</peak-output-packet-rate> <bandwidth>1000000</bandwidth> <load-interval>9</load-interval> <output-load>0</output-load> <input-load>0</input-load> <reliability>255</reliability> </data-rates> <interface-statistics> <stats-type>full</stats-type> <full-interface-stats> <packets-received>0</packets-received> <bytes-received>0</bytes-received> <packets-sent>0</packets-sent> <bytes-sent>0</bytes-sent> <multicast-packets-received>0</multicast-packets-received> <broadcast-packets-received>0</broadcast-packets-received> <multicast-packets-sent>0</multicast-packets-sent> <broadcast-packets-sent>0</broadcast-packets-sent> <output-drops>0</output-drops> <output-queue-drops>0</output-queue-drops> <input-drops>0</input-drops> <input-queue-drops>0</input-queue-drops> <runt-packets-received>0</runt-packets-received> <giant-packets-received>0</giant-packets-received> <throttled-packets-received>0</throttled-packets-received> <parity-packets-received>0</parity-packets-received> <unknown-protocol-packets-received>0</unknown-protocol-packets-received> <input-errors>0</input-errors> <crc-errors>0</crc-errors> <input-overruns>0</input-overruns> <framing-errors-received>0</framing-errors-received> <input-ignored-packets>0</input-ignored-packets> <input-aborts>0</input-aborts> <output-errors>0</output-errors> <output-underruns>0</output-underruns> <output-buffer-failures>0</output-buffer-failures> <output-buffers-swapped-out>0</output-buffers-swapped-out> <applique>0</applique> <resets>0</resets> <carrier-transitions>0</carrier-transitions> <availability-flag>0</availability-flag> <last-data-time>1490888108</last-data-time> <seconds-since-last-clear-counters>0</seconds-since-last-clear-counters> <last-discontinuity-time>1489787869</last-discontinuity-time> <seconds-since-packet-received>4294967295</seconds-since-packet-received> <seconds-since-packet-sent>4294967295</seconds-since-packet-sent> </full-interface-stats> </interface-statistics> <if-index>0</if-index> </interface> <interface> <interface-name>GigabitEthernet0/0/0/6</interface-name> <interface-handle>GigabitEthernet0/0/0/6</interface-handle> <interface-type>IFT_GETHERNET</interface-type> <hardware-type-string>GigabitEthernet</hardware-type-string> <state>im-state-admin-down</state> <line-state>im-state-admin-down</line-state> <encapsulation>ether</encapsulation> <encapsulation-type-string>ARPA</encapsulation-type-string> <mtu>1514</mtu> <is-l2-transport-enabled>false</is-l2-transport-enabled> <state-transition-count>0</state-transition-count> <last-state-transition-time>1100377</last-state-transition-time> <is-dampening-enabled>false</is-dampening-enabled> <speed>1000000</speed> <duplexity>im-attr-duplex-full</duplexity> <media-type>im-attr-media-other</media-type> <link-type>im-attr-link-type-force</link-type> <in-flow-control>im-attr-flow-control-off</in-flow-control> <out-flow-control>im-attr-flow-control-off</out-flow-control> <mac-address> <address>52:54:00:ff:d2:b1</address> </mac-address> <burned-in-address> <address>52:54:00:ff:d2:b1</address> </burned-in-address> <carrier-delay> <carrier-delay-up>10</carrier-delay-up> <carrier-delay-down>0</carrier-delay-down> </carrier-delay> <bandwidth>1000000</bandwidth> <max-bandwidth>1000000</max-bandwidth> <is-l2-looped>false</is-l2-looped> <description></description> <data-rates> <input-data-rate>0</input-data-rate> <input-packet-rate>0</input-packet-rate> <output-data-rate>0</output-data-rate> <output-packet-rate>0</output-packet-rate> <peak-input-data-rate>0</peak-input-data-rate> <peak-input-packet-rate>0</peak-input-packet-rate> <peak-output-data-rate>0</peak-output-data-rate> <peak-output-packet-rate>0</peak-output-packet-rate> <bandwidth>1000000</bandwidth> <load-interval>9</load-interval> <output-load>0</output-load> <input-load>0</input-load> <reliability>255</reliability> </data-rates> <interface-statistics> <stats-type>full</stats-type> <full-interface-stats> <packets-received>0</packets-received> <bytes-received>0</bytes-received> <packets-sent>0</packets-sent> <bytes-sent>0</bytes-sent> <multicast-packets-received>0</multicast-packets-received> <broadcast-packets-received>0</broadcast-packets-received> <multicast-packets-sent>0</multicast-packets-sent> <broadcast-packets-sent>0</broadcast-packets-sent> <output-drops>0</output-drops> <output-queue-drops>0</output-queue-drops> <input-drops>0</input-drops> <input-queue-drops>0</input-queue-drops> <runt-packets-received>0</runt-packets-received> <giant-packets-received>0</giant-packets-received> <throttled-packets-received>0</throttled-packets-received> <parity-packets-received>0</parity-packets-received> <unknown-protocol-packets-received>0</unknown-protocol-packets-received> <input-errors>0</input-errors> <crc-errors>0</crc-errors> <input-overruns>0</input-overruns> <framing-errors-received>0</framing-errors-received> <input-ignored-packets>0</input-ignored-packets> <input-aborts>0</input-aborts> <output-errors>0</output-errors> <output-underruns>0</output-underruns> <output-buffer-failures>0</output-buffer-failures> <output-buffers-swapped-out>0</output-buffers-swapped-out> <applique>0</applique> <resets>0</resets> <carrier-transitions>0</carrier-transitions> <availability-flag>0</availability-flag> <last-data-time>1490888108</last-data-time> <seconds-since-last-clear-counters>0</seconds-since-last-clear-counters> <last-discontinuity-time>1489787869</last-discontinuity-time> <seconds-since-packet-received>4294967295</seconds-since-packet-received> <seconds-since-packet-sent>4294967295</seconds-since-packet-sent> </full-interface-stats> </interface-statistics> <if-index>0</if-index> </interface> <interface> <interface-name>MgmtEth0/0/CPU0/0</interface-name> <interface-handle>MgmtEth0/0/CPU0/0</interface-handle> <interface-type>IFT_ETHERNET</interface-type> <hardware-type-string>Management Ethernet</hardware-type-string> <state>im-state-up</state> <line-state>im-state-up</line-state> <encapsulation>ether</encapsulation> <encapsulation-type-string>ARPA</encapsulation-type-string> <mtu>1514</mtu> <is-l2-transport-enabled>false</is-l2-transport-enabled> <state-transition-count>1</state-transition-count> <last-state-transition-time>1100222</last-state-transition-time> <is-dampening-enabled>false</is-dampening-enabled> <speed>1000000</speed> <duplexity>im-attr-duplex-unknown</duplexity> <media-type>im-attr-media-other</media-type> <link-type>im-attr-link-type-auto</link-type> <in-flow-control>im-attr-flow-control-off</in-flow-control> <out-flow-control>im-attr-flow-control-off</out-flow-control> <mac-address> <address>52:54:00:ff:99:42</address> </mac-address> <burned-in-address> <address>52:54:00:ff:99:42</address> </burned-in-address> <carrier-delay> <carrier-delay-up>10</carrier-delay-up> <carrier-delay-down>0</carrier-delay-down> </carrier-delay> <bandwidth>1000000</bandwidth> <max-bandwidth>1000000</max-bandwidth> <is-l2-looped>false</is-l2-looped> <description></description> <arp-information> <arp-timeout>14400</arp-timeout> <arp-type-name>ARPA</arp-type-name> <arp-is-learning-disabled>false</arp-is-learning-disabled> </arp-information> <ip-information> <ip-address>10.85.112.123</ip-address> <subnet-mask-length>25</subnet-mask-length> </ip-information> <data-rates> <input-data-rate>192</input-data-rate> <input-packet-rate>392</input-packet-rate> <output-data-rate>70</output-data-rate> <output-packet-rate>105</output-packet-rate> <peak-input-data-rate>0</peak-input-data-rate> <peak-input-packet-rate>0</peak-input-packet-rate> <peak-output-data-rate>0</peak-output-data-rate> <peak-output-packet-rate>0</peak-output-packet-rate> <bandwidth>1000000</bandwidth> <load-interval>9</load-interval> <output-load>0</output-load> <input-load>0</input-load> <reliability>255</reliability> </data-rates> <interface-statistics> <stats-type>full</stats-type> <full-interface-stats> <packets-received>228836760</packets-received> <bytes-received>13447429857</bytes-received> <packets-sent>56486840</packets-sent> <bytes-sent>4095136965</bytes-sent> <multicast-packets-received>1042005</multicast-packets-received> <broadcast-packets-received>174752</broadcast-packets-received> <multicast-packets-sent>0</multicast-packets-sent> <broadcast-packets-sent>21</broadcast-packets-sent> <output-drops>0</output-drops> <output-queue-drops>0</output-queue-drops> <input-drops>0</input-drops> <input-queue-drops>0</input-queue-drops> <runt-packets-received>0</runt-packets-received> <giant-packets-received>0</giant-packets-received> <throttled-packets-received>0</throttled-packets-received> <parity-packets-received>0</parity-packets-received> <unknown-protocol-packets-received>0</unknown-protocol-packets-received> <input-errors>0</input-errors> <crc-errors>0</crc-errors> <input-overruns>0</input-overruns> <framing-errors-received>0</framing-errors-received> <input-ignored-packets>0</input-ignored-packets> <input-aborts>0</input-aborts> <output-errors>0</output-errors> <output-underruns>0</output-underruns> <output-buffer-failures>0</output-buffer-failures> <output-buffers-swapped-out>0</output-buffers-swapped-out> <applique>0</applique> <resets>0</resets> <carrier-transitions>1</carrier-transitions> <availability-flag>0</availability-flag> <last-data-time>1490888108</last-data-time> <seconds-since-last-clear-counters>0</seconds-since-last-clear-counters> <last-discontinuity-time>1489787864</last-discontinuity-time> <seconds-since-packet-received>0</seconds-since-packet-received> <seconds-since-packet-sent>0</seconds-since-packet-sent> </full-interface-stats> </interface-statistics> <if-index>0</if-index> </interface> <interface> <interface-name>Null0</interface-name> <interface-handle>Null0</interface-handle> <interface-type>IFT_NULL</interface-type> <hardware-type-string>Null interface</hardware-type-string> <state>im-state-up</state> <line-state>im-state-up</line-state> <encapsulation>null</encapsulation> <encapsulation-type-string>Null</encapsulation-type-string> <mtu>1500</mtu> <is-l2-transport-enabled>false</is-l2-transport-enabled> <state-transition-count>1</state-transition-count> <last-state-transition-time>1100254</last-state-transition-time> <is-dampening-enabled>false</is-dampening-enabled> <bandwidth>0</bandwidth> <max-bandwidth>0</max-bandwidth> <is-l2-looped>false</is-l2-looped> <description></description> <data-rates> <input-data-rate>0</input-data-rate> <input-packet-rate>0</input-packet-rate> <output-data-rate>0</output-data-rate> <output-packet-rate>0</output-packet-rate> <peak-input-data-rate>0</peak-input-data-rate> <peak-input-packet-rate>0</peak-input-packet-rate> <peak-output-data-rate>0</peak-output-data-rate> <peak-output-packet-rate>0</peak-output-packet-rate> <bandwidth>0</bandwidth> <load-interval>9</load-interval> <output-load>0</output-load> <input-load>0</input-load> <reliability>255</reliability> </data-rates> <interface-statistics> <stats-type>full</stats-type> <full-interface-stats> <packets-received>0</packets-received> <bytes-received>0</bytes-received> <packets-sent>0</packets-sent> <bytes-sent>0</bytes-sent> <multicast-packets-received>0</multicast-packets-received> <broadcast-packets-received>0</broadcast-packets-received> <multicast-packets-sent>0</multicast-packets-sent> <broadcast-packets-sent>0</broadcast-packets-sent> <output-drops>0</output-drops> <output-queue-drops>0</output-queue-drops> <input-drops>0</input-drops> <input-queue-drops>0</input-queue-drops> <runt-packets-received>0</runt-packets-received> <giant-packets-received>0</giant-packets-received> <throttled-packets-received>0</throttled-packets-received> <parity-packets-received>0</parity-packets-received> <unknown-protocol-packets-received>0</unknown-protocol-packets-received> <input-errors>0</input-errors> <crc-errors>0</crc-errors> <input-overruns>0</input-overruns> <framing-errors-received>0</framing-errors-received> <input-ignored-packets>0</input-ignored-packets> <input-aborts>0</input-aborts> <output-errors>0</output-errors> <output-underruns>0</output-underruns> <output-buffer-failures>0</output-buffer-failures> <output-buffers-swapped-out>0</output-buffers-swapped-out> <applique>0</applique> <resets>0</resets> <carrier-transitions>0</carrier-transitions> <availability-flag>0</availability-flag> <last-data-time>1490888108</last-data-time> <seconds-since-last-clear-counters>0</seconds-since-last-clear-counters> <last-discontinuity-time>1489787884</last-discontinuity-time> <seconds-since-packet-received>4294967295</seconds-since-packet-received> <seconds-since-packet-sent>4294967295</seconds-since-packet-sent> </full-interface-stats> </interface-statistics> <if-index>0</if-index> </interface> </interface-xr> </interfaces> </data> ''') golden_output = {'get.return_value': etree_holder()} def test_golden(self): self.device = Mock(**self.golden_output) intf_obj = ShowEthernetTags(device=self.device) intf_obj.context = Context.yang.value.split() parsed_output = intf_obj.parse() self.assertEqual(parsed_output,self.golden_parsed_output) empty_parsed_output = {'interface': {'GigabitEthernet0/0/0/0': {'sub_interface': {'GigabitEthernet0/0/0/0.501': {}}}}} class empty_etree_holder(): def __init__(self): self.data = ET.fromstring(''' <data> <interfaces xmlns="http://cisco.com/ns/yang/Cisco-IOS-XR-pfi-im-cmd-oper"> <interface-xr> <interface> <interface-name>GigabitEthernet0/0/0/0.501</interface-name> <interface-handle>GigabitEthernet0/0/0/0.501</interface-handle> <interface-type>IFT_VLAN_SUBIF</interface-type> <hardware-type-string>VLAN sub-interface(s)</hardware-type-string> <state>im-state-up</state> <line-state>im-state-up</line-state> <encapsulation>dot1q</encapsulation> <encapsulation-type-string>802.1Q</encapsulation-type-string> <mtu>1522</mtu> <is-l2-transport-enabled>false</is-l2-transport-enabled> <state-transition-count>1</state-transition-count> <last-state-transition-time>1100222</last-state-transition-time> <is-dampening-enabled>false</is-dampening-enabled> <speed>1000000</speed> <mac-address> <address>52:54:00:ff:50:0e</address> </mac-address> <carrier-delay> <carrier-delay-up>0</carrier-delay-up> <carrier-delay-down>0</carrier-delay-down> </carrier-delay> <bandwidth>1000000</bandwidth> <max-bandwidth>1000000</max-bandwidth> <is-l2-looped>false</is-l2-looped> <parent-interface-name>GigabitEthernet0/0/0/0</parent-interface-name> <description></description> <encapsulation-information> </encapsulation-information> <data-rates> <input-data-rate>0</input-data-rate> <input-packet-rate>0</input-packet-rate> <output-data-rate>0</output-data-rate> <output-packet-rate>0</output-packet-rate> <peak-input-data-rate>0</peak-input-data-rate> <peak-input-packet-rate>0</peak-input-packet-rate> <peak-output-data-rate>0</peak-output-data-rate> <peak-output-packet-rate>0</peak-output-packet-rate> <bandwidth>1000000</bandwidth> <load-interval>9</load-interval> <output-load>0</output-load> <input-load>0</input-load> <reliability>255</reliability> </data-rates> <interface-statistics> <stats-type>full</stats-type> <full-interface-stats> <packets-received>0</packets-received> <bytes-received>0</bytes-received> <packets-sent>0</packets-sent> <bytes-sent>0</bytes-sent> <multicast-packets-received>0</multicast-packets-received> <broadcast-packets-received>0</broadcast-packets-received> <multicast-packets-sent>0</multicast-packets-sent> <broadcast-packets-sent>0</broadcast-packets-sent> <output-drops>0</output-drops> <output-queue-drops>0</output-queue-drops> <input-drops>0</input-drops> <input-queue-drops>0</input-queue-drops> <runt-packets-received>0</runt-packets-received> <giant-packets-received>0</giant-packets-received> <throttled-packets-received>0</throttled-packets-received> <parity-packets-received>0</parity-packets-received> <unknown-protocol-packets-received>0</unknown-protocol-packets-received> <input-errors>0</input-errors> <crc-errors>0</crc-errors> <input-overruns>0</input-overruns> <framing-errors-received>0</framing-errors-received> <input-ignored-packets>0</input-ignored-packets> <input-aborts>0</input-aborts> <output-errors>0</output-errors> <output-underruns>0</output-underruns> <output-buffer-failures>0</output-buffer-failures> <output-buffers-swapped-out>0</output-buffers-swapped-out> <applique>0</applique> <resets>0</resets> <carrier-transitions>0</carrier-transitions> <availability-flag>0</availability-flag> <last-data-time>1490888108</last-data-time> <seconds-since-last-clear-counters>0</seconds-since-last-clear-counters> <last-discontinuity-time>1489787915</last-discontinuity-time> <seconds-since-packet-received>4294967295</seconds-since-packet-received> <seconds-since-packet-sent>4294967295</seconds-since-packet-sent> </full-interface-stats> </interface-statistics> <if-index>0</if-index> </interface> </interface-xr> </interfaces> </data> ''') empty_output = {'get.return_value': empty_etree_holder()} def test_empty(self): self.device1 = Mock(**self.empty_output) intf_obj = ShowEthernetTags(device=self.device1) intf_obj.context = Context.yang.value.split() parsed_output = intf_obj.parse() self.assertEqual(parsed_output,self.empty_parsed_output) if __name__ == '__main__': unittest.main()
2.234375
2
pycket/prims/hash.py
namin/pycket
129
12788878
#! /usr/bin/env python # -*- coding: utf-8 -*- import sys from pycket import impersonators as imp from pycket import values, values_string from pycket.hash.base import W_HashTable, W_ImmutableHashTable, w_missing from pycket.hash.simple import ( W_EqvMutableHashTable, W_EqMutableHashTable, W_EqvImmutableHashTable, W_EqImmutableHashTable, make_simple_mutable_table, make_simple_mutable_table_assocs, make_simple_immutable_table, make_simple_immutable_table_assocs) from pycket.hash.equal import W_EqualHashTable from pycket.impersonators.baseline import W_ImpHashTable, W_ChpHashTable from pycket.cont import continuation, loop_label from pycket.error import SchemeException from pycket.prims.expose import default, expose, procedure, define_nyi from rpython.rlib import jit, objectmodel _KEY = 0 _VALUE = 1 _KEY_AND_VALUE = 2 _PAIR = 3 PREFIXES = ["unsafe-mutable", "unsafe-immutable"] def prefix_hash_names(base): result = [base] for pre in PREFIXES: result.append("%s-%s" % (pre, base)) return result @expose(prefix_hash_names("hash-iterate-first"), [W_HashTable]) def hash_iterate_first(ht): if ht.length() == 0: return values.w_false return values.W_Fixnum.ZERO @expose(prefix_hash_names("hash-iterate-next"), [W_HashTable, values.W_Fixnum]) def hash_iterate_next(ht, pos): return ht.hash_iterate_next(pos) @objectmodel.specialize.arg(4) def hash_iter_ref(ht, n, env, cont, returns): from pycket.interpreter import return_value, return_multi_vals try: w_key, w_val = ht.get_item(n) if returns == _KEY: return return_value(w_key, env, cont) if returns == _VALUE: return return_value(w_val, env, cont) if returns == _KEY_AND_VALUE: vals = values.Values._make2(w_key, w_val) return return_multi_vals(vals, env, cont) if returns == _PAIR: vals = values.W_Cons.make(w_key, w_val) return return_value(vals, env, cont) assert False, "unknown return code" except KeyError: raise SchemeException("hash-iterate-key: invalid position") except IndexError: raise SchemeException("hash-iterate-key: invalid position") @expose(prefix_hash_names("hash-iterate-key"), [W_HashTable, values.W_Fixnum], simple=False) def hash_iterate_key(ht, pos, env, cont): return hash_iter_ref(ht, pos.value, env, cont, returns=_KEY) @expose(prefix_hash_names("hash-iterate-value"), [W_HashTable, values.W_Fixnum], simple=False) def hash_iterate_value(ht, pos, env, cont): return hash_iter_ref(ht, pos.value, env, cont, returns=_VALUE) @expose(prefix_hash_names("hash-iterate-key+value"), [W_HashTable, values.W_Fixnum], simple=False) def hash_iterate_key_value(ht, pos, env, cont): return hash_iter_ref(ht, pos.value, env, cont, returns=_KEY_AND_VALUE) @expose(prefix_hash_names("hash-iterate-pair"), [W_HashTable, values.W_Fixnum], simple=False) def hash_iterate_pair(ht, pos, env, cont): return hash_iter_ref(ht, pos.value, env, cont, returns=_PAIR) @expose("hash-for-each", [W_HashTable, procedure, default(values.W_Object, values.w_false)], simple=False) def hash_for_each(ht, f, try_order, env, cont): # FIXME: implmeent try-order? -- see hash-map return hash_for_each_loop(ht, f, 0, env, cont) @loop_label def hash_for_each_loop(ht, f, index, env, cont): from pycket.interpreter import return_value try: w_key, w_value = ht.get_item(index) except KeyError: return hash_for_each_loop(ht, f, index + 1, env, cont) except IndexError: return return_value(values.w_void, env, cont) return f.call([w_key, w_value], env, hash_for_each_cont(ht, f, index, env, cont)) @continuation def hash_for_each_cont(ht, f, index, env, cont, _vals): return hash_for_each_loop(ht, f, index + 1, env, cont) @expose("hash-map", [W_HashTable, procedure, default(values.W_Object, values.w_false)], simple=False) def hash_map(h, f, try_order, env, cont): # FIXME : If try-order? is true, then the order of keys and values # passed to proc is normalized under certain circumstances, such # as when the keys are all symbols and hash is not an # impersonator. from pycket.interpreter import return_value acc = values.w_null return hash_map_loop(f, h, 0, acc, env, cont) # f.enable_jitting() # return return_value(w_missing, env, # hash_map_cont(f, h, 0, acc, env, cont)) @loop_label def hash_map_loop(f, ht, index, w_acc, env, cont): from pycket.interpreter import return_value try: w_key, w_value = ht.get_item(index) except KeyError: return hash_map_loop(f, ht, index + 1, w_acc, env, cont) except IndexError: return return_value(w_acc, env, cont) after = hash_map_cont(f, ht, index, w_acc, env, cont) return f.call([w_key, w_value], env, after) @continuation def hash_map_cont(f, ht, index, w_acc, env, cont, _vals): from pycket.interpreter import check_one_val w_val = check_one_val(_vals) w_acc = values.W_Cons.make(w_val, w_acc) return hash_map_loop(f, ht, index + 1, w_acc, env, cont) @jit.elidable def from_assocs(assocs, fname): if not assocs.is_proper_list(): raise SchemeException("%s: expected proper list" % fname) keys = [] vals = [] while isinstance(assocs, values.W_Cons): val, assocs = assocs.car(), assocs.cdr() if not isinstance(val, values.W_Cons): raise SchemeException("%s: expected list of pairs" % fname) keys.append(val.car()) vals.append(val.cdr()) return keys[:], vals[:] @expose("make-weak-hasheq", [default(values.W_List, values.w_null)]) def make_weak_hasheq(assocs): # FIXME: not actually weak return make_simple_mutable_table_assocs(W_EqMutableHashTable, assocs, "make-weak-hasheq") @expose("make-weak-hasheqv", [default(values.W_List, values.w_null)]) def make_weak_hasheqv(assocs): # FIXME: not actually weak return make_simple_mutable_table_assocs(W_EqvMutableHashTable, assocs, "make-weak-hasheqv") @expose(["make-weak-hash", "make-late-weak-hasheq"], [default(values.W_List, None)]) def make_weak_hash(assocs): if assocs is None: return W_EqualHashTable([], [], immutable=False) return W_EqualHashTable(*from_assocs(assocs, "make-weak-hash"), immutable=False) @expose("make-immutable-hash", [default(values.W_List, values.w_null)]) def make_immutable_hash(assocs): keys, vals = from_assocs(assocs, "make-immutable-hash") return W_EqualHashTable(keys, vals, immutable=True) @expose("make-immutable-hasheq", [default(values.W_List, values.w_null)]) def make_immutable_hasheq(assocs): return make_simple_immutable_table_assocs(W_EqImmutableHashTable, assocs, "make-immutable-hasheq") @expose("make-immutable-hasheqv", [default(values.W_List, values.w_null)]) def make_immutable_hasheqv(assocs): return make_simple_immutable_table_assocs(W_EqvImmutableHashTable, assocs, "make-immutable-hasheq") @expose("hash") def hash(args): if len(args) % 2 != 0: raise SchemeException("hash: key does not have a corresponding value") keys = [args[i] for i in range(0, len(args), 2)] vals = [args[i] for i in range(1, len(args), 2)] return W_EqualHashTable(keys, vals, immutable=True) @expose("hasheq") def hasheq(args): if len(args) % 2 != 0: raise SchemeException("hasheq: key does not have a corresponding value") keys = [args[i] for i in range(0, len(args), 2)] vals = [args[i] for i in range(1, len(args), 2)] return make_simple_immutable_table(W_EqImmutableHashTable, keys, vals) @expose("hasheqv") def hasheqv(args): if len(args) % 2 != 0: raise SchemeException("hasheqv: key does not have a corresponding value") keys = [args[i] for i in range(0, len(args), 2)] vals = [args[i] for i in range(1, len(args), 2)] return make_simple_immutable_table(W_EqvImmutableHashTable, keys, vals) @expose("make-hash", [default(values.W_List, values.w_null)]) def make_hash(pairs): return W_EqualHashTable(*from_assocs(pairs, "make-hash")) @expose("make-hasheq", [default(values.W_List, values.w_null)]) def make_hasheq(pairs): return make_simple_mutable_table_assocs(W_EqMutableHashTable, pairs, "make-hasheq") @expose("make-hasheqv", [default(values.W_List, values.w_null)]) def make_hasheqv(pairs): return make_simple_mutable_table_assocs(W_EqvMutableHashTable, pairs, "make-hasheqv") @expose("hash-set!", [W_HashTable, values.W_Object, values.W_Object], simple=False) def hash_set_bang(ht, k, v, env, cont): if ht.immutable(): raise SchemeException("hash-set!: given immutable table") return ht.hash_set(k, v, env, cont) @continuation def hash_set_cont(key, val, env, cont, _vals): from pycket.interpreter import check_one_val table = check_one_val(_vals) return table.hash_set(key, val, env, return_table_cont(table, env, cont)) @continuation def return_table_cont(table, env, cont, _vals): from pycket.interpreter import return_value return return_value(table, env, cont) @expose("hash-set", [W_HashTable, values.W_Object, values.W_Object], simple=False) def hash_set(table, key, val, env, cont): from pycket.interpreter import return_value if not table.immutable(): raise SchemeException("hash-set: not given an immutable table") # Fast path if isinstance(table, W_ImmutableHashTable): new_table = table.assoc(key, val) return return_value(new_table, env, cont) return hash_copy(table, env, hash_set_cont(key, val, env, cont)) @continuation def hash_ref_cont(default, k, env, cont, _vals): from pycket.interpreter import return_value, check_one_val val = check_one_val(_vals) if val is not w_missing: return return_value(val, env, cont) if default is None: raise SchemeException("key %s not found"%k.tostring()) if default.iscallable(): return default.call([], env, cont) return return_value(default, env, cont) @expose("hash-ref", [W_HashTable, values.W_Object, default(values.W_Object, None)], simple=False) def hash_ref(ht, k, default, env, cont): return ht.hash_ref(k, env, hash_ref_cont(default, k, env, cont)) @expose("hash-remove!", [W_HashTable, values.W_Object], simple=False) def hash_remove_bang(ht, k, env, cont): if ht.immutable(): raise SchemeException("hash-remove!: expected mutable hash table") return ht.hash_remove_inplace(k, env, cont) @expose("hash-remove", [W_HashTable, values.W_Object], simple=False) def hash_remove(ht, k, env, cont): if not ht.immutable(): raise SchemeException("hash-remove: expected immutable hash table") return ht.hash_remove(k, env, cont) @continuation def hash_clear_cont(ht, env, cont, _vals): return hash_clear_loop(ht, env, cont) def hash_clear_loop(ht, env, cont): from pycket.interpreter import return_value if ht.length() == 0: return return_value(values.w_void, env, cont) w_k, w_v = ht.get_item(0) return ht.hash_remove_inplace(w_k, env, hash_clear_cont(ht, env, cont)) @expose("hash-clear!", [W_HashTable], simple=False) def hash_clear_bang(ht, env, cont): from pycket.interpreter import return_value if ht.is_impersonator(): ht.hash_clear_proc(env, cont) return hash_clear_loop(ht, env, cont) else: ht.hash_empty() return return_value(values.w_void, env, cont) define_nyi("hash-clear", [W_HashTable]) @expose("hash-count", [W_HashTable]) def hash_count(hash): return values.W_Fixnum(hash.length()) @continuation def hash_keys_subset_huh_cont(keys_vals, hash_2, idx, env, cont, _vals): from pycket.interpreter import return_value, check_one_val val = check_one_val(_vals) if val is values.w_false: return return_value(values.w_false, env, cont) else: return hash_keys_subset_huh_loop(keys_vals, hash_2, idx + 1, env, cont) @loop_label def hash_keys_subset_huh_loop(keys_vals, hash_2, idx, env, cont): from pycket.interpreter import return_value if idx >= len(keys_vals): return return_value(values.w_true, env, cont) else: return hash_ref([hash_2, keys_vals[idx][0], values.w_false], env, hash_keys_subset_huh_cont(keys_vals, hash_2, idx, env, cont)) @jit.elidable def uses_same_eq_comparison(hash_1, hash_2): h_1 = hash_1 h_2 = hash_2 if hash_1.is_impersonator() or hash_1.is_chaperone(): h_1 = hash_1.get_proxied() if hash_2.is_impersonator() or hash_2.is_chaperone(): h_2 = hash_2.get_proxied() if isinstance(h_1, W_EqualHashTable): return isinstance(h_2, W_EqualHashTable) elif isinstance(h_1, W_EqMutableHashTable) or isinstance(h_1, W_EqImmutableHashTable): return isinstance(h_2, W_EqMutableHashTable) or isinstance(h_2, W_EqImmutableHashTable) elif isinstance(h_1, W_EqvMutableHashTable) or isinstance(h_1, W_EqvImmutableHashTable): return isinstance(h_2, W_EqvMutableHashTable) or isinstance(h_2, W_EqvImmutableHashTable) else: return False @expose("hash-keys-subset?", [W_HashTable, W_HashTable], simple=False) def hash_keys_subset_huh(hash_1, hash_2, env, cont): if not uses_same_eq_comparison(hash_1, hash_2): raise SchemeException("hash-keys-subset?: given hash tables do not use the same key comparison -- first table : %s - second table: %s" % (hash_1.tostring(), hash_2.tostring())) return hash_keys_subset_huh_loop(hash_1.hash_items(), hash_2, 0, env, cont) @continuation def hash_copy_ref_cont(keys, idx, src, new, env, cont, _vals): from pycket.interpreter import check_one_val val = check_one_val(_vals) return new.hash_set(keys[idx][0], val, env, hash_copy_set_cont(keys, idx, src, new, env, cont)) @continuation def hash_copy_set_cont(keys, idx, src, new, env, cont, _vals): return hash_copy_loop(keys, idx + 1, src, new, env, cont) @loop_label def hash_copy_loop(keys, idx, src, new, env, cont): from pycket.interpreter import return_value if idx >= len(keys): return return_value(new, env, cont) return src.hash_ref(keys[idx][0], env, hash_copy_ref_cont(keys, idx, src, new, env, cont)) def hash_copy(src, env, cont): from pycket.interpreter import return_value if isinstance(src, W_ImmutableHashTable): new = src.make_copy() return return_value(new, env, cont) new = src.make_empty() if src.length() == 0: return return_value(new, env, cont) return hash_copy_loop(src.hash_items(), 0, src, new, env, cont) expose("hash-copy", [W_HashTable], simple=False)(hash_copy) # FIXME: not implemented @expose("equal-hash-code", [values.W_Object]) def equal_hash_code(v): # only for improper path cache entries if isinstance(v, values.W_Cons): if v.is_proper_list(): return values.W_Fixnum.ZERO nm = v.car() p = v.cdr() if isinstance(nm, values_string.W_String) and \ isinstance(p, values.W_Path) and \ isinstance(p.path, str): return values.W_Fixnum(objectmodel.compute_hash((nm.tostring(), p.path))) return values.W_Fixnum.ZERO @expose("equal-secondary-hash-code", [values.W_Object]) def equal_secondary_hash_code(v): return values.W_Fixnum.ZERO @expose("eq-hash-code", [values.W_Object]) def eq_hash_code(v): t = type(v) if t is values.W_Fixnum: return v if t is values.W_Flonum: hash = objectmodel.compute_hash(v.value) elif t is values.W_Character: hash = objectmodel.compute_hash(v.value) else: hash = objectmodel.compute_hash(v) return values.W_Fixnum(hash) @expose("eqv-hash-code", [values.W_Object]) def eqv_hash_code(v): hash = v.hash_eqv() return values.W_Fixnum(hash)
2.234375
2
bootloader/waflib/Tools/bison.py
BA7JCM/pyinstaller
0
12788879
<gh_stars>0 #! /usr/bin/env python # encoding: utf-8 # WARNING! Do not edit! https://waf.io/book/index.html#_obtaining_the_waf_file from waflib import Task from waflib.TaskGen import extension class bison(Task.Task): color = 'BLUE' run_str = '${BISON} ${BISONFLAGS} ${SRC[0].abspath()} -o ${TGT[0].name}' ext_out = ['.h'] @extension('.y', '.yc', '.yy') def big_bison(self, node): has_h = '-d' in self.env.BISONFLAGS outs = [] if node.name.endswith('.yc'): outs.append(node.change_ext('.tab.cc')) if has_h: outs.append(node.change_ext('.tab.hh')) else: outs.append(node.change_ext('.tab.c')) if has_h: outs.append(node.change_ext('.tab.h')) tsk = self.create_task('bison', node, outs) tsk.cwd = node.parent.get_bld() self.source.append(outs[0]) def configure(conf): conf.find_program('bison', var='BISON') conf.env.BISONFLAGS = ['-d']
2.140625
2
sharpen_image.py
danielskatz/parsl-example
9
12788880
import sys try: from PIL import Image, ImageFilter except ImportError: print("error:", sys.argv[0], "requires Pillow - install it via 'pip install Pillow'") sys.exit(2) if len(sys.argv) != 3: print("error - usage:", sys.argv[0], "input_file output_file") sys.exit(2) input_filename = sys.argv[1] output_filename = sys.argv[2] #Read image try: im = Image.open(input_filename) except OSError: print("error - can't open file:", input_file) sys.exit(2) #Apply a filter to the image im_sharp = im.filter(ImageFilter.SHARPEN) #Save the filtered image to a new file im_sharp.save(output_filename, 'JPEG')
3.328125
3
helbing_model.py
csebastiao/helbing_clogging
0
12788881
<gh_stars>0 # -*- coding: utf-8 -*- """ Model based on the Helbing model of "Simulation dynamical features of escape panic" to simulate fish going through a hole in a wall. """ import numpy as np import sys import pandas as pd from sklearn.neighbors import NearestNeighbors class Fish(): """ Fish that wants to swim to an objective out of the box. Attributes ---------- size : float Radius of the sphere representing the size of the fish mass : float Mass of the fish position : numpy.array Position of the fish as an array [x, y] speed : numpy.array Speed of the fish as an array [vx, vy] desired_speed : float Instant speed that the fish wants to have objective : numpy.array Actual position of the objective that the fish want to go to first_objective : numpy.array First objective of the fish, once attained, change to the second second_objective : numpy.array Second objective of the fish, once the first is attained force : numpy.array Force and social force on the fish as an array [fx, fy] char_time : float Characteristic time for a fish to get to his desired speed color : str Color of the fish, changes when the objective changes. """ def __init__(self, s, m, pos, tau, des_v, fobj, sobj): self.size = s self.mass = m self.position = pos self.speed = np.array([0., 0.]) self.desired_speed = des_v self.objective = fobj self.first_objective = fobj self.second_objective = sobj self.force = np.array([0., 0.]) self.char_time = tau self.color = 'b' def getCoords(self): " Returns the position of the fish" return self.position def getColor(self): " Returns the color of the fish" return self.color def getSize(self): " Returns the radius of the fish" return self.size def objective_speed(self): """ Returns the objective speed of the fish, which is what speed the fish wants to go, depending on his own position, the position of the objective and his desired speed. That way, the closer the objective is the smaller the objective speed is, even though his desired speed is a constant. Returns ------- numpy.array Objective speed of the fish """ return (self.desired_speed* (self.objective - self.position)/ np.linalg.norm((self.objective - self.position))) def get_neighbors(self, fish_list, N = 8): """ Finds the N neighbors of a fish Parameters ---------- fish_list : list List of every fish. N : int, optional Number of neighbors for a fish. The default is 5. Returns ------- n_list : list List of the N fish considered as the neighbors. Notes ---------- See also get_positions() """ neigh = NearestNeighbors(n_neighbors = N) pos_list, exc_list = get_positions(self, fish_list) neigh.fit(pos_list) #fit the NN to the values closest = neigh.kneighbors([self.position], return_distance = False) #just take indices n_list = [] for i in closest[0] : n_list.append(exc_list[i]) return n_list def force_friction(self, other_fish, A, B, k, kappa): """ Returns the force of friction of a fish onto another. Parameters ---------- other_fish : Fish Fish with who we measure the force of friction. A : float Repulsion constant. B : float Repulsion constant. k : float Body force constant. kappa : float Sliding friction force constant. Returns ------- numpy.array Returns the force of friction of other_fish on self, as [fx, fy]. Notes ---------- See also gfunc() """ d = np.linalg.norm((self.position - other_fish.position)) #distance n = np.array((self.position - other_fish.position) / d) #normalized # vector going from the other fish to self sum_r = self.size + other_fish.size #sum of the radius of both fish t = np.array([-n[1], n[0]]) #normalized tangential vector tvd = np.dot((self.speed - other_fish.speed), t) #tangential velocity # difference rep_f = A * np.exp((sum_r - d)/B) * n #repulsion force bod_f = k * gfunc(sum_r - d, d, sum_r) * n #body force sli_f = kappa * gfunc(sum_r - d, d, sum_r) * tvd * t #sliding #friction force return rep_f + bod_f + sli_f def force_wall(self, wall, A, B, k, kappa): """ Returns the force between the fish and the walls. Parameters ---------- wall : list Position of the upper and down coordinates of the hole in the wall, as [[xu, yu], [xd, yd]] A : float Repulsion constant. B : float Repulsion constant. k : float Body force constant. kappa : float Sliding friction force constant. Returns ------- numpy.array Returns the force of friction of the wall on self, as [fx, fy]. Notes ---------- See also gfunc() """ #find the closest position of the wall to the fish if self.position[1] > wall[0][1] or self.position[1] < wall[1][1] : if self.position[0] - wall[0][0] < 0: wall_pos = [wall[0][0], self.position[1]] #Make a thickness to the walls, as thick as the hole_size elif self.position[0] - wall[0][0] < wall[0][1] + wall[1][1]: wall_pos = [self.position[0] + 0.1, self.position[1]] else : wall_pos = [wall[0][0], self.position[1]] elif self.position[1] >= 0 : wall_pos = wall[0] elif self.position[1] < 0 : wall_pos = wall[1] d = np.linalg.norm((self.position - wall_pos)) #see force_friction n = np.array((self.position - wall_pos) / d) sum_r = self.size t = np.array([-n[1], n[0]]) tvd = np.dot(self.speed, t) rep_f = A * np.exp((sum_r - d)/B) * n bod_f = k * gfunc(sum_r - d, d, sum_r) * n sli_f = kappa * gfunc(sum_r - d, d, sum_r) * tvd * t return rep_f + bod_f - sli_f def total_force(self, fish_list, wall, A, B, k, kappa): """ Returns the total force exerted on a fish from other fish, the wall and the urge to go to the objective. Parameters ---------- other_fish : Fish Fish with who we measure the force of friction. wall : list Position of the upper and down coordinates of the hole in the wall, as [[xu, yu], [xd, yd]] A : float Repulsion constant. B : float Repulsion constant. k : float Body force constant. kappa : float Sliding friction force constant. Returns ------- numpy.array Total force on the fish as an array [fx, fy]. Notes ---------- See also get_neighbors(), force_friction(), and force_wall() """ ff = np.array([0.,0.]) neighbors = self.get_neighbors(fish_list) #get every neighbors for j in range(len(neighbors)): #add friction_force for each one ff += self.force_friction(neighbors[j], A, B, k, kappa) fw = self.force_wall(wall, A, B, k, kappa) fs = self.mass * (self.objective_speed() - self.speed)/self.char_time return fs + ff + fw def update_force(self, fish_list, wall, A, B, k, kappa): " Update the force on the fish, see total_force()" self.force = self.total_force(fish_list, wall, A, B, k, kappa) def update_status(self, dt): " Update the position and speed of the fish, see verlet_alg()" self.position, self.speed = verlet_alg(self.position, self.speed, self.force, self.mass, dt) # def update_objective(self, d = 0.2): # " Update the position of the actual objective of the fish" # if self.objective == self.first_objective: # if (self.position[0] > self.objective[0]) or ( # angle_between(-(self.objective - self.position), # [-1, 0]) < (np.pi/12)) or ( # np.linalg.norm((self.position - # self.first_objective[0])) < d ) : # self.objective = self.second_objective # self.color = 'orange' #change the color with the objective # elif self.objective == self.second_objective: # if (angle_between(-(self.objective - self.position), [-1, 0]) > # (np.pi/12)) and (np.linalg.norm(( # self.position - self.first_objective)) > d) and ( # self.position[0] < self.first_objective[0]): # self.objective = self.first_objective # self.color = 'b' #change the color with the objective def update_objective(self, d = 0.35): " Update the position of the actual objective of the fish" if self.objective == self.first_objective: if ((np.linalg.norm((self.position - self.first_objective)) < d) or (self.position[0] > self.first_objective[0])) : self.objective = self.second_objective self.color = 'orange' #change the color with the objective def evolveTimeStep(self, fish_list, wall, A, B, k, kappa, dt): " Make one timestep with an update of the characteristics of the fish" self.update_force(fish_list, wall, A, B, k, kappa) self.update_status(dt) self.update_objective() def verlet_first(pos, vel, force, m, delta_t): "First part of the Verlet algorithm, see verlet_alg" r = np.array(pos) v = np.array(vel) a = np.array(force/m) return r + delta_t * v + 0.5 * a * delta_t**2, v + 0.5 * delta_t * a def verlet_second(pos, vel, force, m, delta_t): "Second part of the Verlet algorithm, see verlet_alg" r = np.array(pos) v = np.array(vel) a = np.array(force/m) return r, v + 0.5 * delta_t * a def verlet_alg(pos, vel, force, m, delta_t): """ Verlet algorithm for the evolution of the position of a particle. Need to be on two steps because we need "half-time" values. Useful because energy is conserved (with small oscillations around the value). Parameters ---------- pos : numpy.array Initial position as an array [x, y]. vel : numpy.array Initial speed as an array [vx, vy]. force : numpy.array Initial force as an array [fx, fy]. m : float Mass of the particle. delta_t : float Time passed between the initial and final time. Returns ------- r : numpy.array Updated position as an array [x, y]. v : numpy.array Updated speed as an array [vx, vy]. """ r, v = verlet_first(pos, vel, force, m, delta_t) r, v = verlet_second(r, v, force, m, delta_t) return r, v def gfunc(x, d, r): "g function from Helbing, useful for contact forces." if d > r : return 0 else : return x def get_positions(target, fish_list): """ We want to get the positions (and the list of corresponding fish) of every fish except the target fish. Parameters ---------- target : Fish Target fish. fish_list : list List of every fish. Returns ------- pos_list : list List of positions of every fish except the target fish. exc_list : list List of every fish except the target fish. """ pos_list = [] exc_list = [] for fish in fish_list: if fish == target : pass else : pos_list.append([fish.position[0], fish.position[1]]) exc_list.append(fish) return pos_list, exc_list def unit_vector(vector): """ Returns the unit vector of the vector. """ return vector / np.linalg.norm(vector) def angle_between(v1, v2): """ Returns the angle in radians between vectors 'v1' and 'v2'. """ v1_u = unit_vector(v1) v2_u = unit_vector(v2) return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) def crystal_init_pos(xmin, xmax, ymin, ymax, n): """ Initialized ordered positions of n**2 object in a box between xmin and xmax and ymin and ymax such that they are not on top of each other. We use n such that we know that there is an integer as a square root of the number of object to get the same number of full rows and columns. Parameters ---------- xmin : float Minimal x value. xmax : float Maximal x value. ymin : float Minimal y value. ymax : float Maximal y value. n : int Square root of the number of fish. Returns ------- xp : list x coordinates of every ordered object. yp : list y coordinates of every ordered object. """ xp = [] yp = [] if n == 1: #avoid division by 0, put the only object in the middle xp.append((xmax - xmin)/2 + xmin) yp.append((ymax - ymin)/2 + ymin) else : for i in range(n): #variation of y then x, so columns after columns for j in range(n): xp.append(xmin + (i/(n-1))*(xmax-xmin)) yp.append(ymin + (j/(n-1))*(ymax-ymin)) return xp, yp def no_overlap(x, y, s, others): """ Verifies that there is no overlap between the target and other objects, they're all circles but with different sizes. Parameters ---------- x : float x-position of the target. y : float y-position of the target. s : float Radius of the target. others : list Other object listed as [[x1, y1, s1], [x2, y2, s2], ...]. Returns ------- bool True if there is no overlap, False otherwise. """ for o in others : if np.linalg.norm(np.array([x,y]) - np.array(o[:2])) < s + o[2]: return False else : pass return True def random_init_pos(C, R, size, others): """ Returns a random initial position within a circle of center C and radius R of an circle with a radius size, with no overlap with other objects. Parameters ---------- C : list Position of the center of the circle, as [xc, yc]. R : float Radius of the circle. size : float Radius of the randomly initialized object. others : list Other object listed as [[x1, y1, s1], [x2, y2, s2], ...]. Returns ------- list Position and raidus of the object as [x, y, size]. Notes ---------- See also no_overlap(). """ pos = [] while len(pos) < 1: r = R * np.sqrt(np.random.random()) #random polar coordinates theta = np.random.random() * 2 * np.pi x = C[0] + r * np.cos(theta) #swith to cartesian coordinates y = C[1] + r * np.sin(theta) if no_overlap(x, y, size, others) == True: pos.append([x, y, size]) #add if there is no overlap with others else : pass return pos[0] def no_overlap_random_pos(C, R, size_list, N): """ Returns random initial position of circle object of various radii within a circle of center C and radius R. Parameters ---------- C : list Position of the center of the circle, as [xc, yc]. R : float Radius of the circle. size_list : list List of the radii of the objects we want to randomly initialize. N : int Number of objects. Raises ------ ValueError If there is not as much radii as the number of objects wanted. Returns ------- numpy.array Array of the position of every object, as [[x1, y1], ..., [xN, yN]]. Notes ---------- See also random_init_pos(). """ pos_list = [] if len(size_list) != N : raise ValueError('Not as much radii as object') for i in range(N): pos_list.append(random_init_pos(C, R, size_list[i], pos_list)) return np.array(pos_list)[:,:2] def make_fish(L, N, rmin, rmax, C, R, m, tau, desired_speed, first_obj, second_obj, init = 'random'): """ Makes a list of fish with various parameters Parameters ---------- L : float Size of the aquarium. N : int Number of fish. rmin : float Minimal radius. rmax : float Maximal radius. C : list Position of the center of the circle, as [xc, yc]. R : float Radius of the circle. m : float Mass of the fish. tau : float Characteristic time of acceleration. desired_speed : float Desired maximal speed of the fish. first_obj : list Position of the first objective, as [x, y]. second_obj : list Position of the second objective, as [x, y]. init : str, optional How the initialization of the positions is made. Can either be 'crystal', where they are ordered as a square with a fixed distance between each fish, or with 'random' as random positions of the fish within a circle. The default is 'random'. Raises ------ ValueError If the init option is not used correctly, with either 'random' or 'crystal'. Returns ------- fish_list : list List of fish. Notes ---------- See also Fish(), no_overlap_random_pos(), and crystal_init_pos(). """ if init == 'random': fish_list = [] size_list = [] for i in range(N): size_list.append(np.random.uniform(rmin, rmax)) pos_list = no_overlap_random_pos(C, R, size_list, N) for i in range(N): fish_list.append(Fish(size_list[i], m, pos_list[i], tau, desired_speed, first_obj, second_obj)) elif init == 'crystal': xx, yy = crystal_init_pos(-2*L + L/3, L/2 - L/3, -L + L/3, L - L/3, n ) for i in range(N): radius = np.random.random(rmin, rmax) fish_list.append(Fish(radius, m, [xx[i], yy[i]], tau, ds, fobj, sobj)) else : raise ValueError('Wrong init option') return fish_list def helbing_constant(): "Return constant from the original article Helbing et. al, 2000" m = 80. #mass A = 2. * 10**3 #amplitude of long-range repulsion B = 0.08 #characteristic distance for long-range repulsion ds = 0.8 #desired speed k = 1.2 * 10**5 #body force constant kappa = 2.4 * 10**5 #friction force constant rmin = 0.25 #minimum radius rmax = 0.35 #maximum radius tau = 0.5 #characteristic time for acceleration return m, A, B, ds, k, kappa, rmin, rmax, tau def adaptative_timestep(fish_list, default_dt = 0.01, v_changelimit = 0.01, tms_mul = 0.95): """ Returns the timestep adapted to the biggest velocity change, such that nothing due to a too big of a timestep occurs, such as described for Helbing et al., 2000. Parameters ---------- fish_list : list List of every fish. default_dt : float, optional Initial timestep. The default is 0.01. v_changelimit : float, optional Maximum velocity change in a timestep. The default is 0.01. tms_mul : float, optional Multiplier used to reduce the timestep. The default is 0.95. Returns ------- t : float Adaptative timestep. """ max_acc = 0 for fish in fish_list : #find the highest acceleration if max_acc < np.linalg.norm(fish.force)/fish.mass : max_acc = np.linalg.norm(fish.force)/fish.mass t = default_dt while t * max_acc > v_changelimit: #while velocity change superior to limit t *= tms_mul #reduce the timestep to reduce the velocity change return t L = 10 m, A, B, ds, k, kappa, rmin, rmax, tau = helbing_constant() ds = 1.2 kappa = 4.8 * 10**5 uw = [L/2, 2 * rmin] dw = [L/2, -(2 * rmin)] #Place first objective on the hole fobj = [(1/2) * L + (uw[1] - dw[1])/2 , 0] #Place second objective further away so there is no jam near the exit sobj = [(11/4) * L, 0] #Number of individual is N, with an integer square root n n = 7 N = n**2 #Position and radius of the circle of random initial position with no overlap C = [-L/3, 0] R = L/2 for i in range(10): fish_list = make_fish(L, N, rmin, rmax, C, R, m, tau, ds, fobj, sobj) #Put this way, we get timestep, fish id, x, y, radius tmax = 150 hist = [] f_hist = [] ts = 0 timer = 0.01 while ts < tmax: f_count = 0 for fish in fish_list : fish.update_force(fish_list, [uw, dw], A, B, k, kappa) dt = adaptative_timestep(fish_list, default_dt = 0.01, v_changelimit = 0.01) ts += dt for fish in fish_list : fish.update_status(dt) fish.update_objective() hist.append([ts, f_count, fish.getCoords()[0], fish.getCoords()[1], fish.getSize(), fish.getColor()]) if ts >= timer : f_hist.append([ts, f_count, fish.getCoords()[0], fish.getCoords()[1], fish.getSize(), fish.getColor()]) f_count += 1 if ts >= timer : timer += 0.01 sys.stdout.write("\r{0}%".format(round((ts-dt)/tmax*100,2))) sys.stdout.flush() #We create the corresponding pandas DataFrame df = pd.DataFrame(hist, columns =['Time', 'FishID', 'X', 'Y', 'R', 'C']) #We save it into a csv file title = 'history_49_ds12_ka48_{}.csv'.format(i) df.to_csv(title, index=False) #We create the corresponding pandas DataFrame df2 = pd.DataFrame(f_hist, columns =['Time', 'FishID', 'X', 'Y', 'R', 'C']) #We save it into a csv file title = 'fixed_history_49_ds12_ka48_{}.csv'.format(i) df2.to_csv(title, index=False)
3.703125
4
desafiosCursoEmVideo/ex056.py
gomesGabriel/Pythonicos
1
12788882
print('\033[33m-=-\033[m' * 20) print('\033[33m************* Analisador completo *************\033[m') print('\033[33m-=-\033[m' * 20) si = 0 iv = 0 nv = 0 tm = 0 for c in range(1, 4): print('---- {}ª Pessoa ----' .format(c)) n = str(input('Nome: ')).strip() i = int(input('Idade: ')) s = str(input('Sexo [S/M]: ')).strip() si += i if c == 1 and s in 'Mm': iv = i nv = n if s in 'Mm' and i > id: iv = i nv = n if s in 'Ff' and i < 20: tm += 1 print('A média de idade do grupo é: {}' .format(si / 4)) print('O homem mais velho tem {} e se chama: {}' .format(iv, nv)) print('Ao todo são {} mulheres com menos de 20 anos.' .format(tm))
3.09375
3
visualise/colormap.py
TimoWilken/scworldedit
0
12788883
#!/usr/bin/python3 """Colormaps map data to colors for visualisation. To use a colormap, call one of its color_* methods with data in a suitable format. """ from abc import ABCMeta, abstractmethod from array import array from itertools import chain class ColorMap(metaclass=ABCMeta): """The base color map. Custom color maps should inherit from this class and override the color_heatmap(self, dataset) method. """ @staticmethod def _parse_html_color(color): r"""Parse a color conforming to the regex #?\d\d?\d\d?\d\d?\d?\d?. The parsed color may be in one of the following formats, each with an optional hash ("#") character in front: ["#RRGGBB", "#RGB", "#RRGGBBAA", "#RGBA"]. """ color = color.translate({ord('#'): None}) cl = len(color) // 3 # len of one RGBA component r, g, b, a = color[:cl], color[cl:2*cl], color[2*cl:3*cl], color[3*cl:] if cl == 1: r, g, b, a = map(lambda c: 2*c, (r, g, b, a)) return int(r, 16), int(g, 16), int(b, 16), int(a, 16) if a else 255 @abstractmethod def color_heatmap(self, dataset): """Transform heatmap data into pixel rows to write to a PNG file.""" return NotImplemented class AbsoluteColorMap(ColorMap): """A user-defined colormap mapping absolute values to colors.""" def __init__(self, colors): """Initialise a new color map.""" self.default = self._parse_html_color(colors.get('default', '#0000')) self.colormap = {int(k): self._parse_html_color(c) for k, c in colors.items() if k.isdigit()} def color_heatmap(self, dataset): """Transform heatmap data into pixel rows to write to a PNG file.""" coord_data = dataset.by_coordinates(relative=False) for y in range(dataset.bounds.height): yield array('B', chain.from_iterable( self.colormap.get(coord_data[(x, y)], self.default) if (x, y) in coord_data else (0, 0, 0, 0) for x in range(dataset.bounds.width) )) class DefaultColorMap(ColorMap): """The default greyscale colormap to use if no user-provided one exists.""" def color_heatmap(self, dataset): """Transform heatmap data into pixel rows to write to a PNG file.""" coord_data = dataset.by_coordinates(relative=True) for y in range(dataset.bounds.height): yield array('B', map(round, chain.from_iterable( (*((255 * coord_data[(x, y)],) * 3), 255) if (x, y) in coord_data else (0, 0, 0, 0) for x in range(dataset.bounds.width) )))
3.796875
4
_unittests/ut_sql/test_database_join.py
mohamedelkansouli/Ensae_py2
0
12788884
<reponame>mohamedelkansouli/Ensae_py2<filename>_unittests/ut_sql/test_database_join.py """ @brief test log(time=13s) """ import sys import os import unittest from pyquickhelper.loghelper import fLOG, unzip from pyquickhelper.pycode import get_temp_folder try: import src except ImportError: path = os.path.normpath( os.path.abspath( os.path.join( os.path.split(__file__)[0], "..", ".."))) if path not in sys.path: sys.path.append(path) import src from src.pyensae.sql import Database class TestDatabaseJoin (unittest.TestCase): _memo_SQL1 = """SELECT query AS query, profile_QSSH.pos AS profile_QSSH_pos, type AS type, bucket AS bucket, max_nb AS max_nb, sum_difftime AS sum_difftime, profile_QSSH.url AS url, url_QSSH.pos AS url_QSSH_pos, co AS co, nb_view AS nb_view, sum_nb_view AS sum_nb_view, sum_difftime_view AS sum_difftime_view, nb_click AS nb_click, sum_nb_click AS sum_nb_click, sum_difftime_click AS sum_difftime_click FROM profile_QSSH JOIN url_QSSH ON profile_QSSH.url == url_QSSH.url """ _memo_SQL2 = """SELECT query AS query, profile_QSSH.pos AS pos, type AS type, bucket AS bucket, max_nb AS max_nb, sum_difftime AS sum_difftime, profile_QSSH.url AS url, co AS co, nb_view AS nb_view, sum_nb_view AS sum_nb_view, sum_difftime_view AS sum_difftime_view, nb_click AS nb_click, sum_nb_click AS sum_nb_click, sum_difftime_click AS sum_difftime_click FROM profile_QSSH INNER JOIN url_QSSH ON profile_QSSH.url == url_QSSH.url AND profile_QSSH.pos == url_QSSH.pos WHERE bucket == 'bu###1' """ def test_join_bis(self): fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__") filename = os.path.join(os.path.split( __file__)[0], "data", "database_linked.zip") temp = get_temp_folder(__file__, "temp_join_bis") filename = unzip(filename, temp) assert os.path.exists(filename) db = Database(filename, LOG=fLOG) db.connect() sql = "SELECT COUNT(*) FROM profile_QSSH" exe = db.execute_view(sql) assert exe[0][0] == 16 sql, fields = db.inner_join("profile_QSSH", "url_QSSH", "url", None, execute=False, create_index=False, unique=False) sql = sql.strip(" \n\r\t") tep = TestDatabaseJoin._memo_SQL1.strip(" \n\r\t") if sql.replace(" ", "") != tep.replace(" ", ""): print(sql) raise Exception("sql queries should be identifical") assert fields == [('query', 'query'), ('profile_QSSH.pos', 'profile_QSSH_pos'), ('type', 'type'), ('bucket', 'bucket'), ('max_nb', 'max_nb'), ('sum_difftime', 'sum_difftime'), ('profile_QSSH.url', 'url'), ('url_QSSH.pos', 'url_QSSH_pos'), ('co', 'co'), ('nb_view', 'nb_view'), ('sum_nb_view', 'sum_nb_view'), ('sum_difftime_view', 'sum_difftime_view'), ('nb_click', 'nb_click'), ('sum_nb_click', 'sum_nb_click'), ('sum_difftime_click', 'sum_difftime_click')] view = db.execute_view(sql) assert len(view) == 2 sql, fields = db.inner_join("profile_QSSH", "url_QSSH", ("url", "pos"), None, execute=False, create_index=False, where="bucket == 'bu###1'") sql = sql.strip(" \n\r\t") tep = TestDatabaseJoin._memo_SQL2.strip(" \n\r\t") if sql.replace(" ", "") != tep.replace(" ", ""): for a, b in zip(sql.split("\n"), tep.split("\n")): print("res", a) print("exp", b) print(a == b) assert sql.replace(" ", "") == tep.replace(" ", "") assert fields == [('query', 'query'), ('profile_QSSH.pos', 'pos'), ('type', 'type'), ('bucket', 'bucket'), ('max_nb', 'max_nb'), ('sum_difftime', 'sum_difftime'), ('profile_QSSH.url', 'url'), ('co', 'co'), ('nb_view', 'nb_view'), ('sum_nb_view', 'sum_nb_view'), ('sum_difftime_view', 'sum_difftime_view'), ('nb_click', 'nb_click'), ('sum_nb_click', 'sum_nb_click'), ('sum_difftime_click', 'sum_difftime_click')] view = db.execute_view(sql) assert len(view) == 1 db.close() def test_histogram(self): fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__") filename = os.path.join(os.path.split( __file__)[0], "data", "database_linked.zip") temp = get_temp_folder(__file__, "temp_histogram") filename = unzip(filename, temp) assert os.path.exists(filename) db = Database(filename, LOG=fLOG) db.connect() sql = db.histogram("url_QRW2", col_sums=["sum_nb_click"], columns=("pos", "url")) view = db.execute_view(sql) assert len(view) == 38216 sql = db.histogram("url_QRW2", col_sums=["sum_nb_click"], columns="url") view = db.execute_view(sql) assert len(view) == 28436 sql = db.histogram("url_QRW2", col_sums=["sum_nb_click"], columns="pos", values=[1, 2, 3, 4, 5]) view = db.execute_view(sql) assert view == [(1, 2370, 87049), (2, 5734, 11522), (3, 4009, 5383), (4, 4304, 1778), (5, 21799, 3588)] sql = db.histogram("url_QRW2", col_sums=["sum_nb_click"], columns="pos", values={"pos123": [1, 2, 3], "others": [4, 5, 6, 7, 8, 9, 10]}) view = db.execute_view(sql) assert view == [('none', 21, 0), ('others', 26082, 5366), ('pos123', 12113, 103954)] db.close() def test_histogram2(self): fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__") filename = os.path.join(os.path.split( __file__)[0], "data", "database_linked.zip") temp = get_temp_folder(__file__, "temp_histogram2") filename = unzip(filename, temp) assert os.path.exists(filename) db = Database(filename, LOG=fLOG) db.connect() sql = db.histogram("url_QRW2", values={"cat1": [(1, 1), (1, 0)], "cat2": [ (1, 10), (2, 10), (2, 1)]}, col_sums=["sum_nb_click"], columns=("pos", "co")) view = db.execute_view(sql) assert view == [('cat1', 1115, 15), ('cat2', 3792, 411), ('none', 33309, 108894)] db.close() if __name__ == "__main__": unittest.main()
2.0625
2
bootstrap/act-bootstrap.py
geirskjo/act-bootstrap
0
12788885
<reponame>geirskjo/act-bootstrap<gh_stars>0 #!/usr/bin/env python3 import argparse import json import os import sys from logging import critical, warning import act def parseargs(): """ Parse arguments """ parser = argparse.ArgumentParser(description="ACT Bootstrap data model") parser.add_argument( "--userid", type=int, dest="user_id", required=True, help="User ID") parser.add_argument( "--object-types", dest="object_types_filename", required=True, help="Object type defintions (json)") parser.add_argument( "--fact-types", dest="fact_types_filename", required=True, help="Fact type defintions (json)") parser.add_argument( "--meta-fact-types", dest="meta_fact_types_filename", required=True, help="Meta Fact type defintions (json)") parser.add_argument( "--logfile", dest="log_file", help="Log to file (default = stdout)") parser.add_argument( "--loglevel", dest="log_level", default="info", help="Loglevel (default = info)") parser.add_argument( "--act-baseurl", dest="act_baseurl", required=True, help="API URI") return parser.parse_args() def create_object_types(client, object_types_filename): if not os.path.isfile(object_types_filename): critical("Object defintion file not found: %s" % object_types_filename) sys.exit(1) try: object_types = json.loads(open(object_types_filename).read()) except json.decoder.JSONDecodeError: critical("Unable to parse file as json: %s" % object_types_filename) sys.exit(1) existing_object_types = [object_type.name for object_type in client.get_object_types()] # Create all objects for object_type in object_types: name = object_type["name"] validator = object_type.get("validator", act.DEFAULT_VALIDATOR) if name in existing_object_types: warning("Object type %s already exists" % name) continue client.object_type(name=name, validator_parameter=validator).add() def create_fact_types(client, fact_types_filename): # Create fact type with allowed bindings to ALL objects # We want to change this later, but keep it like this to make it simpler # when evaluating the data model if not os.path.isfile(fact_types_filename): critical("Facts defintion file not found: %s" % fact_types_filename) try: fact_types = json.loads(open(fact_types_filename).read()) except json.decoder.JSONDecodeError: critical("Unable to parse file as json: %s" % fact_types_filename) sys.exit(1) for fact_type in fact_types: name = fact_type["name"] validator = fact_type.get("validator", act.DEFAULT_VALIDATOR) object_bindings = fact_type.get("objectBindings", []) if not object_bindings: client.create_fact_type_all_bindings( name, validator_parameter=validator) else: client.create_fact_type(name, validator=validator, object_bindings=object_bindings) def create_meta_fact_types(client, meta_fact_types_filename): # Create fact type with allowed bindings to ALL objects # We want to change this later, but keep it like this to make it simpler # when evaluating the data model if not os.path.isfile(meta_fact_types_filename): critical("Meta Fact defintions file not found: %s" % meta_fact_types_filename) try: meta_fact_types = json.loads(open(meta_fact_types_filename).read()) except json.decoder.JSONDecodeError: critical("Unable to parse file as json: %s" % meta_fact_types_filename) sys.exit(1) for meta_fact_type in meta_fact_types: name = meta_fact_type["name"] validator = meta_fact_type.get("validator", act.DEFAULT_VALIDATOR) fact_bindings = meta_fact_type.get("factBindings", []) if not fact_bindings: client.create_meta_fact_type_all_bindings(name, validator_parameter=validator) else: client.create_meta_fact_type(name, fact_bindings=fact_bindings, validator=validator) if __name__ == "__main__": args = parseargs() client = act.Act( args.act_baseurl, args.user_id, args.log_level, args.log_file, "act-types") create_object_types( client, object_types_filename=args.object_types_filename) create_fact_types(client, fact_types_filename=args.fact_types_filename) create_meta_fact_types(client, meta_fact_types_filename=args.meta_fact_types_filename)
2.515625
3
app/contracts/migrations/0025_auto_20180211_1442.py
snakrani/discovery
0
12788886
# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('vendors', '0028_auto_20180211_1350'), ('contracts', '0024_auto_20180205_0342'), ] operations = [ migrations.RunSQL("UPDATE django_content_type SET app_label = 'contracts' WHERE app_label = 'contract';"), migrations.RunSQL("ALTER TABLE IF EXISTS contract_contract RENAME TO contracts_contract;"), migrations.RunSQL("ALTER TABLE IF EXISTS contract_fpdsload RENAME TO contracts_fpdsload;"), migrations.RunSQL("ALTER TABLE IF EXISTS contract_placeofperformance RENAME TO contracts_placeofperformance;"), ]
1.617188
2
setup.py
numpde/bugs
0
12788887
<reponame>numpde/bugs import setuptools # python setup.py sdist bdist_wheel # twine upload dist/* && rm -rf build dist *.egg-info setuptools.setup( name="bugs", version="0.0.2", author="RA", author_email="<EMAIL>", keywords="python essentials", description="Python essential imports.", long_description="Python essential imports. [Info](https://github.com/numpde/bugs).", long_description_content_type="text/markdown", url="https://github.com/numpde/bugs", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.6', install_requires=['pandas', 'plox', 'inclusive', 'tcga', 'more_itertools'], # Required for includes in MANIFEST.in #include_package_data=True, test_suite="nose.collector", tests_require=["nose"], )
1.367188
1
library/library.py
jatinmg97/library
0
12788888
<filename>library/library.py import pandas as pd import uuid import sqlite3 import os from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.naive_bayes import GaussianNB from sklearn.ensemble import RandomForestClassifier from sklearn.svm import SVC from sklearn import model_selection import numpy as np from sklearn.metrics import balanced_accuracy_score from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score from sklearn.metrics import auc from sklearn.metrics import f1_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.linear_model import Ridge from sklearn.linear_model import Lasso from scipy.spatial.distance import cdist from sklearn.mixture import GaussianMixture from sklearn.cluster import KMeans from sklearn.metrics import adjusted_mutual_info_score from sklearn.metrics import adjusted_rand_score from sklearn.cluster import DBSCAN from sqlalchemy import create_engine from urllib import parse import time from sklearn.linear_model import SGDClassifier import traceback parameters={} PK={} #saves da def data(name): PK={} Data_Table = pd.DataFrame(columns=['Data_Name','Data_ID']) if not os.path.isfile('Data_Table.csv'): Data_Table.to_csv('Data_Table.csv',header='column_names',index=False) else: Data_Table=pd.read_csv('Data_Table.csv') #aaa2=(Data_Table.Data_Name=='name').all() #name A=Data_Table[Data_Table['Data_Name'].astype(str).str[:].str.contains(name)] b=A.empty ID=A['Data_ID'] if b==True: PK={} PK[name] = uuid.uuid4() PK2=pd.DataFrame.from_dict(PK, orient='index') PK2 = PK2.reset_index() Data_Table2=PK2 Data_Table2.columns = ['Data_Name', 'Data_ID'] Data_Table2['Data_Name'] = Data_Table2['Data_Name'].astype(str) Data_Table2['Data_ID'] = Data_Table2['Data_ID'].astype(str) # column_names=["Data_Name","Data_ID" if not os.path.isfile('Data_Table.csv'): Data_Table2.to_csv('Data_Table.csv',header='column_names',index=False) else: Data_Table2.to_csv('Data_Table.csv',mode='a',header=False, index=False) ID=Data_Table2['Data_ID'] else: ID=A['Data_ID'] return ID def func(lr): #parameters[x]=x.get_params() #PK={} #global Model_ID parameters={} PK={} #PK3={} #PK[lr]=time.time() import datetime now = datetime.datetime.now() idz=now.strftime('%Y-%m-%dT%H:%M:%S') + ('-%02d' % (now.microsecond / 10000)) PK[lr] = uuid.uuid4() PK2=pd.DataFrame.from_dict(PK, orient='index') PK2 = PK2.reset_index() Model_ID=PK2 Model_ID.columns = ['Model_Detail', 'Model_ID'] Model_ID['Model_Detail'] = Model_ID['Model_Detail'].astype(str) Model_ID['Model_ID'] = Model_ID['Model_ID'].astype(str) Model_ID['Time_Stamp']=idz sep='(' x=Model_ID['Model_Detail'][0] x1=x.split(sep,1)[0] Model_ID['Model_Name']=x1 #Model_ID['Model_SF']= Model_ID['x'] return Model_ID k={} def func1(x,d): #ID=d.loc[0,'Model_ID'] try: ID=d #name=x print (x) print(ID) parameters={} k={} global Parameters3 parameters[x]=x.get_params() Parameters2=pd.DataFrame.from_dict(parameters, orient='index') Parameters3 = Parameters2.reset_index() Parameters3=Parameters3.melt(id_vars=["index"], var_name=["Param_Name"], value_name="Value") Parameters3.columns = ['Model_Name','Param_Name','Value'] print (Parameters3) Parameters3['Model_Name'] = Parameters3['Model_Name'].astype(str) Parameters3['Value'] = Parameters3['Value'].astype(str) Parameters3 = Parameters3[Parameters3.Value != "nan"] Parameters3['Model_ID']=ID #Parameters3.loc[Parameters3.Model_Name==name, 'Model_ID']=ID #Parameters4['Model_ID']= np.where(Parameters3['Model_Name']== name, ID,'NA') #Parameters3['Model_ID']= np.where(Parameters3['Model_ID']== 'NA', ID,'NA') #Parameters3.loc[:,"Model_ID"] = ID l=Parameters3['Param_Name'] l=l.tolist() # l=[1,2,3,4] k={} for i in l: k[i]=uuid.uuid4() #Parameters3.loc[:,"TuningID"]= k[i] PK2=pd.DataFrame.from_dict(k, orient='index') PK2 = PK2.reset_index() PK2.columns = ['Model_Name', 'Tuning_ID'] PK2=PK2.drop(['Model_Name'],axis=1) #Tuning_ID=PK2 Parameters3['Tuning_ID']=PK2['Tuning_ID'] Parameters3=Parameters3.drop(['Model_Name'],axis=1) except: var = traceback.format_exc() with open("error.txt", "a") as myfile: myfile.write(var) return Parameters3 def func3(x,X_train,y_train,y): #ID=y.loc[0,'Model_ID'] try: global Goodness_of_fit global var ID=y scoring = 'r2' scoring2 = 'neg_mean_squared_error' scoring3 = 'explained_variance' #scoring4 = 'balanced_accuracy' scoring5 = 'neg_mean_absolute_error' seed = 7 kfold = model_selection.KFold(n_splits=2, random_state=seed) r2={} MSE={} EV={} ME={} MAE={} results={} results2={} results = model_selection.cross_val_score(x, X_train, y_train,cv=kfold, scoring=scoring) results2 = model_selection.cross_val_score(x, X_train, y_train, cv=kfold, scoring=scoring2) results3 = model_selection.cross_val_score(x, X_train, y_train, cv=kfold, scoring=scoring3) # results4 = model_selection.cross_val_score(x, X_train, y_train, cv=kfold, scoring=scoring4) results5 = model_selection.cross_val_score(x, X_train, y_train, cv=kfold, scoring=scoring5) r2[x]=results.mean() MSE[x]=results2.mean() EV[x]=results3.mean() # ME[x]=results4.mean() MAE[x]=results5.mean() r_squared=pd.DataFrame.from_dict(r2, orient='index') r_squared = r_squared.reset_index() mse=pd.DataFrame.from_dict(MSE, orient='index') mse = mse.reset_index() EV=pd.DataFrame.from_dict(EV, orient='index') EV = EV.reset_index() MAE=pd.DataFrame.from_dict(MAE, orient='index') MAE = MAE.reset_index() r_squared["g_fit"] = "R_SQUARED" r_squared.columns=['Model_Name', 'Value',"Goodness_of_fit"] mse["g_fit"] = "MSE" mse.columns=['Model_Name', 'Value',"Goodness_of_fit"] EV["g_fit"] = "EV" EV.columns=['Model_Name', 'Value',"Goodness_of_fit"] MAE["g_fit"] = "MAE" MAE.columns=['Model_Name', 'Value',"Goodness_of_fit"] Goodness_of_fit=r_squared Goodness_of_fit['Model_Name'] = Goodness_of_fit['Model_Name'].astype(str) Goodness_of_fit=r_squared.append(mse) Goodness_of_fit=Goodness_of_fit.append(EV) Goodness_of_fit=Goodness_of_fit.append(MAE) Goodness_of_fit['Model_Name'] = Goodness_of_fit['Model_Name'].astype(str) #Model_ID=ID Goodness_of_fit['Model_ID']=ID Goodness_of_fit = Goodness_of_fit.reset_index() Goodness_of_fit= Goodness_of_fit.drop(['index'],axis=1) m=Goodness_of_fit['Value'] m=m.tolist() m=[1,2,3,4] d={} for i in m: d[i]=uuid.uuid4() # Parameters3.loc[:,"TuningID"]= k[i] PK3=pd.DataFrame.from_dict(d, orient='index') PK3 = PK3.reset_index() PK3.columns = ['Model_Name','GF_ID2'] #Tuning_ID=PK2 Goodness_of_fit['GF_ID']=PK3['GF_ID2'] #Goodness_of_fit.loc[:,"Model_ID"] = ID Goodness_of_fit=Goodness_of_fit.drop(['Model_Name'],axis=1) except: var = traceback.format_exc() with open("error.txt", "a") as myfile: myfile.write(var) return Goodness_of_fit def func4(x,X_train,y_train,y): #ID=y.loc[0,'Model_ID'] global Goodness_of_fit global var try: ID=y #seed = 7 sep='(' ad=str(x) ad=ad.split(sep,1)[0] if ad=="XGBClassifier": pred = x.predict(X_train) #pred = [round(value) for value in pred] pred = np.asarray([np.argmax(line) for line in pred]) else: pred=x.predict(X_train) Accuracy={} AUC={} Precision={} f1_score2={} recall_score2={} results={} results2={} results = accuracy_score(y_train,pred,normalize=True) #results2 = auc(y_train,pred) results2= balanced_accuracy_score(y_train, pred) results3 = f1_score(y_train,pred, average='weighted') results4 = precision_score(y_train,pred,average='weighted') results5 = recall_score(y_train,pred,average='weighted') Accuracy[x]=results AUC[x]=results2 Precision[x]=results3 f1_score2[x]=results4 recall_score2[x]=results5 Accuracy=pd.DataFrame.from_dict(Accuracy, orient='index') Accuracy = Accuracy.reset_index() Balanced_Accuracy=pd.DataFrame.from_dict(AUC, orient='index') Balanced_Accuracy = Balanced_Accuracy.reset_index() Precision=pd.DataFrame.from_dict(Precision, orient='index') Precision = Precision.reset_index() f1_score2=pd.DataFrame.from_dict(f1_score2, orient='index') f1_score2 = f1_score2.reset_index() recall_score2=pd.DataFrame.from_dict(recall_score2, orient='index') recall_score2 = recall_score2.reset_index() Accuracy["g_fit"] = "Accuracy" Accuracy.columns=['Model_Name', 'Value',"Goodness_of_fit"] Balanced_Accuracy["g_fit"] = "Balanced_Accuracy" Balanced_Accuracy.columns=['Model_Name', 'Value',"Goodness_of_fit"] Precision["g_fit"] = "Precision" Precision.columns=['Model_Name', 'Value',"Goodness_of_fit"] f1_score2["g_fit"] = "f1_score" f1_score2.columns=['Model_Name', 'Value',"Goodness_of_fit"] recall_score2["g_fit"] = "recall_score" recall_score2.columns=['Model_Name', 'Value',"Goodness_of_fit"] Goodness_of_fit=Accuracy Goodness_of_fit['Model_Name'] = Goodness_of_fit['Model_Name'].astype(str) Goodness_of_fit=Accuracy.append(Balanced_Accuracy) Goodness_of_fit=Goodness_of_fit.append(Precision) Goodness_of_fit=Goodness_of_fit.append(f1_score2) Goodness_of_fit=Goodness_of_fit.append(recall_score2) Goodness_of_fit['Model_Name'] = Goodness_of_fit['Model_Name'].astype(str) #Model_ID=ID Goodness_of_fit['Model_ID']=ID Goodness_of_fit = Goodness_of_fit.reset_index() Goodness_of_fit= Goodness_of_fit.drop(['index'],axis=1) m=Goodness_of_fit['Value'] m=[1,2,3,4,5] d={} for i in m: d[i]=uuid.uuid4() # Parameters3.loc[:,"TuningID"]= k[i] PK3=pd.DataFrame.from_dict(d, orient='index') PK3 = PK3.reset_index() PK3.columns = ['Model_Name','GF_ID2'] #Tuning_ID=PK2 Goodness_of_fit['GF_ID']=PK3['GF_ID2'] #Goodness_of_fit.loc[:,"Model_ID"] = ID Goodness_of_fit=Goodness_of_fit.drop(['Model_Name'],axis=1) # Goodness_of_fit=Goodness_of_fit[['Goodness_of_fit', 'Value', 'Model_Id', 'GF_ID']] except: var = traceback.format_exc() with open("error.txt", "a") as myfile: myfile.write(var) return Goodness_of_fit def cluster(kmeans,X_test,y_test,y): x=str(kmeans) #sep='(' #x1=x.split(sep,1)[0] if "SpectralClustering" in x: pred=kmeans.fit_predict(X_test) elif "AgglomerativeClustering" in x: pred=kmeans.fit_predict(X_test) elif "DBSCAN" in x: pred=kmeans.fit_predict(X_test) else: pred=kmeans.predict(X_test) score2=adjusted_mutual_info_score(y_test,pred) score3=adjusted_rand_score(y_test,pred) if "AgglomerativeClustering" in x: score4="0" elif "DBSCAN" in x: score4="0" else: score4=sum(np.min(cdist(X_test, kmeans.cluster_centers_, 'euclidean'), axis=1)) / X_test.shape[0] ID=y Score={} rand={} wss={} results={} results2={} Score[kmeans]=score2 rand[kmeans]=score3 wss[kmeans]=score4 Adjusted_mutual_info=pd.DataFrame.from_dict(Score, orient='index') Adjusted_mutual_info = Adjusted_mutual_info.reset_index() Adjusted_rand_info=pd.DataFrame.from_dict(rand, orient='index') Adjusted_rand_info = Adjusted_rand_info.reset_index() WSS=pd.DataFrame.from_dict(wss, orient='index') WSS = WSS.reset_index() Adjusted_mutual_info["g_fit"] = "Adjusted_mutual_info" Adjusted_mutual_info.columns=['Model_Name', 'Value',"Goodness_of_fit"] Adjusted_rand_info["g_fit"] = "Adjusted_rand_info" Adjusted_rand_info.columns=['Model_Name', 'Value',"Goodness_of_fit"] WSS["g_fit"] = "WSS" WSS.columns=['Model_Name', 'Value',"Goodness_of_fit"] Goodness_of_fit=Adjusted_mutual_info Goodness_of_fit['Model_Name'] = Goodness_of_fit['Model_Name'].astype(str) Goodness_of_fit=Goodness_of_fit.append(Adjusted_rand_info) Goodness_of_fit=Goodness_of_fit.append(WSS) Goodness_of_fit['Model_Name'] = Goodness_of_fit['Model_Name'].astype(str) #Model_ID=ID Goodness_of_fit['Model_ID']=ID Goodness_of_fit = Goodness_of_fit.reset_index() Goodness_of_fit= Goodness_of_fit.drop(['index'],axis=1) m=Goodness_of_fit['Value'] m=[1,2,3] d={} for i in m: d[i]=uuid.uuid4() # Parameters3.loc[:,"TuningID"]= k[i] PK3=pd.DataFrame.from_dict(d, orient='index') PK3 = PK3.reset_index() PK3.columns = ['Model_Name','GF_ID2'] #Tuning_ID=PK2 Goodness_of_fit['GF_ID']=PK3['GF_ID2'] #Goodness_of_fit.loc[:,"Model_ID"] = ID Goodness_of_fit=Goodness_of_fit.drop(['Model_Name'],axis=1) return Goodness_of_fit def final(lr,X_train,y_train): Model_Table=func(lr) ID=Model_Table.loc[0,'Model_ID'] HP_Table=func1(lr,ID) GF_Table=func3(lr,X_train,y_train,ID) aaa=[Model_Table,HP_Table,GF_Table] Model_Table=aaa[0] HP_Table=aaa[1] GF_Table=aaa[2] if not os.path.isfile('Model_Table.csv'): Model_Table.to_csv('Model_Table.csv',header='column_names') else: Model_Table.to_csv('Model_Table.csv',mode='a',header=False) if not os.path.isfile('HP_Table.csv'): HP_Table.to_csv('HP_Table.csv',header='column_names') else: HP_Table.to_csv('HP_Table.csv',mode='a',header=False) if not os.path.isfile('GF_Table.csv'): GF_Table.to_csv('GF_Table.csv',header='column_names') else: GF_Table.to_csv('GF_Table.csv',mode='a',header=False) return [Model_Table,HP_Table,GF_Table] def csv(aaa): Model_Table=aaa[0] HP_Table=aaa[1] GF_Table=aaa[2] if not os.path.isfile('Model_Table.csv'): Model_Table.to_csv('Model_Table.csv',header='column_names') else: Model_Table.to_csv('Model_Table.csv',mode='a',header=False,index=False) if not os.path.isfile('HP_Table.csv'): HP_Table.to_csv('HP_Table.csv',header='column_names') else: HP_Table.to_csv('HP_Table.csv',mode='a',header=False,index=False) if not os.path.isfile('GF_Table.csv'): GF_Table.to_csv('GF_Table.csv',header='column_names') else: GF_Table.to_csv('GF_Table.csv',mode='a',header=False,index=False) return Model_Table class data: def __init__(self,path): self.path=path def save(self): path=self.path PK={} Data_Table = pd.DataFrame(columns=['Data_Name','Data_ID']) if not os.path.isfile('Data_Table.csv'): Data_Table.to_csv('Data_Table.csv',header='column_names',index=False) else: Data_Table=pd.read_csv('Data_Table.csv') #aaa2=(Data_Table.Data_Name=='name').all() #name A=Data_Table[Data_Table['Data_Name'].astype(str).str[:].str.contains(path)] b=A.empty ID=A['Data_ID'] if b==True: PK={} PK[path] = uuid.uuid4() PK2=pd.DataFrame.from_dict(PK, orient='index') PK2 = PK2.reset_index() Data_Table2=PK2 Data_Table2.columns = ['Data_Name', 'Data_ID'] Data_Table2['Data_Name'] = Data_Table2['Data_Name'].astype(str) Data_Table2['Data_ID'] = Data_Table2['Data_ID'].astype(str) # column_names=["Data_Name","Data_ID" if not os.path.isfile('Data_Table.csv'): Data_Table2.to_csv('Data_Table.csv',header='column_names',index=False) else: Data_Table2.to_csv('Data_Table.csv',mode='a',header=False, index=False) ID=Data_Table2['Data_ID'] print("Generated a new DID") else: ID=A['Data_ID'] print("Found your DID") return print("DID: " +ID ) class tracker: def __init__(self,lr,X_test,y_test): self.lr=lr self.X_test=X_test self.y_test=y_test def save(self):#,lr=None,X_test=None,y_test=None): lr=self.lr X_test=self.X_test y_test=self.y_test #k=self.k Model_Table=func(lr) ID=Model_Table.loc[0,'Model_ID'] HP_Table=func1(lr,ID) from sklearn.utils.testing import all_estimators from sklearn import base estimators = all_estimators() x=[] for name, class_ in estimators: if issubclass(class_, base.ClassifierMixin): x.append(name) x.append("XGBClassifier") y=[] for name, class_ in estimators: if issubclass(class_, base.RegressorMixin): y.append(name) y.append("XGBRegressor") z=[] for name, class_ in estimators: if issubclass(class_, base.ClusterMixin): z.append(name) sep='(' ad=str(lr) ad=ad.split(sep,1)[0] if ad in y: GF_Table=func3(lr,X_test,y_test,ID) elif ad in x: GF_Table=func4(lr,X_test,y_test,ID) else: GF_Table=cluster(lr,X_test,y_test,ID) #GF_Table=func3(lr,X_test,y_test,ID) aaa=[Model_Table,HP_Table,GF_Table] Model_Table=aaa[0] HP_Table=aaa[1] GF_Table=aaa[2] if not os.path.isfile('Model_Table.csv'): Model_Table.to_csv('Model_Table.csv',header='column_names',index=False) else: Model_Table.to_csv('Model_Table.csv',mode='a',header=False,index=False) if not os.path.isfile('HP_Table.csv'): HP_Table.to_csv('HP_Table.csv',header='column_names',index=False) else: HP_Table.to_csv('HP_Table.csv',mode='a',header=False,index=False) if not os.path.isfile('GF_Table.csv'): GF_Table.to_csv('GF_Table.csv',header='column_names',index=False) else: GF_Table.to_csv('GF_Table.csv',mode='a',header=False,index=False) engine = create_engine('mssql+pyodbc://admin_login:%s@<EMAIL>/IICS_Logs?driver=SQL+Server+Native+Client+11.0'% parse.unquote_plus('Miracle@123')) connection = engine.connect() Model_Table.to_sql('Model_Table', con = engine, if_exists = 'append', chunksize = 1000,index=False) HP_Table.to_sql('HP_Table', con = engine, if_exists = 'append', chunksize = 1000,index=False) GF_Table.to_sql('GF_Table', con = engine, if_exists = 'append', chunksize = 1000,index=False) nice=[Model_Table,HP_Table,GF_Table] nice2="Tables updated !" return print(nice, nice2) def classification(self):#,lr=None,X_test=None,y_test=None): lr=self.lr X_test=self.X_test y_test=self.y_test Model_Table=func(lr) ID=Model_Table.loc[0,'Model_ID'] HP_Table=func1(lr,ID) GF_Table=func4(lr,X_test,y_test,ID) aaa=[Model_Table,HP_Table,GF_Table] Model_Table=aaa[0] HP_Table=aaa[1] GF_Table=aaa[2] if not os.path.isfile('Model_Table.csv'): Model_Table.to_csv('Model_Table.csv',header='column_names') else: Model_Table.to_csv('Model_Table.csv',mode='a',header=False) if not os.path.isfile('HP_Table.csv'): HP_Table.to_csv('HP_Table.csv',header='column_names') else: HP_Table.to_csv('HP_Table.csv',mode='a',header=False) if not os.path.isfile('GF_Table.csv'): GF_Table.to_csv('GF_Table.csv',header='column_names') else: GF_Table.to_csv('GF_Table.csv',mode='a',header=False) return [Model_Table,HP_Table,GF_Table] def clustering(self):#,lr=None,X_test=None,y_test=None): lr=self.lr X_test=self.X_test y_test=self.y_test Model_Table=func(lr) ID=Model_Table.loc[0,'Model_ID'] HP_Table=func1(lr,ID) GF_Table=cluster(lr,X_test,y_test,ID) aaa=[Model_Table,HP_Table,GF_Table] Model_Table=aaa[0] HP_Table=aaa[1] GF_Table=aaa[2] if not os.path.isfile('Model_Table.csv'): Model_Table.to_csv('Model_Table.csv',header='column_names') else: Model_Table.to_csv('Model_Table.csv',mode='a',header=False) if not os.path.isfile('HP_Table.csv'): HP_Table.to_csv('HP_Table.csv',header='column_names') else: HP_Table.to_csv('HP_Table.csv',mode='a',header=False) if not os.path.isfile('GF_Table.csv'): GF_Table.to_csv('GF_Table.csv',header='column_names') else: GF_Table.to_csv('GF_Table.csv',mode='a',header=False) return print( [Model_Table,HP_Table,GF_Table] +"Tables Updated !")
2.265625
2
Lesson_7/Task_13.py
AlexHarf/Selenium_training
0
12788889
<gh_stars>0 import pytest from selenium import webdriver from selenium.webdriver.support.select import Select from selenium.webdriver.support.wait import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.common.by import By @pytest.fixture def driver(request): wd = webdriver.Chrome() request.addfinalizer(wd.quit) return wd def test(driver): driver.implicitly_wait(5) driver.get("http://localhost/litecart/en/") for i in range(0, 3): menu = driver.find_elements_by_css_selector("div#box-most-popular.box a:not([data-fancybox-group])") menu[i].click() size = driver.find_elements_by_name("options[Size]") if len(size) > 0: size_select = Select(driver.find_element_by_name('options[Size]')) size_select.select_by_value('Small') driver.find_element_by_name("add_cart_product").click() WebDriverWait(driver, 10).until(EC.text_to_be_present_in_element((By.CSS_SELECTOR, "div#cart span.quantity"), str(i+1))) driver.get("http://localhost/litecart/en/") driver.find_element_by_css_selector("div#cart a:last-child").click() product_number = driver.find_elements_by_css_selector("td.item") for c in product_number: table_item=driver.find_element_by_class_name("item") driver.find_element_by_name("remove_cart_item").click() WebDriverWait(driver, 10).until(EC.staleness_of(table_item))
2.5625
3
skills/dff_friendship_skill/dialogflows/flows/starter_states.py
oserikov/dream
34
12788890
from enum import Enum, auto class State(Enum): USR_START = auto() # # SYS_GENRE = auto() # USR_GENRE = auto() # SYS_WEEKDAY = auto() USR_WHAT_FAV = auto() SYS_CHECK_POSITIVE = auto() SYS_CHECK_NEGATIVE = auto() SYS_CHECK_NEUTRAL = auto() SYS_GET_REASON = auto() USR_REPEAT = auto() SYS_AGREED = auto() SYS_DISAGREED = auto() USR_ASSENT_YES = auto() USR_ASSENT_NO = auto() USR_MY_FAV = auto() SYS_YES = auto() SYS_NO = auto() USR_WHY = auto() USR_MY_FAV_STORY = auto() # USR_WEEKDAY = auto() SYS_FRIDAY = auto() USR_FRIDAY = auto() SYS_SMTH = auto() USR_MY_FAV_DAY = auto() # SYS_ERR = auto() USR_ERR = auto()
2.96875
3
common_configs/logging/stdlib.py
nigma/django-common-configs
5
12788891
<filename>common_configs/logging/stdlib.py #-*- coding: utf-8 -*- """ Django logging configuration Defined loggers: - <catch all> - django - django.startup - django.request - django.db.backends - django.commands - django.security.DisallowedHost - app.* - for project app loggers - boto - celery - requests - raven - sentry.errors Defined handlers: - mail_admins - console - console_celery - sentry """ from __future__ import absolute_import, division, print_function, unicode_literals import sys import logging from configurations import values logging.captureWarnings(True) class Logging(object): #: Default handler LOGGING_DEFAULT_HANDLER = values.Value("console") #: Default handler for celery LOGGING_CELERY_HANDLER = values.Value("console_celery") #: Default formatter LOGGING_DEFAULT_FORMATTER = values.Value("console") #: Add request-id to each log line (requires https://github.com/dabapps/django-log-request-id) LOGGING_ADD_REQUEST_ID = values.BooleanValue(True) #: Use sentry for error logging LOGGING_USE_SENTRY = values.BooleanValue(True) def get_request_id_filters(self): return ["request_id"] if self.LOGGING_ADD_REQUEST_ID else [] def get_sentry_handlers(self): return ["sentry"] if self.LOGGING_USE_SENTRY else [] def get_logging_filters(self): filters = { "require_debug_false": { "()": "django.utils.log.RequireDebugFalse" } } if self.LOGGING_ADD_REQUEST_ID: filters["request_id"] = { "()": "log_request_id.filters.RequestIDFilter" } return filters def get_logging_formatters(self): return { "standard": { "format": "%(asctime)s - %(levelname)-5s [%(name)s:%(lineno)s] %(message)s", "datefmt": "%Y-%m-%d %H:%M:%S" }, "verbose": { "format": "%(asctime)s - %(levelname)-5s %(module)s [%(name)s:%(lineno)s] %(process)d %(thread)d %(message)s", "datefmt": "%Y-%m-%d %H:%M:%S" }, "console": { "format": "%(asctime)s - %(levelname)-5s [%(name)s:%(lineno)s] %(message)s", "datefmt": "%H:%M:%S" }, "heroku": { "format": "%(levelname)-5s request_id=%(request_id)s [%(name)s:%(lineno)s] %(message)s" }, "celery": { "format": "%(levelname)-5s [%(processName)s:%(name)s:%(lineno)s] [%(task_name)s(%(task_id)s)] %(message)s" } } def get_logging_handlers(self): stream = sys.stdout handlers = { "mail_admins": { "level": "ERROR", "filters": ["require_debug_false"] + self.get_request_id_filters(), "class": "django.utils.log.AdminEmailHandler", "include_html": False, }, "console": { "level": "DEBUG", "filters": self.get_request_id_filters(), "class": "logging.StreamHandler", "formatter": "heroku", "stream": stream }, "console_celery": { "level": "INFO", "class": "logging.StreamHandler", "formatter": "celery", "stream": stream } } if self.LOGGING_USE_SENTRY: handlers["sentry"] = { "level": "ERROR", "filters": self.get_request_id_filters(), "class": "raven.contrib.django.raven_compat.handlers.SentryHandler", } return handlers def get_loggers(self): handlers = [self.LOGGING_DEFAULT_HANDLER] + self.get_sentry_handlers() return { "": { "handlers": handlers, "level": "WARNING", }, "boto": { "handlers": handlers, "level": "INFO", "propagate": True }, "django": { "handlers": handlers, "level": "WARNING", "propagate": False, }, "django.startup": { "handlers": handlers, "level": "INFO", "propagate": False }, "django.request": { "handlers": ["mail_admins"], "level": "ERROR", "propagate": True }, "django.db.backends": { "level": "ERROR", "handlers": handlers, "propagate": False }, "django.commands": { "handlers": ["mail_admins"], "level": "ERROR", "propagate": True }, "django.security.DisallowedHost": { "handlers": [], "propagate": False, }, "app": { "handlers": handlers, "level": "DEBUG", "propagate": False }, "celery": { "handlers": handlers, "level": "INFO", "propagate": False }, "requests": { "handlers": handlers, "level": "WARNING", "propagate": False }, "oauthlib": { "handlers": handlers, "level": "INFO", "propagate": False }, "raven": { "level": "DEBUG", "handlers": [self.LOGGING_DEFAULT_HANDLER], "propagate": False }, "sentry.errors": { "level": "DEBUG", "handlers": [self.LOGGING_DEFAULT_HANDLER], "propagate": False }, } def LOGGING(self): """ Fully configured Django logging """ return { "version": 1, "disable_existing_loggers": False, "root": { "level": "WARNING", "handlers": [self.LOGGING_DEFAULT_HANDLER] + self.get_sentry_handlers(), }, "formatters": self.get_logging_formatters(), "filters": self.get_logging_filters(), "handlers": self.get_logging_handlers(), "loggers": self.get_loggers() }
1.96875
2
tests/test_sia_client.py
fcoach66/pysiaalarm
0
12788892
# -*- coding: utf-8 -*- """Class for tests of pysiaalarm.""" import json import logging import random import socket import threading import time import pytest from mock import patch from pysiaalarm import InvalidAccountFormatError from pysiaalarm import InvalidAccountLengthError from pysiaalarm import InvalidKeyFormatError from pysiaalarm import InvalidKeyLengthError from pysiaalarm import SIAAccount from pysiaalarm import SIAClient from pysiaalarm import SIAEvent from tests.test_client import client_program from tests.test_utils import create_test_items _LOGGER = logging.getLogger(__name__) KEY = "<KEY>" ACCOUNT = "1111" HOST = "localhost" PORT = 7777 def func(event: SIAEvent): """Pass for testing.""" pass class testSIA(object): """Class for pysiaalarm tests.""" @pytest.mark.parametrize( "line, account, type, code", [ ( '98100078"*SIA-DCS"5994L0#AAA[5AB718E008C616BF16F6468033A11326B0F7546CAB230910BCA10E4DEBA42283C436E4F8EFF50931070DDE36D5BB5F0C', "AAA", "", "", ), ( '2E680078"SIA-DCS"6002L0#AAA[|Nri1/CL501]_14:12:04,09-25-2019', "AAA", "Closing Report", "CL", ), ], ) def test_event_parsing(self, line, account, type, code): """Test event parsing methods.""" event = SIAEvent(line) assert event.code == code assert event.type == type assert event.account == account @pytest.mark.parametrize( "key, account, port, error", [ ("ZZZZZZZZZZZZZZZZ", ACCOUNT, 7777, InvalidKeyFormatError), ("158888888888888", ACCOUNT, 7777, InvalidKeyLengthError), ("1688888888888888", ACCOUNT, 7777, None), ("23888888888888888888888", ACCOUNT, 7777, InvalidKeyLengthError), ("248888888888888888888888", ACCOUNT, 7777, None), ("3188888888888888888888888888888", ACCOUNT, 7777, InvalidKeyLengthError), ("32888888888888888888888888888888", ACCOUNT, 7777, None), (KEY, "22", 7777, InvalidAccountLengthError), (KEY, "ZZZ", 7777, InvalidAccountFormatError), ], ) def test_sia_key_account_errors(self, key, account, port, error): """Test sia client behaviour.""" try: SIAClient( host="", port=port, accounts=[SIAAccount(account_id=account, key=key)], function=func, ) assert False if error else True except Exception as exp: assert isinstance(exp, error) @pytest.mark.parametrize("config_file", [("tests\\unencrypted_config.json")]) def test_client(self, config_file): """Test the client. Arguments: config_file {str} -- Filename of the config. """ try: with open(config_file, "r") as f: config = json.load(f) except: # noqa: E722 config = {"host": HOST, "port": PORT, "account_id": ACCOUNT, "key": None} events = [] def func_append(event: SIAEvent): events.append(event) siac = SIAClient( host="", port=config["port"], accounts=[SIAAccount(account_id=config["account_id"], key=config["key"])], function=func_append, ) siac.start() tests = [ {"code": False, "crc": False, "account": False, "time": False}, {"code": True, "crc": False, "account": False, "time": False}, {"code": False, "crc": True, "account": False, "time": False}, {"code": False, "crc": False, "account": True, "time": False}, {"code": False, "crc": False, "account": False, "time": True}, ] t = threading.Thread( target=client_program, name="test_client", args=(config, 1, tests) ) t.daemon = True t.start() # stops after the five events have been sent. # run for 30 seconds time.sleep(30) siac.stop() assert siac.counts == { "events": 5, "valid_events": 1, "errors": { "crc": 1, "timestamp": 1, "account": 1, "code": 1, "format": 0, "user_code": 0, }, } assert len(events) == 1
2.109375
2
third/imagelib/06_dlib.py
gottaegbert/penter
13
12788893
<filename>third/imagelib/06_dlib.py """ # https://github.com/vipstone/faceai # https://www.cnblogs.com/vipstone/p/8964656.html 下载训练模型 训练模型用于是人脸识别的关键,用于查找图片的关键点。 下载地址:http://dlib.net/files/ 下载文件:shape_predictor_68_face_landmarks.dat.bz2 当然你也可以训练自己的人脸关键点模型,这个功能会放在后面讲。 下载好的模型文件,我的存放地址是:C:\Python36\Lib\site-packages\dlib-data\shape_predictor_68_face_landmarks.dat.bz2 解压:shape_predictor_68_face_landmarks.dat.bz2得到文件:shape_predictor_68_face_landmarks.dat """ def demo1(): import cv2 import dlib path = "lenna.jpg" img = cv2.imread(path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 人脸分类器 detector = dlib.get_frontal_face_detector() # 获取人脸检测器 # r"E:\bigdata\ai\dlib\data\shape_predictor_68_face_landmarks.dat" # r"E:\bigdata\ai\dlib\data\shape_predictor_5_face_landmarks.dat" predictor = dlib.shape_predictor( r"E:\bigdata\ai\dlib\data\shape_predictor_68_face_landmarks.dat" ) dets = detector(gray, 1) for face in dets: shape = predictor(img, face) # 寻找人脸的68个标定点 # 遍历所有点,打印出其坐标,并圈出来 for pt in shape.parts(): pt_pos = (pt.x, pt.y) cv2.circle(img, pt_pos, 2, (0, 255, 0), 1) cv2.imshow("image", img) cv2.waitKey(0) cv2.destroyAllWindows() # 视频识别人脸 def demo2(): import cv2 import dlib detector = dlib.get_frontal_face_detector() # 使用默认的人类识别器模型 def discern(img): gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) dets = detector(gray, 1) for face in dets: left = face.left() top = face.top() right = face.right() bottom = face.bottom() cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 2) cv2.imshow("image", img) else: cv2.imshow("image", img) cap = cv2.VideoCapture(r"E:\bigdata\ai\video\1.mp4") while (1): ret, img = cap.read() discern(img) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() # cnn狗识别 def demo3(): import cv2 import dlib cnn_face_detector = dlib.cnn_face_detection_model_v1(r"E:\bigdata\ai\dlib\data\mmod_dog_hipsterizer.dat") for f in ["../imagelib/dog1.png"]: # opencv 读取图片,并显示 img = cv2.imread(f, cv2.IMREAD_COLOR) # opencv的bgr格式图片转换成rgb格式 b, g, r = cv2.split(img) img2 = cv2.merge([r, g, b]) # 进行检测 dets = cnn_face_detector(img, 1) # 打印检测到的人脸数 print("Number of faces detected: {}".format(len(dets))) # 遍历返回的结果 # 返回的结果是一个mmod_rectangles对象。这个对象包含有2个成员变量:dlib.rectangle类,表示对象的位置;dlib.confidence,表示置信度。 for i, d in enumerate(dets): face = d.rect print( "Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}".format(i, face.left(), face.top(), face.right(), d.rect.bottom(), d.confidence)) # 在图片中标出人脸 left = face.left() top = face.top() right = face.right() bottom = face.bottom() cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 3) cv2.namedWindow(f, cv2.WINDOW_AUTOSIZE) cv2.imshow(f, img) k = cv2.waitKey(0) cv2.destroyAllWindows()
3.296875
3
src/run.py
iWeya/palestra-web-scraping-para-cacar-pontos-na-escola
1
12788894
import json from requests_html import HTMLSession from helpers import compare_trees, get_content_tree MAIN_URL = r'http://docente.ifrn.edu.br/abrahaolopes/2017.1-integrado/2.02401.1v-poo' def main(): session = HTMLSession() current_tree = get_content_tree(MAIN_URL, session) with open('storage/tree.json', 'r') as stored_tree_file: stored_tree = json.load(stored_tree_file) difference = compare_trees( stored_tree, current_tree ) if difference: for item in difference: category = item['category'].upper() category = category.rjust(8) path = item['path'] url = item['url'] print( f'{category} | {path}' ) print( f'{url}\n' ) with open('storage/tree.json', 'w') as stored_tree_file: stored_tree_file.write( json.dumps(current_tree) ) if __name__ == "__main__": main()
2.984375
3
linAlgVis.py
testinggg-art/Linear_Algebra_With_Python
1,719
12788895
<filename>linAlgVis.py import matplotlib.pyplot as plt import numpy as np import numpy def linearCombo(a, b, c): '''This function is for visualizing linear combination of standard basis in 3D. Function syntax: linearCombo(a, b, c), where a, b, c are the scalar multiplier, also the elements of the vector. ''' fig = plt.figure(figsize = (10,10)) ax = fig.add_subplot(projection='3d') ######################## Standard basis and Scalar Multiplid Vectors######################### vec = np.array([[[0, 0, 0, 1, 0, 0]], # e1 [[0, 0, 0, 0, 1, 0]], # e2 [[0, 0, 0, 0, 0, 1]], # e3 [[0, 0, 0, a, 0, 0]], # a* e1 [[0, 0, 0, 0, b, 0]], # b* e2 [[0, 0, 0, 0, 0, c]], # c* e3 [[0, 0, 0, a, b, c]]]) # ae1 + be2 + ce3 colors = ['b','b','b','r','r','r','g'] for i in range(vec.shape[0]): X, Y, Z, U, V, W = zip(*vec[i,:,:]) ax.quiver(X, Y, Z, U, V, W, length=1, normalize=False, color = colors[i] ,arrow_length_ratio = .08, pivot = 'tail', linestyles = 'solid',linewidths = 3, alpha =.6) #################################Plot Rectangle Boxes############################## dlines = np.array([[[a, 0, 0],[a, b, 0]], [[0, b, 0],[a, b, 0]], [[0, 0, c],[a, b, c]], [[0, 0, c],[a, 0, c]], [[a, 0, c],[a, b, c]], [[0, 0, c],[0, b, c]], [[0, b, c],[a, b, c]], [[a, 0, 0],[a, 0, c]], [[0, b, 0],[0, b, c]], [[a, b, 0],[a, b, c]]]) colors = ['k','k','g','k','k','k','k','k','k'] for i in range(dlines.shape[0]): ax.plot(dlines[i,:,0], dlines[i,:,1], dlines[i,:,2], lw =3, ls = '--', color = 'black', alpha=0.5) #################################Annotation######################################## ax.text(x = a, y = b, z = c, s= ' $(%0.d, %0.d, %.0d)$'% (a, b, c), size = 18) ax.text(x = a, y = 0, z = 0, s= ' $%0.d e_1 = (%0.d, 0, 0)$'% (a, a), size = 15) ax.text(x = 0, y = b, z = 0, s= ' $%0.d e_2 = (0, %0.d, 0)$'% (b, b), size = 15) ax.text(x = 0, y = 0, z = c, s= ' $%0.d e_3 = (0, 0, %0.d)$' %(c, c), size = 15) #################################Axis Setting###################################### ax.grid() ax.set_xlim([0, a+1]) ax.set_ylim([0, b+1]) ax.set_zlim([0, c+1]) ax.set_xlabel('x-axis', size = 18) ax.set_ylabel('y-axis', size = 18) ax.set_zlabel('z-axis', size = 18) ax.set_title('Vector $(%0.d, %0.d, %.0d)$ Visualization' %(a, b, c), size = 20) ax.view_init(elev=20., azim=15) if __name__ == '__main__': a = 7 b = 4 c = 9 linearCombo(a, b, c) def linearComboNonStd(a, b, c, vec1, vec2, vec3): '''This function is for visualizing linear combination of non-standard basis in 3D. Function syntax: linearCombo(a, b, c, vec1, vec2, vec3), where a, b, c are the scalar multiplier, ve1, vec2 and vec3 are the basis. ''' fig = plt.figure(figsize = (10,10)) ax = fig.add_subplot(projection='3d') ########################Plot basis############################## vec1 = np.array([[0, 0, 0, vec1[0], vec1[1], vec1[2]]]) X, Y, Z, U, V, W = zip(*vec1) ax.quiver(X, Y, Z, U, V, W, length=1, normalize=False, color = 'blue',arrow_length_ratio = .08, pivot = 'tail', linestyles = 'solid',linewidths = 3) vec2 = np.array([[0, 0, 0, vec2[0], vec2[1], vec2[2]]]) X, Y, Z, U, V, W = zip(*vec2) ax.quiver(X, Y, Z, U, V, W, length=1, normalize=False, color = 'blue',arrow_length_ratio = .08, pivot = 'tail', linestyles = 'solid',linewidths = 3) vec3 = np.array([[0, 0, 0, vec3[0], vec3[1], vec3[2]]]) X, Y, Z, U, V, W = zip(*vec3) ax.quiver(X, Y, Z, U, V, W, length=1, normalize=False, color = 'blue',arrow_length_ratio = .08, pivot = 'tail', linestyles = 'solid',linewidths = 3) ###########################Plot Scalar Muliplied Vectors#################### avec1 = a * vec1 X, Y, Z, U, V, W = zip(*avec1) ax.quiver(X, Y, Z, U, V, W, length=1, normalize=False, color = 'red', alpha = .6,arrow_length_ratio = a/100, pivot = 'tail', linestyles = 'solid',linewidths = 3) bvec2 = b * vec2 X, Y, Z, U, V, W = zip(*bvec2) ax.quiver(X, Y, Z, U, V, W, length=1, normalize=False, color = 'red', alpha = .6,arrow_length_ratio = b/100, pivot = 'tail', linestyles = 'solid',linewidths = 3) cvec3 = c * vec3 X, Y, Z, U, V, W = zip(*cvec3) ax.quiver(X, Y, Z, U, V, W, length=1, normalize=False, color = 'red', alpha = .6,arrow_length_ratio = c/100, pivot = 'tail', linestyles = 'solid',linewidths = 3) combo = avec1 + bvec2 + cvec3 X, Y, Z, U, V, W = zip(*combo) ax.quiver(X, Y, Z, U, V, W, length=1, normalize=False, color = 'green', alpha = .7,arrow_length_ratio = np.linalg.norm(combo)/300, pivot = 'tail', linestyles = 'solid',linewidths = 3) #################################Plot Rectangle Boxes############################## point1 = [avec1[0, 3], avec1[0, 4], avec1[0, 5]] point2 = [avec1[0, 3]+bvec2[0, 3], avec1[0, 4]+bvec2[0, 4], avec1[0, 5]+bvec2[0, 5]] line1 = np.array([point1, point2]) ax.plot(line1[:,0], line1[:,1], line1[:,2], lw =3, ls = '--', color = 'black', alpha=0.5) point1 = [bvec2[0, 3], bvec2[0, 4], bvec2[0, 5]] point2 = [avec1[0, 3]+bvec2[0, 3], avec1[0, 4]+bvec2[0, 4], avec1[0, 5]+bvec2[0, 5]] line1 = np.array([point1, point2]) ax.plot(line1[:,0], line1[:,1], line1[:,2], lw =3, ls = '--', color = 'black', alpha=0.5) point1 = [bvec2[0, 3], bvec2[0, 4], bvec2[0, 5]] point2 = [cvec3[0, 3]+bvec2[0, 3], cvec3[0, 4]+bvec2[0, 4], cvec3[0, 5]+bvec2[0, 5]] line1 = np.array([point1, point2]) ax.plot(line1[:,0], line1[:,1], line1[:,2], lw =3, ls = '--', color = 'black', alpha=0.5) point1 = [cvec3[0, 3], cvec3[0, 4], cvec3[0, 5]] point2 = [cvec3[0, 3]+bvec2[0, 3], cvec3[0, 4]+bvec2[0, 4], cvec3[0, 5]+bvec2[0, 5]] line1 = np.array([point1, point2]) ax.plot(line1[:,0], line1[:,1], line1[:,2], lw =3, ls = '--', color = 'black', alpha=0.5) point1 = [cvec3[0, 3], cvec3[0, 4], cvec3[0, 5]] point2 = [cvec3[0, 3]+avec1[0, 3], cvec3[0, 4]+avec1[0, 4], cvec3[0, 5]+avec1[0, 5]] line1 = np.array([point1, point2]) ax.plot(line1[:,0], line1[:,1], line1[:,2], lw =3, ls = '--', color = 'black', alpha=0.5) point1 = [avec1[0, 3], avec1[0, 4], avec1[0, 5]] point2 = [cvec3[0, 3]+avec1[0, 3], cvec3[0, 4]+avec1[0, 4], cvec3[0, 5]+avec1[0, 5]] line1 = np.array([point1, point2]) ax.plot(line1[:,0], line1[:,1], line1[:,2], lw =3, ls = '--', color = 'black', alpha=0.5) ## point1 = [avec1[0, 3]+bvec2[0, 3]+cvec3[0, 3], avec1[0, 4]+bvec2[0, 4]+cvec3[0, 4], avec1[0, 5]+bvec2[0, 5]+cvec3[0, 5]] point2 = [cvec3[0, 3]+avec1[0, 3], cvec3[0, 4]+avec1[0, 4], cvec3[0, 5]+avec1[0, 5]] line1 = np.array([point1, point2]) ax.plot(line1[:,0], line1[:,1], line1[:,2], lw =3, ls = '--', color = 'black', alpha=0.5) ## point1 = [avec1[0, 3]+bvec2[0, 3]+cvec3[0, 3], avec1[0, 4]+bvec2[0, 4]+cvec3[0, 4], avec1[0, 5]+bvec2[0, 5]+cvec3[0, 5]] point2 = [cvec3[0, 3]+bvec2[0, 3], cvec3[0, 4]+bvec2[0, 4], cvec3[0, 5]+bvec2[0, 5]] line1 = np.array([point1, point2]) ax.plot(line1[:,0], line1[:,1], line1[:,2], lw =3, ls = '--', color = 'black', alpha=0.5) ## point1 = [avec1[0, 3]+bvec2[0, 3]+cvec3[0, 3], avec1[0, 4]+bvec2[0, 4]+cvec3[0, 4], avec1[0, 5]+bvec2[0, 5]+cvec3[0, 5]] point2 = [bvec2[0, 3]+avec1[0, 3], bvec2[0, 4]+avec1[0, 4], bvec2[0, 5]+avec1[0, 5]] line1 = np.array([point1, point2]) ax.plot(line1[:,0], line1[:,1], line1[:,2], lw =3, ls = '--', color = 'black', alpha=0.5) #################################Annotation######################################## ax.text(x = vec1[0,3], y = vec1[0,4], z = vec1[0,5], s= ' $v_1 =(%0.d, %0.d, %.0d)$'% (vec1[0,3], vec1[0,4], vec1[0,4]), size = 8) ax.text(x = vec2[0,3], y = vec2[0,4], z = vec2[0,5], s= ' $v_2 =(%0.d, %0.d, %.0d)$'% (vec2[0,3], vec2[0,4], vec2[0,4]), size = 8) ax.text(x = vec3[0,3], y = vec3[0,4], z = vec3[0,5], s= ' $v_3= (%0.d, %0.d, %.0d)$'% (vec3[0,3], vec3[0,4], vec3[0,4]), size = 8) ax.text(x = avec1[0,3], y = avec1[0,4], z = avec1[0,5], s= ' $%.0d v_1 =(%0.d, %0.d, %.0d)$'% (a, avec1[0,3], avec1[0,4], avec1[0,4]), size = 8) ax.text(x = bvec2[0,3], y = bvec2[0,4], z = bvec2[0,5], s= ' $%.0d v_2 =(%0.d, %0.d, %.0d)$'% (b, bvec2[0,3], bvec2[0,4], bvec2[0,4]), size = 8) ax.text(x = cvec3[0,3], y = cvec3[0,4], z = cvec3[0,5], s= ' $%.0d v_3= (%0.d, %0.d, %.0d)$'% (c, cvec3[0,3], cvec3[0,4], cvec3[0,4]), size = 8) # ax.text(x = 0, y = b, z = 0, s= ' $%0.d e_2 = (0, %0.d, 0)$'% (b, b), size = 15) # ax.text(x = 0, y = 0, z = c, s= ' $%0.d e_3 = (0, 0, %0.d)$' %(c, c), size = 15) #################################Axis Setting###################################### ax.grid() ax.set_xlim([0, 15]) ax.set_ylim([0, 15]) ax.set_zlim([0, 15]) ax.set_xlabel('x-axis', size = 18) ax.set_ylabel('y-axis', size = 18) ax.set_zlabel('z-axis', size = 18) #ax.set_title('Vector $(%0.d, %0.d, %.0d)$ Visualization' %(a, b, c), size = 20) ax.view_init(elev=20., azim=15) if __name__ == '__main__': a = 2 b = 3 c = 4 vec1 = np.array([2,1,0]) vec2 = np.array([0,3,1]) vec3 = np.array([1,2,3]) linearComboNonStd(a, b, c, vec1,vec2,vec3)
3.765625
4
cosmoz/joysticks.py
T-K-233/arduino-python
2
12788896
<gh_stars>1-10 ''' Adapted from Xbox-360-Controller-for-Python https://github.com/r4dian/Xbox-360-Controller-for-Python Modified by -T.K.- Aug 2018 ''' import ctypes import time import sys from operator import itemgetter, attrgetter from itertools import count, starmap from pyglet import event class XINPUT_GAMEPAD(ctypes.Structure): _fields_ = [ ('buttons', ctypes.c_ushort), # wButtons ('4', ctypes.c_ubyte), # bLeftTrigger ('5', ctypes.c_ubyte), # bLeftTrigger ('0', ctypes.c_short), # sThumbLX ('1', ctypes.c_short), # sThumbLY ('2', ctypes.c_short), # sThumbRx ('3', ctypes.c_short), # sThumbRy ] class XINPUT_STATE(ctypes.Structure): _fields_ = [ ('packet_number', ctypes.c_ulong), # dwPacketNumber ('gamepad', XINPUT_GAMEPAD), # Gamepad ] class XINPUT_VIBRATION(ctypes.Structure): _fields_ = [("l_motor", ctypes.c_ushort), ("r_motor", ctypes.c_ushort)] class XINPUT_BATTERY_INFORMATION(ctypes.Structure): _fields_ = [("BatteryType", ctypes.c_ubyte), ("BatteryLevel", ctypes.c_ubyte)] xinput = ctypes.windll.xinput1_4 def struct_dict(struct): ''' take a ctypes.Structure and return its field/value pairs as a dict. >>> 'buttons' in struct_dict(XINPUT_GAMEPAD) True >>> struct_dict(XINPUT_GAMEPAD)['buttons'].__class__.__name__ 'CField' ''' get_pair = lambda field_type: ( field_type[0], getattr(struct, field_type[0])) return dict(list(map(get_pair, struct._fields_))) def get_bit_values(number, size=32): ''' Get bit values as a list for a given number >>> get_bit_values(1) == [0]*31 + [1] True >>> get_bit_values(0xDEADBEEF) [1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L] You may override the default word size of 32-bits to match your actual application. >>> get_bit_values(0x3, 2) [1L, 1L] >>> get_bit_values(0x3, 4) [0L, 0L, 1L, 1L] ''' res = list(gen_bit_values(number)) res.reverse() # 0-pad the most significant bit res = [0] * (size - len(res)) + res return res def gen_bit_values(number): ''' Return a zero or one for each bit of a numeric value up to the most significant 1 bit, beginning with the least significant bit. ''' number = int(number) while number: yield number & 0x1 number >>= 1 class XBoxController(event.EventDispatcher): ''' A stateful wrapper, using pyglet event model, that binds to one XInput device and dispatches events when states change. ''' max_devices = 4 def __init__(self, device_number, normalize_axes=True): self.device_number = device_number values = vars() del values['self'] self.__dict__.update(values) super(XBoxController, self).__init__() self._last_state = self.get_state() self.axis = [0.0] * 6 self.button = [0] * 16 # Set the method that will be called to normalize the values for analog axis. choices = [self.translate_identity, self.translate_using_data_size] self.translate = choices[normalize_axes] self.get_state() def translate_using_data_size(self, value, data_size): # normalizes analog data to [0,1] for unsigned data and [-0.5,0.5] for signed data data_bits = 8 * data_size return float(value) / (2 ** data_bits - 1) def translate_identity(self, value, data_size=None): return value def get_state(self): 'Get the state of the controller represented by this object' state = XINPUT_STATE() res = xinput.XInputGetState(self.device_number, ctypes.byref(state)) if res == 0: # SUCCESS self.state = 1 return state if res == 1167: # DEVICE_NOT_CONNECTED self.state = 0 else: raise RuntimeError('Unknown error %d attempting to get state of device %d' % (res, self.device_number)) def is_connected(self): return self._last_state is not None def set_vibration(self, left_motor, right_motor): 'Control the speed of both motors seperately' XInputSetState = xinput.XInputSetState XInputSetState.argtypes = [ctypes.c_uint, ctypes.POINTER(XINPUT_VIBRATION)] XInputSetState.restype = ctypes.c_uint vibration = XINPUT_VIBRATION(int(left_motor * 65535), int(right_motor * 65535)) XInputSetState(self.device_number, ctypes.byref(vibration)) def get_battery_information(self): 'Get battery type & charge level' BATTERY_DEVTYPE_GAMEPAD = 0x00 BATTERY_DEVTYPE_HEADSET = 0x01 XInputGetBatteryInformation = xinput.XInputGetBatteryInformation XInputGetBatteryInformation.argtypes = [ctypes.c_uint, ctypes.c_ubyte, ctypes.POINTER(XINPUT_BATTERY_INFORMATION)] XInputGetBatteryInformation.restype = ctypes.c_uint battery = XINPUT_BATTERY_INFORMATION(0,0) XInputGetBatteryInformation(self.device_number, BATTERY_DEVTYPE_GAMEPAD, ctypes.byref(battery)) ''' define BATTERY_TYPE_DISCONNECTED 0x00 define BATTERY_TYPE_WIRED 0x01 define BATTERY_TYPE_ALKALINE 0x02 define BATTERY_TYPE_NIMH 0x03 define BATTERY_TYPE_UNKNOWN 0xFF define BATTERY_LEVEL_EMPTY 0x00 define BATTERY_LEVEL_LOW 0x01 define BATTERY_LEVEL_MEDIUM 0x02 define BATTERY_LEVEL_FULL 0x03 ''' batt_type = 'Unknown' if battery.BatteryType == 0xFF else ['Disconnected', 'Wired', 'Alkaline', 'Nimh'][battery.BatteryType] level = ['Empty', 'Low', 'Medium', 'Full'][battery.BatteryLevel] return batt_type, level def handle_changed_state(self, state): 'Dispatch various events as a result of the state changing' self.dispatch_event('on_state_changed', state) self.dispatch_axis_events(state) self.dispatch_button_events(state) def dispatch_axis_events(self, state): axis_fields = dict(XINPUT_GAMEPAD._fields_) axis_fields.pop('buttons') for axis, type in list(axis_fields.items()): old_val = getattr(self._last_state.gamepad, axis) new_val = getattr(state.gamepad, axis) data_size = ctypes.sizeof(type) old_val = self.translate(old_val, data_size) new_val = self.translate(new_val, data_size) # an attempt to add deadzones and dampen noise if ((old_val!=new_val and (new_val>0.08000000000000000 or new_val<-0.08000000000000000) and abs(old_val-new_val) > 0.00000000500000000) or (axis=='4' or axis=='5') and new_val==0 and abs(old_val-new_val) > 0.00000000500000000): self.dispatch_event('on_axis', axis, new_val) def dispatch_button_events(self, state): changed = state.gamepad.buttons ^ self._last_state.gamepad.buttons changed = get_bit_values(changed, 16) buttons_state = get_bit_values(state.gamepad.buttons, 16) changed.reverse() buttons_state.reverse() button_numbers = count(1) changed_buttons = list(filter(itemgetter(0), list(zip(changed, button_numbers, buttons_state)))) tuple(starmap(self.dispatch_button_event, changed_buttons)) def dispatch_button_event(self, changed, number, pressed): self.dispatch_event('on_button', number-1, pressed) # -1 to restore index to 0 def on_axis(self, axis, value): self.axis[int(axis)] = value * 2 def on_button(self, button, pressed): self.button[button] = pressed def refresh(self): state = self.get_state() try: if state.packet_number != self._last_state.packet_number: self.handle_changed_state(state) except: pass self._last_state = state @staticmethod def init_all(): devices = list(map(XBoxController, list(range(XBoxController.max_devices)))) devices = [d for d in devices if d.is_connected()] print('%d joysticks found.' % len(devices)) return devices list(map(XBoxController.register_event_type, [ 'on_state_changed', 'on_axis', 'on_button', ])) import keyboard class Keyboard: def __init__(self, suppress=False): self.keys = [] self.state = 1 self._suppress = suppress def _process_keys(self, e): if e.event_type == 'down': e = e.scan_code if e not in self.keys: self.keys.append(e) elif e.event_type == 'up': e = e.scan_code if e in self.keys: self.keys.remove(e) def key(self, key): if type(key) == int: return key in self.keys def refresh(self): keyboard.hook(self._process_keys, suppress=self._suppress)
2.5625
3
nn/neuron.py
prabhatnagarajan/rl_lib
1
12788897
<gh_stars>1-10 from activation import * import numpy as np from pdb import set_trace class Neuron: # takes in initialized weights with the final term def __init__(self, init_weights, init_bias, activation=Activation.sigmoid): self.weights = init_weights self.bias = init_bias obj = Activation() self.activation = obj.sigmoid def apply(self, input): return self.activation(np.dot(input, self.weights) + self.bias)
3.09375
3
app/models/keyword.py
Simple2B/twitter-bot
0
12788898
<reponame>Simple2B/twitter-bot<filename>app/models/keyword.py<gh_stars>0 from app import db from app.models.utils import ModelMixin class Keyword(db.Model, ModelMixin): __tablename__ = 'keywords' id = db.Column(db.Integer, primary_key=True) word = db.Column(db.String(60), unique=True, nullable=False)
1.96875
2
src/upbgui/frame_button.py
SIGSEGV-666/UPBGUI
1
12788899
from .widget import Widget, BGUI_DEFAULT, BGUI_NO_THEME, BGUI_CENTERED from .frame import Frame from .label import Label FBSTYLE_CLASSIC = 0 FBSTYLE_SOLID = 1 class FrameButton(Widget): """A clickable frame-based button.""" theme_section = 'FrameButton' theme_options = { 'Color': (0.4, 0.4, 0.4, 1), 'BorderSize': 1, 'BorderColor': (0, 0, 0, 1), 'LabelSubTheme': '', 'SolidBaseColor': (0.4, 0.4, 0.4, 1.0), 'SolidHoverColor': (0.6, 0.6, 0.6, 1.0), 'SolidClickColor': (0.9, 0.9, 0.9, 1.0) } def __init__(self, parent, name=None, base_color=None, text="", font=None, pt_size=None, aspect=None, size=[1, 1], pos=[0, 0], sub_theme='', text_color=None, border_size=None, border_color=None, style=FBSTYLE_CLASSIC, hover_color=None, click_color=None, options=BGUI_DEFAULT): """ :param parent: the widget's parent :param name: the name of the widget :param base_color: the color of the button :param text: the text to display (this can be changed later via the text property) :param font: the font to use :param pt_size: the point size of the text to draw (defaults to 30 if None) :param aspect: constrain the widget size to a specified aspect ratio :param size: a tuple containing the width and height :param pos: a tuple containing the x and y position :param sub_theme: name of a sub_theme defined in the theme file (similar to CSS classes) :param options: various other options """ Widget.__init__(self, parent, name, aspect, size, pos, sub_theme, options) self.style = style self.frame = Frame(self, size=[1, 1], pos=[0, 0], options=BGUI_NO_THEME) self.label = Label(self, text=text, font=font, pt_size=pt_size, pos=[0, 0], sub_theme=self.theme['LabelSubTheme'], options=BGUI_DEFAULT | BGUI_CENTERED) if self.style == FBSTYLE_SOLID: self.solid_basecolor = (self.theme['SolidBaseColor'] if not base_color else (base_color)) self.solid_hovercolor = (self.theme['SolidHoverColor'] if not hover_color else (hover_color)) self.solid_clickcolor = (self.theme['SolidClickColor'] if not click_color else (click_color)) if not base_color: base_color = self.theme['Color'] self.base_color = base_color self.frame.border = (border_size if border_size is not None else self.theme['BorderSize']) self.frame.border_color = (border_color if border_color else self.theme['BorderColor']) self.light = [ self.base_color[0] + 0.15, self.base_color[1] + 0.15, self.base_color[2] + 0.15, self.base_color[3]] self.dark = [ self.base_color[0] - 0.15, self.base_color[1] - 0.15, self.base_color[2] - 0.15, self.base_color[3]] if text_color: self.label.color = text_color if self.style == FBSTYLE_CLASSIC: self.frame.colors = (self.dark, self.dark, self.light, self.light) elif self.style == FBSTYLE_SOLID: self.frame.colors = (self.solid_basecolor,)*4 @property def text(self): return self.label.text @text.setter def text(self, value): self.label.text = value @property def color(self): if self.style == FBSTYLE_CLASSIC: return self.base_color elif self.style == FBSTYLE_SOLID: return self.solid_basecolor @color.setter def color(self, value): if self.style == FBSTYLE_CLASSIC: self.base_color = value self.light = ( self.base_color[0] + 0.15, self.base_color[1] + 0.15, self.base_color[2] + 0.15, self.base_color[3]) self.dark = ( self.base_color[0] - 0.15, self.base_color[1] - 0.15, self.base_color[2] - 0.15, self.base_color[3]) self.frame.colors = (self.dark, self.dark, self.light, self.light) elif self.style == FBSTYLE_SOLID: self.solid_basecolor = value def _handle_hover(self): if self.style == FBSTYLE_CLASSIC: light = self.light[:] dark = self.dark[:] # Lighten button when hovered over. for n in range(3): light[n] += .1 dark[n] += .1 self.frame.colors = (dark, dark, light, light) elif self.style == FBSTYLE_SOLID: self.frame.colors = (self.solid_hovercolor,)*4 def _handle_active(self): if self.style == FBSTYLE_CLASSIC: light = self.light[:] dark = self.dark[:] # Darken button when clicked. for n in range(3): light[n] -= .1 dark[n] -= .1 self.frame.colors = (light, light, dark, dark) elif self.style == FBSTYLE_SOLID: self.frame.colors = (self.solid_clickcolor,)*4 def _draw(self): """Draw the button""" # Draw the children before drawing an additional outline Widget._draw(self) # Reset the button's color if self.style == FBSTYLE_CLASSIC: self.frame.colors = (self.dark, self.dark, self.light, self.light) elif self.style == FBSTYLE_SOLID: self.frame.colors = (self.solid_basecolor,)*4
3.09375
3
pybites_tools/zen.py
Timfrazer/pybites-tools
0
12788900
import sys from io import StringIO def zen_of_python() -> list[str]: """ Dump the Zen of Python into a variable https://stackoverflow.com/a/23794519 """ zen = StringIO() old_stdout = sys.stdout sys.stdout = zen import this # noqa F401 sys.stdout = old_stdout return zen.getvalue().splitlines() def main(): import pyperclip zen = "\n".join(zen_of_python()) pyperclip.copy(zen) print("The Zen of Python has been copied to your clipboard") if __name__ == "__main__": main()
3.28125
3
betfair/exceptions.py
mkapuza/betfair.py
94
12788901
# -*- coding: utf-8 -*- class BetfairError(Exception): pass class NotLoggedIn(BetfairError): pass class LoginError(BetfairError): def __init__(self, response, data): self.response = response self.status_code = response.status_code self.message = data.get('loginStatus', 'UNKNOWN') super(LoginError, self).__init__(self.message) class AuthError(BetfairError): def __init__(self, response, data): self.response = response self.status_code = response.status_code self.message = data.get('error', 'UNKNOWN') super(AuthError, self).__init__(self.message) class ApiError(BetfairError): def __init__(self, response, data): self.response = response self.status_code = response.status_code try: error_data = data['error']['data']['APINGException'] self.message = error_data.get('errorCode', 'UNKNOWN') self.details = error_data.get('errorDetails') except KeyError: self.message = 'UNKNOWN' self.details = None super(ApiError, self).__init__(self.message)
2.5625
3
test/test_weight_calculator.py
canvas-gamification/canvas-weight-calculator
0
12788902
import unittest from exceptions import RangeValidationException from weight_calculator import calculate_weights, validate_rages, validate_grades_and_ranges class WeightCalculatorTest(unittest.TestCase): def range_validation_tests(self): self.assertFalse(validate_rages({ "Final": [50, 60], "Midterms": [25, 30], "Assignments": [0, 5] })) self.assertFalse(validate_rages({ "Final": [50, 60], "Midterms": [50, 60], "Assignments": [10, 15] })) self.assertFalse(validate_rages({ "Final": [50, 60], "Midterms": [25, 30], "Assignments": [15, 10] })) self.assertTrue(validate_rages({ "Final": [50, 60], "Midterms": [25, 30], "Assignments": [5, 15] })) def range_and_weights_validation_tests(self): self.assertFalse(validate_grades_and_ranges( { "Final": [100, 50], "Midterms": [50, 100], "Assignments": [100, 0, 100] }, { "Final": [50, 60], "Midterms": [25, 30], "Assignments": [15, 10] })) self.assertFalse(validate_grades_and_ranges( { "Finals": [100, 50], "Midterms": [50, 100], "Assignments": [100, 0, 100] }, { "Final": [50, 60], "Midterms": [25, 30], "Assignments": [10, 15] })) self.assertFalse(validate_grades_and_ranges( { "Final": [100, 50], "Midterms": [50, 100], "Assignments": [100, 0, 100] }, { "Finals": [50, 60], "Midterms": [25, 30], "Assignments": [10, 15] })) self.assertTrue(validate_grades_and_ranges( { "Final": [100, 50], "Midterms": [50, 100], "Assignments": [100, 0, 100] }, { "Final": [50, 60], "Midterms": [25, 30], "Assignments": [10, 15] })) def test(self): ranges = { "Final": [50, 60], "Midterms": [30, 40], "Assignments": [5, 30], } bad_ranges = { "Finals": [50, 60], "Midterms": [30, 40], "Assignments": [5, 30], } grades = { "Final": [100, 100, 99], "Midterms": [50, 60], "Assignments": [0, 0, 2, 5, 10] } weights = calculate_weights(grades, ranges) self.assertEqual(weights, { "Final": 60, "Midterms": 35, "Assignments": 5, }) self.assertRaises(RangeValidationException, calculate_weights, bad_ranges, ranges)
3.015625
3
torchnmf/nmf.py
akashpalrecha/pytorch-NMF
0
12788903
import torch import torch.nn.functional as F from torch.nn import Parameter from .metrics import Beta_divergence from .base import Base from tqdm import tqdm def _mu_update(param, pos, gamma, l1_reg, l2_reg, constant_rows=None): if param.grad is None: return # prevent negative term, very likely to happen with kl divergence multiplier:torch.Tensor = F.relu(pos - param.grad, inplace=True) if l1_reg > 0: pos.add_(l1_reg) if l2_reg > 0: if pos.shape != param.shape: pos = pos + l2_reg * param else: pos.add_(l2_reg * param) multiplier.div_(pos) if gamma != 1: multiplier.pow_(gamma) # Fill the first `constant_rows` of the multiplier with 1s # to leave them unchanged if constant_rows is not None: multiplier[:constant_rows,:].fill_(1.0) param.mul_(multiplier) class _NMF(Base): def __init__(self, W_size, H_size, rank): super().__init__() self.rank = rank self.W = Parameter(torch.rand(*W_size).double()) self.H = Parameter(torch.rand(*H_size).double()) def forward(self, H=None, W=None): if H is None: H = self.H if W is None: W = self.W return self.reconstruct(H, W) def reconstruct(self, H, W): raise NotImplementedError def get_W_positive(self, WH, beta, H_sum) -> (torch.Tensor, None or torch.Tensor): raise NotImplementedError def get_H_positive(self, WH, beta, W_sum) -> (torch.Tensor, None or torch.Tensor): raise NotImplementedError def fit(self, V, W=None, H=None, fix_h_rows=None, update_W=True, update_H=True, update_H_after_iter=None, beta=1, tol=1e-5, min_loss=None, max_iter=200, min_iter=20, verbose=0, initial='random', alpha=0, l1_ratio=0, lower_thresh=1e-8, ): self.fix_neg.value = lower_thresh V = self.fix_neg(V) if W is None: pass # will do special initialization in thre future else: self.W.data.copy_(W) self.W.requires_grad = update_W if H is None: pass else: self.H.data.copy_(H) self.H.requires_grad = update_H if update_H_after_iter is None: update_H_after_iter = max_iter if beta < 1: gamma = 1 / (2 - beta) elif beta > 2: gamma = 1 / (beta - 1) else: gamma = 1 l1_reg = alpha * l1_ratio l2_reg = alpha * (1 - l1_ratio) loss_scale = torch.prod(torch.tensor(V.shape)).float() H_sum, W_sum = None, None with tqdm(total=max_iter, disable=not verbose) as pbar: for n_iter in range(max_iter): if n_iter >= update_H_after_iter: update_H = True self.H.requires_grad = True if self.W.requires_grad: self.zero_grad() WH = self.reconstruct(self.H.detach(), self.W) loss = Beta_divergence(self.fix_neg(WH), V, beta) loss.backward() with torch.no_grad(): positive_comps, H_sum = self.get_W_positive(WH, beta, H_sum) _mu_update(self.W, positive_comps, gamma, l1_reg, l2_reg) W_sum = None if self.H.requires_grad: self.zero_grad() WH = self.reconstruct(self.H, self.W.detach()) loss = Beta_divergence(self.fix_neg(WH), V, beta) loss.backward() with torch.no_grad(): positive_comps, W_sum = self.get_H_positive(WH, beta, W_sum) _mu_update(self.H, positive_comps, gamma, l1_reg, l2_reg, fix_h_rows) H_sum = None loss = loss.div_(loss_scale).item() pbar.set_postfix(loss=loss) # pbar.set_description('Beta loss=%.4f' % error) pbar.update() if not n_iter: loss_init = loss elif (previous_loss - loss) / loss_init < tol and n_iter >= min_iter: if min_loss is not None and loss > min_loss: pass else: break previous_loss = loss return n_iter def fit_transform(self, *args, **kwargs): n_iter = self.fit(*args, **kwargs) return n_iter, self.forward() class NMF(_NMF): def __init__(self, Vshape, rank=None): self.K, self.M = Vshape if not rank: rank = self.K super().__init__((self.K, rank), (rank, self.M), rank) def reconstruct(self, H, W): return W @ H def get_W_positive(self, WH, beta, H_sum): H = self.H if beta == 1: if H_sum is None: H_sum = H.sum(1) denominator = H_sum[None, :] else: if beta != 2: WH = WH.pow(beta - 1) WHHt = WH @ H.t() denominator = WHHt return denominator, H_sum def get_H_positive(self, WH, beta, W_sum): W = self.W if beta == 1: if W_sum is None: W_sum = W.sum(0) # shape(n_components, ) denominator = W_sum[:, None] else: if beta != 2: WH = WH.pow(beta - 1) WtWH = W.t() @ WH denominator = WtWH return denominator, W_sum def sort(self): _, maxidx = self.W.data.max(0) _, idx = maxidx.sort() self.W.data = self.W.data[:, idx] self.H.data = self.H.data[idx] class NMFD(_NMF): def __init__(self, Vshape, T=1, rank=None): self.K, self.M = Vshape if not rank: rank = self.K self.pad_size = T - 1 super().__init__((self.K, rank, T), (rank, self.M - T + 1), rank) def reconstruct(self, H, W): return F.conv1d(H[None, :], W.flip(2), padding=self.pad_size)[0] def get_W_positive(self, WH, beta, H_sum): H = self.H if beta == 1: if H_sum is None: H_sum = H.sum(1) denominator = H_sum[None, :, None] else: if beta != 2: WH = WH.pow(beta - 1) WHHt = F.conv1d(WH[:, None], H[:, None]) denominator = WHHt return denominator, H_sum def get_H_positive(self, WH, beta, W_sum): W = self.W if beta == 1: if W_sum is None: W_sum = W.sum((0, 2)) denominator = W_sum[:, None] else: if beta != 2: WH = WH.pow(beta - 1) WtWH = F.conv1d(WH[None, :], W.transpose(0, 1))[0] denominator = WtWH return denominator, W_sum def sort(self): _, maxidx = self.W.data.sum(2).max(0) _, idx = maxidx.sort() self.W.data = self.W.data[:, idx] self.H.data = self.H.data[idx] class NMF2D(_NMF): def __init__(self, Vshape, win=1, rank=None): try: F, T = win except: F = T = win if len(Vshape) == 3: self.channel, self.K, self.M = Vshape else: self.K, self.M = Vshape self.channel = 1 self.pad_size = (F - 1, T - 1) super().__init__((self.channel, rank, F, T), (rank, self.K - F + 1, self.M - T + 1), rank) def reconstruct(self, H, W): out = F.conv2d(H[None, ...], W.flip((2, 3)), padding=self.pad_size)[0] if self.channel == 1: return out[0] return out def get_W_positive(self, WH, beta, H_sum): H = self.H if beta == 1: if H_sum is None: H_sum = H.sum((1, 2)) denominator = H_sum[None, :, None, None] else: if beta != 2: WH = WH.pow(beta - 1) WH = WH.view(self.channel, 1, self.K, self.M) WHHt = F.conv2d(WH, H[:, None]) denominator = WHHt return denominator, H_sum def get_H_positive(self, WH, beta, W_sum): W = self.W if beta == 1: if W_sum is None: W_sum = W.sum((0, 2, 3)) denominator = W_sum[:, None, None] else: if beta != 2: WH = WH.pow(beta - 1) WH = WH.view(1, self.channel, self.K, self.M) WtWH = F.conv2d(WH, W.transpose(0, 1))[0] denominator = WtWH return denominator, W_sum def sort(self): raise NotImplementedError class NMF3D(_NMF): def __init__(self, Vshape: tuple, rank: int = None, win=1): try: T, H, W = win except: T = H = W = win if len(Vshape) == 4: self.channel, self.N, self.K, self.M = Vshape else: self.N, self.K, self.M = Vshape self.channel = 1 self.pad_size = (T - 1, H - 1, W - 1) if not rank: rank = self.K super().__init__((self.channel, rank, T, H, W), (rank, self.N - T + 1, self.K - H + 1, self.M - W + 1), rank) def reconstruct(self, H, W): out = F.conv3d(H[None, ...], W.flip((2, 3, 4)), padding=self.pad_size)[0] if self.channel == 1: return out[0] return out def get_W_positive(self, WH, beta, H_sum): H = self.H if beta == 1: if H_sum is None: H_sum = H.sum((1, 2, 3)) denominator = H_sum[None, :, None, None, None] else: if beta != 2: WH = WH.pow(beta - 1) WH = WH.view(self.channel, 1, self.N, self.K, self.M) WHHt = F.conv3d(WH, H[:, None]) denominator = WHHt return denominator, H_sum def get_H_positive(self, WH, beta, W_sum): W = self.W if beta == 1: if W_sum is None: W_sum = W.sum((0, 2, 3, 4)) denominator = W_sum[:, None, None, None] else: if beta != 2: WH = WH.pow(beta - 1) WH = WH.view(1, self.channel, self.N, self.K, self.M) WtWH = F.conv3d(WH, W.transpose(0, 1))[0] denominator = WtWH return denominator, W_sum def sort(self): raise NotImplementedError
2.34375
2
src/utils.py
yoichi1484/sake_embedding
2
12788904
from gensim.models import KeyedVectors import pprint import json PATH_DATA = '../data/sake_dataset_v1.json' def preprocessing(sake_data): return sake_data.strip().replace(' ', '_') def fix_data(data): fixed_data = [] for k, v in sorted(data.items(), key=lambda x:x[0]): if 'mean' in v: fixed_data.append('{}:{}'.format(k, v['mean'])) elif type(v) == list: for _v in v: _v = preprocessing(_v) fixed_data.append('{}:{}'.format(k, _v)) else: v = preprocessing(v) fixed_data.append('{}:{}'.format(k, v)) return fixed_data def load_dataset(path = PATH_DATA): with open(path) as f: dataset = json.load(f) return dataset def load_sake_embedding(path): return KeyedVectors.load_word2vec_format(path) class SearchAPI(): def __init__(self, path = PATH_DATA): self.dataset = load_dataset(path)['dataset'] def and_search(self, *args): """ This function returns sake data that contain the queries Args: queries Return: data (list) that contain the queries Example: >>> api = SearchAPI() >>> results = api.and_search("brand:英勲", "rice:祝") >>> pprint.pprint(results[0], width=40) {'alcohol_rate': {'max': '15.00', 'mean': '15.00', 'min': '15.00'}, 'amino_acid_content': {'max': '', 'mean': '', 'min': ''}, 'brand': '英勲', ... } """ result = self.dataset for query in args: result = self._filtering(query, result) return result def _filtering(self, query, dataset): return [d for d in dataset if query in fix_data(d)]
2.921875
3
visigoth/stimuli/elementarray.py
mwaskom/visigoth
2
12788905
"""Psychopy ElementArrayStim with flexible pedestal luminance. Psychopy authors have said on record that this functionality should exist in Psychopy itself. Future users of this code should double check as to whether that has been implemented and if this code can be excised. Note however that we have also added some functinoality to set the contrast in a way that depends on the pedestal, which may not get added. This module is adapted from a similar extension to GratingStim Original credit to https://github.com/nwilming/PedestalGrating/ Covered under the PsychoPy license, as it is a simple extension of prior code: Copyright (C) 2015 <NAME> Distributed under the terms of the GNU General Public License (GPL). """ from __future__ import division import pyglet pyglet.options['debug_gl'] = False import ctypes # noqa: 402 GL = pyglet.gl from psychopy.visual.elementarray import ElementArrayStim # noqa: 402 from psychopy.visual.basevisual import MinimalStim, TextureMixin # noqa: 402 try: from psychopy.visual import shaders except ImportError: from psychopy import _shadersPyglet as shaders # Framgent shader for the gabor stimulus. This is needed to add the pedestal to # the color values for each location. I'm keeping it in this file to make the # stimulus fairly self contained and to avoid messing with anything else. # Almost a one to one copy of the original psychopy shader. fragSignedColorTexMask = ''' uniform sampler2D texture, mask; uniform float pedestal; void main() { vec4 textureFrag = texture2D(texture,gl_TexCoord[0].st); vec4 maskFrag = texture2D(mask,gl_TexCoord[1].st); gl_FragColor.a = gl_Color.a*maskFrag.a*textureFrag.a; gl_FragColor.rgb = ((pedestal+1.0)/2.0) + ((textureFrag.rgb * (gl_Color.rgb*2.0-1.0)+1.0)/2.0) -0.5; } ''' class ElementArray(ElementArrayStim, MinimalStim, TextureMixin): """Field of elements that are independently controlled and rapidly drawn. This stimulus class defines a field of elements whose behaviour can be independently controlled. Suitable for creating 'global form' stimuli or more detailed random dot stimuli. This stimulus can draw thousands of elements without dropping a frame, but in order to achieve this performance, uses several OpenGL extensions only available on modern graphics cards (supporting OpenGL2.0). See the ElementArray demo. """ def __init__(self, win, units=None, fieldPos=(0.0, 0.0), fieldSize=(1.0, 1.0), fieldShape='circle', nElements=100, sizes=2.0, xys=None, rgbs=None, colors=(1.0, 1.0, 1.0), colorSpace='rgb', opacities=None, depths=0, fieldDepth=0, oris=0, sfs=1.0, contrs=1, phases=0, elementTex='sin', elementMask='gauss', texRes=48, interpolate=True, name=None, autoLog=False, maskParams=None, pedestal=None): super(ElementArray, self).__init__( win, units=units, fieldPos=fieldPos, fieldSize=fieldSize, fieldShape=fieldShape, nElements=nElements, sizes=sizes, xys=xys, rgbs=rgbs, colors=colors, colorSpace=colorSpace, opacities=opacities, depths=depths, fieldDepth=fieldDepth, oris=oris, sfs=sfs, contrs=contrs, phases=phases, elementTex=elementTex, elementMask=elementMask, texRes=texRes, interpolate=interpolate, name=name, autoLog=autoLog, maskParams=maskParams) # Set the default pedestal assuming a gray window color pedestal = win.background_color if pedestal is None else pedestal self.pedestal = pedestal self._progSignedTexMask = shaders.compileProgram( shaders.vertSimple, fragSignedColorTexMask) @property def pedestal_contrs(self): """Stimulus contrast, accounting for pedestal""" return self.contrs / (self.pedestal + 1) @pedestal_contrs.setter def pedestal_contrs(self, values): """Stimulus contrast, accounting for pedestal.""" adjusted_values = values * (self.pedestal + 1) self.contrs = adjusted_values def draw(self, win=None): """Draw the stimulus in its relevant window. You must call this method after every win.update() if you want the stimulus to appear on that frame and then update the screen again. """ if win is None: win = self.win self._selectWindow(win) if self._needVertexUpdate: self._updateVertices() if self._needColorUpdate: self.updateElementColors() if self._needTexCoordUpdate: self.updateTextureCoords() # scale the drawing frame and get to centre of field GL.glPushMatrix() # push before drawing, pop after # push the data for client attributes GL.glPushClientAttrib(GL.GL_CLIENT_ALL_ATTRIB_BITS) # GL.glLoadIdentity() self.win.setScale('pix') cpcd = ctypes.POINTER(ctypes.c_double) GL.glColorPointer(4, GL.GL_DOUBLE, 0, self._RGBAs.ctypes.data_as(cpcd)) GL.glVertexPointer(3, GL.GL_DOUBLE, 0, self.verticesPix.ctypes.data_as(cpcd)) # setup the shaderprogram _prog = self._progSignedTexMask GL.glUseProgram(_prog) # set the texture to be texture unit 0 GL.glUniform1i(GL.glGetUniformLocation(_prog, b"texture"), 0) # mask is texture unit 1 GL.glUniform1i(GL.glGetUniformLocation(_prog, b"mask"), 1) # BEGIN ADDED CODE GL.glUniform1f(GL.glGetUniformLocation(_prog, b"pedestal"), self.pedestal) # END ADDED CODE # bind textures GL.glActiveTexture(GL.GL_TEXTURE1) GL.glBindTexture(GL.GL_TEXTURE_2D, self._maskID) GL.glEnable(GL.GL_TEXTURE_2D) GL.glActiveTexture(GL.GL_TEXTURE0) GL.glBindTexture(GL.GL_TEXTURE_2D, self._texID) GL.glEnable(GL.GL_TEXTURE_2D) # setup client texture coordinates first GL.glClientActiveTexture(GL.GL_TEXTURE0) GL.glTexCoordPointer(2, GL.GL_DOUBLE, 0, self._texCoords.ctypes) GL.glEnableClientState(GL.GL_TEXTURE_COORD_ARRAY) GL.glClientActiveTexture(GL.GL_TEXTURE1) GL.glTexCoordPointer(2, GL.GL_DOUBLE, 0, self._maskCoords.ctypes) GL.glEnableClientState(GL.GL_TEXTURE_COORD_ARRAY) GL.glEnableClientState(GL.GL_COLOR_ARRAY) GL.glEnableClientState(GL.GL_VERTEX_ARRAY) GL.glDrawArrays(GL.GL_QUADS, 0, self.verticesPix.shape[0] * 4) # unbind the textures GL.glActiveTexture(GL.GL_TEXTURE1) GL.glBindTexture(GL.GL_TEXTURE_2D, 0) GL.glDisable(GL.GL_TEXTURE_2D) # main texture GL.glActiveTexture(GL.GL_TEXTURE0) GL.glBindTexture(GL.GL_TEXTURE_2D, 0) GL.glDisable(GL.GL_TEXTURE_2D) # disable states GL.glDisableClientState(GL.GL_COLOR_ARRAY) GL.glDisableClientState(GL.GL_VERTEX_ARRAY) GL.glDisableClientState(GL.GL_TEXTURE_COORD_ARRAY) GL.glUseProgram(0) GL.glPopClientAttrib() GL.glPopMatrix()
2.046875
2
ansible-simple-exports-demo.py
j-sims/isilon-ansible-demos
0
12788906
#!/usr/bin/env python import json import yaml sharesFilename = 'simple-exports.json' with open(sharesFilename, 'r') as f: shares = json.load(f) ### For Loop to write out playbook for each cluster for cluster in shares['clusters']: playbookFilename = 'playbook-simple-exports-%s.yml' % cluster['name'] with open(playbookFilename, 'w') as playbook: play = [ { 'hosts': 'localhost', 'name': 'Isilon New NFS Export with URI module', 'tasks': [], } ] startsession = { 'name': 'get isilon API session IDs', 'register': 'results_login', 'uri': { 'body': {'password': cluster['password'], 'services': ['platform', 'namespace'], 'username': cluster['username']}, 'body_format': 'json', 'method': 'POST', 'status_code': 201, 'url': 'https://' + cluster['name'] +':8080/session/1/session', 'validate_certs': False } } play[0]['tasks'].append(startsession) for export in cluster['exports']: createexport = { 'name': 'make NFS Export', 'uri': { 'body': { 'description': export['description'], 'paths': export['paths'], 'zone': export['zone']}, 'body_format': 'json', 'headers': {'Cookie': 'isisessid={{ results_login.cookies.isisessid }}', 'X-CSRF-Token': '{{ results_login.cookies.isicsrf }}', 'referer': 'https://'+cluster['name']+':8080'}, 'method': 'POST', 'status_code': 201, 'url': 'https://'+cluster['name']+':8080/platform/4/protocols/nfs/exports', 'validate_certs': False, } } play[0]['tasks'].append(createexport) endsession = { 'name': 'Delete isilon API session IDs', 'register': 'results_DEL_cookie', 'uri': { 'headers': { 'Cookie': 'isisessid={{ results_login.cookies.isisessid }}', 'X-CSRF-Token': '{{ results_login.cookies.isicsrf }}', 'referer': 'https://'+cluster['name']+':8080', }, 'method': 'DELETE', 'status_code': 204, 'url': 'https://'+cluster['name']+':8080/session/1/session', 'validate_certs': False, } } play[0]['tasks'].append(endsession) yaml.safe_dump(play, playbook, default_flow_style=False)
2.109375
2
app.py
eocode/Queens
0
12788907
<gh_stars>0 """ Start app """ from app import queen if __name__ == "__main__": """Main function for run application""" queen.run()
1.46875
1
core/utils/k8s.py
kubesys/kubevm
0
12788908
<reponame>kubesys/kubevm import socket import time import traceback import operator from json import dumps import os, sys from sys import exit from kubernetes import client, config from kubernetes.client import V1DeleteOptions from kubernetes.client.rest import ApiException import logging import logging.handlers try: from utils import constants from utils.exception import BadRequest except: import constants from exception import BadRequest TOKEN = constants.KUBERNETES_TOKEN_FILE VM_PLURAL = constants.KUBERNETES_PLURAL_VM VMP_PLURAL = constants.KUBERNETES_PLURAL_VMP VMD_PLURAL = constants.KUBERNETES_PLURAL_VMD VMDI_PLURAL = constants.KUBERNETES_PLURAL_VMDI VMDSN_PLURAL = constants.KUBERNETES_PLURAL_VMDSN VM_KIND = constants.KUBERNETES_KIND_VM VMP_KIND = constants.KUBERNETES_KIND_VMP VMD_KIND = constants.KUBERNETES_KIND_VMD VMDI_KIND = constants.KUBERNETES_KIND_VMDI VMDSN_KIND = constants.KUBERNETES_KIND_VMDSN VERSION = constants.KUBERNETES_API_VERSION GROUP = constants.KUBERNETES_GROUP config.load_kube_config(config_file=TOKEN) LOG = '/var/log/virtctl.log' RETRY_TIMES = 15 def set_logger(header, fn): logger = logging.getLogger(header) handler1 = logging.StreamHandler() handler2 = logging.handlers.RotatingFileHandler(filename=fn, maxBytes=10000000, backupCount=10) logger.setLevel(logging.DEBUG) handler1.setLevel(logging.ERROR) handler2.setLevel(logging.DEBUG) formatter = logging.Formatter("%(asctime)s %(name)s %(lineno)s %(levelname)s %(message)s") handler1.setFormatter(formatter) handler2.setFormatter(formatter) logger.addHandler(handler1) logger.addHandler(handler2) return logger k8s_logger = set_logger(os.path.basename(__file__), LOG) resources = {} kind_plural = {VM_KIND:VM_PLURAL, VMP_KIND:VMP_PLURAL, VMD_KIND:VMD_PLURAL, VMDI_KIND:VMDI_PLURAL, VMDSN_KIND:VMDSN_PLURAL} for kind,plural in kind_plural.items(): resource = {} resource['version'] = VERSION resource['group'] = GROUP resource['plural'] = plural resources[kind] = resource def get(name, kind): jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=resources[kind]['group'], version=resources[kind]['version'], namespace='default', plural=resources[kind]['plural'], name=name) return jsondict def create(name, data, kind): hostname = get_hostname_in_lower_case() jsondict = {'spec': {'volume': {}, 'nodeName': hostname, 'status': {}}, 'kind': kind, 'metadata': {'labels': {'host': hostname}, 'name': name}, 'apiVersion': '%s/%s' % (resources[kind]['group'], resources[kind]['version'])} jsondict = updateJsonRemoveLifecycle(jsondict, data) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') return client.CustomObjectsApi().create_namespaced_custom_object( group=resources[kind]['group'], version=resources[kind]['version'], namespace='default', plural=resources[kind]['plural'], body=body) def update(name, data, kind): return client.CustomObjectsApi().replace_namespaced_custom_object( group=resources[kind]['group'], version=resources[kind]['version'], namespace='default', plural=resources[kind]['plural'], name=name, body=data) def delete(name, data, kind): k8s_logger.debug('deleteVMBackupdebug %s' % name) return client.CustomObjectsApi().delete_namespaced_custom_object( group=resources[kind]['group'], version=resources[kind]['version'], namespace='default', plural=resources[kind]['plural'], name=name, body=data) def addPowerStatusMessage(jsondict, reason, message): if jsondict: status = {'conditions': {'state': {'waiting': {'message': message, 'reason': reason}}}} spec = get_spec(jsondict) if spec: spec['status'] = status return jsondict def get_spec(jsondict): spec = jsondict.get('spec') if not spec: raw_object = jsondict.get('raw_object') if raw_object: spec = raw_object.get('spec') return spec def deleteLifecycleInJson(jsondict): if jsondict: spec = get_spec(jsondict) if spec: lifecycle = spec.get('lifecycle') if lifecycle: del spec['lifecycle'] return jsondict def updateJsonRemoveLifecycle(jsondict, body): if jsondict: spec = get_spec(jsondict) if spec: lifecycle = spec.get('lifecycle') if lifecycle: del spec['lifecycle'] spec.update(body) return jsondict def hasLifeCycle(jsondict): if jsondict: spec = get_spec(jsondict) if spec: lifecycle = spec.get('lifecycle') if lifecycle: return True return False def removeLifecycle(jsondict): if jsondict: spec = get_spec(jsondict) if spec: lifecycle = spec.get('lifecycle') if lifecycle: del spec['lifecycle'] return jsondict def get_hostname_in_lower_case(): return 'vm.%s' % socket.gethostname().lower() def changeNode(jsondict, newNodeName): if jsondict: jsondict['metadata']['labels']['host'] = newNodeName spec = get_spec(jsondict) if spec: nodeName = spec.get('nodeName') if nodeName: spec['nodeName'] = newNodeName return jsondict def replaceData(jsondict): all_kind = {'VirtualMachine': 'domain', 'VirtualMachinePool': 'pool', 'VirtualMachineDisk': 'volume', 'VirtualMachineDiskImage': 'volume', 'VirtualMachineDiskSnapshot': 'volume', 'VirtualMachineBackup': 'backup'} mkind = jsondict['kind'] mn = jsondict['metadata']['name'] k8s = K8sHelper(mkind) current = k8s.get(mn) host = jsondict['metadata']['labels']['host'] # nodename = jsondicts[i]['metadata']['labels']['host'] changeNode(current, host) if jsondict: key = all_kind[mkind] if 'spec' in jsondict.keys() and isinstance(jsondict['spec'], dict) and key in jsondict['spec'].keys(): data = jsondict['spec'][key] if current: current['spec'][key] = data return current def get_node_name(jsondict): if jsondict: return jsondict['metadata']['labels']['host'] return None def list_node(): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) jsondict = client.CoreV1Api().list_node().to_dict() return jsondict except ApiException as e: if e.reason == 'Not Found': return False else: time.sleep(3) except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) k8s_logger.debug(traceback.format_exc()) k8s_logger.debug("sleep 3 sec") time.sleep(3) raise BadRequest('can not get node info from k8s.') class K8sHelper(object): def __init__(self, kind): self.kind = kind def exist(self, name): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=resources[self.kind]['group'], version=resources[self.kind][ 'version'], namespace='default', plural=resources[self.kind]['plural'], name=name) return True except ApiException as e: if e.reason == 'Not Found': return False else: time.sleep(3) except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) k8s_logger.debug(traceback.format_exc()) k8s_logger.debug("sleep 3 sec") time.sleep(3) raise BadRequest('can not get %s %s response from k8s.' % (self.kind, name)) def get(self, name): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=resources[self.kind]['group'], version=resources[self.kind][ 'version'], namespace='default', plural=resources[self.kind]['plural'], name=name) return jsondict except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) k8s_logger.debug(traceback.format_exc()) k8s_logger.debug("sleep 3 sec") time.sleep(3) raise BadRequest('can not get %s %s on k8s.' % (self.kind, name)) def get_data(self, name, key): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=resources[self.kind]['group'], version=resources[self.kind][ 'version'], namespace='default', plural=resources[self.kind]['plural'], name=name) if 'spec' in jsondict.keys() and isinstance(jsondict['spec'], dict) and key in jsondict['spec'].keys(): return jsondict['spec'][key] return None except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) time.sleep(3) raise BadRequest('can not get %s %s on k8s.' % (self.kind, name)) def get_create_jsondict(self, name, key, data): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) hostname = get_hostname_in_lower_case() jsondict = {'spec': {'volume': {}, 'nodeName': hostname, 'status': {}}, 'kind': self.kind, 'metadata': {'labels': {'host': hostname}, 'name': name}, 'apiVersion': '%s/%s' % (resources[self.kind]['group'], resources[self.kind]['version'])} jsondict = updateJsonRemoveLifecycle(jsondict, {key: data}) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') return body except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) time.sleep(3) raise BadRequest('can not get %s %s data on k8s.' % (self.kind, name)) def create(self, name, key, data): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) if self.exist(name): return hostname = get_hostname_in_lower_case() jsondict = {'spec': {'volume': {}, 'nodeName': hostname, 'status': {}}, 'kind': self.kind, 'metadata': {'labels': {'host': hostname}, 'name': name}, 'apiVersion': '%s/%s' % (resources[self.kind]['group'], resources[self.kind]['version'])} jsondict = updateJsonRemoveLifecycle(jsondict, {key: data}) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') return client.CustomObjectsApi().create_namespaced_custom_object( group=resources[self.kind]['group'], version=resources[self.kind]['version'], namespace='default', plural=resources[self.kind]['plural'], body=body) except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) k8s_logger.debug(traceback.format_exc()) k8s_logger.debug("sleep 3 sec") time.sleep(3) error_print(500, 'can not create %s %s on k8s.' % (self.kind, name)) def add_label(self, name, domain): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) if not self.exist(name): return jsondict = self.get(name) jsondict['metadata']['labels']['domain'] = domain # jsondict = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') # jsondict = updateJsonRemoveLifecycle(jsondict, {key: data}) return client.CustomObjectsApi().replace_namespaced_custom_object( group=resources[self.kind]['group'], version=resources[self.kind]['version'], namespace='default', plural=resources[self.kind]['plural'], name=name, body=jsondict) except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) k8s_logger.debug(traceback.format_exc()) k8s_logger.debug("sleep 3 sec") time.sleep(3) raise BadRequest('can not modify %s %s on k8s.' % (self.kind, name)) def update(self, name, key, data): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) if not self.exist(name): return jsondict = self.get(name) if 'spec' in jsondict.keys() and isinstance(jsondict['spec'], dict) and key in jsondict['spec'].keys() \ and operator.eq(jsondict['spec'][key], data) == 0: return jsondict = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') jsondict = updateJsonRemoveLifecycle(jsondict, {key: data}) return client.CustomObjectsApi().replace_namespaced_custom_object( group=resources[self.kind]['group'], version=resources[self.kind]['version'], namespace='default', plural=resources[self.kind]['plural'], name=name, body=jsondict) except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) k8s_logger.debug(traceback.format_exc()) k8s_logger.debug("sleep 3 sec") time.sleep(3) raise BadRequest('can not modify %s %s on k8s.' % (self.kind, name)) def updateAll(self, name, jsondict): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) if not self.exist(name): return jsondict = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') jsondict = deleteLifecycleInJson(jsondict) return client.CustomObjectsApi().replace_namespaced_custom_object( group=resources[self.kind]['group'], version=resources[self.kind]['version'], namespace='default', plural=resources[self.kind]['plural'], name=name, body=jsondict) except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) k8s_logger.debug(traceback.format_exc()) k8s_logger.debug("sleep 3 sec") time.sleep(3) raise BadRequest('can not modify %s %s on k8s.' % (self.kind, name)) def createAll(self, name, jsondict): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) if self.exist(name): return jsondict = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') jsondict = deleteLifecycleInJson(jsondict) return client.CustomObjectsApi().create_namespaced_custom_object( group=resources[self.kind]['group'], version=resources[self.kind]['version'], namespace='default', plural=resources[self.kind]['plural'], body=jsondict) except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) k8s_logger.debug(traceback.format_exc()) k8s_logger.debug("sleep 3 sec") time.sleep(3) raise BadRequest('can not modify %s %s on k8s.' % (self.kind, name)) def delete(self, name): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) return client.CustomObjectsApi().delete_namespaced_custom_object( group=resources[self.kind]['group'], version=resources[self.kind]['version'], namespace='default', plural=resources[self.kind]['plural'], name=name, body=V1DeleteOptions()) except ApiException as e: if e.reason == 'Not Found': return else: time.sleep(3) except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) k8s_logger.debug(traceback.format_exc()) k8s_logger.debug("sleep 3 sec") time.sleep(3) raise BadRequest('can not delete %s %s on k8s.' % (self.kind, name)) def delete_lifecycle(self, name): for i in range(RETRY_TIMES): try: config.load_kube_config(TOKEN) if not self.exist(name): return jsondict = self.get(name) if hasLifeCycle(jsondict): jsondict = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') jsondict = removeLifecycle(jsondict) return client.CustomObjectsApi().replace_namespaced_custom_object( group=resources[self.kind]['group'], version=resources[self.kind]['version'], namespace='default', plural=resources[self.kind]['plural'], name=name, body=jsondict) else: return except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1 or repr(e).find( 'ApiException') != -1: config.load_kube_config(TOKEN) k8s_logger.debug(traceback.format_exc()) k8s_logger.debug("sleep 3 sec") time.sleep(3) raise BadRequest('can not delete lifecycle %s %s on k8s.' % (self.kind, name)) def change_node(self, name, newNodeName): if not self.exist(name): return jsondict = self.get(name) if jsondict: jsondict = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') jsondict['metadata']['labels']['host'] = newNodeName spec = get_spec(jsondict) if spec: nodeName = spec.get('nodeName') if nodeName: spec['nodeName'] = newNodeName self.updateAll(name, jsondict) def error_print(code, msg, data=None): if data is None: print(dumps({"result": {"code": code, "msg": msg}, "data": {}})) exit(1) else: print(dumps({"result": {"code": code, "msg": msg}, "data": data})) exit(1) if __name__ == '__main__': # data = { # 'domain': 'cloudinit', # 'pool': 'migratepoolnodepool22' # } helper = K8sHelper(VMP_KIND) # backup_helper.create('backup1', 'backup', data) # print(backup_helper.add_label('vmbackup2', 'cloudinit')) # print get_all_node_ip() # get_pools_by_path('/var/lib/libvirt/cstor/1709accf174vccaced76b0dbfccdev/1709accf174vccaced76b0dbfccdev') # k8s = K8sHelper('VirtualMachineDisk') # disk1 = k8s.get('disk33333clone') # print dumps(disk1) # k8s.delete('disk33333clone1') # k8s.create('disk33333clone1', 'volume', disk1['spec']['volume']) # disk1['spec']['volume']['filename'] = 'lalalalalalala' # k8s.update('disk33333clone1', 'volume', disk1['spec']['volume'])
1.679688
2
upload/views.py
travelgeezer/sasukekun-flask
0
12788909
from flask import request, Blueprint, send_file from sasukekun_flask.utils import v1, format_response from sasukekun_flask.config import API_IMAGE from .models import PasteFile ONE_MONTH = 60 * 60 * 24 * 30 upload = Blueprint('upload', __name__) @upload.route(v1('/upload/'), methods=['GET', 'POST']) def upload_file(): if request.method == 'GET': paste_files = PasteFile.objects.all() data = [paste_file.json for paste_file in paste_files] return format_response(data=data) elif request.method == 'POST': uploaded_file = request.files['file'] w = request.form.get('w') h = request.form.get('h') if not uploaded_file: format_response(code=400, info='not file') if False and w and h: paste_file = PasteFile.rsize(uploaded_file, w, h) # TODO: fix issues else: paste_file = PasteFile.create_by_uploaded_file(uploaded_file) paste_file.save() return format_response(data=paste_file.to_dict()) @upload.route(v1('/upload/<filehash>/', base=API_IMAGE), methods=['GET']) def download(filehash): paste_file = PasteFile.get_by_filehash(filehash) return send_file( open(paste_file.path, 'rb'), mimetype='application/octet-stream', cache_timeout=ONE_MONTH, as_attachment=True, attachment_filename=paste_file.filename.encode('utf-8'))
2.390625
2
storages/backends/s3refreshablesession.py
Techainer/django-storages
2
12788910
from uuid import uuid4 from datetime import datetime from time import time import boto3 from boto3 import Session from botocore.credentials import RefreshableCredentials from botocore.session import get_session from botocore.credentials import InstanceMetadataFetcher from storages.utils import setting import logging class InstanceMetadataBotoSession: METHOD = 'iam-role' CANONICAL_NAME = 'Ec2InstanceMetadata' """ Boto Helper class which lets us create refreshable session, so that we can cache the client or resource. Usage ----- session = BotoSession().refreshable_session() client = session.client("s3") # we now can cache this client object without worrying about expiring credentials """ def __init__( self, region_name: str = None, session_name: str = None, ): """ Initialize `BotoSession` Parameters ---------- region_name : str (optional) Default region when creating new connection. session_name : str (optional) An identifier for the assumed role session. (required when `sts_arn` is given) """ self.region_name = region_name # read why RoleSessionName is important https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts.html self.session_name = session_name or uuid4().hex self._role_fetcher = InstanceMetadataFetcher(timeout=setting("S3_CREDENTIALS_TIMEOUT", 1000), num_attempts=3) self.access_key = None self.secret_key = None self.security_token = None def __get_session_credentials(self): """ Get session credentials """ fetcher = self._role_fetcher # We do the first request, to see if we get useful data back. # If not, we'll pass & move on to whatever's next in the credential # chain. metadata = fetcher.retrieve_iam_role_credentials() if not metadata: return None logging.debug('Found credentials from IAM Role: %s', metadata['role_name']) # We manually set the data here, since we already made the request & # have it. When the expiry is hit, the credentials will auto-refresh # themselves. credentials = RefreshableCredentials.create_from_metadata( metadata, method=self.METHOD, refresh_using=fetcher.retrieve_iam_role_credentials, ) self.access_key = credentials.access_key self.secret_key = credentials.secret_key self.security_token = credentials.token return credentials def refreshable_session(self) -> Session: """ Get refreshable boto3 session. """ try: # get refreshable credentials refreshable_credentials = RefreshableCredentials.create_from_metadata( metadata=self.__get_session_credentials(), refresh_using=self._role_fetcher.retrieve_iam_role_credentials, method=self.METHOD, ) # attach refreshable credentials current session session = get_session() session._credentials = refreshable_credentials session.set_config_variable("region", self.region_name) autorefresh_session = Session(botocore_session=session) return autorefresh_session except: return boto3.session.Session()
2.34375
2
cellpainting2/reporting.py
apahl/cellpainting2
0
12788911
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ ######### Reporting ######### *Created on Thu Jun 8 14:40 2017 by <NAME>* Tools for creating HTML Reports.""" import time import base64 import os import gc import os.path as op from string import Template from io import BytesIO as IO import pandas as pd from rdkit.Chem import AllChem as Chem from rdkit.Chem import Draw import numpy as np from PIL import Image, ImageChops import matplotlib.pyplot as plt from cellpainting2 import tools as cpt from cellpainting2 import report_templ as cprt from cellpainting2 import processing as cpp cp_config = cpt.load_config("config") # cp_plates = cpt.load_config("plates") IPYTHON = cpt.is_interactive_ipython() if IPYTHON: from IPython.core.display import HTML ACT_PROF_PARAMETERS = cp_config["Parameters"] ACT_CUTOFF_PERC = cp_config["Cutoffs"]["ActCutoffPerc"] ACT_CUTOFF_PERC_H = cp_config["Cutoffs"]["ActCutoffPercH"] ACT_CUTOFF_PERC_REF = cp_config["Cutoffs"]["ActCutoffPercRef"] OVERACT_H = cp_config["Cutoffs"]["OverActH"] LIMIT_ACTIVITY_H = cp_config["Cutoffs"]["LimitActivityH"] LIMIT_ACTIVITY_L = cp_config["Cutoffs"]["LimitActivityL"] LIMIT_CELL_COUNT_H = cp_config["Cutoffs"]["LimitCellCountH"] LIMIT_CELL_COUNT_L = cp_config["Cutoffs"]["LimitCellCountL"] LIMIT_SIMILARITY_H = cp_config["Cutoffs"]["LimitSimilarityH"] LIMIT_SIMILARITY_L = cp_config["Cutoffs"]["LimitSimilarityL"] PARAMETER_HELP = cp_config["ParameterHelp"] # get positions of the compartments in the list of parameters x = 1 XTICKS = [x] for comp in ["Median_Cytoplasm", "Median_Nuclei"]: for idx, p in enumerate(ACT_PROF_PARAMETERS[x:], 1): if p.startswith(comp): XTICKS.append(idx + x) x += idx break XTICKS.append(len(ACT_PROF_PARAMETERS)) Draw.DrawingOptions.atomLabelFontFace = "DejaVu Sans" Draw.DrawingOptions.atomLabelFontSize = 18 try: from misc_tools import apl_tools AP_TOOLS = True # Library version VERSION = apl_tools.get_commit(__file__) # I use this to keep track of the library versions I use in my project notebooks print("{:45s} ({})".format(__name__, VERSION)) except ImportError: AP_TOOLS = False print("{:45s} ({})".format(__name__, time.strftime( "%y%m%d-%H:%M", time.localtime(op.getmtime(__file__))))) try: # Try to import Avalon so it can be used for generation of 2d coordinates. from rdkit.Avalon import pyAvalonTools as pyAv USE_AVALON_2D = True except ImportError: print(" * Avalon not available. Using RDKit for 2d coordinate generation.") USE_AVALON_2D = False try: import holoviews as hv hv.extension("bokeh") HOLOVIEWS = True except ImportError: HOLOVIEWS = False print("* holoviews could not be import. heat_hv is not available.") def check_2d_coords(mol, force=False): """Check if a mol has 2D coordinates and if not, calculate them.""" if not force: try: mol.GetConformer() except ValueError: force = True # no 2D coords... calculate them if force: if USE_AVALON_2D: pyAv.Generate2DCoords(mol) else: mol.Compute2DCoords() def mol_from_smiles(smi, calc_2d=True): mol = Chem.MolFromSmiles(smi) if not mol: mol = Chem.MolFromSmiles("*") else: if calc_2d: check_2d_coords(mol) return mol def autocrop(im, bgcolor="white"): if im.mode != "RGB": im = im.convert("RGB") bg = Image.new("RGB", im.size, bgcolor) diff = ImageChops.difference(im, bg) bbox = diff.getbbox() if bbox: return im.crop(bbox) return None # no contents def get_value(str_val): if not str_val: return "" try: val = float(str_val) if "." not in str_val: val = int(val) except ValueError: val = str_val return val def isnumber(x): """Returns True, if x is a number (i.e. can be converted to float).""" try: float(x) return True except ValueError: return False def convert_bool(dict, dkey, true="Yes", false="No", default="n.d."): if dkey in dict: if dict[dkey]: dict[dkey] = true else: dict[dkey] = false else: dict[dkey] = default def load_image(path, well, channel): image_fn = "{}/{}_w{}.jpg".format(path, well, channel) im = Image.open(image_fn) return im def b64_mol(mol, size=300): img_file = IO() try: img = autocrop(Draw.MolToImage(mol, size=(size, size))) except UnicodeEncodeError: print(Chem.MolToSmiles(mol)) mol = Chem.MolFromSmiles("C") img = autocrop(Draw.MolToImage(mol, size=(size, size))) img.save(img_file, format='PNG') b64 = base64.b64encode(img_file.getvalue()) b64 = b64.decode() img_file.close() return b64 def b64_img(im, format="JPEG"): if isinstance(im, IO): needs_close = False img_file = im else: needs_close = True img_file = IO() im.save(img_file, format=format) b64 = base64.b64encode(img_file.getvalue()) b64 = b64.decode() if needs_close: img_file.close() return b64 def mol_img_tag(mol, options=None): tag = """<img {} src="data:image/png;base64,{}" alt="Mol"/>""" if options is None: options = "" img_tag = tag.format(options, b64_mol(mol)) return img_tag def img_tag(im, format="jpeg", options=None): tag = """<img {} src="data:image/{};base64,{}" alt="Image"/>""" if options is None: options = "" b = b64_img(im, format=format) img_tag = tag.format(options, format.lower(), b) return img_tag def load_control_images(src_dir): image_dir = op.join(src_dir, "images") ctrl_images = {} for ch in range(1, 6): im = load_image(image_dir, "H11", ch) ctrl_images[ch] = img_tag(im, options='style="width: 250px;"') return ctrl_images def sanitize_filename(fn): result = fn.replace(":", "_").replace(",", "_").replace(".", "_") return result def write(text, fn): with open(fn, "w") as f: f.write(text) def write_page(page, title="Report", fn="index.html", templ=cprt.HTML_INTRO): t = Template(templ + page + cprt.HTML_EXTRO) result = t.substitute(title=title) write(result, fn=fn) def assign_colors(rec): act_cutoff_high = ACT_CUTOFF_PERC_H if "Toxic" in rec: if rec["Toxic"]: rec["Col_Toxic"] = cprt.COL_RED else: rec["Col_Toxic"] = cprt.COL_GREEN else: rec["Col_Toxic"] = cprt.COL_WHITE if "Pure_Flag" in rec: if rec["Pure_Flag"] == "Ok": rec["Col_Purity"] = cprt.COL_GREEN elif rec["Pure_Flag"] == "Warn": rec["Col_Purity"] = cprt.COL_YELLOW elif rec["Pure_Flag"] == "Fail": rec["Col_Purity"] = cprt.COL_RED else: rec["Col_Purity"] = cprt.COL_WHITE else: rec["Col_Purity"] = cprt.COL_WHITE if rec["Rel_Cell_Count"] >= LIMIT_CELL_COUNT_H: rec["Col_Cell_Count"] = cprt.COL_GREEN elif rec["Rel_Cell_Count"] >= LIMIT_CELL_COUNT_L: rec["Col_Cell_Count"] = cprt.COL_YELLOW else: rec["Col_Cell_Count"] = cprt.COL_RED if rec["Activity"] > act_cutoff_high: rec["Col_Act"] = cprt.COL_RED elif rec["Activity"] >= LIMIT_ACTIVITY_H: rec["Col_Act"] = cprt.COL_GREEN elif rec["Activity"] >= LIMIT_ACTIVITY_L: rec["Col_Act"] = cprt.COL_YELLOW else: rec["Col_Act"] = cprt.COL_RED if rec["Act_Flag"] == "active": rec["Col_Act_Flag"] = cprt.COL_GREEN else: rec["Col_Act_Flag"] = cprt.COL_RED def remove_colors(rec): for k in rec.keys(): if k.startswith("Col_"): rec[k] = cprt.COL_WHITE def overview_report(df, cutoff=LIMIT_SIMILARITY_L / 100, highlight=False, mode="cpd"): """mode `int` displays similarities not to references but to other internal compounds (just displays the `Similarity` column).""" cpp.load_resource("SIM_REFS") sim_refs = cpp.SIM_REFS detailed_cpds = [] if isinstance(df, cpp.DataSet): df = df.data t = Template(cprt.OVERVIEW_TABLE_HEADER) if "int" in mode: tbl_header = t.substitute(sim_entity="to another Test Compound") else: tbl_header = t.substitute(sim_entity="to a Reference") report = [cprt.OVERVIEW_TABLE_INTRO, tbl_header] row_templ = Template(cprt.OVERVIEW_TABLE_ROW) idx = 0 for _, rec in df.iterrows(): act_cutoff_low = ACT_CUTOFF_PERC act_cutoff_high = ACT_CUTOFF_PERC_H idx += 1 well_id = rec["Well_Id"] mol = mol_from_smiles(rec.get("Smiles", "*")) rec["mol_img"] = mol_img_tag(mol) rec["idx"] = idx if "Pure_Flag" not in rec: rec["Pure_Flag"] = "n.d." rec["Act_Flag"] = "active" rec["Max_Sim"] = "" rec["Link"] = "" rec["Col_Sim"] = cprt.COL_WHITE has_details = True if rec["Activity"] < act_cutoff_low: has_details = False rec["Act_Flag"] = "inactive" # print(rec) # similar references are searched for non-toxic compounds with an activity >= LIMIT_ACTIVITY_L if rec["Activity"] < LIMIT_ACTIVITY_L or rec["Activity"] > act_cutoff_high or rec["Toxic"] or rec["OverAct"] > OVERACT_H: similars_determined = False if rec["OverAct"] > OVERACT_H: rec["Max_Sim"] = "Overact." rec["Col_Sim"] = cprt.COL_RED else: similars_determined = True assign_colors(rec) convert_bool(rec, "Toxic") if has_details: detailed_cpds.append(well_id) details_fn = sanitize_filename(well_id) plate = rec["Plate"] rec["Link"] = '<a href="../{}/details/{}.html">Detailed<br>Report</a>'.format( plate, details_fn) if similars_determined: if "int" in mode: # similar = {"Similarity": [rec["Similarity"]]} similar = pd.DataFrame( {"Well_Id": [well_id], "Similarity": [rec["Similarity"]]}) else: similar = sim_refs[sim_refs["Well_Id"] == well_id].compute() similar = similar.sort_values("Similarity", ascending=False).reset_index() if len(similar) > 0: max_sim = round( similar["Similarity"][0] * 100, 1) # first in the list has the highest similarity rec["Max_Sim"] = max_sim if max_sim >= LIMIT_SIMILARITY_H: rec["Col_Sim"] = cprt.COL_GREEN elif max_sim >= LIMIT_SIMILARITY_L: rec["Col_Sim"] = cprt.COL_YELLOW else: rec["Col_Sim"] = cprt.COL_WHITE print("ERROR: This should not happen (Max_Sim).") else: rec["Max_Sim"] = "< {}".format(LIMIT_SIMILARITY_L) rec["Col_Sim"] = cprt.COL_RED if not highlight: # remove all coloring again: remove_colors(rec) report.append(row_templ.substitute(rec)) report.append(cprt.TABLE_EXTRO) return "\n".join(report), detailed_cpds def sim_ref_table(similar): cpp.load_resource("REFERENCES") df_refs = cpp.REFERENCES table = [cprt.TABLE_INTRO, cprt.REF_TABLE_HEADER] templ = Template(cprt.REF_TABLE_ROW) for idx, rec in similar.iterrows(): rec = rec.to_dict() ref_id = rec["Ref_Id"] ref_data = df_refs[df_refs["Well_Id"] == ref_id] if cpp.is_dask(ref_data): ref_data = ref_data.compute() if len(ref_data) == 0: print(rec) raise ValueError("BUG: ref_data should not be empty.") ref_data = ref_data.copy() ref_data = ref_data.fillna("&mdash;") rec.update(ref_data.to_dict("records")[0]) mol = mol_from_smiles(rec.get("Smiles", "*")) rec["Sim_Format"] = "{:.1f}".format(rec["Similarity"] * 100) rec["Tan_Format"] = "{:.1f}".format(rec["Tanimoto"] * 100) if rec["Tan_Format"] == np.nan: rec["Tan_Format"] = "&mdash;" rec["mol_img"] = mol_img_tag(mol) rec["idx"] = idx + 1 link = "../../{}/details/{}.html".format(rec["Plate"], sanitize_filename(rec["Well_Id"])) rec["link"] = link row = templ.substitute(rec) table.append(row) table.append(cprt.TABLE_EXTRO) return "\n".join(table) def changed_parameters_table(act_prof, val, parameters=ACT_PROF_PARAMETERS): changed = cpt.parameters_from_act_profile_by_val( act_prof, val, parameters=parameters) table = [] templ = Template(cprt.PARM_TABLE_ROW) for idx, p in enumerate(changed, 1): p_elmnts = p.split("_") p_module = p_elmnts[2] p_name = "_".join(p_elmnts[1:]) rec = { "idx": idx, "Parameter": p_name, "Help_Page": PARAMETER_HELP[p_module] } row = templ.substitute(rec) table.append(row) return "\n".join(table), changed def parm_stats(parameters): result = [] channels = ["_Mito", "_Ph_golgi", "_Syto", "_ER", "Hoechst"] for ch in channels: cnt = len([p for p in parameters if ch in p]) result.append(cnt) return result def parm_hist(increased, decreased, hist_cache): # try to load histogram from cache: if op.isfile(hist_cache): result = open(hist_cache).read() return result labels = [ "Mito", "Golgi / Membrane", "RNA / Nucleoli", "ER", "Nuclei" ] inc_max = max(increased) dec_max = max(decreased) max_total = max([inc_max, dec_max]) if max_total == 0: result = "No compartment-specific parameters were changed." return result inc_norm = [v / max_total for v in increased] dec_norm = [v / max_total for v in decreased] n_groups = 5 dpi = 96 # plt.rcParams['axes.titlesize'] = 25 plt.style.use("seaborn-white") plt.style.use("seaborn-pastel") plt.style.use("seaborn-talk") plt.rcParams['axes.labelsize'] = 25 plt.rcParams['xtick.labelsize'] = 20 plt.rcParams['ytick.labelsize'] = 20 plt.rcParams['legend.fontsize'] = 20 size = (1500, 1000) figsize = (size[0] / dpi, size[1] / dpi) fig, ax = plt.subplots(figsize=figsize) index = np.arange(n_groups) bar_width = 0.25 plt.bar(index, inc_norm, bar_width, color='#94caef', label='Inc') plt.bar(index + bar_width, dec_norm, bar_width, color='#ffdd1a', label='Dec') plt.xlabel('Cell Compartment') plt.ylabel('rel. Occurrence') plt.xticks(index + bar_width / 2, labels, rotation=45) plt.legend() plt.tight_layout() img_file = IO() plt.savefig(img_file, bbox_inches='tight', format="jpg") result = img_tag(img_file, format="jpg", options='style="width: 800px;"') img_file.close() # important, otherwise the plots will accumulate and fill up memory: plt.close() open(hist_cache, "w").write(result) # cache the histogram return result def heat_mpl(df, id_prop="Compound_Id", cmap="bwr", show=True, colorbar=True, biosim=False, chemsim=False, method="dist_corr", sort_parm=False, parm_dict=None, plot_cache=None): # try to load heatmap from cache: if plot_cache is not None and op.isfile(plot_cache): result = open(plot_cache).read() return result if "dist" in method.lower(): profile_sim = cpt.profile_sim_dist_corr else: profile_sim = cpt.profile_sim_tanimoto df_len = len(df) img_size = 15 if show else 17 plt.style.use("seaborn-white") plt.style.use("seaborn-pastel") plt.style.use("seaborn-talk") plt.rcParams['axes.labelsize'] = 25 # plt.rcParams['legend.fontsize'] = 20 plt.rcParams['figure.figsize'] = (img_size, 1.1 + 0.47 * (df_len - 1)) plt.rcParams['axes.labelsize'] = 25 plt.rcParams['ytick.labelsize'] = 20 plt.rcParams['xtick.labelsize'] = 15 fs_text = 18 y_labels = [] fp_list = [] max_val = 3 # using a fixed color range now min_val = -3 ylabel_templ = "{}{}{}" ylabel_cs = "" ylabel_bs = "" id_prop_list = [] for ctr, (_, rec) in enumerate(df.iterrows()): if sort_parm: if ctr == 0: compartments = ["Median_Cells", "Median_Cytoplasm", "Median_Nuclei"] parm_list = [] for comp in compartments: parm_comp = [x for x in ACT_PROF_PARAMETERS if x.startswith(comp)] val_list = [rec[x] for x in parm_comp] parm_sorted = [x for _, x in sorted(zip(val_list, parm_comp))] parm_list.extend(parm_sorted) else: parm_list = ACT_PROF_PARAMETERS fp = [rec[x] for x in ACT_PROF_PARAMETERS] fp_view = [rec[x] for x in parm_list] fp_list.append(fp_view) id_prop_list.append(rec[id_prop]) if chemsim: if ctr == 0: mol = mol_from_smiles(rec.get("Smiles", "*")) if len(mol.GetAtoms()) > 1: ylabel_cs = "Chem | " mol_fp = Chem.GetMorganFingerprint(mol, 2) # ECFC4 else: # no Smiles present in the DataFrame ylabel_cs = "" chemsim = False else: q = rec.get("Smiles", "*") if len(q) < 2: ylabel_cs = " | " else: sim = cpt.chem_sim(mol_fp, q) * 100 ylabel_cs = "{:3.0f}% | ".format(sim) if biosim: if ctr == 0: prof_ref = fp ylabel_bs = " Bio | " else: sim = profile_sim(prof_ref, fp) * 100 ylabel_bs = "{:3.0f}% | ".format(sim) ylabel = ylabel_templ.format(ylabel_cs, ylabel_bs, rec[id_prop]) y_labels.append(ylabel) # m_val = max(fp) # this was the calculation of the color range # if m_val > max_val: # max_val = m_val # m_val = min(fp) # if m_val < min_val: # min_val = m_val if isinstance(parm_dict, dict): parm_dict["Parameter"] = parm_list for i in range(len(id_prop_list)): parm_dict[str(id_prop_list[i])] = fp_list[i].copy() # calc the colorbar range max_val = max(abs(min_val), max_val) # invert y axis: y_labels = y_labels[::-1] fp_list = fp_list[::-1] Z = np.asarray(fp_list) plt.xticks(XTICKS) plt.yticks(np.arange(df_len) + 0.5, y_labels) plt.pcolor(Z, vmin=-max_val, vmax=max_val, cmap=cmap) plt.text(XTICKS[1] // 2, -1.1, "Cells", horizontalalignment='center', fontsize=fs_text) plt.text(XTICKS[1] + ((XTICKS[2] - XTICKS[1]) // 2), -1.1, "Cytoplasm", horizontalalignment='center', fontsize=fs_text) plt.text(XTICKS[2] + ((XTICKS[3] - XTICKS[2]) // 2), -1.1, "Nuclei", horizontalalignment='center', fontsize=fs_text) if colorbar and len(df) > 3: plt.colorbar() plt.tight_layout() if show: plt.show() else: img_file = IO() plt.savefig(img_file, bbox_inches='tight', format="jpg") result = img_tag(img_file, format="jpg", options='style="width: 900px;"') img_file.close() # important, otherwise the plots will accumulate and fill up memory: plt.clf() plt.close() gc.collect() if plot_cache is not None: # cache the plot open(plot_cache, "w").write(result) return result def heat_hv(df, id_prop="Compound_Id", cmap="bwr", invert_y=False): if not HOLOVIEWS: raise ImportError("# holoviews library could not be imported") df_parm = df[[id_prop] + ACT_PROF_PARAMETERS].copy() df_len = len(df_parm) col_bar = False if df_len < 3 else True values = list(df_parm.drop(id_prop, axis=1).values.flatten()) max_val = max(values) min_val = min(values) max_val = max(abs(min_val), max_val) hm_opts = dict(width=950, height=40 + 30 * df_len, tools=['hover'], invert_yaxis=invert_y, xrotation=90, labelled=[], toolbar='above', colorbar=col_bar, xaxis=None, colorbar_opts={"width": 10}) hm_style = {"cmap": cmap} opts = {'HeatMap': {'plot': hm_opts, "style": hm_style}} df_heat = cpt.melt(df_parm, id_prop=id_prop) heatmap = hv.HeatMap(df_heat).redim.range(Value=(-max_val, max_val)) return heatmap(opts) def show_images(plate_full_name, well): """For interactive viewing in the notebook.""" if not IPYTHON: return src_dir = op.join(cp_config["Paths"]["SrcPath"], plate_full_name) ctrl_images = load_control_images(src_dir) image_dir = op.join(src_dir, "images") templ_dict = {} for ch in range(1, 6): im = load_image(image_dir, well, ch) templ_dict["Img_{}_Cpd".format(ch)] = img_tag( im, options='style="width: 250px;"') templ_dict["Img_{}_Ctrl".format(ch)] = ctrl_images[ch] tbody_templ = Template(cprt.IMAGES_TABLE) table = cprt.TABLE_INTRO + \ tbody_templ.substitute(templ_dict) + cprt.HTML_EXTRO return HTML(table) def get_data_for_wells(well_ids): cpp.load_resource("DATASTORE") data = cpp.DATASTORE result = data[data["Well_Id"].isin(well_ids)] if cpp.is_dask(result): result = result.compute() result["_sort"] = pd.Categorical( result["Well_Id"], categories=well_ids, ordered=True) result = result.sort_values("_sort") result.drop("_sort", axis=1, inplace=False) return result def detailed_report(rec, src_dir, ctrl_images): # print(rec) cpp.load_resource("SIM_REFS") sim_refs = cpp.SIM_REFS date = time.strftime("%d-%m-%Y %H:%M", time.localtime()) image_dir = op.join(src_dir, "images") well_id = rec["Well_Id"] # act_prof = [rec[x] for x in ACT_PROF_PARAMETERS] mol = mol_from_smiles(rec.get("Smiles", "*")) if "Pure_Flag" not in rec: rec["Pure_Flag"] = "n.d." templ_dict = rec.copy() log2_vals = [(x, rec[x]) for x in ACT_PROF_PARAMETERS] parm_table = [] for idx, x in enumerate(log2_vals, 1): parm_table.extend(["<tr><td>", str(idx), "</td>", # omit the "Median_" head of each parameter "<td>", x[0][7:], "</td>", '<td align="right">', "{:.2f}".format(x[1]), "</td></tr>\n"]) templ_dict["Parm_Table"] = "".join(parm_table) df_heat = pd.DataFrame([rec]) templ_dict["Date"] = date templ_dict["mol_img"] = mol_img_tag(mol, options='class="cpd_image"') if templ_dict["Is_Ref"]: if not isinstance(templ_dict["Trivial_Name"], str) or templ_dict["Trivial_Name"] == "": templ_dict["Trivial_Name"] = "&mdash;" if not isinstance(templ_dict["Known_Act"], str) or templ_dict["Known_Act"] == "": templ_dict["Known_Act"] = "&mdash;" t = Template(cprt.DETAILS_REF_ROW) templ_dict["Reference"] = t.substitute(templ_dict) else: templ_dict["Reference"] = "" well = rec["Metadata_Well"] for ch in range(1, 6): im = load_image(image_dir, well, ch) templ_dict["Img_{}_Cpd".format(ch)] = img_tag( im, options='style="width: 250px;"') templ_dict["Img_{}_Ctrl".format(ch)] = ctrl_images[ch] act_cutoff_high = ACT_CUTOFF_PERC_H if rec["Rel_Cell_Count"] < LIMIT_CELL_COUNT_L: templ_dict["Ref_Table"] = "Because of compound toxicity, no similarity was determined." elif rec["Activity"] < LIMIT_ACTIVITY_L: templ_dict["Ref_Table"] = "Because of low induction (&lt; {}%), no similarity was determined.".format(LIMIT_ACTIVITY_L) elif rec["Activity"] > act_cutoff_high: templ_dict["Ref_Table"] = "Because of high induction (&gt; {}%), no similarity was determined.".format(act_cutoff_high) elif rec["OverAct"] > OVERACT_H: templ_dict["Ref_Table"] = "Because of high similarity to the overactivation profile (&gt; {}%), no similarity was determined.".format(OVERACT_H) else: similar = sim_refs[sim_refs["Well_Id"] == well_id].compute() if len(similar) > 0: similar = similar.sort_values("Similarity", ascending=False).reset_index().head(5) ref_tbl = sim_ref_table(similar) templ_dict["Ref_Table"] = ref_tbl sim_data = get_data_for_wells(similar["Ref_Id"].values) df_heat = pd.concat([df_heat, sim_data]) else: templ_dict["Ref_Table"] = "No similar references found." cache_path = op.join(cp_config["Dirs"]["DataDir"], "plots", rec["Plate"]) if not op.isdir(cache_path): os.makedirs(cache_path, exist_ok=True) hm_fn = sanitize_filename(rec["Well_Id"] + ".txt") hm_cache = op.join(cache_path, hm_fn) templ_dict["Heatmap"] = heat_mpl(df_heat, id_prop="Compound_Id", cmap="bwr", show=False, colorbar=True, plot_cache=hm_cache) t = Template(cprt.DETAILS_TEMPL) report = t.substitute(templ_dict) return report def full_report(df, src_dir, report_name="report", plate=None, cutoff=0.6, highlight=False): report_full_path = op.join(cp_config["Dirs"]["ReportDir"], report_name) overview_fn = op.join(report_full_path, "index.html") date = time.strftime("%d-%m-%Y %H:%M", time.localtime()) cpt.create_dirs(op.join(report_full_path, "details")) if isinstance(df, cpp.DataSet): df = df.data print("* creating overview...") header = "{}\n<h2>Cell Painting Overview Report</h2>\n".format(cprt.LOGO) title = "Overview" if plate is not None: title = plate header += "<h3>Plate {}</h3>\n".format(plate) header += "<p>({})</p>\n".format(date) if highlight: highlight_legend = cprt.HIGHLIGHT_LEGEND else: highlight_legend = "" overview, detailed_cpds = overview_report(df, cutoff=cutoff, highlight=highlight) overview = header + overview + highlight_legend write_page(overview, title=title, fn=overview_fn, templ=cprt.OVERVIEW_HTML_INTRO) # print(detailed_cpds) print("* creating detailed reports...") print(" * loading control images...") ctrl_images = load_control_images(src_dir) print(" * writing individual reports...") df_detailed = df[df["Well_Id"].isin(detailed_cpds)] ctr = 0 df_len = len(df_detailed) for _, rec in df_detailed.iterrows(): ctr += 1 if not IPYTHON and ctr % 10 == 0: print(" ({:3d}%)\r".format(int(100 * ctr / df_len)), end="") well_id = rec["Well_Id"] fn = op.join(report_full_path, "details", "{}.html".format(sanitize_filename(well_id))) title = "{} Details".format(well_id) # similar = detailed_cpds[well_id] details = detailed_report(rec, src_dir, ctrl_images) write_page(details, title=title, fn=fn, templ=cprt.DETAILS_HTML_INTRO) print("* done. ") if IPYTHON: return HTML('<a href="{}">{}</a>'.format(overview_fn, "Overview"))
2.1875
2
post/models.py
Yash1256/Django-Intern
1
12788912
<reponame>Yash1256/Django-Intern from django.db import models from registration.models import Author # Create your models here. class Post(models.Model): author_id = models.IntegerField(null=False) title = models.CharField(max_length=255, null=False) description = models.CharField(max_length=500, null=False) content = models.TextField(null=False) date = models.DateField(null=False) class Meta: db_table = "posts" verbose_name = "Post" verbose_name_plural = "Posts" @property def author(self): return Author.objects.get(pk=self.author_id) def __str__(self): return f"{self.title}({self.author.name})"
2.6875
3
backend/api_v2/migrations/0008_auto_20170101_0105.py
AstroMatt/subjective-time-perception
0
12788913
# -*- coding: utf-8 -*- # Generated by Django 1.10.4 on 2017-01-01 01:05 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('api_v2', '0007_auto_20170101_0101'), ] operations = [ migrations.AlterField( model_name='trial', name='percentage_all', field=models.FloatField(blank=True, help_text='Percentage Coefficient - all', null=True, verbose_name='P'), ), migrations.AlterField( model_name='trial', name='percentage_blue', field=models.FloatField(blank=True, help_text='Percentage Coefficient - blue', null=True, verbose_name='PB'), ), migrations.AlterField( model_name='trial', name='percentage_red', field=models.FloatField(blank=True, help_text='Percentage Coefficient - red', null=True, verbose_name='PR'), ), migrations.AlterField( model_name='trial', name='percentage_white', field=models.FloatField(blank=True, help_text='Percentage Coefficient - white', null=True, verbose_name='PW'), ), migrations.AlterField( model_name='trial', name='time_mean_all', field=models.FloatField(blank=True, help_text='Time Coefficient Mean - all', null=True, verbose_name='TM'), ), migrations.AlterField( model_name='trial', name='time_mean_blue', field=models.FloatField(blank=True, help_text='Time Coefficient Mean - blue', null=True, verbose_name='TMB'), ), migrations.AlterField( model_name='trial', name='time_mean_red', field=models.FloatField(blank=True, help_text='Time Coefficient Mean - red', null=True, verbose_name='TMR'), ), migrations.AlterField( model_name='trial', name='time_mean_white', field=models.FloatField(blank=True, help_text='Time Coefficient Mean - white', null=True, verbose_name='TMW'), ), migrations.AlterField( model_name='trial', name='time_stdev_all', field=models.FloatField(blank=True, help_text='Time Coefficient Standard Deviation - all', null=True, verbose_name='TSD'), ), migrations.AlterField( model_name='trial', name='time_stdev_blue', field=models.FloatField(blank=True, help_text='Time Coefficient Standard Deviation - blue', null=True, verbose_name='TSDB'), ), migrations.AlterField( model_name='trial', name='time_stdev_red', field=models.FloatField(blank=True, help_text='Time Coefficient Standard Deviation - red', null=True, verbose_name='TSDR'), ), migrations.AlterField( model_name='trial', name='time_stdev_white', field=models.FloatField(blank=True, help_text='Time Coefficient Standard Deviation - white', null=True, verbose_name='TSDW'), ), migrations.AlterField( model_name='trial', name='timeout', field=models.FloatField(help_text='Seconds per color', verbose_name='Timeout'), ), ]
1.5625
2
lib/utils/meta_manager.py
kbase/sample_search_api
0
12788914
<reponame>kbase/sample_search_api<filename>lib/utils/meta_manager.py from utils.re_utils import execute_query SAMPLE_NODE_COLLECTION = "samples_nodes" SAMPLE_SAMPLE_COLLECTION = "samples_sample" META_AQL_TEMPLATE = f""" let version_ids = (for sample_id in @sample_ids let doc = DOCUMENT({SAMPLE_SAMPLE_COLLECTION}, sample_id.id) RETURN {{ 'id': doc.id, 'version_id': doc.vers[sample_id.version - 1], 'version': sample_id.version }} ) let node_metas = (for version_id in version_ids for node in {SAMPLE_NODE_COLLECTION} FILTER node.id == version_id.id AND node.uuidver == version_id.version_id LIMIT @num_sample_ids let cmeta_keys = (FOR meta IN node.cmeta RETURN meta.ok ) let ucmeta_keys = (FOR meta IN node.ucmeta RETURN CONCAT("custom:", meta.ok) ) RETURN APPEND(cmeta_keys, ucmeta_keys) ) RETURN UNIQUE(FLATTEN(node_metas)) """ class MetadataManager: def __init__(cls, re_api_url, re_admin_token=None): cls.re_api_url = re_api_url cls.re_admin_token = re_admin_token def get_sampleset_meta(self, sample_ids, user_token): # use the user token if an admin token is not provided query_params = {"sample_ids": sample_ids, 'num_sample_ids': len(sample_ids)} run_token = self.re_admin_token if self.re_admin_token else user_token ret = execute_query( META_AQL_TEMPLATE, self.re_api_url, run_token, query_params ) return ret['results'][0]
2.09375
2
utils/replay.py
xuzhiyuan1528/KTM-DRL
10
12788915
import numpy as np import torch # https://github.com/sfujim/TD3/blob/ade6260da88864d1ab0ed592588e090d3d97d679/utils.py class ReplayBuffer(object): def __init__(self, state_dim, action_dim, max_size=int(1e6)): self.max_size = max_size self.ptr = 0 self.size = 0 self.state = np.zeros((max_size, state_dim)) self.action = np.zeros((max_size, action_dim)) self.next_state = np.zeros((max_size, state_dim)) self.reward = np.zeros((max_size, 1)) self.not_done = np.zeros((max_size, 1)) self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") def add(self, state, action, next_state, reward, done): self.state[self.ptr] = state self.action[self.ptr] = action self.next_state[self.ptr] = next_state self.reward[self.ptr] = reward self.not_done[self.ptr] = 1. - done self.ptr = (self.ptr + 1) % self.max_size self.size = min(self.size + 1, self.max_size) def sample(self, batch_size): ind = np.random.randint(0, self.size, size=batch_size) return ( torch.from_numpy(self.state[ind]).float().to(self.device), torch.from_numpy(self.action[ind]).float().to(self.device), torch.from_numpy(self.next_state[ind]).float().to(self.device), torch.from_numpy(self.reward[ind]).float().to(self.device), torch.from_numpy(self.not_done[ind]).float().to(self.device) ) def sample_np(self, batch_size): ind = np.random.randint(0, self.size, size=batch_size) return ( np.float32(self.state[ind]), np.float32(self.action[ind]), np.float32(self.next_state[ind]), np.float32(self.reward[ind]), np.float32(self.not_done[ind]) ) def save(self, fdir): np.save(fdir + '/sample-state', self.state[:self.size]) np.save(fdir + '/sample-action', self.action[:self.size]) np.save(fdir + '/sample-nstate', self.next_state[:self.size]) np.save(fdir + '/sample-reward', self.reward[:self.size]) np.save(fdir + '/sample-ndone', self.not_done[:self.size]) def load(self, fdir): state = np.load(fdir + '/sample-state.npy', allow_pickle=True) action = np.load(fdir + '/sample-action.npy', allow_pickle=True) nstate = np.load(fdir + '/sample-nstate.npy', allow_pickle=True) reward = np.load(fdir + '/sample-reward.npy', allow_pickle=True) ndone = np.load(fdir + '/sample-ndone.npy', allow_pickle=True) for s, a, ns, r, nd in zip(state, action, nstate, reward, ndone): self.add(s, a, ns, r, 1. - nd) def reset(self): self.ptr = 0 self.size = 0
2.40625
2
scripts/codepost/codepostAbet.py
cbourke/ComputerScienceII
20
12788916
""" This script interfaces with the codepost.io API to produce exemplar reports for ABET accreditation. For a particular assignment, a report includes an assignment summary (basic info and stats) as well as the full assessment of 3 student examples of an A, B, and C submission. The report includes all line-by-line grader comments (and point deductions) as well as source files. Source files are formatted in markdown. In the codepost.io web client the comments would be embedded directly in the source files but for this report they are collected in the summary. A, B, and C examples are automatically chosen from all graded submissions. The top submission is chosen for the A example while the B/C are chosen to be the closest to a 85%/75% score based on the total number of points of the assignment. The report is written to both a markdown-formatted output file as well as a PDF version (which is produced from the markdown using pandoc/latex via a system call so these are expected to be installed and available). You can run this script in one of two modes: you can provide either a single assignment ID which will produce a single report for that assignment only, or you can provide a course ID which will produce (separate) reports for all assignments in the course. In either case, the IDs must be valid codepost.io IDs. Optionally, you can provide your own codepost API key via the command line, otherwise it must be specified in the config.py file. """ import argparse import os import codepost from config import config parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument('--codePostApiKey', type=str, default=config.codePostApiKey, help='Optionally provide a codepost API key to use. By default the API key in the config.py file is used.') group = parser.add_mutually_exclusive_group(required=True) group.add_argument('--codePostCourseId', type=int, help='Generates ABET reports for *every* assignment in the provided codepost course.') group.add_argument('--codePostAssignmentId', type=int, help='Generates a single ABET report for the provided codepost assignment.') args = parser.parse_args() def submissionToMarkdown(submission,title,assignmentPts): """ Returns both a summary and source files of the provided submission as markdown-formatted strings. """ details = "" result = f""" ## {title} Example * Student(s): {submission.students} * Score: {submission.grade:.1f} / {assignmentPts:.1f} = {(100*submission.grade/assignmentPts):.2f}% """ for fileId in submission.files: f = codepost.file.retrieve(id=fileId.id) fileName = f.name # fools be puttin unicode shite in their source, so... fileContents = f.code.encode('utf-8').decode('ascii','ignore') fileExtension = f.extension fileGraderCommentIds = [x.id for x in f.comments] result += f" * Source File: `{fileName}`\n" details += f"## {title} Example - `{fileName}`\n" details += f"```{fileExtension}\n{fileContents}\n```\n" for commentId in fileGraderCommentIds: c = codepost.comment.retrieve(id=commentId) cleanText = c.text.replace("\n\n", "\n") result += f" * Lines {c.startLine:d} - {c.endLine:d} (-{c.pointDelta:.1f}): {cleanText:s}\n" return result, details def getAssignmentReport(assignment): """ Produces an ABET assignment report (as a markdown-formatted string) for the given assignment (which is expected to be a codepost API object) by pulling all relevant data as well as source code files (and grader comments) for randomly selected A, B and C samples """ courseId = assignment.course course = codepost.course.retrieve(id=courseId) courseName = course.name coursePeriod = course.period assignmentName = assignment.name assignmentPts = assignment.points assignmentMean = assignment.mean assignmentMedian = assignment.median summary = f""" # {courseName} - {coursePeriod} ## {assignmentName} * Points: {assignmentPts} * Mean: {assignmentMean} * Median: {assignmentMedian}\n\n""" # find ideal A, B, C samples submissions = assignment.list_submissions() aSubmission = submissions[0] bSubmission = submissions[0] cSubmission = submissions[0] # we only expect 1 submission per student since submissions are via our # scripts, but in any case, find the 3 closest to A=max%, B = 85%, C = 75% for submission in submissions: if submission.grade > aSubmission.grade: aSubmission = submission if abs(submission.grade / assignmentPts - .85) < abs(bSubmission.grade / assignmentPts - .85): bSubmission = submission if abs(submission.grade / assignmentPts - .75) < abs(cSubmission.grade / assignmentPts - .75): cSubmission = submission aSummary, aDetail = submissionToMarkdown(aSubmission,"A",assignmentPts) bSummary, bDetail = submissionToMarkdown(bSubmission,"B",assignmentPts) cSummary, cDetail = submissionToMarkdown(cSubmission,"C",assignmentPts) return summary + aSummary + bSummary + cSummary + "\n\n" + aDetail + bDetail + cDetail def produceCourseReports(courseId): """ Produces ABET reports (as both md and pdf files) for all assignments in the specified course """ course = codepost.course.retrieve(id=courseId) for a in course.assignments: assignmentId = a.id produceAssignmentReport(assignmentId) def produceAssignmentReport(assignmentId): """ Produces a single report (as an md and pdf file) for the specified assignment """ a = codepost.assignment.retrieve(id=assignmentId) assignmentName = a.name baseFileName = assignmentName.replace(" ", "_") assignmentId = a.id report = getAssignmentReport(a) fileNameMd = baseFileName + ".md" fileNamePdf = baseFileName + ".pdf" f = open(fileNameMd, "w") f.write(report) f.close() os.system("pandoc -s -V geometry:margin=1in -o "+fileNamePdf+" "+fileNameMd) return None codePostApiKey = args.codePostApiKey codepost.configure_api_key(codePostApiKey) if args.codePostCourseId: produceCourseReports(args.codePostCourseId) elif args.codePostAssignmentId: produceAssignmentReport(args.codePostAssignmentId) else: print("ERROR: neither course ID nor assignment ID specified")
2.90625
3
integrations/tensorflow/bindings/python/iree/tf/support/tf_utils_test.py
schoppmp/iree
0
12788917
# Lint as: python3 # Copyright 2020 Google LLC # # 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 # # https://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. """Tests for iree.tf.support.tf_utils.""" from absl.testing import parameterized from iree.tf.support import tf_utils import numpy as np import tensorflow as tf class UtilsTests(tf.test.TestCase, parameterized.TestCase): @parameterized.named_parameters([('int8_to_i8', np.int8, 'i8'), ('int32_to_i32', np.int32, 'i32'), ('float32_to_f32', np.float32, 'f32'), ('float64_to_f64', np.float64, 'f64')]) def test_to_mlir_type(self, numpy_type, mlir_type): self.assertEqual(tf_utils.to_mlir_type(numpy_type), mlir_type) @parameterized.named_parameters([ ('single_i32', [np.array([1, 2], dtype=np.int32)], '2xi32=1 2'), ('single_f32', [np.array([1, 2], dtype=np.float32)], '2xf32=1.0 2.0'), ]) def test_save_input_values(self, inputs, inputs_str): self.assertEqual(tf_utils.save_input_values(inputs), inputs_str) def test_apply_function(self): inputs = [1, [2, 3], (4, 5), {'6': 6, '78': [7, 8]}] expected = [0, [1, 2], (3, 4), {'6': 5, '78': [6, 7]}] result = tf_utils.apply_function(inputs, lambda x: x - 1) self.assertEqual(result, expected) self.assertNotEqual(inputs, expected) @parameterized.named_parameters([ { 'testcase_name': 'all the same', 'array_c': np.array([0, 1, 2]), 'array_d': np.array(['0', '1', '2']), 'array_e': np.array([0.0, 0.1, 0.2]), 'tar_same': True, }, { 'testcase_name': 'wrong int', 'array_c': np.array([1, 1, 2]), 'array_d': np.array(['0', '1', '2']), 'array_e': np.array([0.0, 0.1, 0.2]), 'tar_same': False, }, { 'testcase_name': 'wrong string', 'array_c': np.array([0, 1, 2]), 'array_d': np.array(['a', '1', '2']), 'array_e': np.array([0.0, 0.1, 0.2]), 'tar_same': False, }, { 'testcase_name': 'wrong float', 'array_c': np.array([0, 1, 2]), 'array_d': np.array(['0', '1', '2']), 'array_e': np.array([1.0, 0.1, 0.2]), 'tar_same': False, }, ]) def test_recursive_check_same(self, array_c, array_d, array_e, tar_same): # yapf: disable ref = { 'a': 1, 'b': [ {'c': np.array([0, 1, 2])}, {'d': np.array(['0', '1', '2'])}, {'e': np.array([0.0, 0.1, 0.2])} ], } tar = { 'a': 1, 'b': [ {'c': array_c}, {'d': array_d}, {'e': array_e} ], } # yapf: enable same, _ = tf_utils.check_same(ref, tar, rtol=1e-6, atol=1e-6) self.assertEqual(tar_same, same) if __name__ == '__main__': tf.test.main()
1.914063
2
imbedding/parameter.py
bjih1999/Uhzzuda_project
0
12788918
import pandas as pd import numpy as np texts = [] f = open('preprocess/jull_review.csv', 'r') for line in f.readlines(): oneline = line.replace("\n", "").split(",") oneline = list(filter(None, oneline)) texts.append(oneline) print(len(texts)) from gensim.models.doc2vec import Doc2Vec, TaggedDocument documents = [TaggedDocument(doc, [i]) for i, doc in enumerate(texts)] doc2vec_model = Doc2Vec(documents, vector_size=100, window=10, min_count=30, workers=4) doc2vec_model.save('doc2vec_v=100_dm0.model') doc2vec_model = Doc2Vec(documents, vector_size=100, window=10, min_count=30, workers=4) doc2vec_model.save('doc2vec_v=100_dm1.model')
2.484375
2
run.py
iTecAI/roomdash
0
12788919
import subprocess, json, sys, time from selenium import webdriver from selenium.webdriver.firefox.options import Options with open('config.json', 'r') as f: CONFIG = json.load(f) proc = subprocess.Popen([sys.executable, 'server.py'], stdout=sys.stdout) print('Waiting for server to start.') time.sleep(4) options = Options() options.add_argument(f'--kiosk http://{CONFIG["host"]}:{str(CONFIG["port"])}') driver = webdriver.Firefox(firefox_options=options) driver.get(f'http://{CONFIG["host"]}:{str(CONFIG["port"])}') driver.fullscreen_window() proc.wait()
2.390625
2
variantgrid/settings/env/_settings_template.py
SACGF/variantgrid
5
12788920
from variantgrid.settings.components.celery_settings import * # pylint: disable=wildcard-import, unused-wildcard-import from variantgrid.settings.components.default_settings import * # pylint: disable=wildcard-import, unused-wildcard-import from variantgrid.settings.components.seqauto_settings import * # pylint: disable=wildcard-import, unused-wildcard-import # ANNOTATION_ENTREZ_EMAIL = '<EMAIL>' WEB_HOSTNAME = 'yourdomain.com' WEB_IP = '127.0.0.1' ALLOWED_HOSTS = ["localhost", WEB_HOSTNAME, WEB_IP] # SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTOCOL', 'https') # PEDIGREE_MADELINE2_COMMAND = "madeline2"
1.296875
1
two_sum/two_sum_test.py
kevinzen/learning
0
12788921
import unittest from two_sum.solution import Solution class MyTestCase(unittest.TestCase): def test_two_sum(self): s = Solution() nums = [2,7,11,15] target = 9 result = s.twoSum(nums, target) self.assertEqual(result, [0,1]) nums = [-1,-2,-3,-4,-5] target = -8 result = s.twoSum(nums, target) self.assertEqual(result, [2,4]) def test_two_sum_two_pass_hash(self): s = Solution() nums = [2,7,11,15] target = 9 result = s.twoSumTwoPassHash(nums, target) self.assertEqual(result, [0,1]) nums = [-1,-2,-3,-4,-5] target = -8 result = s.twoSumTwoPassHash(nums, target) self.assertEqual(result, [2,4]) def test_two_sum_one_pass_hash(self): s = Solution() # nums = [2,7,11,15] # target = 9 # result = s.twoSumOnePassHash(nums, target) # self.assertEqual(result, [0,1]) # # # nums = [-1,-2,-3,-4,-5] # target = -8 # result = s.twoSumOnePassHash(nums, target) # self.assertEqual(result, [2,4]) # nums = [3,3] target = 6 result = s.twoSumOnePassHash(nums, target) self.assertEqual(result, [0,1])
3.59375
4
spyder/plugins/editor/fallback/tests/conftest.py
seryj/spyder
0
12788922
# -*- coding: utf-8 -*- # Copyright © Spyder Project Contributors # Licensed under the terms of the MIT License # (see spyder/__init__.py for details) import pytest from spyder.plugins.editor.fallback.actor import FallbackActor from spyder.plugins.editor.lsp.tests.conftest import qtbot_module @pytest.fixture(scope='module') def fallback(qtbot_module, request): fallback = FallbackActor(None) qtbot_module.addWidget(fallback) with qtbot_module.waitSignal(fallback.sig_fallback_ready, timeout=30000): fallback.start() def teardown(): fallback.stop() request.addfinalizer(teardown) return fallback
2.109375
2
resnet.py
geodekid/resnet1
12
12788923
<reponame>geodekid/resnet1 # You will learn how to build very deep convolutional networks, using Residual Networks (ResNets) # In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by He et al., allow you to train much deeper networks than were previously practically feasible. # Let's import packages import numpy as np from keras import layers from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D from keras.models import Model, load_model from keras.preprocessing import image from keras.utils import layer_utils from keras.utils.data_utils import get_file from keras.applications.imagenet_utils import preprocess_input import pydot from IPython.display import SVG from keras.utils.vis_utils import model_to_dot from keras.utils import plot_model from resnets_utils import * from keras.initializers import glorot_uniform import scipy.misc from matplotlib.pyplot import imshow %matplotlib inline import keras.backend as K K.set_image_data_format('channels_last') K.set_learning_phase(1) # Identity block def identity_block(X, f, filters, stage, block): """ Implementation of the identity block as defined in Figure 3 Arguments: X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev) f -- integer, specifying the shape of the middle CONV's window for the main path filters -- python list of integers, defining the number of filters in the CONV layers of the main path stage -- integer, used to name the layers, depending on their position in the network block -- string/character, used to name the layers, depending on their position in the network Returns: X -- output of the identity block, tensor of shape (n_H, n_W, n_C) """ # defining name basis conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' # Retrieve Filters F1, F2, F3 = filters # Save the input value. You'll need this later to add back to the main path. X_shortcut = X # First component of main path X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X) X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X) X = Activation('relu')(X) # Second component of main path X = Conv2D(filters = F2, kernel_size=(f,f), strides = (1,1), padding='same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3, name = bn_name_base + '2b')(X) X = Activation('relu')(X) # Third component of main path X = Conv2D(filters = F3, kernel_size=(1,1), strides = (1,1), padding="valid", name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X) # Final step: Add shortcut value to main path, and pass it through a RELU activation X = Add()([X,X_shortcut]) X = Activation('relu')(X) return X # Creating a TF graph and session tf.reset_default_graph() with tf.Session() as test: np.random.seed(1) A_prev = tf.placeholder("float", [3, 4, 4, 6]) X = np.random.randn(3, 4, 4, 6) A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a') test.run(tf.global_variables_initializer()) out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0}) print("out = " + str(out[0][1][1][0])) # The convolutional block # GRADED FUNCTION: convolutional_block def convolutional_block(X, f, filters, stage, block, s = 2): """ Implementation of the convolutional block as defined in Figure 4 Arguments: X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev) f -- integer, specifying the shape of the middle CONV's window for the main path filters -- python list of integers, defining the number of filters in the CONV layers of the main path stage -- integer, used to name the layers, depending on their position in the network block -- string/character, used to name the layers, depending on their position in the network s -- Integer, specifying the stride to be used Returns: X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C) """ # defining name basis conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' # Retrieve Filters F1, F2, F3 = filters # Save the input value X_shortcut = X ##### MAIN PATH ##### # First component of main path X = Conv2D(filters = F1, kernel_size= (1, 1), strides = (s,s),padding="valid", name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X) X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X) X = Activation('relu')(X) # Second component of main path X = Conv2D(filters = F2, kernel_size=(f,f), strides=(1,1), name = conv_name_base + '2b', padding="same",kernel_initializer = glorot_uniform(seed=0))(X) X = BatchNormalization(axis = 3, name= bn_name_base + '2b')(X) X = Activation('relu')(X) # Third component of main path X = Conv2D(filters = F3, kernel_size=(1,1), strides = (1,1), name= conv_name_base + '2c',padding="valid", kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X) ##### SHORTCUT PATH #### X_shortcut = Conv2D(filters = F3, kernel_size= (1,1), strides=(s,s), name=conv_name_base + '1', padding="valid", kernel_initializer=glorot_uniform(seed=0))(X_shortcut) X_shortcut = BatchNormalization(axis=3, name=bn_name_base+'1')(X_shortcut) # Final step: Add shortcut value to main path, and pass it through a RELU activation X = Add()([X_shortcut,X]) X = Activation("relu")(X) return X tf.reset_default_graph() with tf.Session() as test: np.random.seed(1) A_prev = tf.placeholder("float", [3, 4, 4, 6]) X = np.random.randn(3, 4, 4, 6) A = convolutional_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a') test.run(tf.global_variables_initializer()) out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0}) print("out = " + str(out[0][1][1][0])) # ResNet 50 def ResNet50(input_shape = (64, 64, 3), classes = 6): """ Implementation of the popular ResNet50 the following architecture: CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3 -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER Arguments: input_shape -- shape of the images of the dataset classes -- integer, number of classes Returns: model -- a Model() instance in Keras """ # Define the input as a tensor with shape input_shape X_input = Input(input_shape) # Zero-Padding X = ZeroPadding2D((3, 3))(X_input) # Stage 1 X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X) X = BatchNormalization(axis = 3, name = 'bn_conv1')(X) X = Activation('relu')(X) X = MaxPooling2D((3, 3), strides=(2, 2))(X) # Stage 2 X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1) X = identity_block(X, 3, [64, 64, 256], stage=2, block='b') X = identity_block(X, 3, [64, 64, 256], stage=2, block='c') # Stage 3 X = convolutional_block(X, f=3, filters = [128,128,512], stage = 3, block='a', s=2) X = identity_block(X, 3, filters = [128,128,512],stage=3, block='b') X = identity_block(X, 3, filters = [128,128,512], stage=3, block='c') X = identity_block(X, 3, filters = [128,128,512], stage =3, block='d') # Stage 4 X = convolutional_block(X, f=3, filters = [256,256,1024],stage=4, block='a', s=2) X = identity_block(X, 3, filters = [256,256,1024], stage=4, block='b') X = identity_block(X, 3, filters = [256, 256, 1024], stage=4, block='c') X = identity_block(X, 3, filters= [256,256,1024], stage=4, block='d') X = identity_block(X, 3, filters=[256,256,1024], stage=4, block='e') X = identity_block(X, 3, filters=[256,256,1024], stage=4, block='f') # Stage 5 X = convolutional_block(X, f=3, filters=[256,256,2048], stage=5,block='a', s=3) X = identity_block(X, 3, filters=[256,256,2048], stage=5, block='b') X = identity_block(X,3, filters=[256,256,2048], stage=5, block='c') # AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)" X = AveragePooling2D((2,2), name='avg_pool')(X) # output layer X = Flatten()(X) X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X) # Create model model = Model(inputs = X_input, outputs = X, name='ResNet50') return model # Run the following code to build the model's graph. If your implementation is not correct you will know it by checking your accuracy when running model.fit(...) below. model = ResNet50(input_shape = (64, 64, 3), classes = 6) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # The model is ready to be trained. The only thing you need is a dataset. # Use any dataset of your choice. # You're on your own now, you now have the required tools to build ResNet. I am providing a sample of what it may look like from HERE X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset() # Normalize image vectors X_train = X_train_orig/255. X_test = X_test_orig/255. # Convert training and test labels to one hot matrices Y_train = convert_to_one_hot(Y_train_orig, 6).T Y_test = convert_to_one_hot(Y_test_orig, 6).T print ("number of training examples = " + str(X_train.shape[0])) print ("number of test examples = " + str(X_test.shape[0])) print ("X_train shape: " + str(X_train.shape)) print ("Y_train shape: " + str(Y_train.shape)) print ("X_test shape: " + str(X_test.shape)) print ("Y_test shape: " + str(Y_test.shape)) model.fit(X_train, Y_train, epochs = 2, batch_size = 32) preds = model.evaluate(X_test, Y_test) print ("Loss = " + str(preds[0])) print ("Test Accuracy = " + str(preds[1])) # Transfer Learning, you can use a pretrained ResNet 50 here, available online model = load_model('ResNet50.h5') preds = model.evaluate(X_test, Y_test) print ("Loss = " + str(preds[0])) print ("Test Accuracy = " + str(preds[1])) #ResNet50 is a powerful model for image classification when it is trained for an adequate number of iterations. # That's it . Congratulations on learning Residual Networks. Thank you for watching.
3.421875
3
utilities.py
tdrmk/pyklotski
0
12788924
from pygame.draw import rect as draw_rect def darken_color(color, factor): return tuple(int(c * factor) for c in color) def draw_piece(surf, color, left, top, width, height, size): padding_factor = 0.025 shadow_factor = 0.085 margin_factor = 0.05 base_color = color margin_color = darken_color(color, 0.8) bottom_color = darken_color(color, 0.4) # Applying padding padding = int(size * padding_factor) left, top = left + padding, top + padding width, height = width - 2 * padding, height - 2 * padding size = size - 2 * padding # Applying shadow effect shadow = int(size * shadow_factor) top_rect = (left, top, width - shadow, height - shadow) bottom_rect = (left + shadow, top + shadow, width - shadow, height - shadow) draw_rect(surf, bottom_color, bottom_rect) draw_rect(surf, base_color, top_rect) # Draw margins draw_rect(surf, margin_color, top_rect, int(size * margin_factor))
3.703125
4
{{cookiecutter.project_slug}}/src/{{cookiecutter.package_name}}/exceptions.py
pcrespov/cookiecutter-simcore-pyservice
0
12788925
<reponame>pcrespov/cookiecutter-simcore-pyservice<filename>{{cookiecutter.project_slug}}/src/{{cookiecutter.package_name}}/exceptions.py """ All exceptions used in the {{ cookiecutter.package_name }} code base are defined here. """ class ServiceException(Exception): """ Base exception class. All service-specific exceptions should subclass this class. """
1.421875
1
api/reports/migrations/0001_initial.py
Egor4ik325/rankrise
0
12788926
<gh_stars>0 # Generated by Django 3.2.9 on 2022-01-02 17:46 import uuid import django.db.models.deletion import model_utils.fields from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ("contenttypes", "0002_remove_content_type_name"), ] operations = [ migrations.CreateModel( name="Report", fields=[ ( "id", model_utils.fields.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ), ("title", models.CharField(max_length=100, verbose_name="title")), ("description", models.TextField(verbose_name="description")), ( "object_pk", models.CharField(max_length=100, verbose_name="object ID"), ), ( "created", models.DateTimeField(auto_now_add=True, verbose_name="Created"), ), ( "content_type", models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name="reports", to="contenttypes.contenttype", verbose_name="content type", ), ), ( "reporter", models.ForeignKey( default=None, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name="reports", to=settings.AUTH_USER_MODEL, verbose_name="Reporter", ), ), ], options={ "verbose_name": "Report", "verbose_name_plural": "Reports", "ordering": ["created"], }, ), ]
1.820313
2
python/paddle/trainer_config_helpers/tests/configs/test_gated_unit_layer.py
shenchaohua/Paddle
3
12788927
<gh_stars>1-10 from paddle.trainer_config_helpers import * data = data_layer(name='input', size=256) glu = gated_unit_layer( size=512, input=data, act=TanhActivation(), gate_attr=ExtraLayerAttribute(error_clipping_threshold=100.0), gate_param_attr=ParamAttr(initial_std=1e-4), gate_bias_attr=ParamAttr(initial_std=1), inproj_attr=ExtraLayerAttribute(error_clipping_threshold=100.0), inproj_param_attr=ParamAttr(initial_std=1e-4), inproj_bias_attr=ParamAttr(initial_std=1), layer_attr=ExtraLayerAttribute(error_clipping_threshold=100.0)) outputs(glu)
2.03125
2
ModularER_2D/gym_rem2D/morph/circular_module.py
FrankVeenstra/gym_rem2D
27
12788928
#!/usr/bin/env python """ Standard BOX 2D module with single joint """ import gym_rem2D.morph.module_utility as mu from gym_rem.utils import Rot from enum import Enum import numpy as np from Controller import m_controller import random import math from gym_rem2D.morph import abstract_module from gym_rem2D.morph import simple_module as sm import Box2D as B2D from Box2D.b2 import (edgeShape, fixtureDef, polygonShape, revoluteJointDef, contactListener) class Connection(Enum): """Available connections for standard 2D module""" left = (1.,0.,0.) right = (-1.,0.,0.) top = (0.,1.0,0.) class Circular2D(abstract_module.Module): """Standard 2D module""" def __init__(self, theta=0, size=(0.1,0.1, 0.0)): self.theta = theta % 2 # double check self.size = np.array(size) assert self.size.shape == (3,), "Size must be a 3 element vector! : this is a 2D module but takes in a three dimensional size vector for now. Third entry is ignored" self.connection_axis = np.array([0., 0., 1.]) self.orientation = Rot.from_axis(self.connection_axis, -self.theta * (np.pi / 2.)) # NOTE: The fudge factor is to avoid colliding with the plane once # spawned self.position = np.array([0., self.size[2] / 2. + 0.002, 0.]) # uses only x and y self._children = {} self.controller = m_controller.Controller() # relative scales self.radius = 0.25 self.angle = math.pi/2 self.type = "CIRCLE" self.MIN_RADIUS = 0.25 self.MAX_RADIUS = 0.5 self.MIN_ANGLE = math.pi/4 self.MAX_ANGLE = math.pi*2 self.torque = 50 #self.joint = None # needs joint def limitWH(self): """Limit morphology to bounds""" if self.radius > self.MAX_RADIUS: self.radius = self.MAX_RADIUS elif self.radius < self.MIN_RADIUS: self.radius = self.MIN_RADIUS if self.angle >self.MAX_ANGLE: self.angle = self.MAX_ANGLE elif self.angle < self.MIN_ANGLE: self.angle = self.MIN_ANGLE def mutate(self, MORPH_MUTATION_RATE,MUTATION_RATE,MUT_SIGMA): """ To mutate the shape and controller stored in the modules. """ #return if random.uniform(0,1) < MORPH_MUTATION_RATE: self.radius = random.gauss(self.radius, MUT_SIGMA) if random.uniform(0,1) < MORPH_MUTATION_RATE: self.angle = random.gauss(self.angle,MUT_SIGMA * math.pi) self.limitWH() if self.controller is not None: self.controller.mutate(MUTATION_RATE,MUT_SIGMA, self.angle) def setMorph(self,val1, val2, val3): # values are between -1 and 1 self.radius = val1 + 1.5 # val2 is not used since radius self.angle = self.MIN_ANGLE +(((val3 + 1.0)*0.5) * (self.MAX_ANGLE-self.MIN_ANGLE)) # limit values self.limitWH() def __setitem__(self, key, module): if not isinstance(key, Connection): raise TypeError("Key: '{}' is not a Connection type".format(key)) if key in self._children: raise ModuleAttached() if key not in self.available: raise ConnectionObstructed() # Add module as a child self._children[key] = module # Calculate connection point direction = self.orientation.rotate(np.array(key.value)) position = self.position + (direction * self.size) / 2. # Update parent pointer of module module.update(self, position, direction) def update(self, parent=None, pos=None, direction=None): # Update own orientation first in case we have been previously # connected self.orientation = Rot.from_axis(self.connection_axis, -self.theta * (np.pi / 2.)) # Update position in case parent is None self.position = np.array([0., 0., self.size[2] / 2. + 0.002]) # Reset connection in case parent is None self.connection = None # Call super to update orientation super().update(parent, pos, direction) # If parent is not None we need to update position and connection point if self.parent is not None: # Update center position for self # NOTE: We add a little fudge factor to avoid overlap self.position = pos + (direction * self.size * 1.01) / 2. # Calculate connection points for joint conn = np.array([0., 0., -self.size[2] / 2.]) parent_conn = parent.orientation.T.rotate(pos - parent.position) self.connection = (parent_conn, conn) # Update potential children self.update_children() def update_children(self): for conn in self._children: direction = self.orientation.rotate(np.array(conn.value)) position = self.position + (direction * self.size) / 2. self._children[conn].update(self, position, direction) def spawn(self): orient = self.orientation.as_quat() cuid = B2D.b2CircleShape cuid.m_p.Set(self.position) if (self.parent): self.joint = B2D.b2RevoluteJoint() return cuid def get_global_position_of_connection_site(self,con=None, parent_component = None): if con is None: con = Connection.left # get intersection of rectangle from width and height local_position = [] # 2d array local_angle = (con.value[0] * (self.angle)) # positive for left, negative for right # position relative to y directional vector if parent_component: local_angle+=parent_component.angle x = math.cos(local_angle+ math.pi/2)*self.radius y = math.sin(local_angle+ math.pi/2)*self.radius local_position.append(x) local_position.append(y) if parent_component is None: return local_position,local_angle global_position = [local_position[0]+parent_component.position[0], local_position[1]+parent_component.position[1]] return global_position, local_angle def create(self,world,TERRAIN_HEIGHT,module=None,node=None,connection_site=None, p_c=None, module_list=None, position = None): # get module height and width if p_c is not None and connection_site is None: raise("When you want to attach a new component to a parent component, you have to supply", "a connection_site object with it. This connection_site object defines where to anchor", "the joint in between to components") n_radius = self.radius angle = 0 pos = [7,10,0]; if position is not None: pos = position if (p_c is not None): local_pos_x =math.cos(connection_site.orientation.x+ math.pi/2) * n_radius local_pos_y =math.sin(connection_site.orientation.x+ math.pi/2) * n_radius pos[0] = (local_pos_x) + connection_site.position.x pos[1] = (local_pos_y) + connection_site.position.y # This module will create one component that will be temporarily stored in ncomponent new_component = None # This module will create one joint (if a parent component is present) that will be temporarily stored in njoint njoint = None components = [] joints = [] if connection_site: angle += connection_site.orientation.x if (pos[1] - n_radius < TERRAIN_HEIGHT): #TODO CHANGE TO TERRAIN_HEIGT OR DO CHECK ELSEWHERE if node is not None: node.component = None return components,joints else: fixture = fixtureDef( shape=B2D.b2CircleShape(radius =n_radius), density=1, friction=0.1, restitution=0.0, categoryBits=0x0020, maskBits=0x001 ) new_component = world.CreateDynamicBody( position=(pos[0],pos[1]), angle = angle, fixtures = fixture) color = [255,255,255] if node is not None and module_list is not None: color = world.cmap(node.type/len(module_list)) elif node is not None and module_list is None: print("Note: cannot assign a color to the module since the 'module_list' is not passed as an argument") # move to component creator new_component.color1 = (color[0],color[1],color[2]) new_component.color2 = (color[0],color[1],color[2]) components.append(new_component) if node is not None: node.component = [new_component] if connection_site is not None: joint = mu.create_joint(world, p_c,new_component,connection_site, angle, self.torque) joints.append(joint) return components, joints
3.109375
3
level_05.py
katsukaree/chapter-weasel
0
12788929
#!/usr/bin/env python3 import requests import base64 import re from levels_credentials import credentials level_url = credentials[5]["url"] level_username = credentials[5]["level"] level_password = credentials[5]["password"] next_level_url = credentials[6]["url"] next_level_username = credentials[6]["level"] credentials = "%s:%s" % (level_username, level_password) auth_creds = base64.b64encode(credentials.encode("ascii")) heads = {"Authorization": "Basic %s" % auth_creds.decode("ascii"), "Referer": next_level_url} cooks = {"loggedin": "1"} response = requests.get(level_url, headers=heads, cookies=cooks) data = response.text strings = re.split('\n|:|\s|<|>', data) next_password = strings[strings.index(next_level_username) + 2] print(next_password)
2.71875
3
python/testData/refactoring/rename/referencesInsideFStringsNotReportedAsStringOccurrences.py
tgodzik/intellij-community
2
12788930
<filename>python/testData/refactoring/rename/referencesInsideFStringsNotReportedAsStringOccurrences.py def func(): v<caret>ar = 42 s = f'{var}'
1.46875
1
libs/modules/trader.py
meetri/cryptolib
0
12788931
<reponame>meetri/cryptolib import os,sys,talib,numpy,math,time,datetime from influxdbwrapper import InfluxDbWrapper from coincalc import CoinCalc from exchange import Exchange class Trader(object): def __init__(self, market = None, exchange=None, currency=None): self.influxdb = InfluxDbWrapper.getInstance() self.market = market if exchange is None: exchange = "bittrex" self.exchange = exchange self.cs = None self.indicators = None self.timeframe = None self.cssize = None self.candle_seconds = 0 self.candle_remaining = 0 self.candle_last_time = None if currency is not None: self.market = CoinCalc.getInstance().get_market(currency) def set_currency(self,currency): self.market = CoinCalc.getInstance().get_market(currency) return self def project_volume( volkey = "basevolume" ): size = self.cssize m = 1 if size[-1] == "m": m = 60 elif size[-1] == "h": m = 3600 elif size[-1] == "d": m = 86400 sec_ofs = float(size[0:-1]) * m ts = time.time() % sec_ofs remaining = sec_ofs - ts rem = sec_ofs / ( sec_ofs - remaining) return cs[volkey][-1] * rem def getCandleRemaining(self): rem = None if self.candle_last_time is not None: ts = time.time() - self.candle_last_time if ts < self.candle_remaining: return self.candle_remaining - ts return rem def get_candlesticks(self, timeframe = "1h", size = "1m", dateOffset = "now()" , base_size="1m"): self.timeframe = timeframe self.cssize = size m = 1 if size[-1] == "m": m = 60 elif size[-1] == "h": m = 3600 elif size[-1] == "d": m = 86400 sec_ofs = float(size[0:-1]) * m ts = time.time() % sec_ofs if len(base_size) > 0: dateOffset = (datetime.datetime.utcnow() - datetime.timedelta(seconds=ts) + datetime.timedelta(seconds=sec_ofs)).strftime('%Y-%m-%dT%H:%M:%SZ') pres = self.influxdb.raw_query("""SELECT SUM(base_volume) AS base_volume, SUM(volume) AS volume, MAX(high) as high, MIN(low) as low, FIRST(open) as open, LAST(close) AS close FROM "market_ohlc" WHERE market='{0}' AND exchange='{5}' AND time < '{1}' AND time > '{1}' - {2} AND period='{4}' GROUP BY time({3})""".format(self.market,dateOffset,timeframe,size,base_size,self.exchange)) points = pres.get_points() else: points = self.influxdb.raw_query("""select base_volume, volume, open, high, low, close FROM "market_ohlc" WHERE market='{0}' AND exchange='{4}' AND time < {1} AND time > {1} - {2} AND period='{3}'""".format(self.market,dateOffset,timeframe,size,self.exchange)).get_points() cs = self.clear_candlesticks() psize = 0 for point in points: if point["volume"] == None: continue #point["volume"] = 0 #if point["base_volume"] == None: # point["base_volume"] = 0 psize += 1 cs["low"].extend([point["low"]]) cs["high"].extend([point["high"]]) cs["closed"].extend([point["close"]]) cs["open"].extend([point["open"]]) cs["volume"].extend([float(point["volume"])]) cs["basevolume"].extend([float(point["base_volume"])]) cs["time"].extend([point["time"]]) self.candle_remaining = sec_ofs - ts self.candle_seconds = sec_ofs self.candle_last_time = time.time() if psize == 0: raise Exception("no market data for {} at {}".format(self.market,dateOffset)) self.cs = { "low": numpy.array(cs["low"]), "high": numpy.array(cs["high"]), "closed": numpy.array(cs["closed"]), "volume": numpy.array(cs["volume"]), "basevolume": numpy.array(cs["basevolume"]), "open": numpy.array(cs["open"]), "time": cs["time"], "remaining": numpy.array(cs["remaining"]), "projected_volume": numpy.array(cs["projected_volume"]), "projected_basevolume": numpy.array(cs["projected_basevolume"]), } Exchange.getInstance().set_market_value(self.market, self.cs["closed"][-1] ) return self.cs def x_get_candlesticks(self, timeframe = "1h", size = "5m", dateOffset = "now()" ): self.timeframe = timeframe self.cssize = size points = self.influxdb.raw_query("""select LAST(basevolume) as basevolume, LAST(volume) as volume, FIRST(last) as open, LAST(last) as closed, MAX(last) as high, MIN(last) as low FROM "market_summary" WHERE marketname='{0}' and time < {1} and time > {1} - {2} group by time({3})""".format(self.market,dateOffset,timeframe,size)).get_points() cs = self.clear_candlesticks() psize = 0 for point in points: psize += 1 cs["low"].extend([point["low"]]) cs["high"].extend([point["high"]]) cs["closed"].extend([point["closed"]]) cs["open"].extend([point["open"]]) cs["volume"].extend([point["volume"]]) cs["basevolume"].extend([point["basevolume"]]) cs["time"].extend([point["time"]]) if psize == 0: raise Exception("no market data for {} at {}".format(self.market,dateOffset)) def fix_gaps(lst): for idx,val in enumerate(lst): if val == None: if idx > 0: lst[idx] = lst[idx-1] if idx == 0: lst[idx] = 0 fix_gaps(cs["low"]) fix_gaps(cs["high"]) fix_gaps(cs["closed"]) fix_gaps(cs["open"]) fix_gaps(cs["volume"]) fix_gaps(cs["basevolume"]) fix_gaps(cs["time"]) self.cs = { "low": numpy.array(cs["low"]), "high": numpy.array(cs["high"]), "closed": numpy.array(cs["closed"]), "volume": numpy.array(cs["volume"]), "basevolume": numpy.array(cs["basevolume"]), "open": numpy.array(cs["open"]), "time": cs["time"] } Exchange.getInstance().set_market_value(self.market, self.cs["closed"][-1] ) return self.cs def xget_candlesticks(self, timeframe = "1h", size = "5m" ): self.timeframe = timeframe self.cssize = size points = self.influxdb.raw_query("""select FIRST(last) as open, LAST(last) as closed, MAX(last) as high, MIN(last) as low, (LAST(basevolume)+LAST(volume)) as volume FROM "market_summary" WHERE marketname='{}' and time > now() - {} group by time({})""".format(self.market,timeframe,size)).get_points() cs = self.clear_candlesticks() for point in points: cs["low"].extend([point["low"]]) cs["high"].extend([point["high"]]) cs["closed"].extend([point["closed"]]) cs["open"].extend([point["open"]]) cs["volume"].extend([point["volume"]]) cs["basevolume"].extend([point["basevolume"]]) cs["time"].extend([point["time"]]) def fix_gaps(lst): for idx,val in enumerate(lst): if val == None: if idx > 0: lst[idx] = lst[idx-1] if idx == 0: lst[idx] = 0 fix_gaps(cs["low"]) fix_gaps(cs["high"]) fix_gaps(cs["closed"]) fix_gaps(cs["open"]) fix_gaps(cs["volume"]) fix_gaps(cs["basevolume"]) fix_gaps(cs["time"]) self.cs = { "low": numpy.array(cs["low"]), "high": numpy.array(cs["high"]), "closed": numpy.array(cs["closed"]), "volume": numpy.array(cs["volume"]), "basevolume": numpy.array(cs["basevolume"]), "open": numpy.array(cs["open"]), "time": cs["time"] } Exchange.getInstance().set_market_value(self.market, self.cs["closed"][-1] ) return self.cs def clear_candlesticks(self): return { "open": [], "closed": [], "high": [], "low": [], "volume": [], "basevolume": [], "time":[], "opening":[],"closing":[],"remaining":[],"projected_volume":[],"projected_basevolume":[] }
2.53125
3
__init__.py
merialdo/research.mojito
0
12788932
<gh_stars>0 from mojito.mojito import Mojito from mojito.chart import chart
1.140625
1
conf/__init__.py
FredrikM97/Medical-ROI
0
12788933
<filename>conf/__init__.py """This package includes all the modules related to data loading and preprocessing. To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset. """ import os import json from utils import load_configs """ def load_configs(): configs = {} for pos_json in os.listdir('.'): if pos_json.endswith('.json'): with open('configs/' +pos_json) as json_file: for name, config in json.load(json_file).items(): if name in configs: raise Exception(f"Config from {pos_json} with name {name} already exists!") configs.update({name:config}) return configs """ def load_config(name): return load_configs('configs/')[name]
2.71875
3
sharpy-sc2/sharpy/plans/require/gas.py
etzhang416/sharpy-bot-eco
0
12788934
import warnings import sc2 from sharpy.plans.require.require_base import RequireBase class Gas(RequireBase): """Require that a specific number of minerals are "in the bank".""" def __init__(self, vespene_requirement: int): assert vespene_requirement is not None and isinstance(vespene_requirement, int) super().__init__() self.vespene_requirement = vespene_requirement def check(self) -> bool: if self.ai.vespene > self.vespene_requirement: return True return False class RequiredGas(Gas): def __init__(self, vespene_requirement: int): warnings.warn("'RequiredGas' is deprecated, use 'Gas' instead", DeprecationWarning, 2) super().__init__(vespene_requirement)
2.84375
3
src/__init__.py
bkatwal/distributed-kafka-consumer-python
2
12788935
import logging import os # set the default logging level to info logging.basicConfig(level=logging.INFO) ROOT_SRC_DIR = os.path.dirname(os.path.abspath(__file__)) USERNAME = os.environ.get('APP_USERNAME', 'admin') PASSWORD = os.environ.get('APP_PASSWORD', '<PASSWORD>') WORKER_NUM_CPUS = os.environ.get('WORKER_NUM_CPUS', .25) SASL_USERNAME = os.environ.get('SASL_USERNAME', None) SASL_PASSWORD = os.environ.get('SASL_PASSWORD', None) SECURITY_PROTOCOL = os.environ.get('SECURITY_PROTOCOL', 'PLAINTEXT') SASL_MECHANISM = os.environ.get('SASL_MECHANISM') WORKER_CONFIG_PATH = os.environ.get('WORKER_CONFIG_PATH', '/../config/consumer_config.json') RAY_HEAD_ADDRESS = os.environ.get('RAY_HEAD_ADDRESS', 'auto') LOCAL_MODE = os.environ.get('LOCAL_MODE', 'Y')
1.890625
2
ddweb/apps/images/views.py
neic/ddweb
0
12788936
<gh_stars>0 import os from django.contrib.auth.decorators import permission_required from django.contrib.contenttypes.models import ContentType from django.core.urlresolvers import reverse from django.shortcuts import render from django.views.decorators.http import require_POST from jfu.http import upload_receive, UploadResponse, JFUResponse from ddweb.apps.images.models import Image @permission_required("image.add_image") def uploadForm(request, content_type, object_id): ct = ContentType.objects.get(model=content_type) associatedObject = ct.get_object_for_this_type(pk=object_id) context = { "associatedObject": associatedObject, "content_type": content_type, "object_id": object_id, } return render(request, "upload.html", context) @require_POST @permission_required("image.add_image", raise_exception=True) def upload(request): # The assumption here is that jQuery File Upload # has been configured to send files one at a time. # If multiple files can be uploaded simulatenously, # 'file' may be a list of files. image = upload_receive(request) content_type = ContentType.objects.get(model=request.POST["content_type"]) object_id = request.POST["object_id"] instance = Image(image=image, content_type=content_type, object_id=object_id) instance.save() basename = os.path.basename(instance.image.path) file_dict = { "name": basename, "size": image.size, "url": instance.image.url, "deleteUrl": reverse("jfu_delete", kwargs={"pk": instance.pk}), "deleteType": "POST", } return UploadResponse(request, file_dict) @require_POST @permission_required("image.delete_image", raise_exception=True) def upload_delete(request, pk): success = True try: instance = Image.objects.get(pk=pk) os.unlink(instance.image.path) instance.delete() except Image.DoesNotExist: success = False return JFUResponse(request, success)
2.125
2
mmdet/det_core/utils/mAP_utils.py
Karybdis/mmdetection-mini
834
12788937
import numpy as np from multiprocessing import Pool from ..bbox import bbox_overlaps # https://zhuanlan.zhihu.com/p/34655990 def calc_PR_curve(pred, label): pos = label[label == 1] # 正样本 threshold = np.sort(pred)[::-1] # pred是每个样本的正例预测概率值,逆序 label = label[pred.argsort()[::-1]] precision = [] recall = [] tp = 0 fp = 0 ap = 0 # 平均精度 for i in range(len(threshold)): if label[i] == 1: tp += 1 recall.append(tp / len(pos)) precision.append(tp / (tp + fp)) # 近似曲线下面积 ap += (recall[i] - recall[i - 1]) * precision[i] else: fp += 1 recall.append(tp / len(pos)) precision.append(tp / (tp + fp)) return precision, recall, ap def tpfp_voc(det_bboxes, gt_bboxes, iou_thr=0.5): num_dets = det_bboxes.shape[0] num_gts = gt_bboxes.shape[0] # tp和fp都是针对预测个数而言,不是gt个数 tp = np.zeros(num_dets, dtype=np.float32) fp = np.zeros(num_dets, dtype=np.float32) # 如果gt=0,那么所有预测框都算误报,所有预测bbox位置的fp都设置为1 if gt_bboxes.shape[0] == 0: fp[...] = 1 return tp, fp if num_dets == 0: return tp, fp ious = bbox_overlaps(det_bboxes[:, :4], gt_bboxes).numpy() # print(ious) # 对于每个预测框,找到最匹配的gt iou ious_max = ious.max(axis=1) # 对于每个预测框,找到最匹配gt的索引 ious_argmax = ious.argmax(axis=1) # 按照预测概率分支降序排列 sort_inds = np.argsort(-det_bboxes[:, -1]) gt_covered = np.zeros(num_gts, dtype=bool) # 多对一情况下,除了概率分值最大且大于阈值的预测框算tp外,其他框全部算fp for i in sort_inds: # 如果大于iou,则表示匹配 if ious_max[i] >= iou_thr: matched_gt = ious_argmax[i] # 每个gt bbox只匹配一次,且是和预测概率最大的匹配,不是按照iou if not gt_covered[matched_gt]: gt_covered[matched_gt] = True tp[i] = 1 else: fp[i] = 1 else: fp[i] = 1 return tp, fp def _average_precision(recalls, precisions, mode='voc2007'): recalls = recalls[np.newaxis, :] precisions = precisions[np.newaxis, :] assert recalls.shape == precisions.shape and recalls.ndim == 2 num_scales = recalls.shape[0] ap = np.zeros(num_scales, dtype=np.float32) if mode == 'voc2012': # 平滑后就是标准的PR曲线算法 zeros = np.zeros((num_scales, 1), dtype=recalls.dtype) ones = np.ones((num_scales, 1), dtype=recalls.dtype) mrec = np.hstack((zeros, recalls, ones)) mpre = np.hstack((zeros, precisions, zeros)) # 写法比较高级,高效 for i in range(mpre.shape[1] - 1, 0, -1): mpre[:, i - 1] = np.maximum(mpre[:, i - 1], mpre[:, i]) # 每段区间内,精度都是取最大值,也就是水平线 for i in range(num_scales): ind = np.where(mrec[i, 1:] != mrec[i, :-1])[0] # 找到召回率转折点,表示x轴移动区间索引 ap[i] = np.sum( (mrec[i, ind + 1] - mrec[i, ind]) * mpre[i, ind + 1]) # 每段面积和 elif mode == 'voc2007': # 11点法,需要平平滑处理 for i in range(num_scales): for thr in np.arange(0, 1 + 1e-3, 0.1): precs = precisions[i, recalls[i, :] >= thr] prec = precs.max() if precs.size > 0 else 0 ap[i] += prec ap /= 11 else: raise ValueError( 'Unrecognized mode, only "area" and "11points" are supported') return ap # code ref from mmdetection def voc_eval_map(results, annotations, iou_thr=0.5, name='voc2007', nproc=4): """ :param results: list[list],外层list是指代图片编号,内层list是指代类别编号, 假设一共20个类,则内层list长度为20,每个List内部是numpy矩阵,nx5表示每张图片对应的每个类别的检测bbox,xyxyconf格式 :param annotations:和results一样 :param iou_thr: 是否算TP的阈值,voc默认是0.5 :param name: 采用哪一种评估指标,voc2007是11点,voc2012是标准pr曲线计算 :return: """ assert len(results) == len(annotations) num_imgs = len(results) # 图片个数 num_classes = len(results[0]) # positive class num pool = Pool(nproc) eval_results = [] for i in range(num_classes): cls_dets = [img_res[i] for img_res in results] cls_gts = [img_res[i] for img_res in annotations] tpfp = pool.starmap( tpfp_voc, zip(cls_dets, cls_gts, [iou_thr for _ in range(num_imgs)])) # 得到每个预测bbox的tp和fp情况 tp, fp = tuple(zip(*tpfp)) # 统计gt bbox数目 num_gts = 0 for j, bbox in enumerate(cls_gts): num_gts += bbox.shape[0] # 合并所有图片所有预测bbox cls_dets = np.vstack(cls_dets) num_dets = cls_dets.shape[0] # 检测bbox个数 # 以上计算出了每个预测bbox的tp和fp情况 # 此处计算精度和召回率,写的比较高级 sort_inds = np.argsort(-cls_dets[:, -1]) # 按照预测概率分值降序排列 # 仔细思考这种写法,其实是c3_pr_roc.py里面calc_PR_curve的高级快速写法 tp = np.hstack(tp)[sort_inds][None] fp = np.hstack(fp)[sort_inds][None] tp = np.cumsum(tp, axis=1) fp = np.cumsum(fp, axis=1) eps = np.finfo(np.float32).eps recalls = tp / np.maximum(num_gts, eps) precisions = tp / np.maximum((tp + fp), eps) recalls = recalls[0, :] precisions = precisions[0, :] # print('recalls', recalls, 'precisions', precisions) ap = _average_precision(recalls, precisions, name)[0] eval_results.append({ 'num_gts': num_gts, 'num_dets': num_dets, 'recall': recalls, 'precision': precisions, 'ap': ap }) pool.close() aps = [] for cls_result in eval_results: if cls_result['num_gts'] > 0: aps.append(cls_result['ap']) mean_ap = np.array(aps).mean().item() if aps else 0.0 return mean_ap
2.140625
2
azad/utils.py
CoAxLab/azad
6
12788938
import cloudpickle import numpy as np import torch from typing import List, Tuple from scipy.linalg import eigh def save_checkpoint(state, filename='checkpoint.pkl'): data = cloudpickle.dumps(state) with open(filename, 'wb') as fi: fi.write(data) def load_checkpoint(filename='checkpoint.pkl'): with open(filename, 'rb') as fi: return cloudpickle.load(fi)
2.34375
2
python/chapter2/sshcmd.py
xiaostar2016/KaliTest
1
12788939
<gh_stars>1-10 #!/usr/bin/env python # coding=utf-8 import threading import paramiko import subprocess def ssh_command(ip, user, passwd, command): client = paramiko.SSHClient() # paramiko支持用密钥认证代,实际环境推荐使用密钥认证,这里设置账号和密码认证 # client.load_host_keys('/home/justin/.ssh/known_hosts') # 设置自动添加和保存目标SSH服务器的SSH密钥 client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) client.connect(ip, username=user, password=<PASSWORD>) # 连接 ssh_session = client.get_transport().open_session() # 打开会话 if ssh_session.active: ssh_session.exec_command(command) print ssh_session.recv(1024) return # 调用函数,以用户pi机器密码连接自己的树莓派,并执行id这个命令 ssh_command('192.168.3.11', 'pi', 'raspberry', 'id')
2.515625
3
Interview/evenNumberList.py
dnootana/Python
1
12788940
<reponame>dnootana/Python # Enter your code here. Read input from STDIN. Print output to STDOUT def evenlist(): list = input("Enter numbers : ") L = list.split() E = [] for i in range(len(L)): num = int(L[i],10) if(num%2==0): E.append(L[i]) print("Even numbers are : ",E)
3.734375
4
impstall/core.py
ryanniehaus/impstall
2
12788941
<reponame>ryanniehaus/impstall #!/usr/bin/env python ''' This module can be used to import python packages and install them if not already installed. ''' import os import sys import subprocess import tempfile import urllib _pipSetupUrl = 'https://bootstrap.pypa.io/get-pip.py' PIP_OPTIONS=[] INSTALL_PIP_OPTIONS=[] PYTHON_EXE_PATH=sys.executable HTTP_PROXY=None HTTPS_PROXY=None def _updateModVarsFromEnv(): if os.environ.get('PYTHON_EXE_PATH') is not None: global PYTHON_EXE_PATH PYTHON_EXE_PATH=os.environ['PYTHON_EXE_PATH'] if os.environ.get('INSTALL_PIP_OPTIONS') is not None: global INSTALL_PIP_OPTIONS INSTALL_PIP_OPTIONS=os.environ['INSTALL_PIP_OPTIONS'] if os.environ.get('PIP_OPTIONS') is not None: global PIP_OPTIONS PIP_OPTIONS=os.environ['PIP_OPTIONS'] global HTTPS_PROXY if os.environ.get('https_proxy') is not None and os.environ['https_proxy']!='': HTTPS_PROXY=os.environ['https_proxy'] elif os.environ.get('HTTPS_PROXY') is not None and os.environ['HTTPS_PROXY']!='': HTTPS_PROXY=os.environ['HTTPS_PROXY'] if HTTPS_PROXY is not None: if os.environ.get('https_proxy') is None: os.environ['https_proxy']=HTTPS_PROXY if os.environ.get('HTTPS_PROXY') is None: os.environ['HTTPS_PROXY']=HTTPS_PROXY global HTTP_PROXY if os.environ.get('http_proxy') is not None and os.environ['http_proxy']!='': HTTP_PROXY=os.environ['http_proxy'] elif os.environ.get('HTTP_PROXY') is not None and os.environ['HTTP_PROXY']!='': HTTP_PROXY=os.environ['HTTP_PROXY'] if HTTP_PROXY is not None: if os.environ.get('http_proxy') is None: os.environ['http_proxy']=HTTP_PROXY if os.environ.get('HTTP_PROXY') is None: os.environ['HTTP_PROXY']=HTTP_PROXY def _installWithPip(pipName, pythonExePath=eval('PYTHON_EXE_PATH'), getPipOpts=eval('INSTALL_PIP_OPTIONS'), pipOpts=eval('PIP_OPTIONS')): ''' :param pipName: :return: ''' pipAvail = False try: import pip as pip pipAvail = True except ImportError: pass proxyArgs = [] if HTTP_PROXY is not None: proxyArgs.append('--proxy='+HTTP_PROXY) elif HTTPS_PROXY is not None: proxyArgs.append('--proxy='+HTTPS_PROXY) if not pipAvail: print 'Downloading pip installer:', _pipSetupUrl tmpDir = tempfile.gettempdir() pipSetupFilePath = os.path.join(tmpDir, os.path.basename(_pipSetupUrl)) urllib.urlretrieve(_pipSetupUrl, pipSetupFilePath) pipSetupArgs = [pythonExePath, pipSetupFilePath] pipSetupArgs.extend(proxyArgs) pipSetupArgs.extend(getPipOpts) print 'Executing pip installer:', ' '.join(pipSetupArgs) subprocess.Popen(pipSetupArgs) pipAvail = False try: import pip as pip pipAvail = True except ImportError: pass if pipAvail: pipArgs=proxyArgs pipArgs.extend(pipOpts) pipArgs.extend(['install', pipName]) print 'Installing', pipName + ':', 'pip', ' '.join(pipArgs) pip.main(pipArgs) else: print 'Pip not available...' #Look at pypi repo for installers #Download and use installer if available def set_pip_options(pipOptions=[]): global PIP_OPTIONS PIP_OPTIONS=pipOptions def get_pip_options(): return PIP_OPTIONS def set_pip_installer_options(pipInstallerOptions=[]): global INSTALL_PIP_OPTIONS INSTALL_PIP_OPTIONS=pipInstallerOptions def get_pip_installer_options(): return INSTALL_PIP_OPTIONS def set_custom_python_exe_path(pythonExePath=sys.executable): global PYTHON_EXE_PATH PYTHON_EXE_PATH=pythonExePath def get_current_python_exe_path(): return PYTHON_EXE_PATH def set_http_proxy(httpProxy=None): global HTTP_PROXY HTTP_PROXY=httpProxy def get_http_proxy(): return HTTP_PROXY def set_https_proxy(httpsProxy=None): global HTTPS_PROXY HTTPS_PROXY=httpsProxy def get_https_proxy(): return HTTPS_PROXY def impstall(module, items={}, pipPackage=None): ''' This is the main function of the module. It will import `importName` if it can. If not, it will try to install it. First, it tries to import the module. If pip is not installed, it tries to install pip. If that fails, it tries to install from pip. If the pip install fails or the module fails to install from pip, we try to find a module installer on the internet. If that fails, an exception is raised. :param module: str This is the name of the module that we want to import or import from. It should be the name that would be used in a standard import statement. :param pipPackage: str, optional This is the name of the module as it would be requested through pip. If not provided, it is set to `module` :return: N/A ''' baseModule=module.split('.')[0] packageAlreadyInstalled = False try: exec('import '+baseModule) packageAlreadyInstalled = True except ImportError: pass if not packageAlreadyInstalled: if pipPackage is None: pipPackage = baseModule _updateModVarsFromEnv() _installWithPip(pipPackage) if len(items) == 0: builtImportString = 'import ' + module else: builtImportString = 'from ' + module + ' import ' tempIdx = 0 for key in items: if tempIdx > 0: builtImportString += ', ' builtImportString += key if items[key] is not None and items[key] != '': builtImportString += ' as ' + items[key] tempIdx += 1 exec (builtImportString, sys._getframe(1).f_globals)
2.140625
2
protopigeon/__init__.py
gregorynicholas/proto-pigeon
0
12788942
<filename>protopigeon/__init__.py<gh_stars>0 from protorpc.messages import * from protorpc.protojson import * from .translators import *
1.140625
1
src/tests/test_inference.py
Islandora-Image-Segmentation/Newspaper-Navigator-API
0
12788943
<filename>src/tests/test_inference.py import os from PIL import Image from inference import predict from . import TEST_ASSETS_DIR def test_inference_one(): """ Test for the segmentation ML model. This test requires the model weights `model_final.pth` to be present in src/resources. """ img = Image.open(os.path.join(TEST_ASSETS_DIR, "test_image_one.png")) result = predict(img) assert len(result.bounding_boxes) > 0 def test_inference_two(): """ Test for the segmentation ML model. This test requires the model weights `model_final.pth` to be present in src/resources. """ img = Image.open(os.path.join(TEST_ASSETS_DIR, "test_image_two.png")) result = predict(img) assert len(result.bounding_boxes) > 0
2.5
2
Python learnings/Django projects/advcbv/basic_app/admin.py
warpalatino/public
1
12788944
from django.contrib import admin from .models import School,Student # Register your models here. admin.site.register(School) admin.site.register(Student)
1.46875
1
mp_gui/layout/__init__.py
kerryeon/mp_python
1
12788945
<filename>mp_gui/layout/__init__.py __all__ = ['MpGui'] from mp_gui.layout.main import MpGuiLinker as MpGui
1.25
1
exps/Baseline-Complement/models/model.py
Championchess/Generative-3D-Part-Assembly
80
12788946
""" B-Complement Input: part point clouds: B x P x N x 3 Output: R and T: B x P x(3 + 4) Losses: Center L2 Loss, Rotation L2 Loss, Rotation Chamder-Distance Loss """ import torch from torch import nn import torch.nn.functional as F import sys, os import numpy as np BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.join(BASE_DIR, '../utils')) from cd.chamfer import chamfer_distance from quaternion import qrot import ipdb from scipy.optimize import linear_sum_assignment # PointNet Front-end class PartPointNet(nn.Module): def __init__(self, feat_len): super(PartPointNet, self).__init__() self.conv1 = nn.Conv1d(3, 64, 1) self.conv2 = nn.Conv1d(64, 64, 1) self.conv3 = nn.Conv1d(64, 64, 1) self.conv4 = nn.Conv1d(64, 128, 1) #self.conv5 = nn.Conv1d(128, 1024, 1) self.bn1 = nn.BatchNorm1d(64) self.bn2 = nn.BatchNorm1d(64) self.bn3 = nn.BatchNorm1d(64) self.bn4 = nn.BatchNorm1d(128) #self.bn5 = nn.BatchNorm1d(1024) self.mlp1 = nn.Linear(128, feat_len) self.bn6 = nn.BatchNorm1d(feat_len) """ Input: B x N x 3 Output: B x F """ def forward(self, x): x = x.permute(0, 2, 1) x = torch.relu(self.bn1(self.conv1(x))) x = torch.relu(self.bn2(self.conv2(x))) x = torch.relu(self.bn3(self.conv3(x))) x = torch.relu(self.bn4(self.conv4(x))) #x = torch.relu(self.bn5(self.conv5(x))) x = x.max(dim=-1)[0] x = torch.relu(self.bn6(self.mlp1(x))) return x # PointNet Back-end class PoseDecoder(nn.Module): def __init__(self, feat_len): super(PoseDecoder, self).__init__() self.mlp1 = nn.Linear(feat_len, 512) self.mlp2 = nn.Linear(512, 256) self.trans = nn.Linear(256, 3) self.quat = nn.Linear(256, 4) self.quat.bias.data.zero_() """ Input: B x (2F + P + 16) Output: B x 7 """ def forward(self, feat): feat = torch.relu(self.mlp1(feat)) feat = torch.relu(self.mlp2(feat)) trans = torch.tanh(self.trans(feat)) # consider to remove torch.tanh if not using PartNet normalization quat_bias = feat.new_tensor([[[1.0, 0.0, 0.0, 0.0]]]) quat = self.quat(feat).add(quat_bias) quat = quat / (1e-12 + quat.pow(2).sum(dim=-1, keepdim=True)).sqrt() out = torch.cat([trans, quat.squeeze(0)], dim=-1) return out class Network(nn.Module): def __init__(self, conf): super(Network, self).__init__() self.conf = conf self.part_pointnet = PartPointNet(conf.feat_len) self.pose_decoder = PoseDecoder(2 * conf.feat_len + conf.max_num_part + 16) """ Input: B x P x N x 3, B x P, B x P x P, B x 7 Output: B x P x (3 + 4) """ def forward(self,seq, part_pcs, part_valids, instance_label, gt_part_pose): batch_size = part_pcs.shape[0] num_part = part_pcs.shape[1] num_point = part_pcs.shape[2] pred_part_poses = np.zeros((batch_size, num_part, 7)) pred_part_poses = torch.tensor(pred_part_poses).to(self.conf.device) # generate random_noise random_noise = np.random.normal(loc=0.0, scale=1.0, size=[batch_size, num_part, 16]).astype( np.float32) # B x P x 16 random_noise = torch.tensor(random_noise).to(self.conf.device) for iter in range(num_part): select_ind = seq[:,iter].int().tolist() batch_ind = [i for i in range(len(select_ind))] if iter == 0: cur_pred_pose = gt_part_pose # B x 7 pred_part_poses= pred_part_poses.float() pred_part_poses[batch_ind,select_ind,:] = cur_pred_pose cur_pred_center = cur_pred_pose[:, :3].unsqueeze(1).repeat(1, num_point, 1) # B x N x 3 cur_pred_qrot = cur_pred_pose[:, 3:].unsqueeze(1).repeat(1, num_point, 1) # B x N x 4 cur_part = cur_pred_center + qrot(cur_pred_qrot, part_pcs[batch_ind,select_ind, :, :])# B x N x 3 cur_part = cur_part.unsqueeze(1) # B x 1 x N x 3 cur_shape = cur_part # B x batch_ind,select_ind x N x 3 else: cur_shape_feat = self.part_pointnet(cur_shape.view(batch_size, -1, 3)) # B x F cur_part_feat = self.part_pointnet(part_pcs[batch_ind,select_ind, :, :])# B x F cat_feat = torch.cat([cur_shape_feat, cur_part_feat, instance_label[batch_ind,select_ind, :].contiguous(), random_noise[batch_ind,select_ind, :].contiguous()], dim=-1) # B x (2F + P + 16) cur_pred_pose = self.pose_decoder(cat_feat) # B x 7 pred_part_poses[batch_ind,select_ind, :] = cur_pred_pose cur_pred_center = cur_pred_pose[:, :3].unsqueeze(1).repeat(1, num_point, 1) # B x N x 3 cur_pred_qrot = cur_pred_pose[:, 3:].unsqueeze(1).repeat(1, num_point, 1) # B x N x 4 cur_part = cur_pred_center + qrot(cur_pred_qrot, part_pcs[batch_ind,select_ind, :, :]) # B x N x 3 cur_part = cur_part.unsqueeze(1) # B x 1 x N x 3 cur_shape = torch.cat([cur_shape, cur_part], dim=1) # B x select_ind x N x 3 pred_part_poses = pred_part_poses.double() * part_valids.unsqueeze(2).double() return pred_part_poses.float() """ Input: * x N x 3, * x 3, * x 4, * x 3, * x 4, Output: *, * (two lists) """ def linear_assignment(self, pts, centers1, quats1, centers2, quats2): cur_part_cnt = pts.shape[0] num_point = pts.shape[1] with torch.no_grad(): cur_quats1 = quats1.unsqueeze(1).repeat(1, num_point, 1) cur_centers1 = centers1.unsqueeze(1).repeat(1, num_point, 1) cur_pts1 = qrot(cur_quats1, pts) + cur_centers1 cur_quats2 = quats2.unsqueeze(1).repeat(1, num_point, 1) cur_centers2 = centers2.unsqueeze(1).repeat(1, num_point, 1) cur_pts2 = qrot(cur_quats2, pts) + cur_centers2 cur_pts1 = cur_pts1.unsqueeze(1).repeat(1, cur_part_cnt, 1, 1).view(-1, num_point, 3) cur_pts2 = cur_pts2.unsqueeze(0).repeat(cur_part_cnt, 1, 1, 1).view(-1, num_point, 3) dist1, dist2 = chamfer_distance(cur_pts1, cur_pts2, transpose=False) dist_mat = (dist1.mean(1) + dist2.mean(1)).view(cur_part_cnt, cur_part_cnt) rind, cind = linear_sum_assignment(dist_mat.cpu().numpy()) return rind, cind """ Input: B x P x 3, B x P x 3, B x P Output: B """ def get_trans_l2_loss(self, trans1, trans2, valids): loss_per_data = (trans1 - trans2).pow(2).sum(dim=-1) loss_per_data = (loss_per_data * valids).sum(1) / valids.sum(1) return loss_per_data """ Input: B x P x N x 3, B x P x 4, B x P x 4, B x P Output: B """ def get_rot_l2_loss(self, pts, quat1, quat2, valids): batch_size = pts.shape[0] num_point = pts.shape[2] pts1 = qrot(quat1.unsqueeze(2).repeat(1, 1, num_point, 1), pts) pts2 = qrot(quat2.unsqueeze(2).repeat(1, 1, num_point, 1), pts) loss_per_data = (pts1 - pts2).pow(2).sum(-1).mean(-1) loss_per_data = (loss_per_data * valids).sum(1) / valids.sum(1) return loss_per_data """ Input: B x P x N x 3, B x P x 4, B x P x 4, B x P Output: B """ def get_rot_cd_loss(self, pts, quat1, quat2, valids, device): batch_size = pts.shape[0] num_point = pts.shape[2] pts1 = qrot(quat1.unsqueeze(2).repeat(1, 1, num_point, 1), pts) pts2 = qrot(quat2.unsqueeze(2).repeat(1, 1, num_point, 1), pts) dist1, dist2 = chamfer_distance(pts1.view(-1, num_point, 3), pts2.view(-1, num_point, 3), transpose=False) loss_per_data = torch.mean(dist1, dim=1) + torch.mean(dist2, dim=1) loss_per_data = loss_per_data.view(batch_size, -1) loss_per_data = loss_per_data.to(device) loss_per_data = (loss_per_data * valids).sum(1) / valids.sum(1) return loss_per_data # def get_total_cd_loss(self, pts, quat1, quat2, valids, center1, center2, device): batch_size = pts.shape[0] num_part = pts.shape[1] num_point = pts.shape[2] center1 = center1.unsqueeze(2).repeat(1,1,num_point,1) center2 = center2.unsqueeze(2).repeat(1,1,num_point,1) pts1 = qrot(quat1.unsqueeze(2).repeat(1, 1, num_point, 1), pts) + center1 pts2 = qrot(quat2.unsqueeze(2).repeat(1, 1, num_point, 1), pts) + center2 dist1, dist2 = chamfer_distance(pts1.view(-1, num_point, 3), pts2.view(-1, num_point, 3), transpose=False) loss_per_data = torch.mean(dist1, dim=1) + torch.mean(dist2, dim=1) loss_per_data = loss_per_data.view(batch_size, -1) thre = 0.01 loss_per_data = loss_per_data.to(device) acc = [[0 for i in range(num_part)]for j in range(batch_size)] for i in range(batch_size): for j in range(num_part): if loss_per_data[i,j] < thre and valids[i,j]: acc[i][j] = 1 loss_per_data = (loss_per_data * valids).sum(1) / valids.sum(1) return loss_per_data , acc def get_shape_cd_loss(self, pts, quat1, quat2, valids, center1, center2, device): batch_size = pts.shape[0] num_part = pts.shape[1] num_point = pts.shape[2] center1 = center1.unsqueeze(2).repeat(1,1,num_point,1) center2 = center2.unsqueeze(2).repeat(1,1,num_point,1) pts1 = qrot(quat1.unsqueeze(2).repeat(1, 1, num_point, 1), pts) + center1 pts2 = qrot(quat2.unsqueeze(2).repeat(1, 1, num_point, 1), pts) + center2 pts1 = pts1.view(batch_size,num_part*num_point,3) pts2 = pts2.view(batch_size,num_part*num_point,3) dist1, dist2 = chamfer_distance(pts1, pts2, transpose=False) valids = valids.unsqueeze(2).repeat(1,1,1000).view(batch_size,-1) dist1 = dist1 * valids dist2 = dist2 * valids loss_per_data = torch.mean(dist1, dim=1) + torch.mean(dist2, dim=1) loss_per_data = loss_per_data.to(device) return loss_per_data def get_sym_point(self, point, x, y, z): if x: point[0] = - point[0] if y: point[1] = - point[1] if z: point[2] = - point[2] return point.tolist() def get_possible_point_list(self, point, sym): sym = torch.tensor([1.0, 1.0, 1.0]) point_list = [] if sym.equal(torch.tensor([0.0, 0.0, 0.0])): point_list.append(self.get_sym_point(point, 0, 0, 0)) elif sym.equal(torch.tensor([1.0, 0.0, 0.0])): point_list.append(self.get_sym_point(point, 0, 0, 0)) point_list.append(self.get_sym_point(point, 1, 0, 0)) elif sym.equal(torch.tensor([0.0, 1.0, 0.0])): point_list.append(self.get_sym_point(point, 0, 0, 0)) point_list.append(self.get_sym_point(point, 0, 1, 0)) elif sym.equal(torch.tensor([0.0, 0.0, 1.0])): point_list.append(self.get_sym_point(point, 0, 0, 0)) point_list.append(self.get_sym_point(point, 0, 0, 1)) elif sym.equal(torch.tensor([1.0, 1.0, 0.0])): point_list.append(self.get_sym_point(point, 0, 0, 0)) point_list.append(self.get_sym_point(point, 1, 0, 0)) point_list.append(self.get_sym_point(point, 0, 1, 0)) point_list.append(self.get_sym_point(point, 1, 1, 0)) elif sym.equal(torch.tensor([1.0, 0.0, 1.0])): point_list.append(self.get_sym_point(point, 0, 0, 0)) point_list.append(self.get_sym_point(point, 1, 0, 0)) point_list.append(self.get_sym_point(point, 0, 0, 1)) point_list.append(self.get_sym_point(point, 1, 0, 1)) elif sym.equal(torch.tensor([0.0, 1.0, 1.0])): point_list.append(self.get_sym_point(point, 0, 0, 0)) point_list.append(self.get_sym_point(point, 0, 1, 0)) point_list.append(self.get_sym_point(point, 0, 0, 1)) point_list.append(self.get_sym_point(point, 0, 1, 1)) else: point_list.append(self.get_sym_point(point, 0, 0, 0)) point_list.append(self.get_sym_point(point, 1, 0, 0)) point_list.append(self.get_sym_point(point, 0, 1, 0)) point_list.append(self.get_sym_point(point, 0, 0, 1)) point_list.append(self.get_sym_point(point, 1, 1, 0)) point_list.append(self.get_sym_point(point, 1, 0, 1)) point_list.append(self.get_sym_point(point, 0, 1, 1)) point_list.append(self.get_sym_point(point, 1, 1, 1)) return point_list def get_min_l2_dist(self, list1, list2, center1, center2, quat1, quat2): list1 = torch.tensor(list1) # m x 3 list2 = torch.tensor(list2) # n x 3 len1 = list1.shape[0] len2 = list2.shape[0] center1 = center1.unsqueeze(0).repeat(len1, 1) center2 = center2.unsqueeze(0).repeat(len2, 1) quat1 = quat1.unsqueeze(0).repeat(len1, 1) quat2 = quat2.unsqueeze(0).repeat(len2, 1) list1 = list1.to(self.conf.device) list2 = list2.to(self.conf.device) list1 = center1 + qrot(quat1, list1) list2 = center2 + qrot(quat2, list2) mat1 = list1.unsqueeze(1).repeat(1, len2, 1) mat2 = list2.unsqueeze(0).repeat(len1, 1, 1) mat = (mat1 - mat2) * (mat1 - mat2) mat = mat.sum(dim=-1) return mat.min() """ Contact point loss metric Date: 2020/5/22 Input B x P x 3, B x P x 4, B x P x P x 4, B x P x 3 Ouput B """ def get_contact_point_loss(self, center, quat, contact_points, sym_info): batch_size = center.shape[0] num_part = center.shape[1] contact_point_loss = torch.zeros(batch_size) total_num = 0 count = 0 for b in range(batch_size): sum_loss = 0 for i in range(num_part): for j in range(num_part): if contact_points[b, i, j, 0]: contact_point_1 = contact_points[b, i, j, 1:] contact_point_2 = contact_points[b, j, i, 1:] sym1 = sym_info[b, i] sym2 = sym_info[b, j] point_list_1 = self.get_possible_point_list(contact_point_1, sym1) point_list_2 = self.get_possible_point_list(contact_point_2, sym2) dist = self.get_min_l2_dist(point_list_1, point_list_2, center[b, i, :], center[b, j, :], quat[b, i, :], quat[b, j, :]) # 1 if dist < 0.01: count += 1 total_num += 1 sum_loss += dist contact_point_loss[b] = sum_loss return contact_point_loss, count, total_num
2.21875
2
tests/test_dynamo.py
FernandoGarzon/dmwmclient
1
12788947
import pytest from dmwmclient import Client @pytest.mark.asyncio async def test_cycle(): client = Client() dynamo = client.dynamo cycle = await dynamo.latest_cycle() assert type(cycle) is dict assert set(cycle.keys()) == {'cycle', 'partition_id', 'timestamp', 'comment'} @pytest.mark.asyncio async def test_detail(): client = Client() dynamo = client.dynamo df = await dynamo.site_detail('T2_PK_NCP', 34069) assert set(df.columns) == {'condition', 'condition_id', 'decision', 'name', 'site', 'size'} assert df.sum()['size'] == 99787.18272119202
2.109375
2
tests/cases/base.py
chop-dbhi/varify
6
12788948
<gh_stars>1-10 from django.contrib.auth.models import User from django.core.cache import cache from django_rq import get_queue, get_connection from rq.queue import get_failed_queue from django.test import TestCase, TransactionTestCase class AuthenticatedBaseTestCase(TestCase): def setUp(self): self.user = User.objects.create_user(username='test', password='<PASSWORD>') self.client.login(username='test', password='<PASSWORD>') class QueueTestCase(TransactionTestCase): def setUp(self): cache.clear() get_queue('variants').empty() get_queue('default').empty() get_failed_queue(get_connection()).empty()
2.015625
2
avalonbot/cards.py
AvantiShri/avalon-bot
0
12788949
from __future__ import division, print_function, absolute_import import random from collections import OrderedDict #Mimicking Enums, but having the values be strings class CardType(object): LOYAL_SERVANT_OF_ARTHUR="LOYAL_SERVANT_OF_ARTHUR" MERLIN="MERLIN" PERCIVAL="PERCIVAL" MINION_OF_MORDRED="MINION_OF_MORDRED" ASSASSIN="ASSASSIN" MORGANA="MORGANA" MORDRED="MORDRED" OBERON="OBERON" #Mimicking Enums, but having the values be strings class Team(object): GOOD="GOOD" EVIL="EVIL" class Card(object): def __init__(self, team, card_type, special_abilities): self.team = team self.card_type = card_type self.special_abilities = special_abilities def get_additional_info_to_provide_to_player(self, game): raise NotImplementedError() def get_card_summary(self): return OrderedDict([("Team", str(self.team)), ("Special abilities", self.special_abilities)]) class LoyalServantOfArthur(Card): def __init__(self): Card.__init__(self, team=Team.GOOD, card_type=CardType.LOYAL_SERVANT_OF_ARTHUR, special_abilities="This card has no special abilities.") def get_additional_info_to_provide_to_player(self, game): return ("As a loyal servant, you don't have any additional info" +" beyond what the other cards in the game are. Review those" +" cards and their abilities to strategize.") class Merlin(Card): def __init__(self): Card.__init__(self, team=Team.GOOD, card_type=CardType.MERLIN, special_abilities=("You will be given information on who" +" the players on the evil team are, *with the exception of" +" MORDRED* (if MORDRED is present in the game)." +" You will not know the specific roles of the players on the" +" evil team you are told about. If PERCIVAL is in the game, your" +" identity will be made known to them - however, if" +" MORGANA is also in the game, PERCIVAL will also be given" +" MORGANA's name and won't be told which of the two of you" +" is the real MERLIN. You should not be too" +" obvious about being MERLIN or else the evil team will win by" +" assassinating you at the end.")) def get_additional_info_to_provide_to_player(self, game): evil_team_players = [] for player in game.players: if (player.card.team==Team.EVIL and player.card.card_type != CardType.MORDRED): evil_team_players.append(player) return ("You know that the following players are on the evil team: " +", ".join(sorted(str(x) for x in evil_team_players))) class Percival(Card): def __init__(self): Card.__init__(self, team=Team.GOOD, card_type=CardType.PERCIVAL, special_abilities=( "You will be given the names of players who are either" +" MORGANA or MERLIN, but you will not be told who is who." +" Note that if MORGANA is absent from the game, you will just" +" be given the name of MERLIN.")) def get_additional_info_to_provide_to_player(self, game): morgana_or_merlin = [] for player in game.players: if (player.card.card_type==CardType.MORGANA or player.card.card_type==CardType.MERLIN): morgana_or_merlin.append(player) return ("These players are either MORGANA or MERLIN" +" (if MORGANA is absent from the game, this is just" +" the name of MERLIN): " +" & ".join(sorted(str(x) for x in morgana_or_merlin))) class BadGuy(Card): def __init__(self, card_type, special_abilities): Card.__init__(self, team=Team.EVIL, card_type=card_type, special_abilities=special_abilities) def get_additional_info_to_provide_to_player(self, game): evil_team_players = [] for player in game.players: if (player.card.team==Team.EVIL and player.card.card_type != CardType.OBERON): evil_team_players.append(player) return ("You know that the following players are on the evil team: " +", ".join(sorted(str(x) for x in evil_team_players))) class Assassin(BadGuy): def __init__(self): super(Assassin, self).__init__( card_type=CardType.ASSASSIN, special_abilities = ( "At the end of the game, you will take the" +" final call on who MERLIN is likely to be. If you guess right," +" the evil team wins.")) class MinionOfMordred(BadGuy): def __init__(self): super(MinionOfMordred, self).__init__( card_type=CardType.MINION_OF_MORDRED, special_abilities="This card has no special abilities.") class Morgana(BadGuy): def __init__(self): super(Morgana, self).__init__( card_type=CardType.MORGANA, special_abilities = ( "PERCIVAL will be given your" +" name along with the name of the person playing MERLIN, but" +" PERCIVAL will not be told who is who. You should try to figure" +" out who PERCIVAL is and convince PERCIVAL that you are MERLIN.")) class Mordred(BadGuy): def __init__(self): super(Mordred, self).__init__( card_type=CardType.MORDRED, special_abilities=( "MERLIN does not know you are on the evil team" +" (this is a major advantage for the evil team).")) class Oberon(BadGuy): def __init__(self): super(Oberon, self).__init__( card_type=CardType.OBERON, special_abilities = ( "The other players on the evil team won't know that" +" you are also on the evil team, and similarly, you don't" +" know who the other players on the evil team are (this is a" +" disadvantage for the evil team).")) def get_additional_info_to_provide_to_player(self, game): return ("None - unlike all the other players on the evil team," +" you don't have any information about who is on the" +" evil team!") card_type_to_class = { "LOYAL_SERVANT_OF_ARTHUR": LoyalServantOfArthur, "MERLIN": Merlin, "PERCIVAL": Percival, "MINION_OF_MORDRED": MinionOfMordred, "ASSASSIN": Assassin, "MORGANA": Morgana, "MORDRED": Mordred, "OBERON": Oberon }
3.46875
3
utils/__init__.py
mpi2/impc-reference-harvester
0
12788950
import configparser import os import logging dirname = os.path.dirname(__file__) filename = os.path.join(dirname, '../config.ini') config = configparser.ConfigParser() config.read_file(open(filename)) logger = logging.getLogger() handler = logging.StreamHandler() formatter = logging.Formatter( '%(asctime)s %(name)-12s %(levelname)-8s %(message)s') handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(logging.INFO)
2.265625
2