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import os import sys import keras import matplotlib.pyplot as plt import numpy as np import pandas as pd import tensorflow as tf from keras.callbacks import CSVLogger, History from keras.layers import BatchNormalization, Dense, Dropout, Input from keras.models import Model # from .IntegratedGradient import integrated_gradients """ Created by <NAME> on 6/15/18. Email : <EMAIL> or <EMAIL> Website: http://ce.sharif.edu/~naghipourfar Github: https://github.com/naghipourfar Skype: mn7697np """ def loadGradients(path="../Results/IntegratedGradient/integrated_gradients.csv"): return pd.read_csv(path, header=None) def save_summaries_for_each_feature(feature_importance, path="./IntegratedGradient/Summaries/"): for i in range(feature_importance.shape[1]): description = feature_importance[i].describe() description.to_csv(path + "feature_{0}.txt".format(i)) def analyze_top_100_features_for_each_sample(feature_importance): top_importances = [] for i in range(feature_importance.shape[0]): importances = feature_importance.iloc[i, :] importances = list(reversed(sorted(abs(importances)))) top_100_importances = importances[:100] top_importances.append(top_100_importances) np.savetxt(fname="./top100_deeplift.csv", X=np.array(top_importances), delimiter=',') def plot_heatmaps(feature_importances, path="./IntegratedGradient/Heatmaps/"): plt.rcParams["figure.figsize"] = 5, 2 for i in range(feature_importances.shape[0]): y = feature_importances.iloc[i, :] fig, ax = plt.subplots(nrows=1, sharex='all') # extent = [x[0] - (x[1] - x[0]) / 2., x[-1] + (x[1] - x[0]) / 2., 0, 1] heatmap = ax.imshow(y[np.newaxis, :], cmap="plasma", aspect="auto") ax.set_yticks([]) # ax.set_xlim(extent[0], extent[1]) plt.tight_layout() plt.colorbar(heatmap) plt.savefig(path + "sample_{0}.png".format(i)) plt.close() def plot_distributions(feature_importance, path="../Results/IntegratedGradient/DistPlots/"): import seaborn as sns for i in range(feature_importance.shape[1]): plt.figure() sns.distplot(feature_importance[i]) plt.xlabel("Feature Importance") plt.ylabel("Density") plt.title("Feature_{0} Distribution of Importance".format(i)) plt.savefig(path + "feature_{0}.png".format(i)) plt.close() def plot_distribution(feature_importance, path="../Results/IntegratedGradient/"): file_name = "distribution.png" feature_importance = feature_importance.as_matrix() # Convert to numpy ndarray new_shape = (feature_importance.shape[0] * feature_importance.shape[1],) feature_importance = np.reshape(feature_importance, newshape=new_shape) import seaborn as sns sns.distplot(feature_importance) plt.xlabel("Feature Importance") plt.ylabel("Density") plt.title("Distribution of all feature importances") plt.savefig(path + file_name) plt.close() def box_plot(feature_importance, path="../Results/IntegratedGradient/"): pass def calculate_statistical_criteria(feature_importance=None, criteria="absolute_error", path="../Results/IntegratedGradient/"): file_name = "intgrad_" + criteria + ".csv" feature_importance = feature_importance.as_matrix() # Convert to np.ndarray statistical_criteria = np.zeros(shape=(feature_importance.shape[1], 1)) if criteria == "absolute_error": num_features = feature_importance.shape[1] statistical_criteria = np.array([[np.max(feature_importance[:, i]) - np.min( feature_importance[:, i])] for i in range(num_features)]) elif criteria == "relative_error": statistical_criteria = np.array([[(np.max(feature_importance[:, i]) - np.min( feature_importance[:, i])) / (np.max(feature_importance[:, i]))] for i in range(feature_importance.shape[1])]) np.savetxt(fname=path + file_name, X=statistical_criteria, delimiter=",") def plot_statistical_criteria(criteria="absolute_error", data_path="../Results/IntegratedGradient/", save_path="../Results/IntegratedGradient/"): data_path = data_path + "intgrad_" + criteria + ".csv" save_path = save_path + "intgrad_" + criteria + ".png" statistical_criteria = pd.read_csv(data_path, header=None).as_matrix() import seaborn as sns sns.distplot(statistical_criteria) if criteria == "absolute_error": plt.xlabel("Absolute Error") plt.title("Distribution of Absolute Error") elif criteria == "relative_error": plt.xlabel("Relative Error") plt.title("Distribution of Relative Error") plt.ylabel("Density") plt.savefig(save_path) plt.close() def make_summary_data(feature_importance, path="../Results/IntegratedGradient/"): file_name = "summaries.csv" feature_importance = feature_importance num_features = feature_importance.shape[1] all_describtions = np.zeros( shape=(num_features, 4)) # mean - std - min - max for i in range(num_features): describtion = feature_importance[i].describe() describtion = describtion.iloc[[1, 2, 3, 7]].as_matrix() all_describtions[i] = describtion.T print(all_describtions.shape) np.savetxt(fname=path + file_name, X=all_describtions, delimiter=',') def compute_integrated_gradient(machine="damavand", save_path="../Results/IntegratedGradient/", verbose=1): file_name = "integrated_gradient.csv" if machine == "damavand": mrna_address = "~/f/Behrooz/dataset_local/fpkm_normalized.csv" else: mrna_address = "../Data/fpkm_normalized.csv" m_rna = pd.read_csv(mrna_address, header=None) model = keras.models.load_model("../Results/classifier.h5") ig = integrated_gradients(model, verbose=verbose) num_samples = m_rna.shape[0] num_features = m_rna.shape[1] feature_importances = np.zeros(shape=(num_samples, num_features)) for i in range(num_samples): feature_importances[i] = ig.explain(m_rna.as_matrix()[i, :]) if verbose == 1: sys.stdout.flush() sys.stdout.write('\r') sys.stdout.write("Progress: " + str((i / 10787) * 100) + " %") sys.stdout.flush() if verbose == 1: sys.stdout.flush() sys.stdout.write('\r') sys.stdout.write("Progress: " + str((10787 / 10787) * 100) + " %") sys.stdout.flush() np.savetxt(fname="../Results/IntegratedGradient/integrated_gradients.csv", X=np.array(feature_importances), delimiter=',') return pd.DataFrame(feature_importances) machine = "local" if __name__ == '__main__': general_path = "../Results/IntegratedGradient/" data_path = general_path + "integrated_gradients.csv" summary_path = general_path + "summary.csv" distplot_path = general_path + "distribution.png" if os.path.exists(data_path): feature_importance = loadGradients(path=data_path) print("Data has been loaded!") else: feature_importance = compute_integrated_gradient( machine=machine, save_path=data_path, verbose=1) print("Data has been computed and saved!") # plot_distribution(feature_importance, path=distplot_path) # print("General Distribution has been drawn!") calculate_statistical_criteria( feature_importance, criteria="absolute_error") print("Statistical Criteria AE Calculation has been finished!") plot_statistical_criteria(criteria="absolute_error") print("Statistical Criteria AE Distribution plot has been drawn!") # calculate_statistical_criteria(None, criteria="relative_error") # print("Statistical Criteria RE Calculation has been finished!") # plot_statistical_criteria(criteria="relative_error") # print("Statistical Criteria RE Distribution plot has been drawn!") # make_summary_data(feature_importance) # print("Summary of all features has been made!") print("Finished!")
StarcoderdataPython
3209562
# -*- coding: utf-8 -*- import simpy, scipy, numpy, random from src.core.linear_congruential_generator import LinearCongruentialGenerator RANDOM_SEED = 42 NUM_COMPONENTES_INDEPENDENTES = 2 QUANTIDADE_TESTES = 5 TEMPO_SIMULACAO = 7 * 24 def tef_uniform(): """return a random value from uniform distribuition""" return scipy.random.uniform(0.0, 8.0) # hours def tef_expo(): """return a random value from exponential distribuition""" return scipy.random.standard_exponential(10) # hours def tempo_do_equipamento_funcionando(): """Return actual processing time for a concrete part.""" return random.normalvariate(10.0, 2.0) def tef(componente_id): """Return time until next failure for a machine.""" if componente_id == 1: return tef_uniform() # TEF COMPONENTE A elif componente_id == 2: return random.expovariate(10) # TEF COMPONENTE B def calcular_z(r1, r2): return numpy.sqrt(-2 * numpy.log(r1)) * numpy.sin(2 * 180 * r2) + numpy.random.randint(8, 10) class EquipamentoDoisComponentesIndependentes(object): def __init__(self, env, name, equipamento_funcionando): self.env = env self.name = name self.tempo_entre_falhas_total = 0 self.broken = False self.componente_A = 1 self.componente_B = 2 # Start "working" and "break_machine" processes for this machine. self.process = env.process(self.working(equipamento_funcionando)) env.process(self.break_machine(self.componente_A)) env.process(self.break_machine(self.componente_B)) def working(self, reparador_de_componente): lcg = LinearCongruentialGenerator() while True: r1 = lcg.generate_random_numbers(1).pop() r2 = lcg.generate_random_numbers(1, initial_seed=r1).pop() + 10 # Gero um x aleatorio usando o Linear Congruential Generator done_in = abs(calcular_z(r1, r2)) # Espero um componente voltar do tempo falhando (simulacao desse evento, periodo falhando, evento) while done_in: try: start = self.env.now yield self.env.timeout(done_in) done_in = 0 except simpy.Interrupt: self.broken = True done_in -= self.env.now - start # Salva o tempo em que o componente esteve falhando self.tempo_entre_falhas_total += done_in # Chama um reparador de componente para faze voltar a funcionar with reparador_de_componente.request(priority=1) as req: yield req yield self.env.timeout(tempo_do_equipamento_funcionando()) self.broken = False def break_machine(self, componente_id): # Funcao de quebra de componente # a funcao tef sendo chamada abaixo leva em consideracao o tipo de componente # se for o componente que segue uma distribuicao uniforme em horas vai ser usado tef_uniform para esse componente # do contrario vai ser usado tef_expo para o outro componente do equipamento while True: yield self.env.timeout(tef(componente_id)) if not self.broken: # Only break the machine if it is currently working. self.process.interrupt() # Analyis/results print('Equipamento - 2 Componentes Independentes\n') print('Resultados depois de %s testes, cada teste de 1 semana (em horas).\n' % QUANTIDADE_TESTES) media_tempo_falhas = 0 for teste_semanal in range(QUANTIDADE_TESTES): # Setup and start the simulation random.seed(RANDOM_SEED) # This helps reproducing the results # Create an environment and start the setup process env = simpy.Environment() equipamento_funcionando = simpy.PreemptiveResource(env, capacity=1) equipamento = EquipamentoDoisComponentesIndependentes(env, 'Equipamento %d', equipamento_funcionando) # Execute! env.run(until=TEMPO_SIMULACAO) print('%s no teste nro %d executou 7 [dias] * 24 [horas] {= %d horas}, falhou %d [horas].\n' % (equipamento.name, teste_semanal, TEMPO_SIMULACAO, equipamento.tempo_entre_falhas_total)) media_tempo_falhas += equipamento.tempo_entre_falhas_total print('Em media o equipamento falha por %d / semana' % (media_tempo_falhas / QUANTIDADE_TESTES)) #Equipamento - 2 Componentes Independentes #Resultados depois de 5 testes, cada teste de 1 semana (em horas). #Equipamento %d no teste nro 0 executou 7 [dias] * 24 [horas] {= 168 horas}, falhou 68 [horas]. #Equipamento %d no teste nro 1 executou 7 [dias] * 24 [horas] {= 168 horas}, falhou 85 [horas]. #Equipamento %d no teste nro 2 executou 7 [dias] * 24 [horas] {= 168 horas}, falhou 64 [horas]. #Equipamento %d no teste nro 3 executou 7 [dias] * 24 [horas] {= 168 horas}, falhou 84 [horas]. #Equipamento %d no teste nro 4 executou 7 [dias] * 24 [horas] {= 168 horas}, falhou 84 [horas]. #Em media o equipamento falha por 77 / semana #Process finished with exit code 0
StarcoderdataPython
47041
"""Tests for RandoPony admin views and functionality. """ from datetime import datetime import unittest from unittest.mock import patch from pyramid import testing from pyramid_mailer import get_mailer from sqlalchemy import create_engine from randopony.models.meta import ( Base, DBSession, ) class TestCoreAdminViews(unittest.TestCase): """Unit tests for core admin interface views. """ def _get_target_class(self): from randopony.views.admin.core import AdminViews return AdminViews def _make_one(self, *args, **kwargs): return self._get_target_class()(*args, **kwargs) def setUp(self): self.config = testing.setUp() engine = create_engine('sqlite://') DBSession.configure(bind=engine) Base.metadata.create_all(engine) def tearDown(self): DBSession.remove() testing.tearDown() def test_home(self): """admin home view has expected template variables """ from randopony import __pkg_metadata__ as version request = testing.DummyRequest() admin = self._make_one(request) tmpl_vars = admin.home() self.assertEqual( tmpl_vars, {'version': version.number + version.release}) def test_wranglers_list(self): """admin wranglers view has expected template variables """ from randopony import __pkg_metadata__ as version request = testing.DummyRequest() request.matchdict['list'] = 'wranglers' admin = self._make_one(request) tmpl_vars = admin.items_list() self.assertEqual( tmpl_vars['version'], version.number + version.release) self.assertEqual(tmpl_vars['list'], 'wranglers') self.assertEqual(tmpl_vars['list_title'], 'Pony Wranglers') self.assertEqual(tmpl_vars['action'], 'edit') def test_wranglers_list_order(self): """admin wranglers list is alpha ordered by email """ from randopony.models import Administrator admin1 = Administrator(email='<EMAIL>', password_hash='<PASSWORD>') admin2 = Administrator(email='<EMAIL>', password_hash='<PASSWORD>') DBSession.add_all((admin1, admin2)) request = testing.DummyRequest() request.matchdict['list'] = 'wranglers' admin = self._make_one(request) tmpl_vars = admin.items_list() admins = [a.email for a in tmpl_vars['items'].all()] self.assertEqual( admins, '<EMAIL> <EMAIL>'.split()) def test_delete_cancel(self): """admin delete cancel leaves item in database """ from randopony.models import Administrator admin = Administrator(email='<EMAIL>', password_hash='<PASSWORD>') DBSession.add(admin) self.config.add_route('admin.list', '/admin/{list}/') request = testing.DummyRequest(post={'cancel': 'cancel'}) request.matchdict['list'] = 'wranglers' request.matchdict['item'] = '<EMAIL>' admin = self._make_one(request) admin.delete() wrangler = DBSession.query(Administrator).first() self.assertEqual(wrangler.email, '<EMAIL>') def test_delete_wrangler_confirmation(self): """admin delete confirmation view for wrangler has exp template vars """ from randopony import __pkg_metadata__ as version self.config.add_route('admin.list', '/admin/{list}/') request = testing.DummyRequest() request.matchdict['list'] = 'wranglers' request.matchdict['item'] = '<EMAIL>' admin = self._make_one(request) tmpl_vars = admin.delete() self.assertEqual( tmpl_vars, { 'version': version.number + version.release, 'list': 'wranglers', 'item': '<EMAIL>', 'item_type': 'administrator', }) def test_delete_wrangler(self): """admin delete for wrangler deletes item from database """ from sqlalchemy.orm.exc import NoResultFound from randopony.models import Administrator admin = Administrator(email='<EMAIL>', password_hash='<PASSWORD>') DBSession.add(admin) self.config.add_route('admin.list', '/admin/{list}/') request = testing.DummyRequest(post={'delete': 'delete'}) request.matchdict['list'] = 'wranglers' request.matchdict['item'] = '<EMAIL>' admin = self._make_one(request) admin.delete() query = DBSession.query(Administrator) with self.assertRaises(NoResultFound): query.filter_by(email='<EMAIL>').one() def test_delete_brevet(self): """admin delete for brevet deletes item from database """ from sqlalchemy.orm.exc import NoResultFound from randopony.models import core from randopony.models import Brevet brevet = Brevet( region='LM', distance=200, date_time=datetime(2012, 11, 11, 7, 0, 0), route_name='11th Hour', start_locn='Bean Around the World Coffee, Lonsdale Quay, ' '123 Carrie Cates Ct, North Vancouver', organizer_email='<EMAIL>', ) DBSession.add(brevet) self.config.add_route('admin.list', '/admin/{list}/') request = testing.DummyRequest(post={'delete': 'delete'}) request.matchdict['list'] = 'brevets' request.matchdict['item'] = str(brevet) with patch.object(core, 'datetime') as mock_datetime: mock_datetime.today.return_value = datetime(2012, 11, 1, 12, 55, 42) admin = self._make_one(request) admin.delete() with self.assertRaises(NoResultFound): Brevet.get_current().one() class TestEmailToOrganizer(unittest.TestCase): """Unit tests for email_to_organizer admin function re: event URLs. """ def _call_email_to_organizer(self, *args, **kwargs): from randopony.views.admin.core import email_to_organizer return email_to_organizer(*args, **kwargs) def setUp(self): from randopony.models import EmailAddress self.config = testing.setUp( settings={ 'mako.directories': 'randopony:templates', }) self.config.include('pyramid_mailer.testing') self.config.include('pyramid_mako') self.config.add_route( 'admin.populaires.view', '/admin/brevet/{item}') self.config.add_route( 'brevet', '/brevets/{region}/{distance}/{date}') self.config.add_route( 'brevet.rider_emails', '/brevets/{region}/{distance}/{date}/rider_emails/{uuid}') engine = create_engine('sqlite://') DBSession.configure(bind=engine) Base.metadata.create_all(engine) from_randopony = EmailAddress( key='from_randopony', email='<EMAIL>', ) admin_email = EmailAddress( key='admin_email', email='<EMAIL>', ) DBSession.add_all((from_randopony, admin_email)) def tearDown(self): DBSession.remove() testing.tearDown() def test_email_to_organizer_catches_missing_google_doc_id(self): """email_to_organizer return error flash if google_doc_id not set """ from randopony.models import Brevet brevet = Brevet( region='VI', distance=200, date_time=datetime(2013, 3, 3, 7, 0), route_name='Chilly 200', start_locn='<NAME>, 3131 Millgrove St, Victoria', organizer_email='<EMAIL>', registration_end=datetime(2013, 3, 2, 12, 0), ) DBSession.add(brevet) request = testing.DummyRequest() request.matchdict.update({ 'region': 'VI', 'distance': '200', 'date': '03Mar2013', }) date = '03Mar2013' event_page_url = request.route_url( 'brevet', region=brevet.region, distance=brevet.distance, date=date) rider_emails_url = request.route_url( 'brevet.rider_emails', region=brevet.region, distance=brevet.distance, date=date, uuid=brevet.uuid) flash = self._call_email_to_organizer( request, brevet, event_page_url, rider_emails_url) self.assertEqual( flash, [ 'error', 'Google Drive rider list must be created before email to ' 'organizer(s) can be sent' ]) def test_email_to_organizer_sends_email(self): """email_to_organizer sends message & sets expected flash message """ from randopony.models import Brevet brevet = Brevet( region='VI', distance=200, date_time=datetime(2013, 3, 3, 7, 0), route_name='<NAME>', start_locn='<NAME>, 3131 Millgrove St, Victoria', organizer_email='<EMAIL>', registration_end=datetime(2013, 3, 2, 12, 0), google_doc_id='spreadsheet:1234', ) DBSession.add(brevet) request = testing.DummyRequest() request.matchdict.update({ 'region': 'VI', 'distance': '200', 'date': '03Mar2013', }) date = '03Mar2013' event_page_url = request.route_url( 'brevet', region=brevet.region, distance=brevet.distance, date=date) rider_emails_url = request.route_url( 'brevet.rider_emails', region=brevet.region, distance=brevet.distance, date=date, uuid=brevet.uuid) mailer = get_mailer(request) flash = self._call_email_to_organizer( request, brevet, event_page_url, rider_emails_url) self.assertEqual(len(mailer.outbox), 1) self.assertEqual( flash, ['success', 'Email sent to VI200 03Mar2013 organizer(s)']) def test_email_to_organizer_message(self): """email_to_organizer message has expected content """ from randopony.models import ( EmailAddress, Brevet, ) brevet = Brevet( region='VI', distance=200, date_time=datetime(2013, 3, 3, 7, 0), route_name='Chilly 200', start_locn='<NAME>, 3131 Millgrove St, Victoria', organizer_email='<EMAIL>', registration_end=datetime(2013, 3, 2, 12, 0), google_doc_id='spreadsheet:123' ) DBSession.add(brevet) request = testing.DummyRequest() request.matchdict.update({ 'region': 'VI', 'distance': '200', 'date': '03Mar2013', }) date = '03Mar2013' event_page_url = request.route_url( 'brevet', region=brevet.region, distance=brevet.distance, date=date) rider_emails_url = request.route_url( 'brevet.rider_emails', region=brevet.region, distance=brevet.distance, date=date, uuid=brevet.uuid) mailer = get_mailer(request) self._call_email_to_organizer( request, brevet, event_page_url, rider_emails_url) msg = mailer.outbox[0] self.assertEqual(msg.subject, 'RandoPony URLs for VI200 03Mar2013') from_randopony = ( DBSession.query(EmailAddress) .filter_by(key='from_randopony').first().email) self.assertEqual(msg.sender, from_randopony) self.assertEqual(msg.recipients, ['<EMAIL>']) self.assertIn( 'The URL is <http://example.com/brevets/VI/200/03Mar2013>.', msg.body) self.assertIn( 'rider list URL is <https://spreadsheets.google.com/ccc?key=123>.', msg.body) self.assertIn( 'email address list URL is <http://example.com/brevets/' 'VI/200/03Mar2013/rider_emails/' 'ba8e8e00-dd42-5c6c-9b30-b65ce9c8df26>.', msg.body) self.assertIn( 'Pre-registration on the pony closes at 12:00 on 2013-03-02', msg.body) self.assertIn('send email to <<EMAIL>>.', msg.body) def test_email_to_organizer_multi_organizer(self): """email to organizer has expected to list for multi-organizer event """ from randopony.models import Brevet brevet = Brevet( region='VI', distance=200, date_time=datetime(2013, 3, 3, 7, 0), route_name='Chilly 200', start_locn='Chez Croy, 3131 Millgrove St, Victoria', organizer_email='<EMAIL>, <EMAIL>', registration_end=datetime(2013, 3, 2, 12, 0), google_doc_id='spreadsheet:1234' ) DBSession.add(brevet) request = testing.DummyRequest() request.matchdict.update({ 'region': 'VI', 'distance': '200', 'date': '03Mar2013', }) date = '03Mar2013' event_page_url = request.route_url( 'brevet', region=brevet.region, distance=brevet.distance, date=date) rider_emails_url = request.route_url( 'brevet.rider_emails', region=brevet.region, distance=brevet.distance, date=date, uuid=brevet.uuid) mailer = get_mailer(request) self._call_email_to_organizer( request, brevet, event_page_url, rider_emails_url) msg = mailer.outbox[0] self.assertEqual( msg.recipients, ['<EMAIL>', '<EMAIL>']) class TestEmailToWebmaster(unittest.TestCase): """Unit tests for email_to_webmaster admin function re: event page URL. """ def _call_email_to_webmaster(self, *args, **kwargs): from randopony.views.admin.core import email_to_webmaster return email_to_webmaster(*args, **kwargs) def setUp(self): from randopony.models import EmailAddress self.config = testing.setUp( settings={ 'mako.directories': 'randopony:templates', }) self.config.include('pyramid_mailer.testing') self.config.include('pyramid_mako') self.config.add_route( 'admin.populaires.view', '/admin/populaire/{item}') self.config.add_route( 'populaire', '/populaires/{short_name}') engine = create_engine('sqlite://') DBSession.configure(bind=engine) Base.metadata.create_all(engine) from_randopony = EmailAddress( key='from_randopony', email='<EMAIL>', ) club_webmaster = EmailAddress( key='club_webmaster', email='<EMAIL>', ) admin_email = EmailAddress( key='admin_email', email='<EMAIL>', ) DBSession.add_all((from_randopony, club_webmaster, admin_email)) def tearDown(self): DBSession.remove() testing.tearDown() def test_email_to_webmaster_sends_email(self): """email_to_webmaster sends message & sets expected flash message """ from randopony.models import Populaire populaire = Populaire( event_name='Victoria Populaire', short_name='VicPop', distance='50 km, 100 km', date_time=datetime(2011, 3, 27, 10, 0), start_locn='University of Victoria, Parking Lot #2 ' '(Gabriola Road, near McKinnon Gym)', organizer_email='<EMAIL>', registration_end=datetime(2011, 3, 24, 12, 0), entry_form_url='http://www.randonneurs.bc.ca/VicPop/' 'VicPop11_registration.pdf', google_doc_id='spreadsheet:1234' ) DBSession.add(populaire) request = testing.DummyRequest() request.matchdict['item'] = 'VicPop' event_page_url = request.route_url( 'populaire', short_name=populaire.short_name) mailer = get_mailer(request) flash = self._call_email_to_webmaster( request, populaire, event_page_url) self.assertEqual(len(mailer.outbox), 1) self.assertEqual( flash, ['success', 'Email with VicPop page URL sent to webmaster']) def test_email_to_webmaster_message(self): """email_to_webmaster message has expected content """ from randopony.models import Populaire populaire = Populaire( event_name='Victoria Populaire', short_name='VicPop', distance='50 km, 100 km', date_time=datetime(2011, 3, 27, 10, 0), start_locn='University of Victoria, Parking Lot #2 ' '(Gabriola Road, near McKinnon Gym)', organizer_email='<EMAIL>', registration_end=datetime(2011, 3, 24, 12, 0), entry_form_url='http://www.randonneurs.bc.ca/VicPop/' 'VicPop11_registration.pdf', google_doc_id='spreadsheet:1234' ) DBSession.add(populaire) request = testing.DummyRequest() request.matchdict['item'] = 'VicPop' event_page_url = request.route_url( 'populaire', short_name=populaire.short_name) mailer = get_mailer(request) self._call_email_to_webmaster(request, populaire, event_page_url) msg = mailer.outbox[0] self.assertEqual( msg.subject, 'RandoPony Pre-registration page for VicPop') self.assertEqual(msg.sender, '<EMAIL>') self.assertEqual(msg.recipients, ['<EMAIL>']) self.assertIn('page for the VicPop event has been added', msg.body) self.assertIn( 'The URL is <http://example.com/populaires/VicPop>.', msg.body) self.assertIn('send email to <<EMAIL>>.', msg.body) class TestFinalizeFlashMsg(unittest.TestCase): """Unit tests for finalize_flash_msg function. """ def _call_finalize_flash_msg(self, *args, **kwargs): from randopony.views.admin.core import finalize_flash_msg return finalize_flash_msg(*args, **kwargs) def test_finalize_flash_msg_error(self): """flash 1st element is error when error present in flash list """ request = testing.DummyRequest() self._call_finalize_flash_msg(request, 'success foo error bar'.split()) flash = request.session.pop_flash() self.assertEqual(flash[0], 'error') def test_finalize_flash_msg_success(self): """flash 1st element is success when error not present in flash list """ request = testing.DummyRequest() self._call_finalize_flash_msg( request, 'success foo sucess bar'.split()) flash = request.session.pop_flash() self.assertEqual(flash[0], 'success') def test_finalize_flash_msg_content(self): """flash[1:] are msgs w/o error or success elements of flash list """ request = testing.DummyRequest() self._call_finalize_flash_msg(request, 'success foo error bar'.split()) flash = request.session.pop_flash() self.assertEqual(flash[1:], 'foo bar'.split())
StarcoderdataPython
3349997
#!usr/bin/env python # Python Imports import os import time from tkinter import Tk, Button, Label, filedialog # Third party modules import cv2 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt from PIL import Image, ImageTk from mtcnn.mtcnn import MTCNN # Developer metadata. __author__ = "<NAME>." __copyright__ = "Copyright 2020, <NAME>." __credits__ = "<NAME>." __license__ = "MIT" __version__ = "1.0" __maintainer__ = "<NAME>." __status__ = "Prototype" # Global Functions. # I dislike doing this, but Tkinter leaves me no choice. root = Tk() image_panel = Label(root, image='') female = Label(root, text='') male = Label(root, text='') # Constants IMAGE_WIDTH = 500 IMAGE_HEIGHT = 500 def load_labels(filename): with open(filename, 'r') as f: return [line.strip() for line in f.readlines()] def analyze_face(): interpreter = tf.lite.Interpreter( model_path=os.path.join('models', 'gender-recognition.tflite')) interpreter.allocate_tensors() input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() # Check the type of the input tensor. floating_model = input_details[0]['dtype'] == np.float32 # NxHxWxC, H:1, W:2 height = input_details[0]['shape'][1] width = input_details[0]['shape'][2] image = Image.open('tmp.jpg').resize((width, height)) # add N dim input_data = np.expand_dims(image, axis=0) if floating_model: input_data = (np.float32(input_data) - 127.5) / 127.5 interpreter.set_tensor(input_details[0]['index'], input_data) start_time = time.time() interpreter.invoke() stop_time = time.time() output_data = interpreter.get_tensor(output_details[0]['index']) results = np.squeeze(output_data) top_k = results.argsort()[-5:][::-1] labels = load_labels(os.path.join('models', 'labels.txt')) print(labels) for i in top_k: if floating_model: print('{:08.6f}: {}'.format(float(results[i]), labels[i])) else: print('{:08.6f}: {}'.format(float(results[i] / 255.0), labels[i])) print('Time: {:.3f}ms'.format((stop_time - start_time) * 1000)) return results def get_highlighted_face(filename, faces): data = plt.imread(filename) plt.imshow(data) ax = plt.gca() x, y, width, height = faces[0]['box'] rect = plt.Rectangle((x, y), width, height, fill=False, color='red') ax.add_patch(rect) plt.savefig('plot.png', bbox_inches='tight') def get_cropped_faces(filename, faces): """ Only gets the first detected face! Returns cropped data. """ try: # data = plt.imread(filename) # x1, y1, width, height = faces[0]['box'] # x2, y2 = x1 + width, y1 + height # plt.axis('off') # plt.imshow(data[y1:y2, x1:x2]) # plt.imsave('tmp.jpg', data[y1:y2, x1:x2]) # get_highlighted_face(filename, faces) bounding_box = faces[0]['box'] cv2.rectangle(filename, (bounding_box[0], bounding_box[1]), (bounding_box[0] + bounding_box[2], bounding_box[1] + bounding_box[3]), (255, 0, 0), 5) cropped_image = filename[bounding_box[1]:bounding_box[1] + bounding_box[3], bounding_box[0]:bounding_box[0]+bounding_box[2]] cv2.imwrite('tmp.jpg', cv2.cvtColor(cropped_image, cv2.COLOR_RGB2BGR)) cv2.imwrite('plot.png', cv2.cvtColor(filename, cv2.COLOR_RGB2BGR)) except Exception as ex: print(ex) Label(root, text='No face detected!').pack() return def detect_faces(image): """ Detect faces using MTCNN. """ face_detector_mtcnn = MTCNN() # image_to_detect = plt.imread(image) image_to_detect = cv2.cvtColor(cv2.imread(image), cv2.COLOR_BGR2RGB) try: faces = face_detector_mtcnn.detect_faces(image_to_detect) print(faces) except Exception as ex: print(ex) Label(root, text='Something is wrong! Please try again!').pack() return get_cropped_faces(image_to_detect, faces) def open_file(): filename = filedialog.askopenfilename(title='Select an image!') return filename def open_image(): image_panel.pack_forget() male.pack_forget() female.pack_forget() image_to_open = open_file() detect_faces(image_to_open) results = analyze_face() # Show full image in the tkinter's screen. image = Image.open('plot.png') image.thumbnail((IMAGE_WIDTH, IMAGE_HEIGHT), Image.ANTIALIAS) image.save('plot.png') image = Image.open('plot.png') rendered_image = ImageTk.PhotoImage(image) # image_panel = Label(root, image=rendered_image) # image_panel.image = rendered_image # image_panel.pack() female_str = 'Female: ' + str(results[0]) male_str = 'Male: ' + str(results[1]) print(male_str, female_str) image_panel.image = rendered_image image_panel.config(image=rendered_image) female.configure(text=female_str) male.configure(text=male_str) image_panel.pack() female.pack() male.pack() os.remove('tmp.jpg') os.remove('plot.png') def clear_screen(image_panel, female, male): image_panel.destroy() female.destroy() male.destroy() def define_user_interface(root): root.title('Face Classifier Application') root.geometry('800x800') root.resizable(width=True, height=True) Label(root, text='Welcome to the Face Recognition Application!').pack() Label(root, text='Powered by MTCNN, TensorFlow, and Deep Learning!').pack() Button(root, text='Classify your face!', command=open_image).pack() def main(): define_user_interface(root) root.mainloop() if __name__ == '__main__': main()
StarcoderdataPython
111273
#!/usr/bin/python import socket import bluetooth, subprocess nearby_devices = bluetooth.discover_devices(duration=4,lookup_names=True, flush_cache=True, lookup_class=False) TCP_IP = '44:44:1B:04:13:7D' TCP_PORT = 13854 BUFFER_SIZE = 2048 name = 'Sichiray' # Device name addr = '44:44:1B:04:13:7D' # Device Address port = 13854 # RFCOMM port passkey = "0000" # passkey of the device you want to connect # kill any "bluetooth-agent" process that is already running subprocess.call("kill -9 `pidof bluetooth-agent`",shell=True) # Start a new "bluetooth-agent" process where XXXX is the passkey status = subprocess.call("bluetooth-agent " + passkey + " &",shell=True) # Now, connect in the same way as always with PyBlueZ try: s = bluetooth.BluetoothSocket(bluetooth.RFCOMM) s.connect((addr,port)) except bluetooth.btcommon.BluetoothError as err: # Error handler pass
StarcoderdataPython
146604
<gh_stars>0 from launchable.testpath import FilePathNormalizer import os.path import pathlib import subprocess import sys import tempfile import unittest class TestFilePathNormalizer(unittest.TestCase): def test_relative_path(self): n = FilePathNormalizer() relpath = os.path.join('foo', 'bar', 'baz') self.assertEqual(relpath, n.relativize(relpath)) def test_base_path(self): base = os.path.abspath('base') relpath = os.path.join('foo', 'bar', 'baz') abspath = os.path.join(base, 'foo', 'bar', 'baz') n = FilePathNormalizer(base_path=base) self.assertEqual(relpath, n.relativize(relpath)) self.assertEqual(relpath, n.relativize(abspath)) def test_normalize_path(self): relpath = os.path.join('foo', 'bar', 'baz') non_normalized = os.path.join('foo', 'bar', 'omit', '..', 'baz') n = FilePathNormalizer() self.assertEqual(relpath, n.relativize(non_normalized)) def test_no_base_path_inference(self): base = os.path.abspath('base') relpath = os.path.join('foo', 'bar', 'baz') abspath = os.path.join(base, 'foo', 'bar', 'baz') n = FilePathNormalizer(no_base_path_inference=True) self.assertEqual(relpath, n.relativize(relpath)) self.assertEqual(abspath, n.relativize(abspath)) n = FilePathNormalizer(base_path=base, no_base_path_inference=True) self.assertEqual(relpath, n.relativize(relpath)) self.assertEqual(relpath, n.relativize(abspath)) @unittest.skipIf(sys.platform.startswith( "win" ), "tempfile creates 8.3 filenames, and it's hard to deal with them. Practically, we don't see them often, so do not support them now until it's needed." ) def test_inference_git(self): with tempfile.TemporaryDirectory() as tempdirname: temppath = pathlib.PurePath(tempdirname) base = str(temppath.joinpath("gitrepo")) relpath = os.path.join('foo', 'bar', 'baz') abspath = os.path.join(base, 'foo', 'bar', 'baz') self._run_command(['git', 'init', base]) n = FilePathNormalizer() self.assertEqual(relpath, n.relativize(abspath)) @unittest.skipIf(sys.platform.startswith( "win" ), "tempfile creates 8.3 filenames, and it's hard to deal with them. Practically, we don't see them often, so do not support them now until it's needed. Also when this runs on Windows, GIT_AUTHOR_NAME etc. is ignored and fails." ) def test_inference_git_submodule(self): with tempfile.TemporaryDirectory() as tempdirname: temppath = pathlib.PurePath(tempdirname) self._run_command( ['git', 'init', str(temppath.joinpath("submod"))]) self._run_command( ['git', 'commit', '--allow-empty', '--message', 'test commit'], cwd=str(temppath.joinpath("submod"))) self._run_command( ['git', 'init', str(temppath.joinpath("gitrepo"))]) self._run_command([ 'git', 'submodule', 'add', str(temppath.joinpath("submod")), 'submod' ], cwd=str(temppath.joinpath("gitrepo"))) base = str(temppath.joinpath("gitrepo")) relpath = os.path.join('submod', 'foo', 'bar', 'baz') abspath = os.path.join(base, 'submod', 'foo', 'bar', 'baz') n = FilePathNormalizer() self.assertEqual(relpath, n.relativize(abspath)) def _run_command(self, args, cwd=None): # Use check_output here to capture stderr for a better error message. try: subprocess.check_output(args, cwd=cwd, stderr=subprocess.PIPE, universal_newlines=True, env={ "GIT_AUTHOR_NAME": "Test User <<EMAIL>>", "GIT_COMMITTER_NAME": "Test User <<EMAIL>>", }) except subprocess.CalledProcessError as e: self.fail( "Failed to execute a command: {}\nSTDOUT: {}\nSTDERR: {}\n". format(e, e.stdout, e.stderr)) if __name__ == '__main__': unittest.main()
StarcoderdataPython
1748100
<gh_stars>0 #!/usr/bin/python # # ICD model file helper: given a target directory tree, generate # command-model.conf that sends all commands to assemblies in that # tree, and subscribe-model.conf that subscribes to all events # published by assemblies in that tree. import os import sys import re import fnmatch from pyhocon import ConfigFactory ## main # if (len(sys.argv) < 4): print """genIcd: Generate command-model.conf that sends all commands, and subscribe-model.conf that subscribes to all events, for assemblies under the input model path. usage: genIcd <input model path> <output command-model file> <output subscribe-model file> [skip description string 1] [skip description string 2] ... Note that the optional [skip description string] arguments are case-insensitve pattern matches to the first line of the description for commands and events. Also, these strings are going into a regex, so be careful to escape special characters. The command/event will be skipped if *any* of the skip strings are matched. example: The following would send all NFIRAOS commands, and subscribe to all NFIRAOS events, with the exception of any commands/events with (engineering) appearing in the first line genIcd ICD-Model-Files/NFIRAOS-Model-Files command-model.conf subscribe-model.conf "\(engineering\)" """ sys.exit(1) in_dir = sys.argv[1] cmd_name = sys.argv[2] sub_name = sys.argv[3] if len(sys.argv) >= 5: skipstrings = sys.argv[4:] else: skipstrings = None # use a dictionary to keep track of differences between parsing # commands and events types = {} types[' cmd'] = { 'outname':cmd_name, # name of the output file 'startstr': 'send = [\n', # start file with this 'endstr' : ']\n', # end file with this 'space' : ' ', # space at start of each output line 'inname':'command-model.conf', # name of model file to parse 'key':'receive' # key pointing to array to iterate over } types['event'] = { 'outname':sub_name, # name of the output file 'startstr': 'subscribe {\n events = [\n', # start file with this 'endstr' : ' ]\n}\n', # end file with this 'space' : ' ', # space at start of each output line 'inname':'publish-model.conf', # name of model file to parse 'root':'publish', # optional root containing key 'key':'events' # key pointing to array to iterate over } # open output files and store in dictionary for type in types: outname = types[type]['outname'] try: f = open(outname,"w") types[type]['outfile'] = f f.write(types[type]['startstr']) except Exception as e: print "Error creating output file " + outname + \ "({0}): {1}".format(e.errno, e.strerror) sys.exit(1) for root, dirs, files in os.walk(in_dir): # skip hidden files = [f for f in files if not f[0] == '.'] dirs[:] = [d for d in sorted(dirs) if not d[0] == '.'] # Parse different types of input files for type in types: d = types[type] outfile = d['outfile'] spc = d['space'] if d['inname'] in files: f = root+'/'+d['inname'] try: conf = ConfigFactory.parse_file(f) subsystem = conf['subsystem'] component = conf['component'] if 'root' in d: a = conf[d['root']][d['key']] else: a = conf[d['key']] outfile.write('\n%s// %s %s\n' % (spc,subsystem,component)) for r in a: name = r['name'] if skipstrings and 'description' in r: # Check for skip strings in 1st line of description skip = False for skipstr in skipstrings: if re.search("\A.*"+skipstr,r['description'], re.IGNORECASE): skip = True break if skip: print type, subsystem, component, name, \ '[***SKIPPED***]' continue print type, subsystem, component, name s = """%s{ %s subsystem = %s %s component = %s %s name = %s %s} """ % (spc,spc,subsystem,spc,component,spc,name,spc) outfile.write(s) except Exception as e: pass for type in types: try: f = types[type]['outfile'] f.write(types[type]['endstr']) f.close() except Exception as e: print "Error finalizing output file ({0}): {1}".format(e.errno, e.strerror) sys.exit(1)
StarcoderdataPython
1629160
<filename>samples/openapi3/client/petstore/python-legacy/test/test_format_test.py # coding: utf-8 """ OpenAPI Petstore This spec is mainly for testing Petstore server and contains fake endpoints, models. Please do not use this for any other purpose. Special characters: \" \\ # noqa: E501 The version of the OpenAPI document: 1.0.0 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import unittest import datetime import petstore_api from petstore_api.models.format_test import FormatTest # noqa: E501 from petstore_api.rest import ApiException class TestFormatTest(unittest.TestCase): """FormatTest unit test stubs""" def setUp(self): pass def tearDown(self): pass def make_instance(self, include_optional): """Test FormatTest include_option is a boolean, when False only required params are included, when True both required and optional params are included """ # model = petstore_api.models.format_test.FormatTest() # noqa: E501 if include_optional : return FormatTest( integer = 10, int32 = 20, int64 = 56, number = 32.1, float = 54.3, double = 67.8, decimal = 1, string = 'a', byte = 'YQ==', binary = bytes(b'blah'), date = datetime.datetime.strptime('1975-12-30', '%Y-%m-%d').date(), date_time = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'), uuid = '72f98069-206d-4f12-9f12-3d1e525a8e84', password = '<PASSWORD>', pattern_with_digits = '0480728880', pattern_with_digits_and_delimiter = 'image_480' ) else : return FormatTest( number = 32.1, byte = 'YQ==', date = datetime.datetime.strptime('1975-12-30', '%Y-%m-%d').date(), password = '<PASSWORD>', ) def testFormatTest(self): """Test FormatTest""" inst_req_only = self.make_instance(include_optional=False) inst_req_and_optional = self.make_instance(include_optional=True) if __name__ == '__main__': unittest.main()
StarcoderdataPython
3284676
<filename>thonny/plugins/paren_matcher.py import io import token from thonny import get_workbench from thonny.codeview import CodeViewText from thonny.shell import ShellText import time _OPENERS = {")": "(", "]": "[", "}": "{"} TOKTYPES = {token.LPAR, token.RPAR, token.LBRACE, token.RBRACE, token.LSQB, token.RSQB} BLANK_LINE_REGEX_STR = r"\n\s*\r?\n" BLANK_LINE_REGEX_STR = r"^\s*$" BLOCK_START_REGEX_STR = r"^\s*(class|def|while|elif|with|try|except|finally) " class ParenMatcher: def __init__(self, text): self.text = text self._update_scheduling_id = None self._delayed_scheduling_id = None self._tokens_cache = {} def schedule_update(self, delay=None): if self._update_scheduling_id is not None: self.text.after_cancel(self._update_scheduling_id) if delay is not None: self._update_scheduling_id = self.text.after(delay, self.perform_update) else: self._update_scheduling_id = self.text.after_idle(self.perform_update) def perform_update(self): try: self.update_highlighting() finally: self._update_scheduled = False def invalidate_token_cache(self): self._tokens_cache = {} def update_highlighting(self): clear_highlighting(self.text) if get_workbench().get_option("view.paren_highlighting"): self._update_highlighting_for_active_range() def _update_highlighting_for_active_range(self): start_index = "1.0" end_index = self.text.index("end") # Try to reduce search range for better performance. index = self._find_block_start("@0,0 linestart", True) if index: start_index = index lower_right = "@%d,%d" % (self.text.winfo_width(), self.text.winfo_height()) index = self._find_block_start(lower_right + " lineend", False) if index: end_index = index self._highlight(start_index, end_index) def _find_block_start(self, start_position, backwards): while True: index = self.text.search( BLOCK_START_REGEX_STR, start_position, regexp=True, backwards=backwards, stopindex="1.0" if backwards else "end", ) if not index: break tags = self.text.tag_names(index) if "string3" in tags or "open_string3" in tags: # not a block start if backwards: start_position = index else: start_position = index + " +1c" else: break return index def _highlight(self, start_index, end_index): stack = [] cursor_row, cursor_col = map(int, self.text.index("insert").split(".")) for t in self._get_paren_tokens(start_index, end_index): if t.string in "([{": stack.append(t) elif not stack: # stack is empty, ie. found a closer without opener close_index = "%d.%d" % (t.start[0], t.end[1]) self.text.tag_add("unclosed_expression", start_index, close_index) break elif stack[-1].string != _OPENERS[t.string]: # incorrect closure opener = stack[-1] open_index = "%d.%d" % opener.start self.text.tag_add("unclosed_expression", open_index, end_index) break else: # found a pair opener = stack[-1] closer = t # if cursor is right after opener or closer then highlight both if ( cursor_row == opener.start[0] and cursor_col == opener.end[1] or cursor_row == closer.start[0] and cursor_col == closer.end[1] ): self.text.tag_add("surrounding_parens", "%d.%d" % closer.start) self.text.tag_add("surrounding_parens", "%d.%d" % opener.start) stack.pop() if stack: # something was left without closure opener = stack[-1] open_index = "%d.%d" % opener.start self.text.tag_add("unclosed_expression", open_index, end_index) def _get_paren_tokens(self, start_index, end_index): import tokenize if (start_index, end_index) in self._tokens_cache: return self._tokens_cache[(start_index, end_index)] start_row, start_col = map(int, start_index.split(".")) source = self.text.get(start_index, end_index) # prepend source with empty lines and spaces to make # token rows and columns match with widget indices source = ("\n" * (start_row - 1)) + (" " * start_col) + source result = [] try: tokens = tokenize.tokenize(io.BytesIO(source.encode("utf-8")).readline) for token in tokens: # if token.string != "" and token.string in "()[]{}": if token.exact_type in TOKTYPES: result.append(token) except Exception: # happens eg when parens are unbalanced or there is indentation error or ... pass if start_index == "1.0" and end_index == "end": self._tokens_cache[(start_index, end_index)] = result return result class ShellParenMatcher(ParenMatcher): def _update_highlighting_for_active_range(self): # TODO: check that cursor is in this range index_parts = self.text.tag_prevrange("command", "end") if index_parts: start_index, end_index = index_parts self._highlight(start_index, end_index) def _update_highlighting(event, text_changed, need_update, delay=None): text = event.widget if not hasattr(text, "paren_matcher"): if isinstance(text, CodeViewText): text.paren_matcher = ParenMatcher(text) elif isinstance(text, ShellText): text.paren_matcher = ShellParenMatcher(text) else: return if text_changed: text.paren_matcher.invalidate_token_cache() if need_update: text.paren_matcher.schedule_update(delay) def update_highlighting_full(event): _update_highlighting(event, True, True) def clear_highlighting(text): text.tag_remove("surrounding_parens", "0.1", "end") text.tag_remove("unclosed_expression", "0.1", "end") _last_move_time = 0 def update_highlighting_move(event): global _last_move_time # needs delay because selecting with mouse causes many events # and I don't know how to distinguish selection from other moves t = time.time() if t - _last_move_time > 0.1: delay = None else: delay = 300 _last_move_time = t _update_highlighting(event, False, True, delay=delay) def update_highlighting_edit_cw(event): if isinstance(event.text_widget, CodeViewText): event.widget = event.text_widget trivial = event.get("trivial_for_parens", False) _update_highlighting(event, True, not trivial) if trivial: event.text_widget.tag_remove("surrounding_parens", "0.1", "end") def load_plugin() -> None: wb = get_workbench() wb.set_default("view.paren_highlighting", True) wb.bind("TextInsert", update_highlighting_edit_cw, True) wb.bind("TextDelete", update_highlighting_edit_cw, True) wb.bind_class("CodeViewText", "<<VerticalScroll>>", update_highlighting_move, True) wb.bind_class("CodeViewText", "<<CursorMove>>", update_highlighting_move, True) wb.bind_class("ShellText", "<<TextChange>>", update_highlighting_full, True) wb.bind_class("ShellText", "<<CursorMove>>", update_highlighting_full, True) wb.bind("<<UpdateAppearance>>", update_highlighting_full, True)
StarcoderdataPython
47946
# Listing_23-11.py # Copyright Warren & <NAME>, 2013 # Released under MIT license http://www.opensource.org/licenses/mit-license.php # Version $version ---------------------------- # Crazy Eights - the main loop with scoring added # Note that this is not a complete program. It needs to be put together # with the other parts of Crazy Eights to make a working program. done = False p_total = c_total = 0 while not done: game_done = False blocked = 0 init_cards() # set up deck and create player's and computer's hands while not game_done: player_turn() if len(p_hand) == 0: # player won game_done = True print print "You won!" # display game score here p_points = 0 for card in c_hand: p_points += card.value # add up points from computer's remaining cards p_total += p_points # add points from this game to the total print"You got %i points for computer's hand" % p_points if not game_done: computer_turn() if len(c_hand) == 0: # computer won game_done = True print print "Computer won!" # display game score here c_points = 0 for card in p_hand: c_points += card.value # add up points from player's remaining cards c_total += c_points # add points from this game to the total print"Computer got %i points for your hand" % c_points if blocked >= 2: # both player and computer blocked, so both get points game_done = True print "Both players blocked. GAME OVER." player_points = 0 for card in c_hand: p_points += card.value p_total += p_points c_points = 0 for card in p_hand: c_points += card.value c_total += c_points # print points for this game print"You got %i points for computer's hand" % p_points print"Computer got %i points for your hand" % c_points play_again = raw_input("Play again (Y/N)? ") if play_again.lower().startswith('y'): done = False # print total points so far print "\nSo far, you have %i points" % p_total print "and the computer has %i points.\n" % c_total else: done = True # print final totals print "\n Final Score:" print "You: %i Computer: %i" % (p_total, c_total)
StarcoderdataPython
3226121
from constants.headers import ROUTING_PROVISIONING from constants.nodetype import TYPE_PROVISIONING from util.nodetype import NodeType class Self: # Singleton instance _instance = None node_id: bytes node_type: NodeType def __new__(cls): if cls._instance is None: cls._instance = super(Self, cls).__new__(cls) cls.node_id = ROUTING_PROVISIONING cls.node_type = NodeType(TYPE_PROVISIONING) return cls._instance def reset(self): self.node_id = ROUTING_PROVISIONING self.node_type = NodeType(TYPE_PROVISIONING)
StarcoderdataPython
1708827
<gh_stars>1-10 import collections import itertools import math import sys import os import cv2 import numpy import tensorflow as tf import common import model def letter_probs_to_code(letter_probs): output = "".join(common.CHARS[i] for i in numpy.argmax(letter_probs, axis=1)) return output.replace("_", "") def predict(list_image_path, param_values): # Load the model which detects number plates over a sliding window. x, y, params = model.get_detect_model() # Execute the model at each scale. gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9) with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess: for image_name in list_image_path: im_gray = cv2.imread(image_name, cv2.IMREAD_GRAYSCALE) / 255. im_gray = cv2.resize(im_gray, (128, 64)) print("-------------") feed_dict = {x: numpy.stack([im_gray])} feed_dict.update(dict(zip(params, param_values))) y_val = sess.run(y, feed_dict=feed_dict) letter_probs = (y_val[0, 0, 0, 1:].reshape( 10, len(common.CHARS))) letter_probs = common.softmax(letter_probs) present_prob = common.sigmoid(y_val[0, 0, 0, 0]) print("input", image_name) print("output", letter_probs_to_code(letter_probs)) if __name__ == "__main__": f = numpy.load(sys.argv[2]) param_values = [f[n] for n in sorted(f.files, key=lambda s: int(s[4:]))] list_images = ["{}/{}".format(sys.argv[1], file_name) for file_name in os.listdir(sys.argv[1])] predict(list_image_path=list_images, param_values=param_values)
StarcoderdataPython
1688984
import os from platform import architecture, system from subprocess import Popen from sys import stderr SENNA_DIR = 'senna/' TMP_FILENAME = 'senna.tmp' TMP_OUT_FILENAME = 'senna.out.tmp' class SennaWrapper: __senna_executable = None __senna_location = None def __init__(self): self.__senna_location = self.__det_location() self.__senna_executable = self.__det_executable() def __det_location(self): return os.path.join(os.path.dirname(os.path.abspath(__file__)), SENNA_DIR) def __det_executable(self): os_name = system() executable = '' if os_name == 'Linux': bits = architecture()[0] if bits == '64bit': executable='senna-linux64' elif bits == '32bit': executable='senna-linux32' else: executable='senna' elif os_name == 'Windows': executable='senna-win32.exe' elif os_name == 'Darwin': executable='senna-osx' else: raise Exception("Unable to determine the current OS!") return os.path.join(self.__senna_location, executable) def srl(self, sentence, verbose=False): tmp_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), TMP_FILENAME) tmp_out_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), TMP_OUT_FILENAME) if verbose: print('SENNA temporary files: {}, {}'.format(tmp_filename, tmp_out_filename)) with open(tmp_filename, 'w') as tmp: tmp.write(sentence) tmp.close() tmp_file = open(tmp_filename, 'r') tmp_out_file = open(tmp_out_filename, 'w') command = self.__senna_executable + ' -srl ' current_dir = os.getcwd() os.chdir(self.__senna_location) if verbose: print('SENNA command: {}'.format(command)) process = Popen(command, stdin=tmp_file, stdout=tmp_out_file, shell=True) else: process = Popen(command, stdin=tmp_file, stdout=tmp_out_file, stderr=open(os.devnull, 'w'), shell=True) process.wait() out, err = process.communicate() os.chdir(current_dir) if verbose: print('Removing SENNA temporary files...') tmp_file.close() os.remove(tmp_filename) tmp_out_file.close() assert not process.returncode, 'ERROR: SENNA exited with a non-zero code status.' with open(tmp_out_filename, 'r') as out_file: pred_list = list() predicates = {} statements = {} for line in out_file.readlines(): line_list = line.split() # Line: Terms | Verbs | SRL_0 | SRL_1 | ... # Idx: [0] [1] [2] [3] ... # *** Number of predicates (verbs) == Number of statements == Number of SRL columns *** if len(line_list) > 1 and not line_list[1] == '-': # get predicates (verbs) predicates[line_list[1]] = [] pred_list.append(line_list[1]) # list to iterate later - dictionary will not keep keys in order stat_list = [[] for l in range(len(pred_list))] # to be filled with each statement's contents out_file.seek(0) for line in out_file.readlines(): line_list = line.split() if len(line_list) < 1: continue term = line_list[0] pred_offset = 1 pred_index = 0 for key in pred_list: # iterate over list instead of dictionary pred_dict = predicates[key] pred_index += 1 srl_tag = line_list[pred_offset + pred_index] if not (srl_tag == 'O' or srl_tag == 'S-V'): srl = '-'.join(srl_tag.split('-')[1:]) if srl_tag.startswith('S-') or srl_tag.startswith('B-'): # Single occurrence or beginning of a composite. pred_dict.append((srl, term)) predicates[key] = pred_dict continue last_tuple = pred_dict[-1] pred_dict[-1] = (srl, last_tuple[1] + ' ' + term) predicates[key] = pred_dict srl_offset = 2 for i in range(len(stat_list)): if not (line_list[srl_offset + i] == 'O'): stat_list[i].append(term) out_file.close() os.remove(tmp_out_filename) for i in range(len(pred_list)): statements[pred_list[i]] = ' '.join(stat_list[i]) return predicates, statements
StarcoderdataPython
3376234
<reponame>mathur/modelstruct<gh_stars>0 def ClassFactory(name, argnames, BaseClass=BaseClass): def __init__(self, **kwargs): for key, value in kwargs.items(): if key not in argnames: raise TypeError("Argument %s not valid for %s" % (key, self.__class__.__name__)) setattr(self, key, value) BaseClass.__init__(self, name[:-len("Class")]) newclass = type(name, (BaseClass,),{"__init__": __init__}) return newclass
StarcoderdataPython
1709509
#Learning Python """This is a multiline Comment.""" import datetime time = datetime.datetime.now() print(time) print("Hello, World!") x = 5 y = 10 print(x) print(y) print(10*5) name = "John" a = "awesome" print("Python is " + a) print(x + y) #If you try to combine a string and a number, Python will give you an error print(name) p=int(1) q=int(1.8) r=float(3.8) s=float(3) print(p,q,r,s) print(name.lower()) print(name.upper()) z = input("Enter Your Name: ") print("Hello," + z) if x==5: print "X is five" while x < 10: print(x) x += 1 if x!=4: print("X is not Four") print("X is greater than 3 and less than 6:", x>3 and x<6) fruits=["Apple","Banana","Mango","Strawberry"] fruits[1]="Grapes" fruits.append("Cherry") print(fruits) fruits.pop(1) print(fruits) for x in fruits: print(x) if x == "Mango": break
StarcoderdataPython
68638
# -*- coding: utf-8 -*- # @Date : 2016-02-21 15:43:36 # @Author : <EMAIL> import base64 import json import urllib from sgmllib import SGMLParser from time import time import tornado.gen import tornado.web from sqlalchemy.orm.exc import NoResultFound from tornado.httpclient import HTTPRequest, AsyncHTTPClient,HTTPError from config import loginurl1,runurl from mod.models.mysql.pe_models import PeDetailCache class RunningParser(SGMLParser): def __init__(self): SGMLParser.__init__(self) def getRunningTable(self, text): self.table = [] self.row = [] self.flag = 0 self.form = 0 # _cookies = cookies # _url = 'http://zccx.seu.edu.cn/SportWeb/gym/gymExercise/gymExercise_query_result_2.jsp?xh=%s'%(card_id) self.feed(text) def start_form(self, attrs): self.form += 1 def end_form(self): self.form -= 1 msg = {} msg['sign_date'] = self.row[3] msg['sign_time'] = self.row[4] msg['sign_effect'] = self.row[6] self.table.append(msg) self.row = [] def start_td(self, attrs): self.flag += 1 def end_td(self): self.flag -= 1 def handle_data(self, text): if self.flag == 3 and self.form == 1: self.row.append(text) class pedetailHandler(tornado.web.RequestHandler): @property def db(self): return self.application.db def on_finish(self): self.db.close() def get(self): self.write('Herald Web Service') @tornado.web.asynchronous @tornado.gen.engine def post(self): retjson = {'code':200,'content':''} cardnum = self.get_argument('cardnum',default=None) password = self.get_argument('password',default=None) status = None if not (cardnum and password): retjson['code'] = 400 retjson['content'] = 'params lack' else: # read from cache try: status = self.db.query(PeDetailCache).filter(PeDetailCache.cardnum == cardnum).one() if status.date > int(time())-10000 and status.text != '*': self.write(base64.b64decode(status.text)) self.finish() return except NoResultFound: status = PeDetailCache(cardnum = cardnum,text = '*',date = int(time())) self.db.add(status) try: self.db.commit() except: self.db.rollback() try: client = AsyncHTTPClient() data = { "Login.Token1" : cardnum, "Login.Token2" : password, 'goto' : "http://mynew.seu.edu.cn/loginSuccess.portal", 'gotoOnFail' : "http://mynew.seu.edu.cn/loginFailure.portal" } data1 = { 'IDToken0': '', 'IDToken1':cardnum, 'IDToken2':password, 'IDButton':'Submit', 'goto':'http://zccx.seu.edu.cn/', 'gx_charset':'gb2312' } cookie1 = '' request = HTTPRequest( loginurl1, method='POST', body = urllib.urlencode(data1), follow_redirects=False ) initcookie = '' try: response = yield client.fetch(request) except HTTPError as e: initcookie = e.response.headers['Set-Cookie'] init_cookie1 = initcookie.split(';')[4].split(',')[1]#+initcookie.split(';')[0] header = { 'Host':'zccx.seu.edu.cn', 'Accept': 'textml,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/45.0.2454.101 Safari/537.36', 'Referer':'http://zccx.seu.edu.cn/', 'Connection':'Keep-alive', 'Accept-Encoding': 'gzip, deflate', 'Accept-Language': 'zh-CN,zh;q=0.8', 'Cookie':init_cookie1+';'+cookie1+';'+';amblcookie=02' } request = HTTPRequest( runurl, method='GET', headers = header ) response = yield client.fetch(request) cookie1 = response.headers['Set-Cookie'] header['Cookie'] = init_cookie1+';'+cookie1+';'+';amblcookie=02' getpeurl = "http://zccx.seu.edu.cn/SportWeb/gym/gymExercise/gymExercise_query_result_2.jsp?xh=%s"%(cardnum) request = HTTPRequest( getpeurl, headers = header, request_timeout=8 ) response = yield client.fetch(request) spider = RunningParser() spider.getRunningTable(response.body) retjson['content'] = spider.table except HTTPError as e: retjson['code'] = 500 retjson['content'] = str(e) ret = json.dumps(retjson, ensure_ascii=False, indent=2) self.write(ret) self.finish() # refresh cache if retjson['code'] == 200: status.date = int(time()) status.text = base64.b64encode(ret) self.db.add(status) try: self.db.commit() except Exception,e: self.db.rollback() finally: self.db.remove()
StarcoderdataPython
4827839
# -*- coding: utf-8 -*- ############################################################################## # # Copyright (c) 2009 CamptoCamp. All rights reserved. # @author <NAME> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # ############################################################################## from openerp import netsvc from openerp.osv import fields, orm class res_company(orm.Model): """override company to add currency update""" def _multi_curr_enable(self, cr, uid, ids, field_name, arg, context={}): "check if multi company currency is enabled" result = {} if self.pool.get('ir.model.fields').search(cr, uid, [('name', '=', 'company_id'), ('model', '=', 'res.currency')])==[]: enable = 0 else: enable = 1 for id in ids: result[id] = enable return result def button_refresh_currency(self, cr, uid, ids, context=None): """Refrech the currency !!for all the company now""" currency_updater_obj = self.pool.get('currency.rate.update') try: currency_updater_obj.run_currency_update(cr, uid) except Exception, e: return False return True def on_change_auto_currency_up(self, cr, uid, id, value): """handle the activation of the currecny update on compagnies. There are two ways of implementing multi_company currency, the currency is shared or not. The module take care of the two ways. If the currency are shared, you will only be able to set auto update on one company, this will avoid to have unusefull cron object running. If yours currency are not share you will be able to activate the auto update on each separated company""" if len(id) : id = id[0] else : return {} enable = self.browse(cr, uid, id).multi_company_currency_enable compagnies = self.search(cr, uid, []) activate_cron = 'f' if not value : # this statement is here beacaus we do no want to save in case of error self.write(cr, uid, id,{'auto_currency_up':value}) for comp in compagnies : if self.browse(cr, uid, comp).auto_currency_up: activate_cron = 't' break self.pool.get('currency.rate.update').save_cron( cr, uid, {'active':activate_cron} ) return {} else : for comp in compagnies : if comp != id and not enable: if self.browse(cr, uid, comp).multi_company_currency_enable: #we ensure taht we did not have write a true value self.write(cr, uid, id,{'auto_currency_up':False}) return { 'value':{ 'auto_currency_up':False }, 'warning':{ 'title':"Warning", 'message': 'Yon can not activate auto currency '+\ 'update on more thant one company with this '+ 'multi company configuration' } } self.write(cr, uid, id,{'auto_currency_up':value}) for comp in compagnies : if self.browse(cr, uid, comp).auto_currency_up: activate_cron = 't' self.pool.get('currency.rate.update').save_cron( cr, uid, {'active':activate_cron} ) break return {} def on_change_intervall(self, cr, uid, id, interval) : ###Function that will update the cron ###freqeuence self.pool.get('currency.rate.update').save_cron( cr, uid, {'interval_type':interval} ) compagnies = self.search(cr, uid, []) for comp in compagnies : self.write(cr, uid, comp,{'interval_type':interval}) return {} _inherit = "res.company" _columns = { ### activate the currency update 'auto_currency_up': fields.boolean('Automatical update of the currency this company'), 'services_to_use' : fields.one2many( 'currency.rate.update.service', 'company_id', 'Currency update services' ), ###predifine cron frequence 'interval_type': fields.selection( [ ('days','Day(s)'), ('weeks', 'Week(s)'), ('months', 'Month(s)') ], 'Currency update frequence', help="""changing this value will also affect other compagnies""" ), ###function field that allows to know the ###mutli company currency implementation 'multi_company_currency_enable' : fields.function( _multi_curr_enable, method=True, type='boolean', string="Multi company currency", help='if this case is not check you can'+\ ' not set currency is active on two company' ), }
StarcoderdataPython
3378901
# Copyright 2013 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. from buildbot.scheduler import Scheduler from buildbot.scheduler import Triggerable import collections # This file contains useful functions for masters whose slaves run recipes. def AddSchedulersAndTriggers(buildmaster_config=None, slave_list=None, scheduler_name=None, branch=None): """Adds schedulers and triggers to the BuildmasterConfig based on the the slave_list. This function relies on certain structure in the slave_list, in particular the custom 'triggered_by' property, which is not yet commonly used to define triggers. Returns a dictionary mapping builder name, for those builders which invoke triggers, to the (synthesized) name of the trigger. TODO(kbr): this function does not yet support builders with multiple slaves behind them, but could be updated to do so. Throws an Exception if a non-existent builder is mentioned in another builder's 'triggered_by' property. Arguments: buildmaster_config: a BuildmasterConfig into which the 'schedulers' property will be defined. slave_list: a SlavesList constructed from slaves.cfg or builders.pyl. scheduler_name: the name of the Scheduler for the polling (not triggered) builders. """ c = buildmaster_config polling_builders = [] # Maps the parent builder to a set of the names of the builders it triggers. trigger_map = collections.defaultdict(list) # Maps the name of the parent builder to the (synthesized) name of its # trigger, wrapped in a list. trigger_name_map = {} next_group_id = 0 for slave in slave_list.slaves: builder = slave['builder'] parent_builder = slave.get('triggered_by') if parent_builder == 'none': # Uses recipe-side triggers. Don't add to trigger maps. pass elif parent_builder is not None: if slave_list.GetSlave(builder=parent_builder) is None: raise Exception('Could not find parent builder %s for builder %s' % (parent_builder, builder)) trigger_map[parent_builder].append(builder) if parent_builder not in trigger_name_map: trigger_name_map[parent_builder] = 'trigger_group_%d' % next_group_id next_group_id += 1 else: polling_builders.append(builder) s_gpu = Scheduler(name=scheduler_name, branch=branch, treeStableTimer=60, builderNames=polling_builders) c['schedulers'] = [s_gpu] for name, builders in trigger_map.iteritems(): c['schedulers'].append(Triggerable(name=trigger_name_map[name], builderNames=builders)) return trigger_name_map def AddRecipeBasedBuilders(buildmaster_config=None, slave_list=None, annotator=None, trigger_name_map=None): """Writes builders which use recipes to the BuildmasterConfig's 'builders' list, using the AnnotatorFactory's BaseFactory. Specifies some common factory properties for these builders. Arguments: buildmaster_config: a BuildmasterConfig into which the 'builders' property will be defined. slave_list: a SlavesList constructed from slaves.cfg or builders.pyl. annotator: an AnnotatorFactory instance. trigger_name_map: the trigger name map returned by AddSchedulersAndTriggers, above. """ builders = [] for slave in slave_list.slaves: if 'recipe' in slave: factory_properties = { 'test_results_server': 'test-results.appspot.com', 'generate_gtest_json': True, 'build_config': slave['build_config'] } if 'perf_id' in slave: factory_properties['show_perf_results'] = True factory_properties['perf_id'] = slave['perf_id'] name = slave['builder'] builder = { 'name': name, 'factory': annotator.BaseFactory( slave['recipe'], factory_properties, [trigger_name_map[name]] if name in trigger_name_map else None), 'gatekeeper': slave.get('gatekeeper_categories', ''), } # Don't specify auto_reboot unless the slave does, to let # master_utils' default take effect. if 'auto_reboot' in slave: builder['auto_reboot'] = slave['auto_reboot'] builders.append(builder) buildmaster_config['builders'] = builders
StarcoderdataPython
1728840
<reponame>jnngu/codingdatingsite<filename>styledating/main.py from flask import Flask, render_template, request, redirect, url_for from flask_login import LoginManager, UserMixin, login_required, login_user, logout_user, current_user import sqlite3 import hashlib import re from util import * from user import * from autograde import * app = Flask(__name__) app.secret_key = open('secret_key.txt').read() login_manager = LoginManager() login_manager.init_app(app) @login_manager.user_loader def load_user(user_id): return User(user_id) @app.route('/') def index(): return render_template("index.html") @app.route('/register') def register(): return render_template("registration.html") @app.route('/process_registration', methods=['POST']) def process_registration(): f = request.form if not f['username'].isalnum(): return f"username can only contain letters and numbers" L = sql_execute('''SELECT * FROM users WHERE username=?''', (f['username'],)) print(L) if len(L) > 0: return f"sorry; username {f['username']} already taken" num = sql_execute('''SELECT MAX(id) from users''') print(num) newid = 1 if len(num) == 0 or num[0][0] == None else 1 + num[0][0] sql_execute('''INSERT INTO users VALUES (?,?,?,?,?,?,?,?)''',\ (newid,f['username'],f['name'],'',hashpw(f['pass']),"","","")) pfp = request.files['pfp'] pfp.save('static/pfps/'+f['username']) #return f"registered! You are user #{newid}<br>" + render_template("login.html") return redirect(url_for('login')) @app.route('/login') def login(): return render_template("login.html") @app.route('/process_login', methods=['POST']) def process_login(): f = request.form L = sql_execute('''SELECT name FROM users WHERE username=? AND pass_hash=?''',\ (f['username'], hashpw(f['pass']))) print(L) if len(L) == 0 or L[0][0] == None: return "sorry, login failed\n" + render_template("login.html") user = User(f['username']) login_user(user) return redirect(url_for('evaluations')) @app.route("/logout") @login_required def logout(): logout_user() return redirect(url_for('index')) dct = {1:'pal', 2:'robot', 3:'rotated'} fcn_names = {1:'palindrome', 2:'judge', 3:'rotate'} @app.route('/code/<int:n>') @login_required def coding_chal(n): if n < 1 or n > 3: return "???" else: sc = open("challenges/"+dct[n]+"_sc.py").read() question_text = open("challenges/"+dct[n]+"_desc.txt").read().split('\n') return render_template("code_chall.html",\ n=n,question_text=question_text,code=sc,results=["No results yet!"],done=None) @app.route('/process_language', methods=['POST']) def process_language(): n = int(request.form['n']) if request.form['language'] == "Python": sc = open("challenges/"+dct[n]+"_sc.py").read() question_text = open("challenges/"+dct[n]+"_desc.txt").read().split('\n') return render_template("code_chall.html",\ n=n,question_text=question_text,code=sc,results=["No results yet!"],done=None,lang='python') else: sc = open("challenges/"+dct[n]+"_sc.c").read() question_text = open("challenges/"+dct[n]+"_desc.txt").read().split('\n') return render_template("code_chall.html",\ n=n,question_text=question_text,code=sc,results=["No results yet!"],done=None,lang='c') @app.route('/process_code', methods=['POST']) @login_required def process_code(): n = int(request.form['cnum']) if n < 1 or n > 3: return "???" else: question_text = open("challenges/"+dct[n]+"_desc.txt").read().split('\n') user_code = request.form['code_submission'] tc_filename ="challenges/"+dct[n]+"_testcases.txt" sol_filename = "challenges/"+dct[n]+"_sol.txt" lang = request.form['language'] (numWrong,results) = runAutograder(lang.lower(),user_code,fcn_names[n],tc_filename, sol_filename) results = results.split('\n') if numWrong == 0: #TODO: user_code is a string with the user's submitted code. # current_user.username is the user's username. # @ Brandon. current_user.updatedb(n,user_code) return render_template("code_chall.html",n=n,\ question_text=question_text,code=user_code,results=results,\ done=(numWrong == 0)) @app.route('/matches') @app.route('/matches/<string:typ>/<string:other>') @login_required def matches(typ=None,other=None): if other != None: sql_execute('''insert into actions values(?,?,?)''', (typ,current_user.username,other)) if current_user.numdone() < 3: return render_template("matches.html",user=current_user,curr=current_user) best_match = current_user.compute_best_match() return render_template("matches.html",user=best_match,curr=current_user) @app.route('/evaluations') @login_required def evaluations(): return render_template("evaluations.html",user=current_user) @app.route('/profile') @login_required def profile(): return render_template("profile.html", user=current_user, isMe=True) @app.route('/mutual') @login_required def mutual(): matches = current_user.get_mutual_matches() return render_template("mutual.html", matches=matches, user=current_user) if __name__ == '__main__': app.run(debug=False)
StarcoderdataPython
197656
<reponame>GEOS-ESM/AeroApps<filename>src/Components/rtms/geo_vlidort/geo_vlidort_AI_lc2.py #!/usr/bin/env python # -W ignore::DeprecationWarning """ Runscript for running geo_vlidort.x on NCCS May 2017 <EMAIL> """ from datetime import datetime, timedelta from dateutil.parser import parse import os import shutil import sys import subprocess from distutils.dir_util import mkpath import numpy as np import math import time import glob import shutil from netCDF4 import Dataset from optparse import OptionParser # Command-line args jobsmax = 150 dt = timedelta(hours=1) archive = '/archive/u/rgovinda/osse2/' class JOBS(object): def handle_jobs(self): # Figure out how many jobs you need to submit runlen = len(self.dirstring) if self.nodemax is not None: numjobs = sum(self.nodemax_list) else: numjobs = runlen devnull = open(os.devnull, 'w') if numjobs <= jobsmax: countRun = runlen node_tally = numjobs else: if self.nodemax is not None: keep_adding = True node_tally = 0 countRun = 0 while(keep_adding): if (node_tally + self.nodemax_list[countRun] <= jobsmax): node_tally = node_tally + self.nodemax_list[countRun] countRun = countRun + 1 else: keep_adding = False else: countRun = jobsmax node_tally = jobsmax workingJobs = np.arange(countRun) # Submit first JOBSMAX jobs jobid = np.empty(0) for i in workingJobs: s = self.dirstring[i] os.chdir(s) jobid = np.append(jobid,subprocess.check_output(['qsub',self.runfile])) os.chdir(self.cwd) # Monitor jobs 1-by-1 # Add a new job when one finishes # Until they are all done stat = subprocess.call(['qstat -u pcastell'], shell=True, stdout=devnull) while (stat == 0): stat = subprocess.call(['qstat -u pcastell'], shell=True, stdout=devnull) finishedJobs = np.empty(0,dtype='int') for ii,i in enumerate(workingJobs): s = jobid[i] s = s.strip('\n') finished = False # Check to see if this job is finished if self.nodemax is not None and self.nodemax_list[i] > 1: # Loop through the nodes working on this job finishedCNT = 0 for a in np.arange(self.nodemax_list[i]): a = a + 1 try: result = subprocess.check_output(['squeue','-j',s+'_'+ str(a)],stderr=subprocess.STDOUT) if (s+'_'+ str(a) not in result): #print ' Job Not Found! '+s+'_'+ str(a) finishedCNT = finishedCNT + 1 except subprocess.CalledProcessError as e: #print ' ERROR Job Not Found! '+s+'_'+ str(a) finishedCNT = finishedCNT + 1 if (finishedCNT == self.nodemax_list[i]): finished = True else: result = subprocess.call(['qstat',s], stdout=devnull) if (result != 0): finished = True # if the job is finished clean up the workspace if finished: #print 'Job finished, cleaning up', s, i finishedJobs = np.append(finishedJobs,ii) errcheck = self.check_for_errors(i,s) if (errcheck is False): self.destroy_workspace(i,s) else: print 'Jobid ',s,' exited with errors' # finished checking up on all the jobs # Remove finished jobs from the currently working list if len(finishedJobs) != 0: print 'deleting finishedJobs',finishedJobs,jobid[workingJobs[finishedJobs]] if self.nodemax is not None: node_tally = node_tally - sum(self.nodemax_list[workingJobs[finishedJobs]]) else: node_tally = node_tally - len(finishedJobs) workingJobs = np.delete(workingJobs,finishedJobs) # Add more jobs if needed # reinitialize stat variable if (runlen > countRun) and (node_tally < jobsmax): #print 'adding new jobs' if self.nodemax is not None: keep_adding = True newRun = 0 while (keep_adding): if (node_tally + self.nodemax_list[countRun + newRun] <= jobsmax): node_tally = node_tally + self.nodemax_list[countRun + newRun] newRun = newRun + 1 if (countRun + newRun == runlen): keep_adding = False else: keep_adding = False else: newRun = jobsmax - node_tally node_tally = jobsmax newjobs = countRun + np.arange(newRun) workingJobs = np.append(workingJobs, newjobs) for i in newjobs: s = self.dirstring[i] os.chdir(s) jobid = np.append(jobid,subprocess.check_output(['qsub',runfile])) os.chdir(self.cwd) countRun = countRun + newRun stat = subprocess.call(['qstat -u pcastell'], shell=True, stdout=devnull) print 'Waiting 1 minutes' time.sleep(60) # Exited while loop print 'All jobs done' def check_for_errors(self,i,jobid): os.chdir(self.dirstring[i]) error = False if self.nodemax is None: nodemax = None else: nodemax = self.nodemax_list[i] if self.nodemax is not None and nodemax > 1: for a in np.arange(nodemax): a = a + 1 errfile = 'slurm_' +jobid + '_' + str(a) + '.err' statinfo = os.stat(errfile) if (statinfo.st_size != 0): error = True else: errfile = 'slurm_' +jobid + '.err' statinfo = os.stat(errfile) if (statinfo.st_size != 0): error = True os.chdir(self.cwd) return error class WORKSPACE(JOBS): """ Create Working Directories and RC files """ def __init__(self,startdate,enddate,options): self.cloud = False for oo in options.__dict__: if (type(options.__dict__[oo]) is str) and (options.__dict__[oo].lower() == 'none'): self.__dict__[oo] = None else: self.__dict__[oo] = options.__dict__[oo] if self.nodemax is not None: self.nodemax = int(self.nodemax) self.nccs = self.nccs + '/' + self.instname.upper() + '/DATA/' self.prefix = self.nccs + 'workdir/' self.indir = self.nccs self.outdir = self.nccs + 'LevelC2' if (self.additional_output): self.addoutdir = self.nccs + 'LevelC2' else: self.addoutdir = None # check for layout keyword. # figure out number of tiles if self.layout is None: self.ntiles = 1 else: self.ntiles = int(self.layout[0])*int(self.layout[1]) if type(self.channels) is str: if ',' in self.channels: self.channels = self.channels.replace(' ','').split(',') else: self.channels = self.channels.split() if type(self.runmode) is str: if ',' in self.runmode: self.runmodeL = self.runmode.replace(' ','').split(',') else: self.runmodeL = self.runmode.split() self.startdateL = startdate self.enddateL = enddate ################################################## #### # Loop through dates # Create working directories, SLURM scripts, and RC-files #### ################################################## # save run directory if self.verbose: print '++Saving run directory',os.getcwd() self.cwd = os.getcwd() #initialize arrays to hold directory names self.dirstring = [] self.outdirstring = [] self.nodemax_list = [] if (self.additional_output): self.addoutdirstring = [] for startdate,enddate in zip(self.startdateL,self.enddateL): # Loop over dates while (startdate <= enddate): # loop through tiles for tile in np.arange(self.ntiles): if self.layout is not None: laycode = self.layout + str(tile) else: laycode = None # check to see if there is any work to do # only simulating land pixels with limited SZAs and Cloud fractions numpixels = self.prefilter(startdate,layout=laycode) if numpixels>0: if (numpixels <= 1000 and self.nodemax is not None): nodemax = 1 elif self.nodemax is not None: nodemax = int(self.nodemax) else: nodemax = None for runmode in self.runmodeL: # Runmode code self.runmode = runmode self.code = runmode for i, ch in enumerate(self.channels): # Create working directories for intermediate outputs # create output directories # save directory names dirlist = self.make_workspace(startdate,ch,nodemax=nodemax,layout=laycode) if (self.additional_output): workdir, outdir, addoutdir_ = dirlist else: workdir, outdir = dirlist self.dirstring.append(workdir) self.outdirstring.append(outdir) self.nodemax_list.append(nodemax) if (self.additional_output): self.addoutdirstring.append(addoutdir_) # Create rcfiles self.make_rcfile(workdir,startdate,ch,nodemax=nodemax, layout=laycode) startdate = startdate + dt self.nodemax_list = np.array(self.nodemax_list) def get_from_archive(self,path): ii = path.find('/c1440_NR/') archive = self.archive + path[ii:] if not os.path.exists(os.path.dirname(path)): os.makedirs(os.path.dirname(path)) # Copy from archive. Note: overwrites file if it exists. try: shutil.copyfile(archive,path) except IOError: print 'Could not find archive ',archive, ' to put in ', path sys.exit() def put_in_archive(self,path): ii = path.find('/c1440_NR/') archive = self.archive + path[ii:] # Copy to archive. Note: overwrites file if it exists. if not os.path.exists(os.path.dirname(archive)): os.makedirs(os.path.dirname(archive)) try: shutil.copyfile(path,archive) except IOError: print 'Could not put '+path+' in archive ',archive sys.exit() def prefilter(self,date,layout=None): if self.verbose: print '++Checking for good pixels in prefilter' g5dir = self.indir + '/LevelB/'+ 'Y'+ str(date.year) + '/M' + str(date.month).zfill(2) + '/D' + str(date.day).zfill(2) nymd = str(date.year) + str(date.month).zfill(2) + str(date.day).zfill(2) hour = str(date.hour).zfill(2) if layout is None: met = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.met_Nv.' + nymd + '_' + hour + 'z.nc4' geom = g5dir + '/' + self.instname.lower() + '.lb2.angles.' + nymd + '_' + hour + 'z.nc4' land = self.indir + '/LevelB/invariant/' + self.instname.lower() + '-g5nr.lb2.asm_Nx.nc4' aer = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.aer_Nv.' + nymd + '_' + hour + 'z.nc4' else: met = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.met_Nv.' + nymd + '_' + hour + 'z_' + layout +'.nc4' geom = g5dir + '/' + self.instname.lower() + '.lb2.angles.' + nymd + '_' + hour + 'z_' + layout +'.nc4' land = self.indir + '/LevelB/invariant/' + self.instname.lower() + '-g5nr.lb2.asm_Nx_' + layout + '.nc4' aer = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.aer_Nv.' + nymd + '_' + hour + 'z_' + layout +'.nc4' if self.verbose: print '++Opening metfile ',met if not os.path.exists(met): self.get_from_archive(met) ncMet = Dataset(met) Cld = np.squeeze(ncMet.variables[u'CLDTOT'][:]) f = np.where(Cld <= float(self.CLDMAX)) ncMet.close() if len(f[0]) == 0: return False, 0 if not os.path.exists(geom): self.get_from_archive(geom) ncGeom = Dataset(geom) SZA = np.squeeze(ncGeom.variables[u'solar_zenith'][:]) VZA = np.squeeze(ncGeom.variables[u'sensor_zenith'][:]) ncGeom.close() if not os.path.exists(land): self.get_from_archive(land) ncLand = Dataset(land) FRLAND = np.squeeze(ncLand.variables[u'FRLAND'][:]) ncLand.close() SZA = SZA[f] VZA = VZA[f] FRLAND = FRLAND[f] f = np.where(VZA < 80) if len(f[0]) == 0: return 0 SZA = SZA[f] FRLAND = FRLAND[f] f = np.where(SZA < 80) if len(f[0]) == 0: return 0 FRLAND = FRLAND[f] f = np.where(FRLAND < 0.99) if len(f[0]) == 0: return 0 return len(f[0]) def make_workspace(self,date,ch,nodemax=None,layout=None): # Get necessary files from archive if needed g5dir = self.indir + '/LevelB/'+ 'Y'+ str(date.year) + '/M' + str(date.month).zfill(2) + '/D' + str(date.day).zfill(2) nymd = str(date.year) + str(date.month).zfill(2) + str(date.day).zfill(2) hour = str(date.hour).zfill(2) if (layout is None) or (self.cloud is True) : chm = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.chm_Nv.' + nymd + '_' + hour + 'z.nc4' aer = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.aer_Nv.' + nymd + '_' + hour + 'z.nc4' else: chm = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.chm_Nv.' + nymd + '_' + hour + 'z_' + layout +'.nc4' aer = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.aer_Nv.' + nymd + '_' + hour + 'z_' + layout +'.nc4' if (not os.path.exists(chm)) and (self.cloud is False): self.get_from_archive(chm) if not os.path.exists(aer): self.get_from_archive(aer) # creating working directory dirname = '{}/{}.{}T{}.{}.{}'.format(self.prefix,self.instname.lower(),date.date(),str(date.hour).zfill(2),ch,self.code) jobname = '{}.{}T{}.{}'.format(self.instname.lower(),date.date(),str(date.hour).zfill(2),ch) if layout is not None: dirname = dirname + '.' + layout jobname = jobname + '.' + layout bindir = dirname if os.path.exists(dirname): shutil.rmtree(dirname) os.makedirs(dirname) # outdirname outdir = '{}/Y{}/M{}/D{}'.format(self.outdir,date.year,str(date.month).zfill(2),str(date.day).zfill(2)) if self.addoutdir is not None: addoutdir = '{}/Y{}/M{}/D{}'.format(self.addoutdir,date.year,str(date.month).zfill(2),str(date.day).zfill(2)) # create some symlinks to shared files os.chdir(dirname) if not os.path.isfile(os.path.basename(self.execFile)): os.symlink(self.execFile,os.path.basename(self.execFile)) if not os.path.exists('ExtData'): os.symlink(self.cwd+'/ExtData','ExtData') if self.cloud is True: if not os.path.exists('ExtDataCloud'): os.symlink(self.cwd+'/ExtDataCloud','ExtDataCloud') if not os.path.isfile('Chem_MieRegistry.rc'): os.symlink(self.cwd+'/Chem_MieRegistry.rc','Chem_MieRegistry.rc') if not os.path.isfile('clean_mem.sh'): os.symlink(self.cwd+'/clean_mem.sh','clean_mem.sh') # Create slurm runfile - using self.runfile as template source = open(self.cwd+'/'+self.runfile,'r') destination = open(self.runfile,'w') for line in source: if (line[0:18] == '#SBATCH --job-name'): destination.write('#SBATCH --job-name='+jobname+'\n') elif (line[0:16] == '#SBATCH --output'): if (nodemax is not None and nodemax > 1): destination.write('#SBATCH --output='+'slurm'+"_%A_%a.out"+'\n') else: destination.write('#SBATCH --output='+'slurm'+"_%j.out"+'\n') elif (line[0:15] == '#SBATCH --error'): if (nodemax is not None and nodemax > 1): destination.write('#SBATCH --error='+'slurm'+"_%A_%a.err"+'\n') else: destination.write('#SBATCH --error='+'slurm'+"_%j.err"+'\n') elif (line[0:15] == '#SBATCH --array'): if (nodemax is not None and nodemax > 1): destination.write('#SBATCH --array=1-'+str(nodemax)+'\n') elif (line[0:13] == 'setenv GEOBIN'): destination.write('setenv GEOBIN '+bindir+'\n') elif (line[0:13] == 'setenv OUTDIR'): destination.write('setenv OUTDIR '+outdir+'\n') if not os.path.exists(outdir): mkpath(outdir) elif (line[0:16] == 'setenv ADDOUTDIR'): if (self.addoutdir is not None): destination.write('setenv ADDOUTDIR '+addoutdir+'\n') if not os.path.exists(addoutdir): mkpath(addoutdir) else: destination.write('setenv ADDOUTDIR None \n') elif (line[0:8] == '$RUN_CMD'): if (nodemax is not None and nodemax > 1): destination.write('$RUN_CMD ./'+ os.path.basename(self.execFile) +' geo_vlidort.rc ${SLURM_ARRAY_TASK_ID}'+'\n') else: destination.write('$RUN_CMD ./'+ os.path.basename(self.execFile) + ' geo_vlidort.rc'+'\n') else: destination.write(line) source.close() destination.close() # Change wavelength number in Aod_EOS.rc source = open(self.cwd+'/Aod_EOS.rc','r') destination = open('Aod_EOS.rc','w') a = float(ch)*1e-3 for line in source: if (line[0:11] == 'r_channels:'): destination.write('r_channels: '+'{:0.3f}e-6'.format(a)+'\n') else: destination.write(line) source.close() destination.close() # Go back to original run directory os.chdir(self.cwd) if (self.addoutdir is not None): return dirname, outdir, addoutdir else: return dirname, outdir def make_rcfile(self,dirname,date,ch,nodemax=None,layout=None): os.chdir(dirname) fch = float(ch) rcfile = open('geo_vlidort.rc','w') rcfile.write('INDIR: '+self.indir+'\n') rcfile.write('OUTDIR: .\n') rcfile.write('DATE: '+str(date.year)+str(date.month).zfill(2)+str(date.day).zfill(2)+'\n') rcfile.write('TIME: '+str(date.hour).zfill(2)+'\n') rcfile.write('INSTNAME: ' + self.instname.lower() + '\n') if (self.runmode == 'scalar'): rcfile.write('SCALAR: true\n') else: rcfile.write('SCALAR: false\n') rcfile.write('CHANNELS: '+ch+'\n') rcfile.write('CLDMAX: '+self.CLDMAX+'\n') if nodemax is not None: rcfile.write('NODEMAX: '+ str(nodemax) + '\n') if self.version is not None: rcfile.write('VERSION: '+self.version+'\n') if layout is not None: rcfile.write('LAYOUT: '+layout+'\n') rcfile.close() os.chdir(self.cwd) def final_cleanup(self): # put LevelB aer files in archive or remove # -------------------- # Loop over dates for startdate,enddate in zip(self.startdateL,self,enddateL): date = startdate while (date <= enddate): g5dir = self.indir + '/LevelB/'+ 'Y'+ str(date.year) + '/M' + str(date.month).zfill(2) + '/D' + str(date.day).zfill(2) nymd = str(date.year) + str(date.month).zfill(2) + str(date.day).zfill(2) hour = str(date.hour).zfill(2) if self.layout is not None: # loop through tiles for tile in np.arange(self.ntiles): laycode = self.layout + str(tile) met = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.met_Nv.' + nymd + '_' + hour + 'z_' + laycode +'.nc4' chm = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.chm_Nv.' + nymd + '_' + hour + 'z_' + laycode +'.nc4' aer = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.aer_Nv.' + nymd + '_' + hour + 'z_' + laycode +'.nc4' geom = g5dir + '/' + self.instname.lower() + '.lb2.angles.' + nymd + '_' + hour + 'z_' + laycode +'.nc4' land = self.indir + '/LevelB/invariant/' + self.instname.lower() + '-g5nr.lb2.asm_Nx_' + laycode + '.nc4' if self.archive_lb: self.put_in_archive(met) self.put_in_archive(chm) self.put_in_archive(aer) self.put_in_archive(geom) self.put_in_archive(land) if not self.keep_lb: os.remove(met) os.remove(chm) os.remove(aer) os.remove(geom) os.remove(land) else: met = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.met_Nv.' + nymd + '_' + hour + 'z.nc4' chm = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.chm_Nv.' + nymd + '_' + hour + 'z.nc4' aer = g5dir + '/' + self.instname.lower() + '-g5nr.lb2.aer_Nv.' + nymd + '_' + hour + 'z.nc4' geom = g5dir + '/' + self.instname.lower() + '.lb2.angles.' + nymd + '_' + hour + 'z.nc4' land = self.indir + '/LevelB/invariant/' + self.instname.lower() + '-g5nr.lb2.asm_Nx.nc4' if self.archive_lb: self.put_in_archive(met) self.put_in_archive(chm) self.put_in_archive(aer) self.put_in_archive(geom) self.put_in_archive(land) if not self.keep_lb: os.remove(met) os.remove(chm) os.remove(aer) os.remove(geom) os.remove(land) date += dt def destroy_workspace(self,i,jobid): os.chdir(self.dirstring[i]) os.remove('Aod_EOS.rc') os.remove('Chem_MieRegistry.rc') os.remove(os.path.basename(self.execFile)) os.remove('clean_mem.sh') os.remove('ExtData') os.remove('geo_vlidort.rc') os.remove(self.runfile) if self.nodemax is None: nodemax = None else: nodemax = self.nodemax_list[i] if self.additional_output: addoutdir = self.addoutdirstring[i] else: addoutdir = None outdir = self.outdirstring[i] if self.profile is False: if nodemax is not None and nodemax > 1: for a in np.arange(nodemax): a = a + 1 errfile = 'slurm_' +jobid + '_' + str(a) + '.err' outfile = 'slurm_' +jobid + '_' + str(a) + '.out' if self.verbose: print '++cleaning up errfile', errfile print '++cleaning up outfile', outfile os.remove(errfile) os.remove(outfile) os.remove('slurm_%A_%a.out') else: errfile = 'slurm_' +jobid + '.err' os.remove(errfile) outfile = 'slurm_' +jobid + '.out' os.remove(outfile) def move_file(filelist,dest): for movefile in filelist: if os.path.exists(dest+'/'+movefile): os.remove(dest+'/'+movefile) shutil.move(movefile,dest) #runscript to combine files if nodemax is not None and nodemax > 1: outfilelist = glob.glob('*.lc2.*.nc4') self.combine_files(outfilelist) outfilelist = glob.glob('*.lc2.*.nc4') move_file(outfilelist,outdir) else: outfilelist = glob.glob('*.lc2.*.nc4') move_file(outfilelist,outdir) if (addoutdir is not None): if nodemax is not None and nodemax > 1: outfilelist = glob.glob('*.add.*.nc4') self.combine_files(outfilelist) outfilelist = glob.glob('*.add.*.nc4') move_file(outfilelist,addoutdir) else: outfilelist = glob.glob('*.add.*.nc4') move_file(outfilelist,addoutdir) os.chdir(self.cwd) if self.profile is False: os.rmdir(self.dirstring[i]) def combine_files(self,filelist): mergedfile = filelist[0] parts = mergedfile.split('.') mergedfile = '' for p in parts[0:-2]: mergedfile = mergedfile + p + '.' mergedfile = mergedfile + 'nc4' #mergedfile = mergedfile[:-5] + 'nc4' os.rename(filelist[0],mergedfile) filelist = filelist[1:] if type(filelist) is str: filelist = filelist.split() ncmergedfile = Dataset(mergedfile, mode='r+') for filename in filelist: ncfile = Dataset(filename) for var in ncmergedfile.variables: vardata = ncfile.variables[var][:] mergedata = ncmergedfile.variables[var][:] if (np.ma.is_masked(vardata)): mergedata[~vardata.mask] = vardata[~vardata.mask] ncmergedfile.variables[var][:] = mergedata ncfile.close() os.remove(filename) ncmergedfile.close() ######################################################### if __name__ == "__main__": # Defaults # ------------ instname = 'tempo' version = '1.0' channels = '388' nodemax = 10 layout = None CLDMAX = '0.01' nccs = '/discover/nobackup/projects/gmao/osse2/pub/c1440_NR/OBS/' runmode = 'vector' runfile = 'geo_vlidort_lc2.j' execFile = '/discover/nobackup/pcastell/workspace/GAAS/src/Components/geo_vlidort/geo_vlidort_AI.x' #Flags # verbose = False # additional_output = False # profile = False # keep_lb = False # archive_lb = False # Parse command line options # ------------------------------ parser = OptionParser(usage="Usage: %prog [options] startdate enddate") parser.add_option("-I", "--instname", dest="instname", default=instname, help="Instrument name (default=%s)"\ %instname ) parser.add_option("-V", "--version_string", dest="version", default=version, help="Version name (default=%s)"\ %version ) parser.add_option("-c", "--channels", dest="channels", default=channels, help="Channels (default=%s)"\ %channels ) parser.add_option("-a", "--additional", action="store_true", dest="additional_output",default=False, help="Turn on writing additional_output. (default=False)") parser.add_option("-v", "--verbose",action="store_true", dest="verbose", default=False, help="Verbose (default=False)" ) parser.add_option("--keep_lb",action="store_true", dest="keep_lb", default=False, help="keep LevelB files - do not remove after geo_vlidort is finished (default=False)" ) parser.add_option("--archive_lb",action="store_true", dest="archive_lb", default=False, help="archive LevelB files - copy to archive dir after geo_vlidort is finished (default=False)" ) parser.add_option("-n", "--nodemax", dest="nodemax", default=nodemax, help="Max number of nodes to use. "\ "(default=%s)"\ %nodemax ) parser.add_option("-l", "--layout", dest="layout", default=layout, help="Layout of domain. Used for breaking up high-res domains (e.g. GOES-R)"\ "(default=%s)"\ %layout ) parser.add_option("-C", "--cldmax", dest="CLDMAX", default=CLDMAX, help="Maximum cloud fraction allowed. "\ "(default=%s)"\ %CLDMAX ) parser.add_option("-d", "--dir", dest="nccs", default=nccs, help="Root level directory for inputs/outputs "\ "(default=%s)"\ %nccs ) parser.add_option("-p", "--profile", action="store_true",dest="profile", default=False, help="Leave slurm output files in working directory. (default=False)") parser.add_option("-r", "--runmode", dest="runmode", default=runmode, help="VLIDORT run mode. Either 'scalar' or 'vector' "\ "(default=%s)"\ %runmode ) parser.add_option("-f", "--runfile", dest="runfile", default=runfile, help="slurm script template "\ "(default=%s)"\ %runfile ) parser.add_option("-e", "--execfile", dest="execFile", default=execFile, help="geo_vlidort executable "\ "(default=%s)"\ %execFile ) parser.add_option("-A", "--archive", dest="archive", default=archive, help="where to look for missing data on archive"\ "(default=%s)"\ %archive ) ################ ### # End of uper inputs ### ################ (options, args) = parser.parse_args() if len(args) == 2: startdate, enddate = args else: parser.error("must have 2 arguments: startdate and enddate") # Setup workspace and runscript # ------------------------------ if ',' in startdate: startdate = startdate.split(',') enddate = enddate.split(',') startL = [] endL = [] for s in startdate: startL.append(parse(s)) for e in enddate: endL.append(parse(e)) else: startdate = [parse(startdate)] enddate = [parse(enddate)] workspace = WORKSPACE(startdate,enddate,options) # Submit and Handle Jobs # ----------------------------- if (workspace.dirstring) > 0: workspace.handle_jobs() workspace.final_cleanup() else: print 'No model hours to run'
StarcoderdataPython
4812237
<reponame>lachlants/denet import sys import time import math import denet.common.logging as logging import denet.multi.network as network from denet.multi.worker import WorkerProcess #handles communication with update server class UpdateClient(): def __init__(self, epoch_start, subset_start, subset_num, sock=None, async=False, mpi=False, count_offset=0, count_delta=1): #ensure valid epoch / subset self.subset_num = subset_num if subset_start >= self.subset_num: self.epoch = epoch_start + 1 self.subset = 0 else: self.epoch = epoch_start self.subset = subset_start - 1 #for non-async subset num must if async: self.epoch_stride = subset_num else: self.epoch_stride = count_delta*int(math.ceil(subset_num / count_delta)) self.mpi = mpi self.sock = sock self.server_exists = not self.sock is None if not sock is None: self.server_name = "MPI" if self.mpi else sock.getsockname() self.async = async self.count_delta = count_delta self.count_offset = epoch_start*self.epoch_stride + subset_start if not async: self.count_offset += count_offset - count_delta - (subset_start%count_delta) logging.info("Start update client - epoch:", self.epoch, "epoch stride:", self.epoch_stride, "subset_num:", self.subset_num, "async:", self.async, "MPI:", self.mpi, "count delta:", self.count_delta, "count offset:", self.count_offset) #server commands def cmd_count(self, peek=False): assert self.server_exists network.send_json(self.sock, {"cmd": "count", "peek": peek}, self.mpi) r = network.recv_json(self.sock, self.mpi); return r["count"] def cmd_update(self, model_update_delta, model_update): assert self.server_exists network.send_json(self.sock, {"cmd": "update", "data": model_update_delta.export_json()}, self.mpi) model_update.import_json(network.recv_json(self.sock, self.mpi)) return model_update def cmd_sync(self, model_update, initial=False): assert self.server_exists cmd_json = {"cmd" : "sync", "initial":initial} if initial: cmd_json["data"] = model_update.export_json() network.send_json(self.sock, cmd_json, self.mpi) model_update.import_json(network.recv_json(self.sock, self.mpi)) return model_update #returns index of next subset to process (returns -1 if epoch has finished) #overcomplicated to handle all three cases! def get_subset_next(self): if self.server_exists: if self.async: #peek counter to see if next subset is in new epoch epoch_next = int((self.cmd_count(peek = True) + self.count_offset) // self.epoch_stride) if (epoch_next != self.epoch): self.epoch += 1 self.subset = -1 else: #get next update self.subset = (self.cmd_count() + self.count_offset)%self.epoch_stride else: self.count_offset += self.count_delta epoch_next = int(self.count_offset // self.epoch_stride) if self.epoch != epoch_next: self.count_offset -= self.count_delta self.epoch += 1 self.subset = -1 else: self.subset = min(self.count_offset%self.epoch_stride, self.subset_num-1) else: self.subset += 1 if self.subset >= self.subset_num: self.epoch += 1 self.subset = -1 return self.subset #send model to server def update(self, model_update_delta, model_update, workers): if self.server_exists: #send model delta and update workers logging.info("Sending updates to server:", self.server_name) ts = time.time() self.cmd_update(model_update_delta, model_update) for worker in workers: worker.update(model_update) logging.info("Update took %0.1f sec"%(time.time() - ts)) #synchonize all nodes def sync(self, model_update, workers, initial=False): if self.async: logging.info("Synchronizing with update server:", self.server_name) ts = time.time() self.cmd_sync(model_update, initial) #update workers for worker in workers: worker.update(model_update) logging.info("Sync took %0.1f sec"%(time.time() - ts))
StarcoderdataPython
175616
# -*- coding: utf-8 -*- import math import collections import bisect import heapq import time import random import itertools import sys """ created by shhuan at 2017/10/20 14:23 """ MAXN = 2000000+5 G = collections.defaultdict(list) def addEdge(s, t): G[s].append(t) G[t].append(s) N = 0 siz = [0] * MAXN def predfs(v, fa): siz[v] = 1 for u in G[v]: if u != fa: predfs(u, v) siz[v] += siz[u] def fincent(v, fa): for u in G[v]: if u != fa and siz[u] > N//2: return fincent(u, v) return v topo = [] def dfs(v, fa): global topo for u in G[v]: if u != fa: dfs(u, v) topo.append((v, fa)) ans = [] def genans(v, fa): global topo topo = [] dfs(v, fa) topo.pop() iter = v for now in topo: ans.append((fa, iter, now[0])) ans.append((now[0], now[1], v)) iter = now[0] ans.append((fa, iter, v)) N = int(input()) for i in range(N-1): s, t = map(int, input().split()) addEdge(s, t) predfs(1, -1) cent = fincent(1, -1) cent2 = 0 for u in G[cent]: if siz[u]*2 == N: cent2 = u for u in G[cent]: if u != cent2: genans(u, cent) if cent2: for u in G[cent2]: if u != cent: genans(u, cent2) print(len(ans)) for row in ans: print(' '.join(map(str, row))) print(siz[:N+1])
StarcoderdataPython
123832
<reponame>kamadorueda/oblivion # Standard imports from typing import Tuple # pylint: disable=too-many-lines # Primes up to 4 ** 8 * 16 (1048576) PRIMES: Tuple[int, ...] = ( 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997, 1009, 1013, 1019, 1021, 1031, 1033, 1039, 1049, 1051, 1061, 1063, 1069, 1087, 1091, 1093, 1097, 1103, 1109, 1117, 1123, 1129, 1151, 1153, 1163, 1171, 1181, 1187, 1193, 1201, 1213, 1217, 1223, 1229, 1231, 1237, 1249, 1259, 1277, 1279, 1283, 1289, 1291, 1297, 1301, 1303, 1307, 1319, 1321, 1327, 1361, 1367, 1373, 1381, 1399, 1409, 1423, 1427, 1429, 1433, 1439, 1447, 1451, 1453, 1459, 1471, 1481, 1483, 1487, 1489, 1493, 1499, 1511, 1523, 1531, 1543, 1549, 1553, 1559, 1567, 1571, 1579, 1583, 1597, 1601, 1607, 1609, 1613, 1619, 1621, 1627, 1637, 1657, 1663, 1667, 1669, 1693, 1697, 1699, 1709, 1721, 1723, 1733, 1741, 1747, 1753, 1759, 1777, 1783, 1787, 1789, 1801, 1811, 1823, 1831, 1847, 1861, 1867, 1871, 1873, 1877, 1879, 1889, 1901, 1907, 1913, 1931, 1933, 1949, 1951, 1973, 1979, 1987, 1993, 1997, 1999, 2003, 2011, 2017, 2027, 2029, 2039, 2053, 2063, 2069, 2081, 2083, 2087, 2089, 2099, 2111, 2113, 2129, 2131, 2137, 2141, 2143, 2153, 2161, 2179, 2203, 2207, 2213, 2221, 2237, 2239, 2243, 2251, 2267, 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StarcoderdataPython
3259775
<reponame>brandenc40/groupme-bot<filename>groupme_bot/bot.py from __future__ import annotations import re from collections import OrderedDict from json.decoder import JSONDecodeError from typing import Any, List, Callable, Optional import httpx from starlette.requests import Request from starlette.responses import PlainTextResponse from starlette.types import Scope, Receive, Send from .attachment import Attachment, MentionsAttachment from .callback import Callback from .groupme import GroupMe _success_response = PlainTextResponse('Success') class HandlerPatternExistsError(Exception): pass class Context(object): __slots__ = ('_bot', '_callback') def __init__(self, bot: Bot, callback: Callback): """ Context provided to every handler/scheduled function run by the bot. This provides a clean object containing bot the bot in use and the Callback that was sent. :param Bot bot: :param Callback callback: """ self._bot: Bot = bot self._callback: Callback = callback @property def bot(self) -> Bot: return self._bot @property def callback(self) -> Callback: return self._callback class Bot(GroupMe): __slots__ = ('bot_name', 'bot_id', 'groupme_api_token', 'group_id', '_handler_functions', '_jobs') def __init__(self, bot_name: str, bot_id: str, groupme_api_token: str, group_id: str): """ The Bot class represents a single bot that can contains multiple callback handlers and scheduled jobs. Bots are run using the Router class. :param bot_name: A unique name for this bot to be known as within this code. Does not impact the name displayed in the GroupMe app. :param bot_id: The Bot ID provided by GroupMe. :param groupme_api_token: The GroupMe API token for access to group details. :param group_id: The id of the GroupMe group in which the bot exists. """ super().__init__(groupme_api_token) self.bot_name = bot_name self.bot_id = bot_id self.groupme_api_token = groupme_api_token self.group_id = group_id self._handler_functions: OrderedDict = OrderedDict() self._jobs = [] @property def cron_jobs(self) -> List[dict]: """ All list of cron jobs associated with this bot. :return List[dict]: """ return self._jobs def __str__(self): return f"{self.bot_name}: {len(self._handler_functions)} callback handlers, " \ f"{len(self._jobs)} cron jobs at {hex(id(self))}" async def __call__(self, scope: Scope, receive: Receive, send: Send): req = Request(scope, receive, send) try: callback_dict = await req.json() except JSONDecodeError as e: response = PlainTextResponse( "400 Bad Request. Unable to parse JSON. Error: " + str(e), status_code=400) await response(scope, receive, send) return callback = Callback(callback_dict) if callback.sender_type != 'user': # only reply to users await _success_response(scope, receive, send) return text = callback.text.lower().strip() for pattern, func in self._handler_functions.items(): if re.search(pattern, text): try: func(Context(self, callback)) await _success_response(scope, receive, send) return except Exception as e: response = PlainTextResponse(str(e), status_code=500) await response(scope, receive, send) return await _success_response(scope, receive, send) return def add_callback_handler(self, regex_pattern: str, func: Callable[[Context], Any]) -> None: """ Registers a regex pattern as to a bot handler function. If the regex pattern is found in a message from a GroupMe user, the function will be called. :param regex_pattern: The pattern to search for in the message text :param Callable[[Context], Any] func: The function to be called when the pattern is matched """ if regex_pattern in self._handler_functions: raise HandlerPatternExistsError(f"The pattern `{regex_pattern}` is already registered to a handler") self._handler_functions[regex_pattern] = func def add_cron_job(self, func: Callable[[Context], Any], **kwargs) -> None: """ Registers a function to be run on set cron schedule. Uses APScheduler cron trigger. Details here: https://apscheduler.readthedocs.io/en/stable/modules/triggers/cron.html Available arguments: - year (int|str) – 4-digit year - month (int|str) – month (1-12) - day (int|str) – day of the (1-31) - week (int|str) – ISO week (1-53) - day_of_week (int|str) – number or name of weekday (0-6 or mon,tue,wed,thu,fri,sat,sun) - hour (int|str) – hour (0-23) - minute (int|str) – minute (0-59) - second (int|str) – second (0-59) - start_date (datetime|str) – earliest possible date/time to trigger on (inclusive) - end_date (datetime|str) – latest possible date/time to trigger on (inclusive) - timezone (datetime.tzinfo|str) – time zone to use for the date/time calculations (defaults to scheduler timezone) - jitter (int|None) – advance or delay the job execution by jitter seconds at most. :param Callable[[Context], Any] func: The function to be called when the cron trigger is triggered """ self._jobs.append({ 'func': func, 'trigger': 'cron', 'args': (Context(self, Callback({})),), 'kwargs': kwargs }) def post_message(self, msg: str, attachments: Optional[List[Attachment]] = None) -> httpx.Response: """ Posts a bot message to the group with optional attachments. :param str msg: The message to be sent :param Optional[List[Attachment]] attachments: Attachments to send in the message :return requests.Response: The POST request response object """ if attachments: attachments = [attachment.to_dict() for attachment in attachments] else: attachments = [] data = { "bot_id": self.bot_id, "text": msg, "attachments": attachments } response = httpx.post( 'https://api.groupme.com/v3/bots/post', json=data, headers={'Content-Type': 'application/json'} ) response.raise_for_status() return response def mention_all(self) -> None: """ Mentions everybody in the group so they receive a notification """ text = '' user_ids = [] loci = [] group = self.get_group(self.group_id) for member in group['members']: user_ids.append(member['user_id']) loci.append([len(text), len(member['nickname']) + 1]) text += '@{} '.format(member['nickname']) self.post_message(text, [MentionsAttachment(loci=loci, user_ids=user_ids)])
StarcoderdataPython
8980
from __future__ import print_function # Python 2/3 compatibility from gremlin_python import statics from gremlin_python.structure.graph import Graph from gremlin_python.process.graph_traversal import __ from gremlin_python.process.strategies import * from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection #initializing the graph object graph = Graph() #creating connection with the remote remoteConn = DriverRemoteConnection('wss://<endpoint>:8182/gremlin','g') g = graph.traversal().withRemote(DriverRemoteConnection('wss://<endpoint>:8182/gremlin','g')) print('Connection created.') #clearing out all the vertices to start fresh g.V().drop().iterate() print('Deleting everything and starting clean.') #Adding some vertices (nodes) gerald = g.addV('person').property('age','81').property('first_name','Gerald').property('stays_in','Portland').next() edith = g.addV('person').property('age','78').property('first_name','Edith').property('stays_in','Portland').next() peter = g.addV('person').property('age','52').property('first_name','Shane').property('stays_in','Seattle').next() mary = g.addV('person').property('age','50').property('first_name','Mary').property('stays_in','Seattle').next() betty = g.addV('person').property('age','19').property('first_name','Betty').property('stays_in','Chicago').next() print('Added some vertices (nodes).') #Adding relationships (edges) edge = g.V().has('first_name', 'Gerald').addE('husband_of').to(g.V().has('first_name', 'Edith')).property('married_since','1947').next() edge = g.V().has('first_name', 'Edith').addE('wife_of').to(g.V().has('first_name', 'Gerald')).property('married_since','1947').next() edge = g.V().has('first_name', 'Shane').addE('son_of').to(g.V().has('first_name', 'Gerald')).property('known_since','1964').next() edge = g.V().has('first_name', 'Gerald').addE('father_of').to(g.V().has('first_name', 'Shane')).property('known_since','1964').next() edge = g.V().has('first_name', 'Shane').addE('son_of').to(g.V().has('first_name', 'Edith')).property('known_since','1964').next() edge = g.V().has('first_name', 'Edith').addE('mother_of').to(g.V().has('first_name', 'Shane')).property('known_since','1964').next() edge = g.V().has('first_name', 'Shane').addE('husband_of').to(g.V().has('first_name', 'Mary')).property('known_since','1989').next() edge = g.V().has('first_name', 'Mary').addE('wife_of').to(g.V().has('first_name', 'Shane')).property('known_since','1989').next() edge = g.V().has('first_name', 'Shane').addE('father_of').to(g.V().has('first_name', 'Betty')).property('known_since','1991').next() edge = g.V().has('first_name', 'Betty').addE('daughter_of').to(g.V().has('first_name', 'Shane')).property('known_since','1991').next() edge = g.V().has('first_name', 'Mary').addE('mother_of').to(g.V().has('first_name', 'Betty')).property('known_since','1991').next() edge = g.V().has('first_name', 'Betty').addE('daughter_of').to(g.V().has('first_name', 'Mary')).property('known_since','1991').next() #print out all the node's first names print('\n Printing first name from all nodes:') print(g.V().first_name.toList()) #print out all the properties of person whose's first name is Shane print('\n Printing all properties of person whose first name is Shane:') print(g.V().has('person','first_name','Shane').valueMap().next()) #traversing the graph starting with Betty to then Shane to then Edith print('\n Finding Betty and then looking up her parents:') print(g.V().has('first_name', 'Betty').out('daughter_of').out('son_of').valueMap().toList()) #Print out all the nodes print('\n Printing out all the nodes:') people = g.V().valueMap().toList() print(people) #Print out all the connections (edges) print('\n Print out all the connections (edges):') connections = g.E().valueMap().toList() print(connections) #Closing the connection remoteConn.close() print('Connection closed!')
StarcoderdataPython
3200577
<filename>deploy/scripts/combine_charts.py #! /usr/bin/env python3 """ Update the Helm chart version with the specified version. """ import argparse from pathlib import Path from jinja2 import Environment, PackageLoader, select_autoescape helm_dir = Path(__file__).resolve().parent.parent / "helm" # Map the chart names to their location. This is useful for updating # dependencies (in Chart.yaml) as well as the charts. helm_charts = [ helm_dir / "aws-login", helm_dir / "thecombine", helm_dir / "thecombine" / "charts" / "backend", helm_dir / "thecombine" / "charts" / "database", helm_dir / "thecombine" / "charts" / "frontend", helm_dir / "thecombine" / "charts" / "maintenance", helm_dir / "cert-proxy-client", helm_dir / "cert-proxy-server", helm_dir / "create-admin-user", ] def parse_args() -> argparse.Namespace: """Define command line arguments for parser.""" # Parse user command line arguments parser = argparse.ArgumentParser( description="Update the version and appVersions for the Helm charts.", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument( "version", help="New version for the Helm charts.", ) return parser.parse_args() def generate(version: str) -> None: print("New version: {version}") version_config = { "version": { "aws_login": "v0.2.0", "thecombine": f"v{version}", "cert_proxy_client": f"v{version}", "cert_proxy_server": f"v{version}", "create_admin_user": f"v{version}", } } for chart_dir in helm_charts: # Initialize the Jinja2 environment jinja_env = Environment( loader=PackageLoader("combine_charts", str(chart_dir)), autoescape=select_autoescape(["html", "xml"]), trim_blocks=False, lstrip_blocks=True, ) template = jinja_env.get_template("Chart.yaml.j2") final_chart = chart_dir / "Chart.yaml" print(f"Writing: {final_chart}") final_chart.write_text(template.render(version_config)) def main() -> None: args = parse_args() generate(args.version) if __name__ == "__main__": main()
StarcoderdataPython
1714802
<gh_stars>0 import os from typing import Optional, List import tweepy class Twitter: def __init__(self) -> None: super().__init__() bearer_token = os.environ.get("TWITTER_API_BEARER_TOKEN") self.twitter = tweepy.Client(bearer_token) def get_user_id(self, dev) -> Optional[int]: try: response = self.twitter.get_user(username=dev) except tweepy.errors.HTTPException: response = None if not response or not response.data: return None return response.data.id def get_followers(self, dev: str) -> Optional[List[int]]: try: user_id = self.get_user_id(dev) except tweepy.errors.HTTPException: return None response = self.twitter.get_users_followers(user_id) if not response.data: return [] return [user.id for user in response.data]
StarcoderdataPython
3212504
<filename>caffe2/python/operator_test/mean_op_test.py # Copyright (c) 2016-present, Facebook, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################## from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from caffe2.python import core from hypothesis import given import hypothesis.strategies as st import caffe2.python.hypothesis_test_util as hu import numpy as np import unittest class TestMean(hu.HypothesisTestCase): @given( k=st.integers(1, 5), n=st.integers(1, 10), m=st.integers(1, 10), in_place=st.booleans(), seed=st.integers(0, 2**32 - 1), **hu.gcs ) def test_mean(self, k, n, m, in_place, seed, gc, dc): np.random.seed(seed) input_names = [] input_vars = [] for i in range(k): X_name = 'X' + str(i) input_names.append(X_name) var = np.random.randn(n, m).astype(np.float32) input_vars.append(var) def mean_ref(*args): return [np.mean(args, axis=0)] op = core.CreateOperator( "Mean", input_names, ['Y' if not in_place else 'X0'], ) self.assertReferenceChecks( device_option=gc, op=op, inputs=input_vars, reference=mean_ref, ) self.assertGradientChecks( device_option=gc, op=op, inputs=input_vars, outputs_to_check=0, outputs_with_grads=[0], ) self.assertDeviceChecks(dc, op, input_vars, [0]) if __name__ == "__main__": unittest.main()
StarcoderdataPython
3396704
<reponame>sanqingqu/ros_lane_departure_warning<gh_stars>1-10 #!/usr/bin/python3 import torch, os, cv2, sys import argparse from PIL import Image from model.model import parsingNet from utils.common import merge_config from utils.dist_utils import dist_print import torch import scipy.special, tqdm import numpy as np import torchvision.transforms as transforms # from data.dataset import LaneTestDataset from data.constant import culane_row_anchor, tusimple_row_anchor def lane_detect_func(model, img_transform, src_img_f, des_img_f): src_img_0 = cv2.imread(src_img_f) img_w, img_h = src_img_0.shape[1], src_img_0.shape[0] src_img_1 = cv2.cvtColor(src_img_0, cv2.COLOR_BGR2RGB) src_img_1 = Image.fromarray(src_img_1) src_img_t = img_transform(src_img_1).cuda().unsqueeze(0) with torch.no_grad(): out = model(src_img_t) col_sample = np.linspace(0, 800 - 1, args.griding_num) col_sample_w = col_sample[1] - col_sample[0] out_j = out[0].data.cpu().numpy() out_j = out_j[:, ::-1, :] prob = scipy.special.softmax(out_j[:-1, :, :], axis=0) idx = np.arange(args.griding_num) + 1 idx = idx.reshape(-1, 1, 1) loc = np.sum(prob * idx, axis=0) out_j = np.argmax(out_j, axis=0) loc[out_j == args.griding_num] = 0 out_j = loc vis_img = cv2.imread(src_img_f) for i in range(out_j.shape[1]): if np.sum(out_j[:, i] != 0) > 4: for k in range(out_j.shape[0]): if out_j[k, i] > 0: ppp = (int(out_j[k, i] * col_sample_w * img_w / 800) - 1, int(img_h * (row_anchor[cls_num_per_lane-1-k]/288)) - 1 ) if i == 0: cv2.circle(vis_img, ppp, 5, (255,0,0),-1) elif i == 1: cv2.circle(vis_img, ppp, 5, (0,255,0),-1) elif i == 2: cv2.circle(vis_img, ppp, 5, (0,0,255),-1) elif i == 3: cv2.circle(vis_img, ppp, 5, (255,255,255),-1) vis_img = cv2.resize(vis_img, (img_w//2, img_h//2)) cv2.imwrite(des_img_f, vis_img) if __name__ == "__main__": torch.backends.cudnn.benchmark = True script_dir = sys.path[0] opts = argparse.ArgumentParser("Parameter for UFLD method based lane detection.") # opts.add_argument("--dataset", default="Tusimple") # opts.add_argument("--model_path", default=os.path.join(script_dir, "weight", "tusimple_18.pth")) opts.add_argument("--dataset", default="CULane") opts.add_argument("--model_path", default=os.path.join(script_dir, "weight", "culane_18.pth")) opts.add_argument("--backbone", default='18') opts.add_argument("--griding_num", default=100) opts.add_argument("--src_img_f", default="/home/ztp/Downloads/Test_ColorImage_road05/ColorImage_road05/171206_064746439_Camera_6.jpg") # opts.add_argument("--src_img_f", default="/home/ztp/Downloads/benchmark_velocity_supp/supp_img/0042.jpg") args = opts.parse_args() if args.dataset == 'CULane': cls_num_per_lane = 18 args.griding_num = 200 row_anchor = culane_row_anchor elif args.dataset == 'Tusimple': cls_num_per_lane = 56 args.griding_num = 100 row_anchor = tusimple_row_anchor else: raise NotImplementedError net = parsingNet(pretrained = False, backbone=args.backbone,cls_dim = (args.griding_num+1,cls_num_per_lane,4), use_aux=False).cuda() # we dont need auxiliary segmentation in testing state_dict = torch.load(args.model_path, map_location='cpu')['model'] compatible_state_dict = {} for k, v in state_dict.items(): if 'module.' in k: compatible_state_dict[k[7:]] = v else: compatible_state_dict[k] = v net.load_state_dict(compatible_state_dict, strict=False) net.cuda() net.eval() img_transforms = transforms.Compose([ transforms.Resize((288, 800)), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ]) des_img_dir = "/home/ztp/Projects/qingdao/lane_test/" src_img_dir = "/home/ztp/Downloads/lane_marking_examples/road02/ColorImage/Record002/Camera_5" img_list = os.listdir(src_img_dir) print(len(img_list)) src_img_list = [] with torch.no_grad(): for img_f in tqdm.tqdm(img_list): src_img_f = os.path.join(src_img_dir, img_f) des_img_f = os.path.join(des_img_dir, img_f) lane_detect_func(net, img_transforms, src_img_f, des_img_f)
StarcoderdataPython
1705957
<filename>alpharotate/utils/gwd.py # -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys import tensorflow as tf sys.path.append('../..') # from utils.gaussian_wasserstein_distance import get_element1, get_element4 from alpharotate.libs.utils.coordinate_convert import * # from alpharotate.libs.utils.rbbox_overlaps import rbbx_overlaps # from alpharotate.libs.utils.iou_cpu import get_iou_matrix def iou_rotate_calculate2(boxes1, boxes2): ious = [] if boxes1.shape[0] != 0: boxes1[:, 2] += 1.0 boxes1[:, 3] += 1.0 boxes2[:, 2] += 1.0 boxes2[:, 3] += 1.0 area1 = boxes1[:, 2] * boxes1[:, 3] area2 = boxes2[:, 2] * boxes2[:, 3] for i in range(boxes1.shape[0]): temp_ious = [] r1 = ((boxes1[i][0], boxes1[i][1]), (boxes1[i][2], boxes1[i][3]), boxes1[i][4]) r2 = ((boxes2[i][0], boxes2[i][1]), (boxes2[i][2], boxes2[i][3]), boxes2[i][4]) int_pts = cv2.rotatedRectangleIntersection(r1, r2)[1] if int_pts is not None: order_pts = cv2.convexHull(int_pts, returnPoints=True) int_area = cv2.contourArea(order_pts) inter = int_area * 1.0 / (area1[i] + area2[i] - int_area + 1e-4) # if boxes1[i][2] < 0.1 or boxes1[i][3] < 0.1 or boxes2[i][2] < 0.1 or boxes2[i][3] < 0.1: # inter = 0 inter = max(0.0, min(1.0, inter)) temp_ious.append(inter) else: temp_ious.append(0.0) ious.append(temp_ious) return np.array(ious, dtype=np.float32) def diou_rotate_calculate(boxes1, boxes2): if boxes1.shape[0] != 0: area1 = boxes1[:, 2] * boxes1[:, 3] area2 = boxes2[:, 2] * boxes2[:, 3] d = (boxes1[:, 0] - boxes2[:, 0]) ** 2 + (boxes1[:, 1] - boxes2[:, 1]) boxes1_ = forward_convert(boxes1, with_label=False) boxes2_ = forward_convert(boxes2, with_label=False) xmin = np.minimum(np.min(boxes1_[:, 0::2]), np.min(boxes2_[:, 0::2])) xmax = np.maximum(np.max(boxes1_[:, 0::2]), np.max(boxes2_[:, 0::2])) ymin = np.minimum(np.min(boxes1_[:, 1::2]), np.min(boxes2_[:, 1::2])) ymax = np.maximum(np.max(boxes1_[:, 1::2]), np.max(boxes2_[:, 1::2])) c = (xmax - xmin) ** 2 + (ymax - ymin) ** 2 ious = [] for i in range(boxes1.shape[0]): r1 = ((boxes1[i][0], boxes1[i][1]), (boxes1[i][2], boxes1[i][3]), boxes1[i][4]) r2 = ((boxes2[i][0], boxes2[i][1]), (boxes2[i][2], boxes2[i][3]), boxes2[i][4]) int_pts = cv2.rotatedRectangleIntersection(r1, r2)[1] if int_pts is not None: order_pts = cv2.convexHull(int_pts, returnPoints=True) int_area = cv2.contourArea(order_pts) iou = int_area * 1.0 / (area1[i] + area2[i] - int_area) else: iou = 0.0 ious.append(iou) ious = np.array(ious) dious = ious - d / c else: dious = [] return np.reshape(np.array(dious, dtype=np.float32), [-1, 1]) def gaussian_wasserstein_distance_(boxes1, boxes2): boxes1 = coordinate_present_convert(boxes1, -1) boxes1[:, 4] += 90 boxes1[:, 4] *= (-np.pi / 180) boxes2 = coordinate_present_convert(boxes2, -1) boxes2[:, 4] += 90 boxes2[:, 4] *= (-np.pi / 180) dis = (boxes1[:, 0] - boxes2[:, 0])**2 + (boxes1[:, 1] - boxes2[:, 1])**2 + \ ((boxes1[:, 2] / 2 * np.cos(boxes1[:, 4])**2 + boxes1[:, 3] / 2 * np.sin(boxes1[:, 4])**2) - (boxes2[:, 2] / 2 * np.cos(boxes2[:, 4])**2 + boxes2[:, 3] / 2 * np.sin(boxes2[:, 4])**2))**2 + \ 2*((boxes1[:, 2] / 2 * np.cos(boxes1[:, 4])* np.sin(boxes1[:, 4] - boxes1[:, 3] / 2 * np.cos(boxes1[:, 4]) * np.sin(boxes1[:, 4]))) - (boxes2[:, 2] / 2 * np.cos(boxes2[:, 4]) * np.sin(boxes2[:, 4] - boxes2[:, 3] / 2 * np.cos(boxes2[:, 4]) * np.sin(boxes2[:, 4]))))**2 + \ ((boxes1[:, 2] / 2 * np.sin(boxes1[:, 4]) ** 2 + boxes1[:, 3] / 2 * np.cos(boxes1[:, 4]) ** 2) - ( boxes2[:, 2] / 2 * np.sin(boxes2[:, 4]) ** 2 + boxes2[:, 3] / 2 * np.cos(boxes2[:, 4]) ** 2)) ** 2 return dis def gaussian_wasserstein_distance(boxes1, boxes2): from alpharotate.utils import get_element1, get_element4 boxes1 = coordinate_present_convert(boxes1, -1) boxes1[:, 4] += 90 boxes1[:, 4] *= (-np.pi / 180) # boxes2 = coordinate_present_convert(boxes2, -1) boxes2[:, 4] += 90 boxes2[:, 4] *= (-np.pi / 180) element1 = get_element1(boxes1[:, 2], boxes1[:, 3], boxes1[:, 4], boxes2[:, 2], boxes2[:, 3], boxes2[:, 4]) element4 = get_element4(boxes1[:, 2], boxes1[:, 3], boxes1[:, 4], boxes2[:, 2], boxes2[:, 3], boxes2[:, 4]) dis = (boxes1[:, 0] - boxes2[:, 0])**2 + (boxes1[:, 1] - boxes2[:, 1])**2 + (element1 + element4) return dis def wasserstein_diss_sigma(sigma1, sigma2): wasserstein_diss_item2 = tf.linalg.matmul(sigma1, sigma1) + tf.linalg.matmul(sigma2, sigma2) - 2 * tf.linalg.sqrtm( tf.linalg.matmul(tf.linalg.matmul(sigma1, tf.linalg.matmul(sigma2, sigma2)), sigma1)) wasserstein_diss_item2 = tf.linalg.trace(wasserstein_diss_item2) return wasserstein_diss_item2 def gwd(boxes1, boxes2): x1, y1, w1, h1, theta1 = tf.unstack(boxes1, axis=1) x2, y2, w2, h2, theta2 = tf.unstack(boxes2, axis=1) x1 = tf.reshape(x1, [-1, 1]) y1 = tf.reshape(y1, [-1, 1]) h1 = tf.reshape(h1, [-1, 1]) w1 = tf.reshape(w1, [-1, 1]) theta1 = tf.reshape(theta1, [-1, 1]) x2 = tf.reshape(x2, [-1, 1]) y2 = tf.reshape(y2, [-1, 1]) h2 = tf.reshape(h2, [-1, 1]) w2 = tf.reshape(w2, [-1, 1]) theta2 = tf.reshape(theta2, [-1, 1]) theta1 *= (np.pi / 180) theta2 *= (np.pi / 180) sigma1_1 = w1 / 2 * tf.cos(theta1) ** 2 + h1 / 2 * tf.sin(theta1) ** 2 sigma1_2 = w1 / 2 * tf.sin(theta1) * tf.cos(theta1) - h1 / 2 * tf.sin(theta1) * tf.cos(theta1) sigma1_3 = w1 / 2 * tf.sin(theta1) * tf.cos(theta1) - h1 / 2 * tf.sin(theta1) * tf.cos(theta1) sigma1_4 = w1 / 2 * tf.sin(theta1) ** 2 + h1 / 2 * tf.cos(theta1) ** 2 sigma1 = tf.reshape(tf.concat([sigma1_1, sigma1_2, sigma1_3, sigma1_4], axis=-1), [-1, 2, 2]) sigma2_1 = w2 / 2 * tf.cos(theta2) ** 2 + h2 / 2 * tf.sin(theta2) ** 2 sigma2_2 = w2 / 2 * tf.sin(theta2) * tf.cos(theta2) - h2 / 2 * tf.sin(theta2) * tf.cos(theta2) sigma2_3 = w2 / 2 * tf.sin(theta2) * tf.cos(theta2) - h2 / 2 * tf.sin(theta2) * tf.cos(theta2) sigma2_4 = w2 / 2 * tf.sin(theta2) ** 2 + h2 / 2 * tf.cos(theta2) ** 2 sigma2 = tf.reshape(tf.concat([sigma2_1, sigma2_2, sigma2_3, sigma2_4], axis=-1), [-1, 2, 2]) wasserstein_diss_item1 = (x1 - x2) ** 2 + (y1 - y2) ** 2 wasserstein_diss_item2 = tf.reshape(wasserstein_diss_sigma(sigma1, sigma2), [-1, 1]) wasserstein_diss = wasserstein_diss_item1 + wasserstein_diss_item2 return sigma1, sigma2, wasserstein_diss def sigma(a, w, h): R = np.array([[np.cos(a), -np.sin(a)], [np.sin(a), np.cos(a)]]) sig = np.array([[w/2, 0], [0, h/2]]) res = np.dot(R, sig) res = np.dot(res, R.T) return res if __name__ == '__main__': from alpharotate.utils import get_element1, get_element4 boxes1 = np.array([[50, 50, 10, 70, -30], [50, 50, 100, 700, -30]], np.float32) boxes2 = np.array([[10, 40, 10, 70, -30], [10, 40, 100, 700, -40]], np.float32) # boxes1 = np.array([ # prediction box # [50, 50, 10, 70, -35], # 90 <--> 180 # [50, 50, 70, 10, -90.5], # 90 PoA + EoE # [50, 50, 70, 10, -90.5], # 180 PoA # [50, 50, 40, 40, -35], # 180 w=h # ], np.float32) # # boxes2 = np.array([ # ground truth # [50, 50, 70, 10, 55], # [50, 50, 10, 70, -0.5], # [50, 50, 70, 10, 89.5], # [50, 50, 40, 40, 55], # ], np.float32) print('iou', iou_rotate_calculate2(boxes1, boxes2).reshape(-1,)) # [0.9999996 0.9999998 0.9999998 1. ] # print(diou_rotate_calculate(boxes1, boxes2).reshape(-1,)) # [0.9999997 0.99999994 0.99999994 1. ] # print(gaussian_wasserstein_distance(boxes1, boxes2)) # [6.1035156e-05 3.1062821e-04 3.1062821e-04 0.0000000e+00] # tmp = np.maximum(np.log(gaussian_wasserstein_distance(boxes1, boxes2)+1e-3), 0) # print(np.log(gaussian_wasserstein_distance(boxes1, boxes2))) # print(tmp/(1+tmp)) # print(np.argsort(iou_rotate_calculate2(boxes1, boxes2).reshape(-1, )*-1)) # print(np.argsort(diou_rotate_calculate(boxes1, boxes2).reshape(-1, )*-1)) # print(np.argsort(np.array(gaussian_wasserstein_distance(boxes1, boxes2)))) # # # print(sigma(-np.pi*35/180, 10, 70)) # # print(sigma(np.pi*(90-35)/180, 70, 10)) # sigma1_tf1, sigma2_tf1, gwd_tf1 = gwd(coordinate_present_convert(boxes1, -1, False), coordinate_present_convert(boxes2, -1, False)) sigma1_tf2, sigma2_tf2, gwd_tf2 = gwd(boxes1, boxes2) # sigma1_tf2 = tf.linalg.eigvalsh(sigma1_tf2) with tf.Session() as sess: sigma1_tf_1, sigma2_tf_1, gwd_tf_1 = sess.run([sigma1_tf1, sigma2_tf1, gwd_tf1]) sigma1_tf_2, sigma2_tf_2, gwd_tf_2 = sess.run([sigma1_tf2, sigma2_tf2, gwd_tf2]) # print(sigma1_tf_1) # print(sigma2_tf_1) # print('**'*10) print(sigma1_tf_2) # print(sigma2_tf_2) print('**' * 10) # print(np.reshape(gwd_tf_1, [-1, ])) # print(np.argsort(np.reshape(gwd_tf_1, [-1, ]))) print('gwd', np.reshape(gwd_tf_2, [-1, ])) print(1-1/(np.sqrt(gwd_tf_2)+1)) # print(np.argsort(np.reshape(gwd_tf_2, [-1, ]))) # gwd_tf_2 = np.maximum(np.log(gwd_tf_2 + 1e-3), 0.0) # gwd_tf_2_ = np.maximum(np.log(gwd_tf_2 + 1e-3), 0.0) # print(gwd_tf_2_/(5+gwd_tf_2_)) # # gwd_tf_2 = np.maximum(np.log(gwd_tf_2 + 1e-3), 0.0) # print(gwd_tf_2) # print(1-1 / (5 + gwd_tf_2)) # print(gwd_tf_2_ / (2 + gwd_tf_2_)) # print(gwd_tf_2_ / (3 + gwd_tf_2_)) # print(gwd_tf_2_ / (5 + gwd_tf_2_))
StarcoderdataPython
1619284
<filename>examples/blink.py if __name__ == '__main__': from time import sleep from pino.config import Config from pino.ino import HIGH, LOW, OUTPUT, Arduino, Comport config = Config("./examples/sample.yaml") com = Comport.derive(config.comport) \ .deploy() \ .connect() # com = Comport() \ # .set_port("/dev/ttyACM0") \ # .set_baudrate(115200) \ # .set_timeout(1.) \ # .set_warmup(2.) \ # .deploy() \ # .connect() ino = Arduino(com) ino.apply_pinmode_settings(config.pinmode) LED_BUILTIN = 13 # ino.set_pinmode(LED_BUILTIN, OUTPUT) for _ in range(10): ino.digital_write(LED_BUILTIN, HIGH) sleep(1) ino.digital_write(LED_BUILTIN, LOW) sleep(1)
StarcoderdataPython
1660585
<filename>poc/blockbuilder.py #utility to help making big builds, if you it 2 blocks, it fills the gap in between from mcpi.minecraft import Minecraft from mcpi import block mc = Minecraft.create() pos = mc.player.getPos() mc.player.setPos(pos.x, pos.y +5, pos.x) noOfPos = 0 while True: blockHits = mc.events.pollBlockHits() for blockHit in blockHits: noOfPos += 1 if noOfPos == 1: firstPos = blockHit.pos elif noOfPos == 2: secondPos = blockHit.pos noOfPos = 0 blockType = mc.getBlockWithData(blockHit.pos) #if its a book case, use AIR instead if blockType.id == 47: blockType = block.Block(0) mc.setBlocks(firstPos, secondPos, blockType.id, blockType.data)
StarcoderdataPython
3311925
<gh_stars>0 from unityagents import UnityEnvironment import numpy as np import random import torch from collections import deque import matplotlib.pyplot as plt import time import gc import cv2 import logging import sys logger = logging.getLogger("unityagents") logger.propagate = False env = UnityEnvironment(file_name="../VisualBanana_Linux/Banana.x86_64") # get the default brain brain_name = env.brain_names[0] brain = env.brains[brain_name] # reset the environment env_info = env.reset(train_mode=True)[brain_name] # number of agents in the environment print('Number of agents:', len(env_info.agents)) # number of actions action_size = brain.vector_action_space_size print('Number of actions:', action_size) # examine the state space state = env_info.visual_observations[0] print('States look like:', state.shape) #state = np.squeeze(state) #state *= 256 #state = state.astype(np.uint8) #state = cv2.cvtColor(state, cv2.COLOR_RGB2GRAY) #cv2.imshow('display', state) #cv2.imshow('display', cv2.resize(state, (256, 256), interpolation=cv2.INTER_NEAREST)) #cv2.imshow('display', cv2.resize(state[...,0], (512, 512))) #cv2.waitKey(0) #plt.imshow(np.squeeze(state)) #plt.show() #state = np.mean(state, axis=3) #state = np.squeeze(state) state = np.dot(state, np.array([0.299, 0.587, 0.114])) #state = state.reshape(state.shape[1], state.shape[2], 1) #print(state) print('States look like:', state.shape) # -> (1, 84, 84) #plt.imshow(np.squeeze(state), cmap='gray') #plt.show() env.close() def preprocess(state): state = np.dot(state, np.array([0.299, 0.587, 0.114])) state *= 256 return state.astype(np.uint8) #return np.transpose(state, (2, 0, 1)) FRAME_SIZE = 4 print("FRAME_SIZE:{}".format(FRAME_SIZE), file=sys.stderr) SIM_RESET_INTERVAL = 500 from dqn_agent import Agent from double_dqn_agent import DDQNAgent from duel_double_dqn_agent import DuelDDQNAgent #agent = Agent(state_size=(FRAME_SIZE, state.shape[1], state.shape[2]), action_size=action_size, seed=0) #agent = DDQNAgent(state_size=(FRAME_SIZE, state.shape[1], state.shape[2]), action_size=action_size, seed=0) agent = DuelDDQNAgent(state_size=(FRAME_SIZE, state.shape[1], state.shape[2]), action_size=action_size, seed=0) print("Agent's Name:{}".format(agent.__class__.__name__), file=sys.stderr) basename = '' def dqn(n_episodes=int(5e4), max_t=1000, eps_start=1.0, eps_end=0.01, eps_decay=0.999): """Deep Q-Learning. Params ====== n_episodes (int): maximum number of training episodes max_t (int): maximum number of timesteps per episode eps_start (float): starting value of epsilon, for epsilon-greedy action selection eps_end (float): minimum value of epsilon eps_decay (float): multiplicative factor (per episode) for decreasing epsilon """ scores = [] # list containing scores from each episode scores_window = deque(maxlen=100) # last 100 scores eps = eps_start # initialize epsilon obs_queue = deque(maxlen=FRAME_SIZE) start = time.time() for i_episode in range(1, n_episodes+1): if i_episode % SIM_RESET_INTERVAL == 1: env = UnityEnvironment(file_name="../VisualBanana_Linux/Banana.x86_64") env_info = env.reset(train_mode=True)[brain_name] obs = env_info.visual_observations[0] obs = np.squeeze(obs) obs = preprocess(obs) obs_queue.append(obs) state = None score = 0 while True: if state is not None: action = agent.act(state, eps) else: action = random.choice(np.arange(action_size)) env_info = env.step(action)[brain_name] # send the action to the environment obs = env_info.visual_observations[0] # get the next state obs = np.squeeze(obs) obs = preprocess(obs) #cv2.imshow('display', cv2.resize(np.flip(obs, 2), (528, 528), interpolation=cv2.INTER_NEAREST)) # cv2.imshow('display', cv2.resize(obs, (256, 256), interpolation=cv2.INTER_NEAREST)) # ch = cv2.waitKey(300) # if ch == 27: # import sys # sys.exit() obs_queue.append(obs) reward = env_info.rewards[0] # get the reward done = env_info.local_done[0] if len(obs_queue) == FRAME_SIZE: next_state = np.array(obs_queue) #print('next_state.shape', next_state.shape) if state is not None: agent.step(state, action, reward, next_state, done) state = next_state score += reward #time.sleep(0.2) if done: obs_queue.clear() break scores_window.append(score) # save most recent score scores.append(score) # save most recent score if agent.l_step > 0: eps = max(eps_end, eps_decay*eps) # decrease epsilon current = time.time() elapsed = current - start elapsed_str = time.strftime("%H:%M:%S", time.gmtime(elapsed)) print('\rEpisode {}\tAverage Score: {:.2f}\t{}\t{}\t{}\t{}\t{}'.format(i_episode, np.mean(scores_window), elapsed_str, action, agent.l_step, len(agent.memory), eps), end="", file=sys.stderr) if i_episode % 100 == 0: print('\rEpisode {}\tAverage Score: {:.2f}\t{}\t{}\t{}\t{}\t{}'.format(i_episode, np.mean(scores_window), elapsed_str, action, agent.l_step, len(agent.memory), eps), file=sys.stderr) if np.mean(scores_window)>=14.0: print('\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}'.format(i_episode-100, np.mean(scores_window))) break if i_episode % SIM_RESET_INTERVAL == 0: env.close() time_str = time.strftime("%Y-%m-%d_%I-%M-%S%p") global basename basename = agent.__class__.__name__ + time_str torch.save(agent.qnetwork_local.state_dict(), './checkpoint_' + basename + '.pth') return scores dqn_scores = dqn() ## plot the scores fig = plt.figure() ax = fig.add_subplot(111) plt.plot(np.arange(len(dqn_scores)), dqn_scores) plt.ylabel('Score') plt.xlabel('Episode #') print('\nsave learning graph on {}.'.format(basename), file=sys.stderr) plt.savefig('learning_graph_' + basename + '.png') np.save('learning_graph_' + basename + '.npy', dqn_scores) #plt.show() #
StarcoderdataPython
3287115
__author__ = '<NAME>' __email__ = '<EMAIL>' __date__ = '14/Mar/2017' import sys import requests import config from utils import feed_utils def add_user_to_project(project_id, user_id, membership_type): try: json_body = {"membership_type": membership_type} response = requests.post(config.api_url("projects/%s/users/%s" % (project_id, user_id)), json=json_body, auth=(config.API_KEY, config.API_SECRET)) return response.status_code == requests.codes.ok except: feed_utils.error(sys.exc_info()[0]) return None def delete_user_from_project(project_id, user_id): try: response = requests.delete(config.api_url("projects/%s/users/%s" % (project_id, user_id)), auth=(config.API_KEY, config.API_SECRET)) return response.status_code == requests.codes.no_content except: feed_utils.error(sys.exc_info()[0]) return None def get_user_in_project(project_id, user_id): try: response = requests.get(config.api_url("projects/%s/users/%s" % (project_id, user_id)), auth=(config.API_KEY, config.API_SECRET)) if response.status_code == requests.codes.ok: json_response = response.json() return int(json_response["membership_type"]) except: feed_utils.error(sys.exc_info()[0]) return None def get_project(project_id): try: response = requests.get(config.api_url("projects/%s" % project_id), auth=(config.API_KEY, config.API_SECRET)) if response.status_code == requests.codes.ok: return response.json() except: feed_utils.error(sys.exc_info()[0]) return None def get_project_ids(): try: ids = [] response = requests.get(config.api_url("projects/ids"), auth=(config.API_KEY, config.API_SECRET)) if response.status_code == requests.codes.ok: json_response = response.json() for uid in json_response: ids.append(uid[u'id']) return ids except: feed_utils.error(sys.exc_info()[0]) return None def add_project(name, description): try: json_body = {"name": name, "description": description} response = requests.post(config.api_url("projects"), json=json_body, auth=(config.API_KEY, config.API_SECRET)) if response.status_code == requests.codes.created: return response.json()["id"] except: feed_utils.error(sys.exc_info()[0]) return None def delete_project(project_id): try: response = requests.delete(config.api_url("projects/%s" % project_id), auth=(config.API_KEY, config.API_SECRET)) return response.status_code == requests.codes.no_content except: feed_utils.error(sys.exc_info()[0]) return False def search_projects(project_id=None, name=None): try: json_body = {} if project_id: json_body['id'] = project_id if name: json_body['name'] = name response = requests.post(config.api_url("projects/query"), json=json_body, auth=(config.API_KEY, config.API_SECRET)) if response.status_code == requests.codes.ok: return response.json() except: feed_utils.error(sys.exc_info()[0]) return None
StarcoderdataPython
1728498
import itertools as it import bisect def gen_primes(): D = {} n = 2 while True: if n not in D: yield n D[n * n] = [n] else: for p in D[n]: D.setdefault(p + n, []).append(p) del D[n] n += 1 if __name__ == "__main__": ps = tuple(it.takewhile(lambda x: x<=1000000, gen_primes())) ss = tuple(it.accumulate(ps)) N = int(input()) for _ in range(N): M = int(input()) print(ss[bisect.bisect_right(ps, M)-1])
StarcoderdataPython
1610282
<gh_stars>1-10 #!/usr/bin/python2 """ This code finds a free port """ import socket # find a free port def get_free_port(): """get a single random port""" sock = socket.socket() sock.bind(('', 0)) port = sock.getsockname()[1] sock.close() return port print(get_free_port())
StarcoderdataPython
169993
<reponame>tcmal/ah-project from tkinter import * from tkinter.ttk import * import tkinter.filedialog as filedialog import tkinter.messagebox as messagebox from config import LocalFile from hash import sha256 from common import bool_to_tick from views import ViewHasBackButton from views.verify import VerifyHistoryView class FilePermissionsView(ViewHasBackButton): def __init__(self, app, frame, file): self.app = app self.frame = frame self.file = file # Initialise widgets ## Tree self.tree = Treeview(frame, selectmode="browse") self.tree.bind('<<TreeviewSelect>>', self.select_change) self.tree['columns'] = ('bio') ### Column configuration self.tree.column('#0', width=500, minwidth=100) self.tree.heading('#0', text='Name') self.tree.column('bio', minwidth=250) self.tree.heading('bio', text='Bio') ### Loading to start with self.tree.insert("", 'end', 'loading', text="Loading...") self.tree.grid(row=0, column=0, rowspan=5, padx=5) ## Back button self.add_back_button(row=0, column=1, sticky=N+E+W) ## Checkboxes for reading and writing self.allow_read = IntVar() self.read_checkbox = Checkbutton(self.frame, text="Read", state=DISABLED, variable=self.allow_read) self.read_checkbox.grid(row=1, column=1, sticky=E+W) self.allow_write = IntVar() self.write_checkbox = Checkbutton(self.frame, text="Write", state=DISABLED, variable=self.allow_write) self.write_checkbox.grid(row=2, column=1, sticky=E+W) ## Save changes button self.submit_button = Button(frame, text="Submit", state=DISABLED, command=self.submit) self.submit_button.grid(row=3, column=1, sticky=E+W) ## Draw the GUI self.app.tk.update() # Get the list of users and permissions self.update_data() # Returns a tuple (allow_read, allow_write) def get_permissions(self, username): if username not in self.permissions.keys(): return (False, False) else: return (self.permissions[username]['allow_read'], self.permissions[username]['allow_write']) def update_data(self): # Clear list self.tree.focus("") self.select_change(None) self.tree.delete(*self.tree.get_children()) # Get a list of users from the server self.users = {} self.app.ensure_authorised() res = self.app.get('directory').json() ## If it fails, Show error and go home if not res['success']: messagebox.showerror('Error', 'Error getting user list from server: %s' % res['message']) self.back_home() return ## Insert each user for user in res['users']: self.insert_user(user) # Get the permissions for this file self.app.ensure_authorised() res = self.app.get( 'file/getPermissions?file_id=%s' % (self.file['id']) ).json() if not res['success']: messagebox.showerror('Error getting file permissions', res['message']) self.back_home() return self.permissions = res['permissions'] def insert_user(self, user): ## Don't display the user if user['name'] == self.app.config.username: return ## Store the full object for later self.users[user['name']] = user ## Add to the treeview self.tree.insert("", 'end', user['name'], text=user['name'], values=( user['bio'].split("\n")[0],)) # Called when user selects def select_change(self, _event): # Make sure inputs aren't usable when they shouldn't be ## Disable all when no user selected if self.tree.focus() == "": self.submit_button.config(state=DISABLED) self.write_checkbox.config(state=DISABLED) self.read_checkbox.config(state=DISABLED) return username = self.tree.focus() ## Enable whenever anything is selected self.submit_button.config(state=NORMAL) self.write_checkbox.config(state=NORMAL) self.read_checkbox.config(state=NORMAL) # Get the permissions for that user and update checkboxes ## If they're not listed, they don't have any permissions allow_read, allow_write = self.get_permissions(username) self.allow_read.set(allow_read) self.allow_write.set(allow_write) def submit(self): # Get the selected username if self.tree.focus() == "": return selected_name = self.tree.focus() selected = self.users[selected_name] # Get the desired permissions allow_read = self.allow_read.get() > 0 allow_write = self.allow_write.get() > 0 # Tell the server self.app.ensure_authorised() res = self.app.post('file/setPermissions', json={ 'file_id': self.file['id'], 'target': selected_name, 'allow_read': allow_read, 'allow_write': allow_write }).json() if not res['success']: messagebox.showerror('Error setting file permissions', res['message']) # Update the data self.update_data()
StarcoderdataPython
3337362
# -*- coding: utf-8 -*- # Generated by Django 1.11.15 on 2018-09-26 13:08 from __future__ import unicode_literals import django.core.validators from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('partner', '0077_auto_20180911_0946'), ] operations = [ migrations.AlterField( model_name='partner', name='legal_name', field=models.TextField(max_length=255, validators=[django.core.validators.MinLengthValidator(1)]), ), ]
StarcoderdataPython
4811051
# Copyright 2018 Luddite Labs Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Comment block ------------- """ from docutils import nodes class CommentMixin: def visit_comment(self, node): # Here we can pass indent as child_indent=3 or next=dict(indent=3, ...). # Difference is what child_indent will be inherited by all children # and 'next' indent will be ued only by first child and it's children. # In this case it's the same because we construct all blocks here. # We even can pass indent to the second open_block. self.open_block(top_margin=1, child_indent=3) self.block.add_boxed('..') self.open_block(top_margin=None, bottom_margin=1) self.block.add_text(node.rawsource) self.close_block() self.close_block() raise nodes.SkipNode def depart_comment(self, node): pass
StarcoderdataPython
3232450
<filename>app/grandchallenge/workstation_configs/migrations/0001_initial.py<gh_stars>1-10 # Generated by Django 2.2.6 on 2019-10-15 09:57 import uuid import django.core.validators import django.db.models.deletion import django_extensions.db.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)] operations = [ migrations.CreateModel( name="LookUpTable", fields=[ ( "id", models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name="ID", ), ), ( "title", models.CharField(max_length=255, verbose_name="title"), ), ( "description", models.TextField( blank=True, null=True, verbose_name="description" ), ), ( "slug", django_extensions.db.fields.AutoSlugField( blank=True, editable=False, populate_from="title", verbose_name="slug", ), ), ( "color", models.TextField( validators=[ django.core.validators.RegexValidator( regex="^\\[(?:((?: ?-?\\d*(?:\\.\\d+)? ){3}(?:-?\\d*(?:\\.\\d+)?)) ?, ?)+((?:-?\\d*(?:\\.\\d+)? ){3}(?:\\d*(:?\\.\\d+)? ?))\\]$" ) ] ), ), ( "alpha", models.TextField( validators=[ django.core.validators.RegexValidator( regex="^\\[(?:((?: ?-?\\d*(?:\\.\\d+)? ){1}(?:-?\\d*(?:\\.\\d+)?)) ?, ?)+((?:-?\\d*(?:\\.\\d+)? ){1}(?:\\d*(:?\\.\\d+)? ?))\\]$" ) ] ), ), ( "color_invert", models.TextField( blank=True, validators=[ django.core.validators.RegexValidator( regex="^\\[(?:((?: ?-?\\d*(?:\\.\\d+)? ){3}(?:-?\\d*(?:\\.\\d+)?)) ?, ?)+((?:-?\\d*(?:\\.\\d+)? ){3}(?:\\d*(:?\\.\\d+)? ?))\\]$" ) ], ), ), ( "alpha_invert", models.TextField( blank=True, validators=[ django.core.validators.RegexValidator( regex="^\\[(?:((?: ?-?\\d*(?:\\.\\d+)? ){1}(?:-?\\d*(?:\\.\\d+)?)) ?, ?)+((?:-?\\d*(?:\\.\\d+)? ){1}(?:\\d*(:?\\.\\d+)? ?))\\]$" ) ], ), ), ("range_min", models.SmallIntegerField(default=0)), ("range_max", models.SmallIntegerField(default=4095)), ("relative", models.BooleanField(default=False)), ( "color_interpolation", models.CharField( choices=[ ("RGB", "RGB"), ("HLS", "HLS"), ("HLSpos", "HLS Positive"), ("HLSneg", "HLS Negative"), ("Constant", "Constant"), ], default="RGB", max_length=8, ), ), ( "color_interpolation_invert", models.CharField( choices=[ ("RGB", "RGB"), ("HLS", "HLS"), ("HLSpos", "HLS Positive"), ("HLSneg", "HLS Negative"), ("Constant", "Constant"), ], default="RGB", max_length=8, ), ), ], options={"ordering": ("title",), "abstract": False}, ), migrations.CreateModel( name="WindowPreset", fields=[ ( "id", models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name="ID", ), ), ( "title", models.CharField(max_length=255, verbose_name="title"), ), ( "description", models.TextField( blank=True, null=True, verbose_name="description" ), ), ( "slug", django_extensions.db.fields.AutoSlugField( blank=True, editable=False, populate_from="title", verbose_name="slug", ), ), ( "width", models.PositiveIntegerField( validators=[ django.core.validators.MinValueValidator( limit_value=1 ) ] ), ), ("center", models.IntegerField()), ], options={"ordering": ("title",), "abstract": False}, ), migrations.CreateModel( name="WorkstationConfig", fields=[ ( "id", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ), ("created", models.DateTimeField(auto_now_add=True)), ("modified", models.DateTimeField(auto_now=True)), ( "title", models.CharField(max_length=255, verbose_name="title"), ), ( "description", models.TextField( blank=True, null=True, verbose_name="description" ), ), ( "slug", django_extensions.db.fields.AutoSlugField( blank=True, editable=False, populate_from="title", verbose_name="slug", ), ), ( "default_slab_thickness_mm", models.DecimalField( blank=True, decimal_places=2, max_digits=4, null=True, validators=[ django.core.validators.MinValueValidator( limit_value=0.01 ) ], ), ), ( "default_slab_render_method", models.CharField( blank=True, choices=[ ("MAX", "Maximum"), ("MIN", "Minimum"), ("AVG", "Average"), ], max_length=3, ), ), ( "default_orientation", models.CharField( blank=True, choices=[ ("A", "Axial"), ("C", "Coronal"), ("S", "Sagittal"), ], max_length=1, ), ), ( "default_overlay_interpolation", models.CharField( blank=True, choices=[ ("NN", "NearestNeighbour"), ("TL", "Trilinear"), ], default="NN", max_length=2, ), ), ("show_image_info_plugin", models.BooleanField(default=True)), ("show_display_plugin", models.BooleanField(default=True)), ( "creator", models.ForeignKey( null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, ), ), ( "default_overlay_lut", models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to="workstation_configs.LookUpTable", ), ), ( "default_window_preset", models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name="workstation_default_window_presets", to="workstation_configs.WindowPreset", ), ), ( "window_presets", models.ManyToManyField( blank=True, related_name="workstation_window_presets", to="workstation_configs.WindowPreset", ), ), ], options={"ordering": ("created", "creator"), "abstract": False}, ), ]
StarcoderdataPython
1700219
<reponame>bheinzerling/dougu import re from pathlib import Path from .io import sentencepiece_load from .embeddingutil import load_word2vec_file class BPEmb(): """ Load a BPEmb model, preprocess text, encode/decode BPE using sentencepiece. """ def __init__( self, *, lang="en", vs=10000, dim=100, model_file="data/bpemb/data/{lang}/{lang}.wiki.bpe.vs{vs}.model", bpemb_file="data/bpemb/data/{lang}/{lang}.wiki.bpe.vs{vs}.d{dim}.w2v.bin", # NOQA spm_emb_idxs_match=True, preproc=True, encode_extra_options=None, add_pad=False, vs_fallback=True): if vs_fallback: available = BPEmb.available_vocab_sizes(lang, model_file) if not available: raise ValueError("No BPEmb models for " + model_file) if vs not in available: available = sorted(available) _vs = vs if vs < available[0]: vs = available[0] else: vs = available[-1] print(f"BPEmb fallback: {lang} from vocab size {_vs} to {vs}") self.lang = lang self.vocab_size = self.vs = vs self.dim = dim self.model_file = model_file.format(lang=lang, vs=vs) self.bpemb_file = bpemb_file.format(lang=lang, vs=vs, dim=dim) self.spm = sentencepiece_load(self.model_file) if encode_extra_options: self.spm.SetEncodeExtraOptions(encode_extra_options) self.emb = load_word2vec_file(self.bpemb_file, add_pad=add_pad) assert self.dim == self.emb.vectors.shape[1] self.preproc = preproc self.BOS = self.spm.PieceToId("<s>") self.EOS = self.spm.PieceToId("</s>") def __repr__(self): return self.__class__.__name__ + \ f"(lang={self.lang}, vs={self.vocab_size}, dim={self.dim})" def encode_as_pieces(self, tokens): if self.preproc: tokens = self.do_preproc(tokens) return list(map(self.spm.EncodeAsPieces, tokens)) def encode_as_ids(self, tokens): if self.preproc: tokens = self.do_preproc(tokens) return list(map(self.spm.EncodeAsIds, tokens)) def encode_as_ids_with_eos(self, tokens): if self.preproc: tokens = self.do_preproc(tokens) return list(map( lambda t: self.spm.EncodeAsIds(t) + [self.EOS], tokens)) def encode_as_ids_with_bos_eos(self, tokens): if self.preproc: tokens = self.do_preproc(tokens) return list(map( lambda t: [self.BOS] + self.spm.EncodeAsIds(t) + [self.EOS], tokens)) def decode_ids(self, ids): try: return list(map(self.spm.DecodeIds, ids)) except TypeError: try: return self.spm.DecodeIds(ids.tolist()) except TypeError: return list(map(self.spm.DecodeIds, ids.tolist())) def do_preproc(self, tokens): return map(lambda t: re.sub("\d", "0", t.lower()), tokens) @property def pieces(self): return self.emb.index2word @property def words(self): return self.pieces @staticmethod def available_vocab_sizes( lang, model_file="data/bpemb/data/{lang}/{lang}.wiki.bpe.vs{vs}.model", vocab_sizes=[1000, 3000, 5000, 10000, 25000, 50000, 100000]): if lang.startswith("multi_"): vocab_sizes = [100000, 200000, 320000, 1000000] return set( vs for vs in vocab_sizes if Path(model_file.format(lang=lang, vs=vs)).exists())
StarcoderdataPython
3338427
<gh_stars>1-10 ''' 1. GaussianMap method TopoGraph generation im2skeleton.py skeleton2topoMap.py graphClass.py reprsntAndMatching.py --- 2. Generate water throughtput for each path-pixel, generate passage on top of the count, close the room by break the passage.(where river is the skeleton pixel, rainDrop is each pixel of map) rainFlow.py RMsaddle.py passageGen.py roomExpand.py rain_main.py will be utilized to testing this script '''
StarcoderdataPython
1615242
# Copyright 2018 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. """Unit tests.""" import pytest from google.rpc import status_pb2 from google.cloud import bigtable_admin_v2 from google.cloud.bigtable_admin_v2.proto import bigtable_table_admin_pb2 from google.cloud.bigtable_admin_v2.proto import table_pb2 from google.longrunning import operations_pb2 from google.protobuf import empty_pb2 class MultiCallableStub(object): """Stub for the grpc.UnaryUnaryMultiCallable interface.""" def __init__(self, method, channel_stub): self.method = method self.channel_stub = channel_stub def __call__(self, request, timeout=None, metadata=None, credentials=None): self.channel_stub.requests.append((self.method, request)) response = None if self.channel_stub.responses: response = self.channel_stub.responses.pop() if isinstance(response, Exception): raise response if response: return response class ChannelStub(object): """Stub for the grpc.Channel interface.""" def __init__(self, responses=[]): self.responses = responses self.requests = [] def unary_unary(self, method, request_serializer=None, response_deserializer=None): return MultiCallableStub(method, self) class CustomException(Exception): pass class TestBigtableTableAdminClient(object): def test_create_table(self): # Setup Expected Response name = 'name3373707' expected_response = {'name': name} expected_response = table_pb2.Table(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request parent = client.instance_path('[PROJECT]', '[INSTANCE]') table_id = 'tableId-895419604' table = {} response = client.create_table(parent, table_id, table) assert expected_response == response assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.CreateTableRequest( parent=parent, table_id=table_id, table=table) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_create_table_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request parent = client.instance_path('[PROJECT]', '[INSTANCE]') table_id = 'tableId-895419604' table = {} with pytest.raises(CustomException): client.create_table(parent, table_id, table) def test_create_table_from_snapshot(self): # Setup Expected Response name = 'name3373707' expected_response = {'name': name} expected_response = table_pb2.Table(**expected_response) operation = operations_pb2.Operation( name='operations/test_create_table_from_snapshot', done=True) operation.response.Pack(expected_response) # Mock the API response channel = ChannelStub(responses=[operation]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request parent = client.instance_path('[PROJECT]', '[INSTANCE]') table_id = 'tableId-895419604' source_snapshot = 'sourceSnapshot-947679896' response = client.create_table_from_snapshot(parent, table_id, source_snapshot) result = response.result() assert expected_response == result assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.CreateTableFromSnapshotRequest( parent=parent, table_id=table_id, source_snapshot=source_snapshot) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_create_table_from_snapshot_exception(self): # Setup Response error = status_pb2.Status() operation = operations_pb2.Operation( name='operations/test_create_table_from_snapshot_exception', done=True) operation.error.CopyFrom(error) # Mock the API response channel = ChannelStub(responses=[operation]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request parent = client.instance_path('[PROJECT]', '[INSTANCE]') table_id = 'tableId-895419604' source_snapshot = 'sourceSnapshot-947679896' response = client.create_table_from_snapshot(parent, table_id, source_snapshot) exception = response.exception() assert exception.errors[0] == error def test_list_tables(self): # Setup Expected Response next_page_token = '' tables_element = {} tables = [tables_element] expected_response = { 'next_page_token': next_page_token, 'tables': tables } expected_response = bigtable_table_admin_pb2.ListTablesResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request parent = client.instance_path('[PROJECT]', '[INSTANCE]') paged_list_response = client.list_tables(parent) resources = list(paged_list_response) assert len(resources) == 1 assert expected_response.tables[0] == resources[0] assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.ListTablesRequest( parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_list_tables_exception(self): channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request parent = client.instance_path('[PROJECT]', '[INSTANCE]') paged_list_response = client.list_tables(parent) with pytest.raises(CustomException): list(paged_list_response) def test_get_table(self): # Setup Expected Response name_2 = 'name2-1052831874' expected_response = {'name': name_2} expected_response = table_pb2.Table(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') response = client.get_table(name) assert expected_response == response assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.GetTableRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_get_table_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') with pytest.raises(CustomException): client.get_table(name) def test_delete_table(self): channel = ChannelStub() client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') client.delete_table(name) assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.DeleteTableRequest( name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_delete_table_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') with pytest.raises(CustomException): client.delete_table(name) def test_modify_column_families(self): # Setup Expected Response name_2 = 'name2-1052831874' expected_response = {'name': name_2} expected_response = table_pb2.Table(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') modifications = [] response = client.modify_column_families(name, modifications) assert expected_response == response assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.ModifyColumnFamiliesRequest( name=name, modifications=modifications) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_modify_column_families_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') modifications = [] with pytest.raises(CustomException): client.modify_column_families(name, modifications) def test_drop_row_range(self): channel = ChannelStub() client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') client.drop_row_range(name) assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.DropRowRangeRequest( name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_drop_row_range_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') with pytest.raises(CustomException): client.drop_row_range(name) def test_generate_consistency_token(self): # Setup Expected Response consistency_token = 'consistencyToken-<PASSWORD>' expected_response = {'consistency_token': consistency_token} expected_response = bigtable_table_admin_pb2.GenerateConsistencyTokenResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') response = client.generate_consistency_token(name) assert expected_response == response assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.GenerateConsistencyTokenRequest( name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_generate_consistency_token_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') with pytest.raises(CustomException): client.generate_consistency_token(name) def test_check_consistency(self): # Setup Expected Response consistent = True expected_response = {'consistent': consistent} expected_response = bigtable_table_admin_pb2.CheckConsistencyResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') consistency_token = 'consistency<PASSWORD>' response = client.check_consistency(name, consistency_token) assert expected_response == response assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.CheckConsistencyRequest( name=name, consistency_token=consistency_token) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_check_consistency_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') consistency_token = 'consistencyToken-<PASSWORD>' with pytest.raises(CustomException): client.check_consistency(name, consistency_token) def test_snapshot_table(self): # Setup Expected Response name_2 = 'name2-1052831874' data_size_bytes = 2110122398 description_2 = 'description2568623279' expected_response = { 'name': name_2, 'data_size_bytes': data_size_bytes, 'description': description_2 } expected_response = table_pb2.Snapshot(**expected_response) operation = operations_pb2.Operation( name='operations/test_snapshot_table', done=True) operation.response.Pack(expected_response) # Mock the API response channel = ChannelStub(responses=[operation]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') cluster = 'cluster872092154' snapshot_id = 'snapshotId-168585866' description = 'description-1724546052' response = client.snapshot_table(name, cluster, snapshot_id, description) result = response.result() assert expected_response == result assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.SnapshotTableRequest( name=name, cluster=cluster, snapshot_id=snapshot_id, description=description) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_snapshot_table_exception(self): # Setup Response error = status_pb2.Status() operation = operations_pb2.Operation( name='operations/test_snapshot_table_exception', done=True) operation.error.CopyFrom(error) # Mock the API response channel = ChannelStub(responses=[operation]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request name = client.table_path('[PROJECT]', '[INSTANCE]', '[TABLE]') cluster = 'cluster872092154' snapshot_id = 'snapshotId-168585866' description = 'description-1724546052' response = client.snapshot_table(name, cluster, snapshot_id, description) exception = response.exception() assert exception.errors[0] == error def test_get_snapshot(self): # Setup Expected Response name_2 = 'name2-1052831874' data_size_bytes = 2110122398 description = 'description-1724546052' expected_response = { 'name': name_2, 'data_size_bytes': data_size_bytes, 'description': description } expected_response = table_pb2.Snapshot(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request name = client.snapshot_path('[PROJECT]', '[INSTANCE]', '[CLUSTER]', '[SNAPSHOT]') response = client.get_snapshot(name) assert expected_response == response assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.GetSnapshotRequest( name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_get_snapshot_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request name = client.snapshot_path('[PROJECT]', '[INSTANCE]', '[CLUSTER]', '[SNAPSHOT]') with pytest.raises(CustomException): client.get_snapshot(name) def test_list_snapshots(self): # Setup Expected Response next_page_token = '' snapshots_element = {} snapshots = [snapshots_element] expected_response = { 'next_page_token': next_page_token, 'snapshots': snapshots } expected_response = bigtable_table_admin_pb2.ListSnapshotsResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request parent = client.cluster_path('[PROJECT]', '[INSTANCE]', '[CLUSTER]') paged_list_response = client.list_snapshots(parent) resources = list(paged_list_response) assert len(resources) == 1 assert expected_response.snapshots[0] == resources[0] assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.ListSnapshotsRequest( parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_list_snapshots_exception(self): channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request parent = client.cluster_path('[PROJECT]', '[INSTANCE]', '[CLUSTER]') paged_list_response = client.list_snapshots(parent) with pytest.raises(CustomException): list(paged_list_response) def test_delete_snapshot(self): channel = ChannelStub() client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup Request name = client.snapshot_path('[PROJECT]', '[INSTANCE]', '[CLUSTER]', '[SNAPSHOT]') client.delete_snapshot(name) assert len(channel.requests) == 1 expected_request = bigtable_table_admin_pb2.DeleteSnapshotRequest( name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_delete_snapshot_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = bigtable_admin_v2.BigtableTableAdminClient(channel=channel) # Setup request name = client.snapshot_path('[PROJECT]', '[INSTANCE]', '[CLUSTER]', '[SNAPSHOT]') with pytest.raises(CustomException): client.delete_snapshot(name)
StarcoderdataPython
3397675
import time import os import os.path as path import sys import yaml import json import math import numpy as np import pandas as pd from getting_data import load_sample from ranker_helper import get_scores from s2search_score_pdp import compute_pdp pd.options.display.float_format = '{:,.10f}'.format pd.set_option('display.expand_frame_repr', False) model_dir = './s2search_data' data_dir = str(path.join(os.getcwd(), 'pipelining')) ranker = None data_loading_line_limit = 1000 def read_conf(exp_dir_path): conf_path = path.join(exp_dir_path, 'conf.yml') with open(str(conf_path), 'r') as f: conf = yaml.safe_load(f) return conf.get('description'), conf.get('samples'), conf.get('sample_from_other_exp'), def save_pdp_to_npz(exp_dir_path, npz_file_path, *args, **kws): scores_dir = path.join(exp_dir_path, 'scores') if not path.exists(str(scores_dir)): os.mkdir(str(scores_dir)) print(f'\tsave PDP data to {npz_file_path}') np.savez_compressed(npz_file_path, *args, **kws) def compute_diagonal_pdp(f1_name, f2_name, query, paper_data): print(f'\tgetting diagonal pdp for {f1_name}, {f2_name}') st = time.time() variant_data = [] for i in range(len(paper_data)): # pick the i th features value f1_value_that_is_used = paper_data[i][f1_name] f2_value_that_is_used = paper_data[i][f2_name] # replace it to all papers for p in paper_data: new_data = {**p} new_data[f1_name] = f1_value_that_is_used new_data[f2_name] = f2_value_that_is_used variant_data.append(new_data) scores = get_scores(query, variant_data, task_name='hs') scores_split = np.array_split(scores, len(paper_data)) pdp_value = [np.mean(x) for x in scores_split] et = round(time.time() - st, 6) print(f'\tcompute {len(scores)} diagonal pdp within {et} sec') return pdp_value def get_pdp_data_if_exist(output_exp_dir, output_data_sample_name, data_exp_name, data_sample_name, f1_name, f2_name, query, paper_data): f1_pdp_data = None f2_pdp_data = None f1_f2_diagonal_pdp_data = None f1_pdp_from_source_path = path.join(data_dir, data_exp_name, 'scores', f"{data_sample_name}_pdp_{f1_name}.npz") f1_pdp_from_here_path = path.join(output_exp_dir, 'scores', f"{output_data_sample_name}_pdp_{f1_name}.npz") f1_data_exist = False if os.path.exists(f1_pdp_from_source_path): f1_pdp_data = [np.mean(pdps) for pdps in np.load(f1_pdp_from_source_path)['arr_0']] f1_data_exist = True if os.path.exists(f1_pdp_from_here_path): f1_pdp_data = [np.mean(pdps) for pdps in np.load(f1_pdp_from_source_path)['arr_0']] f1_data_exist = True f2_pdp_from_source_path = path.join(data_dir, data_exp_name, 'scores', f"{data_sample_name}_pdp_{f2_name}.npz") f2_pdp_from_here_path = path.join(output_exp_dir, 'scores', f"{output_data_sample_name}_pdp_{f2_name}.npz") f2_data_exist = False if os.path.exists(f2_pdp_from_source_path): f2_pdp_data = [np.mean(pdps) for pdps in np.load(f2_pdp_from_source_path)['arr_0']] f2_data_exist = True if os.path.exists(f2_pdp_from_here_path): f2_pdp_data = [np.mean(pdps) for pdps in np.load(f2_pdp_from_source_path)['arr_0']] f2_data_exist = True f1_f2_data_exist = False f1_f2_diagonal_pdp_path = path.join(output_exp_dir, 'scores', f"{output_data_sample_name}_diagonal_pdp_{f1_name}_{f2_name}.npz") if os.path.exists(f1_f2_diagonal_pdp_path): f1_f2_diagonal_pdp_data = np.load(f1_f2_diagonal_pdp_path)['arr_0'] f1_f2_data_exist = True if not f1_data_exist: f1_pdp_data = compute_pdp(paper_data, query, f1_name) save_pdp_to_npz(output_exp_dir, f1_pdp_from_here_path, f1_pdp_data) if not f2_data_exist: f2_pdp_data = compute_pdp(paper_data, query, f2_name) save_pdp_to_npz(output_exp_dir, f2_pdp_from_here_path, f2_pdp_data) if not f1_f2_data_exist: f1_f2_diagonal_pdp_data = compute_diagonal_pdp(f1_name, f2_name, query, paper_data) save_pdp_to_npz(output_exp_dir, f1_f2_diagonal_pdp_path, f1_f2_diagonal_pdp_data) return f1_pdp_data, f2_pdp_data, f1_f2_diagonal_pdp_data def compute_and_save(output_exp_dir, output_data_sample_name, query, data_exp_name, data_sample_name): df = load_sample(data_exp_name, data_sample_name) paper_data = json.loads(df.to_json(orient='records')) feature_list = [ 'title', 'abstract', 'venue', 'authors', 'year', 'n_citations' ] hs_metric = pd.DataFrame(columns=['f1', 'f1', 'hs', 'hs_sqrt']) hs_metrix = np.zeros([len(feature_list), len(feature_list)]) hs_sqrt_metrix = np.zeros([len(feature_list), len(feature_list)]) for f1_idx in range(len(feature_list)): for f2_idx in range(f1_idx + 1, len(feature_list)): f1_name = feature_list[f1_idx] f2_name = feature_list[f2_idx] f1_pdp_data, f2_pdp_data, f1_f2_diagonal_pdp_data = \ get_pdp_data_if_exist(output_exp_dir, output_data_sample_name, data_exp_name, data_sample_name, f1_name, f2_name, query, paper_data) numerators = f1_f2_diagonal_pdp_data - f1_pdp_data - f2_pdp_data numerators *= numerators numerator = np.sum(numerators) denominators = f1_f2_diagonal_pdp_data * f1_f2_diagonal_pdp_data denominator = np.sum(denominators) h_jk = numerator / denominator h_jk_sqrt = math.sqrt(numerator) hs_metric.loc[len(hs_metric.index)] = [f1_name, f2_name, h_jk, h_jk_sqrt] hs_metrix[f1_idx][f2_idx] = h_jk hs_metrix[f2_idx][f1_idx] = h_jk hs_sqrt_metrix[f1_idx][f2_idx] = h_jk_sqrt hs_sqrt_metrix[f2_idx][f1_idx] = h_jk_sqrt npz_file_path = path.join(output_exp_dir, 'scores', f"{output_data_sample_name}_hs_metrix.npz") save_pdp_to_npz(output_exp_dir, npz_file_path, hs_metrix=hs_metrix, hs_sqrt_metrix=hs_sqrt_metrix) print(hs_metric.sort_values(by=['hs'], ascending=False)) # print(hs_metric) def get_hstatistic_and_save_score(exp_dir_path): des, sample_configs, sample_from_other_exp = read_conf(exp_dir_path) tested_sample_list = [] for sample_name in sample_configs: if sample_name in sample_from_other_exp.keys(): other_exp_name, data_file_name = sample_from_other_exp.get(sample_name) tested_sample_list.append({'exp_name': other_exp_name, 'data_sample_name': sample_name, 'data_source_name': data_file_name.replace('.data', '')}) else: tested_sample_list.append({'exp_name': exp_name, 'data_sample_name': sample_name, 'data_source_name': sample_name}) for tested_sample_config in tested_sample_list: tested_sample_name = tested_sample_config['data_sample_name'] tested_sample_data_source_name = tested_sample_config['data_source_name'] tested_sample_from_exp = tested_sample_config['exp_name'] task = sample_configs.get(tested_sample_name) if task != None: print(f'computing ale for {tested_sample_name}') for t in task: try: query = t['query'] compute_and_save( exp_dir_path, tested_sample_name, query, tested_sample_from_exp, tested_sample_data_source_name) except FileNotFoundError as e: print(e) else: print(f'**no config for tested sample {tested_sample_name}') if __name__ == '__main__': if len(sys.argv) > 1: exp_list = sys.argv[1:] for exp_name in exp_list: exp_dir_path = path.join(data_dir, exp_name) if path.isdir(exp_dir_path): get_hstatistic_and_save_score(exp_dir_path) else: print(f'**no exp dir {exp_dir_path}')
StarcoderdataPython
4830494
<gh_stars>10-100 from collections import OrderedDict import os import sys sys.path = [os.path.abspath(os.path.dirname(__file__))] + sys.path os.environ['is_test_suite'] = 'True' import classifiers as classifier_tests import regressors as regressor_tests training_parameters = { 'model_names': ['GradientBoosting', 'XGB', 'DeepLearning', 'LGBM'] } # Make this an OrderedDict so that we run the tests in a consistent order test_names = OrderedDict([ ('optimize_final_model_classification', classifier_tests.optimize_final_model_classification), ('getting_single_predictions_classification', classifier_tests.getting_single_predictions_classification), ('feature_learning_getting_single_predictions_classification', classifier_tests.feature_learning_getting_single_predictions_classification), ('getting_single_predictions_multilabel_classification', classifier_tests.getting_single_predictions_multilabel_classification), ('categorical_ensembling_classification', classifier_tests.categorical_ensembling_classification), ('feature_learning_categorical_ensembling_getting_single_predictions_classification', classifier_tests.feature_learning_categorical_ensembling_getting_single_predictions_classification) # ('optimize_final_model_regression', regressor_tests.optimize_final_model_regression), # ('getting_single_predictions_regression', regressor_tests.getting_single_predictions_regression), # ('feature_learning_getting_single_predictions_regression', regressor_tests.feature_learning_getting_single_predictions_regression), # ('categorical_ensembling_regression', regressor_tests.categorical_ensembling_regression), # ('feature_learning_categorical_ensembling_getting_single_predictions_regression', regressor_tests.feature_learning_categorical_ensembling_getting_single_predictions_regression) ]) def test_generator(): for model_name in training_parameters['model_names']: for test_name, test in test_names.items(): test_model_name = model_name if '_classification' in test_name and model_name is not None: test_model_name = model_name + 'Classifier' elif '_regression' in test_name and model_name is not None: test_model_name = model_name + 'Regressor' test.description = str(test_model_name) + '_' + test_name yield test, test_model_name
StarcoderdataPython
3263952
# Generated by Django 2.1.3 on 2019-10-22 03:05 from django.db import migrations, models import phonenumber_field.modelfields class Migration(migrations.Migration): dependencies = [ ('w2r', '0001_initial'), ] operations = [ migrations.AlterField( model_name='registro', name='telefono', field=phonenumber_field.modelfields.PhoneNumberField(blank=True, max_length=128, null=True, region=None), ), migrations.AlterField( model_name='registro', name='tiempo_10k', field=models.TimeField(blank=True, null=True), ), migrations.AlterField( model_name='registro', name='tiempo_15k', field=models.TimeField(blank=True, null=True), ), migrations.AlterField( model_name='registro', name='tiempo_21k', field=models.TimeField(blank=True, null=True), ), migrations.AlterField( model_name='registro', name='tiempo_42k', field=models.TimeField(blank=True, null=True), ), migrations.AlterField( model_name='registro', name='tiempo_5k', field=models.TimeField(blank=True, null=True), ), ]
StarcoderdataPython
3268772
<gh_stars>0 from rest_framework.renderers import JSONRenderer from rest_framework.negotiation import DefaultContentNegotiation class JSONDefaultRendererContentNegotiation(DefaultContentNegotiation): """http://www.django-rest-framework.org/api-guide/content-negotiation/#example""" def select_renderer(self, request, renderers, format_suffix): """Given a request and a list of renderers, return a two-tuple of: (renderer, media type) If format not in querystring then use JSON as the default renderer, otherwise defer to the select_renderer of the DefaultContentNegotiation. """ if 'format' not in request.GET: return (JSONRenderer(), JSONRenderer.media_type) return super().select_renderer(request, renderers, format_suffix)
StarcoderdataPython
1681838
from django.contrib import admin from django.contrib.auth.models import Group from .models import Item, Contact class ItemAdmin(admin.ModelAdmin): list_display = ('price', 'description', 'seller', ) class ContactAdmin(admin.ModelAdmin): list_display = ('name', 'email', 'location', 'item', 'interest', ) admin.site.register(Item, ItemAdmin) admin.site.register(Contact, ContactAdmin) admin.site.unregister(Group)
StarcoderdataPython
3203393
from multiprocessing.pool import AsyncResult, ThreadPool __thread_pools = {} def __get_thread_pool(name) -> ThreadPool: if name not in __thread_pools: __thread_pools[name] = ThreadPool(10) return __thread_pools[name] def in_background(fn) -> AsyncResult: return __get_thread_pool('default').apply_async(fn)
StarcoderdataPython
1700951
<reponame>sdispater/poet # -*- coding: utf-8 -*- from poet.installer import Installer from poet.repositories import PyPiRepository from poet.package import PipDependency foo_dependency_123 = PipDependency('foo', '1.2.3') foo_dependency_133 = PipDependency('foo', '1.3.3') bar_dependency_321 = PipDependency('bar', '3.2.1') baz_dependency_233 = PipDependency('baz', '2.3.3') def test_resolve_update_actions_up_to_date(command): installer = Installer(command, PyPiRepository()) current_deps = [ PipDependency('foo', '1.2.3'), PipDependency('bar', '3.2.1') ] deps = [ PipDependency('foo', '1.2.3'), PipDependency('bar', '3.2.1') ] actions = installer._resolve_update_actions( deps, current_deps ) assert [] == actions def test_resolve_update_actions_upgrade(command): installer = Installer(command, PyPiRepository()) current_deps = [ foo_dependency_123, bar_dependency_321 ] deps = [ foo_dependency_133, PipDependency('bar', '3.2.1') ] actions = installer._resolve_update_actions( deps, current_deps ) expected = [ ('update', foo_dependency_123, foo_dependency_133) ] assert expected == actions def test_resolve_update_actions_install(command): installer = Installer(command, PyPiRepository()) current_deps = [ foo_dependency_123 ] deps = [ foo_dependency_123, bar_dependency_321 ] actions = installer._resolve_update_actions( deps, current_deps ) expected = [ ('install', None, bar_dependency_321) ] assert expected == actions def test_resolve_update_actions_remove(command): installer = Installer(command, PyPiRepository()) current_deps = [ foo_dependency_123, bar_dependency_321 ] deps = [ foo_dependency_123 ] actions = installer._resolve_update_actions( deps, current_deps ) expected = [ ('remove', None, bar_dependency_321) ] assert expected == actions def test_resolve_update_actions_all(command): installer = Installer(command, PyPiRepository()) current_deps = [ foo_dependency_123, bar_dependency_321 ] deps = [ foo_dependency_133, baz_dependency_233 ] actions = installer._resolve_update_actions( deps, current_deps ) expected = [ ('update', foo_dependency_123, foo_dependency_133), ('install', None, baz_dependency_233), ('remove', None, bar_dependency_321) ] assert expected == actions
StarcoderdataPython
1761471
<filename>bento/private/_yaku/examples/example4.py import os import sys from yaku.context \ import \ get_bld, get_cfg from yaku.scheduler \ import \ run_tasks def configure(ctx): # The tool ctask works as follows: # - When a tool is *loaded* (load_tool), get its configure function (dummy # is setup if no configure is found) # - When a tool is *used* (use_tool), run its configure function # - given a list of candidates, run the detect function for every candidate # until detect returns True # - if one candidate found, record its name and run its setup function # - if none found, fails tools = ctx.use_tools(["ctasks"]) def build(ctx): builder = ctx.builders["ctasks"] builder.program("main", [os.path.join("src", "main.c")]) if __name__ == "__main__": ctx = get_cfg() configure(ctx) ctx.store() ctx = get_bld() build(ctx) run_tasks(ctx) ctx.store()
StarcoderdataPython
3220480
<gh_stars>0 import logging import subprocess import configparser import os class SSH: def __init__(self, username, ip, port, identity_file): self.username = username self.ip = ip self.port = port self.identity_file = identity_file def connection_str(self): return '{}@{}'.format(self.username, self.ip) def run(self, command): if isinstance(command, str): command = [command] logging.debug("Connect to %s:%s and run %s", self.connection_str(), self.port, " ".join(command)) raw = subprocess.check_output(['ssh', self.connection_str(), '-p', self.port, '-i', self.identity_file] + command) return raw.decode('utf-8') def create_ssh(ip_script, username, port, identity): logging.debug("Retrieve IP with %s", ip_script) ip_raw = subprocess.check_output(ip_script, shell=True) ip = ip_raw.decode('utf-8').strip() logging.info("Found backup device at %s", ip) return SSH(username, ip, port, identity) def expanduser(path): if path.startswith("~/"): username = os.environ.get('SUDO_USER', '') path = "~" + username + path[1:] return os.path.expanduser(path) def read_config(args): config = configparser.ConfigParser() config.read(expanduser(args.config)) return config def setup_logging(verbose, quiet, logfile): streamlevel = logging.INFO if verbose: streamlevel = logging.DEBUG elif quiet: streamlevel = logging.WARNING logging.basicConfig(format='%(levelname)-10s - %(message)s', level=streamlevel) rootlogger = logging.getLogger() filelevel = logging.DEBUG filehandler = logging.FileHandler(filename=logfile) filehandler.setLevel(filelevel) filehandler.setFormatter(logging.Formatter('[%(asctime)s] %(levelname)-10s - %(message)s')) rootlogger.addHandler(filehandler)
StarcoderdataPython
3347719
# -*- coding: utf-8 -*- # ------------------------------------------------------------------------------ # FEDERAL UNIVERSITY OF UBERLANDIA # Faculty of Electrical Engineering # Biomedical Engineering Lab # ------------------------------------------------------------------------------ # Author: <NAME> # Contact: <EMAIL> # Git: www.github.com/italogfernandes # ------------------------------------------------------------------------------ # Description: # Based on opencv docs and tutorials # http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html ------------------------------------------------------------------------------ import numpy as np import matplotlib.pyplot as plt import pandas as pd # ------------------------------------------------------------------------------
StarcoderdataPython
1739851
<reponame>bbhunter/Ghostwriter<gh_stars>1-10 """This contains customizations for displaying the Shepherd application models in the admin panel.""" # Django Imports from django.contrib import admin # 3rd Party Libraries from import_export.admin import ImportExportModelAdmin from .models import ( ActivityType, AuxServerAddress, Domain, DomainNote, DomainServerConnection, DomainStatus, HealthStatus, History, ServerHistory, ServerNote, ServerProvider, ServerRole, ServerStatus, StaticServer, TransientServer, WhoisStatus, ) from .resources import DomainResource, StaticServerResource def enable_dns_resets(modeladmin, request, queryset): queryset.update(reset_dns=True) enable_dns_resets.short_description = "Enable DNS resets for selected Domains" def disable_dns_resets(modeladmin, request, queryset): queryset.update(reset_dns=False) disable_dns_resets.short_description = "Disable DNS resets for selected Domains" @admin.register(ActivityType) class ActivityTypeAdmin(admin.ModelAdmin): pass @admin.register(AuxServerAddress) class AuxServerAddressAdmin(admin.ModelAdmin): list_display = ("primary", "ip_address", "static_server") list_filter = ("primary",) list_display_links = ("ip_address",) @admin.register(DomainServerConnection) class DomainServerConnectionAdmin(admin.ModelAdmin): list_display = ( "subdomain", "domain_name", "endpoint", "static_server", "transient_server", ) list_display_links = ("subdomain", "domain_name", "endpoint") fieldsets = ( ( "Domain Information", {"fields": ("domain", "subdomain", "endpoint")}, ), ( "Server Connection", {"fields": ("static_server", "transient_server")}, ), ) @admin.register(History) class HistoryAdmin(admin.ModelAdmin): list_display = ("domain", "client", "activity_type", "start_date", "end_date") list_filter = ("activity_type", "client") list_display_links = ("domain", "client") fieldsets = ( ( "Domain Checkout Information", {"fields": ("domain", "client", "project")}, ), ( "Domain Use Information", {"fields": ("operator", "activity_type", "start_date", "end_date")}, ), ("Misc", {"fields": ("note",)}), ) @admin.register(DomainNote) class DomainNoteAdmin(admin.ModelAdmin): list_display = ("operator", "timestamp", "domain") list_filter = ("domain",) list_display_links = ("operator", "timestamp", "domain") @admin.register(DomainStatus) class DomainStatusAdmin(admin.ModelAdmin): pass @admin.register(Domain) class DomainAdmin(ImportExportModelAdmin): actions = [enable_dns_resets, disable_dns_resets] resource_class = DomainResource list_display = ( "domain_status", "name", "whois_status", "health_status", "last_health_check", "registrar", "reset_dns", "note", ) list_filter = ( "domain_status", "whois_status", "health_status", "registrar", "auto_renew", "reset_dns", ) list_display_links = ("domain_status", "name") list_editable = ("reset_dns",) readonly_fields = ('last_health_check',) fieldsets = ( ("General Information", {"fields": ("name", "domain_status", "creation", "expiration", "auto_renew")}), ( "Health Status", { "fields": ( "last_health_check", "whois_status", "health_status", "categorization", "burned_explanation", ) }, ), ( "DNS Status", { "fields": ( "dns", "reset_dns", ) }, ), ("Misc", {"fields": ("note",)}), ) @admin.register(HealthStatus) class HealthStatusAdmin(admin.ModelAdmin): pass @admin.register(ServerHistory) class ServerHistoryRoleAdmin(admin.ModelAdmin): list_display = ( "server_name", "ip_address", "client", "activity_type", "server_role", "start_date", "end_date", ) list_filter = ("activity_type", "client") list_display_links = ("server_name", "ip_address", "client") fieldsets = ( ( "Server Checkout Information", {"fields": ("server", "client", "project")}, ), ( "Server Use Information", { "fields": ( "operator", "activity_type", "server_role", "start_date", "end_date", ) }, ), ("Misc", {"fields": ("note",)}), ) @admin.register(ServerNote) class ServerNoteAdmin(admin.ModelAdmin): list_display = ("operator", "timestamp", "server") list_filter = ("server",) list_display_links = ("operator", "timestamp", "server") @admin.register(ServerProvider) class ServerProviderRoleAdmin(admin.ModelAdmin): pass @admin.register(ServerRole) class ServerRoleAdmin(admin.ModelAdmin): pass @admin.register(ServerStatus) class ServerStatusRoleAdmin(admin.ModelAdmin): pass @admin.register(StaticServer) class StaticServerAdmin(ImportExportModelAdmin): resource_class = StaticServerResource list_display = ("name", "ip_address", "server_status", "server_provider") list_filter = ("server_status", "server_provider") list_display_links = ("name", "ip_address") fieldsets = ( ( "Basic Server Information", {"fields": ("ip_address", "name", "server_status", "server_provider")}, ), ( "Misc", { "fields": ( "last_used_by", "note", ) }, ), ) @admin.register(TransientServer) class TransientServerRoleAdmin(admin.ModelAdmin): list_display = ( "name", "ip_address", "aux_address", "server_role", "activity_type", "server_provider", ) list_filter = ("server_provider", "server_role", "activity_type") list_display_links = ("name", "ip_address") fieldsets = ( ("Basic Server Information", {"fields": ("name", "ip_address", "aux_address")}), ( "Server Use Information", {"fields": ("server_provider", "server_role", "activity_type", "operator")}, ), ("Misc", {"fields": ("note",)}), ) @admin.register(WhoisStatus) class WhoisStatusAdmin(admin.ModelAdmin): pass
StarcoderdataPython
4815639
#!/usr/bin/python3 from services.assistant_services_base import AssistantServicesBase from .skill import SkillInput, Skill from neo4j import GraphDatabase from typing import List class FAQSkill(Skill): """Lets the assistant answer FAQ questions for the user based on LTU website info.""" def __init__(self): """Initializes a new instance of the FAQSkill class.""" # The database driver will be initialized in the perform_setup method. self._driver = None def matches_command(self, skill_input: SkillInput) -> bool: """Returns a Boolean value indicating whether this skill can be used to handle the given command.""" return self._is_question_for_curriculum(skill_input) or \ self._is_question_for_who_has_role(skill_input) def execute_for_command(self, skill_input: SkillInput, services: AssistantServicesBase): """Executes this skill on the given command input.""" if self._is_question_for_curriculum(skill_input): self._handle_question_for_curriculum(skill_input) elif self._is_question_for_who_has_role(skill_input): self._handle_question_for_who_has_role(skill_input) else: services.user_interaction_service.speak("Sorry, I cannot answer this kind of question yet.") def perform_setup(self, services: AssistantServicesBase): """Executes any setup work necessary for this skill before it can be used.""" try: self._driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password")) except Exception as e: raise RuntimeError("Failed to establish database connection.") from e if not services.settings_service.faq_initialized: self._set_up_database() services.settings_service.faq_initialized = True services.settings_service.save_settings() else: print("FAQ database already initialized.") # Various helper functions for this skill are to be defined below. def _handle_question_for_curriculum(self, skill_input: SkillInput): """Handles a question regarding the curriculum.""" # TODO: Fill this in to have the skill actually process the question and speak the result. pass def _handle_question_for_who_has_role(self, skill_input: SkillInput): """Handles a question regarding which staff member has a role.""" # TODO: Fill this in to have the skill actually process the question and speak the result. pass def _is_question_for_curriculum(self, skill_input: SkillInput) -> bool: """Determines whether the `SkillInput` is for a question regarding the curriculum.""" # TODO: Fill this in and return either True or False. pass def _is_question_for_who_has_role(self, skill_input: SkillInput) -> bool: """Determines whether the `SkillInput` is for a question regarding which staff member has a role.""" # TODO: Fill this in and return either True or False. pass def _run_query_multiple_results(self, query: str) -> List[str]: """Runs the provided Cypher query expected to find multiple results, and returns a list of strings.""" # TODO pass def _run_query_single_result(self, query: str) -> str: """Runs the provided Cypher query expected to find one result, and return a single string.""" # TODO pass def _set_up_database(self): """Sets up the database used for answering questions by crawling the LTU website.""" # TODO: Fill this in. raise NotImplementedError("The FAQSkill database setup has not been implemented yet.")
StarcoderdataPython
3399324
from distutils.core import setup name = 'pyPaSWAS' setup( #Information about the package name=name, version='0.1.0', description='Python implementation of Smith-Waterman on CUDA', author='<NAME>', author_email='<EMAIL>', url='http://trac.nbic.nl/' + name.lower(), license='', #TODO Provide a license # We probably need more requirements, such as pycuda requires=['numpy', 'Bio (>1.54)', 'pycuda (>=2012.1)'], # What we'll package as part of this dist packages=[name, name + '.Core', name + '.Core.cfg', name + '.Core.test'], )
StarcoderdataPython
3358519
""" Convert a Binary Search Tree to a sorted Circular Doubly-Linked List in place. You can think of the left and right pointers as synonymous to the predecessor and successor pointers in a doubly-linked list. For a circular doubly linked list, the predecessor of the first element is the last element, and the successor of the last element is the first element. We want to do the transformation in place. After the transformation, the left pointer of the tree node should point to its predecessor, and the right pointer should point to its successor. You should return the pointer to the smallest element of the linked list. Example 1: Input: root = [4,2,5,1,3] Output: [1,2,3,4,5] Explanation: The figure below shows the transformed BST. The solid line indicates the successor relationship, while the dashed line means the predecessor relationship. Example 2: Input: root = [2,1,3] Output: [1,2,3] Example 3: Input: root = [] Output: [] Explanation: Input is an empty tree. Output is also an empty Linked List. Example 4: Input: root = [1] Output: [1] Solution: 1. Recursion 2. Iteration """ """ # Definition for a Node. class Node: def __init__(self, val, left=None, right=None): self.val = val self.left = left self.right = right """ # Iterative inorder traversal # Time: O(N), N is the tree size # Space: O(H), H is the tree height, H = logN class Solution: def treeToDoublyList(self, root: 'Node') -> 'Node': if root == None: return None dummy = Node(-1) pre = dummy cur = root # inorder traverse stack = [] while cur or stack: while cur: stack.append(cur) cur = cur.left cur = stack.pop() cur.left = pre pre.right = cur pre = cur cur = cur.right # now 'pre' node points to the last element, 'dummy' node's right pointer points to the first element pre.right = dummy.right dummy.right.left = pre return dummy.right # Recursion # Time: O(N) # Space: O(logN), tree height class Solution: def treeToDoublyList(self, root: 'Node') -> 'Node': def helper(node): """ Performs standard inorder traversal: left -> node -> right and links all nodes into DLL """ nonlocal last, first if node: # left helper(node.left) # node if last: # link the previous node (last) # with the current one (node) last.right = node node.left = last else: # keep the smallest node # to close DLL later on first = node last = node # right helper(node.right) if not root: return None # the smallest (first) and the largest (last) nodes first, last = None, None helper(root) # close DLL last.right = first first.left = last return first
StarcoderdataPython
3373009
import pickle from PIL import Image import matplotlib.pyplot as plt import cv2 import copy #read through open-cv img = cv2.imread("mining_map.png") img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #read through PIL im = Image.open("mining_map.png") pix = im.load() Goldlist = {} for x in range (0,im.width): for y in range (0,im.height): if pix[x,y]==(255,102,0,255) or pix[x,y]==(255,255,0,255): now=(x,y) Goldlist[now]=True #open trained Q-table filename = "Dict50.pk" #the filename is in the format of Dict(timesoftraining).pk with open(filename, 'rb') as fi: d = pickle.load(fi) #instantiate final path of the agent path = {} #instantiate results x, y = 0, 0 reward = 0 result = [] prev_ind = 8 for i in range (100): #no more than 100 steps temp = copy.copy(img[y,x]) img[y,x]=[255, 255, 255] #small animation for visualization plt.imshow(img) plt.show(block=False) plt.pause(0.6) #0.7 seconds between frames plt.close() img[y,x] = copy.copy(temp) #update agent's path coordinate = (y,x) path[coordinate]=[255, 255, 255] #add value in coordinate form result.append("("+str(x)+", "+str(y)+')') #prevents infinite looping if ((x,y) in Goldlist): Goldlist[(x,y)] = False booleans = tuple(Goldlist.values()) ind = 10*y+x move_ind = d[booleans][ind].index(max(d[booleans][ind])) if (abs(move_ind-prev_ind)==2): maximum = d[booleans][ind][move_ind] min_diff = maximum second_index = -1 for j in range (4): if (j==move_ind): continue diff = maximum - d[booleans][ind][j] if (diff<min_diff): min_diff = diff second_index = j move_ind = second_index #traceback action if (move_ind == 0): y = y - 1 #move up if (move_ind == 1): x = x + 1 #move right if (move_ind == 2): y = y + 1 #move down if (move_ind == 3): x = x - 1 #move left prev_ind = move_ind #update reward if (pix[x,y] == (153,0,0,255)): reward = reward - 1 * 0.1 elif (pix[x,y] == (0,0,0,255)): reward = reward - 1 * 0.01 elif (pix[x,y] == (255,102,0,255)): reward = reward + 10 * 0.95 else: reward = reward + 100 * 0.90 #terminate when all gold mines have been collected if (booleans == (False,False,False,False,False,False)): break print(result) print(path) print("Final Reward: " + str(reward)) #shows the entire path of the agent for i in path: img[i[0],i[1]]=path[i] plt.imshow(img) plt.show()
StarcoderdataPython
1607291
<filename>owl_system/AutomatizacionGUI.py<gh_stars>0 #pip install tk import tkinter as tk from tkinter import ttk from tkinter import filedialog import os.path import webbrowser from AutomatizacionEmpleado import AutomatizacionEmpleado class Application(ttk.Frame): expediente = '' def __init__(self, main_window): super().__init__(main_window) main_window.title('GestionExpedienteElectronico') main_window.resizable(False, False) main_window.geometry('350x165') self.create_oneProcessWidgets() def create_oneProcessWidgets(self): """ ### GUI GestionExpedienteElectronico_Version1 """ self.label = tk.Label(self, text=r"HammerDev99", fg="blue", cursor="hand2") self.label.pack(side=tk.BOTTOM, padx=10, pady=10) self.label.bind("<Button-1>", lambda e: self.callback("https://github.com/HammerDev99/GestionExpedienteElectronico_Version1")) self.label1 = tk.Label(self, text='Seleccione la carpeta del expediente electrónico') self.label1.pack(pady=15) self.scrollbar = tk.Scrollbar(self, orient=tk.HORIZONTAL) self.scrollbar.pack(fill="x", padx=5) self.entry1 = tk.Entry (self, width = 50, xscrollcommand=self.scrollbar.set) self.entry1.config(state=tk.DISABLED) self.entry1.insert("end", str(dir(tk.Scrollbar))) self.entry1.pack(fill="x", before=self.scrollbar) self.scrollbar.config(command=self.entry1.xview) self.pathExpediente = tk.Button(self, text = 'Agregar carpeta', command = self.obtenerExpediente, height = 1, width = 17) self.pathExpediente.pack(side=tk.LEFT) self.label5 = tk.Label(self, text='') self.label5.pack(side=tk.LEFT) self.aceptar = tk.Button(self, text = 'Aceptar', command = self.procesaCarpeta, height = 1, width = 7) self.aceptar.pack(side=tk.RIGHT, padx=3) self.cancelar = tk.Button(self, text = 'Cancelar', fg = 'red', command = self.master.destroy, height = 1, width = 7) self.cancelar.pack(side=tk.RIGHT, before=self.aceptar) self.pack() def callback(self, url): """ @modules: webbrowser """ webbrowser.open_new(url) def obtenerExpediente(self): """ - Obtiene ruta seleccionada por usuario, Actualiza variable global expediente Ejecuta funcion agregaNombreBase enviando la ruta y parametro False """ folder_selected = filedialog.askdirectory() self.expediente = folder_selected self.agregaNombreBase(folder_selected, False) def procesaCarpeta(self): """ - Crea objeto y llama metodo process() """ if self.expediente != '': if tk.messagebox.askyesno(message="Se procesarán los archivos que contiene la carpeta \""+ os.path.basename(self.expediente) +"\". \n¿Desea continuar?.", title=os.path.basename(self.expediente)): obj = AutomatizacionEmpleado(self.expediente, "") self.mensaje(obj.process()) else: self.mensaje(6) else: self.mensaje(3) def mensaje(self, result): """ @param: result tipo int @modules: tkinter - Utiliza la GUI para enviar mensaje """ switcher={ 0:'Procedimiento detenido. No se encontraron los archivos indicados en el índice', 1:'Procedimiento finalizado', 2:'Archivos sin procesar', 3:'Seleccione una carpeta para procesar', 4:'Agregue archivos a la lista', 5:'La carpeta electrónica del expediente se encuentra actualizada', 6:'Procedimiento detenido' } if result != None: tk.messagebox.showinfo(message=switcher.get(result), title=os.path.basename(self.expediente)) def agregaNombreBase(self, items, bandera): """ @param: items tipo List, bandera tipo bool; items contiene nombre(s) de archivo(s) @widgets: listbox1, entry1 - Crea lista auxiliar con nombres base de items - Bandera controla Widget a modificar """ nombres = [] if bandera: for x in items: nombres.append(os.path.basename(x)) self.listbox1.delete(0, tk.END) self.listbox1.insert(0, *nombres) #self.listaNombres = nombres else: self.entry1.config(state=tk.NORMAL) self.entry1.delete(0, tk.END) self.entry1.insert(0, items) self.entry1.config(state=tk.DISABLED) main_window = tk.Tk() app = Application(main_window) app.mainloop()
StarcoderdataPython
1688620
<filename>core/objs/rh_gasto_suportado.py # !/usr/bin/env python3 # -*- encoding: utf-8 -*- """ ERP+ """ __author__ = '<NAME>' __credits__ = [] __version__ = "1.0" __maintainer__ = "<NAME>" __status__ = "Development" __model_name__ = 'rh_gasto_suportado.RHGastoSuportado' import auth, base_models from orm import * from form import * class RHGastoSuportado(Model, View): def __init__(self, **kargs): Model.__init__(self, **kargs) self.__name__ = 'rh_gasto_suportado' self.__title__= 'Gatos Suportados em Salario' self.__model_name__ = __model_name__ self.__list_edit_mode__ = 'inline' self.__order_by__='valor desc' self.__auth__ = { 'read':['All'], 'write':['All'], 'create':['All'], 'delete':['Gestor'], 'full_access':['Gestor'] } self.__get_options__ = ['nome'] self.rh_recibo_salario = parent_field(view_order=1 , name='Recibo', size=70,onlist=True, column='periodo', model_name ='rh_recibo_salario.RHReciboSalario') self.nome = string_field(view_order=2,name='Descrição',size=100) self.valor = currency_field(view_order=3,name='Valor',size=70, sum=True)
StarcoderdataPython
3265501
<gh_stars>1-10 import numpy as np from TDTensor import TDTensor from trainingDataGeneratorTensor import datageneratorTensor as datagenerator def experiment(data_train, data_valid, data_test, ku, kv, kr, max_epoch = 10, SGDstep = 0.001): # def experiment(data_train, data_valid, data_test, K, max_epoch=10, SGDstep=0.001): training = datagenerator(data_train) valid = datagenerator(data_valid) test = datagenerator(data_test) # model = MF() # model = CD() # model.fit(training, valid, K = K, max_epoch = max_epoch, SGDstep = SGDstep) model = TDTensor() model.fit(training, valid, ku, kv, kr, max_epoch=max_epoch, SGDstep=SGDstep) perf = performance(test, model) return perf def performance(test, model): mse = model.loss(test) msre = 0.0 Nsamp = 0 for samp in test.sample(random = False): uid, iid, lid, value = samp value_pred = model.predict(uid, iid, lid) msre += np.power((value - value_pred) / value, 2.0) Nsamp += 1 msre = msre / Nsamp return [mse, msre] if __name__ == "__main__": # print experiment(data_train = "data/synthetic_N500_M500_L3_K5_0.7train", # data_valid = "data/synthetic_N500_M500_L3_K5_0.1valid", # data_test = "data/synthetic_N500_M500_L3_K5_0.2test", # ku = 15, kv = 15, kr = 15, # # K = 15, # max_epoch = 1000, SGDstep = 0.001) print experiment(data_train="data/TDTsynthetic_N500_M500_L3_ku20_kv20_kr10_0.7train", data_valid="data/TDTsynthetic_N500_M500_L3_ku20_kv20_kr10_0.1valid", data_test="data/TDTsynthetic_N500_M500_L3_ku20_kv20_kr10_0.2test", ku=20, kv=20, kr=10, # K = 100, max_epoch=1000, SGDstep=0.001)
StarcoderdataPython
1777839
<filename>rapidsms/contrib/handlers/handlers/keyword.py #!/usr/bin/env python # vim: ai ts=4 sts=4 et sw=4 import re from django.core.exceptions import ObjectDoesNotExist from ..exceptions import HandlerError from .base import BaseHandler class KeywordHandler(BaseHandler): """ This handler type can be subclassed to create simple keyword-based handlers. When a message is received, it is checked against the mandatory ``keyword`` attribute (a regular expression) for a prefix match. For example:: >>> class AbcHandler(KeywordHandler): ... keyword = "abc" ... ... def help(self): ... self.respond("Here is some help.") ... ... def handle(self, text): ... self.respond("You said: %s." % text) If the keyword is matched and followed by some text, the ``handle`` method is called:: >>> AbcHandler.test("abc waffles") ['You said: waffles.'] If *just* the keyword is matched, the ``help`` method is called:: >>> AbcHandler.test("abc") ['Here is some help.'] All other messages are silently ignored (as usual), to allow other apps or handlers to catch them. """ #: A string specifying a regular expression matched against the #: beginning of the message. Not case sensitive. keyword = None def help(self): """Called when the keyword matches but no text follows""" raise NotImplementedError def handle(self, text): """Called when the keyword matches and text follows :param text: The text that follows the keyword. Any whitespace between the keyword and the text is not included. """ raise NotImplementedError @classmethod def _keyword(cls): if hasattr(cls, "keyword") and cls.keyword: # The 'keyword' is inside non-grouping parentheses so that a # user could set the keyword to a regex - e.g. # keyword = r'one|two|three' prefix = r""" ^\s* # discard leading whitespace (?:{keyword}) # require the keyword or regex [\s,;:]* # consume any whitespace , ; or : ([^\s,;:].*)? # capture rest of line if any, starting # with the first non-whitespace $ # match all the way to the end """.format(keyword=cls.keyword) return re.compile(prefix, re.IGNORECASE|re.VERBOSE) raise HandlerError('KeywordHandler must define a keyword.') @classmethod def dispatch(cls, router, msg): keyword = cls._keyword() match = keyword.match(msg.text) if match is None: return False # spawn an instance of this handler, and stash # the low(er)-level router and message object inst = cls(router, msg) # if any non-whitespace content was send after the keyword, send # it along to the handle method. the instance can always find # the original text via self.msg if it really needs it. text = match.group(1) if text is not None and text.strip() != "": try: inst.handle(text) # special case: if an object was expected but not found, # return the (rather appropriate) "%s matching query does # not exist." message. this can, of course, be overridden by # catching the exception within the ``handle`` method. except ObjectDoesNotExist, err: return inst.respond_error( unicode(err)) # another special case: if something was miscast to an int # (it was probably a string from the ``text``), return a # more friendly (and internationalizable) error. except ValueError, err: p = r"^invalid literal for int\(\) with base (\d+?): '(.+?)'$" m = re.match(p, unicode(err)) # allow other valueerrors to propagate. if m is None: raise return inst.respond_error( "Not a valid number: %(string)s" % dict( string=m.group(2))) # if we received _just_ the keyword, with # no content, some help should be sent back else: inst.help() return True
StarcoderdataPython
1794757
<filename>string/string_half_alike.py<gh_stars>0 """ 1704. Determine if String Halves Are Alike https://leetcode.com/problems/determine-if-string-halves-are-alike/ You are given a string s of even length. Split this string into two halves of equal lengths, and let a be the first half and b be the second half. Two strings are alike if they have the same number of vowels ('a', 'e', 'i', 'o', 'u', 'A', 'E', 'I', 'O', 'U'). Notice that s contains uppercase and lowercase letters. Return true if a and b are alike. Otherwise, return false. Example: Input: s = "book" Output: true Explanation: a = "bo" and b = "ok". a has 1 vowel and b has 1 vowel. Therefore, they are alike. Input: s = "textbook" Output: false Explanation: a = "text" and b = "book". a has 1 vowel whereas b has 2. Therefore, they are not alike. Notice that the vowel o is counted twice. """ # Runtime: 40ms class Solution: def halvesAreAlike(self, s: str) -> bool: vowels = ('a', 'e', 'i', 'o', 'u', 'A', 'E', 'I', 'O', 'U') first, second = s[:int(len(s) / 2)], s[int(len(s) / 2):] first_count, second_count = 0, 0 for letter in first: if letter in vowels: first_count += 1 for letter in second: if letter in vowels: second_count += 1 return first_count == second_count
StarcoderdataPython
1698884
<gh_stars>0 from .pyfdb import *
StarcoderdataPython
1738474
<reponame>skylovead/StrategyEase-Python-SDK<filename>tests/strategyease_sdk/joinquant/test_executor.py # -*- coding: utf-8 -*- import collections import datetime import inspect import logging import os import unittest import six from six.moves import configparser if six.PY2: ConfigParser = configparser.RawConfigParser else: ConfigParser = configparser.ConfigParser from strategyease_sdk import JoinQuantExecutor @unittest.skip("integration class") class JoinQuantExecutorTest(unittest.TestCase): def setUp(self): logging.basicConfig(level=logging.INFO) config = ConfigParser() dir_path = os.path.dirname(os.path.realpath(__file__)) config.read('{}/../../config/config.ini'.format(dir_path)) self.Order = collections.namedtuple('Order', ['is_buy', 'security', 'price', 'amount']) self.executor = JoinQuantExecutor(host=config.get('StrategyEase', 'host')) def test_buy_stock(self): mock_order = self.Order mock_order.is_buy = True mock_order.order_id = 1 mock_order.security = '000001.XSHE' mock_order.price = 11.11 mock_order.amount = 100 mock_order.limit = 11.11 mock_order.add_time = datetime.datetime.now() response = self.executor.execute(mock_order) print(inspect.stack()[0][3] + ' - ' + response.text) json = response.json(); if response.status_code == 200: self.assertTrue(json['id']) elif response.status_code == 400: self.assertTrue(json['message']) else: self.fail() def test_sell_stock(self): mock_order = self.Order mock_order.is_buy = False mock_order.order_id = 2 mock_order.security = '000001.XSHE' mock_order.price = 11.11 mock_order.amount = 100 mock_order.limit = 11.11 mock_order.add_time = datetime.datetime.now() response = self.executor.execute(mock_order) print(inspect.stack()[0][3] + ' - ' + response.text) json = response.json(); if response.status_code == 200: self.assertTrue(json['id']) elif response.status_code == 400: self.assertTrue(json['message']) else: self.fail() def test_cancel(self): mock_order = self.Order mock_order.is_buy = True mock_order.order_id = 3 mock_order.security = '000001.XSHE' mock_order.price = 9.01 mock_order.amount = 100 mock_order.limit = 9.01 mock_order.add_time = datetime.datetime.now() self.executor.execute(mock_order) response = self.executor.cancel(mock_order) print(inspect.stack()[0][3] + ' - ' + response.text) if response.status_code != 200: self.fail()
StarcoderdataPython
3319115
<gh_stars>0 from Jumpscale import j from .SerializerBase import SerializerBase class SerializerJSXObject(SerializerBase): def __init__(self): SerializerBase.__init__(self) def dumps(self, obj, test=True): """ obj is the dataobj for JSX j.data.serializers.jsxdata.dumps(.. :param obj: :param test: if True will be slow !!! :return: """ assert isinstance(obj, j.data.schema._JSXObjectClass) try: obj._capnp_obj.clear_write_flag() data = obj._capnp_obj.to_bytes_packed() except Exception as e: # need to catch exception much better (more narrow) obj._capnp_obj_ = obj._capnp_obj.as_builder() data = obj._capnp_obj_.to_bytes_packed() version = 3 if obj.id: objid = obj.id else: objid = 0 data2 = ( version.to_bytes(1, "little") + int(objid).to_bytes(4, "little") + bytes(bytearray.fromhex(obj._schema._md5)) + data ) if test: u = self.loads(data=data2) assert u.id == obj.id # self._log_debug("DUMPS:%s:%s" % (version, obj.id), data=obj._ddict) return data2 def loads(self, data, bcdb=None): """ j.data.serializers.jsxdata.loads(.. :param data: :return: obj """ assert data versionnr = int.from_bytes(data[0:1], byteorder="little") if versionnr == 3: obj_id = int.from_bytes(data[1:5], byteorder="little") md5bin = data[5:21] md5 = md5bin.hex() data2 = data[21:] schema = j.data.schema.get_from_md5(md5) obj = schema.new(capnpdata=data2, bcdb=bcdb) obj.id = obj_id if obj.id == 0: obj.id = None return obj else: raise j.exceptions.Base("version wrong, versionnr found:%s (needs to be 1 or 10)" % versionnr)
StarcoderdataPython
9759
# SQL output is imported as a pandas dataframe variable called "df" # Source: https://stackoverflow.com/questions/19441730/trimmed-mean-with-percentage-limit-in-python import pandas as pd import matplotlib.pyplot as plt from scipy.stats import tmean, scoreatpercentile import numpy as np def trimmean(arr, percent): lower_limit = scoreatpercentile(arr, percent) upper_limit = scoreatpercentile(arr, 100-percent) return tmean(arr, limits=(lower_limit, upper_limit), inclusive=(False, False)) my_result = trimmean(df["amt_paid"].values,10)
StarcoderdataPython
3337266
<filename>common_pyutil/io.py from typing import List, Callable, Union def prompt(string: str, p_t: Union[str, set], p_f: Union[str, set]) -> bool: """Prompt for input and return a boolean value. Args: string: Prefix string to input p_t: Any of the `True` prompts p_f: Any of the `False` prompts """ x = input(string).strip() if isinstance(p_t, str): p_t = {p_t} if isinstance(p_f, str): p_f = {p_f} valid_inputs = {*p_t, *p_f} while x not in valid_inputs: x = input(string + f"\nPlease type one of {valid_inputs} ").strip() if x in p_t: return True elif x in p_f: return False def prompt_yes_no(string: str) -> bool: return prompt(string + " (yes or no) ", "yes", "no") def prompt_y_n(string: str) -> bool: return prompt(string + " (y, n) ", {"yes", "y"}, {"no", "n"})
StarcoderdataPython
4807473
from typing import List, Dict, Any, Iterable from dataclasses import dataclass @dataclass( init=True, frozen=True, ) class TableKey: collection: str name: str version: int @staticmethod def from_row(row: List[str]) -> "TableKey": return TableKey(collection=row[1], name=row[2], version=int(row[3])) def row_matches(self, row: List[str]) -> bool: return self == TableKey.from_row(row) def format(self) -> str: return "{coll}-{name}-v{ver}".format( coll=self.collection, name=self.name, ver=self.version )
StarcoderdataPython
146519
# -- coding: utf-8 -- import os from unittest import main, TestCase from selenium import webdriver from selenium.webdriver.common.keys import Keys from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC class TestSelenium(TestCase): def setUp(self): self.driver = webdriver.Firefox() self.waiter = WebDriverWait(self.driver, timeout=60) self.email = os.environ['TESTING_EMAIL'] self.password = os.environ['TESTING_PASSWORD'] with open(os.path.dirname(__file__) + '/../../fabfile/hosts.txt', 'rU') as f: hosts = f.read().split(',') if len(hosts) == 0: raise Exception('Please insert a test host') self.live_site = 'http://' + hosts[0] def switch_to_other_window(self, in_handle): ''' Switch to the first window found with a handle that's not in_handle ''' def predicate(driver): for check_handle in driver.window_handles: if check_handle != in_handle: driver.switch_to_window(check_handle) return True return False return predicate def test_create_page_and_preview(self): self.driver.get(self.live_site) main_window = self.driver.current_window_handle self.driver.find_element_by_id('signin').click() # wait until the persona window's available and switch to it self.waiter.until(self.switch_to_other_window(main_window)) self.waiter.until(EC.visibility_of_element_located((By.ID, 'authentication_email'))).send_keys(self.email) # there are many possible submit buttons on this page # so grab the whole row and then click the first button submit_row = self.driver.find_element_by_css_selector('.submit.cf.buttonrow') submit_row.find_element_by_css_selector('.isStart.isAddressInfo').click() # ajax, wait until the element is visible self.waiter.until(EC.visibility_of_element_located((By.ID, 'authentication_password'))).send_keys(self.password) submit_row.find_element_by_css_selector('.isReturning.isTransitionToSecondary').click() # switch back to the main window self.driver.switch_to.window(main_window) # make sure we're back to Chime # self.assertEquals(self.driver.title, 'Tasks | Chime') main_url = self.driver.current_url # start a new task self.waiter.until(EC.visibility_of_element_located((By.NAME, 'task_description'))).send_keys('foobar') self.driver.find_element_by_class_name('create').click() # create a new page self.waiter.until(EC.visibility_of_element_located((By.XPATH, "//form[@action='.']/input[@type='text']"))).send_keys('foobar') self.driver.find_element_by_xpath("//form[@action='.']/button[@type='submit']").click() # add some content to that page self.waiter.until(EC.visibility_of_element_located((By.NAME, 'en-title'))).send_keys('foo') test_page_content = 'This is some test content.', Keys.RETURN, Keys.RETURN, '# This is a test h1', Keys.RETURN, Keys.RETURN, '> This is a test pull-quote' self.driver.find_element_by_id('en-body markItUp').send_keys(*test_page_content) # save the page self.driver.find_element_by_xpath("//button[@value='Save']").click() # preview the page self.waiter.until(EC.presence_of_element_located((By.XPATH, "//a[@target='_blank']"))).click() # wait until the preview window's available and switch to it self.waiter.until(self.switch_to_other_window(main_window)) self.waiter.until(EC.presence_of_element_located((By.TAG_NAME, 'body'))) self.driver.close() self.driver.switch_to.window(main_window) # go back to the main page self.driver.get(main_url) # delete our branch self.driver.find_element_by_xpath("//form[@action='/update']/button[@value='abandon']").click() def tearDown(self): self.driver.close() if __name__ == '__main__': main()
StarcoderdataPython
1616744
<gh_stars>0 from wms.database import sql as _sql create_connection = _sql.create_connection
StarcoderdataPython
3337366
<filename>Python_Projects/OS-app/Chat application.py """ This is the central file of the application. It will update and download the main software so you will never need to come here to obtain the newest version of the software! I plan on expanding this concept, but for now I will only stay with this app. """ from urllib import request import os from time import sleep import subprocess online_path = "https://raw.githubusercontent.com/Eis3/Server/master/Python_Projects/OS-app/" # Should never change. path = os.getcwd() + "\\" # app boot def boot(): os.system("python startup.py") exit() # app update all def update(): download_path = "" print("Downloading...") for line in request.urlopen(online_path + "UPDATE_FILES.txt").read().decode().split("\n"): line = line.strip("\r") if "path=" in line: download_path = line.split("=")[1] continue if line == "": continue try: get_file(download_path, line) print("Downloading files from '" + download_path + "/" + line) except EnvironmentError: print("Error") boot() # Function to obtain the file from the "cloud" def get_file(file_path, name): # Checks if the directory exists. if file_path == "": pass elif not os.path.exists(file_path): os.makedirs(file_path) # TODO: Allow for folders in folders print("requesting " + name) # Take care of .png's if name[-4:] == ".png": print("Downloading") with open(file_path + name, "wb") as file: file.write(request.urlopen(online_path + file_path + name).read()) # Files that are not images. else: with open(file_path + name, "w") as file: # makes the files pass with open(file_path + name, "a") as file: # prints into the file for line in request.urlopen(online_path + file_path + name).read().decode(): file.write(line) try: # version check with open("config.txt", "r") as file: for line in file: # find version=X.X variable = line.strip("\n").split("=") # 0 = name 1 = value if variable[0] == "version": print("Found the current version to be " + str(variable[1])) break # get most updated version for line in request.urlopen(online_path + "config.txt").read().decode().split("\n"): server_variable = line.split("=") # 0 = name 1 = value if server_variable[0] == "version": print("The server requires version " + str(server_variable[1])) break # if up to date if str(server_variable[1]) == str(variable[1]): print("Your version is up to date!\nBooting...") boot() exit() else: user_respond = input("Your version is not up-to-date! We highly recomend that you update!\nClick enter to continue or type in \"noupdate\" to cancel the updating") if user_respond == "noupdate": boot() else: update() exit() # config.txt not found except FileNotFoundError: print("config.txt not found!") answer = str(input("Is this your first start up? YES/NO\n> ")).upper() if "N" in answer: print("Please contact the support as the current version can't be obtained!\nIt must be that the program is" + "simply not installed or that you have mistakenly deleted the file.") input("Press enter to close...") else: update()
StarcoderdataPython
1649688
<reponame>kennydn99/tf-pose-estimation import argparse import logging import time import math import cv2 import numpy as np from tf_pose.estimator import TfPoseEstimator from tf_pose.networks import get_graph_path, model_wh logger = logging.getLogger("TfPoseEstimator-WebCam") logger.setLevel(logging.DEBUG) ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) formatter = logging.Formatter("[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s") ch.setFormatter(formatter) logger.addHandler(ch) fps_time = 0 def str2bool(v): return v.lower() in ("yes", "true", "t", "1") if __name__ == "__main__": parser = argparse.ArgumentParser(description="tf-pose-estimation realtime webcam") parser.add_argument("--camera", type=int, default=0) parser.add_argument( "--resize", type=str, default="0x0", help="if provided, resize images before they are processed. default=0x0, Recommends : 432x368 or 656x368 or 1312x736 ", ) parser.add_argument( "--resize-out-ratio", type=float, default=4.0, help="if provided, resize heatmaps before they are post-processed. default=1.0", ) parser.add_argument( "--model", type=str, default="mobilenet_thin", help="cmu / mobilenet_thin / mobilenet_v2_large / mobilenet_v2_small", ) parser.add_argument( "--show-process", type=bool, default=False, help="for debug purpose, if enabled, speed for inference is dropped.", ) parser.add_argument( "--tensorrt", type=str, default="False", help="for tensorrt process." ) args = parser.parse_args() logger.debug("initialization %s : %s" % (args.model, get_graph_path(args.model))) w, h = model_wh(args.resize) if w > 0 and h > 0: e = TfPoseEstimator( get_graph_path(args.model), target_size=(w, h), trt_bool=str2bool(args.tensorrt), ) else: e = TfPoseEstimator( get_graph_path(args.model), target_size=(432, 368), trt_bool=str2bool(args.tensorrt), ) logger.debug("cam read+") cam = cv2.VideoCapture(args.camera) ret_val, image = cam.read() logger.info("cam image=%dx%d" % (image.shape[1], image.shape[0])) #calculate angle function given three joints def calculate_angle(x,y,z): x = np.array(x) y = np.array(y) z = np.array(z) #calculate angle between three coordinates rad = np.arctan2(z[1]-y[1], z[0]-y[0]) - np.arctan2(x[1]-y[1], x[0]-y[0]) angle = np.abs(180 * rad / np.pi) #max angle = 180 degrees if angle > 180.0: angle = 360 - angle return angle #find perpendicular coordinates function - pass in two joint coordinates def perpCoord(a,b): a = np.array(a) b = np.array(b) # find length between a and b length = np.linalg.norm(a-b) #get direction vector = [delta x, delta y] dv = [b[0] - a[0], b[1] - a[1]] if dv[0] == 0 or dv[1] == 0: c = [b[0], b[1] + length] return c, dv #get magnitude var = math.sqrt(dv[0]*dv[0] + dv[1]*dv[1]) dv[0] = dv[0]/var dv[1] = dv[1]/var #invert direction vector coordinate and swap dv[0], dv[1] = -dv[1], dv[0] # new line starting at b pointing in direction of dv if b[0] < a[0]: c = [b[0] - dv[0] * length, b[1] - dv[1] * length] else: c = [b[0] + dv[0] * length, b[1] + dv[1] * length] return c while True: ret_val, image = cam.read() logger.debug("image process+") humans = e.inference( image, resize_to_default=(w > 0 and h > 0), upsample_size=args.resize_out_ratio, ) logger.debug("postprocess+") image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) try: for human in humans: #save coordinates of shoulder, hip, and elbow & neck neck = [human.body_parts[1].x, human.body_parts[1].y] RS = [human.body_parts[2].x, human.body_parts[2].y] RE = [human.body_parts[3].x, human.body_parts[3].y] LS = [human.body_parts[5].x, human.body_parts[5].y] LE = [human.body_parts[6].x, human.body_parts[6].y] #Calculate the angle for right side pc = perpCoord(neck, RS) angle = calculate_angle(pc, RS, RE) formatted_angle = "{:.2f}".format(angle) #calculate angle for left side left_pc = perpCoord(neck, LS) left_angle = calculate_angle(left_pc, LS, LE) left_formatted_angle = "{:.2f}".format(left_angle) #visualizing angles on image img_h, img_w = image.shape[:2] cv2.putText( image, formatted_angle, tuple(np.multiply(RS, [img_w, img_h]).astype(int)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA ) cv2.putText( image, left_formatted_angle, tuple(np.multiply(LS, [img_w, img_h]).astype(int)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA ) #visualize perpendicular coordinate cv2.circle( image, tuple(np.multiply(pc, [img_w, img_h]).astype(int)), 3, (0,0,255), thickness=3, lineType=8, shift=0, ) cv2.circle( image, tuple(np.multiply(left_pc, [img_w, img_h]).astype(int)), 3, (0,0,255), thickness=3, lineType=8, shift=0, ) except: pass logger.debug("show+") cv2.putText( image, "FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2, ) cv2.imshow("tf-pose-estimation result", image) fps_time = time.time() if cv2.waitKey(1) == 27: break logger.debug("finished+") cv2.destroyAllWindows()
StarcoderdataPython
1792219
<reponame>gferreira/zdogpy<filename>Lib/zDogPy/boilerplate.py '''Boilerplate & utils''' import math TAU = math.pi * 2 def lerp(a, b, t): return (b - a) * t + a # def powerMultipliers(a): # if a == 2: # return a * a # elif a == 3: # return a * a * a # elif a == 4: # return a * a * a * a # elif a == 5: # return a * a * a * a * a # def easeInOut(alpha, power): # if power == 1: # return alpha # alpha = max(0, min(1, alpha)) # isFirstHalf = alpha < 0.5 # slope = isFirstHalf if alpha else 1 - alpha # slope = slope / 0.5 # # make easing steeper with more multiples # powerMultiplier = powerMultipliers[power] or powerMultipliers[2] # curve = powerMultiplier(slope) # curve = curve / 2 # return isFirstHalf if curve else 1 - curve def hexToRGB(hexColor): hexColor = hexColor.lstrip('#') if len(hexColor) == 3: hexColor = ''.join([c*2 for c in hexColor]) L = len(hexColor) rgb = [] for i in range(0, L, L // 3): rgb += (int(hexColor[i:i + L // 3], 16) / 255,) return tuple(rgb) def RGBToHex(rgbColor): r, g, b = rgbColor r, g, b = int(r * 255), int(g * 255), int(b * 255) return '%02x%02x%02x' % (r, g, b)
StarcoderdataPython
3317989
import os from filefind.config import load_config def test_post_process_config(): config = load_config(['-i', '*.cpp *.h', '--include', '*.hpp']) assert config.include == ['*.cpp', '*.h', '*.hpp'] assert config.exclude == [] assert config.directory == os.path.abspath('.') def test_include_as_position_arguments(): config = load_config(['-i', '*.cpp *.h', '*.hpp']) assert config.include == ['*.cpp', '*.h', '*.hpp']
StarcoderdataPython
1785387
<reponame>lyclyc52/nerf_with_slot_attention import os val = [0, 3, 4, 23, 24, 40, 41, 42, 43, 45, 46, 48, 58, 59, 60] for i in range(100,0, -1): t = 'data/nerf_synthetic/clevr_1/train2/r_{:d}.png'.format(i-1) s = 'data/nerf_synthetic/clevr_1/train2/r_{:d}.png'.format(i) os.rename(t, s)
StarcoderdataPython
156215
# MIT License # # Copyright (c) 2017-2019 <NAME> # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys import os import argparse import pandas as pd import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import joblib try: import urllib urllib_HTTP_Error = urllib.error.HTTPError except AttributeError: import urllib2 urllib_HTTP_Error = urllib2.HTTPError from .version import __version__ # noqa F401 from tindetheus import config from tindetheus import export_embeddings from tindetheus import tindetheus_align from tindetheus.tinder_client import client import tindetheus.facenet_clone.facenet as facenet import tindetheus.image_processing as imgproc import tindetheus.machine_learning as ml try: FileNotFoundError except NameError: FileNotFoundError = IOError def catch_http_error(e, args, retries, facebook_token, x_auth_token): print(e) print("Closing session.") retries -= 1 if retries > 0: print("Let's try again. \n Retries left:", retries) main(args, facebook_token, x_auth_token=x_auth_token, retries=retries) else: print("Out of retries. Exiting.") def main(args, facebook_token, x_auth_token=None, retries=20): # There are three function choices: browse, build, like # browse: review new tinder profiles and store them in your database # train: use machine learning to create a new model that likes and dislikes # profiles based on your historical preference # like: use your machine leanring model to like new tinder profiles if args.function == 'browse': try: my_sess = client(facebook_token, args.distance, args.model_dir, likes_left=args.likes, x_auth_token=x_auth_token) my_sess.browse() except urllib_HTTP_Error as e: catch_http_error(e, args, retries, facebook_token, x_auth_token) elif args.function == 'train': # align the database tindetheus_align.main() # export the embeddings from the aligned database export_embeddings.main(model_dir=args.model_dir, image_batch=args.image_batch) # calculate the n average embedding per profiles X, y = ml.calc_avg_emb() # fit and save a logistic regression model to the database ml.fit_log_reg(X, y) elif args.function == 'validate': print('\n\nAttempting to validate the dataset...\n\n') valdir = 'validation' # align the validation dataset tindetheus_align.main(input_dir=valdir, output_dir=valdir+'_aligned') # export embeddings # y is the image list, X is the embedding_array image_list, emb_array = export_embeddings.main(model_dir=args.model_dir, # noqa E501 data_dir=valdir+'_aligned', # noqa E501 image_batch=args.image_batch, # noqa E501 embeddings_name='val_embeddings.npy', # noqa E501 labels_name='val_labels.npy', # noqa E501 labels_strings_name='val_label_strings.npy', # noqa E501 return_image_list=True) # noqa E501 # print(image_list) # convert the image list to a numpy array to take advantage of # numpy array slicing image_list = np.array(image_list) print('\n\nEvaluating trained model\n \n') model = joblib.load('log_reg_model.pkl') yhat = model.predict(emb_array) # print(yhat) # 0 should be dislike, and 1 should be like # if this is backwards, there is probably a bug... dislikes = yhat == 0 likes = yhat == 1 imgproc.show_images(image_list[dislikes], holdon=True, title='Dislike') print('\n\nGenerating plots...\n\n') plt.title('Dislike') imgproc.show_images(image_list[likes], holdon=True, title='Like') plt.title('Like') cols = ['Image name', 'Model prediction (0=Dislike, 1=Like)'] results = np.array((image_list, yhat)).T print('\n\nSaving results to validation.csv\n\n') my_results_DF = pd.DataFrame(results, columns=cols) my_results_DF.to_csv('validation.csv') plt.show() elif args.function == 'like': try: print('... Loading the facenet model ...') print('... be patient this may take some time ...') with tf.Graph().as_default(): with tf.Session() as sess: # pass the tf session into client object my_sess = client(facebook_token, args.distance, args.model_dir, likes_left=args.likes, tfsess=sess, x_auth_token=x_auth_token) # Load the facenet model facenet.load_model(my_sess.model_dir) print('Facenet model loaded successfully!!!') # automatically like users my_sess.like() except urllib_HTTP_Error as e: catch_http_error(e, args, retries, facebook_token, x_auth_token) elif args.function == 'like_folder': print('Copying al_database profiles into either al/like or al/dislike') # make folders if not os.path.exists('al'): os.makedirs('al') if not os.path.exists('al/like'): os.makedirs('al/like') if not os.path.exists('al/dislike'): os.makedirs('al/dislike') # load the auto like database al_data = np.load('al_database.npy', allow_pickle=True) # copy profile images to either al/like or al/dislike for user in al_data: imgproc.al_copy_images(user[8], user[0], user[-1]) else: text = '''You must specify a function. Your choices are either tindetheus browse tindetheus train tindetheus like tindetheus validate''' print(text) def parse_arguments(argv, defaults): help_text = '''There are four function choices: browse, train, like, or validate. \n 1) tindetheus browse -- Let's you browse tinder profiles to add to your database. -- Browses tinder profiles in your distance until you run out. -- Asks if you'd like to increase the distance by 5 miles. -- Use to build a database of the tinder profiles you look at. \n 2) tindetheus train -- Trains a model to your Tinder database. -- Uses facenet implementation for facial detection and classification. -- Saves logistic regression model to classify which faces you like and -- dislike. \n 3) tindetheus like -- Automatically like and dislike Tinder profiles based on your historical -- preference. First run browse, then run train, then prosper with like. -- Uses the trained model to automatically like and dislike profiles. -- Profiles where a face isn't detected are automatically disliked. \n 4) tindetheus validate -- This validate functions applies your personally trained tinder model on -- an external set of images. Place images you'd like to run tindetheus on -- withing a folder within the validation directory. See README for more -- details. The results are saved in validation.csv. \n 5) tindetheus like_folder -- Creates al/like and al/dislike folders based on the profiles you have -- automatically liked. This copies the profile images from al_database -- into al/like or al/disliked based on whether the model liked or -- disliked the profile. \n You can now store all default optional parameters in your environment variables! This means you can set your starting distance, number of likes, and image_batch size without manually specifying the options each time. You can set local environment variables for a local folder using a .env file. This is an example .env file: FACEBOOK_AUTH_TOKEN="TODO" # your facebook token hash # alternatively you can use the XAuthToken # TINDER_AUTH_TOKEN="TODO" TINDETHEUS_MODEL_DIR="/models/20170512-110547" # the location of your facenet model directory # see https://github.com/davidsandberg/facenet#pre-trained-models for other # pretrained facenet models TINDETHEUS_IMAGE_BATCH=1000 # number of images to load in a batch during train # the larger the image_batch size, the faster the training process, at the # cost of additional memory. A 4GB machine may struggle with 1000 images. TINDETHEUS_DISTANCE=5 # Set the starting distance in miles TINDETHEUS_LIKES=100 # set the number of likes you want to use # note that free Tinder users only get 100 likes in 24 hours TINDETHEUS_RETRIES=20 \n Optional arguments will overide config.txt settings. ''' parser = argparse.ArgumentParser(description=help_text, formatter_class=argparse.RawDescriptionHelpFormatter) # noqa: E501 parser.add_argument('function', type=str, help='browse, train, like, validate, or like_folder') parser.add_argument('--distance', type=int, help='Set the starting distance in miles.' 'Tindetheus will crawl in 5 mile increments from here' '. Default=5.', default=defaults['distance']) parser.add_argument('--image_batch', type=int, help='The number of images to load in the facenet' ' model at one time. This only affects the train ' 'functionality. A larger number will be faster at the' ' cost for larger memory. Default=1000.', default=defaults['image_batch']) parser.add_argument('--model_dir', type=str, help='Location of your ' 'pretrained facenet model. Default="20170512-110547"', default=defaults['model_dir']) parser.add_argument('--likes', type=int, help='Set the number of likes to ' 'use. Note that free Tinder users only get 100 likes ' 'in 24 hour period', default=defaults['likes']) parser.add_argument('--version', action='version', version=__version__) return parser.parse_args(argv) def command_line_run(): # settings to look for defaults = {'facebook_token': config.FACEBOOK_AUTH_TOKEN, 'XAuthToken': config.TINDER_AUTH_TOKEN, 'model_dir': config.TINDETHEUS_MODEL_DIR, 'image_batch': config.TINDETHEUS_IMAGE_BATCH, 'distance': config.TINDETHEUS_DISTANCE, 'likes': config.TINDETHEUS_LIKES, 'retries': config.TINDETHEUS_RETRIES} # parse the supplied arguments args = parse_arguments(sys.argv[1:], defaults) if defaults['facebook_token'] is None and defaults['XAuthToken'] is None: raise('ERROR: No facebook or tinder token has been set.' 'You must supply an auth token in order to use tindetheus!') # run the main function with parsed arguments main(args, defaults['facebook_token'], defaults['XAuthToken'], retries=defaults['retries']) if __name__ == '__main__': command_line_run()
StarcoderdataPython
151013
<filename>parlai/tasks/genderation_bias/agents.py<gh_stars>1-10 #!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """ Generates a controllable_gen version of a ParlAI task, i.e., for tasks with multi-turn dialogues (episodes), this will generate a task with single example episodes, in which we append to the context a special classification token. In order to use this teacher, specify the task flag as follows: `--task genderation_bias:controllable_task:<ORIGINAL TASK NAME>`. As an example, try running: `parlai display_data -t genderation_bias:controllable_task:convai2` """ from parlai.core.message import Message from parlai.core.opt import Opt from parlai.core.teachers import FixedDialogTeacher from parlai.utils.io import PathManager import parlai.utils.logging as logging from parlai.utils.typing import TShared from parlai.tasks.genderation_bias.build import build from parlai.tasks.genderation_bias.utils import ( flatten_and_classify, get_original_task_module, ) from copy import deepcopy import datetime import glob import json import os from tqdm import tqdm from typing import List, Optional, Tuple class ControllableTaskTeacher(FixedDialogTeacher): """ Generates a controllable_gen version of a ParlAI task, i.e., for tasks with multi- turn dialogues (episodes), this will generate a task with single example episodes, in which we append to the context a special classification token. """ @staticmethod def add_cmdline_args(parser): flattened = parser.add_argument_group('ControllableTaskTeacher Flattening Args') flattened.add_argument( '--flatten-include-labels', type='bool', default=True, help='Include labels in the history when flattening an episode', ) flattened.add_argument( '--flatten-delimiter', type=str, default='\n', help='How to join the dialogue history from previous turns.', ) flattened.add_argument( '--flatten-max-context-length', type=int, default=-1, help='Maximum number of utterances to include per episode. ' 'Default -1 keeps all.', ) agent = parser.add_argument_group('ControllableTaskTeacher Args') agent.add_argument( '--invalidate-cache', type='bool', default=False, help='Set this to True to rebuild the data (may want to do this if ' 'original data has changed or you want to rebuild with new options)', ) agent.add_argument( '--max-examples', type=int, default=-1, help='If greater than zero, will stop building after a certain num of exs', ) agent.add_argument( '--fixed-control', type=str, default='', help='Always append this fixed control string, good for deploy time.', ) # Add the arguments for the task teacher opt = parser.parse_and_process_known_args()[0] tasks = get_original_task_module(opt, multi_possible=True) for task in tasks: if hasattr(task, 'add_cmdline_args'): task.add_cmdline_args(parser) return parser def __init__(self, opt: Opt, shared: TShared = None): assert opt['flatten_delimiter'] == opt.get( 'delimiter', '\n' ), '--flatten-delimiter and --delimiter are set differently, please inspect and set to the same to avoid unexpected results' self.opt = opt if shared and 'data' in shared: self.data = shared['data'] else: self.word_lists = self.build_wordlists(opt) self.data = self._setup_data(opt) super().__init__(opt, shared) self.reset() def num_episodes(self) -> int: return len(self.data) def num_examples(self) -> int: return len(self.data) def _get_save_path(self, datapath: str, date: str) -> str: """ Return save path for the controllable gen data. :param datapath: path to ParlAI Data :param date: current date :return path: return path to save """ return os.path.join( datapath, f"{self.original_task_name.replace(':', '_')}_flattened_controllable_gen_{date}", ) @classmethod def build_wordlists(cls, opt: Opt) -> Tuple[List[str], List[str]]: """ Load list of explicitly gendered words. Words taken from <https://github.com/uclanlp/gn_glove/blob/master/wordlist/>. Examples include brother, girl, actress, husbands, etc. """ build(opt['datapath']) folder = os.path.join(opt['datapath'], 'genderation_bias') male_words = os.path.join(folder, 'male_word_file.txt') female_words = os.path.join(folder, 'female_word_file.txt') with open(male_words, 'r') as f: male = f.read().splitlines() with open(female_words, 'r') as f: female = f.read().splitlines() return male, female def _setup_data(self, opt: Opt) -> List[List[Message]]: """ Flatten and classify the normal task data. Save/load where applicable. :param opt: options dict. """ # create save directory, if it does not already exist self.original_task_name = ':'.join(opt['task'].split(':')[2:]) self.save_dir = self._get_save_path( opt['datapath'], str(datetime.datetime.today()) ) os.makedirs(self.save_dir, exist_ok=True) fname = f"{opt['datatype'].split(':')[0]}.json" self.save_path = os.path.join(self.save_dir, fname) data = self.load_data(opt, fname) if data is not None: # successfully load data return data # build the original teacher original_task_module = get_original_task_module(opt) teacher_opt = deepcopy(opt) teacher_opt['task'] = self.original_task_name teacher = original_task_module(teacher_opt) total_exs = teacher.num_examples() if self.opt['max_examples'] > 0: total_exs = min(self.opt['max_examples'], total_exs) progress_bar = tqdm( total=total_exs, unit='ex', unit_scale=True, desc='Building flattened data' ) all_episodes = [] num_exs = 0 while num_exs < total_exs: current_episode = [] episode_done = False while not episode_done: action = Message(teacher.act()) current_episode.append(action) episode_done = action.get('episode_done', False) num_exs += 1 # flatten the episode into 1-example episodes with context flattened_ep = flatten_and_classify( current_episode, opt['flatten_max_context_length'], include_labels=opt['flatten_include_labels'], delimiter=opt['flatten_delimiter'], word_lists=self.word_lists, ) all_episodes += flattened_ep progress_bar.update(len(flattened_ep)) # save data for future use self.save_data(all_episodes) return all_episodes def load_data(self, opt: Opt, filename: str) -> Optional[List[List[Message]]]: """ Attempt to load pre-build data. Checks for the most recently build data via the date string. :param opt: options dict :param filename: name of (potentially) saved data :return episodes: return list of episodes, if available """ # first check for the most recent date save_dir = self._get_save_path(opt['datapath'], '*') all_dates = [] for fname in glob.glob(os.path.join(save_dir, filename)): date = os.path.split(fname)[0].split('_')[-1] all_dates.append(date) if len(all_dates) > 0: most_recent = os.path.join( self._get_save_path(opt['datapath'], sorted(all_dates)[-1]), filename ) else: # data has not been built yet return None if opt['invalidate_cache']: # invalidate the cache and remove the existing data logging.warn( f' [ WARNING: invalidating cache at {self.save_path} and rebuilding the data. ]' ) if self.save_path == most_recent: os.remove(self.save_path) return None # Loading from most recent date self.save_path = most_recent logging.info(f' [ Data already exists. Loading from: {self.save_path} ]') with PathManager.open(self.save_path, 'rb') as f: data = json.load(f) return data def save_data(self, data: List[List[Message]]): """ Save the data via dumping to a json file. :param data: list of episodes """ try: json_data = json.dumps(data) with PathManager.open(self.save_path, 'w') as f: f.write(json_data) logging.info(f'[ Data successfully saved to path: {self.save_path} ]') except Exception: logging.warn('Data is not json serializable; not saving') def get(self, episode_idx: int, entry_idx: int = 0) -> Message: """ Return a flattened example. If using a fixed control, put that in instead of what was originally in the text. :param episode_idx: index of ep in data :param entry_idx: index of ex in ep :return ex: return an example """ ex = Message(self.data[episode_idx]) if self.opt['fixed_control'] != '': old_text = ' '.join(ex['text'].split(' ')[:-1]) text = f"{old_text} {self.opt['fixed_control']}" ex.force_set('text', text) return ex def share(self): shared = super().share() shared['data'] = self.data return shared class DefaultTeacher(ControllableTaskTeacher): pass
StarcoderdataPython
5034
import logging import os from datetime import datetime from inspect import signature, Parameter from pathlib import Path from pprint import pprint from textwrap import dedent from typing import Optional, Union import fire import tensorflow as tf from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, TerminateOnNaN from tensorflow.keras import Model from spellnn import models from spellnn.data import alphabet from spellnn.data.alphabet import get_chars from spellnn.data.processing import DataProcessor from spellnn.data.util import nb_lines from spellnn.layers.mapping import CharMapping os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # FATAL logging.getLogger('tensorflow').setLevel(logging.FATAL) class Gym: def __init__(self): self.train_dataset: Optional[tf.data.Dataset] = None self.valid_dataset: Optional[tf.data.Dataset] = None self.char2int: Optional[CharMapping] = None self.model: Optional[Model] = None self.nb_train_samples: int = 0 self.nb_valid_samples: int = 0 self.batch_size = 0 def construct_dataset(self, path: str, locale: str, batch_size: int = 32, validation_split: float = 0.3): pprint(locals()) all_chars = [alphabet.START, alphabet.END] + get_chars(locale) char_weights = [0.5 if c.isalpha() and c.islower() else 0.2 if c.isalpha() else 0.1 if c not in {alphabet.START, alphabet.END} else 0 for c in all_chars] self.char2int = CharMapping(chars=all_chars, include_unknown=True) data_processor = DataProcessor(locale=locale, char2id=self.char2int, alphabet=all_chars, alphabet_weighs=char_weights) print('Calculating number of lines in the file...', end=' ') all_samples = nb_lines(path) print(all_samples) self.batch_size = batch_size self.nb_train_samples = int((1 - validation_split) * all_samples) self.nb_valid_samples = all_samples - self.nb_train_samples dataset = tf.data.TextLineDataset(path) self.train_dataset = dataset.take(self.nb_train_samples) self.train_dataset = self.train_dataset.shuffle(10 * batch_size, seed=42, reshuffle_each_iteration=True) self.train_dataset = self.train_dataset.batch(batch_size, drop_remainder=True) self.train_dataset = self.train_dataset.map( lambda b: tf.numpy_function(func=data_processor.process_batch, inp=[b], Tout=['int32', 'int32', 'int32'])) self.train_dataset = self.train_dataset.map(lambda enc_in, dec_in, targ: ((enc_in, dec_in), targ)) self.train_dataset = self.train_dataset.repeat() self.valid_dataset = dataset.skip(self.nb_train_samples) self.valid_dataset = self.valid_dataset.shuffle(10 * batch_size, seed=42, reshuffle_each_iteration=True) self.valid_dataset = self.valid_dataset.batch(batch_size, drop_remainder=True) self.valid_dataset = self.valid_dataset.map( lambda b: tf.numpy_function(func=data_processor.process_batch, inp=[b], Tout=['int32', 'int32', 'int32'])) self.valid_dataset = self.valid_dataset.map(lambda enc_in, dec_in, targ: ((enc_in, dec_in), targ)) self.valid_dataset = self.valid_dataset.repeat() return self def create_model(self, name): arguments = signature(getattr(models, name).__init__) arguments = {k: v.default for k, v in arguments.parameters.items() if v.default is not Parameter.empty and k != 'self'} arguments['nb_symbols'] = len(self.char2int) arg_str = ', '.join([f'{k}=' + str(v) if type(v) != str else f'{k}=' '"' + str(v) + '"' for k, v in arguments.items()]) # print(arg_str) exec(dedent(f''' def create({arg_str}): self.model = {name}(**locals()) return self create.__name__ = {name}.__name__ create.__doc__ = {name}.__init__.__doc__ setattr(self, create.__name__, create) '''), {'self': self, name: getattr(models, name), arg_str: arg_str}) return getattr(self, name) def train(self, epochs: int, monitor_metric='val_acc', patience: int = 5, steps_per_epoch: Union[int, str] = 'auto', validation_steps: Union[int, str] = 'auto', log_dir: str = 'logs', use_multiprocessing: bool = False): pprint(locals()) log_dir = Path(log_dir).joinpath(datetime.now().replace(microsecond=0).isoformat()) model_path = Path(log_dir).joinpath('checkpoints').joinpath('best-model.h5py') model_path = str(model_path) if steps_per_epoch == 'auto': steps_per_epoch = self.nb_train_samples // self.batch_size if validation_steps == 'auto': validation_steps = self.nb_valid_samples // self.batch_size self.model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc']) history = self.model.fit_generator( self.train_dataset.as_numpy_iterator(), steps_per_epoch=steps_per_epoch, validation_data=self.valid_dataset.as_numpy_iterator(), validation_steps=validation_steps, epochs=epochs, use_multiprocessing=use_multiprocessing, workers=os.cpu_count() - 1, callbacks=[ TerminateOnNaN(), TensorBoard(log_dir=log_dir), ModelCheckpoint(model_path, monitor=monitor_metric, verbose=1, save_best_only=True), EarlyStopping(monitor=monitor_metric, patience=patience), ]) return history.history if __name__ == '__main__': cli = Gym() fire.Fire(cli)
StarcoderdataPython
3361305
<reponame>ahmetcagriakca/pythondataintegrator from pdip.data import Entity from sqlalchemy import Column, String, Integer, ForeignKey from sqlalchemy.orm import relationship from scheduler.domain.base import Base class SecretSourceBasicAuthentication(Entity, Base): __tablename__ = "SecretSourceBasicAuthentication" __table_args__ = {"schema": "Secret"} SecretSourceId = Column(Integer, ForeignKey('Secret.SecretSource.Id')) User = Column(String(100), index=False, unique=False, nullable=False) Password = Column(String(100), index=False, unique=False, nullable=False) SecretSource = relationship("SecretSource", back_populates="SecretSourceBasicAuthentications") def __init__(self, SecretSourceId: int = None, User: str = None, Password: str = None, SecretSource=None, *args, **kwargs): super().__init__(*args, **kwargs) self.SecretSourceId: int = SecretSourceId self.User: str = User self.Password: str = Password self.SecretSource = SecretSource
StarcoderdataPython
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# -*- coding: utf-8 -*- """ Deterimines the reflectance based on r and mua. """ import math import helpers.analyticalvalues as av def reflectance(mua, r): """ mua: the absorption coefficient used. r: the radial distance used. """ values = av.analyticalValues(r, mua) # the value of the reflectance is determined return (values.z0 * (values.ueff + values.rho1 ** -1) * math.exp( -values.ueff * values.rho1) / (values.rho1 ** 2) + (values.z0 + 2 * values.zb) * (values.ueff + values.rho2 ** -1) * math.exp( -values.ueff * values.rho2) / (values.rho2 ** 2)) / 4 / math.pi
StarcoderdataPython
173434
# -*- coding: utf-8 -*- # Generated by Django 1.10.7 on 2017-04-14 14:51 from __future__ import unicode_literals from django.db import migrations, models import filebrowser.fields class Migration(migrations.Migration): dependencies = [ ('vvphotos', '0002_auto_20170414_1207'), ] operations = [ migrations.AlterField( model_name='album', name='title', field=models.CharField(blank=True, max_length=255, verbose_name='Title'), ), migrations.AlterField( model_name='photo', name='image', field=filebrowser.fields.FileBrowseField(max_length=200, null=True, verbose_name='Image'), ), ]
StarcoderdataPython
9591
from typing import Optional import pandas as pd from ruptures import Binseg from ruptures.base import BaseCost from sklearn.linear_model import LinearRegression from etna.transforms.base import PerSegmentWrapper from etna.transforms.decomposition.change_points_trend import BaseEstimator from etna.transforms.decomposition.change_points_trend import TDetrendModel from etna.transforms.decomposition.change_points_trend import _OneSegmentChangePointsTrendTransform class _OneSegmentTrendTransform(_OneSegmentChangePointsTrendTransform): """_OneSegmentTrendTransform adds trend as a feature.""" def __init__( self, in_column: str, out_column: str, change_point_model: BaseEstimator, detrend_model: TDetrendModel, **change_point_model_predict_params, ): """Init _OneSegmentTrendTransform. Parameters ---------- in_column: name of column to apply transform to out_column: name of added column change_point_model: model to get trend change points detrend_model: model to get trend from data change_point_model_predict_params: params for change_point_model predict method """ self.out_column = out_column super().__init__( in_column=in_column, change_point_model=change_point_model, detrend_model=detrend_model, **change_point_model_predict_params, ) def transform(self, df: pd.DataFrame) -> pd.DataFrame: """Add column with trend, got from the detrend_model. Parameters ---------- df: data to get trend from Returns ------- pd.DataFrame: df with trend column """ df._is_copy = False series = df[self.in_column] trend_series = self._predict_per_interval_model(series=series) df[self.out_column] = trend_series return df def inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame: """Inverse transform dataframe. Parameters ---------- df: one segment dataframe Returns ------- pd.DataFrame: given dataframe """ return df class _TrendTransform(PerSegmentWrapper): """_TrendTransform adds trend as a feature. Creates column 'regressor_<in_column>_trend'.""" def __init__( self, in_column: str, out_column: str, change_point_model: BaseEstimator, detrend_model: TDetrendModel, **change_point_model_predict_params, ): """Init _TrendTransform. Parameters ---------- in_column: name of column to apply transform to out_column: name of added column change_point_model: model to get trend change points detrend_model: model to get trend in data change_point_model_predict_params: params for change_point_model predict method """ super().__init__( transform=_OneSegmentTrendTransform( in_column=in_column, out_column=out_column, change_point_model=change_point_model, detrend_model=detrend_model, **change_point_model_predict_params, ) ) class TrendTransform(_TrendTransform): """TrendTransform adds trend as a feature. TrendTransform uses Binseg model as a change point detection model in _TrendTransform. """ def __init__( self, in_column: str, out_column: Optional[str] = None, detrend_model: TDetrendModel = LinearRegression(), model: str = "ar", custom_cost: Optional[BaseCost] = None, min_size: int = 2, jump: int = 1, n_bkps: int = 5, pen: Optional[float] = None, epsilon: Optional[float] = None, ): """Init TrendTransform. Parameters ---------- in_column: name of column to apply transform to out_column: name of added column. Don't forget to add regressor prefix if necessary. If not given, use 'regressor_{self.__repr__()}' detrend_model: model to get trend in data model: binseg segment model, ["l1", "l2", "rbf",...]. Not used if 'custom_cost' is not None. custom_cost: binseg custom cost function min_size: minimum segment length necessary to decide it is a stable trend segment jump: jump value can speed up computations: if jump==k, the algo will use every k-th value for change points search. n_bkps: number of change points to find pen: penalty value (>0) epsilon: reconstruction budget (>0) """ self.in_column = in_column self.out_column = out_column self.detrend_model = detrend_model self.model = model self.custom_cost = custom_cost self.min_size = min_size self.jump = jump self.n_bkps = n_bkps self.pen = pen self.epsilon = epsilon super().__init__( in_column=self.in_column, out_column=self.out_column if self.out_column is not None else f"regressor_{self.__repr__()}", change_point_model=Binseg( model=self.model, custom_cost=self.custom_cost, min_size=self.min_size, jump=self.jump ), detrend_model=self.detrend_model, n_bkps=self.n_bkps, pen=self.pen, epsilon=self.epsilon, )
StarcoderdataPython
3264676
import os import sys from datetime import datetime from netCDF4 import date2num from pyniva import Vessel, TimeSeries, token2header import pandas as pd from ferrybox import ferryBoxStationClass as fb import csv __author__ = '<NAME>' __email__ = '<EMAIL>' __created__ = datetime(2019, 1, 22) __modified__ = datetime(2020, 3, 6) __version__ = "1.0" __status__ = "Development" def setup_connection_to_nivadb(): token = '../FBdata/NIVA_TOKEN/nivatestdev-<PASSWORD>.json' return token2header(token) def get_list_of_available_timeseries_for_vessel(vessel_name, vessel_abbreviation, start_date, end_date, variable, use_raw=False): header = setup_connection_to_nivadb() meta_host = "https://api-test.niva.no/v1/metaflow/" tsb_host = "https://api-test.niva.no/v1/tsb/" vessel_list = [v for v in Vessel.list(meta_host, header=header) if hasattr(v, "imo")] for v in vessel_list: if v.name == vessel_name: print("Fetching data for vessel {} ({})".format(v.name, vessel_abbreviation)) # Get signals the vessel signals = v.get_all_tseries(meta_host, header=header) if variable == 'temperature': db_path = "{}/ferrybox/INLET/TEMPERATURE".format(vessel_abbreviation) elif variable == 'salinity': db_path = "{}/ferrybox/CTD/SALINITY".format(vessel_abbreviation) elif variable == 'chla_fluorescence': if not use_raw: db_path = "{}/ferrybox/CHLA_FLUORESCENCE/BIOF_CORR_AND_CALIB".format(vessel_abbreviation) else: db_path = "{}/ferrybox/CHLA_FLUORESCENCE/RAW".format(vessel_abbreviation) elif variable == 'cdom_fluorescence': db_path = "{}/ferrybox/CDOM_FLUORESCENCE/ADJUSTED".format(vessel_abbreviation) elif variable == 'turbidity': db_path = "{}/ferrybox/TURBIDITY".format(vessel_abbreviation) else: db_path = "{}/ferrybox/CHLA_FLUORESCENCE/BIOF_CORR_AND_CALIB".format(vessel_abbreviation) interesting_signals = [db_path, "{}/gpstrack".format(vessel_abbreviation)] int_ts = [ts for sn in interesting_signals for ts in signals if sn in ts.path] for ts in int_ts: print(" Paths found => ", ts.path) if len(int_ts) > 0: data = TimeSeries.get_timeseries_list(tsb_host, int_ts, start_time=start_date, # datetime.utcnow() - timedelta(260), end_time=end_date, # datetime.utcnow()- timedelta(90), name_headers=True, header=header, noffill=True, dt=0) data.to_csv('FA.csv') return data def get_data_around_station(df, st_lon, st_lat, dist): # https://stackoverflow.com/questions/21415661/logical-operators-for-boolean-indexing-in-pandas # Filter out the longitude and latitudes surrounding the station lat/lon print(df['latitude']) return df[ (st_lat - dist < df['latitude']) & (df['latitude'] < st_lat + dist) & (st_lon - 2 * dist < df['longitude']) & ( df['longitude'] < st_lon + 2 * dist)] def get_data_at_station_and_timestamp(df, st_lon, st_lat, timestamp): # https://stackoverflow.com/questions/21415661/logical-operators-for-boolean-indexing-in-pandas # Filter out the nearest observations in time from water sample ind = df.index.get_loc(timestamp, method='nearest') return df.index[ind], df.iloc[ind] def create_station(stationid, df, varname): metadata = ferrybox_metadata(stationid) # Get the data for the station if not metadata["plot_along_latitude"]: df_st = get_data_around_station(df, metadata['longitude'], metadata['latitude'], 0.2) else: df_st = get_data_around_station(df, metadata['longitude'], metadata['latitude'], 0.2) # Create the station return fb.FerryBoxStation(stationid, metadata, df_st, varname) def ferrybox_metadata(stationid): return \ {'VT4': {'name': 'Hvitsten', 'latitude': 59.59, 'longitude': 10.64, 'vessel': 'FA', 'vessel_name': 'MS Color Fantasy', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VT12': {'name': 'Sognesjøen', 'latitude': 60.9804, 'longitude': 4.7568, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VT72': {'name': 'Herøyfjorden og VT71:Skinnbrokleia', 'latitude': 62.3066, 'longitude': 5.5877, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VT80': {'name': 'Djupfest', 'latitude': 63.76542, 'longitude': 9.52296, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.15, 'ybin_dist': 0.1}, 'VT45': {'name': 'Valset', 'latitude': 63.65006, 'longitude': 9.77012, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': False, 'dist': 0.15, 'ybin_dist': 0.1}, 'VT22': {'name': 'Biologisk Stasjon', 'latitude': 63.36, 'longitude': 10.2, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': False, 'dist': 0.13, 'ybin_dist': 0.01}, 'Trondheimsfjorden': {'name': 'Trondheimsfjorden', 'latitude': 63.46, 'longitude': 10.05, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': False, 'dist': 0.05, 'ybin_dist': 0.01}, 'VT23': {'name': 'Trondheimsleia', 'latitude': 63.45737, 'longitude': 8.85324, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.2, 'ybin_dist': 0.01}, 'VT76': {'name': 'Oksebåsneset', 'latitude': 69.82562, 'longitude': 30.11961, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VR23': {'name': 'Blodskytodden', 'latitude': 70.4503, 'longitude': 31.0031, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': False, 'dist': 0.2, 'ybin_dist': 0.05}, 'VR25': {'name': '<NAME>', 'latitude': 70.98425, 'longitude': 28.78323, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': False, 'dist': 0.2, 'ybin_dist': 0.01}, 'VR51': {'name': 'Korsen', 'latitude': 62.0944, 'longitude': 7.0061, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VR31': {'name': 'Tilremsfjorden', 'latitude': 65.6009, 'longitude': 12.2354, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VT3': {'name': 'Torbjørnskjær', 'latitude': 59.0407, 'longitude': 10.7608, 'vessel': 'FA', 'vessel_name': 'MS Color Fantasy', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VT71': {'name': 'Skinnbrokleia', 'latitude': 62.32841, 'longitude': 5.75517, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VR54': {'name': 'Straumsfjorden', 'latitude': 69.502, 'longitude': 18.338, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.2, 'ybin_dist': 0.01}, 'Dk1': {'name': 'Steilene', 'latitude': 59.814999, 'longitude': 10.569384, 'vessel': 'FA', 'vessel_name': 'MS Color Fantasy', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'Im2': {'name': 'Elle', 'latitude': 59.620367, 'longitude': 10.628200, 'vessel': 'FA', 'vessel_name': 'MS Color Fantasy', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'Oslofjorden': {'name': 'Skagerrak', 'latitude': 59.207, 'longitude': 10.7608, 'vessel': 'FA', 'vessel_name': 'MS Color Fantasy', 'plot_along_latitude': True, 'dist': 0.35, 'ybin_dist': 0.05}, 'YO1': {'name': 'Skagerrak', 'latitude': 59.35, 'longitude': 10.7608, 'vessel': 'FA', 'vessel_name': 'MS Color Fantasy', 'plot_along_latitude': True, 'dist': 0.35, 'ybin_dist': 0.01}, 'VR4': {'name': 'Kvænangen', 'latitude': 70.1161, 'longitude': 21.0725, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VR55': {'name': 'Spilderbukta', 'latitude': 69.9664, 'longitude': 21.6887, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VR56': {'name': 'Reisafjorden', 'latitude': 69.9068, 'longitude': 21.0927, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VR58': {'name': 'Ullsfjorden', 'latitude': 69.7544, 'longitude': 19.7701, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.2, 'ybin_dist': 0.01}, 'VR59': {'name': 'Sørfjorden', 'latitude': 69.5711, 'longitude': 19.7185, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VR7': {'name': 'Langfjordnes', 'latitude': 70.6915, 'longitude': 28.0860, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VR21': {'name': 'Bugøynes', 'latitude': 69.9584, 'longitude': 29.8804, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.2, 'ybin_dist': 0.01}, 'VR24': {'name': 'Tanafjorden', 'latitude': 70.7500, 'longitude': 28.3468, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VR52': {'name': 'Broemsneset', 'latitude': 64.47, 'longitude': 11.31, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.1, 'ybin_dist': 0.01}, 'VT42': {'name': 'Korsfjorden', 'latitude': 63.4061, 'longitude': 10.041, 'vessel': 'TF', 'vessel_name': 'MS Trollfjord', 'plot_along_latitude': True, 'dist': 0.2, 'ybin_dist': 0.01}, 'VR7-VR21-VR24': {'name': 'Barentshavet', 'latitude': 70.0, 'longitude': 30.0, 'vessel': 'TF', 'vessel_name': '<NAME>', 'plot_along_latitude': False, 'dist': 1.8, 'ybin_dist': 0.3}, }[stationid] def get_water_sample_data(stationid, df_fb, writer): file = "2020/Oekokyst_FB_AQM_extract_2020.xlsx" df = pd.read_excel(file, sheet_name="WaterChemistry") df_id = df[df["StationCode"] == stationid] metadata = ferrybox_metadata(stationid) st_lon = metadata['longitude'] st_lat = metadata['latitude'] for sample_time, sample_klfa in zip(df_id["SampleDate"], df_id["KlfA µg/l"]): sample_time = pd.to_datetime(sample_time) fb_time, fb_df = get_data_at_station_and_timestamp(df_fb, st_lon, st_lat, sample_time) line = stationid, st_lon, st_lat, sample_time, sample_klfa, fb_time, fb_df["raw_chla_fluorescence"], fb_df[ "longitude"], fb_df["latitude"] writer.writerow(line) # MAIN def main(): substations = ['Dk1', 'Im2', 'YO1', 'VT4', 'Oslofjorden', 'VT4', 'VT12', 'VT80', 'VT45', 'VT22', 'VT23', 'VT76', 'VR23', 'VR25', 'VR51', 'VR31', 'VT3', 'VT71', 'VR54', 'VR4', 'VR58', 'VR21', 'VT42', 'VR7-VR21-VR24', 'VT72'] # 'VR55', 'VR59', 'VR7', 'VR24', 'VR52' 'VT72'] # removed 'VR56' VR55, VR59, VR7, VR24, VR52 'VT72', # substations = ['VT80'] #, 'VT76', 'VR23', 'VR25', 'VT80', 'VT45', 'VT22', 'VT23', 'VT72', # 'VT12'] # ,'VT22','VT22 ytre'] substations = ['Trondheimsfjorden'] # ,'Dk1', 'Im2', 'YO1','VT4'] substations = ['Dk1', 'Im2', 'YO1', 'VT4', 'Oslofjorden'] varnames = ['chla_fluorescence','salinity', 'turbidity', 'temperature'] # varnames = ['chla_fluorescence'] #,'salinity', 'temperature'] start_date = datetime(2020, 1, 1) end_date = datetime(2020, 12, 31) first = True use_preprocessed_data = True ferrybox_calibration = False print("Starting contour plot creation for {} stations".format(len(substations))) for varname in varnames: for stationid in substations: metadata = ferrybox_metadata(stationid) print('Creating station for {} using vessel {} and variable {}'.format(stationid, metadata['vessel_name'], varname)) if not use_preprocessed_data and not ferrybox_calibration: tsbdata = get_list_of_available_timeseries_for_vessel(metadata['vessel_name'], metadata['vessel'], start_date, end_date, varname, use_raw=ferrybox_calibration) elif use_preprocessed_data and not ferrybox_calibration: if metadata['vessel'] == "FA": tsbdata = pd.read_csv("../FBdata/Final_data_2020/FA_corrected.dat", usecols=["time", "salinity", "inlet_temperature", "turbidity_adj", "longitude", "latitude", "chla_fluorescence_c3_adj_corr"]) tsbdata.columns = ["time", "salinity", "temperature","turbidity", "longitude", "latitude","chla_fluorescence"] elif metadata['vessel'] == "TF": tsbdata = pd.read_csv("../FBdata/Final_data_2020/TF_corrected_v2_05022021.dat", usecols=["time", "salinity", "inlet_temperature", "turbidity_adj", "longitude", "latitude", "raw_chla_fluorescence_c3_adj_corr"]) tsbdata.columns = ["time", "salinity", "temperature", "turbidity","longitude", "latitude", "chla_fluorescence"] else: raise Exception("Unable to read data") if not ferrybox_calibration: station = create_station(stationid, tsbdata, varname) station.start_date_jd = date2num(start_date, units=station.refdate, calendar="standard") station.end_date_jd = date2num(end_date, units=station.refdate, calendar="standard") # station.extract_calibration_data(varname) print('Creating contour plot for {}'.format(stationid)) station.create_station_contour_plot() else: if first is True: import csv infile = "ferrybox_calibration_{}{}_to_{}{}.csv".format(start_date.year, start_date.month, end_date.year, end_date.month) if os.path.exists(infile): os.remove(infile) csv_file = open(infile, "a", newline="") writer = csv.writer(csv_file, delimiter=',') header = "StationID", "OBS_LON", "OBS_LAT", "OBS_TIME", \ "OBS_KLFA", "FB_TIME", "FB_KLFA", "FB_LON", "FB_LAT" writer.writerow(header) first = False get_water_sample_data(stationid, tsbdata, writer) if __name__ == '__main__': main()
StarcoderdataPython
1736652
<reponame>lakhlaifi/RedHat-Ansible # # (c) 2017 Red Hat Inc. # # This file is part of Ansible # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. # from __future__ import (absolute_import, division, print_function) __metaclass__ = type DOCUMENTATION = """ --- author: Ansible Networking Team cliconf: vyos short_description: Use vyos cliconf to run command on VyOS platform description: - This vyos plugin provides low level abstraction apis for sending and receiving CLI commands from VyOS network devices. version_added: "2.4" """ import re import json from itertools import chain from ansible.errors import AnsibleConnectionFailure from ansible.module_utils._text import to_text from ansible.module_utils.common._collections_compat import Mapping from ansible.module_utils.network.common.config import NetworkConfig, dumps from ansible.module_utils.network.common.utils import to_list from ansible.plugins.cliconf import CliconfBase class Cliconf(CliconfBase): def get_device_info(self): device_info = {} device_info['network_os'] = 'vyos' reply = self.get('show version') data = to_text(reply, errors='surrogate_or_strict').strip() match = re.search(r'Version:\s*(.*)', data) if match: device_info['network_os_version'] = match.group(1) match = re.search(r'HW model:\s*(\S+)', data) if match: device_info['network_os_model'] = match.group(1) reply = self.get('show host name') device_info['network_os_hostname'] = to_text(reply, errors='surrogate_or_strict').strip() return device_info def get_config(self, flags=None, format=None): if format: option_values = self.get_option_values() if format not in option_values['format']: raise ValueError("'format' value %s is invalid. Valid values of format are %s" % (format, ', '.join(option_values['format']))) if format == 'text': out = self.send_command('show configuration') else: out = self.send_command('show configuration commands') return out def edit_config(self, candidate=None, commit=True, replace=None, comment=None): resp = {} operations = self.get_device_operations() self.check_edit_config_capability(operations, candidate, commit, replace, comment) results = [] requests = [] self.send_command('configure') for cmd in to_list(candidate): if not isinstance(cmd, Mapping): cmd = {'command': cmd} results.append(self.send_command(**cmd)) requests.append(cmd['command']) out = self.get('compare') out = to_text(out, errors='surrogate_or_strict') diff_config = out if not out.startswith('No changes') else None if diff_config: if commit: try: self.commit(comment) except AnsibleConnectionFailure as e: msg = 'commit failed: %s' % e.message self.discard_changes() raise AnsibleConnectionFailure(msg) else: self.send_command('exit') else: self.discard_changes() else: self.send_command('exit') if to_text(self._connection.get_prompt(), errors='surrogate_or_strict').strip().endswith('#'): self.discard_changes() if diff_config: resp['diff'] = diff_config resp['response'] = results resp['request'] = requests return resp def get(self, command=None, prompt=None, answer=None, sendonly=False, output=None, check_all=False): if not command: raise ValueError('must provide value of command to execute') if output: raise ValueError("'output' value %s is not supported for get" % output) return self.send_command(command, prompt=prompt, answer=answer, sendonly=sendonly, check_all=check_all) def commit(self, comment=None): if comment: command = 'commit comment "{0}"'.format(comment) else: command = 'commit' self.send_command(command) def discard_changes(self): self.send_command('exit discard') def get_diff(self, candidate=None, running=None, diff_match='line', diff_ignore_lines=None, path=None, diff_replace=None): diff = {} device_operations = self.get_device_operations() option_values = self.get_option_values() if candidate is None and device_operations['supports_generate_diff']: raise ValueError("candidate configuration is required to generate diff") if diff_match not in option_values['diff_match']: raise ValueError("'match' value %s in invalid, valid values are %s" % (diff_match, ', '.join(option_values['diff_match']))) if diff_replace: raise ValueError("'replace' in diff is not supported") if diff_ignore_lines: raise ValueError("'diff_ignore_lines' in diff is not supported") if path: raise ValueError("'path' in diff is not supported") set_format = candidate.startswith('set') or candidate.startswith('delete') candidate_obj = NetworkConfig(indent=4, contents=candidate) if not set_format: config = [c.line for c in candidate_obj.items] commands = list() # this filters out less specific lines for item in config: for index, entry in enumerate(commands): if item.startswith(entry): del commands[index] break commands.append(item) candidate_commands = ['set %s' % cmd.replace(' {', '') for cmd in commands] else: candidate_commands = str(candidate).strip().split('\n') if diff_match == 'none': diff['config_diff'] = list(candidate_commands) return diff running_commands = [str(c).replace("'", '') for c in running.splitlines()] updates = list() visited = set() for line in candidate_commands: item = str(line).replace("'", '') if not item.startswith('set') and not item.startswith('delete'): raise ValueError('line must start with either `set` or `delete`') elif item.startswith('set') and item not in running_commands: updates.append(line) elif item.startswith('delete'): if not running_commands: updates.append(line) else: item = re.sub(r'delete', 'set', item) for entry in running_commands: if entry.startswith(item) and line not in visited: updates.append(line) visited.add(line) diff['config_diff'] = list(updates) return diff def run_commands(self, commands=None, check_rc=True): if commands is None: raise ValueError("'commands' value is required") responses = list() for cmd in to_list(commands): if not isinstance(cmd, Mapping): cmd = {'command': cmd} output = cmd.pop('output', None) if output: raise ValueError("'output' value %s is not supported for run_commands" % output) try: out = self.send_command(**cmd) except AnsibleConnectionFailure as e: if check_rc: raise out = getattr(e, 'err', e) responses.append(out) return responses def get_device_operations(self): return { 'supports_diff_replace': False, 'supports_commit': True, 'supports_rollback': False, 'supports_defaults': False, 'supports_onbox_diff': True, 'supports_commit_comment': True, 'supports_multiline_delimiter': False, 'supports_diff_match': True, 'supports_diff_ignore_lines': False, 'supports_generate_diff': False, 'supports_replace': False } def get_option_values(self): return { 'format': ['text', 'set'], 'diff_match': ['line', 'none'], 'diff_replace': [], 'output': [] } def get_capabilities(self): result = super(Cliconf, self).get_capabilities() result['rpc'] += ['commit', 'discard_changes', 'get_diff', 'run_commands'] result['device_operations'] = self.get_device_operations() result.update(self.get_option_values()) return json.dumps(result)
StarcoderdataPython
1681928
try: import pyimageocr except Exception as e: print("Error...", e) print('Character : Accuracy') ocr = pyimageocr.OCR(mode='en') # ocr.train("Training") print("Trainning Done !!") a = ocr.pattern_match(file="Test/1-img015-00045.png") print(a) # ocr.tlableshresoldImage() # ocr.getimageHistogram() # ocr.imageShow() # getImageFormFile
StarcoderdataPython
168763
<filename>odoo-13.0/addons/payment/models/__init__.py<gh_stars>0 # -*- coding: utf-8 -*- from . import payment_acquirer from . import account_invoice from . import res_partner from . import account_payment from . import chart_template from . import ir_http from . import res_company
StarcoderdataPython
1668895
<filename>experiments/001_dominance_comparison.py import pickle from pprint import pprint import numpy as np from acs.objective import reduce_objectives from utils.multiobjective import dominates, sort_nondominated # Compares how many solution obtained by NSGA are dominated by the solution # obtained by GA for every pair (repetition, student) ################################################################################ ## Loading ##################################################################### ################################################################################ # (repetitions, students, iterations, individuals, objectives) with open('results/algorithm_results/nsga_2_real.pickle', 'rb') as file: file_results_nsga = pickle.load(file) instance_nsga = file_results_nsga['info']['instance'] instance_size = instance_nsga.num_materials results_nsga = file_results_nsga['data'][3] # (repetitions, students, iterations, objectives) with open('results/algorithm_results/ga_real.pickle', 'rb') as file: file_results_ga = pickle.load(file) instance_ga = file_results_ga['info']['instance'] results_ga = file_results_ga['data'][3] ################################################################################ ## Preparation ################################################################# ################################################################################ # HACK(andre:2020-06-24): Fix the lack of order in the student list of NSGA results nsga_sort = np.argsort(np.array(instance_nsga.learners_keys)) results_nsga = results_nsga[:, nsga_sort, :, :, :] # (repetitions, students, iterations, individuals, objectives) results_ga = results_ga[:, :, :, np.newaxis, :] # Use only the last iteration # (repetitions, students, individuals, objectives) results_nsga = results_nsga[:, :, -1, :, :] results_ga = results_ga[:, :, -1, :, :] num_repetitions = results_nsga.shape[0] num_students = results_nsga.shape[1] num_individuals = results_nsga.shape[2] num_objectives = results_nsga.shape[3] # Calculates NSGA nondominated individuals # (repetitions, students, individuals) nondominated_repetition_mask = np.zeros((num_repetitions, num_students, num_individuals), dtype=bool) nondominated_mask = np.zeros((num_repetitions, num_students, num_individuals), dtype=bool) for i in range(num_repetitions): for j in range(num_students): population = results_nsga[i, j] nondominated_repetition_population = sort_nondominated(population, first_front_only=True, include_repetition=True, sign=-1)[0] nondominated_repetition_mask[i, j, nondominated_repetition_population] = True nondominated_population = sort_nondominated(population, first_front_only=True, include_repetition=False, sign=-1)[0] nondominated_mask[i, j, nondominated_population] = True # Calculates which individual are dominated by GA or by NSGA # (repetitions, students, individuals) repetition_nsga_dominates = np.zeros((num_repetitions, num_students, num_individuals), dtype=bool) repetition_ga_dominates = np.zeros((num_repetitions, num_students, num_individuals), dtype=bool) nsga_dominates = np.zeros((num_repetitions, num_students, num_individuals), dtype=bool) ga_dominates = np.zeros((num_repetitions, num_students, num_individuals), dtype=bool) for i in range(num_repetitions): for j in range(num_students): nondominated_repetition_population = nondominated_repetition_mask[i, j].nonzero()[0] nondominated_population = nondominated_mask[i, j].nonzero()[0] ga_result = reduce_objectives(results_ga[i, j, 0], num_objectives) for k in nondominated_repetition_population: nsga_result = results_nsga[i, j, k] if dominates(nsga_result, ga_result, sign=-1): repetition_nsga_dominates[i, j, k] = True if dominates(ga_result, nsga_result, sign=-1): repetition_ga_dominates[i, j, k] = True for k in nondominated_population: nsga_result = results_nsga[i, j, k] if dominates(nsga_result, ga_result, sign=-1): nsga_dominates[i, j, k] = True if dominates(ga_result, nsga_result, sign=-1): ga_dominates[i, j, k] = True ################################################################################ ## Results ##################################################################### ################################################################################ print('Dimensions: (repetitions, students, individuals, objectives)') print('------------------------------------------------') print('NSGA:', results_nsga.shape) print('GA:', results_ga.shape) print('Instance size: %d materials' % instance_size) print('\n') # With repetition total_nond_rep = nondominated_repetition_mask.sum() nsga_dom_rep = repetition_nsga_dominates.sum() ga_dom_rep = repetition_ga_dominates.sum() neither_rep = total_nond_rep - (nsga_dom_rep + ga_dom_rep) neither_perc_rep = neither_rep / total_nond_rep * 100 nsga_perc_rep = nsga_dom_rep / total_nond_rep * 100 ga_perc_rep = ga_dom_rep / total_nond_rep * 100 # Without repetition total_nond = nondominated_mask.sum() nsga_dom = nsga_dominates.sum() ga_dom = ga_dominates.sum() neither = total_nond - (nsga_dom + ga_dom) neither_perc = neither / total_nond * 100 nsga_perc = nsga_dom / total_nond * 100 ga_perc = ga_dom / total_nond * 100 print('Number of individuals: %d' % nondominated_mask.size) print(' Dominates | GA | Neither | NSGA | Total ') print('---------------+----------------+----------------+----------------+-------') print('w/ repetition | %4d (%6.2f%%) | %4d (%6.2f%%) | %4d (%6.2f%%) | %5d' % (ga_dom_rep, ga_perc_rep, neither_rep, neither_perc_rep, nsga_dom_rep, nsga_perc_rep, total_nond_rep)) print('w/o repetition | %4d (%6.2f%%) | %4d (%6.2f%%) | %4d (%6.2f%%) | %5d' % (ga_dom, ga_perc, neither, neither_perc, nsga_dom, nsga_perc, total_nond)) print('\n') print('GA dominates per student (w/o repetition)') print(' Dominates | GA | Neither | NSGA | Total ') print('---------------+----------------+----------------+----------------+-------') for j in range(num_students): stud_total_nond = nondominated_mask[:, j, :].sum() stud_nsga_dom = nsga_dominates[:, j, :].sum() stud_ga_dom = ga_dominates[:, j, :].sum() stud_neither = stud_total_nond - (stud_nsga_dom + stud_ga_dom) stud_neither_perc = stud_neither / stud_total_nond * 100 stud_nsga_perc = stud_nsga_dom / stud_total_nond * 100 stud_ga_perc = stud_ga_dom / stud_total_nond * 100 print(' Student %2d | %4d (%6.2f%%) | %4d (%6.2f%%) | %4d (%6.2f%%) | %5d' % ((j + 1), stud_ga_dom, stud_ga_perc, stud_neither, stud_neither_perc, stud_nsga_dom, stud_nsga_perc, stud_total_nond))
StarcoderdataPython
3262371
import discord from discord.ext import commands from jikanpy import Jikan from datetime import datetime from dotenv import load_dotenv from os import getenv from services import utils from services.anime import anime from services.search import search from services.schedule import schedule PREFIX = '!' load_dotenv('env') TOKEN = getenv('DISCORD_TOKEN') GUILD = getenv('DISCORD_GUILD') bot = commands.Bot(command_prefix=PREFIX) bot.remove_command('help') @bot.event async def on_ready(): print(f'{bot.user.name} has connected to Discord!') await bot.change_presence(activity=discord.Game(name="!help")) @bot.event async def on_command_error(ctx, error): if isinstance(error, commands.CommandNotFound): return raise error @bot.command(name='disconnect') async def cmd_disconnect(ctx): await bot.close() @bot.command(name='info') async def cmd_info(ctx): embed = discord.Embed( title='Info menu!', description="Server's informations", timestamp=datetime.utcnow(), color=discord.Color.green() ) embed.add_field(name='Server created at', value=f'{ctx.guild.created_at}', inline=False) embed.add_field(name='Server owner', value=f'{ctx.guild.owner}', inline=False) embed.add_field(name='Server Region', value=f'{ctx.guild.region}', inline=False) embed.add_field(name='Server ID', value=f'{ctx.guild.id}', inline=False) embed.set_footer(text='Requested by ' + ctx.message.author.name, icon_url=ctx.message.author.avatar_url) await ctx.send(embed=embed) @bot.command(name='help') async def cmd_help(ctx): embed = discord.Embed( title=f' :rainbow: Urameshi - Help!', description='A simple bot for anime informations', color=discord.Color.green() ) embed.add_field(name=f'- Command: ```{PREFIX}info```', value="Server's informations from this server", inline=False) embed.add_field(name=f'- Command: ```{PREFIX}songs```', value='Name of openings and closings of the researched anime', inline=False) embed.add_field(name=f'- Command: ```{PREFIX}broadcast```', value='Anime broadcast time', inline=False) embed.add_field(name=f'- Command: ```{PREFIX}schedule```', value='Anime schedule for someday in this season (Input week of day)', inline=False) embed.add_field(name=f'- Command: ```{PREFIX}seasons```', value="Animes season prequel and sequel", inline=False) embed.set_footer(text='Requested by ' + ctx.message.author.name, icon_url=ctx.message.author.avatar_url) await ctx.send(embed=embed) @bot.command(name='songs', help='Name of openings and closings of the researched anime') async def cmd_songs(ctx, *name: str): anime_found = search(' '.join(name)) if anime_found['found']: anime_found = anime(anime_found['id'], 'songs') if anime_found['found']: embed = discord.Embed( title=f' :loud_sound: {anime_found["title"]} ({anime_found["title_japanese"]})', color=discord.Color.green() ) embed.set_thumbnail(url=anime_found['image_url']) list_openings_strings = '\n'.join([f"Name: {op}" for op in anime_found['op']]) embed.add_field(name=f'-- Openings: --', value=f"```{list_openings_strings}```", inline=False) embed.set_footer(text='Requested by ' + ctx.message.author.name, icon_url=ctx.message.author.avatar_url) await ctx.send(embed=embed) embed = discord.Embed( title=f' :loud_sound: {anime_found["title"]} ({anime_found["title_japanese"]})', color=discord.Color.green() ) embed.set_thumbnail(url=anime_found['image_url']) list_ending_strings = '\n'.join([f"Name: {end}" for end in anime_found['end']]) embed.add_field(name=f'-- Endings: --', value=f"```{list_ending_strings}```", inline=False) embed.set_footer(text='Requested by ' + ctx.message.author.name, icon_url=ctx.message.author.avatar_url) await ctx.send(embed=embed) if not anime_found['found']: await ctx.send(utils.help_messages(anime_found['error'])) @bot.command(name='schedule', help='Anime schedule for someday in this season (Input week of day)') async def cmd_schedule(ctx, *day: str): animes_today = schedule(' '.join(day)) if animes_today['found']: for anime in animes_today['results']: embed = discord.Embed( title=f' :popcorn: {anime["title"]}', color=discord.Color.green() ) embed.set_thumbnail(url=anime['image_url']) if anime["synopsis"] and len(anime["synopsis"]) > 1022: synopsis = anime['synopsis'] list_synopsis = [] while len(synopsis) > 1000: list_synopsis.append(synopsis[0:999]) synopsis = synopsis[1000:] list_synopsis.append(synopsis) for s in list_synopsis: embed.add_field(name=f':books: Synopsis:', value=f'```{s}```', inline=False) else: embed.add_field(name=f':books: Synopsis:', value=f'```{anime["synopsis"]}```', inline=False) embed.add_field(name=f'Genres:', value=f'```{", ".join([g["name"] for g in anime["genres"]])}```', inline=False) embed.add_field(name=f':globe_with_meridians: Link Mal (Myanimelist.net):', value=f'```{anime["url"]}```', inline=False) embed.set_footer(text='Requested by ' + ctx.message.author.name, icon_url=ctx.message.author.avatar_url) await ctx.send(embed=embed) else: await ctx.send(utils.help_messages(animes_today['error'])) @bot.command(name='seasons', help='Animes season prequel and sequel') async def cmd_season(ctx, *name: str): anime_found = search(' '.join(name)) if anime_found['found']: anime_found = anime(anime_found['id'], 'seasons') if anime_found['found']: while anime_found['found'] and 'Prequel' in anime_found['related']: anime_found = anime(anime_found['related']['Prequel'][0]['mal_id'], 'seasons') while anime_found['found'] and 'Sequel' in anime_found['related']: if anime_found['airing']: embed = discord.Embed( title=f' :popcorn: {anime_found["title"]} ' + f'({anime_found["title_japanese"]})', color=discord.Color.green() ) embed.set_thumbnail(url=anime_found['image_url']) embed.add_field(name=f' Type:', value=f'```{anime_found["type"]}```', inline=False) embed.add_field(name=f':hourglass_flowing_sand: Status:', value=f'```{anime_found["status"]}```', inline=False) if anime_found['premiered']: emoji_season = utils.emoji(anime_found['premiered']) embed.add_field(name=f'- {emoji_season} Season:', value=f'```{anime_found["premiered"]}```', inline=False) embed.add_field(name=f':ballot_box_with_check: Total Episodes:', value=f'```{anime_found["episodes"]}```', inline=False) embed.add_field(name=f':watch: Broadcast in Japan:', value=f'```{anime_found["broadcast"]}```', inline=False) embed.add_field(name=f':globe_with_meridians: Link Mal (Myanimelist.net):', value=f'```{anime_found["url"]}```', inline=False) embed.set_footer(text='Requested by ' + ctx.message.author.name, icon_url=ctx.message.author.avatar_url) else: embed = discord.Embed( title=f' :popcorn: {anime_found["title"]} ' + f'({anime_found["title_japanese"]})', color=discord.Color.green() ) embed.set_thumbnail(url=anime_found['image_url']) embed.add_field(name=f' Type:', value=f'```{anime_found["type"]}```', inline=False) embed.add_field(name=f':hourglass: Status:', value=f'```{anime_found["status"]}```', inline=False) if anime_found['premiered']: emoji_season = utils.emoji(anime_found['premiered']) embed.add_field(name=f'- {emoji_season} Season:', value=f'```{anime_found["premiered"]}```', inline=False) embed.add_field(name=f':ballot_box_with_check: Total Episodes:', value=f'```{anime_found["episodes"]}```', inline=False) embed.add_field(name=f':globe_with_meridians: Link Mal (Myanimelist.net):', value=f'```{anime_found["url"]}```', inline=False) embed.set_footer( text='Requested by ' + ctx.message.author.name, icon_url=ctx.message.author.avatar_url) await ctx.send(embed=embed) anime_found = anime(anime_found['related']['Sequel'][0]['mal_id'], 'seasons') if not anime_found['found']: await ctx.send(utils.help_messages(anime_found('error'))) @bot.command(name='broadcast', help='Anime broadcast time') async def cmd_broadcast(ctx, *name: str): anime_found = search(' '.join(name)) if anime_found['found']: anime_found = anime(anime_found['id'], 'broadcast') if anime_found['found']: emoji_season = utils.emoji(anime_found['premiered']) if anime_found['airing']: embed = discord.Embed( title=f' :popcorn: {anime_found["title"]} ' + f'({anime_found["title_japanese"]})', color=discord.Color.green() ) embed.set_thumbnail(url=anime_found['image_url']) embed.add_field(name=f':hourglass_flowing_sand: Status:', value=f'```{anime_found["status"]}```', inline=False) embed.add_field(name=f'{emoji_season} Season:', value=f'```{anime_found["premiered"]}```', inline=False) embed.add_field(name=f':ballot_box_with_check: Total Episodes:', value=f'```{anime_found["episodes"]}```', inline=False) embed.add_field(name=f':watch: Broadcast in Japan:', value=f'```{anime_found["broadcast"]}```', inline=False) embed.add_field(name=f':globe_with_meridians: Link Mal (Myanimelist.net):', value=f'```{anime_found["url"]}```', inline=False) embed.set_footer(text='Requested by ' + ctx.message.author.name, icon_url=ctx.message.author.avatar_url) else: embed = discord.Embed( title=f' :popcorn: {anime_found["title"]} ' + f'({anime_found["title_japanese"]})', color=discord.Color.green() ) embed.set_thumbnail(url=anime_found['image_url']) embed.add_field(name=f':hourglass: Status:', value=f'```{anime_found["status"]}```', inline=False) embed.add_field(name=f'{emoji_season} Season:', value=f'```{anime_found["premiered"]}```', inline=False) embed.add_field(name=f':ballot_box_with_check: Total Episodes:', value=f'```{anime_found["episodes"]}```', inline=False) embed.add_field(name=f':globe_with_meridians: Link Mal (Myanimelist.net):', value=f'```{anime_found["url"]}```', inline=False) embed.set_footer(text='Requested by ' + ctx.message.author.name, icon_url=ctx.message.author.avatar_url) await ctx.send(embed=embed) if not anime_found['found']: await ctx.send(utils.help_messages(anime_found('error'))) bot.run(TOKEN)
StarcoderdataPython
1799541
<reponame>eneskums/ogrenciBilgiSistemi from django.conf.urls import url from .views import * app_name= 'dersicerik' urlpatterns = [ url(r'^dersicerikTalep/$', dersicerikTalep, name='dersicerikTalep'), url(r'^dersicerikIstek/$', dersicerikIstekleri, name='dersicerikIstek'), url(r'^dersicerikIstekleriDetay/(?P<pk>[0-9]+)/(?P<istek>[0-9]+)/$', dersicerikIstekleriDetay, name='dersicerikIstekleriDetay'), ]
StarcoderdataPython
84736
<filename>vesper/django/app/tests/dtest_model_attributes.py<gh_stars>0 from vesper.django.app.models import ( AnnotationConstraint, AnnotationInfo, Device, DeviceModel, Processor, Station, TagInfo) from vesper.django.app.tests.dtest_case import TestCase class ModelAttributeTests(TestCase): def setUp(self): self._create_shared_test_models() self.test_model_data = self._get_shared_test_model_data() def test_station_attributes(self): expected_attributes = self.test_model_data['stations'] self._assert_model_attributes(Station, expected_attributes) def test_device_model_attributes(self): expected_attributes = self.test_model_data['device_models'] key_attribute_names = frozenset(('manufacturer', 'model')) excluded_attribute_names = frozenset(('num_inputs', 'num_outputs')) self._assert_model_attributes( DeviceModel, expected_attributes, key_attribute_names, excluded_attribute_names) def test_device_attributes(self): expected_attributes = self.test_model_data['devices'] excluded_attribute_names = frozenset(('model',)) self._assert_model_attributes( Device, expected_attributes, excluded_attribute_names=excluded_attribute_names) def test_processor_attributes(self): expected_attributes = self._get_processor_expected_attributes() key_attribute_names = frozenset(('name', 'type')) self._assert_model_attributes( Processor, expected_attributes, key_attribute_names) def _get_processor_expected_attributes(self): aux = self._get_processor_expected_attributes_aux detectors = aux('Detector') classifiers = aux('Classifier') return detectors + classifiers def _get_processor_expected_attributes_aux(self, processor_type): key = processor_type.lower() + 's' model_data = self.test_model_data[key] for data in model_data: data['type'] = processor_type return model_data def test_annotation_constraint_attributes(self): expected_attributes = self.test_model_data['annotation_constraints'] excluded_attribute_names = frozenset(('type', 'extends', 'values')) self._assert_model_attributes( AnnotationConstraint, expected_attributes, excluded_attribute_names=excluded_attribute_names) def test_annotation_attributes(self): expected_attributes = self.test_model_data['annotations'] excluded_attribute_names = frozenset(('constraint',)) self._assert_model_attributes( AnnotationInfo, expected_attributes, excluded_attribute_names=excluded_attribute_names) def test_tag_attributes(self): expected_attributes = self.test_model_data['tags'] excluded_attribute_names = frozenset(('constraint',)) self._assert_model_attributes( TagInfo, expected_attributes, excluded_attribute_names=excluded_attribute_names)
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# @Author : guopeiming # @Contact : <EMAIL>, 163}.com from config import Constants from configparser import ConfigParser class MyConf(ConfigParser): """ MyConf """ def __init__(self, config_file, *args, **kwargs): super(MyConf, self).__init__(*args, **kwargs) self.read(filenames=config_file, encoding='utf-8') def __str__(self): res = "" for sect in self.sections(): res = res + '[' + sect + ']\n' for k, v in self.items(sect): res = res + k + ' : ' + v + '\n' res += '\n' return res @property def char_min_fre(self): return self.getint('Preprocess', 'char_min_fre') @property def word_min_fre(self): return self.getint('Preprocess', 'word_min_fre') @property def bichar_min_fre(self): return self.getint('Preprocess', 'bichar_min_fre') @property def data_path(self): return self.get('Data', 'data_path') @property def dict_data_path(self): return self.get('Data', 'dict_data_path') @property def logits_path(self): return self.get('Data', 'logits_path') @property def cache_3gram_path(self): return self.get('Data', 'cache_3gram_path') @property def pretrained_embed_char(self): return self.getboolean('Embed', 'pretrained_embed_char') @property def pretrained_embed_bichar(self): return self.getboolean('Embed', 'pretrained_embed_bichar') @property def char_gen_oov_uniform(self): return self.getfloat('Embed', 'char_gen_oov_uniform') @property def bichar_gen_oov_uniform(self): return self.getfloat('Embed', 'bichar_gen_oov_uniform') @property def pretrained_char_embed_file(self): return self.get('Embed', 'pretrained_char_embed_file') @property def pretrained_bichar_embed_file(self): return self.get('Embed', 'pretrained_bichar_embed_file') @property def char_gen_oov_mode(self): return self.get('Embed', 'char_gen_oov_mode') @property def bichar_gen_oov_mode(self): return self.get('Embed', 'bichar_gen_oov_mode') @property def seed(self): return self.getint('Train', 'seed') @property def use_cuda(self): return self.getboolean('Train', 'use_cuda') @property def cuda_id(self): return self.getint('Train', 'cuda_id') @property def distillLambda(self): return self.getfloat('Train', 'distillLambda') @property def batch_size(self): return self.getint('Train', 'batch_size') @property def shuffle(self): return self.getboolean('Train', 'shuffle') @property def num_workers(self): return self.getint('Train', 'num_workers') @property def drop_last(self): return self.getboolean('Train', 'drop_last') @property def epoch(self): return self.getint('Train', 'epoch') @property def accumulation_steps(self): return self.getint('Train', 'accumulation_steps') @property def logInterval(self): return self.getint('Train', 'logInterval') @property def valInterval(self): return self.getint('Train', 'valInterval') @property def visuParaInterval(self): return self.getint('Train', 'visuParaInterval') @property def saveInterval(self): return self.getint('Train', 'saveInterval') @property def save_path(self): return self.get('Train', 'save_path') @property def visual_logger_path(self): return self.get('Train', 'visual_logger_path') @property def is_stack(self): return self.getboolean('Model', 'is_stack') @property def char_embed_dim(self): return self.getint('Model', 'char_embed_dim') @property def char_embed_dim_no_static(self): return self.getint('Model', 'char_embed_dim_no_static') @property def bichar_embed_dim(self): return self.getint('Model', 'bichar_embed_dim') @property def bichar_embed_dim_no_static(self): return self.getint('Model', 'bichar_embed_dim_no_static') @property def dropout_embed(self): return self.getfloat('Model', 'dropout_embed') @property def char_embed_max_norm(self): num = self.getfloat('Model', 'char_embed_max_norm') assert num >= 0., 'Char_max_norm must greater than 0.0' return None if abs(num - 0.) < Constants.EPSILON else num @property def bichar_embed_max_norm(self): num = self.getfloat('Model', 'bichar_embed_max_norm') assert num >= 0., 'Word_max_norm must greater than 0.0' return None if abs(num - 0.) < Constants.EPSILON else num @property def encoder_embed_dim(self): return self.getint('Model', 'encoder_embed_dim') @property def dropout_encoder_embed(self): return self.getfloat('Model', 'dropout_encoder_embed') @property def encoder_lstm_hid_size(self): return self.getint('Model', 'encoder_lstm_hid_size') @property def dropout_encoder_hid(self): return self.getfloat('Model', 'dropout_encoder_hid') @property def subword_lstm_hid_size(self): return self.getint('Model', 'subword_lstm_hid_size') @property def word_lstm_hid_size(self): return self.getint('Model', 'word_lstm_hid_size') @property def freeze_bert(self): return self.getboolean('Model', 'freeze_bert') @property def freeze_bert_layers(self): return self.getint('Model', 'freeze_bert_layers') @property def opti_name(self): return self.get('Optimizer', 'name') @property def learning_rate(self): return self.getfloat('Optimizer', 'learning_rate') @property def fine_tune_lr(self): return self.getfloat('Optimizer', 'fine_tune_lr') @property def weight_decay(self): return self.getfloat('Optimizer', 'weight_decay') @property def clip_grad(self): return self.getboolean('Optimizer', 'clip_grad') @property def clip_grad_max_norm(self): return self.getfloat('Optimizer', 'clip_grad_max_norm') @property def warmup_steps(self): return self.getint('Optimizer', 'warmup_steps') @property def lr_decay_factor(self): return self.getfloat('Optimizer', 'lr_decay_factor') @property def momentum(self): return self.getfloat('Optimizer', 'momentum') @property def dampening(self): return self.getfloat('Optimizer', 'dampening') @property def nesterov(self): return self.getboolean('Optimizer', 'nesterov')
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<reponame>wissembrdj/welink class PretreatedQuery: ''' DBpedia resultat ''' def __init__(self, mentions_list, detected_ne): ''' Constructor ''' self.mentions_list=mentions_list self.detected_ne=detected_ne
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# input para capturar a entrada do usuário nome = input("Qual o seu nome: ") idade = input("Qual a sua idade: ") print ("Olá", nome, "sua idade é", idade)
StarcoderdataPython
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from django.views.generic.edit import CreateView from django.contrib import messages from django.urls import reverse_lazy from django.utils.translation import gettext as _ from .forms import ContactForm from .models import ContactsList class ContactFormView(CreateView): template_name = "contact/contact.html" form_class = ContactForm success_url = reverse_lazy("index") def get_context_data(self, **kwargs): context = super(ContactFormView, self).get_context_data(**kwargs) context["contacts"] = ContactsList.objects.all() return context def form_valid(self, form): if form.send_email(): messages.success(self.request, _("Message sent")) else: messages.error(self.request, _("Message not sent, please try again later.")) return super().form_valid(form)
StarcoderdataPython
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import pandas as pd import re import requests from bs4 import BeautifulSoup from time import sleep from .requester import Requester class Crawler: """ """ def __init__(self, url, sarcasm, as_archived=False): self.__url = url self.__sarcasm = sarcasm self.__as_archived = as_archived self.__data = list() self.__requests = list() def set_requests(self, html_class, regex, remove, element="a", shorter=0): """ NOT UPDATED YET Find the last page of a website. e.g. "Pages 1, 2, ..., 34, 35" <- Last page here is 35; Or find the strings rellated to the archived data. e.g. "News by year: 2020, 2019, ...,2005" <- Here is a list[2020, ..., 2005]. In both cases it'll depend if the __as_archived variable is True or False. Parameters ---------- html_class : str Class of the a href attribute related to the maximum number. re_string : raw str Regular expression to find the last page refer. rm_start : int How much to exclude from the beginning of the substring founded by the RE. rm_end : int How much to exclude from the end of the substring founded by the RE. element: str, default "a" Element of the HTML to be founded. shorter: int, default 0 If it's archived style page, set how much to remove from the url to get the pages url list. If necessary. """ find_pages = re.compile(regex) urls = list() html = Requester.get_one_request(self.__url + ("1", "")[self.__as_archived], force=True) bs = BeautifulSoup(html.text, "html.parser") element = str(bs.find_all(element, class_=html_class)) if self.__as_archived == True: for page in find_pages.finditer(element): urls.append(str(self.__url[:-shorter]) + element[page.start()+remove[0]:page.end()-remove[1]]) else: for page in find_pages.finditer(element): pages = int(element[page.start()+remove[0]:page.end()-remove[1]]) + 1 for page in range(1, pages): urls.append(self.__url + str(page)) self.__requests = Requester(urls, num_threads=24).get_requests() def get_raw_data(self, html_class, regex, element="div"): """ NOT UPDATED YET Find some specific part of an HTML class. Parameters ---------- html_class : str Class of the a specified element. re_element : str Regular expression to find a specific HTML element. element : str, default "div" Element of the HTML to be founded. """ find_element = re.compile(regex) raw_data = list() for request in self.__requests: bs = BeautifulSoup(request.text, "html.parser") raw = str(bs.find_all(element, class_=html_class)) for text in find_element.finditer(raw): raw_data.append(raw[text.start():text.end()]) print("[+] Total of {0:04d} raw data collected".format(len(raw_data))) return raw_data def set_data(self, raw_args, regex, remove, html_options, url_prefix=0): """ NOT UPDATED YET Find a specific data of links and title information from a list of HTML specific data. Parameters ---------- re_link : str Regular expression to find a href HTML element. re_title : str Regular expression to find a title HTML element. rm_start : tuple, (int,int) How much to exclude from the beginning of two substrings founded by the two regular expressions: re_link and re_title, in that order. rm_end : tuple, (int,int) How much to exclude from the end of two substrings founded by the two regular expressions: re_link and re_title, in that order. url : str, default "" A substring to add in the beginning of the link founded by the re_link; It's only used if the website URL is different from the original used in the crawling. url_prefix int, default 0 If there's anything to add in the begging of the url string to change in the columns of "link" in the dataset. """ find_link = re.compile(regex[0]) find_title = re.compile(regex[1]) find_text = re.compile(r"<.*?>") # find_text = re.compile(r"<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});") urls = list() for raw in self.get_raw_data(**raw_args): data = [None,None,None,None] data[0] = self.__sarcasm for tmp in find_link.finditer(raw): data[1] = self.__url[:url_prefix] + raw[tmp.start()+remove[0][0]:tmp.end()-remove[0][1]] urls.append(data[1]) for tmp in find_title.finditer(raw): data[2] = raw[tmp.start()+remove[1][0]:tmp.end()-remove[1][1]] if data[2] == [""]: continue data[3] = "" self.__data.append(data) # progress = 0 # requests = Requester(urls, num_threads = 64).get_requests() # print("[+] {0:03d}/{1:03d} data crawled".format(progress,len(requests)), end='\r') # for request in requests: # if request.url != urls[progress]: # progress += 1 # continue # bs = BeautifulSoup(request.text, "html.parser") # text = str(bs.find_all(html_options[0], html_options[1])) # bs = BeautifulSoup(text, "html.parser") # text = str(bs.find_all("p")) # indexs = list() # for t in find_text.finditer(text): # indexs.append([t.start(),t.end()]) # indexs.reverse() # text = list(text) # for i in indexs: # for r in range(i[1], i[0]-1, -1): # text[r] = '' # self.__data[progress][3] = ''.join(text) # progress += 1 # print("[+] {0:03d}/{1:03d} data crawled".format(progress,len(requests)), end='\r') # print() print("[+] Dataframe completed") def get_data(self): return self.__data
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<gh_stars>1-10 for i in range(1, int(input()) + 1): quadrado = i ** 2 cubo = i ** 3 print(f'{i} {quadrado} {cubo}') print(f'{i} {quadrado + 1} {cubo + 1}')
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<reponame>norikuro/vmcjp-slack-service #!/usr/bin/env python import datetime def get_next_time(minutes): now = datetime.datetime.now() next_time = now + datetime.timedelta(minutes=minutes) cron = "cron({} {} {} {} ? {})".format(next_time.minute, next_time.hour, next_time.day, next_time.month, next_time.year) return cron
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<reponame>DirkFi/BigDL from zoo.orca import init_orca_context, stop_orca_context from tensorflow import keras from zoo.pipeline.api.keras.layers import * import argparse import tensorflow as tf import os def bigdl_estimator(): from zoo.orca.learn.bigdl.estimator import Estimator from tensorflow.python.keras.datasets import imdb from tensorflow.python.keras.preprocessing import sequence from zoo.pipeline.api.keras.models import Model from zoo.pipeline.api.keras.objectives import SparseCategoricalCrossEntropy from zoo.orca.data import XShards from zoo.orca.learn.metrics import Accuracy import numpy as np # conf = {"spark.executor.extraJavaOptions": "-Xss512m", "spark.driver.extraJavaOptions": "-Xss512m"} # init_orca_context(cluster_mode="local", cores=8, memory="16g") init_orca_context(cluster_mode="local", cores=4, memory="16g") max_features = 200 max_len = 20 print("running bigdl estimator") (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features) x_train = x_train[:1000] y_train = y_train[:1000] x_test = x_test[-1000:] y_test = y_test[-1000:] print(len(x_train), 'train sequences') print(len(x_test), 'test sequences') print('Pad sequences (samples x time)') x_train = sequence.pad_sequences(x_train, maxlen=max_len) x_test = sequence.pad_sequences(x_test, maxlen=max_len) print('x_train shape:', x_train.shape) print('x_test shape:', x_test.shape) train_pos = np.zeros((len(x_train), max_len), dtype=np.int32) val_pos = np.zeros((len(x_test), max_len), dtype=np.int32) for i in range(0, len(x_train)): train_pos[i, :] = np.arange(max_len) val_pos[i, :] = np.arange(max_len) train_dataset = XShards.partition({"x": (x_train, train_pos), "y": np.array(y_train)}) val_dataset = XShards.partition({"x": (x_test, val_pos), "y": np.array(y_test)}) token_shape = (max_len,) position_shape = (max_len,) token_input = Input(shape=token_shape) position_input = Input(shape=position_shape) O_seq = TransformerLayer.init(vocab=max_features, hidden_size=128, n_head=8, seq_len=max_len)([token_input, position_input]) # Select the first output of the Transformer. The second is the pooled output. O_seq = SelectTable(0)(O_seq) O_seq = GlobalAveragePooling1D()(O_seq) O_seq = Dropout(0.2)(O_seq) outputs = Dense(2, activation='softmax')(O_seq) model = Model([token_input, position_input], outputs) model.summary() batch_size = 64 print("Train started") est = Estimator.from_bigdl(model=model, loss=SparseCategoricalCrossEntropy(), optimizer=Adam(), metrics=[Accuracy()]) est.set_constant_gradient_clipping(0.1, 0.2) est.fit(data=train_dataset, batch_size=batch_size, epochs=1) result = est.evaluate(val_dataset) print(result) est.clear_gradient_clipping() est.set_l2_norm_gradient_clipping(0.5) est.fit(data=train_dataset, batch_size=batch_size, epochs=1) print("Train finished") print("Evaluating started") result = est.evaluate(val_dataset) print(result) print("Evaluating finished") est.save('work/saved_model') # est.load('work/saved_model') print("load and save API finished") est.get_train_summary(tag='Loss') est.get_validation_summary(tag='Top1Accuracy') print("get summary API finished") stop_orca_context() def tf_estimator(): from zoo.orca.learn.tf.estimator import Estimator init_orca_context(cluster_mode="local", cores=4, memory="3g") os.environ["HDF5_USE_FILE_LOCKING"] = 'FALSE' print("running tf estimator") imdb = keras.datasets.imdb (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=1000) # print(train_data) word_index = imdb.get_word_index() word_index = {k: (v + 3) for k, v in word_index.items()} word_index["<PAD>"] = 0 word_index["<START>"] = 1 word_index["<UNK>"] = 2 # unknown word_index["<UNUSED>"] = 3 train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=word_index["<PAD>"], padding='post', maxlen=256) test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=word_index["<PAD>"], padding='post', maxlen=256) model = keras.Sequential() model.add(keras.layers.Embedding(1000, 16)) model.add(keras.layers.GlobalAveragePooling1D()) model.add(keras.layers.Dense(16, activation=tf.nn.relu)) model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid)) model.summary() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc']) x_val = train_data[:1000] partial_x_train = train_data[1000:] y_val = train_labels[:1000] partial_y_train = train_labels[1000:] train_dataset = tf.data.Dataset.from_tensor_slices((partial_x_train, partial_y_train)) validation_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val)) est = Estimator.from_keras(keras_model=model) est.set_constant_gradient_clipping(0.1, 0.2) est.fit(data=train_dataset, batch_size=512, epochs=5, validation_data=validation_dataset) results = est.evaluate(validation_dataset) print(results) est.clear_gradient_clipping() est.set_l2_norm_gradient_clipping(0.1) est.fit(data=train_dataset, batch_size=512, epochs=5, validation_data=validation_dataset) results = est.evaluate(validation_dataset) print(results) est.save('work/saved_model') print("save API finished") # est.save_tf_checkpoint('work/checkpoint') # est.load_tf_checkpoint('work/checkpoint') print("checkpoint save and load API finished") est.save_keras_model('work/keras_model') est.save_keras_weights('work/keras_weights') print("keras model and weights save API finished") # est.load_keras_model('work/keras_model') # est.load_keras_weights('work') print("keras model and weights load API finished") est.get_train_summary(tag='Loss') est.get_validation_summary(tag='Top1Accuracy') # Estimator.load(est, model_path='work/') # Has not been implemented # resutls = est.predict(validation_dataset) # print(results) stop_orca_context() def tf2_estimator(): from zoo.orca.learn.tf2.estimator import Estimator # import ray init_orca_context(cluster_mode="local", cores=4, memory="3g") print("running tf2 estimator") imdb = keras.datasets.imdb (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=1000) # print(train_data) word_index = imdb.get_word_index() word_index = {k: (v + 3) for k, v in word_index.items()} word_index["<PAD>"] = 0 word_index["<START>"] = 1 word_index["<UNK>"] = 2 # unknown word_index["<UNUSED>"] = 3 train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=word_index["<PAD>"], padding='post', maxlen=256) test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=word_index["<PAD>"], padding='post', maxlen=256) model = keras.Sequential() model.add(keras.layers.Embedding(1000, 16)) model.add(keras.layers.GlobalAveragePooling1D()) model.add(keras.layers.Dense(16, activation=tf.nn.relu)) model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid)) model.summary() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc']) x_val = train_data[:1000] partial_x_train = train_data[1000:] y_val = train_labels[:1000] partial_y_train = train_labels[1000:] train_dataset = tf.data.Dataset.from_tensor_slices((partial_x_train, partial_y_train)) validation_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val)) est = Estimator.from_keras(model_creator=model) est.fit(data=train_dataset, batch_size=512, epochs=100, validation_data=validation_dataset) results = est.evaluate(validation_dataset) print(results) est.save('work/saved_model') est.get_train_summary(tag='Loss') est.get_validation_summary(tag='Top1Accuracy') stop_orca_context() def pytorch_estimator(): print("running pytorch estimator") return def openvino_estimator(): print("running openvino estimator") return if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--mode', type=str, default="bigdl", help='The mode for the examples. bigdl, tf, tf2, pytorch or openvino.') args = parser.parse_args() mode = args.mode if mode == "bigdl": bigdl_estimator() elif mode == "tf": tf_estimator() elif mode == "tf2": tf2_estimator() elif mode == "pytorch": pytorch_estimator() else: openvino_estimator()
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# encoding: utf-8 """ line/watchdog.py Created by <NAME> on 2017-07-01. Copyright (c) 2009-2017 Exa Networks. All rights reserved. License: 3-clause BSD. (See the COPYRIGHT file) """ from exabgp.reactor.api.command.command import Command from exabgp.reactor.api.command.limit import match_neighbors from exabgp.reactor.api.command.limit import extract_neighbors from exabgp.protocol.ip import NoNextHop from exabgp.bgp.message import OUT from exabgp.bgp.message.update.attribute import NextHop from exabgp.configuration.static import ParseStaticRoute def register_announce (): pass @Command.register('text','announce route') def announce_route (self, reactor, service, line): def callback (): try: descriptions,command = extract_neighbors(line) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') yield True return changes = self.api_route(command) if not changes: self.log_failure('command could not parse route in : %s' % command) reactor.processes.answer(service,'error') yield True return for change in changes: if not ParseStaticRoute.check(change): self.log_message('invalid route for %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) continue change.nlri.action = OUT.ANNOUNCE reactor.configuration.inject_change(peers,change) self.log_message('route added to %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False reactor.processes.answer_done(service) except ValueError: self.log_failure('issue parsing the route') reactor.processes.answer(service,'error') yield True except IndexError: self.log_failure('issue parsing the route') reactor.processes.answer(service,'error') yield True reactor.asynchronous.schedule(service,line,callback()) return True @Command.register('text','withdraw route') def withdraw_route (self, reactor, service, line): def callback (): try: descriptions,command = extract_neighbors(line) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') yield True return changes = self.api_route(command) if not changes: self.log_failure('command could not parse route in : %s' % command) reactor.processes.answer(service,'error') yield True return for change in changes: # Change the action to withdraw before checking the route change.nlri.action = OUT.WITHDRAW # NextHop is a mandatory field (but we do not require in) if change.nlri.nexthop is NoNextHop: change.nlri.nexthop = NextHop('0.0.0.0') if not ParseStaticRoute.check(change): self.log_message('invalid route for %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) continue if reactor.configuration.inject_change(peers,change): self.log_message('route removed from %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False else: self.log_failure('route not found on %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False reactor.processes.answer_done(service) except ValueError: self.log_failure('issue parsing the route') reactor.processes.answer(service,'error') yield True except IndexError: self.log_failure('issue parsing the route') reactor.processes.answer(service,'error') yield True reactor.asynchronous.schedule(service,line,callback()) return True @Command.register('text','announce vpls') def announce_vpls (self, reactor, service, line): def callback (): try: descriptions,command = extract_neighbors(line) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') yield True return changes = self.api_vpls(command) if not changes: self.log_failure('command could not parse vpls in : %s' % command) reactor.processes.answer(service,'error') yield True return for change in changes: change.nlri.action = OUT.ANNOUNCE reactor.configuration.inject_change(peers,change) self.log_message('vpls added to %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False reactor.processes.answer_done(service) except ValueError: self.log_failure('issue parsing the vpls') reactor.processes.answer(service,'error') yield True except IndexError: self.log_failure('issue parsing the vpls') reactor.processes.answer(service,'error') yield True reactor.asynchronous.schedule(service,line,callback()) return True @Command.register('text','withdraw vpls') def withdraw_vpls (self, reactor, service, line): def callback (): try: descriptions,command = extract_neighbors(line) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') yield True return changes = self.api_vpls(command) if not changes: self.log_failure('command could not parse vpls in : %s' % command) reactor.processes.answer(service,'error') yield True return for change in changes: change.nlri.action = OUT.WITHDRAW if reactor.configuration.inject_change(peers,change): self.log_message('vpls removed from %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False else: self.log_failure('vpls not found on %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False reactor.processes.answer_done(service) except ValueError: self.log_failure('issue parsing the vpls') reactor.processes.answer(service,'error') yield True except IndexError: self.log_failure('issue parsing the vpls') reactor.processes.answer(service,'error') yield True reactor.asynchronous.schedule(service,line,callback()) return True @Command.register('text','announce attributes') def announce_attributes (self, reactor, service, line): def callback (): try: descriptions,command = extract_neighbors(line) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') yield True return changes = self.api_attributes(command,peers) if not changes: self.log_failure('command could not parse route in : %s' % command) reactor.processes.answer(service,'error') yield True return for change in changes: change.nlri.action = OUT.ANNOUNCE reactor.configuration.inject_change(peers,change) self.log_message('route added to %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False reactor.processes.answer_done(service) except ValueError: self.log_failure('issue parsing the route') reactor.processes.answer(service,'error') yield True except IndexError: self.log_failure('issue parsing the route') reactor.processes.answer(service,'error') yield True reactor.asynchronous.schedule(service,line,callback()) return True @Command.register('text','withdraw attributes') def withdraw_attribute (self, reactor, service, line): def callback (): try: descriptions,command = extract_neighbors(line) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') yield True return changes = self.api_attributes(command,peers) if not changes: self.log_failure('command could not parse route in : %s' % command) reactor.processes.answer(service,'error') yield True return for change in changes: change.nlri.action = OUT.WITHDRAW if reactor.configuration.inject_change(peers,change): self.log_message('route removed from %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False else: self.log_failure('route not found on %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False reactor.processes.answer_done(service) except ValueError: self.log_failure('issue parsing the route') reactor.processes.answer(service,'error') yield True except IndexError: self.log_failure('issue parsing the route') reactor.processes.answer(service,'error') yield True reactor.asynchronous.schedule(service,line,callback()) return True @Command.register('text','announce flow') def announce_flow (self, reactor, service, line): def callback (): try: descriptions,command = extract_neighbors(line) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') yield True return changes = self.api_flow(command) if not changes: self.log_failure('command could not parse flow in : %s' % command) reactor.processes.answer(service,'error') yield True return for change in changes: change.nlri.action = OUT.ANNOUNCE reactor.configuration.inject_change(peers,change) self.log_message('flow added to %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False reactor.processes.answer_done(service) except ValueError: self.log_failure('issue parsing the flow') reactor.processes.answer(service,'error') yield True except IndexError: self.log_failure('issue parsing the flow') reactor.processes.answer(service,'error') yield True reactor.asynchronous.schedule(service,line,callback()) return True @Command.register('text','withdraw flow') def withdraw_flow (self, reactor, service, line): def callback (): try: descriptions,command = extract_neighbors(line) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') yield True return changes = self.api_flow(command) if not changes: self.log_failure('command could not parse flow in : %s' % command) reactor.processes.answer(service,'error') yield True return for change in changes: change.nlri.action = OUT.WITHDRAW if reactor.configuration.inject_change(peers,change): self.log_message('flow removed from %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) else: self.log_failure('flow not found on %s : %s' % (', '.join(peers) if peers else 'all peers',change.extensive())) yield False reactor.processes.answer_done(service) except ValueError: self.log_failure('issue parsing the flow') reactor.processes.answer(service,'error') yield True except IndexError: self.log_failure('issue parsing the flow') reactor.processes.answer(service,'error') yield True reactor.asynchronous.schedule(service,line,callback()) return True @Command.register('text','announce eor') def announce_eor (self, reactor, service, command): def callback (self, command, peers): family = self.api_eor(command) if not family: self.log_failure("Command could not parse eor : %s" % command) reactor.processes.answer(service,'error') yield True return reactor.configuration.inject_eor(peers,family) self.log_message("Sent to %s : %s" % (', '.join(peers if peers else []) if peers is not None else 'all peers',family.extensive())) yield False reactor.processes.answer_done(service) try: descriptions,command = extract_neighbors(command) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') return False reactor.asynchronous.schedule(service,command,callback(self,command,peers)) return True except ValueError: self.log_failure('issue parsing the command') reactor.processes.answer(service,'error') return False except IndexError: self.log_failure('issue parsing the command') reactor.processes.answer(service,'error') return False @Command.register('text','announce route-refresh') def announce_refresh (self, reactor, service, command): def callback (self, command, peers): refresh = self.api_refresh(command) if not refresh: self.log_failure("Command could not parse route-refresh command : %s" % command) reactor.processes.answer(service,'error') yield True return reactor.configuration.inject_refresh(peers,refresh) self.log_message("Sent to %s : %s" % (', '.join(peers if peers else []) if peers is not None else 'all peers',refresh.extensive())) yield False reactor.processes.answer_done(service) try: descriptions,command = extract_neighbors(command) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') return False reactor.asynchronous.schedule(service,command,callback(self,command,peers)) return True except ValueError: self.log_failure('issue parsing the command') reactor.processes.answer(service,'error') return False except IndexError: self.log_failure('issue parsing the command') reactor.processes.answer(service,'error') return False @Command.register('text','announce operational') def announce_operational (self, reactor, service, command): def callback (self, command, peers): operational = self.api_operational(command) if not operational: self.log_failure("Command could not parse operational command : %s" % command) reactor.processes.answer(service,'error') yield True return reactor.configuration.inject_operational(peers,operational) self.log_message("operational message sent to %s : %s" % ( ', '.join(peers if peers else []) if peers is not None else 'all peers',operational.extensive() ) ) yield False reactor.processes.answer_done(service) if (command.split() + ['be','safe'])[2].lower() not in ('asm','adm','rpcq','rpcp','apcq','apcp','lpcq','lpcp'): reactor.processes.answer_done(service) return False try: descriptions,command = extract_neighbors(command) peers = match_neighbors(reactor.peers,descriptions) if not peers: self.log_failure('no neighbor matching the command : %s' % command) reactor.processes.answer(service,'error') return False reactor.asynchronous.schedule(service,command,callback(self,command,peers)) return True except ValueError: self.log_failure('issue parsing the command') reactor.processes.answer(service,'error') return False except IndexError: self.log_failure('issue parsing the command') reactor.processes.answer(service,'error') return False
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