xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

89f4b89220e6c9020137c93fa4b93f91dde4a3bd

487

py

Python

push_notifications/ser.py

omss24/django-push-notifications

d3863262addc235a021eeeb08a0c17ecc28df668

[ "MIT" ]

null

push_notifications/ser.py

omss24/django-push-notifications

d3863262addc235a021eeeb08a0c17ecc28df668

[ "MIT" ]

null

push_notifications/ser.py

omss24/django-push-notifications

d3863262addc235a021eeeb08a0c17ecc28df668

[ "MIT" ]

null

from rest_framework import serializers from push_notifications.models import Device, GCMDevice, APNSDevice from consultant.ser import ConsultantTokenSer class DeviceSer(serializers.ModelSerializer): user = ConsultantTokenSer() class Meta: model = Device class GCMDeviceSer(serializers.ModelSerializer): user = ConsultantTokenSer() class Meta: model = GCMDevice class APNSDeviceSer(serializers.ModelSerializer): user = ConsultantTokenSer() class Meta: model = APNSDevice

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487

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d6097551cb5520f6c583c8de98cbd7f8032487b8

2,053

py

Python

python/rikai/spark/sql/generated/RikaiModelSchemaVisitor.py

chunyang/rikai

9eb7f0da68e6c1e27e74de324afdbd8b375df0c3

[ "Apache-2.0" ]

null

python/rikai/spark/sql/generated/RikaiModelSchemaVisitor.py

chunyang/rikai

9eb7f0da68e6c1e27e74de324afdbd8b375df0c3

[ "Apache-2.0" ]

null

python/rikai/spark/sql/generated/RikaiModelSchemaVisitor.py

chunyang/rikai

9eb7f0da68e6c1e27e74de324afdbd8b375df0c3

[ "Apache-2.0" ]

null

# Generated from src/main/antlr4/org/apache/spark/sql/ml/parser/RikaiModelSchema.g4 by ANTLR 4.7.2 from antlr4 import * if __name__ is not None and "." in __name__: from .RikaiModelSchemaParser import RikaiModelSchemaParser else: from RikaiModelSchemaParser import RikaiModelSchemaParser # This class defines a complete generic visitor for a parse tree produced by RikaiModelSchemaParser. class RikaiModelSchemaVisitor(ParseTreeVisitor): # Visit a parse tree produced by RikaiModelSchemaParser#schema. def visitSchema(self, ctx:RikaiModelSchemaParser.SchemaContext): return self.visitChildren(ctx) # Visit a parse tree produced by RikaiModelSchemaParser#unquotedIdentifier. def visitUnquotedIdentifier(self, ctx:RikaiModelSchemaParser.UnquotedIdentifierContext): return self.visitChildren(ctx) # Visit a parse tree produced by RikaiModelSchemaParser#structType. def visitStructType(self, ctx:RikaiModelSchemaParser.StructTypeContext): return self.visitChildren(ctx) # Visit a parse tree produced by RikaiModelSchemaParser#arrayType. def visitArrayType(self, ctx:RikaiModelSchemaParser.ArrayTypeContext): return self.visitChildren(ctx) # Visit a parse tree produced by RikaiModelSchemaParser#nestedStructType. def visitNestedStructType(self, ctx:RikaiModelSchemaParser.NestedStructTypeContext): return self.visitChildren(ctx) # Visit a parse tree produced by RikaiModelSchemaParser#nestedArrayType. def visitNestedArrayType(self, ctx:RikaiModelSchemaParser.NestedArrayTypeContext): return self.visitChildren(ctx) # Visit a parse tree produced by RikaiModelSchemaParser#plainFieldType. def visitPlainFieldType(self, ctx:RikaiModelSchemaParser.PlainFieldTypeContext): return self.visitChildren(ctx) # Visit a parse tree produced by RikaiModelSchemaParser#structField. def visitStructField(self, ctx:RikaiModelSchemaParser.StructFieldContext): return self.visitChildren(ctx) del RikaiModelSchemaParser

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null

0

1

0

1

0

4

d625e5e5ddd3743f74d898ae58c9e4710b27e274

5,586

py

Python

autumn/__init__.py

monash-emu/AuTuMN

fa3b81ef54cf561e0e7364a48f4ff96585dc3310

[ "BSD-2-Clause-FreeBSD" ]

14

2020-03-11T06:15:30.000Z

2022-03-09T03:38:35.000Z

autumn/__init__.py

monash-emu/AuTuMN

fa3b81ef54cf561e0e7364a48f4ff96585dc3310

[ "BSD-2-Clause-FreeBSD" ]

96

2020-01-29T05:10:29.000Z

2022-03-31T01:48:46.000Z

autumn/__init__.py

monash-emu/AuTuMN

fa3b81ef54cf561e0e7364a48f4ff96585dc3310

[ "BSD-2-Clause-FreeBSD" ]

10

2020-04-24T00:38:00.000Z

2021-08-19T16:19:03.000Z

import os import warnings # Ignore future warnings they're annoying. warnings.simplefilter(action="ignore", category=FutureWarning) # Ensure NumPy only uses 1 thread for matrix multiplication, # because NumPy is stupid and tries to use heaps of threads, # which is quite wasteful and makes our models run way more slowly. # https://stackoverflow.com/questions/30791550/limit-number-of-threads-in-numpy os.environ["OMP_NUM_THREADS"] = "1" from autumn.settings import Models, Region from autumn.tools.registry import register_project # TB projects register_project( Models.TB, Region.MARSHALL_ISLANDS, "autumn.projects.tuberculosis.marshall_islands.project" ) register_project(Models.TB, Region.PHILIPPINES, "autumn.projects.tuberculosis.philippines.project") # Example projects register_project(Models.EXAMPLE, Region.PHILIPPINES, "autumn.projects.example.philippines.project") register_project(Models.EXAMPLE, Region.VICTORIA_2020, "autumn.projects.example.victoria.project") # COVID: Victoria state-wide super-model register_project( Models.COVID_19, Region.VICTORIA_2020, "autumn.projects.covid_19.victoria.victoria_2020.project", ) register_project( Models.COVID_19, Region.VICTORIA_2021, "autumn.projects.covid_19.victoria.victoria_2021.project", ) register_project( Models.COVID_19, Region.NORTH_EAST_METRO, "autumn.projects.covid_19.victoria.north_east_metro.project", ) register_project( Models.COVID_19, Region.SOUTH_EAST_METRO, "autumn.projects.covid_19.victoria.south_east_metro.project", ) register_project( Models.COVID_19, Region.WEST_METRO, "autumn.projects.covid_19.victoria.west_metro.project", ) register_project( Models.COVID_19, Region.BARWON_SOUTH_WEST, "autumn.projects.covid_19.victoria.barwon_south_west.project", ) register_project( Models.COVID_19, Region.GIPPSLAND, "autumn.projects.covid_19.victoria.gippsland.project", ) register_project( Models.COVID_19, Region.GRAMPIANS, "autumn.projects.covid_19.victoria.grampians.project", ) register_project( Models.COVID_19, Region.HUME, "autumn.projects.covid_19.victoria.hume.project", ) register_project( Models.COVID_19, Region.LODDON_MALLEE, "autumn.projects.covid_19.victoria.loddon_mallee.project", ) # COVID: European mixing optmization register_project( Models.COVID_19, Region.BELGIUM, "autumn.projects.covid_19.mixing_optimisation.regions.belgium.project", ) register_project( Models.COVID_19, Region.SPAIN, "autumn.projects.covid_19.mixing_optimisation.regions.spain.project", ) register_project( Models.COVID_19, Region.SWEDEN, "autumn.projects.covid_19.mixing_optimisation.regions.sweden.project", ) register_project( Models.COVID_19, Region.UNITED_KINGDOM, "autumn.projects.covid_19.mixing_optimisation.regions.united_kingdom.project", ) register_project( Models.COVID_19, Region.ITALY, "autumn.projects.covid_19.mixing_optimisation.regions.italy.project", ) register_project( Models.COVID_19, Region.FRANCE, "autumn.projects.covid_19.mixing_optimisation.regions.france.project", ) # COVID: Philippines project register_project( Models.COVID_19, Region.CALABARZON, "autumn.projects.covid_19.philippines.calabarzon.project" ) register_project( Models.COVID_19, Region.CENTRAL_VISAYAS, "autumn.projects.covid_19.philippines.central_visayas.project", ) register_project( Models.COVID_19, Region.DAVAO_CITY, "autumn.projects.covid_19.philippines.davao_city.project" ) register_project( Models.COVID_19, Region.DAVAO_REGION, "autumn.projects.covid_19.philippines.davao_region.project", ) register_project( Models.COVID_19, Region.MANILA, "autumn.projects.covid_19.philippines.manila.project" ) register_project( Models.COVID_19, Region.PHILIPPINES, "autumn.projects.covid_19.philippines.philippines.project" ) # COVID: Malaysia project register_project(Models.COVID_19, Region.JOHOR, "autumn.projects.covid_19.malaysia.johor.project") register_project( Models.COVID_19, Region.KUALA_LUMPUR, "autumn.projects.covid_19.malaysia.kuala_lumpur.project" ) register_project( Models.COVID_19, Region.MALAYSIA, "autumn.projects.covid_19.malaysia.malaysia.project" ) register_project(Models.COVID_19, Region.PENANG, "autumn.projects.covid_19.malaysia.penang.project") register_project(Models.COVID_19, Region.SABAH, "autumn.projects.covid_19.malaysia.sabah.project") register_project( Models.COVID_19, Region.SELANGOR, "autumn.projects.covid_19.malaysia.selangor.project" ) # Nepal register_project(Models.COVID_19, Region.NEPAL, "autumn.projects.covid_19.nepal.project") # Sri Lanka register_project( Models.COVID_19, Region.SRI_LANKA, "autumn.projects.covid_19.sri_lanka.sri_lanka.project" ) # Indonesia & Bali register_project(Models.COVID_19, Region.BALI, "autumn.projects.covid_19.indonesia.bali.project") register_project( Models.COVID_19, Region.INDONESIA, "autumn.projects.covid_19.indonesia.indonesia.project" ) # Vietnam register_project( Models.COVID_19, Region.VIETNAM, "autumn.projects.covid_19.vietnam.vietnam.project" ) register_project( Models.COVID_19, Region.HO_CHI_MINH_CITY, "autumn.projects.covid_19.vietnam.ho_chi_minh_city.project", ) register_project(Models.COVID_19, Region.MYANMAR, "autumn.projects.covid_19.myanmar.project") # COVID: Victoria project # FIXME: Parameter validation issues # register_project(Models.COVID_19, Region.VICTORIA, "autumn.projects.covid_19.victoria.project")

28.943005

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0.786609

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0.177465

0.119121

0.198535

0.221224

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0.482628

0.399433

0.090995

0.023635

0

0.034828

0.105621

5,586

192

101

29.09375

0.81205

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true

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0

null

0

1

0

1

null

0

1

0

4

c384e185a028dbe0d164fb2a713c781e03517dfc

146

py

Python

exercise/traceback_test.py

progzc/PythonDemo

0515fee3511bc132bfddf480014f61ce52080616

[ "Apache-2.0" ]

null

exercise/traceback_test.py

progzc/PythonDemo

0515fee3511bc132bfddf480014f61ce52080616

[ "Apache-2.0" ]

null

exercise/traceback_test.py

progzc/PythonDemo

0515fee3511bc132bfddf480014f61ce52080616

[ "Apache-2.0" ]

null

import traceback # 使用traceback模块打印异常信息 # 注意顺序的随机性 try: print('-------------------------') print(1 / 0) except: traceback.print_exc()

14.6

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4

c3a24d5a1423176a71ca5d88b9915517f13181f5

34,574

py

Python

tests/test_gmm.py

EricKightley/sparseklearn

d5d1f42c0572972ea3f4702734f82066ae7270e3

[ "MIT" ]

3

2018-02-08T08:35:54.000Z

2020-02-19T21:50:28.000Z

tests/test_gmm.py

EricKightley/sparseklearn

d5d1f42c0572972ea3f4702734f82066ae7270e3

[ "MIT" ]

1

2020-07-07T05:23:52.000Z

2020-07-08T13:57:48.000Z

tests/test_gmm.py

EricKightley/sparseklearn

d5d1f42c0572972ea3f4702734f82066ae7270e3

[ "MIT" ]

1

2019-10-07T03:56:41.000Z

import unittest import numpy as np from sklearn.mixture.gaussian_mixture import _compute_precision_cholesky from sklearn.mixture.gaussian_mixture import _estimate_log_gaussian_prob from sklearn.mixture.gaussian_mixture import _estimate_gaussian_parameters from sklearn.mixture import GaussianMixture as GMSKL from sparseklearn import GaussianMixture from tests import DataGenerator class TestGaussianMixture(unittest.TestCase): def assertArrayEqual(self, x, y): self.assertTrue(np.allclose(x, y, rtol=1e-6)) def setUp(self): self.td = DataGenerator() #gmm = GaussianMixture(n_components = 3, # num_feat_full = 5, num_feat_comp = 3, num_feat_shared = 1, # num_samp = 4, transform = 'dct', # D_indices = self.td.D_indices, mask = self.td.mask) #self.gmm = gmm def test_fit_sparsifier(self): gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 3, num_feat_shared = 1, num_samp = 4, transform = 'dct', D_indices = self.td.D_indices, mask = self.td.mask) gmm.fit_sparsifier(X = self.td.X) self.assertArrayEqual(self.td.RHDX, gmm.RHDX) self.assertArrayEqual(self.td.mask, gmm.mask) self.assertEqual(self.td.N, gmm.num_samp) self.assertEqual(self.td.Q, gmm.num_feat_comp) self.assertEqual(self.td.P, gmm.num_feat_full) def instantiate_standard_gmm(self, random_state): gmm = GaussianMixture(n_components = self.td.K, num_feat_full = self.td.P, num_feat_comp = self.td.Q, num_feat_shared = self.td.Qs, num_samp = self.td.N, transform = self.td.transform, D_indices = self.td.D_indices, mask = self.td.mask, random_state = random_state) return gmm ########################################################################### ########################################################################### ##### E-STEP ###### ########################################################################### ########################################################################### def test_pairwise_mahalanobis_distances(self): """ pairwise_mahalanobis_distances is a Sparsifier function, but a use case here made me suspect that it's wrong. To confirm this I'm putting a test here first. Will need to make a new one (and probably ammend existing ones that are currently passing but shouldn't be) in that test suite once I am convinced it's the culprit. """ cov_type = 'spherical' rs = np.random.RandomState(10) gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 3, num_feat_shared = 2, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type, random_state = rs) gmm.fit_sparsifier(X = self.td.X) means = rs.rand(gmm.n_components, gmm.num_feat_full) covariances = rs.rand(gmm.n_components) mahadist_test = gmm.pairwise_mahalanobis_distances(means, covariances, cov_type)**2 #undo the rescaling due to compression mahadist_test *= gmm.num_feat_comp/gmm.num_feat_full mahadist_true = np.zeros_like(mahadist_test) for data_ind in range(gmm.num_samp): for comp_ind in range(gmm.n_components): mahadist_true[data_ind, comp_ind] = 1/covariances[comp_ind] * \ np.linalg.norm(gmm.RHDX[data_ind] - means[comp_ind][gmm.mask[data_ind]])**2 self.assertArrayEqual(mahadist_test, mahadist_true) def test_hand_computation_of_log_prob_vs_sklearn(self): """ Something seems wrong with my mahadist computation. Before digging further into the C library to find the error, I want to make sure that the results I think it should give are right. One way to gather evidence in favor of this conclusion is to use the result in the computation of the log probability (this is what led me here in the first place). This test does so, and consequently doesn't actually test any of the code in gmm.py. For this to work the mask must be entirely shared. """ cov_type = 'spherical' rs = np.random.RandomState(10) gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 3, num_feat_shared = 3, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type, random_state = rs) gmm.fit_sparsifier(X = self.td.X) means = rs.rand(gmm.n_components, gmm.num_feat_full) covariances = rs.rand(gmm.n_components) precisions = _compute_precision_cholesky(covariances, cov_type) # this is where we need the mask to be shared, so that all mask rows # equal mask[0] masked_means = means[:, gmm.mask[0]] log_prob_true = _estimate_log_gaussian_prob(gmm.RHDX, masked_means, precisions, cov_type) log_prob_test = np.zeros((gmm.num_samp, gmm.n_components)) for data_ind in range(gmm.num_samp): for comp_ind in range(gmm.n_components): test_const = gmm.num_feat_comp * np.log(2*np.pi) test_logdet = gmm.num_feat_comp * np.log(covariances[comp_ind]) test_mahadist = 1/covariances[comp_ind] * \ np.linalg.norm(gmm.RHDX[data_ind] - means[comp_ind][gmm.mask[data_ind]])**2 log_prob_test[data_ind, comp_ind] = -.5*(test_const + \ test_logdet + test_mahadist) self.assertArrayEqual(log_prob_test, log_prob_true) def test__compute_logdet_array_spherical(self): """ Test spherical logdet under compression on an example computed here. Redundant with test__compute_logdet_array below but was implemented to confirm that test is correct. """ cov_type = 'spherical' rs = np.random.RandomState(10) gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 3, num_feat_shared = 2, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type, random_state = rs) gmm.fit_sparsifier(X = self.td.X) means = rs.rand(gmm.n_components, gmm.num_feat_full) covariances = rs.rand(gmm.n_components) logdet_test = gmm._compute_logdet_array(covariances, 'spherical') logdet_true = gmm.num_feat_comp * np.log(covariances) logdet_true = np.tile(logdet_true, (gmm.num_samp, 1)) self.assertArrayEqual(logdet_test, logdet_true) def test__compute_logdet_array(self): """ Test spherical and diagonal on hard-coded results. """ gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 3, num_feat_shared = 1, num_samp = 4, transform = 'dct', D_indices = self.td.D_indices, mask = self.td.mask) logdet_spherical = gmm._compute_logdet_array(self.td.spherical_covariances, 'spherical') logdet_diag = gmm._compute_logdet_array(self.td.diagonal_covariances, 'diag') self.assertArrayEqual(self.td.correct_logdet_spherical, logdet_spherical) self.assertArrayEqual(self.td.correct_logdet_diag, logdet_diag) def test__compute_log_prob_spherical_no_compression(self): """ Compare the log_prob computation to that of sklearn with no compression. Implemented as a precursor to testing it with compression, to follow. Spherical covariances. """ cov_type = 'spherical' gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 5, num_feat_shared = 5, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type) gmm.fit_sparsifier(X = self.td.X) means = np.random.rand(gmm.n_components, gmm.num_feat_comp) covariances = np.random.rand(gmm.n_components) log_prob_test = gmm._compute_log_prob(means, covariances, cov_type) precisions = _compute_precision_cholesky(covariances, cov_type) log_prob_true = _estimate_log_gaussian_prob(self.td.X, means, precisions, cov_type) self.assertArrayEqual(log_prob_test, log_prob_true) def test__compute_log_prob_spherical_shared_compression(self): """ Compare the log_prob computation to that of sklearn with shared compression. Spherical covariances. """ cov_type = 'spherical' rs = np.random.RandomState(10) gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 3, num_feat_shared = 3, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type, random_state = rs) gmm.fit_sparsifier(X = self.td.X) means = rs.rand(gmm.n_components, gmm.num_feat_full) covariances = rs.rand(gmm.n_components) log_prob_test = gmm._compute_log_prob(means, covariances, cov_type) log_prob_true = np.zeros((gmm.num_samp, gmm.n_components)) for data_ind in range(gmm.num_samp): for comp_ind in range(gmm.n_components): true_const = gmm.num_feat_comp * np.log(2*np.pi) true_logdet = gmm.num_feat_comp * np.log(covariances[comp_ind]) true_mahadist = 1/covariances[comp_ind] * \ np.linalg.norm(gmm.RHDX[data_ind] - means[comp_ind][gmm.mask[data_ind]])**2 log_prob_true[data_ind, comp_ind] = -.5*(true_const + \ true_logdet + true_mahadist) self.assertArrayEqual(log_prob_test, log_prob_true) def test__compute_log_prob_diagonal_no_compression(self): """ Compare the log_prob computation to that of sklearn with no compression. Implemented as a precursor to testing it with compression, to follow. Diagonal covariances. """ cov_type = 'diag' gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 5, num_feat_shared = 5, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type) gmm.fit_sparsifier(X = self.td.X) means = np.random.rand(gmm.n_components, gmm.num_feat_comp) covariances = np.random.rand(gmm.n_components, gmm.num_feat_comp) log_prob_test = gmm._compute_log_prob(means, covariances, cov_type) precisions = _compute_precision_cholesky(covariances, cov_type) log_prob_true = _estimate_log_gaussian_prob(self.td.X, means, precisions, cov_type) self.assertArrayEqual(log_prob_test, log_prob_true) def test__compute_log_prob(self): """ This test should probably get implemented eventually. It corresponds to testing the wrapper around fastLA.pairwise_mahalanobis_distances and gmm._compute_logdet_array. Each of these has tests for spherical and diagonal cases with sparsification. Currently we have tests of: - component functions in _compute_log_prob includes diag and spherical on compressed data with sparsification - _compute_log_prob on dense data tests against sklearn I can't currently think of a way to implement a test for this that isn't a trivial replication of those earlier tests. """ #TODO def test__estimate_log_prob_resp_spherical_no_compression(self): cov_type = 'spherical' gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 5, num_feat_shared = 5, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type) gmm.fit_sparsifier(X = self.td.X) means = np.random.rand(gmm.n_components, gmm.num_feat_comp) covariances = np.random.rand(gmm.n_components) weights = np.random.rand(gmm.n_components) weights /= weights.sum() log_prob_test, log_resp_test, log_prob_norm_test = gmm._estimate_log_prob_resp( weights, means, covariances, cov_type) # find skl's values, pretty ugly to do. precisions = _compute_precision_cholesky(covariances, cov_type) gmm_skl = GMSKL(n_components = 3, covariance_type = cov_type) gmm_skl.means_ = means gmm_skl.precisions_cholesky_ = precisions gmm_skl.weights_ = weights gmm_skl.covariance_type_ = cov_type log_prob_norm_true, log_resp_true = gmm_skl._estimate_log_prob_resp(self.td.X) # if anything is bad later this overwrite with mean seems suspect: log_prob_norm_true = log_prob_norm_true.mean() # now get the log_prob from another function log_prob_true = _estimate_log_gaussian_prob(self.td.X, means, precisions, cov_type) # run the tests self.assertArrayEqual(log_prob_test, log_prob_true) self.assertArrayEqual(log_prob_norm_true, log_prob_norm_test) self.assertArrayEqual(log_resp_true, log_resp_test) def test__estimate_log_prob_resp_diagonal_no_compression(self): cov_type = 'diag' gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 5, num_feat_shared = 5, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type) gmm.fit_sparsifier(X = self.td.X) means = np.random.rand(gmm.n_components, gmm.num_feat_comp) covariances = np.random.rand(gmm.n_components, gmm.num_feat_comp) weights = np.random.rand(gmm.n_components) weights /= weights.sum() log_prob_test, log_resp_test, log_prob_norm_test = gmm._estimate_log_prob_resp( weights, means, covariances, cov_type) # find skl's values, pretty ugly to do. precisions = _compute_precision_cholesky(covariances, cov_type) gmm_skl = GMSKL(n_components = 3, covariance_type = cov_type) gmm_skl.means_ = means gmm_skl.precisions_cholesky_ = precisions gmm_skl.weights_ = weights gmm_skl.covariance_type_ = cov_type log_prob_norm_true, log_resp_true = gmm_skl._estimate_log_prob_resp(self.td.X) # if anything is bad later this overwrite with mean seems suspect: log_prob_norm_true = log_prob_norm_true.mean() # now get the log_prob from another function log_prob_true = _estimate_log_gaussian_prob(self.td.X, means, precisions, cov_type) # run the tests self.assertArrayEqual(log_prob_test, log_prob_true) self.assertArrayEqual(log_prob_norm_true, log_prob_norm_test) self.assertArrayEqual(log_resp_true, log_resp_test) # test failing def test__estimate_log_prob_resp_spherical_shared_compression(self): rs = np.random.RandomState(11) cov_type = 'spherical' gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 3, num_feat_shared = 3, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type, random_state = rs) gmm.fit_sparsifier(X = self.td.X) means = rs.rand(gmm.n_components, gmm.num_feat_full) covariances = rs.rand(gmm.n_components) weights = rs.rand(gmm.n_components) weights /= weights.sum() log_prob_test, log_resp_test, log_prob_norm_test = gmm._estimate_log_prob_resp( weights, means, covariances, cov_type) # find skl's values, pretty ugly to do. precisions = _compute_precision_cholesky(covariances, cov_type) gmm_skl = GMSKL(n_components = 3, covariance_type = cov_type) # we need the mask to be shared so that we can use mask[0] on all means gmm_skl.means_ = means[:, gmm.mask[0]] gmm_skl.precisions_cholesky_ = precisions gmm_skl.weights_ = weights gmm_skl.covariance_type_ = cov_type log_prob_norm_true, log_resp_true = gmm_skl._estimate_log_prob_resp(gmm.RHDX) # if anything is bad later this overwrite with mean seems suspect: log_prob_norm_true = log_prob_norm_true.mean() # now get the log_prob from another function log_prob_true = _estimate_log_gaussian_prob(gmm.RHDX, gmm_skl.means_, precisions, cov_type) # run the tests self.assertArrayEqual(log_prob_test, log_prob_true) self.assertArrayEqual(log_prob_norm_true, log_prob_norm_test) self.assertArrayEqual(log_resp_true, log_resp_test) ########################################################################### ########################################################################### ##### M-STEP ###### ########################################################################### ########################################################################### def test__estimate_gaussian_parameters_spherical_no_compression(self): """ Test _estiamte_gaussian_parameters against sklearn's implementation. Spherical covariances, no compression. """ cov_type = 'spherical' reg_covar = 1e-6 gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 5, num_feat_shared = 5, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type, reg_covar = reg_covar) gmm.fit_sparsifier(X = self.td.X) resp = np.random.rand(gmm.num_samp, gmm.n_components) weights_test, means_test, covariances_test = gmm._estimate_gaussian_parameters(resp, cov_type) # skl counts_true, means_true, covariances_true = _estimate_gaussian_parameters( self.td.X, resp, reg_covar, cov_type) # skl returns counts instead of weights. weights_true = counts_true / gmm.num_samp self.assertArrayEqual(weights_test, weights_true) self.assertArrayEqual(means_test, means_true) self.assertArrayEqual(covariances_test, covariances_true) def test__estimate_gaussian_parameters_diagonal_no_compression(self): """ Test _estiamte_gaussian_parameters against sklearn's implementation. Diagonal covariances, no compression. """ cov_type = 'diag' reg_covar = 1e-6 gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 5, num_feat_shared = 5, num_samp = 4, transform = None, mask = None, D_indices = None, covariance_type = cov_type, reg_covar = reg_covar) gmm.fit_sparsifier(X = self.td.X) resp = np.random.rand(gmm.num_samp, gmm.n_components) weights_test, means_test, covariances_test = gmm._estimate_gaussian_parameters(resp, cov_type) # skl counts_true, means_true, covariances_true = _estimate_gaussian_parameters( self.td.X, resp, reg_covar, cov_type) # skl returns counts instead of weights. weights_true = counts_true / gmm.num_samp self.assertArrayEqual(weights_test, weights_true) self.assertArrayEqual(means_test, means_true) self.assertArrayEqual(covariances_test, covariances_true) def test__estimate_gaussian_means_and_covariances_diagonal_no_compression(self): """ Test _estimate_gaussian_means_and_covariances against hard-coded example. Should be redundant with test__estimate_gaussian_parameters_* tests above, which test against sklearn's results. """ X = np.array([[0,1,0,0], [1,0,0,0], [0,0,1,2]], dtype = np.float64) gmm = GaussianMixture(n_components = 2, num_feat_full = 4, num_feat_comp = 4, num_feat_shared = 4, num_samp = 3, transform = None, D_indices = None, mask = None, reg_covar = 0) gmm.fit_sparsifier(X = X) # note: columns should sum to 1, but don't have to because the weighted # means computation has to renormalize anyway to account for the mask resp = np.array([[.6, .3], [.4, .2], [ 0, .5]], dtype = np.float64) means, covariances = gmm._estimate_gaussian_means_and_covariances(resp, 'diag') correct_means = np.array([[ .4, .6, .0, .0], [ .2, .3, .5, 1.]], dtype=np.float64) correct_covariances = np.array([[.24, .24, 0, 0], [.16, .21, .25, 1]], dtype=np.float64) self.assertArrayEqual(correct_means, means) self.assertArrayEqual(correct_covariances, covariances) def test__estimate_gaussian_weights(self): """ Weights are testsed in test__estimate_gaussian_parameters_* above. Should not need to implement this unless we want to further test on compressed case. """ #TODO return 1 ########################################################################### ########################################################################### ##### Initialization ###### ########################################################################### ########################################################################### def test__initialize_means_case1(self): """ means_init is a 2D array. """ random_state = np.random.RandomState(12) means_init_true = random_state.rand(self.td.K, self.td.P) gmm = GaussianMixture(n_components = self.td.K, num_feat_full = self.td.P, num_feat_comp = self.td.Q, num_feat_shared = self.td.Qs, num_samp = self.td.N, transform = self.td.transform, D_indices = self.td.D_indices, mask = self.td.mask, means_init = means_init_true, random_state = random_state) gmm.fit_sparsifier(X=self.td.X) means_init_test = gmm._initialize_means() self.assertArrayEqual(means_init_test, means_init_true) def test__initialize_means_case2(self): """ means_init is a 3D array. """ random_state = np.random.RandomState(12) n_init = 3 means_init_true = random_state.rand(n_init, self.td.K, self.td.P) gmm = GaussianMixture(n_components = self.td.K, num_feat_full = self.td.P, num_feat_comp = self.td.Q, num_feat_shared = self.td.Qs, num_samp = self.td.N, transform = self.td.transform, D_indices = self.td.D_indices, mask = self.td.mask, means_init = means_init_true, n_init = n_init, random_state = random_state) gmm.fit_sparsifier(X=self.td.X) # first one is discarded for this test _ = gmm._initialize_means() # this should recover the second one means_init_test = gmm._initialize_means() self.assertArrayEqual(means_init_test, means_init_true[1]) def test__initialize_means_case3(self): """ means_init is None, init_params is 'kmpp'. Only checks that the initialized means are of the correct shape. """ random_state = np.random.RandomState(12) gmm = GaussianMixture(n_components = self.td.K, num_feat_full = self.td.P, num_feat_comp = self.td.Q, num_feat_shared = self.td.Qs, num_samp = self.td.N, transform = self.td.transform, D_indices = self.td.D_indices, mask = self.td.mask, means_init = None, init_params = 'kmpp', random_state = random_state) gmm.fit_sparsifier(X=self.td.X) means_init_shape_test = gmm._initialize_means().shape means_init_shape_true = np.array([self.td.K, self.td.P]) self.assertArrayEqual(means_init_shape_test, means_init_shape_true) def test__initialize_means_case4(self): """ means_init is None, init_params is 'random'. Only checks that the initialized means are of the correct shape. """ random_state = np.random.RandomState(12) gmm = GaussianMixture(n_components = self.td.K, num_feat_full = self.td.P, num_feat_comp = self.td.Q, num_feat_shared = self.td.Qs, num_samp = self.td.N, transform = self.td.transform, D_indices = self.td.D_indices, mask = self.td.mask, means_init = None, init_params = 'random', random_state = random_state) gmm.fit_sparsifier(X=self.td.X) means_init_shape_test = gmm._initialize_means().shape means_init_shape_true = np.array([self.td.K, self.td.P]) self.assertArrayEqual(means_init_shape_test, means_init_shape_true) def test__initialize_covariances_case1(self): """ spherical covariance, 1 init. """ random_state = np.random.RandomState(12) means_init_true = random_state.rand(self.td.K, self.td.P) covariances_init_true = random_state.rand(self.td.K) gmm = GaussianMixture(n_components = self.td.K, num_feat_full = self.td.P, num_feat_comp = self.td.Q, num_feat_shared = self.td.Qs, num_samp = self.td.N, transform = self.td.transform, D_indices = self.td.D_indices, mask = self.td.mask, means_init = means_init_true, covariances_init = covariances_init_true, covariance_type = 'spherical', random_state = random_state) gmm.fit_sparsifier(X=self.td.X) means_init = gmm._initialize_means() covariances_init_test = gmm._initialize_covariances(means_init) self.assertArrayEqual(covariances_init_test, covariances_init_true) def test__initialize_covariances_case2(self): """ diagonal covariance, 1 init. """ random_state = np.random.RandomState(12) means_init_true = random_state.rand(self.td.K, self.td.P) covariances_init_true = random_state.rand(self.td.K, self.td.P) gmm = GaussianMixture(n_components = self.td.K, num_feat_full = self.td.P, num_feat_comp = self.td.Q, num_feat_shared = self.td.Qs, num_samp = self.td.N, transform = self.td.transform, D_indices = self.td.D_indices, mask = self.td.mask, means_init = means_init_true, covariances_init = covariances_init_true, covariance_type = 'diag', random_state = random_state) gmm.fit_sparsifier(X=self.td.X) means_init = gmm._initialize_means() covariances_init_test = gmm._initialize_covariances(means_init) self.assertArrayEqual(covariances_init_test, covariances_init_true) def test__initialize_covariances_case3(self): """ No covariances given, just check shape. """ random_state = np.random.RandomState(12) means_init_true = random_state.rand(self.td.K, self.td.P) gmm = GaussianMixture(n_components = self.td.K, num_feat_full = self.td.P, num_feat_comp = self.td.Q, num_feat_shared = self.td.Qs, num_samp = self.td.N, transform = self.td.transform, D_indices = self.td.D_indices, mask = self.td.mask, means_init = means_init_true, covariances_init = None, covariance_type = 'diag', random_state = random_state) gmm.fit_sparsifier(X=self.td.X) means_init = gmm._initialize_means() covariances_init_test = gmm._initialize_covariances(means_init) true_shape = np.array((self.td.K, self.td.P)) self.assertArrayEqual(covariances_init_test.shape, true_shape) def test__initialize_covariances_case4(self): """ diagonal covariance, multi-init. """ random_state = np.random.RandomState(12) n_init = 3 means_init_true = random_state.rand(n_init, self.td.K, self.td.P) covariances_init_true = random_state.rand(n_init, self.td.K, self.td.P) gmm = GaussianMixture(n_components = self.td.K, num_feat_full = self.td.P, num_feat_comp = self.td.Q, num_feat_shared = self.td.Qs, num_samp = self.td.N, transform = self.td.transform, D_indices = self.td.D_indices, mask = self.td.mask, means_init = means_init_true, covariances_init = covariances_init_true, covariance_type = 'diag', n_init = n_init, random_state = random_state) gmm.fit_sparsifier(X=self.td.X) # init means twice to cycle covariances _ = gmm._initialize_means() means_init = gmm._initialize_means() covariances_init_test = gmm._initialize_covariances(means_init) self.assertArrayEqual(covariances_init_test, covariances_init_true[1]) def test__initialize_weights_case3(self): """ multi-init """ random_state = np.random.RandomState(12) n_init = 3 means_init_true = random_state.rand(n_init, self.td.K, self.td.P) weights_init_true = random_state.rand(n_init, self.td.K) weights_init_true /= weights_init_true.sum(axis=1)[:,np.newaxis] gmm = GaussianMixture(n_components = self.td.K, num_feat_full = self.td.P, num_feat_comp = self.td.Q, num_feat_shared = self.td.Qs, num_samp = self.td.N, transform = self.td.transform, D_indices = self.td.D_indices, mask = self.td.mask, means_init = means_init_true, weights_init = weights_init_true, n_init = n_init, covariance_type = 'diag', random_state = random_state) gmm.fit_sparsifier(X=self.td.X) # init means twice to cycle covariances _ = gmm._initialize_means() means_init = gmm._initialize_means() weights_init_test = gmm._initialize_weights(means_init) self.assertArrayEqual(weights_init_test, weights_init_true[1]) def test__init_resp_from_means(self): gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 3, num_feat_shared = 1, num_samp = 4, transform = 'dct', D_indices = self.td.D_indices, mask = self.td.mask) gmm.fit_sparsifier(X=self.td.X) resp_test = gmm._init_resp_from_means(self.td.U) resp_correct = np.array([[0, 1, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0]], dtype = int) def test__initialize_parameters(self): """ Only tests if it runs. """ init_params = 'random' means_init = None gmm = GaussianMixture(n_components = 3, num_feat_full = 5, num_feat_comp = 3, num_feat_shared = 1, num_samp = 4, transform = 'dct', D_indices = self.td.D_indices, mask = self.td.mask, init_params = init_params, means_init = means_init) gmm.fit_sparsifier(HDX=self.td.HDX) gmm._initialize_parameters() ########################################################################### ########################################################################### ##### Fit ###### ########################################################################### ########################################################################### def test_fit(self): """ Catches a case where covariance goes to 0.""" reg_covar = 1e-6 np.random.seed(0) X = np.array([[0,1,0,0], [1,0,0,0], [0,0,1,2]], dtype = np.float64) gmm = GaussianMixture(n_components = 2, covariance_type = 'diag', num_feat_full = 4, num_feat_comp = 2, num_feat_shared = 1, num_samp = 3, transform = None, D_indices = None, mask = None, reg_covar = reg_covar, init_params = 'random', max_iter = 5) gmm.fit(X=X) correct_means = np.array([[0, 0, 1, 0], [1, 0, 0, 0]], dtype = np.float64) correct_covariances = np.ones_like(correct_means)*reg_covar self.assertArrayEqual(gmm.means_, correct_means) self.assertArrayEqual(gmm.covariances_, correct_covariances) if __name__ == '__main__': unittest.main()

51.220741

102

0.586857

4,189

34,574

4.531392

0.0783

0.049942

0.023759

0.042777

0.7662

0.740333

0.714256

0.697345

0.693868

0.688284

0

0.010458

0.297536

34,574

674

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51.296736

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0.135362

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1

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false

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0

1

0

1

0

1

0

null

0

4

c3c29a2e5b1177c7e147cfa8bdfedded391635e1

337

py

Python

CangJie/utils/testing.py

bigdata-ustc/CangJie

a3264082fa0432d257b5c4722b14c55f9092a411

[ "MIT" ]

2

2020-03-04T02:27:29.000Z

2020-05-22T04:07:24.000Z

CangJie/utils/testing.py

bigdata-ustc/CangJie

a3264082fa0432d257b5c4722b14c55f9092a411

[ "MIT" ]

null

CangJie/utils/testing.py

bigdata-ustc/CangJie

a3264082fa0432d257b5c4722b14c55f9092a411

[ "MIT" ]

1

2022-03-12T00:31:59.000Z

# coding: utf-8 # 2020/1/2 @ tongshiwei import random from CangJie import CHI_CHAR __all__ = ["pseudo_sentence"] def _pseudo_sentence(length): return "".join([random.choice(CHI_CHAR) for _ in range(length)]) def pseudo_sentence(num, max_length): return [_pseudo_sentence(random.randint(1, max_length)) for _ in range(num)]

21.0625

80

0.735905

49

337

4.734694

0.55102

0.241379

0.146552

0

0.027682

0.142433

337

15

81

22.466667

0.775087

0.103858

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1

0.285714

false

0

0.285714

0.857143

0

null

1

0

null

0

1

0

1

0

4

7f0dc60b40dd08456791043a9d3a4291a01edf3b

143

py

Python

tanuki/history/urls.py

addisonmaupin/capstone2020

cf8c8e7336aa9866859349838e4f42bc6831679c

[ "MIT" ]

null

tanuki/history/urls.py

addisonmaupin/capstone2020

cf8c8e7336aa9866859349838e4f42bc6831679c

[ "MIT" ]

9

2021-03-19T14:50:48.000Z

2022-03-12T00:47:25.000Z

tanuki/history/urls.py

pabsromo/capstone2020

cf8c8e7336aa9866859349838e4f42bc6831679c

[ "MIT" ]

null

from django.urls import path from . import views app_name = 'history' urlpatterns = [ path('history/', views.history, name='history'), ]

15.888889

52

0.692308

18

143

5.444444

0.555556

0.22449

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0.167832

143

8

53

17.875

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0.153846

0

1

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false

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0.333333

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0.333333

0

1

0

null

1

0

1

0

null

0

1

0

4

613cb859cc616b5297d4f1b783cb516f1faa4533

279

py

Python

tests/test_data.py

thoriuchi0531/tutti

d0fe202864edc9d257654743db6dc44a67a1d7ed

[ "MIT" ]

1

2021-11-14T15:53:38.000Z

tests/test_data.py

thoriuchi0531/fipie

d0fe202864edc9d257654743db6dc44a67a1d7ed

[ "MIT" ]

null

tests/test_data.py

thoriuchi0531/fipie

d0fe202864edc9d257654743db6dc44a67a1d7ed

[ "MIT" ]

null

import pandas as pd from fipie.data import load_example_data def test_load_example_data(): data = load_example_data() assert isinstance(data, pd.DataFrame) def test_instrument_size(): data = load_example_data() # 7 instruments assert data.shape[1] == 7

17.4375

41

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279

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0.314136

0.198953

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0.013333

0.193548

279

15

42

18.6

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0.25

1

0.25

false

0

0.25

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0.5

0

null

1

0

null

0

1

0

4

613fb08490d3b7f7630791e55f0173cb7d3c7a88

74,326

py

Python

osm_ro/httpserver.py

alfonsoegio/RO

9228f51e8a0abb2772c4b75a3462835fe336498a

[ "Apache-2.0" ]

null

osm_ro/httpserver.py

alfonsoegio/RO

9228f51e8a0abb2772c4b75a3462835fe336498a

[ "Apache-2.0" ]

null

osm_ro/httpserver.py

alfonsoegio/RO

9228f51e8a0abb2772c4b75a3462835fe336498a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- ## # Copyright 2015 Telefonica Investigacion y Desarrollo, S.A.U. # This file is part of openmano # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # # For those usages not covered by the Apache License, Version 2.0 please # contact with: nfvlabs@tid.es ## ''' HTTP server implementing the openmano API. It will answer to POST, PUT, GET methods in the appropriate URLs and will use the nfvo.py module to run the appropriate method. Every YAML/JSON file is checked against a schema in openmano_schemas.py module. ''' __author__="Alfonso Tierno, Gerardo Garcia" __date__ ="$17-sep-2014 09:07:15$" import bottle import yaml import threading import logging from openmano_schemas import vnfd_schema_v01, vnfd_schema_v02, \ nsd_schema_v01, nsd_schema_v02, nsd_schema_v03, scenario_edit_schema, \ scenario_action_schema, instance_scenario_action_schema, instance_scenario_create_schema_v01, \ tenant_schema, tenant_edit_schema,\ datacenter_schema, datacenter_edit_schema, datacenter_action_schema, datacenter_associate_schema,\ object_schema, netmap_new_schema, netmap_edit_schema, sdn_controller_schema, sdn_controller_edit_schema, \ sdn_port_mapping_schema, sdn_external_port_schema from .http_tools import errors as httperrors from .http_tools.request_processing import ( format_out, format_in, filter_query_string ) from .wim.http_handler import WimHandler import nfvo import utils from db_base import db_base_Exception from functools import wraps global mydb global url_base global logger url_base="/openmano" logger = None def log_to_logger(fn): ''' Wrap a Bottle request so that a log line is emitted after it's handled. (This decorator can be extended to take the desired logger as a param.) ''' @wraps(fn) def _log_to_logger(*args, **kwargs): actual_response = fn(*args, **kwargs) # modify this to log exactly what you need: logger.info('FROM %s %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url, bottle.response.status) return actual_response return _log_to_logger class httpserver(threading.Thread): def __init__(self, db, admin=False, host='localhost', port=9090, wim_persistence=None, wim_engine=None): #global url_base global mydb global logger #initialization if not logger: logger = logging.getLogger('openmano.http') threading.Thread.__init__(self) self.host = host self.port = port #Port where the listen service must be started if admin==True: self.name = "http_admin" else: self.name = "http" #self.url_preffix = 'http://' + host + ':' + str(port) + url_base mydb = db #self.first_usable_connection_index = 10 #self.next_connection_index = self.first_usable_connection_index #The next connection index to be used #Ensure that when the main program exits the thread will also exit self.handlers = [ WimHandler(db, wim_persistence, wim_engine, url_base) ] self.daemon = True self.setDaemon(True) def run(self, debug=False, quiet=True): bottle.install(log_to_logger) default_app = bottle.app() for handler in self.handlers: default_app.merge(handler.wsgi_app) bottle.run(host=self.host, port=self.port, debug=debug, quiet=quiet) def run_bottle(db, host_='localhost', port_=9090): '''Used for launching in main thread, so that it can be debugged''' server = httpserver(db, host=host_, port=port_) server.run(debug=True) # quiet=True @bottle.route(url_base + '/', method='GET') def http_get(): #print return 'works' #TODO: to be completed @bottle.hook('after_request') def enable_cors(): '''Don't know yet if really needed. Keep it just in case''' bottle.response.headers['Access-Control-Allow-Origin'] = '*' @bottle.route(url_base + '/version', method='GET') def http_get_version(): return nfvo.get_version() # # VNFs # @bottle.route(url_base + '/tenants', method='GET') def http_get_tenants(): logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) select_,where_,limit_ = filter_query_string(bottle.request.query, None, ('uuid','name','description','created_at') ) try: tenants = mydb.get_rows(FROM='nfvo_tenants', SELECT=select_,WHERE=where_,LIMIT=limit_) #change_keys_http2db(content, http2db_tenant, reverse=True) utils.convert_float_timestamp2str(tenants) data={'tenants' : tenants} return format_out(data) except bottle.HTTPError: raise except db_base_Exception as e: logger.error("http_get_tenants error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/tenants/<tenant_id>', method='GET') def http_get_tenant_id(tenant_id): '''get tenant details, can use both uuid or name''' #obtain data logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: from_ = 'nfvo_tenants' select_, where_, limit_ = filter_query_string(bottle.request.query, None, ('uuid', 'name', 'description', 'created_at')) what = 'uuid' if utils.check_valid_uuid(tenant_id) else 'name' where_[what] = tenant_id tenants = mydb.get_rows(FROM=from_, SELECT=select_,WHERE=where_) #change_keys_http2db(content, http2db_tenant, reverse=True) if len(tenants) == 0: bottle.abort(httperrors.Not_Found, "No tenant found with {}='{}'".format(what, tenant_id)) elif len(tenants) > 1: bottle.abort(httperrors.Bad_Request, "More than one tenant found with {}='{}'".format(what, tenant_id)) utils.convert_float_timestamp2str(tenants[0]) data = {'tenant': tenants[0]} return format_out(data) except bottle.HTTPError: raise except db_base_Exception as e: logger.error("http_get_tenant_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/tenants', method='POST') def http_post_tenants(): '''insert a tenant into the catalogue. ''' #parse input data logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content,_ = format_in( tenant_schema ) r = utils.remove_extra_items(http_content, tenant_schema) if r: logger.debug("Remove received extra items %s", str(r)) try: data = nfvo.new_tenant(mydb, http_content['tenant']) return http_get_tenant_id(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_tenants error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/tenants/<tenant_id>', method='PUT') def http_edit_tenant_id(tenant_id): '''edit tenant details, can use both uuid or name''' #parse input data logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content,_ = format_in( tenant_edit_schema ) r = utils.remove_extra_items(http_content, tenant_edit_schema) if r: logger.debug("Remove received extra items %s", str(r)) #obtain data, check that only one exist try: tenant = mydb.get_table_by_uuid_name('nfvo_tenants', tenant_id) #edit data tenant_id = tenant['uuid'] where={'uuid': tenant['uuid']} mydb.update_rows('nfvo_tenants', http_content['tenant'], where) return http_get_tenant_id(tenant_id) except bottle.HTTPError: raise except db_base_Exception as e: logger.error("http_edit_tenant_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/tenants/<tenant_id>', method='DELETE') def http_delete_tenant_id(tenant_id): '''delete a tenant from database, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: data = nfvo.delete_tenant(mydb, tenant_id) return format_out({"result":"tenant " + data + " deleted"}) except bottle.HTTPError: raise except db_base_Exception as e: logger.error("http_delete_tenant_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters', method='GET') def http_get_datacenters(tenant_id): logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: if tenant_id != 'any': #check valid tenant_id nfvo.check_tenant(mydb, tenant_id) select_,where_,limit_ = filter_query_string(bottle.request.query, None, ('uuid','name','vim_url','type','created_at') ) if tenant_id != 'any': where_['nfvo_tenant_id'] = tenant_id if 'created_at' in select_: select_[ select_.index('created_at') ] = 'd.created_at as created_at' if 'created_at' in where_: where_['d.created_at'] = where_.pop('created_at') datacenters = mydb.get_rows(FROM='datacenters as d join tenants_datacenters as td on d.uuid=td.datacenter_id', SELECT=select_,WHERE=where_,LIMIT=limit_) else: datacenters = mydb.get_rows(FROM='datacenters', SELECT=select_,WHERE=where_,LIMIT=limit_) #change_keys_http2db(content, http2db_tenant, reverse=True) utils.convert_float_timestamp2str(datacenters) data={'datacenters' : datacenters} return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_datacenters error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vim_accounts', method='GET') @bottle.route(url_base + '/<tenant_id>/vim_accounts/<vim_account_id>', method='GET') def http_get_vim_account(tenant_id, vim_account_id=None): '''get vim_account list/details, ''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: select_ = ('uuid', 'name', 'dt.datacenter_id as vim_id', 'vim_tenant_name', 'vim_tenant_id', 'user', 'config', 'dt.created_at as created_at', 'passwd') where_ = {'nfvo_tenant_id': tenant_id} if vim_account_id: where_['dt.uuid'] = vim_account_id from_ = 'tenants_datacenters as td join datacenter_tenants as dt on dt.uuid=td.datacenter_tenant_id' vim_accounts = mydb.get_rows(SELECT=select_, FROM=from_, WHERE=where_) if len(vim_accounts) == 0 and vim_account_id: bottle.abort(HTTP_Not_Found, "No vim_account found for tenant {} and id '{}'".format(tenant_id, vim_account_id)) for vim_account in vim_accounts: if vim_account["passwd"]: vim_account["passwd"] = "******" if vim_account['config'] != None: try: config_dict = yaml.load(vim_account['config']) vim_account['config'] = config_dict if vim_account['config'].get('admin_password'): vim_account['config']['admin_password'] = "******" if vim_account['config'].get('vcenter_password'): vim_account['config']['vcenter_password'] = "******" if vim_account['config'].get('nsx_password'): vim_account['config']['nsx_password'] = "******" except Exception as e: logger.error("Exception '%s' while trying to load config information", str(e)) # change_keys_http2db(content, http2db_datacenter, reverse=True) #convert_datetime2str(vim_account) if vim_account_id: return format_out({"datacenter": vim_accounts[0]}) else: return format_out({"datacenters": vim_accounts}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_datacenter_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(HTTP_Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>', method='GET') def http_get_datacenter_id(tenant_id, datacenter_id): '''get datacenter details, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: if tenant_id != 'any': #check valid tenant_id nfvo.check_tenant(mydb, tenant_id) #obtain data what = 'uuid' if utils.check_valid_uuid(datacenter_id) else 'name' where_={} where_[what] = datacenter_id select_=['uuid', 'name','vim_url', 'vim_url_admin', 'type', 'd.config as config', 'description', 'd.created_at as created_at'] if tenant_id != 'any': select_.append("datacenter_tenant_id") where_['td.nfvo_tenant_id']= tenant_id from_='datacenters as d join tenants_datacenters as td on d.uuid=td.datacenter_id' else: from_='datacenters as d' datacenters = mydb.get_rows( SELECT=select_, FROM=from_, WHERE=where_) if len(datacenters)==0: bottle.abort( httperrors.Not_Found, "No datacenter found for tenant with {} '{}'".format(what, datacenter_id) ) elif len(datacenters)>1: bottle.abort( httperrors.Bad_Request, "More than one datacenter found for tenant with {} '{}'".format(what, datacenter_id) ) datacenter = datacenters[0] if tenant_id != 'any': #get vim tenant info vim_tenants = mydb.get_rows( SELECT=("vim_tenant_name", "vim_tenant_id", "user", "passwd", "config"), FROM="datacenter_tenants", WHERE={"uuid": datacenters[0]["datacenter_tenant_id"]}, ORDER_BY=("created", ) ) del datacenter["datacenter_tenant_id"] datacenter["vim_tenants"] = vim_tenants for vim_tenant in vim_tenants: if vim_tenant["passwd"]: vim_tenant["passwd"] = "******" if vim_tenant['config'] != None: try: config_dict = yaml.load(vim_tenant['config']) vim_tenant['config'] = config_dict if vim_tenant['config'].get('admin_password'): vim_tenant['config']['admin_password'] = "******" if vim_tenant['config'].get('vcenter_password'): vim_tenant['config']['vcenter_password'] = "******" if vim_tenant['config'].get('nsx_password'): vim_tenant['config']['nsx_password'] = "******" except Exception as e: logger.error("Exception '%s' while trying to load config information", str(e)) if datacenter['config'] != None: try: config_dict = yaml.load(datacenter['config']) datacenter['config'] = config_dict if datacenter['config'].get('admin_password'): datacenter['config']['admin_password'] = "******" if datacenter['config'].get('vcenter_password'): datacenter['config']['vcenter_password'] = "******" if datacenter['config'].get('nsx_password'): datacenter['config']['nsx_password'] = "******" except Exception as e: logger.error("Exception '%s' while trying to load config information", str(e)) #change_keys_http2db(content, http2db_datacenter, reverse=True) utils.convert_float_timestamp2str(datacenter) data={'datacenter' : datacenter} return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_datacenter_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/datacenters', method='POST') def http_post_datacenters(): '''insert a datacenter into the catalogue. ''' #parse input data logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content,_ = format_in(datacenter_schema, confidential_data=True) r = utils.remove_extra_items(http_content, datacenter_schema) if r: logger.debug("Remove received extra items %s", str(r)) try: data = nfvo.new_datacenter(mydb, http_content['datacenter']) return http_get_datacenter_id('any', data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_datacenters error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/datacenters/<datacenter_id_name>', method='PUT') def http_edit_datacenter_id(datacenter_id_name): '''edit datacenter details, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) #parse input data http_content,_ = format_in( datacenter_edit_schema ) r = utils.remove_extra_items(http_content, datacenter_edit_schema) if r: logger.debug("Remove received extra items %s", str(r)) try: datacenter_id = nfvo.edit_datacenter(mydb, datacenter_id_name, http_content['datacenter']) return http_get_datacenter_id('any', datacenter_id) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_edit_datacenter_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/sdn_controllers', method='POST') def http_post_sdn_controller(tenant_id): '''insert a sdn controller into the catalogue. ''' #parse input data logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content,_ = format_in( sdn_controller_schema ) try: logger.debug("tenant_id: "+tenant_id) #logger.debug("content: {}".format(http_content['sdn_controller'])) data = nfvo.sdn_controller_create(mydb, tenant_id, http_content['sdn_controller']) return format_out({"sdn_controller": nfvo.sdn_controller_list(mydb, tenant_id, data)}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_sdn_controller error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/sdn_controllers/<controller_id>', method='PUT') def http_put_sdn_controller_update(tenant_id, controller_id): '''Update sdn controller''' #parse input data logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content,_ = format_in( sdn_controller_edit_schema ) # r = utils.remove_extra_items(http_content, datacenter_schema) # if r: # logger.debug("Remove received extra items %s", str(r)) try: #logger.debug("tenant_id: "+tenant_id) logger.debug("content: {}".format(http_content['sdn_controller'])) data = nfvo.sdn_controller_update(mydb, tenant_id, controller_id, http_content['sdn_controller']) return format_out({"sdn_controller": nfvo.sdn_controller_list(mydb, tenant_id, controller_id)}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_sdn_controller error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/sdn_controllers', method='GET') def http_get_sdn_controller(tenant_id): '''get sdn controllers list, can use both uuid or name''' try: logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) data = {'sdn_controllers': nfvo.sdn_controller_list(mydb, tenant_id)} return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_sdn_controller error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/sdn_controllers/<controller_id>', method='GET') def http_get_sdn_controller_id(tenant_id, controller_id): '''get sdn controller details, can use both uuid or name''' try: logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) data = nfvo.sdn_controller_list(mydb, tenant_id, controller_id) return format_out({"sdn_controllers": data}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_sdn_controller_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/sdn_controllers/<controller_id>', method='DELETE') def http_delete_sdn_controller_id(tenant_id, controller_id): '''delete sdn controller, can use both uuid or name''' try: logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) data = nfvo.sdn_controller_delete(mydb, tenant_id, controller_id) return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_delete_sdn_controller_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/sdn_mapping', method='POST') def http_post_datacenter_sdn_port_mapping(tenant_id, datacenter_id): '''Set the sdn port mapping for a datacenter. ''' #parse input data logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content, _ = format_in(sdn_port_mapping_schema) # r = utils.remove_extra_items(http_content, datacenter_schema) # if r: # logger.debug("Remove received extra items %s", str(r)) try: data = nfvo.datacenter_sdn_port_mapping_set(mydb, tenant_id, datacenter_id, http_content['sdn_port_mapping']) return format_out({"sdn_port_mapping": data}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_datacenter_sdn_port_mapping error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/sdn_mapping', method='GET') def http_get_datacenter_sdn_port_mapping(tenant_id, datacenter_id): '''get datacenter sdn mapping details, can use both uuid or name''' try: logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) data = nfvo.datacenter_sdn_port_mapping_list(mydb, tenant_id, datacenter_id) return format_out({"sdn_port_mapping": data}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_datacenter_sdn_port_mapping error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/sdn_mapping', method='DELETE') def http_delete_datacenter_sdn_port_mapping(tenant_id, datacenter_id): '''clean datacenter sdn mapping, can use both uuid or name''' try: logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) data = nfvo.datacenter_sdn_port_mapping_delete(mydb, tenant_id, datacenter_id) return format_out({"result": data}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_delete_datacenter_sdn_port_mapping error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/networks', method='GET') #deprecated @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/netmaps', method='GET') @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/netmaps/<netmap_id>', method='GET') def http_getnetmap_datacenter_id(tenant_id, datacenter_id, netmap_id=None): '''get datacenter networks, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) #obtain data try: datacenter_dict = mydb.get_table_by_uuid_name('datacenters', datacenter_id, "datacenter") where_= {"datacenter_id":datacenter_dict['uuid']} if netmap_id: if utils.check_valid_uuid(netmap_id): where_["uuid"] = netmap_id else: where_["name"] = netmap_id netmaps =mydb.get_rows(FROM='datacenter_nets', SELECT=('name','vim_net_id as vim_id', 'uuid', 'type','multipoint','shared','description', 'created_at'), WHERE=where_ ) utils.convert_float_timestamp2str(netmaps) utils.convert_str2boolean(netmaps, ('shared', 'multipoint') ) if netmap_id and len(netmaps)==1: data={'netmap' : netmaps[0]} elif netmap_id and len(netmaps)==0: bottle.abort(httperrors.Not_Found, "No netmap found with " + " and ".join(map(lambda x: str(x[0])+": "+str(x[1]), where_.iteritems())) ) return else: data={'netmaps' : netmaps} return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_getnetwork_datacenter_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/netmaps', method='DELETE') @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/netmaps/<netmap_id>', method='DELETE') def http_delnetmap_datacenter_id(tenant_id, datacenter_id, netmap_id=None): '''get datacenter networks, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) #obtain data try: datacenter_dict = mydb.get_table_by_uuid_name('datacenters', datacenter_id, "datacenter") where_= {"datacenter_id":datacenter_dict['uuid']} if netmap_id: if utils.check_valid_uuid(netmap_id): where_["uuid"] = netmap_id else: where_["name"] = netmap_id #change_keys_http2db(content, http2db_tenant, reverse=True) deleted = mydb.delete_row(FROM='datacenter_nets', WHERE= where_) if deleted == 0 and netmap_id: bottle.abort(httperrors.Not_Found, "No netmap found with " + " and ".join(map(lambda x: str(x[0])+": "+str(x[1]), where_.iteritems())) ) if netmap_id: return format_out({"result": "netmap %s deleted" % netmap_id}) else: return format_out({"result": "%d netmap deleted" % deleted}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_delnetmap_datacenter_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/netmaps/upload', method='POST') def http_uploadnetmap_datacenter_id(tenant_id, datacenter_id): logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: netmaps = nfvo.datacenter_new_netmap(mydb, tenant_id, datacenter_id, None) utils.convert_float_timestamp2str(netmaps) utils.convert_str2boolean(netmaps, ('shared', 'multipoint') ) data={'netmaps' : netmaps} return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_uploadnetmap_datacenter_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/netmaps', method='POST') def http_postnetmap_datacenter_id(tenant_id, datacenter_id): '''creates a new netmap''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) #parse input data http_content,_ = format_in( netmap_new_schema ) r = utils.remove_extra_items(http_content, netmap_new_schema) if r: logger.debug("Remove received extra items %s", str(r)) try: #obtain data, check that only one exist netmaps = nfvo.datacenter_new_netmap(mydb, tenant_id, datacenter_id, http_content) utils.convert_float_timestamp2str(netmaps) utils.convert_str2boolean(netmaps, ('shared', 'multipoint') ) data={'netmaps' : netmaps} return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_postnetmap_datacenter_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/netmaps/<netmap_id>', method='PUT') def http_putnettmap_datacenter_id(tenant_id, datacenter_id, netmap_id): '''edit a netmap''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) #parse input data http_content,_ = format_in( netmap_edit_schema ) r = utils.remove_extra_items(http_content, netmap_edit_schema) if r: logger.debug("Remove received extra items %s", str(r)) #obtain data, check that only one exist try: nfvo.datacenter_edit_netmap(mydb, tenant_id, datacenter_id, netmap_id, http_content) return http_getnetmap_datacenter_id(tenant_id, datacenter_id, netmap_id) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_putnettmap_datacenter_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>/action', method='POST') def http_action_datacenter_id(tenant_id, datacenter_id): '''perform an action over datacenter, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) #parse input data http_content,_ = format_in( datacenter_action_schema ) r = utils.remove_extra_items(http_content, datacenter_action_schema) if r: logger.debug("Remove received extra items %s", str(r)) try: #obtain data, check that only one exist result = nfvo.datacenter_action(mydb, tenant_id, datacenter_id, http_content) if 'net-update' in http_content: return http_getnetmap_datacenter_id(datacenter_id) else: return format_out(result) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_action_datacenter_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/datacenters/<datacenter_id>', method='DELETE') def http_delete_datacenter_id( datacenter_id): '''delete a tenant from database, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: data = nfvo.delete_datacenter(mydb, datacenter_id) return format_out({"result":"datacenter '" + data + "' deleted"}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_delete_datacenter_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>', method='POST') @bottle.route(url_base + '/<tenant_id>/vim_accounts', method='POST') def http_associate_datacenters(tenant_id, datacenter_id=None): '''associate an existing datacenter to a this tenant. ''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) #parse input data http_content,_ = format_in(datacenter_associate_schema, confidential_data=True) r = utils.remove_extra_items(http_content, datacenter_associate_schema) if r: logger.debug("Remove received extra items %s", str(r)) try: vim_account_id = nfvo.create_vim_account(mydb, tenant_id, datacenter_id, **http_content['datacenter']) return http_get_vim_account(tenant_id, vim_account_id) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_associate_datacenters error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vim_accounts/<vim_account_id>', method='PUT') @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>', method='PUT') def http_vim_account_edit(tenant_id, vim_account_id=None, datacenter_id=None): '''associate an existing datacenter to a this tenant. ''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) #parse input data http_content,_ = format_in(datacenter_associate_schema) r = utils.remove_extra_items(http_content, datacenter_associate_schema) if r: logger.debug("Remove received extra items %s", str(r)) try: vim_account_id = nfvo.edit_vim_account(mydb, tenant_id, vim_account_id, datacenter_id=datacenter_id, **http_content['datacenter']) return http_get_vim_account(tenant_id, vim_account_id) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_vim_account_edit error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/datacenters/<datacenter_id>', method='DELETE') @bottle.route(url_base + '/<tenant_id>/vim_accounts/<vim_account_id>', method='DELETE') def http_deassociate_datacenters(tenant_id, datacenter_id=None, vim_account_id=None): '''deassociate an existing datacenter to a this tenant. ''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: data = nfvo.delete_vim_account(mydb, tenant_id, vim_account_id, datacenter_id) return format_out({"result": data}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_deassociate_datacenters error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vim/<datacenter_id>/network/<network_id>/attach', method='POST') def http_post_vim_net_sdn_attach(tenant_id, datacenter_id, network_id): logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content, _ = format_in(sdn_external_port_schema) try: data = nfvo.vim_net_sdn_attach(mydb, tenant_id, datacenter_id, network_id, http_content) return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_vim_net_sdn_attach error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vim/<datacenter_id>/network/<network_id>/detach', method='DELETE') @bottle.route(url_base + '/<tenant_id>/vim/<datacenter_id>/network/<network_id>/detach/<port_id>', method='DELETE') def http_delete_vim_net_sdn_detach(tenant_id, datacenter_id, network_id, port_id=None): logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: data = nfvo.vim_net_sdn_detach(mydb, tenant_id, datacenter_id, network_id, port_id) return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_delete_vim_net_sdn_detach error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vim/<datacenter_id>/<item>', method='GET') @bottle.route(url_base + '/<tenant_id>/vim/<datacenter_id>/<item>/<name>', method='GET') def http_get_vim_items(tenant_id, datacenter_id, item, name=None): logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: data = nfvo.vim_action_get(mydb, tenant_id, datacenter_id, item, name) return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_vim_items error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vim/<datacenter_id>/<item>/<name>', method='DELETE') def http_del_vim_items(tenant_id, datacenter_id, item, name): logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: data = nfvo.vim_action_delete(mydb, tenant_id, datacenter_id, item, name) return format_out({"result":data}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_del_vim_items error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vim/<datacenter_id>/<item>', method='POST') def http_post_vim_items(tenant_id, datacenter_id, item): logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content,_ = format_in( object_schema ) try: data = nfvo.vim_action_create(mydb, tenant_id, datacenter_id, item, http_content) return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_vim_items error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vnfs', method='GET') def http_get_vnfs(tenant_id): logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: if tenant_id != 'any': #check valid tenant_id nfvo.check_tenant(mydb, tenant_id) select_,where_,limit_ = filter_query_string(bottle.request.query, None, ('uuid', 'name', 'osm_id', 'description', 'public', "tenant_id", "created_at") ) if tenant_id != "any": where_["OR"]={"tenant_id": tenant_id, "public": True} vnfs = mydb.get_rows(FROM='vnfs', SELECT=select_, WHERE=where_, LIMIT=limit_) # change_keys_http2db(content, http2db_vnf, reverse=True) utils.convert_str2boolean(vnfs, ('public',)) utils.convert_float_timestamp2str(vnfs) data={'vnfs': vnfs} return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_vnfs error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vnfs/<vnf_id>', method='GET') def http_get_vnf_id(tenant_id,vnf_id): '''get vnf details, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: vnf = nfvo.get_vnf_id(mydb,tenant_id,vnf_id) utils.convert_str2boolean(vnf, ('public',)) utils.convert_float_timestamp2str(vnf) return format_out(vnf) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_vnf_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vnfs', method='POST') def http_post_vnfs(tenant_id): """ Insert a vnf into the catalogue. Creates the flavor and images, and fill the tables at database :param tenant_id: tenant that this vnf belongs to :return: """ # print "Parsing the YAML file of the VNF" # parse input data logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content, used_schema = format_in( vnfd_schema_v01, ("schema_version",), {"0.2": vnfd_schema_v02}) r = utils.remove_extra_items(http_content, used_schema) if r: logger.debug("Remove received extra items %s", str(r)) try: if used_schema == vnfd_schema_v01: vnf_id = nfvo.new_vnf(mydb,tenant_id,http_content) elif used_schema == vnfd_schema_v02: vnf_id = nfvo.new_vnf_v02(mydb,tenant_id,http_content) else: logger.warning('Unexpected schema_version: %s', http_content.get("schema_version")) bottle.abort(httperrors.Bad_Request, "Invalid schema version") return http_get_vnf_id(tenant_id, vnf_id) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_vnfs error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/v3/<tenant_id>/vnfd', method='POST') def http_post_vnfs_v3(tenant_id): """ Insert one or several VNFs in the catalog, following OSM IM :param tenant_id: tenant owner of the VNF :return: The detailed list of inserted VNFs, following the old format """ logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content, _ = format_in(None) try: vnfd_uuid_list = nfvo.new_vnfd_v3(mydb, tenant_id, http_content) vnfd_list = [] for vnfd_uuid in vnfd_uuid_list: vnf = nfvo.get_vnf_id(mydb, tenant_id, vnfd_uuid) utils.convert_str2boolean(vnf, ('public',)) utils.convert_float_timestamp2str(vnf) vnfd_list.append(vnf["vnf"]) return format_out({"vnfd": vnfd_list}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_vnfs error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/vnfs/<vnf_id>', method='DELETE') def http_delete_vnf_id(tenant_id, vnf_id): '''delete a vnf from database, and images and flavors in VIM when appropriate, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) #check valid tenant_id and deletes the vnf, including images, try: data = nfvo.delete_vnf(mydb,tenant_id,vnf_id) #print json.dumps(data, indent=4) return format_out({"result":"VNF " + data + " deleted"}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_delete_vnf_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) #@bottle.route(url_base + '/<tenant_id>/hosts/topology', method='GET') #@bottle.route(url_base + '/<tenant_id>/physicalview/Madrid-Alcantara', method='GET') @bottle.route(url_base + '/<tenant_id>/physicalview/<datacenter>', method='GET') def http_get_hosts(tenant_id, datacenter): '''get the tidvim host hopology from the vim.''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) #print "http_get_hosts received by tenant " + tenant_id + ' datacenter ' + datacenter try: if datacenter == 'treeview': data = nfvo.get_hosts(mydb, tenant_id) else: #openmano-gui is using a hardcoded value for the datacenter result, data = nfvo.get_hosts_info(mydb, tenant_id) #, datacenter) if result < 0: #print "http_get_hosts error %d %s" % (-result, data) bottle.abort(-result, data) else: utils.convert_float_timestamp2str(data) #print json.dumps(data, indent=4) return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_hosts error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<path:path>', method='OPTIONS') def http_options_deploy(path): '''For some reason GUI web ask for OPTIONS that must be responded''' #TODO: check correct path, and correct headers request logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) bottle.response.set_header('Access-Control-Allow-Methods','POST, GET, PUT, DELETE, OPTIONS') bottle.response.set_header('Accept','application/yaml,application/json') bottle.response.set_header('Content-Type','application/yaml,application/json') bottle.response.set_header('Access-Control-Allow-Headers','content-type') bottle.response.set_header('Access-Control-Allow-Origin','*') return @bottle.route(url_base + '/<tenant_id>/topology/deploy', method='POST') def http_post_deploy(tenant_id): '''post topology deploy.''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content, used_schema = format_in( nsd_schema_v01, ("schema_version",), {2: nsd_schema_v02}) #r = utils.remove_extra_items(http_content, used_schema) #if r is not None: print "http_post_deploy: Warning: remove extra items ", r #print "http_post_deploy input: ", http_content try: scenario_id = nfvo.new_scenario(mydb, tenant_id, http_content) instance = nfvo.start_scenario(mydb, tenant_id, scenario_id, http_content['name'], http_content['name']) #print json.dumps(data, indent=4) return format_out(instance) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_deploy error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/topology/verify', method='POST') def http_post_verify(tenant_id): #TODO: # '''post topology verify''' # print "http_post_verify by tenant " + tenant_id + ' datacenter ' + datacenter logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) return # # SCENARIOS # @bottle.route(url_base + '/<tenant_id>/scenarios', method='POST') def http_post_scenarios(tenant_id): '''add a scenario into the catalogue. Creates the scenario and its internal structure in the OPENMANO DB''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content, used_schema = format_in( nsd_schema_v01, ("schema_version",), {2: nsd_schema_v02, "0.3": nsd_schema_v03}) #r = utils.remove_extra_items(http_content, used_schema) #if r is not None: print "http_post_scenarios: Warning: remove extra items ", r #print "http_post_scenarios input: ", http_content try: if used_schema == nsd_schema_v01: scenario_id = nfvo.new_scenario(mydb, tenant_id, http_content) elif used_schema == nsd_schema_v02: scenario_id = nfvo.new_scenario_v02(mydb, tenant_id, http_content, "0.2") elif used_schema == nsd_schema_v03: scenario_id = nfvo.new_scenario_v02(mydb, tenant_id, http_content, "0.3") else: logger.warning('Unexpected schema_version: %s', http_content.get("schema_version")) bottle.abort(httperrors.Bad_Request, "Invalid schema version") #print json.dumps(data, indent=4) #return format_out(data) return http_get_scenario_id(tenant_id, scenario_id) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_scenarios error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/v3/<tenant_id>/nsd', method='POST') def http_post_nsds_v3(tenant_id): """ Insert one or several NSDs in the catalog, following OSM IM :param tenant_id: tenant owner of the NSD :return: The detailed list of inserted NSDs, following the old format """ logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content, _ = format_in(None) try: nsd_uuid_list = nfvo.new_nsd_v3(mydb, tenant_id, http_content) nsd_list = [] for nsd_uuid in nsd_uuid_list: scenario = mydb.get_scenario(nsd_uuid, tenant_id) utils.convert_float_timestamp2str(scenario) nsd_list.append(scenario) data = {'nsd': nsd_list} return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_nsds_v3 error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/scenarios/<scenario_id>/action', method='POST') def http_post_scenario_action(tenant_id, scenario_id): '''take an action over a scenario''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) # parse input data http_content, _ = format_in(scenario_action_schema) r = utils.remove_extra_items(http_content, scenario_action_schema) if r: logger.debug("Remove received extra items %s", str(r)) try: # check valid tenant_id nfvo.check_tenant(mydb, tenant_id) if "start" in http_content: data = nfvo.start_scenario(mydb, tenant_id, scenario_id, http_content['start']['instance_name'], \ http_content['start'].get('description',http_content['start']['instance_name']), http_content['start'].get('datacenter') ) return format_out(data) elif "deploy" in http_content: #Equivalent to start data = nfvo.start_scenario(mydb, tenant_id, scenario_id, http_content['deploy']['instance_name'], http_content['deploy'].get('description',http_content['deploy']['instance_name']), http_content['deploy'].get('datacenter') ) return format_out(data) elif "reserve" in http_content: #Reserve resources data = nfvo.start_scenario(mydb, tenant_id, scenario_id, http_content['reserve']['instance_name'], http_content['reserve'].get('description',http_content['reserve']['instance_name']), http_content['reserve'].get('datacenter'), startvms=False ) return format_out(data) elif "verify" in http_content: #Equivalent to start and then delete data = nfvo.start_scenario(mydb, tenant_id, scenario_id, http_content['verify']['instance_name'], http_content['verify'].get('description',http_content['verify']['instance_name']), http_content['verify'].get('datacenter'), startvms=False ) instance_id = data['uuid'] nfvo.delete_instance(mydb, tenant_id,instance_id) return format_out({"result":"Verify OK"}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_scenario_action error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/scenarios', method='GET') def http_get_scenarios(tenant_id): '''get scenarios list''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: #check valid tenant_id if tenant_id != "any": nfvo.check_tenant(mydb, tenant_id) #obtain data s,w,l=filter_query_string(bottle.request.query, None, ('uuid', 'name', 'osm_id', 'description', 'tenant_id', 'created_at', 'public')) if tenant_id != "any": w["OR"] = {"tenant_id": tenant_id, "public": True} scenarios = mydb.get_rows(SELECT=s, WHERE=w, LIMIT=l, FROM='scenarios') utils.convert_float_timestamp2str(scenarios) utils.convert_str2boolean(scenarios, ('public',) ) data={'scenarios':scenarios} #print json.dumps(scenarios, indent=4) return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_scenarios error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/scenarios/<scenario_id>', method='GET') def http_get_scenario_id(tenant_id, scenario_id): '''get scenario details, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: #check valid tenant_id if tenant_id != "any": nfvo.check_tenant(mydb, tenant_id) #obtain data scenario = mydb.get_scenario(scenario_id, tenant_id) utils.convert_float_timestamp2str(scenario) data={'scenario' : scenario} return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_scenarios error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/scenarios/<scenario_id>', method='DELETE') def http_delete_scenario_id(tenant_id, scenario_id): '''delete a scenario from database, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: #check valid tenant_id if tenant_id != "any": nfvo.check_tenant(mydb, tenant_id) #obtain data data = mydb.delete_scenario(scenario_id, tenant_id) #print json.dumps(data, indent=4) return format_out({"result":"scenario " + data + " deleted"}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_delete_scenario_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/scenarios/<scenario_id>', method='PUT') def http_put_scenario_id(tenant_id, scenario_id): '''edit an existing scenario id''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) http_content,_ = format_in( scenario_edit_schema ) #r = utils.remove_extra_items(http_content, scenario_edit_schema) #if r is not None: print "http_put_scenario_id: Warning: remove extra items ", r #print "http_put_scenario_id input: ", http_content try: nfvo.edit_scenario(mydb, tenant_id, scenario_id, http_content) #print json.dumps(data, indent=4) #return format_out(data) return http_get_scenario_id(tenant_id, scenario_id) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_put_scenario_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/instances', method='POST') def http_post_instances(tenant_id): '''create an instance-scenario''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) # parse input data http_content, used_schema = format_in(instance_scenario_create_schema_v01) r = utils.remove_extra_items(http_content, used_schema) if r is not None: logger.warning("http_post_instances: Warning: remove extra items %s", str(r)) try: #check valid tenant_id if tenant_id != "any": nfvo.check_tenant(mydb, tenant_id) data = nfvo.create_instance(mydb, tenant_id, http_content["instance"]) return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_instances error {}: {}".format(e.http_code, str(e)), exc_info=True) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) # # INSTANCES # @bottle.route(url_base + '/<tenant_id>/instances', method='GET') def http_get_instances(tenant_id): '''get instance list''' try: #check valid tenant_id if tenant_id != "any": nfvo.check_tenant(mydb, tenant_id) #obtain data s,w,l=filter_query_string(bottle.request.query, None, ('uuid', 'name', 'scenario_id', 'tenant_id', 'description', 'created_at')) if tenant_id != "any": w['tenant_id'] = tenant_id instances = mydb.get_rows(SELECT=s, WHERE=w, LIMIT=l, FROM='instance_scenarios') utils.convert_float_timestamp2str(instances) utils.convert_str2boolean(instances, ('public',) ) data={'instances':instances} return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_instances error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/instances/<instance_id>', method='GET') def http_get_instance_id(tenant_id, instance_id): '''get instances details, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: #check valid tenant_id if tenant_id != "any": nfvo.check_tenant(mydb, tenant_id) if tenant_id == "any": tenant_id = None instance = nfvo.get_instance_id(mydb, tenant_id, instance_id) # Workaround to SO, convert vnfs:vms:interfaces:ip_address from ";" separated list to report the first value for vnf in instance.get("vnfs", ()): for vm in vnf.get("vms", ()): for iface in vm.get("interfaces", ()): if iface.get("ip_address"): index = iface["ip_address"].find(";") if index >= 0: iface["ip_address"] = iface["ip_address"][:index] utils.convert_float_timestamp2str(instance) # print json.dumps(instance, indent=4) return format_out(instance) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_instance_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/instances/<instance_id>', method='DELETE') def http_delete_instance_id(tenant_id, instance_id): '''delete instance from VIM and from database, can use both uuid or name''' logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: #check valid tenant_id if tenant_id != "any": nfvo.check_tenant(mydb, tenant_id) if tenant_id == "any": tenant_id = None #obtain data message = nfvo.delete_instance(mydb, tenant_id,instance_id) return format_out({"result":message}) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_delete_instance_id error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/instances/<instance_id>/action', method='POST') def http_post_instance_scenario_action(tenant_id, instance_id): """ take an action over a scenario instance :param tenant_id: tenant where user belongs to :param instance_id: instance indentity :return: """ logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) # parse input data http_content, _ = format_in(instance_scenario_action_schema) r = utils.remove_extra_items(http_content, instance_scenario_action_schema) if r: logger.debug("Remove received extra items %s", str(r)) try: #check valid tenant_id if tenant_id != "any": nfvo.check_tenant(mydb, tenant_id) #print "http_post_instance_scenario_action input: ", http_content #obtain data instance = mydb.get_instance_scenario(instance_id, tenant_id) instance_id = instance["uuid"] data = nfvo.instance_action(mydb, tenant_id, instance_id, http_content) return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_post_instance_scenario_action error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.route(url_base + '/<tenant_id>/instances/<instance_id>/action', method='GET') @bottle.route(url_base + '/<tenant_id>/instances/<instance_id>/action/<action_id>', method='GET') def http_get_instance_scenario_action(tenant_id, instance_id, action_id=None): """ List the actions done over an instance, or the action details :param tenant_id: tenant where user belongs to. Can be "any" to ignore :param instance_id: instance id, can be "any" to get actions of all instances :return: """ logger.debug('FROM %s %s %s', bottle.request.remote_addr, bottle.request.method, bottle.request.url) try: # check valid tenant_id if tenant_id != "any": nfvo.check_tenant(mydb, tenant_id) data = nfvo.instance_action_get(mydb, tenant_id, instance_id, action_id) return format_out(data) except bottle.HTTPError: raise except (nfvo.NfvoException, db_base_Exception) as e: logger.error("http_get_instance_scenario_action error {}: {}".format(e.http_code, str(e))) bottle.abort(e.http_code, str(e)) except Exception as e: logger.error("Unexpected exception: ", exc_info=True) bottle.abort(httperrors.Internal_Server_Error, type(e).__name__ + ": " + str(e)) @bottle.error(400) @bottle.error(401) @bottle.error(404) @bottle.error(403) @bottle.error(405) @bottle.error(406) @bottle.error(409) @bottle.error(503) @bottle.error(500) def error400(error): e={"error":{"code":error.status_code, "type":error.status, "description":error.body}} bottle.response.headers['Access-Control-Allow-Origin'] = '*' return format_out(e)

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null

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4

614274fe73e8e99a09619dde31362d02995e1050

393

py

Python

tools/w3af/w3af/core/data/parsers/__init__.py

sravani-m/Web-Application-Security-Framework

d9f71538f5cba6fe1d8eabcb26c557565472f6a6

[ "MIT" ]

3

2019-04-09T22:59:33.000Z

2019-06-14T09:23:24.000Z

tools/w3af/w3af/core/data/parsers/__init__.py

sravani-m/Web-Application-Security-Framework

d9f71538f5cba6fe1d8eabcb26c557565472f6a6

[ "MIT" ]

null

tools/w3af/w3af/core/data/parsers/__init__.py

sravani-m/Web-Application-Security-Framework

d9f71538f5cba6fe1d8eabcb26c557565472f6a6

[ "MIT" ]

null

import re #URL_RE = ('((http|https):[A-Za-z0-9/](([A-Za-z0-9$_.+!*(),;/?:@&~=-])|%' # '[A-Fa-f0-9]{2})+(#([a-zA-Z0-9][a-zA-Z0-9$_.+!*(),;/?:@&~=%-]*))?)') URL_RE = re.compile('((http|https)://([\w:@\-\./]*?)[^ \0\n\r\t"\'<>]*)', re.U) RELATIVE_URL_RE = re.compile( '((:?[/]{1,2}[\w\-~\.%]+)+\.\w{2,4}(((\?)([\w\-~\.%]*=[\w\-~\.%]*)){1}' '((&)([\w\-~\.%]*=[\w\-~\.%]*))*)?)', re.U)

43.666667

79

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58

393

2.068966

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0.166667

0.2

0.208333

0.2

0

0.046832

0.076336

393

9

80

43.666667

0.283747

0.363868

0

0.592742

0.548387

0

1

0

false

0

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61428cb1bd17c54034a7a91e33314c49c207e56a

1,125

py

Python

src/opendr/perception/object_detection_2d/__init__.py

makistsantekidis/opendr

07dee3b59d3487b9c5a93d6946317178a02c9890

[ "Apache-2.0" ]

217

2020-04-10T16:39:36.000Z

2022-03-30T15:39:04.000Z

src/opendr/perception/object_detection_2d/__init__.py

makistsantekidis/opendr

07dee3b59d3487b9c5a93d6946317178a02c9890

[ "Apache-2.0" ]

79

2021-06-23T10:40:10.000Z

2021-12-16T07:59:42.000Z

src/opendr/perception/object_detection_2d/__init__.py

makistsantekidis/opendr

07dee3b59d3487b9c5a93d6946317178a02c9890

[ "Apache-2.0" ]

29

2021-12-16T09:26:13.000Z

2022-03-29T15:19:18.000Z

from opendr.perception.object_detection_2d.centernet.centernet_learner import CenterNetDetectorLearner from opendr.perception.object_detection_2d.detr.detr_learner import DetrLearner from opendr.perception.object_detection_2d.gem.gem_learner import GemLearner from opendr.perception.object_detection_2d.retinaface.retinaface_learner import RetinaFaceLearner from opendr.perception.object_detection_2d.ssd.ssd_learner import SingleShotDetectorLearner from opendr.perception.object_detection_2d.yolov3.yolov3_learner import YOLOv3DetectorLearner from opendr.perception.object_detection_2d.datasets.wider_person import WiderPersonDataset from opendr.perception.object_detection_2d.datasets.wider_face import WiderFaceDataset from opendr.perception.object_detection_2d.datasets import transforms from opendr.perception.object_detection_2d.utils.vis_utils import draw_bounding_boxes __all__ = ['CenterNetDetectorLearner', 'DetrLearner', 'GemLearner', 'RetinaFaceLearner', 'SingleShotDetectorLearner', 'YOLOv3DetectorLearner', 'WiderPersonDataset', 'WiderFaceDataset', 'transforms', 'draw_bounding_boxes']

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py

Python

Fujitsu/benchmarks/resnet/implementations/mxnet/3rdparty/tvm/python/tvm/relay/op/vision/_make.py

mengkai94/training_results_v0.6

43dc3e250f8da47b5f8833197d74cb8cf1004fc9

[ "Apache-2.0" ]

64

2021-05-02T14:42:34.000Z

2021-05-06T01:35:03.000Z

python/tvm/relay/op/vision/_make.py

ganzhiliang/tvm

b076cad542524cb3744149d953c341b5815f6474

[ "Apache-2.0" ]

23

2019-07-29T05:21:52.000Z

2020-08-31T18:51:42.000Z

python/tvm/relay/op/vision/_make.py

ganzhiliang/tvm

b076cad542524cb3744149d953c341b5815f6474

[ "Apache-2.0" ]

51

2019-07-12T05:10:25.000Z

2021-07-28T16:19:06.000Z

"""Constructor APIs""" from ...._ffi.function import _init_api _init_api("relay.op.vision._make", __name__)

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null

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4

615cd29b421949f6404a4f2577a075608df673f2

236

py

Python

phyutil/phylib/scan/__init__.py

frib-high-level-controls/phyhlc

6486607e3aa0212054a12e9f2ad1a3ef15542f48

[ "BSD-3-Clause" ]

1

2018-03-22T15:18:54.000Z

phyutil/phylib/scan/__init__.py

frib-high-level-controls/phyhlc

6486607e3aa0212054a12e9f2ad1a3ef15542f48

[ "BSD-3-Clause" ]

null

phyutil/phylib/scan/__init__.py

frib-high-level-controls/phyhlc

6486607e3aa0212054a12e9f2ad1a3ef15542f48

[ "BSD-3-Clause" ]

null

""" Lazy load library form either local scan service or remote RESTful based scan server. The library would be loaded on the fly according SCAN_SRV_URL variable. Created on Apr 20, 2015 @author: shen """ from scanlib import ScanLib

21.454545

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null

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1

0

1

0

1

0

4

616c8fd06283a13b04de9a1628d72b207df008b1

78

py

Python

tests/pack_test.py

ryuichi1208/CRUD-frame-flask

9b0c6453a276f4035c1acda2b548ff5fe7f6e4e8

[ "Apache-2.0" ]

1

2019-08-18T08:21:26.000Z

tests/pack_test.py

ryuichi1208/CRUD

9b0c6453a276f4035c1acda2b548ff5fe7f6e4e8

[ "Apache-2.0" ]

6

2021-03-31T19:21:35.000Z

2022-03-11T23:56:16.000Z

tests/pack_test.py

ryuichi1208/CRUD

9b0c6453a276f4035c1acda2b548ff5fe7f6e4e8

[ "Apache-2.0" ]

null

from src import * create.print_pack() rb.read_book_info("data/sample.json")

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1

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0.102564

78

5

38

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true

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null

0

1

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1

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4

618a002b89a23502680282c3e9d117347d37c250

69

py

Python

redwind/wsgi.py

kylewm/redwind

7ad807b5ab2dd74a8d470dbea9dd4baf5567d9c6

[ "BSD-2-Clause" ]

35

2015-01-08T03:26:39.000Z

2020-09-16T00:42:17.000Z

redwind/wsgi.py

kylewm/redwind

7ad807b5ab2dd74a8d470dbea9dd4baf5567d9c6

[ "BSD-2-Clause" ]

47

2015-01-05T23:22:08.000Z

2021-02-02T21:43:26.000Z

redwind/wsgi.py

kylewm/redwind

7ad807b5ab2dd74a8d470dbea9dd4baf5567d9c6

[ "BSD-2-Clause" ]

10

2015-02-20T00:51:37.000Z

2022-01-11T10:59:32.000Z

from . import create_app application = create_app('../redwind.cfg')

17.25

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0.73913

9

69

5.444444

0.777778

0.367347

0

0.115942

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3

43

23

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0

0.202899

0

1

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0

null

1

0

1

0

null

0

1

0

4

4ef8a3824851f6b9f56ee4c14582a2a48239163a

121

py

Python

django_gotolong/gweight/admin.py

ParikhKadam/gotolong

839beb8aa37055a2078eaa289b8ae05b62e8905e

[ "BSD-2-Clause", "BSD-3-Clause" ]

15

2019-12-06T16:19:45.000Z

2021-08-20T13:22:22.000Z

django_gotolong/gweight/admin.py

ParikhKadam/gotolong

839beb8aa37055a2078eaa289b8ae05b62e8905e

[ "BSD-2-Clause", "BSD-3-Clause" ]

14

2020-12-08T10:45:05.000Z

2021-09-21T17:23:45.000Z

django_gotolong/gweight/admin.py

ParikhKadam/gotolong

839beb8aa37055a2078eaa289b8ae05b62e8905e

[ "BSD-2-Clause", "BSD-3-Clause" ]

9

2020-01-01T03:04:29.000Z

2021-04-18T08:42:30.000Z

from django.contrib import admin # Register your models here. from .models import Gweight admin.site.register(Gweight)

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4

f62f593191a0a4b056b492ed4b0c18f0f7cec798

706

py

Python

core/models/managers/UsuarioManager.py

roimpacta/exemplos

cbfe7c81fc14932697c02eb63bec7d7e4a2c5d5a

[ "Apache-2.0" ]

null

core/models/managers/UsuarioManager.py

roimpacta/exemplos

cbfe7c81fc14932697c02eb63bec7d7e4a2c5d5a

[ "Apache-2.0" ]

null

core/models/managers/UsuarioManager.py

roimpacta/exemplos

cbfe7c81fc14932697c02eb63bec7d7e4a2c5d5a

[ "Apache-2.0" ]

null

from django.contrib.auth.models import AbstractBaseUser, UserManager, BaseUserManager from django.db import models class UsuarioManager(BaseUserManager): use_in_migrations = True def _create_user(self, ra, password, **extra_fields): if not ra: raise ValueError('RA precisa ser preenchido') user = self.model(ra=ra, **extra_fields) user.set_password(password) user.save(using=self._db) return user def create_user(self, ra, password, **extra_fields): return self._create_user(ra, password, **extra_fields) def create_superuser(self, ra, password, **extra_fields): return self._create_user(ra, password, **extra_fields)

35.3

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null

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1

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4

f6337b2484b8660d5938695c2b487b77db4d2994

1,932

py

Python

tests/test_metrics/test___init__.py

dmayo/brain-score

3ab4258152c9e3f8c7d29afb10158b184dbcebbe

[ "MIT" ]

52

2019-12-13T06:43:44.000Z

2022-02-21T07:47:39.000Z

tests/test_metrics/test___init__.py

dmayo/brain-score

3ab4258152c9e3f8c7d29afb10158b184dbcebbe

[ "MIT" ]

104

2019-12-06T18:08:54.000Z

2022-03-31T23:57:51.000Z

tests/test_metrics/test___init__.py

dmayo/brain-score

3ab4258152c9e3f8c7d29afb10158b184dbcebbe

[ "MIT" ]

32

2019-12-05T14:31:14.000Z

2022-03-10T02:04:45.000Z

import numpy as np from brainio.assemblies import DataAssembly from brainscore.metrics import Score class TestScoreRaw: def test_sel(self): score = Score([1, 2], coords={'a': [1, 2]}, dims=['a']) score.attrs['raw'] = DataAssembly([0, 2, 1, 3], coords={'a': [1, 1, 2, 2]}, dims=['a']) sel_score = score.sel(a=1) np.testing.assert_array_equal(sel_score.raw['a'], [1, 1]) def test_isel(self): score = Score([1, 2], coords={'a': [1, 2]}, dims=['a']) score.attrs['raw'] = DataAssembly([0, 2, 1, 3], coords={'a': [1, 1, 2, 2]}, dims=['a']) sel_score = score.isel(a=0) np.testing.assert_array_equal(sel_score.raw['a'], [1, 1]) def test_sel_no_apply_raw(self): score = Score([1, 2], coords={'a': [1, 2]}, dims=['a']) score.attrs['raw'] = DataAssembly([0, 2, 1, 3], coords={'a': [1, 1, 2, 2]}, dims=['a']) sel_score = score.sel(a=1, _apply_raw=False) np.testing.assert_array_equal(sel_score.raw['a'], [1, 1, 2, 2]) def test_squeeze(self): score = Score([[1, 2]], coords={'s': [0], 'a': [1, 2]}, dims=['s', 'a']) score.attrs['raw'] = DataAssembly([[0, 2, 1, 3]], coords={'s': [0], 'a': [1, 1, 2, 2]}, dims=['s', 'a']) sel_score = score.squeeze('s') np.testing.assert_array_equal(sel_score.raw.dims, ['a']) def test_mean(self): score = Score([1, 2], coords={'a': [1, 2]}, dims=['a']) score.attrs['raw'] = DataAssembly([0, 2, 1, 3], coords={'a': [1, 1, 2, 2]}, dims=['a']) mean_score = score.mean('a') np.testing.assert_array_equal(mean_score.raw['a'], [1, 1, 2, 2]) def test_mean_no_apply_raw(self): score = Score([1, 2], coords={'a': [1, 2]}, dims=['a']) score.attrs['raw'] = DataAssembly([0, 2, 1, 3], coords={'a': [1, 1, 2, 2]}, dims=['a']) mean_score = score.mean('a', _apply_raw=True) assert mean_score.raw == 1.5

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0

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4

f672a6d387ce80b4a9a8b4f81c34ce28cfc98655

3,704

py

Python

bcg/_nbdev.py

eschmidt42/bcg

3b35de4327d0cfdbabbe784dfc693d695fd013b6

[ "Apache-2.0" ]

1

2022-01-17T07:03:14.000Z

bcg/_nbdev.py

eschmidt42/bcg

3b35de4327d0cfdbabbe784dfc693d695fd013b6

[ "Apache-2.0" ]

2

2021-09-28T01:41:23.000Z

2022-02-26T07:12:27.000Z

bcg/_nbdev.py

eschmidt42/bcg

3b35de4327d0cfdbabbe784dfc693d695fd013b6

[ "Apache-2.0" ]

1

2022-01-17T07:04:49.000Z

# AUTOGENERATED BY NBDEV! DO NOT EDIT! __all__ = ["index", "modules", "custom_doc_links", "git_url"] index = {"GenVars": "00_basics.ipynb", "CommonCauses": "00_basics.ipynb", "Instruments": "00_basics.ipynb", "EffectModifiers": "00_basics.ipynb", "Treatments": "00_basics.ipynb", "initialize": "00_basics.ipynb", "generate": "00_basics.ipynb", "get_obs": "00_basics.ipynb", "CommonCauses.initialize": "00_basics.ipynb", "CommonCauses.generate": "00_basics.ipynb", "CommonCauses.get_obs": "00_basics.ipynb", "Instruments.initialize": "00_basics.ipynb", "Instruments.get_obs": "00_basics.ipynb", "Instruments.generate": "00_basics.ipynb", "EffectModifiers.initialize": "00_basics.ipynb", "EffectModifiers.get_obs": "00_basics.ipynb", "EffectModifiers.generate": "00_basics.ipynb", "stochastically_convert_to_binary": "00_basics.ipynb", "Treatments.initialize": "00_basics.ipynb", "Treatments.generate": "00_basics.ipynb", "Treatments.get_obs": "00_basics.ipynb", "Outcomes": "00_basics.ipynb", "plot_target_vs_rest": "00_basics.ipynb", "plot_var_hists": "00_basics.ipynb", "show_correlations": "00_basics.ipynb", "get_Xy": "00_basics.ipynb", "get_model_feel": "00_basics.ipynb", "get_feature_importance": "00_basics.ipynb", "get_partial_dependencies": "00_basics.ipynb", "plot_partial_dependencies": "00_basics.ipynb", "GraphGenerator": "00_basics.ipynb", "get_only_Xi_to_Y": "00_basics.ipynb", "GraphGenerator.get_only_Xi_to_Y": "00_basics.ipynb", "get_Xi_to_Y_with_ccs_and_such": "00_basics.ipynb", "GraphGenerator.get_Xi_to_Y_with_ccs_and_such": "00_basics.ipynb", "vis_g": "00_basics.ipynb", "GraphGenerator.vis_g": "00_basics.ipynb", "get_gml": "00_basics.ipynb", "GraphGenerator.get_gml": "00_basics.ipynb", "CausalGraph": "02_causal_model.ipynb", "show_graph": "02_causal_model.ipynb", "view_graph": "02_causal_model.ipynb", "CausalGraph.view_graph": "02_causal_model.ipynb", "get_ancestors": "02_causal_model.ipynb", "CausalGraph.get_ancestors": "02_causal_model.ipynb", "cut_edges": "02_causal_model.ipynb", "CausalGraph.cut_edges": "02_causal_model.ipynb", "get_causes": "02_causal_model.ipynb", "CausalGraph.get_causes": "02_causal_model.ipynb", "get_instruments": "02_causal_model.ipynb", "CausalGraph.get_instruments": "02_causal_model.ipynb", "get_effect_modifiers": "02_causal_model.ipynb", "CausalGraph.get_effect_modifiers": "02_causal_model.ipynb", "CausalModel": "02_causal_model.ipynb", "identify_effect": "02_causal_model.ipynb", "construct_backdoor": "02_causal_model.ipynb", "construct_instrumental_variable": "02_causal_model.ipynb", "CausalModel.construct_backdoor": "02_causal_model.ipynb", "CausalModel.construct_instrumental_variable": "02_causal_model.ipynb", "CausalModel.identify_effect": "02_causal_model.ipynb", "RegressionEstimator": "02_causal_model.ipynb", "get_Xy_with_products": "02_causal_model.ipynb", "estimate_effect": "02_causal_model.ipynb", "CausalModel.estimate_effect": "02_causal_model.ipynb"} modules = ["basics.py", "causal_model.py"] doc_url = "https://fastai.github.io/bcg/" git_url = "https://github.com/fastai/bcg/tree/master/" def custom_doc_links(name): return None

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1

0

1

0

null

0

4

f674eacdb8914ef2cd91737543492e75adc3bdd5

152

py

Python

aoc20171202a.py

BarnabyShearer/aoc

4feb66c668b068f0f42ad99b916e80732eba5a2d

[ "MIT" ]

null

aoc20171202a.py

BarnabyShearer/aoc

4feb66c668b068f0f42ad99b916e80732eba5a2d

[ "MIT" ]

null

aoc20171202a.py

BarnabyShearer/aoc

4feb66c668b068f0f42ad99b916e80732eba5a2d

[ "MIT" ]

null

def check(line): return max(line) - min(line) def aoc(data): return sum([check([int(x) for x in line.split()]) for line in data.split("\n")])

21.714286

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6

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null

0

1

0

1

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4

9ca592f62fa6011f23e2615ebf9b5413756c6e71

49

py

Python

torchbiomed/datasets/__init__.py

aicentral/torchbiomed

661b3e4411f7e57f4c5cbb56d02998d2d8bddfdb

[ "BSD-3-Clause" ]

106

2017-03-24T09:36:18.000Z

2021-11-30T11:31:22.000Z

torchbiomed/datasets/__init__.py

aicentral/torchbiomed

661b3e4411f7e57f4c5cbb56d02998d2d8bddfdb

[ "BSD-3-Clause" ]

4

2017-05-11T04:06:48.000Z

2021-04-16T09:38:18.000Z

torchbiomed/datasets/__init__.py

aicentral/torchbiomed

661b3e4411f7e57f4c5cbb56d02998d2d8bddfdb

[ "BSD-3-Clause" ]

37

2017-05-11T07:25:06.000Z

2022-01-16T16:06:42.000Z

from .luna16 import LUNA16 __all__ = ('LUNA16')

12.25

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null

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4

9cafd721fe6d01b5f1bc056090f0209840dec272

153

py

Python

server/learning/login/api_urls.py

kantanand/insmartapps

4ab54bb41101e43b5edaac9795509584f01c5c92

[ "MIT" ]

3

2016-05-01T18:39:08.000Z

2019-02-19T11:55:40.000Z

server/learning/login/api_urls.py

kantanand/insmartapps

4ab54bb41101e43b5edaac9795509584f01c5c92

[ "MIT" ]

1

2016-04-28T16:41:24.000Z

2016-06-11T19:11:14.000Z

server/learning/login/api_urls.py

kantanand/insmartapps

4ab54bb41101e43b5edaac9795509584f01c5c92

[ "MIT" ]

null

from django.conf.urls import url from rest_framework_jwt.views import obtain_jwt_token urlpatterns = [ url(r'^get-auth-token/', obtain_jwt_token), ]

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1

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null

0

1

0

4

9ccb1093180f78123e9f1ee8fdfac29431e8638b

8,830

py

Python

gen_1.py

heyfaraday/CMB_test

ff4c63bd5797dec02c23338c67e761ef62c87338

[ "MIT" ]

null

gen_1.py

heyfaraday/CMB_test

ff4c63bd5797dec02c23338c67e761ef62c87338

[ "MIT" ]

null

gen_1.py

heyfaraday/CMB_test

ff4c63bd5797dec02c23338c67e761ef62c87338

[ "MIT" ]

1

2022-02-13T04:24:42.000Z

import numpy as np from math import sqrt, pi, sin, cos from lib import minkowski L_max_field = 7 L_max_polynom = 7 N = 8 def coef_1(in_l, in_m): if in_l != 0: return sqrt((in_l - in_m) * (2.0 * in_l + 1.0) / ((in_l + in_m) * (2.0 * in_l - 1.0))) if in_l == 0: return 0.0 # P_ generation P_ = np.zeros((N / 2 + 1, L_max_polynom + 1, L_max_polynom + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N P_[j][0][0] = 1.0 / sqrt(4.0 * pi) for m in xrange(0, L_max_polynom): P_[j][m + 1][m + 1] = - P_[j][m][m] * sin(theta) * sqrt(2.0 * m + 3.0) / sqrt(2.0 * m + 2.0) for m in xrange(0, L_max_polynom): P_[j][m][m + 1] = P_[j][m][m] * cos(theta) * sqrt(2.0 * m + 3.0) for m in xrange(0, L_max_polynom - 1): for l in xrange(m + 2, L_max_polynom + 1): P_[j][m][l] = ((2.0 * l - 1.0) * sqrt((l - m) * (2.0 * l + 1.0)) / sqrt((l + m) * (2.0 * l - 1.0)) * P_[j][m][l - 1] * cos(theta) - (l + m - 1.0) * sqrt((l - m) * (l - 1.0 - m) * (2.0 * l + 1.0)) / sqrt((l + m) * (l - 1.0 + m) * (2.0 * l - 3.0)) * P_[j][m][l - 2]) / \ (l - m) for m in xrange(1, L_max_polynom + 1): for l in xrange(m, L_max_polynom + 1): P_[j][m][l] *= sqrt(2.0) # F_x generation - np.imag + np.real F_x = np.zeros((N / 2 + 1, L_max_polynom + 1, L_max_polynom + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N for l in xrange(2, L_max_polynom + 1): for m in xrange(0, l + 1): F_x[j][m][l] = m * P_[j][m][l] / sin(theta) # F_y generation - np.real + np.imag F_y = np.zeros((N / 2 + 1, L_max_polynom + 1, L_max_polynom + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N for l in xrange(2, L_max_polynom + 1): for m in xrange(0, l + 1): F_y[j][m][l] = l * cos(theta) / sin(theta) * P_[j][m][l] - \ 1.0 / sin(theta) * (l + m) * coef_1(l, m) * P_[j][m][l - 1] # F_xy generation - np.imag + np.real F_xy = np.zeros((N / 2 + 1, L_max_polynom + 1, L_max_polynom + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N for l in xrange(2, L_max_polynom + 1): for m in xrange(0, l + 1): F_xy[j][m][l] = m / sin(theta) * ((1.0 / sin(theta)) * (l + m) * P_[j][m][l - 1] * coef_1(l, m) - (l - 1.0) * cos(theta) / sin(theta) * P_[j][m][l]) # F_xx_1 generation - np.real + np.real F_xx_1 = np.zeros((N / 2 + 1, L_max_polynom + 1, L_max_polynom + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N for l in xrange(2, L_max_polynom + 1): for m in xrange(0, l + 1): F_xx_1[j][m][l] = - m * m * P_[j][m][l] / (sin(theta) * sin(theta)) # F_xx_2 generation - np.real + np.real F_xx_2 = np.zeros((N / 2 + 1, L_max_polynom + 1, L_max_polynom + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N for l in xrange(2, L_max_polynom + 1): for m in xrange(0, l + 1): F_xx_2[j][m][l] = (l * cos(theta) / sin(theta) * P_[j][m][l] - 1.0 / sin(theta) * (l + m) * coef_1(l, m) * P_[j][m][l - 1]) * cos(theta) / sin(theta) # F_yy generation - np.real + np.real F_yy = np.zeros((N / 2 + 1, L_max_polynom + 1, L_max_polynom + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N for l in xrange(2, L_max_polynom + 1): for m in xrange(0, l + 1): F_yy[j][m][l] = 0.5 / sin(theta) * ((1.0 / sin(theta)) * (l * l * cos(2.0 * theta) - (l + 2.0) * l + 2.0 * m * m) * P_[j][m][l] + 2.0 * (l + m) * cos(theta) / sin(theta) * coef_1(l, m) * P_[j][m][l - 1]) x = np.zeros((N + 1, N / 2 + 1)) y = np.zeros((N + 1, N / 2 + 1)) for i in xrange(0, N + 1): for j in xrange(0, N / 2 + 1): x[i][j] = (2.0 * i - N) / N * pi y[i][j] = 2.0 * j / N * pi - pi / 2.0 Fa = np.zeros((N / 2 + 1, N)) Fb = np.zeros((N / 2 + 1, N)) T = np.zeros(N) # a_coef = np.random.normal(0.0, 1.0, size=(L_max_polynom + 1, L_max_polynom + 1)) # b_coef = np.random.normal(0.0, 1.0, size=(L_max_polynom + 1, L_max_polynom + 1)) a_coef = np.zeros((L_max_field + 1, L_max_field + 1)) b_coef = np.zeros((L_max_field + 1, L_max_field + 1)) for m in xrange(0, L_max_field + 1): for l in xrange(0, m): a_coef[m][l] = 0.0 b_coef[m][l] = 0.0 for l in xrange(0, L_max_field + 1): b_coef[0][l] = 0.0 a_coef[0][0] = 0.0 b_coef[0][0] = 0.0 a_coef[0][1] = 0.0 a_coef[1][1] = 1.0 b_coef[0][1] = 1.0 b_coef[1][1] = 0.0 C = np.zeros((L_max_field + 1)) for l in xrange(0, L_max_field + 1): C_sum = 0.0 for m in xrange(0, l + 1): C_sum = C_sum + a_coef[m][l] * a_coef[m][l] + b_coef[m][l] * b_coef[m][l] C[l] = C_sum / (2.0 * l + 1.0) sigma_0 = 0.0 for l in xrange(0, L_max_field + 1): sigma_0 += (2.0 * l + 1.0) * C[l] sigma_0 = sqrt(sigma_0 / 4.0 / pi) sigma_1 = 0.0 for l in xrange(0, L_max_field + 1): sigma_1 += l * (l + 1.0) * (2.0 * l + 1.0) * C[l] sigma_1 = sqrt(sigma_1 / 4.0 * pi) sigma_2 = 0.0 for l in xrange(0, L_max_field + 1): sigma_2 += (l + 2.0) * (l - 1.0) * l * (l + 1.0) * (2.0 * l + 1.0) * C[l] sigma_2 = sqrt(sigma_2 / 4.0 * pi) func1 = 0.0 func2 = 0.0 # field generation field = np.zeros((N + 1, N / 2 + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N for m in xrange(0, L_max_field + 1): for l in xrange(m, L_max_field + 1): func1 += a_coef[m][l] * P_[j][m][l] func2 += b_coef[m][l] * P_[j][m][l] Fa[j][m] = func1 Fb[j][m] = func2 func1 = 0.0 func2 = 0.0 T = np.real(np.fft.fft(Fa[j])) + np.imag(np.fft.fft(Fb[j])) field[0:N, j] = T[:] field[N][j] = field[0][j] # field_x generation field_x = np.zeros((N + 1, N / 2 + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N for m in xrange(0, L_max_field + 1): for l in xrange(m, L_max_field + 1): func1 += a_coef[m][l] * F_x[j][m][l] func2 += b_coef[m][l] * F_x[j][m][l] Fa[j][m] = func1 Fb[j][m] = func2 func1 = 0.0 func2 = 0.0 T = - np.imag(np.fft.fft(Fa[j])) + np.real(np.fft.fft(Fb[j])) field_x[0:N, j] = T[:] field_x[N][j] = field_x[0][j] # field_y generation field_y = np.zeros((N + 1, N / 2 + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N for m in xrange(0, L_max_field + 1): for l in xrange(m, L_max_field + 1): func1 += a_coef[m][l] * F_y[j][m][l] func2 += b_coef[m][l] * F_y[j][m][l] Fa[j][m] = func1 Fb[j][m] = func2 func1 = 0.0 func2 = 0.0 T = np.real(np.fft.fft(Fa[j])) + np.imag(np.fft.fft(Fb[j])) field_y[0:N, j] = T[:] field_y[N][j] = field_y[0][j] # field_xx generation field_xx = np.zeros((N + 1, N / 2 + 1)) for j in xrange(1, N / 2): theta = 2 * pi * j / N for m in xrange(0, L_max_field + 1): for l in xrange(m, L_max_field + 1): func1 += a_coef[m][l] * (F_xx_1[j][m][l] + F_xx_2[j][m][l]) func2 += b_coef[m][l] * (F_xx_1[j][m][l] + F_xx_2[j][m][l]) Fa[j][m] = func1 Fb[j][m] = func2 func1 = 0.0 func2 = 0.0 T = np.real(np.fft.fft(Fa[j])) + np.imag(np.fft.fft(Fb[j])) field_xx[0:N, j] = T[:] field_xx[N][j] = field_xx[0][j] # field_yy generation field_yy = np.zeros((N + 1, N / 2 + 1)) for j in xrange(1, N / 2): theta = 2.0 * pi * j / N for m in xrange(0, L_max_field + 1): for l in xrange(m, L_max_field + 1): func1 += a_coef[m][l] * F_yy[j][m][l] func2 += b_coef[m][l] * F_yy[j][m][l] Fa[j][m] = func1 Fb[j][m] = func2 func1 = 0.0 func2 = 0.0 T = np.real(np.fft.fft(Fa[j])) + np.imag(np.fft.fft(Fb[j])) field_yy[0:N, j] = T[:] field_yy[N][j] = field_yy[0][j] # field_xy generation field_xy = np.zeros((N + 1, N / 2 + 1)) for j in xrange(1, N / 2): theta = 2 * pi * j / N for m in xrange(0, L_max_field + 1): for l in xrange(m, L_max_field + 1): func1 += a_coef[m][l] * F_xy[j][m][l] func2 += b_coef[m][l] * F_xy[j][m][l] Fa[j][m] = func1 Fb[j][m] = func2 func1 = 0.0 func2 = 0.0 T = - np.imag(np.fft.fft(Fa[j])) + np.real(np.fft.fft(Fb[j])) field_xy[0:N, j] = T[:] field_xy[N][j] = field_xy[0][j] a = 0.0 na = 0.0 for i in xrange(0, N): for j in xrange(1, N / 2): a += cos(y[i][j]) * field[i][j] * field[i][j] na += cos(y[i][j]) sigma_0_map = sqrt(a / na) field /= sigma_0_map print minkowski.area(y, field) print minkowski.length(x, y, field) print field

27.767296

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0.478029

1,827

8,830

2.16694

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8,830

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null

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4

9cce62689b1c5c32ca70652f2026acc5cece9497

879

py

Python

neighapp/migrations/0002_auto_20210725_1822.py

mohamedissack/Neighbour-App

649ca351bbfeef4ca8f2caa75c2878a178c06cb1

[ "MIT" ]

null

neighapp/migrations/0002_auto_20210725_1822.py

mohamedissack/Neighbour-App

649ca351bbfeef4ca8f2caa75c2878a178c06cb1

[ "MIT" ]

null

neighapp/migrations/0002_auto_20210725_1822.py

mohamedissack/Neighbour-App

649ca351bbfeef4ca8f2caa75c2878a178c06cb1

[ "MIT" ]

null

# Generated by Django 3.2.5 on 2021-07-25 18:22 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('neighapp', '0001_initial'), ] operations = [ migrations.RenameField( model_name='neighbourhood', old_name='hood_description', new_name='neighbourhood_description', ), migrations.RenameField( model_name='neighbourhood', old_name='hood_location', new_name='neighbourhood_location', ), migrations.RenameField( model_name='neighbourhood', old_name='hood_name', new_name='neighbourhood_name', ), migrations.RenameField( model_name='neighbourhood', old_name='hood_photo', new_name='neighbourhood_photo', ), ]

25.852941

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879

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0.278689

0.213115

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879

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1

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1

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null

0

4

143834f608a51b55557e648c315238846f44aa78

74

py

Python

pynmapservice/__init__.py

vix597/vrnmap

30c9e69c63aa8282f9ed2bccb96afa4226912fc9

[ "0BSD" ]

null

pynmapservice/__init__.py

vix597/vrnmap

30c9e69c63aa8282f9ed2bccb96afa4226912fc9

[ "0BSD" ]

null

pynmapservice/__init__.py

vix597/vrnmap

30c9e69c63aa8282f9ed2bccb96afa4226912fc9

[ "0BSD" ]

null

"""PyNmapService - A service to control Nmap with a websocket for VR."""

37

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0.716216

11

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4.818182

0.909091

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0.175676

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0.868852

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null

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null

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1

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1

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null

0

1

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4

148ce824bfce64bbffb187870f97b6123dc6abfb

133

py

Python

nose2/tests/functional/support/scenario/module_import_err/pkg/test_import_err.py

ltfish/nose2

e47363dad10056cf906daf387613c21d74f37e56

[ "BSD-2-Clause" ]

null

nose2/tests/functional/support/scenario/module_import_err/pkg/test_import_err.py

ltfish/nose2

e47363dad10056cf906daf387613c21d74f37e56

[ "BSD-2-Clause" ]

null

nose2/tests/functional/support/scenario/module_import_err/pkg/test_import_err.py

ltfish/nose2

e47363dad10056cf906daf387613c21d74f37e56

[ "BSD-2-Clause" ]

null

raise ValueError('booms') import unittest def test(): pass class Test(unittest.TestCase): def test(self): pass

9.5

30

0.639098

16

133

5.3125

0.6875

0.164706

0

0.255639

133

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10.230769

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0.285714

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0

0.571429

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null

0

null

0

1

0

1

0

1

0

4

1adfbe5a14442d68c9f4504fad47168384a09dd6

961

py

Python

lib/systems/d-aspartic_acid.py

pulsar-chem/BPModule

f8e64e04fdb01947708f098e833600c459c2ff0e

[ "BSD-3-Clause" ]

null

lib/systems/d-aspartic_acid.py

pulsar-chem/BPModule

f8e64e04fdb01947708f098e833600c459c2ff0e

[ "BSD-3-Clause" ]

null

lib/systems/d-aspartic_acid.py

pulsar-chem/BPModule

f8e64e04fdb01947708f098e833600c459c2ff0e

[ "BSD-3-Clause" ]

null

import pulsar as psr def load_ref_system(): """ Returns d-aspartic_acid as found in the IQMol fragment library. All credit to https://github.com/nutjunkie/IQmol """ return psr.make_system(""" H 1.1749 0.2716 -0.8816 C 0.2902 -0.3063 -0.5085 C -0.6411 -0.5180 -1.7035 O -0.8069 0.2223 -2.6543 O -1.3677 -1.6581 -1.7034 H -1.9153 -1.6984 -2.4819 C -0.4682 0.5172 0.5398 C 0.4146 0.9504 1.6942 H -0.9011 1.4221 0.0654 N 0.8376 -1.5441 0.0868 H 1.2665 -2.1008 -0.6201 H 0.1218 -2.0662 0.5461 O 1.4697 1.7781 1.5170 O 0.2747 0.6686 2.8640 H -1.3307 -0.0569 0.9378 H 1.5926 1.9741 0.5965 """)

40.041667

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961

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0.318637

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null

0

1

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4

1ae7b40a189f027b76a7b1c296483fb5c9a8b309

217

py

Python

processmanager/__init__.py

igormacedo/process-manager

c48568d03c83f034d0114228efc919fc38754dc7

[ "MIT" ]

1

2017-09-05T01:27:13.000Z

processmanager/__init__.py

igormacedo/process-manager

c48568d03c83f034d0114228efc919fc38754dc7

[ "MIT" ]

null

processmanager/__init__.py

igormacedo/process-manager

c48568d03c83f034d0114228efc919fc38754dc7

[ "MIT" ]

null

from flask import Flask from flask_socketio import SocketIO, send app = Flask(__name__) app.config['SECRET_KEY'] = 'mysecret' socketio = SocketIO(app) import processmanager.views import processmanager.sockethandler

21.7

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null

0

1

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1

0

4

210178e002cb091da1855ecd2446c4e7026eb996

31,623

py

Python

tests/test_client.py

benjamin-bader/aiohttp

ee35cfe4714cd1b13655958b4625e1570719e9d5

[ "Apache-2.0" ]

null

tests/test_client.py

benjamin-bader/aiohttp

ee35cfe4714cd1b13655958b4625e1570719e9d5

[ "Apache-2.0" ]

null

tests/test_client.py

benjamin-bader/aiohttp

ee35cfe4714cd1b13655958b4625e1570719e9d5

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """Tests for aiohttp/client.py""" import asyncio import inspect import io import unittest import unittest.mock import urllib.parse import aiohttp from aiohttp.client import ClientRequest, ClientResponse try: import chardet except ImportError: # pragma: no cover chardet = None class ClientResponseTests(unittest.TestCase): def setUp(self): self.loop = asyncio.new_event_loop() asyncio.set_event_loop(None) self.connection = unittest.mock.Mock() self.stream = aiohttp.StreamParser(loop=self.loop) self.response = ClientResponse('get', 'http://python.org') def tearDown(self): self.loop.close() def test_del(self): response = ClientResponse('get', 'http://python.org') connection = unittest.mock.Mock() response._setup_connection(connection) with self.assertWarns(ResourceWarning): del response connection.close.assert_called_with() def test_close(self): self.response.connection = self.connection self.response.close() self.assertIsNone(self.response.connection) self.assertTrue(self.connection.release.called) self.response.close() self.response.close() def test_wait_for_100(self): response = ClientResponse( 'get', 'http://python.org', continue100=object()) self.assertTrue(response.waiting_for_continue()) response = ClientResponse( 'get', 'http://python.org') self.assertFalse(response.waiting_for_continue()) def test_repr(self): self.response.status = 200 self.response.reason = 'Ok' self.assertIn( '<ClientResponse(http://python.org) [200 Ok]>', repr(self.response)) def test_read_and_release_connection(self): def side_effect(*args, **kwargs): def second_call(*args, **kwargs): raise aiohttp.EofStream fut = asyncio.Future(loop=self.loop) fut.set_result(b'payload') content.read.side_effect = second_call return fut content = self.response.content = unittest.mock.Mock() content.read.side_effect = side_effect self.response.close = unittest.mock.Mock() res = self.loop.run_until_complete(self.response.read()) self.assertEqual(res, b'payload') self.assertTrue(self.response.close.called) def test_read_and_release_connection_with_error(self): content = self.response.content = unittest.mock.Mock() content.read.return_value = asyncio.Future(loop=self.loop) content.read.return_value.set_exception(ValueError) self.response.close = unittest.mock.Mock() self.assertRaises( ValueError, self.loop.run_until_complete, self.response.read()) self.response.close.assert_called_with(True) def test_release(self): fut = asyncio.Future(loop=self.loop) fut.set_result(b'') content = self.response.content = unittest.mock.Mock() content.readany.return_value = fut self.response.close = unittest.mock.Mock() self.loop.run_until_complete(self.response.release()) self.assertTrue(self.response.close.called) def test_read_and_close(self): self.response.read = unittest.mock.Mock() self.response.read.return_value = asyncio.Future(loop=self.loop) self.response.read.return_value.set_result(b'data') with self.assertWarns(DeprecationWarning): res = self.loop.run_until_complete(self.response.read_and_close()) self.assertEqual(res, b'data') self.assertTrue(self.response.read.called) def test_read_decode_deprecated(self): self.response._content = b'data' self.response.json = unittest.mock.Mock() self.response.json.return_value = asyncio.Future(loop=self.loop) self.response.json.return_value.set_result('json') with self.assertWarns(DeprecationWarning): res = self.loop.run_until_complete(self.response.read(decode=True)) self.assertEqual(res, 'json') self.assertTrue(self.response.json.called) def test_text(self): def side_effect(*args, **kwargs): def second_call(*args, **kwargs): raise aiohttp.EofStream fut = asyncio.Future(loop=self.loop) fut.set_result('{"тест": "пройден"}'.encode('cp1251')) content.read.side_effect = second_call return fut self.response.headers = { 'CONTENT-TYPE': 'application/json;charset=cp1251'} content = self.response.content = unittest.mock.Mock() content.read.side_effect = side_effect self.response.close = unittest.mock.Mock() res = self.loop.run_until_complete(self.response.text()) self.assertEqual(res, '{"тест": "пройден"}') self.assertTrue(self.response.close.called) def test_text_custom_encoding(self): def side_effect(*args, **kwargs): def second_call(*args, **kwargs): raise aiohttp.EofStream fut = asyncio.Future(loop=self.loop) fut.set_result('{"тест": "пройден"}'.encode('cp1251')) content.read.side_effect = second_call return fut self.response.headers = { 'CONTENT-TYPE': 'application/json'} content = self.response.content = unittest.mock.Mock() content.read.side_effect = side_effect self.response.close = unittest.mock.Mock() res = self.loop.run_until_complete( self.response.text(encoding='cp1251')) self.assertEqual(res, '{"тест": "пройден"}') self.assertTrue(self.response.close.called) @unittest.skipIf(chardet is None, "no chardet") def test_text_detect_encoding(self): def side_effect(*args, **kwargs): def second_call(*args, **kwargs): raise aiohttp.EofStream fut = asyncio.Future(loop=self.loop) fut.set_result('{"тест": "пройден"}'.encode('cp1251')) content.read.side_effect = second_call return fut self.response.headers = {'CONTENT-TYPE': 'application/json'} content = self.response.content = unittest.mock.Mock() content.read.side_effect = side_effect self.response.close = unittest.mock.Mock() res = self.loop.run_until_complete(self.response.text()) self.assertEqual(res, '{"тест": "пройден"}') self.assertTrue(self.response.close.called) def test_text_detect_encoding_without_chardet(self): def side_effect(*args, **kwargs): def second_call(*args, **kwargs): raise aiohttp.EofStream fut = asyncio.Future(loop=self.loop) fut.set_result('{"тест": "пройден"}'.encode('cp1251')) content.read.side_effect = second_call return fut self.response.headers = {'CONTENT-TYPE': 'application/json'} content = self.response.content = unittest.mock.Mock() content.read.side_effect = side_effect self.response.close = unittest.mock.Mock() with unittest.mock.patch('aiohttp.client.chardet', None): self.assertRaises(UnicodeDecodeError, self.loop.run_until_complete, self.response.text()) def test_json(self): def side_effect(*args, **kwargs): def second_call(*args, **kwargs): raise aiohttp.EofStream fut = asyncio.Future(loop=self.loop) fut.set_result('{"тест": "пройден"}'.encode('cp1251')) content.read.side_effect = second_call return fut self.response.headers = { 'CONTENT-TYPE': 'application/json;charset=cp1251'} content = self.response.content = unittest.mock.Mock() content.read.side_effect = side_effect self.response.close = unittest.mock.Mock() res = self.loop.run_until_complete(self.response.json()) self.assertEqual(res, {'тест': 'пройден'}) self.assertTrue(self.response.close.called) def test_json_custom_loader(self): self.response.headers = { 'CONTENT-TYPE': 'application/json;charset=cp1251'} self.response._content = b'data' def custom(content): return content + '-custom' res = self.loop.run_until_complete(self.response.json(loads=custom)) self.assertEqual(res, 'data-custom') @unittest.mock.patch('aiohttp.client.client_logger') def test_json_no_content(self, m_log): self.response.headers = { 'CONTENT-TYPE': 'data/octet-stream'} self.response._content = b'' self.response.close = unittest.mock.Mock() res = self.loop.run_until_complete(self.response.json()) self.assertIsNone(res) m_log.warning.assert_called_with( 'Attempt to decode JSON with unexpected mimetype: %s', 'data/octet-stream') def test_json_override_encoding(self): def side_effect(*args, **kwargs): def second_call(*args, **kwargs): raise aiohttp.EofStream fut = asyncio.Future(loop=self.loop) fut.set_result('{"тест": "пройден"}'.encode('cp1251')) content.read.side_effect = second_call return fut self.response.headers = { 'CONTENT-TYPE': 'application/json;charset=utf8'} content = self.response.content = unittest.mock.Mock() content.read.side_effect = side_effect self.response.close = unittest.mock.Mock() res = self.loop.run_until_complete( self.response.json(encoding='cp1251')) self.assertEqual(res, {'тест': 'пройден'}) self.assertTrue(self.response.close.called) @unittest.skipIf(chardet is None, "no chardet") def test_json_detect_encoding(self): def side_effect(*args, **kwargs): def second_call(*args, **kwargs): raise aiohttp.EofStream fut = asyncio.Future(loop=self.loop) fut.set_result('{"тест": "пройден"}'.encode('cp1251')) content.read.side_effect = second_call return fut self.response.headers = {'CONTENT-TYPE': 'application/json'} content = self.response.content = unittest.mock.Mock() content.read.side_effect = side_effect self.response.close = unittest.mock.Mock() res = self.loop.run_until_complete(self.response.json()) self.assertEqual(res, {'тест': 'пройден'}) self.assertTrue(self.response.close.called) def test_json_detect_encoding_without_chardet(self): def side_effect(*args, **kwargs): def second_call(*args, **kwargs): raise aiohttp.EofStream fut = asyncio.Future(loop=self.loop) fut.set_result('{"тест": "пройден"}'.encode('cp1251')) content.read.side_effect = second_call return fut self.response.headers = {'CONTENT-TYPE': 'application/json'} content = self.response.content = unittest.mock.Mock() content.read.side_effect = side_effect self.response.close = unittest.mock.Mock() with unittest.mock.patch('aiohttp.client.chardet', None): self.assertRaises(UnicodeDecodeError, self.loop.run_until_complete, self.response.json()) def test_override_flow_control(self): class MyResponse(ClientResponse): flow_control_class = aiohttp.FlowControlDataQueue response = MyResponse('get', 'http://python.org') response._setup_connection(self.connection) self.assertIsInstance(response.content, aiohttp.FlowControlDataQueue) with self.assertWarns(ResourceWarning): del response class ClientRequestTests(unittest.TestCase): def setUp(self): self.loop = asyncio.new_event_loop() asyncio.set_event_loop(None) self.transport = unittest.mock.Mock() self.connection = unittest.mock.Mock() self.protocol = unittest.mock.Mock() self.protocol.writer.drain.return_value = () self.stream = aiohttp.StreamParser(loop=self.loop) def tearDown(self): self.loop.close() def test_method(self): req = ClientRequest('get', 'http://python.org/') self.assertEqual(req.method, 'GET') req = ClientRequest('head', 'http://python.org/') self.assertEqual(req.method, 'HEAD') req = ClientRequest('HEAD', 'http://python.org/') self.assertEqual(req.method, 'HEAD') def test_version(self): req = ClientRequest('get', 'http://python.org/', version='1.0') self.assertEqual(req.version, (1, 0)) def test_version_err(self): self.assertRaises( ValueError, ClientRequest, 'get', 'http://python.org/', version='1.c') def test_host_port(self): req = ClientRequest('get', 'http://python.org/') self.assertEqual(req.host, 'python.org') self.assertEqual(req.port, 80) self.assertFalse(req.ssl) req = ClientRequest('get', 'https://python.org/') self.assertEqual(req.host, 'python.org') self.assertEqual(req.port, 443) self.assertTrue(req.ssl) req = ClientRequest('get', 'https://python.org:960/') self.assertEqual(req.host, 'python.org') self.assertEqual(req.port, 960) self.assertTrue(req.ssl) def test_host_port_err(self): self.assertRaises( ValueError, ClientRequest, 'get', 'http://python.org:123e/') def test_host_header(self): req = ClientRequest('get', 'http://python.org/') self.assertEqual(req.headers['HOST'], 'python.org') req = ClientRequest('get', 'http://python.org:80/') self.assertEqual(req.headers['HOST'], 'python.org:80') req = ClientRequest('get', 'http://python.org:99/') self.assertEqual(req.headers['HOST'], 'python.org:99') req = ClientRequest('get', 'http://python.org/', headers={'host': 'example.com'}) self.assertEqual(req.headers['HOST'], 'example.com') req = ClientRequest('get', 'http://python.org/', headers={'host': 'example.com:99'}) self.assertEqual(req.headers['HOST'], 'example.com:99') def test_headers(self): req = ClientRequest('get', 'http://python.org/', headers={'Content-Type': 'text/plain'}) self.assertIn('CONTENT-TYPE', req.headers) self.assertEqual(req.headers['CONTENT-TYPE'], 'text/plain') self.assertEqual(req.headers['ACCEPT-ENCODING'], 'gzip, deflate') def test_headers_list(self): req = ClientRequest('get', 'http://python.org/', headers=[('Content-Type', 'text/plain')]) self.assertIn('CONTENT-TYPE', req.headers) self.assertEqual(req.headers['CONTENT-TYPE'], 'text/plain') def test_headers_default(self): req = ClientRequest('get', 'http://python.org/', headers={'ACCEPT-ENCODING': 'deflate'}) self.assertEqual(req.headers['ACCEPT-ENCODING'], 'deflate') def test_invalid_url(self): self.assertRaises( ValueError, ClientRequest, 'get', 'hiwpefhipowhefopw') def test_invalid_idna(self): self.assertRaises( ValueError, ClientRequest, 'get', 'http://\u2061owhefopw.com') def test_no_path(self): req = ClientRequest('get', 'http://python.org') self.assertEqual('/', req.path) def test_basic_auth(self): req = ClientRequest('get', 'http://python.org', auth=aiohttp.helpers.BasicAuth('nkim', '1234')) self.assertIn('AUTHORIZATION', req.headers) self.assertEqual('Basic bmtpbToxMjM0', req.headers['AUTHORIZATION']) def test_basic_auth_utf8(self): req = ClientRequest('get', 'http://python.org', auth=aiohttp.helpers.BasicAuth('nkim', 'секрет', 'utf-8')) self.assertIn('AUTHORIZATION', req.headers) self.assertEqual('Basic bmtpbTrRgdC10LrRgNC10YI=', req.headers['AUTHORIZATION']) def test_basic_auth_tuple_deprecated(self): with self.assertWarns(DeprecationWarning): req = ClientRequest('get', 'http://python.org', auth=('nkim', '1234')) self.assertIn('AUTHORIZATION', req.headers) self.assertEqual('Basic bmtpbToxMjM0', req.headers['AUTHORIZATION']) def test_basic_auth_from_url(self): req = ClientRequest('get', 'http://nkim:1234@python.org') self.assertIn('AUTHORIZATION', req.headers) self.assertEqual('Basic bmtpbToxMjM0', req.headers['AUTHORIZATION']) req = ClientRequest( 'get', 'http://nkim@python.org', auth=aiohttp.helpers.BasicAuth('nkim', '1234')) self.assertIn('AUTHORIZATION', req.headers) self.assertEqual('Basic bmtpbToxMjM0', req.headers['AUTHORIZATION']) def test_no_content_length(self): req = ClientRequest('get', 'http://python.org', loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual('0', req.headers.get('CONTENT-LENGTH')) req = ClientRequest('head', 'http://python.org', loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual('0', req.headers.get('CONTENT-LENGTH')) def test_path_is_not_double_encoded(self): req = ClientRequest('get', "http://0.0.0.0/get/test case") self.assertEqual(req.path, "/get/test%20case") req = ClientRequest('get', "http://0.0.0.0/get/test%2fcase") self.assertEqual(req.path, "/get/test%2fcase") req = ClientRequest('get', "http://0.0.0.0/get/test%20case") self.assertEqual(req.path, "/get/test%20case") def test_params_are_added_before_fragment(self): req = ClientRequest( 'GET', "http://example.com/path#fragment", params={"a": "b"}) self.assertEqual( req.path, "/path?a=b#fragment") req = ClientRequest( 'GET', "http://example.com/path?key=value#fragment", params={"a": "b"}) self.assertEqual( req.path, "/path?key=value&a=b#fragment") def test_cookies(self): req = ClientRequest( 'get', 'http://test.com/path', cookies={'cookie1': 'val1'}) self.assertIn('COOKIE', req.headers) self.assertEqual('cookie1=val1', req.headers['COOKIE']) req = ClientRequest( 'get', 'http://test.com/path', headers={'cookie': 'cookie1=val1'}, cookies={'cookie2': 'val2'}) self.assertEqual('cookie1=val1; cookie2=val2', req.headers['COOKIE']) def test_unicode_get(self): def join(*suffix): return urllib.parse.urljoin('http://python.org/', '/'.join(suffix)) url = 'http://python.org' req = ClientRequest('get', url, params={'foo': 'f\xf8\xf8'}) self.assertEqual('/?foo=f%C3%B8%C3%B8', req.path) req = ClientRequest('', url, params={'f\xf8\xf8': 'f\xf8\xf8'}) self.assertEqual('/?f%C3%B8%C3%B8=f%C3%B8%C3%B8', req.path) req = ClientRequest('', url, params={'foo': 'foo'}) self.assertEqual('/?foo=foo', req.path) req = ClientRequest('', join('\xf8'), params={'foo': 'foo'}) self.assertEqual('/%C3%B8?foo=foo', req.path) def test_query_multivalued_param(self): for meth in ClientRequest.ALL_METHODS: req = ClientRequest( meth, 'http://python.org', params=(('test', 'foo'), ('test', 'baz'))) self.assertEqual(req.path, '/?test=foo&test=baz') def test_post_data(self): for meth in ClientRequest.POST_METHODS: req = ClientRequest( meth, 'http://python.org/', data={'life': '42'}, loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual('/', req.path) self.assertEqual(b'life=42', req.body) self.assertEqual('application/x-www-form-urlencoded', req.headers['CONTENT-TYPE']) @unittest.mock.patch('aiohttp.client.ClientRequest.update_body_from_data') def test_pass_falsy_data(self, _): req = ClientRequest( 'post', 'http://python.org/', data={}, loop=self.loop) req.update_body_from_data.assert_called_once_with({}) def test_get_with_data(self): for meth in ClientRequest.GET_METHODS: req = ClientRequest( meth, 'http://python.org/', data={'life': '42'}) self.assertEqual('/', req.path) self.assertEqual(b'life=42', req.body) def test_bytes_data(self): for meth in ClientRequest.POST_METHODS: req = ClientRequest( meth, 'http://python.org/', data=b'binary data', loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual('/', req.path) self.assertEqual(b'binary data', req.body) self.assertEqual('application/octet-stream', req.headers['CONTENT-TYPE']) def test_files_and_bytes_data(self): with self.assertRaises(ValueError): with self.assertWarns(DeprecationWarning): ClientRequest( 'POST', 'http://python.org/', data=b'binary data', files={'file': b'file data'}) @unittest.mock.patch('aiohttp.client.aiohttp') def test_content_encoding(self, m_http): req = ClientRequest('get', 'http://python.org/', compress='deflate', loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual(req.headers['TRANSFER-ENCODING'], 'chunked') self.assertEqual(req.headers['CONTENT-ENCODING'], 'deflate') m_http.Request.return_value\ .add_compression_filter.assert_called_with('deflate') @unittest.mock.patch('aiohttp.client.aiohttp') def test_content_encoding_header(self, m_http): req = ClientRequest( 'get', 'http://python.org/', headers={'Content-Encoding': 'deflate'}, loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual(req.headers['TRANSFER-ENCODING'], 'chunked') self.assertEqual(req.headers['CONTENT-ENCODING'], 'deflate') m_http.Request.return_value\ .add_compression_filter.assert_called_with('deflate') m_http.Request.return_value\ .add_chunking_filter.assert_called_with(8192) def test_chunked(self): req = ClientRequest( 'get', 'http://python.org/', headers={'TRANSFER-ENCODING': 'gzip'}, loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual('gzip', req.headers['TRANSFER-ENCODING']) req = ClientRequest( 'get', 'http://python.org/', headers={'Transfer-encoding': 'chunked'}, loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual('chunked', req.headers['TRANSFER-ENCODING']) @unittest.mock.patch('aiohttp.client.aiohttp') def test_chunked_explicit(self, m_http): req = ClientRequest( 'get', 'http://python.org/', chunked=True, loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual('chunked', req.headers['TRANSFER-ENCODING']) m_http.Request.return_value\ .add_chunking_filter.assert_called_with(8192) @unittest.mock.patch('aiohttp.client.aiohttp') def test_chunked_explicit_size(self, m_http): req = ClientRequest( 'get', 'http://python.org/', chunked=1024, loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual('chunked', req.headers['TRANSFER-ENCODING']) m_http.Request.return_value\ .add_chunking_filter.assert_called_with(1024) def test_chunked_length(self): req = ClientRequest( 'get', 'http://python.org/', headers={'CONTENT-LENGTH': '1000'}, chunked=1024, loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual(req.headers['TRANSFER-ENCODING'], 'chunked') self.assertNotIn('CONTENT-LENGTH', req.headers) def test_expect100(self): req = ClientRequest('get', 'http://python.org/', expect100=True, loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual('100-continue', req.headers['EXPECT']) self.assertIsNotNone(req._continue) req = ClientRequest('get', 'http://python.org/', headers={'expect': '100-continue'}, loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual('100-continue', req.headers['EXPECT']) self.assertIsNotNone(req._continue) def test_data_stream(self): def gen(): yield b'binary data' return b' result' req = ClientRequest( 'POST', 'http://python.org/', data=gen(), loop=self.loop) self.assertTrue(req.chunked) self.assertTrue(inspect.isgenerator(req.body)) self.assertEqual(req.headers['TRANSFER-ENCODING'], 'chunked') resp = req.send(self.transport, self.protocol) self.assertIsInstance(req._writer, asyncio.Future) self.loop.run_until_complete(resp.wait_for_close()) self.assertIsNone(req._writer) self.assertEqual( self.transport.write.mock_calls[-3:], [unittest.mock.call(b'binary data result'), unittest.mock.call(b'\r\n'), unittest.mock.call(b'0\r\n\r\n')]) def test_data_file(self): req = ClientRequest( 'POST', 'http://python.org/', data=io.BytesIO(b'*' * 2), loop=self.loop) self.assertTrue(req.chunked) self.assertTrue(isinstance(req.body, io.IOBase)) self.assertEqual(req.headers['TRANSFER-ENCODING'], 'chunked') resp = req.send(self.transport, self.protocol) self.assertIsInstance(req._writer, asyncio.Future) self.loop.run_until_complete(resp.wait_for_close()) self.assertIsNone(req._writer) self.assertEqual( self.transport.write.mock_calls[-3:], [unittest.mock.call(b'*' * 2), unittest.mock.call(b'\r\n'), unittest.mock.call(b'0\r\n\r\n')]) def test_data_stream_exc(self): fut = asyncio.Future(loop=self.loop) def gen(): yield b'binary data' yield from fut return b' result' req = ClientRequest( 'POST', 'http://python.org/', data=gen(), loop=self.loop) self.assertTrue(req.chunked) self.assertTrue(inspect.isgenerator(req.body)) self.assertEqual(req.headers['TRANSFER-ENCODING'], 'chunked') @asyncio.coroutine def exc(): yield from asyncio.sleep(0.01, loop=self.loop) fut.set_exception(ValueError) asyncio.async(exc(), loop=self.loop) resp = req.send(self.transport, self.protocol) resp.connection = self.connection self.loop.run_until_complete(req._writer) self.assertTrue(self.connection.close.called) self.assertTrue(self.protocol.set_exception.called) def test_data_stream_not_bytes(self): @asyncio.coroutine def gen(): yield object() return b' result' req = ClientRequest( 'POST', 'http://python.org/', data=gen(), loop=self.loop) req.send(self.transport, self.protocol) self.loop.run_until_complete(req._writer) self.assertTrue(self.protocol.set_exception.called) def test_data_stream_exc_chain(self): fut = asyncio.Future(loop=self.loop) def gen(): yield from fut return b' result' req = ClientRequest( 'POST', 'http://python.org/', data=gen(), loop=self.loop) inner_exc = ValueError() @asyncio.coroutine def exc(): yield from asyncio.sleep(0.01, loop=self.loop) fut.set_exception(inner_exc) asyncio.async(exc(), loop=self.loop) resp = req.send(self.transport, self.protocol) resp.connection = self.connection self.loop.run_until_complete(req._writer) self.assertTrue(self.connection.close.called) self.assertTrue(self.protocol.set_exception.called) outer_exc = self.protocol.set_exception.call_args[0][0] self.assertIsInstance(outer_exc, aiohttp.ClientRequestError) self.assertIs(inner_exc, outer_exc.__context__) self.assertIs(inner_exc, outer_exc.__cause__) def test_data_stream_continue(self): def gen(): yield b'binary data' return b' result' req = ClientRequest( 'POST', 'http://python.org/', data=gen(), expect100=True, loop=self.loop) self.assertTrue(req.chunked) self.assertTrue(inspect.isgenerator(req.body)) def coro(): yield from asyncio.sleep(0.0001, loop=self.loop) req._continue.set_result(1) asyncio.async(coro(), loop=self.loop) req.send(self.transport, self.protocol) self.loop.run_until_complete(req._writer) self.assertEqual( self.transport.write.mock_calls[-3:], [unittest.mock.call(b'binary data result'), unittest.mock.call(b'\r\n'), unittest.mock.call(b'0\r\n\r\n')]) def test_data_continue(self): req = ClientRequest( 'POST', 'http://python.org/', data=b'data', expect100=True, loop=self.loop) def coro(): yield from asyncio.sleep(0.0001, loop=self.loop) req._continue.set_result(1) asyncio.async(coro(), loop=self.loop) req.send(self.transport, self.protocol) self.assertEqual(1, len(self.transport.write.mock_calls)) self.loop.run_until_complete(req._writer) self.assertEqual( self.transport.write.mock_calls[-1], unittest.mock.call(b'data')) def test_close(self): @asyncio.coroutine def gen(): yield from asyncio.sleep(0.00001, loop=self.loop) return b'result' req = ClientRequest( 'POST', 'http://python.org/', data=gen(), loop=self.loop) req.send(self.transport, self.protocol) self.loop.run_until_complete(req.close()) self.assertEqual( self.transport.write.mock_calls[-3:], [unittest.mock.call(b'result'), unittest.mock.call(b'\r\n'), unittest.mock.call(b'0\r\n\r\n')]) def test_custom_response_class(self): class CustomResponse(ClientResponse): def read(self, decode=False): return 'customized!' req = ClientRequest( 'GET', 'http://python.org/', response_class=CustomResponse, loop=self.loop) resp = req.send(self.transport, self.protocol) self.assertEqual('customized!', resp.read())

39.578223

79

0.611074

3,577

31,623

5.287112

0.081353

0.050127

0.036432

0.042566

0.799651

0.762003

0.726681

0.670685

0.64081

0.60496

0

0.011502

0.24947

31,623

798

80

39.62782

0.785329

0.001202

0

0.606383

0

0.136613

0.014897

0

0.231003

0

null

0.00152

0.015198

null

0

null

0

1

0

1

0

null

0

1

0

4

210eace1f941559dd477cf20d2205ab83dd98fe2

8,261

py

Python

pymps/lanczos.py

jacobmanalo/dmrg_tool

8a14b7f33b77f53df4356f090bdcd1a82b12ff20

[ "Apache-2.0" ]

null

pymps/lanczos.py

jacobmanalo/dmrg_tool

8a14b7f33b77f53df4356f090bdcd1a82b12ff20

[ "Apache-2.0" ]

null

pymps/lanczos.py

jacobmanalo/dmrg_tool

8a14b7f33b77f53df4356f090bdcd1a82b12ff20

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Feb 11 11:28:56 2022 Original Code from Dr. Salim Belhaiza https://www.youtube.com/watch?v=S416IbCFeEA&t=185s """ import numpy as np from scipy.sparse import linalg as la import scipy as SP import sys def random_hermitian(n): #A=np.random.rand(n,n) A = SP.sparse.rand(n, n, density=0.01) Adag = A.conjugate().transpose() H = 0.5*(A+Adag) H = H.toarray() return H def eigensolver(d,e,num_evals): evals, evecs = SP.linalg.eigh_tridiagonal(d, e, select='i', select_range=[0,num_evals-1]) return evals, evecs def lanczos_jake(A,num_iter,num_evals): """ Tridiagonalization of matrix A using Gram-Schmidt Orthogonalization i.e. the Lanczos algorithm. To avoid loss of orthogonality due to numerical error, I included a QR decomposition of the Krylov vector matrix Q. Parameters ---------- A : numpy array Matrix num_iter : int Number of Krylov vectors to be generated num_evals : numpy array Number of eigenvalues to be generated. Returns ------- evals : numpy array Array of eigenvalues evecs_transformed : numpy array Array of eigenvectors in the original basis of A """ m = A.shape[0] b = np.random.rand(m) Q = np.zeros((m, num_iter)) q = b / np.linalg.norm(b) Q[:,0] = q for k in range(num_iter): v = np.dot(A, Q[:,k]) #alpha[k] = np.dot(Q[:,k],v) v = v - b[k-1]*Q[:,k-1] - np.dot(Q[:,k],v)*Q[:,k] normv = np.linalg.norm(v) b[k]=normv eps = 1e-12 if normv > eps: q = v/normv if k < num_iter - 1: Q[:,k+1]=q else: print("norm is zero!",k,normv) break Q,_ = np.linalg.qr(Q) Aprime = np.dot(np.transpose(Q),np.dot(A,Q)) evals, evecs = la.eigs(Aprime,k=num_evals,which='SR') evecs_transformed = np.dot(Q,evecs) return evals.real, evecs_transformed def lanczos(A, r0, num_iter, num_evals): """ Tridiagonalization of matrix A using Gram-Schmidt Orthogonalization i.e. the Lanczos algorithm. To avoid loss of orthogonality due to numerical error, I included a QR decomposition of the Krylov vector matrix Q. Parameters ---------- A : numpy array Matrix num_iter : int Number of Krylov vectors to be generated num_evals : numpy array Number of eigenvalues to be generated. Returns ------- evals : numpy array Array of eigenvalues evecs_transformed : numpy array Array of eigenvectors in the original basis of A """ m = A.shape[0] eps = 1e-20 b = r0#np.random.rand(m) norm1 = np.linalg.norm(b) if norm1 < eps: b = np.random.rand(m) Q = np.zeros((m, num_iter)) q = b / np.linalg.norm(b) Q[:,0] = q b = q for k in range(num_iter): #v = np.matmul(A, Q[:,k]) v = A.dot(Q[:,k]) #alpha[k] = np.dot(Q[:,k],v) v = v - b[k-1]*Q[:,k-1] - np.dot(Q[:,k],v)*Q[:,k] normv = np.linalg.norm(v) b[k]=normv if normv > eps: q = v/normv if k < num_iter - 1: Q[:,k+1]=q else: print("norm is zero!",k,normv) break Q,_ = np.linalg.qr(Q) Aprime = np.dot(np.transpose(Q),A.dot(Q)) evals, evecs = la.eigs(Aprime,k=num_evals,which='SR') evecs_transformed = np.dot(Q,evecs) return evals.real, evecs_transformed.real def lanczos2(A, r0, num_iter): """ Obtain the lowest eigevalue and eigenvector of a Linear Operator A Implementation of Lanczos algorithm. Following section 4.4 of the book " Templates for the solution of algebraic eigenvalue problems: a practical guide" Parameters ---------- A : Linear Operator A linaer operador with the matrix a matrix-vector multiplication (matvec) as a member function. r0: ket state compatible with the linear operator Initial guest. Compatible mean that the operation A.matvec(r0) is defined num_iter : int Number max of iteration Returns ------- evals : lowest eigenvalue- float 64 evecs_transformed : ket corresponding to the lowest eigenvalue """ a = [] b = [] shape = r0.shape # for i in range(len(r0.shape)): # shape.append(r0.shape[i]) # #shape = r0.shape v = [] eps = sys.float_info.min tol = 1e-10 eval_ref = 1 ntest = 5 num_iter_max = num_iter + ntest - num_iter%ntest r = r0 norm = np.linalg.norm(r0) b.append(norm) id = 0 for i in range(num_iter_max): id = i if b[i] < abs(eps*eval_ref): r = np.random.rand(*shape) r *= 1./np.linalg.norm(r) Orthogonalize(r,v) b[i] = np.linalg.norm(r) v.append(r/b[i]) r = A.matvec(v[i]) if i > 0: r -= b[i]*v[i-1] a.append(np.sum(v[i]*r)) r -= a[i]*v[i] Orthogonalize(r,v) b.append(np.linalg.norm(r)) if (i+1)%ntest == 0: evals, evecs = SP.linalg.eigh_tridiagonal(a,b[1:id+1],select='i', select_range=[0,0]) error = abs(b[i+1]*evecs[i]) eval_ref = evals if error < tol: break if error > tol: print("Lanczos failed, residual norm = {}".format(error)) #res = np.transpose(v).dot(evecs) evecs_transformed = v[0]*evecs[0] for i in range(1,id): evecs_transformed += v[i]*evecs[i] return evals, evecs_transformed/np.linalg.norm(evecs_transformed) def Orthogonalize(r,v): """ Ortogonalize vector r with the vectors contained in v """ fac = 0.7 tol = 1e-14 n0 = np.linalg.norm(r) for i in range(len(v)): prod = np.sum(r*v[i]) if abs(prod) > tol: r -= prod*v[i] if np.linalg.norm(r)/n0 < fac: return for i in range(len(v)): prod = np.sum(r*v[i]) if abs(prod) > tol: r -= prod*v[i] #def lanczos2t(A, r0, num_iter): # """ # Obtain the lowest eigevalue and eigenvector of a Linear Operator A # Implementation of Lanczos algorithm. # Following section 4.4 of the book " Templates for the solution of algebraic # eigenvalue problems: a practical guide" # Parameters # ---------- # A : Linear Operator # # r0: vector # Initial guest # num_iter : int # Number max of iteration # Returns # ------- # evals : numpy array # Array of eigenvalues # evecs_transformed : numpy array # Array of eigenvectors in the original basis of A # """ # a = [] # b = [] # m = r0.shape[0] # v = [] # eps = sys.float_info.min # tol = 1e-13 # eval_ref = 1 # num_iter_max = num_iter + 5 - num_iter%5 # r = r0 # norm = np.linalg.norm(r0) # b.append(norm) # id = 0 # for i in range(num_iter_max): # id = i # # if b[i] < abs(eps*eval_ref): # r = np.random.rand(m) # r *= 1./np.linalg.norm(r) # Orthogonalize(r,v) # b[i] = np.linalg.norm(r) # # v.append(r/b[i]) # r = A.dot(v[i]) # if i > 0: # r -= b[i]*v[i-1] # a.append(np.dot(v[i],r)) # r -= a[i]*v[i] # Orthogonalize(r,v) # b.append(np.linalg.norm(r)) # if (i+1)%5 == 0: # evals, evecs = SP.linalg.eigh_tridiagonal(a,b[1:id+1],select='i', select_range=[0,0]) # error = abs(b[i+1]*evecs[i]) # eval_ref = evals # if error < tol: # break # if error > tol: # print("Lanczos failed, residual norm = {}".format(error)) # # res = np.transpose(v).dot(evecs) # return evals, res/np.linalg.norm(res)

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null

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4

2114d6302acbdc76e559f24b678d898dd52b2591

546

py

Python

todo/validators.py

abhijit-mitra/fractal_service

234a8ae954eb856ca31e72b003117d8b97ce4171

[ "MIT" ]

1

2020-02-10T17:49:35.000Z

todo/validators.py

abhijit-mitra/fractal_service

234a8ae954eb856ca31e72b003117d8b97ce4171

[ "MIT" ]

5

2020-06-06T00:36:03.000Z

2022-02-10T14:12:37.000Z

todo/validators.py

abhijit-mitra/fractal_service

234a8ae954eb856ca31e72b003117d8b97ce4171

[ "MIT" ]

null

CREATE_TODO = [{ 'field_name': 'bucketName', 'type': str, }, { 'field_name': 'name', 'type': str, }, { 'field_name': 'done', 'type': bool, }, { 'field_name': 'bucketId', 'type': int, 'required': False, 'blank': (True,) }] UPDATE_TODO = [{ 'field_name': 'name', 'type': str, }, { 'field_name': 'done', 'type': bool, 'required': False }, { 'field_name': 'bucketId', 'type': int, 'required': False, 'blank': (True,) }, { 'field_name': 'bucketName', 'type': str }]

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2123b0afaa5ed96d9bc928d0c370baaf17f037f4

91

py

Python

SalesApp/apps.py

Kayarn-Mechatronics/Octello

45f4f73c764ca816918c31ef3ae4889740a68802

[ "Apache-2.0" ]

null

SalesApp/apps.py

Kayarn-Mechatronics/Octello

45f4f73c764ca816918c31ef3ae4889740a68802

[ "Apache-2.0" ]

null

SalesApp/apps.py

Kayarn-Mechatronics/Octello

45f4f73c764ca816918c31ef3ae4889740a68802

[ "Apache-2.0" ]

null

from django.apps import AppConfig class SalesappConfig(AppConfig): name = 'SalesApp'

15.166667

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1

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1

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4

2136715c0e34258bc108b432c3821b98c34b07cd

14,491

py

Python

src/decoders/arcfactored.py

norikinishida/discourse-parsing

7377a78cc32ad6430d256694e31ed9426e7c6340

[ "Apache-2.0" ]

2

2022-02-16T20:41:22.000Z

2022-03-11T18:28:24.000Z

src/decoders/arcfactored.py

norikinishida/discourse-parsing

7377a78cc32ad6430d256694e31ed9426e7c6340

[ "Apache-2.0" ]

null

src/decoders/arcfactored.py

norikinishida/discourse-parsing

7377a78cc32ad6430d256694e31ed9426e7c6340

[ "Apache-2.0" ]

null

import numpy as np RIGHT = 0 LEFT = 1 COMPLETE = 0 INCOMPLETE = 1 class IncrementalEisnerDecoder(object): def __init__(self): self.decoder = EisnerDecoder() def decode(self, arc_scores, edu_ids, # sentence_boundaries, paragraph_boundaries, use_sentence_boundaries, use_paragraph_boundaries, # gold_heads=None): """ Parameters ---------- arc_scores: numpy.ndarray(shape=(n_edus, n_edus), dtype="float") arc_scores: numpy.ndarray(shape=(n_edus, n_edus), dtype="float") edu_ids: list[int] sentence_boundaries: list[(int, int)] paragraph_boundaries: list[(int, int)] use_sentence_boundaries: bool use_paragraph_boundaries: bool gold_heads: numpy.ndarray(shape=(n_edus, n_edus), dtype=np.int32) or None Returns ------- list[(int, int)] """ assert edu_ids[0] == 0 # ROOT arcs = [] # Exclude ROOT new_edu_ids = edu_ids[1:] # Sentence-level parsing if use_sentence_boundaries: target_bnds = sentence_boundaries sub_arcs, new_edu_ids = self.apply_decoder( arc_scores=arc_scores, edu_ids=new_edu_ids, target_bnds=target_bnds, gold_heads=gold_heads) arcs.extend(sub_arcs) # Paragraph-level parsing if use_paragraph_boundaries: if use_sentence_boundaries: target_bnds = paragraph_boundaries else: target_bnds = [(sentence_boundaries[b][0],sentence_boundaries[e][1]) for b,e in paragraph_boundaries] sub_arcs, new_edu_ids = self.apply_decoder( arc_scores=arc_scores, edu_ids=new_edu_ids, target_bnds=target_bnds, gold_heads=gold_heads) arcs.extend(sub_arcs) # Document-level parsing sub_arcs, head = self.decoder.decode_without_root( arc_scores=arc_scores, edu_ids=new_edu_ids, gold_heads=gold_heads) arcs.extend(sub_arcs) # Root attachment arcs.append((0, head)) return arcs def apply_decoder(self, arc_scores, edu_ids, target_bnds, gold_heads): """ Parameters ---------- arc_scores: numpy.ndarray(shape=(n_edus, n_edus), dtype="float") edu_ids: list[int] target_bnds: list[(int, int)] gold_heads: numpy.ndarray(shape=(n_edus, n_edus), dtype=np.int32) Returns ------- list[(int, int)] list[int] """ arcs = [] # list of (int, int) new_edu_ids = [] # list of int for begin_i, end_i in target_bnds: if begin_i == end_i: sub_arcs = [] head = edu_ids[begin_i] else: sub_arcs, head = self.decoder.decode_without_root( arc_scores=arc_scores, edu_ids=edu_ids[begin_i:end_i+1], gold_heads=gold_heads) arcs.extend(sub_arcs) new_edu_ids.append(head) return arcs, new_edu_ids class EisnerDecoder(object): def __init__(self): pass def decode(self, arc_scores, edu_ids, gold_heads=None): """ Parameters ---------- arc_scores: numpy.ndarray(shape=(n_edus, n_edus), dtype="float") edu_ids: list[int] gold_heads: numpy.ndarray(shape=(n_edus, n_edus), dtype=np.int32) or None Returns ------- list[(int, int)] """ assert edu_ids[0] == 0 # ROOT # Initialize charts chart = {} # {(int, int, int, int): float} back_ptr = {} # {(int, int, int, int): float} length = len(edu_ids) # Base case for i in range(length): chart[i, i, LEFT, COMPLETE] = 0.0 chart[i, i, RIGHT, COMPLETE] = 0.0 chart[i, i, LEFT, INCOMPLETE] = 0.0 chart[i, i, RIGHT, INCOMPLETE] = 0.0 for i in range(length): chart[0, i, LEFT, INCOMPLETE] = -np.inf # General case (without ROOT) for d in range(1, length): for i1 in range(1, length - d): # NOTE i3 = i1 + d # Incomplete span # Left tree max_score = -np.inf memo = None arc_score = arc_scores[edu_ids[i3], edu_ids[i1]] if gold_heads is not None: if gold_heads[edu_ids[i1]] != edu_ids[i3]: arc_score += 1.0 for i2 in range(i1, i3): score = arc_score \ + chart[i1, i2, RIGHT, COMPLETE] \ + chart[i2+1, i3, LEFT, COMPLETE] if max_score < score: max_score = score memo = i2 chart[i1, i3, LEFT, INCOMPLETE] = max_score back_ptr[i1, i3, LEFT, INCOMPLETE] = memo # Right tree max_score = -np.inf memo = None arc_score = arc_scores[edu_ids[i1], edu_ids[i3]] if gold_heads is not None: if gold_heads[edu_ids[i3]] != edu_ids[i1]: arc_score += 1.0 for i2 in range(i1, i3): score = arc_score \ + chart[i1, i2, RIGHT, COMPLETE] \ + chart[i2+1, i3, LEFT, COMPLETE] if max_score < score: max_score = score memo = i2 chart[i1, i3, RIGHT, INCOMPLETE] = max_score back_ptr[i1, i3, RIGHT, INCOMPLETE] = memo # Complete span # Left tree max_score = -np.inf memo = None for i2 in range(i1, i3): score = chart[i1, i2, LEFT, COMPLETE] \ + chart[i2, i3, LEFT, INCOMPLETE] if max_score < score: max_score = score memo = i2 chart[i1, i3, LEFT, COMPLETE] = max_score back_ptr[i1, i3, LEFT, COMPLETE] = memo # Right tree max_score = -np.inf memo = None for i2 in range(i1, i3): score = chart[i1, i2+1, RIGHT, INCOMPLETE] \ + chart[i2+1, i3, RIGHT, COMPLETE] if max_score < score: max_score = score memo = i2 + 1 chart[i1, i3, RIGHT, COMPLETE] = max_score back_ptr[i1, i3, RIGHT, COMPLETE] = memo # ROOT attachment # arcs = self.recover_tree(back_ptr, 0, length-1, RIGHT, COMPLETE, arcs=None) # NOTE max_score = -np.inf memo = None for i2 in range(1, length): arc_score = arc_scores[edu_ids[0], edu_ids[i2]] score = arc_score \ + chart[0, 0, RIGHT, COMPLETE] \ + chart[1, i2, LEFT, COMPLETE] \ + chart[i2, length-1, RIGHT, COMPLETE] if max_score < score: max_score = score memo = i2 chart[0, length-1, RIGHT, COMPLETE] = max_score back_ptr[0, length-1, RIGHT, COMPLETE] = memo head = memo # Recovering dependency arcs arcs = [(0, head)] arcs = self.recover_tree(back_ptr, 1, head, LEFT, COMPLETE, arcs=arcs) arcs = self.recover_tree(back_ptr, head, length-1, RIGHT, COMPLETE, arcs=arcs) # Shifting: local position -> global position arcs = [(edu_ids[h], edu_ids[d]) for h,d in arcs] return arcs def decode_without_root(self, arc_scores, edu_ids, gold_heads=None): """ Parameters ---------- arc_scores: numpy.ndarray(shape=(n_edus, n_edus), dtype="float") edu_ids: list[int] gold_heads: numpy.ndarray(shape=(n_edus, n_edus), dtype=np.int32) or None Returns list[(int, int)] int """ assert edu_ids[0] != 0 # No ROOT if len(edu_ids) == 1: return [], edu_ids[0] # Initialize charts chart = {} # {(int, int, int, int): float} back_ptr = {} # {(int, int, int, int): float} length = len(edu_ids) # Base case for i in range(length): chart[i, i, LEFT, COMPLETE] = 0.0 chart[i, i, RIGHT, COMPLETE] = 0.0 chart[i, i, LEFT, INCOMPLETE] = 0.0 chart[i, i, RIGHT, INCOMPLETE] = 0.0 # General case for d in range(1, length): for i1 in range(0, length - d): # NOTE: index "0" does NOT represent ROOT i3 = i1 + d # Incomplete span # Left tree max_score = -np.inf memo = None arc_score = arc_scores[edu_ids[i3], edu_ids[i1]] if gold_heads is not None: if gold_heads[edu_ids[i1]] != edu_ids[i3]: arc_score += 1.0 for i2 in range(i1, i3): score = arc_score \ + chart[i1, i2, RIGHT, COMPLETE] \ + chart[i2+1, i3, LEFT, COMPLETE] if max_score < score: max_score = score memo = i2 chart[i1, i3, LEFT, INCOMPLETE] = max_score back_ptr[i1, i3, LEFT, INCOMPLETE] = memo # Right tree max_score = -np.inf memo = None arc_score = arc_scores[edu_ids[i1], edu_ids[i3]] if gold_heads is not None: if gold_heads[edu_ids[i3]] != edu_ids[i1]: arc_score += 1.0 for i2 in range(i1, i3): score = arc_score \ + chart[i1, i2, RIGHT, COMPLETE] \ + chart[i2+1, i3, LEFT, COMPLETE] if max_score < score: max_score = score memo = i2 chart[i1, i3, RIGHT, INCOMPLETE] = max_score back_ptr[i1, i3, RIGHT, INCOMPLETE] = memo # Complete span # Left tree max_score = -np.inf memo = None for i2 in range(i1, i3): score = chart[i1, i2, LEFT, COMPLETE] \ + chart[i2, i3, LEFT, INCOMPLETE] if max_score < score: max_score = score memo = i2 chart[i1, i3, LEFT, COMPLETE] = max_score back_ptr[i1, i3, LEFT, COMPLETE] = memo # Right tree max_score = -np.inf memo = None for i2 in range(i1, i3): score = chart[i1, i2+1, RIGHT, INCOMPLETE] \ + chart[i2+1, i3, RIGHT, COMPLETE] if max_score < score: max_score = score memo = i2 + 1 chart[i1, i3, RIGHT, COMPLETE] = max_score back_ptr[i1, i3, RIGHT, COMPLETE] = memo # ROOT identification max_score = -np.inf memo = None for i2 in range(0, length): score = chart[0, i2, LEFT, COMPLETE] \ + chart[i2, length-1, RIGHT, COMPLETE] if max_score < score: max_score = score memo = i2 head = memo # Recovering dependency arcs arcs = self.recover_tree(back_ptr, 0, head, LEFT, COMPLETE, arcs=None) arcs = self.recover_tree(back_ptr, head, length-1, RIGHT, COMPLETE, arcs=arcs) # Shifting: local position -> global position arcs = [(edu_ids[h], edu_ids[d]) for h,d in arcs] head = edu_ids[head] return arcs, head def recover_tree(self, back_ptr, i1, i3, direction, complete, arcs=None): """ Parameters ---------- back_ptr: dict[(int, int, int, int), int] i1: int i3: int direction: int complete: int arcs: list[(int, int)] or None Returns ------- list[(int, int)] """ if arcs is None: arcs = [] if i1 == i3: return arcs i2 = back_ptr[i1, i3, direction, complete] if complete == COMPLETE: if direction == LEFT: arcs = self.recover_tree(back_ptr, i1, i2, LEFT, COMPLETE, arcs=arcs) arcs = self.recover_tree(back_ptr, i2, i3, LEFT, INCOMPLETE, arcs=arcs) else: arcs = self.recover_tree(back_ptr, i1, i2, RIGHT, INCOMPLETE, arcs=arcs) arcs = self.recover_tree(back_ptr, i2, i3, RIGHT, COMPLETE, arcs=arcs) else: if direction == LEFT: arcs.append((i3, i1)) arcs = self.recover_tree(back_ptr, i1, i2, RIGHT, COMPLETE, arcs=arcs) arcs = self.recover_tree(back_ptr, i2+1, i3, LEFT, COMPLETE, arcs=arcs) else: arcs.append((i1, i3)) arcs = self.recover_tree(back_ptr, i1, i2, RIGHT, COMPLETE, arcs=arcs) arcs = self.recover_tree(back_ptr, i2+1, i3, LEFT, COMPLETE, arcs=arcs) return arcs

36.047264

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0.459182

1,589

14,491

4.006923

0.069855

0.050887

0.040836

0.030627

0.773206

0.766923

0.719648

0.694519

0.678813

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0.030228

0.445242

14,491

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36.137157

0.761786

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false

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1

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null

0

4

2142da03589a5f59758f0a45ca10464c6cbed83b

55

py

Python

nnuncert/models/pbp/__init__.py

pjoachims/nnuncert

45dede54fdb714926926d719be2c9b9b542b2601

[ "MIT" ]

2

2021-12-30T06:25:43.000Z

2022-01-25T00:41:22.000Z

nnuncert/models/pbp/__init__.py

pjoachims/nnuncert

45dede54fdb714926926d719be2c9b9b542b2601

[ "MIT" ]

1

2022-01-25T00:35:28.000Z

2022-03-28T15:23:16.000Z

nnuncert/models/pbp/__init__.py

pjoachims/nnuncert

45dede54fdb714926926d719be2c9b9b542b2601

[ "MIT" ]

null

from nnuncert.models.pbp.model import PBPModel, PBPPred

55

0.854545

8

55

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1

0

0.072727

55

1

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dcdc351e3f1ef4a2167b8f72258374c86a28517f

308

py

Python

opencv_learn/charpter15/test.py

zhangxinzhou/play_game

854448f8416b2d3f98bb2c3ed0f7d834a61593de

[ "Apache-2.0" ]

null

opencv_learn/charpter15/test.py

zhangxinzhou/play_game

854448f8416b2d3f98bb2c3ed0f7d834a61593de

[ "Apache-2.0" ]

null

opencv_learn/charpter15/test.py

zhangxinzhou/play_game

854448f8416b2d3f98bb2c3ed0f7d834a61593de

[ "Apache-2.0" ]

null

import numpy as np am = np.array( [ [3, 6, 8, 77, 66], [1, 2, 88, 3, 98], [11, 2, 67, 5, 2] ] ) b = np.where(am > 5) print(type(b)) print(b) print("==========") for i in zip(b): print(i) print("==========") print(*b) print("==========") for i in zip(*b): print(i)

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0d3db71ae427d65d2e4455da73eb382d03b40227

609

py

Python

climetlab/core/docstring.py

sylvielamythepaut/climetlab

59516b8a510ad506a12ad32bea9e8b98bdb9abf3

[ "Apache-2.0" ]

1

2021-10-02T12:30:12.000Z

climetlab/core/docstring.py

sylvielamythepaut/climetlab

59516b8a510ad506a12ad32bea9e8b98bdb9abf3

[ "Apache-2.0" ]

null

climetlab/core/docstring.py

sylvielamythepaut/climetlab

59516b8a510ad506a12ad32bea9e8b98bdb9abf3

[ "Apache-2.0" ]

null

# (C) Copyright 2020 ECMWF. # # This software is licensed under the terms of the Apache Licence Version 2.0 # which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. # In applying this licence, ECMWF does not waive the privileges and immunities # granted to it by virtue of its status as an intergovernmental organisation # nor does it submit to any jurisdiction. # # Keep linters happy # N801 = classes should start with uppercase class docstring: # noqa: N801 def __init__(self): pass def __call__(self, func): # func.__doc__ += "Decorated...." return func

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b4c056643da6f1fbb3dd080732017533a6843b7f

375

py

Python

trec2015/cuttsum/pipeline/__init__.py

kedz/cuttsum

992c21192af03fd2ef863f5ab7d10752f75580fa

[ "Apache-2.0" ]

6

2015-09-10T02:22:21.000Z

2021-10-01T16:36:46.000Z

trec2015/cuttsum/pipeline/__init__.py

kedz/cuttsum

992c21192af03fd2ef863f5ab7d10752f75580fa

[ "Apache-2.0" ]

null

trec2015/cuttsum/pipeline/__init__.py

kedz/cuttsum

992c21192af03fd2ef863f5ab7d10752f75580fa

[ "Apache-2.0" ]

2

2018-04-04T10:44:32.000Z

2021-10-01T16:37:26.000Z

from cuttsum.pipeline._article_annotator import ArticlesResource from cuttsum.pipeline._dedupe import DedupedArticlesResource from cuttsum.pipeline._features import SentenceFeaturesResource from cuttsum.pipeline._input_stream import InputStreamResource __all__ = ["ArticlesResource", "DedupedArticlesResource", "SentenceFeaturesResource", "InputStreamResource"]

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b4fcfcdcd2f4bfb8a31e1b613345fad0c5f69146

181

py

Python

pm4pyws/handlers/xes/filtering/versions/__init__.py

ehbasouri/pm4py-ws

9bf5f88848a4aa2873bae86af95d37f64ae1dde1

[ "Apache-2.0" ]

null

pm4pyws/handlers/xes/filtering/versions/__init__.py

ehbasouri/pm4py-ws

9bf5f88848a4aa2873bae86af95d37f64ae1dde1

[ "Apache-2.0" ]

null

pm4pyws/handlers/xes/filtering/versions/__init__.py

ehbasouri/pm4py-ws

9bf5f88848a4aa2873bae86af95d37f64ae1dde1

[ "Apache-2.0" ]

null

from pm4pyws.handlers.xes.filtering.versions import start_activities, end_activities, attributes_pos_trace, \ attributes_neg_trace, attributes_pos_events, attributes_neg_events

60.333333

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0

1

0

1

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4

3701addf0f1b3f3999af1c9ffe555fda45bfbb45

5,022

py

Python

TokenizeTweet.py

jtpastro/twitter_traffic_incident_sensor

d9191e4db4dfe48e689817511e3b97b5901893ac

[ "MIT" ]

null

TokenizeTweet.py

jtpastro/twitter_traffic_incident_sensor

d9191e4db4dfe48e689817511e3b97b5901893ac

[ "MIT" ]

null

TokenizeTweet.py

jtpastro/twitter_traffic_incident_sensor

d9191e4db4dfe48e689817511e3b97b5901893ac

[ "MIT" ]

null

from nltk.tokenize import TweetTokenizer from nltk.stem import RSLPStemmer from unidecode import unidecode as unicodeToAscii from nltk.corpus import stopwords import re class Token(str): timeRegex = re.compile(r'^\d{1,2}((h(\d{2})?)|:\d{2})$') dateRegex = re.compile(r'^\d{1,2}/\d{1,2}(/\d{2,4})?$') wordRegex = re.compile(r'^([a-z-]+|[#@]\w+)$') numberRegex = re.compile(r'^-?\d+([,\.]?\d)*$') def __new__(cls, value): if Token.wordRegex.match(value): obj = str.__new__(cls, value) elif Token.timeRegex.match(value): obj = str.__new__(cls, '__TTKN__') elif Token.dateRegex.match(value): obj = str.__new__(cls, '__DTKN__') elif Token.numberRegex.match(value): obj = str.__new__(cls, '__NTKN__') else: obj = str.__new__(cls, '') obj.value = value return obj class FilterTokenizer(TweetTokenizer): def __init__(self, filterStopwords=True, stemming=False, groupClasses=True): self.groupClasses = groupClasses if stemming: self.stemmer = RSLPStemmer() else: self.stemmer = lambda: None self.stemmer.stem = lambda x: x self.stopwords = [unicodeToAscii(sw) for sw in stopwords.words('portuguese')] if filterStopwords else [] super().__init__(preserve_case=False,reduce_len=True) def tokenize(self, tweet): for tkn in super().tokenize(unicodeToAscii(tweet)): if self.groupClasses: tkn = Token(tkn) if len(tkn) > 1 and not tkn in self.stopwords: yield self.stemmer.stem(tkn) if __name__ == '__main__': tt = FilterTokenizer() tweets = ["Problema mesmo \u00e9 na BR386: diria que h\u00e1 uns 10 km de congestionamento em cada sentido. \nNo C/I, tranca da BR448 at\u00e9 a Ponte do Ca\u00ed.\nNo I/C, tranca antes do acesso ao polo petroqu\u00edmico at\u00e9 a Ponte do Ca\u00ed.\nVai demorar algumas horas pra normalizar ap\u00f3s acidente @GauchaZH", "16h43 - Aproveite o final de semana com consci\u00eancia! \u00c1lcool e dire\u00e7\u00e3o n\u00e3o combinam. #Educa\u00e7\u00e3oEPTCpic.twitter.com/FAHeKL4UKF", "Fim das obras na Av. Crist\u00f3v\u00e3o Colombo com Ramiro Barcelos. Tr\u00e2nsito volta a fluir melhor na regi\u00e3o. Mas Ramiro segue movimentada na descida, rumo \u00e0 Legalidade @GauchaZH", "16h37 - Tr\u00e2nsito totalmente liberado na R. Ramiro Barcelos esq. com a Av. Crist\u00f3v\u00e3o Colombo. Tr\u00e2nsito fluindo bem na regi\u00e3o.", "Regi\u00e3o do Aeroporto bastante movimentada nesta tarde. Sa\u00edda com mais tr\u00e2nsito pela Terceira Perimetral e Sert\u00f3rio. Chegada \u00e0 Capital ainda sem tranqueiras @GauchaZHpic.twitter.com/uCD6lquRpP", " ATEN\u00c7\u00c3O PARA BLOQUEIO pic.twitter.com/1S8bokO7rq", " ATEN\u00c7\u00c3O PARA BLOQUEIO pic.twitter.com/IAttzhDSkU", "O curso EAD \"Pedalando com seguran\u00e7a\" gratuito\n\nInscri\u00e7\u00f5es: https://goo.gl/8aPAmJ\u00a0pic.twitter.com/S0nLHY7eBB", "ATEN\u00c7\u00c3O!!!!https://twitter.com/PRF191RS/status/972483823580647427\u00a0\u2026", "https://gauchazh.clicrbs.com.br/esportes/gauchao/noticia/2018/03/bm-reforca-policiamento-no-entorno-do-beira-rio-e-orienta-deslocamento-de-torcidas-para-o-gre-nal-413-cjelfllns01xs01p46a7ljlfu.html\u00a0\u2026", "BM refor\u00e7a policiamento no entorno do Beira-Rio e orienta deslocamento de torcidas para o Gre-Nal 413. O esquema de seguran\u00e7a, tr\u00e2nsito e locais das concentra\u00e7\u00f5es de torcidas aqui: \nhttps://gauchazh.clicrbs.com.br/esportes/gauchao/noticia/2018/03/bm-reforca-policiamento-no-entorno-do-beira-rio-e-orienta-deslocamento-de-torcidas-para-o-gre-nal-413-cjelfllns01xs01p46a7ljlfu.html\u00a0\u2026 @GauchaZHpic.twitter.com/tYjCSS3umr", "concentra\u00e7\u00e3o na pra\u00e7a do canh\u00e3o, na marinha. sa\u00edda \u00e0s 15h pro est\u00e1dio", "Segundo a EPTC, a tarifa da lota\u00e7\u00e3o pode variar no m\u00ednimo 1,4 vezes o valor do \u00f4nibus. Com a passagem de \u00f4nibus a 4,30, d\u00e1 6,02 em 1,4x. Foi arrendondado pra mais, 6,05, porque se ficasse em 6 a\u00ed seria menos de 1,4x. Entende? hehe", "Pois\u00e9, estamos esclarecendo isso agora. Obrigada pelo toque!", "a\u00ed tem que ver com as torcidas organizadas. s\u00e3o eles que organizam esses transportes.", "N\u00e3o h\u00e1 ciclofaixa? Ande do lado direito junto ao meio fio.pic.twitter.com/plQ5kMuVgU", "Usu\u00e1rios t\u00eam at\u00e9 segunda para recarregar cart\u00e3o TRI sem o reajuste da passagem de \u00f4nibus em Porto Alegre. Mais esclarecimentos sobre as mudan\u00e7as na tarifa em @GauchaZH: https://gauchazh.clicrbs.com.br/porto-alegre/noticia/2018/03/usuarios-tem-ate-segunda-para-recarregar-cartao-tri-sem-o-reajuste-da-passagem-de-onibus-em-porto-alegre-cjelddax701y101r4lbpirec4.html\u00a0\u2026 pic.twitter.com/th6keq4zOy", "a 10/12 1:2 c/i centro/bairro centro-bairro"] for tweet in tweets: print(" ".join([tkn for tkn in tt.tokenize(tweet)]))

102.489796

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2e9a7cbe81552d1fd36c3adbe156d256d2b8546b

193

py

Python

pingdomexport/tests/test_export.py

mattboston/pingdomexport

1cd7acbf813abee0b9a7865b9cd4a1e166d55c37

[ "MIT" ]

4

2018-01-25T09:18:38.000Z

2021-02-12T18:36:08.000Z

pingdomexport/tests/test_export.py

mattboston/pingdomexport

1cd7acbf813abee0b9a7865b9cd4a1e166d55c37

[ "MIT" ]

1

2018-12-04T18:42:06.000Z

2021-05-25T14:03:32.000Z

pingdomexport/tests/test_export.py

mattboston/pingdomexport

1cd7acbf813abee0b9a7865b9cd4a1e166d55c37

[ "MIT" ]

3

2019-04-30T11:52:14.000Z

2021-03-24T20:58:04.000Z

import pytest from pingdomexport import export class TestExport: def test_export_path_unrecognized(self): with pytest.raises(ValueError): export.Export("unrecognized")

24.125

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2ea00266b8ce0afa65518c54a9e3e2b20a676e64

5,746

py

Python

environments/MiniGrid/loggingFunctions.py

vanstrn/RL_public

0e971e40e063b17918460e19728f95d7924af8db

[ "MIT" ]

1

2021-03-19T17:57:51.000Z

environments/MiniGrid/loggingFunctions.py

vanstrn/RL_public

0e971e40e063b17918460e19728f95d7924af8db

[ "MIT" ]

null

environments/MiniGrid/loggingFunctions.py

vanstrn/RL_public

0e971e40e063b17918460e19728f95d7924af8db

[ "MIT" ]

null

import tensorflow as tf import numpy as np import itertools import matplotlib.pyplot as plt import matplotlib.image as mpimg def ConstructSampleMG4R(env,position): grid = env.grid.encode() flagX,flagY = np.unravel_index(np.argmax(grid[:,:,0], axis=None), grid[:,:,0].shape) grid[flagX,flagY] = np.array([ 8, 8, 8 ]) if grid[position[0],position[1],1] == 5: return None grid[position[0],position[1],0] = 10 grid[position[0],position[1],2] = 10 return grid[:,:,np.r_[0,2]] def ConstructSampleMG4RP(env,position): cell = env.grid.get(*position) if cell.type in ["goal",'lava']: return None env.agent_pos = position return env.render(mode = "nah, No render") class ValuePredictionEvaluation(tf.keras.callbacks.Callback): def __init__(self,superEpochs,env,network,imageDir=None,freq=50): self.env = env self.network=network[3] self.imageDir=imageDir self.freq = freq self.superEpochs = superEpochs def on_train_end(self, logs=None): if self.superEpochs%self.freq == 0: self.env.reset() rewardMap = np.zeros([self.env.width,self.env.height]) for i,j in itertools.product(range(self.env.width),range(self.env.height)): grid = ConstructSampleMG4R(self.env,[i,j]) if grid is None: continue value = self.network.predict(np.expand_dims(grid,0)) rewardMap[i,j] = value fig=plt.figure(figsize=(5.5, 5.5)) fig.add_subplot(1,1,1) plt.title("Value Prediction Epoch "+str(self.superEpochs)) imgplot = plt.imshow(rewardMap) fig.colorbar(imgplot) plt.savefig(self.imageDir+"/ValuePred"+str(self.superEpochs)+".png") plt.close() class StatePredictionEvaluation(tf.keras.callbacks.Callback): def __init__(self,env,network,imageDir=None,freq=50): self.env = env self.network=network[0] self.imageDir = imageDir self.freq = freq def on_epoch_end(self,epoch, logs=None): if epoch%self.freq == 0: state = self.env.reset() state_new,reward = self.network.predict(state) fig=plt.figure(figsize=(5.5, 5.5)) fig.add_subplot(1,1,1) plt.title("Predicted Next State Epoch "+str(epoch)) imgplot = plt.imshow(state_new[0,:,:,0],vmin=0, vmax=10) plt.savefig(self.imageDir+"/StatePredEpoch"+str(epoch)+".png") plt.close() class StatePredictionEvaluation_action(tf.keras.callbacks.Callback): def __init__(self,env,network,imageDir=None,freq=50): self.env = env self.network=network[0] self.imageDir = imageDir self.freq = freq def on_epoch_end(self,epoch, logs=None): if epoch%self.freq == 0: state = self.env.reset() fig=plt.figure(figsize=(17, 5.5)) fig.add_subplot(1,5,1) plt.title("State Epoch "+str(epoch)) imgplot = plt.imshow(state[0,:,:,0],vmin=0, vmax=10) for i in range(4): act = np.zeros([1,4]) act[0,i] = 1 state_new,reward = self.network.predict([act,state]) fig.add_subplot(1,5,i+2) plt.title("Predicted Next State Epoch "+str(epoch)) imgplot = plt.imshow(state_new[0,:,:,0],vmin=0, vmax=10) plt.savefig(self.imageDir+"/StatePredEpoch"+str(epoch)+".png") plt.close() class RewardPredictionEvaluation(tf.keras.callbacks.Callback): def __init__(self,env,network,imageDir=None,freq=50): self.env = env self.network=network[0] self.imageDir = imageDir self.freq = freq def on_epoch_end(self,epoch, logs=None): if epoch%self.freq == 0: self.env.reset() rewardMap = np.zeros([self.env.width,self.env.height]) for i,j in itertools.product(range(self.env.width),range(self.env.height)): grid = ConstructSampleMG4R(self.env,[i,j]) if grid is None: continue state_new,reward = self.network.predict(np.expand_dims(grid,0)) rewardMap[i,j] = reward fig=plt.figure(figsize=(5.5, 5.5)) fig.add_subplot(1,1,1) plt.title("Reward Prediction Epoch "+str(epoch)) imgplot = plt.imshow(rewardMap) fig.colorbar(imgplot) plt.savefig(self.imageDir+"/RewardPred"+str(epoch)+".png") plt.close() class RewardPredictionEvaluation_action(tf.keras.callbacks.Callback): def __init__(self,env,network,imageDir=None,freq=50): self.env = env self.network=network[0] self.imageDir = imageDir self.freq = freq def on_epoch_end(self,epoch, logs=None): if epoch%self.freq == 0: self.env.reset() rewardMap = np.zeros([self.env.width,self.env.height]) for i,j in itertools.product(range(self.env.width),range(self.env.height)): grid = ConstructSampleMG4R(self.env,[i,j]) if grid is None: continue act = np.zeros([1,4]) # act[0,i] = 1 state_new,reward = self.network.predict([np.stack([[0,0,0,0]]),np.expand_dims(grid,0)]) rewardMap[i,j] = reward fig=plt.figure(figsize=(5.5, 5.5)) fig.add_subplot(1,1,1) plt.title("Reward Prediction Epoch "+str(epoch)) imgplot = plt.imshow(rewardMap) fig.colorbar(imgplot) plt.savefig(self.imageDir+"/RewardPred"+str(epoch)+".png") plt.close()

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2ea1b5690a4c5c82af8619cc0e9a2fcfb34a9b8a

20

py

Python

example_snippets/multimenus_snippets/Snippets/NumPy/Vectorized (universal) functions/Sums, products, differences within array/cumsum Cumulative sum of the elements along a given axis.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

null

example_snippets/multimenus_snippets/Snippets/NumPy/Vectorized (universal) functions/Sums, products, differences within array/cumsum Cumulative sum of the elements along a given axis.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

null

example_snippets/multimenus_snippets/Snippets/NumPy/Vectorized (universal) functions/Sums, products, differences within array/cumsum Cumulative sum of the elements along a given axis.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

1

2021-02-04T04:51:48.000Z

np.cumsum(a, axis=0)

20

0.7

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null

0

1

0

null

0

1

0

4

2eb0eedeada92d18cbeda5e7f5647f60fe40731b

121

py

Python

setup.py

neerajsharma9195/adversarial-recommendation-systems

46500d18a7175237f07df80af4f9b25e9d4c1188

[ "MIT" ]

2

2021-03-05T17:12:53.000Z

2021-03-19T18:04:20.000Z

setup.py

neerajsharma9195/adversarial-recommendation-systems

46500d18a7175237f07df80af4f9b25e9d4c1188

[ "MIT" ]

1

2021-03-06T00:58:56.000Z

setup.py

neerajsharma9195/adversarial-recommendation-systems

46500d18a7175237f07df80af4f9b25e9d4c1188

[ "MIT" ]

null

from setuptools import setup from Cython.Build import cythonize setup( ext_modules = cythonize("src/models/*.pyx") )

20.166667

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0

4

2edfead133d3f7ee510af74ca2d8c57988538838

31

py

Python

Python/float.py

zSucrilhos/programming

aa0076a4a7084a6064e1e5df258ba0c90cf8ceeb

[ "MIT" ]

null

Python/float.py

zSucrilhos/programming

aa0076a4a7084a6064e1e5df258ba0c90cf8ceeb

[ "MIT" ]

4

2020-07-18T03:27:03.000Z

2020-07-18T03:28:37.000Z

Python/float.py

zSucrilhos/programming

aa0076a4a7084a6064e1e5df258ba0c90cf8ceeb

[ "MIT" ]

null

a=1.23 print(a) print(type(a))

7.75

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0

4

2ef97251288ee2925580a4ac7956e57e50d63431

736

py

Python

Problems/Dynamic Programming/Easy/PascalTriangle/test_pascal_triangle.py

dolong2110/Algorithm-By-Problems-Python

31ecc7367aaabdd2b0ac0af7f63ca5796d70c730

[ "MIT" ]

1

2021-08-16T14:52:05.000Z

Problems/Dynamic Programming/Easy/PascalTriangle/test_pascal_triangle.py

dolong2110/Algorithm-By-Problems-Python

31ecc7367aaabdd2b0ac0af7f63ca5796d70c730

[ "MIT" ]

null

Problems/Dynamic Programming/Easy/PascalTriangle/test_pascal_triangle.py

dolong2110/Algorithm-By-Problems-Python

31ecc7367aaabdd2b0ac0af7f63ca5796d70c730

[ "MIT" ]

null

from unittest import TestCase from pascal_triangle import generate, getRow class Test(TestCase): def test_generate(self): self.assertTrue(generate(1) == [[1]]) self.assertTrue(generate(2) == [[1], [1, 1]]) self.assertTrue(generate(3) == [[1], [1, 1], [1, 2, 1]]) self.assertTrue(generate(4) == [[1], [1, 1], [1, 2, 1], [1, 3, 3, 1]]) self.assertTrue(generate(5) == [[1], [1, 1], [1, 2, 1], [1, 3, 3, 1], [1, 4, 6, 4, 1]]) def test_get_row(self): self.assertTrue(getRow(0) == [1]) self.assertTrue(getRow(1) == [1, 1]) self.assertTrue(getRow(2) == [1, 2, 1]) self.assertTrue(getRow(3) == [1, 3, 3, 1]) self.assertTrue(getRow(4) == [1, 4, 6, 4, 1])

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null

0

1

0

4

2c0a17955f0b3a41b6d6d982db2b74f30e4c43af

215

py

Python

Django/api-basic2/accounts/views.py

sug5806/TIL

2309d8a270e4a7b8961268a40b6492c5db317e37

[ "MIT" ]

null

Django/api-basic2/accounts/views.py

sug5806/TIL

2309d8a270e4a7b8961268a40b6492c5db317e37

[ "MIT" ]

102

2020-02-12T00:10:33.000Z

2022-03-11T23:58:41.000Z

Django/api-basic2/accounts/views.py

sug5806/TIL

2309d8a270e4a7b8961268a40b6492c5db317e37

[ "MIT" ]

null

from rest_framework import generics from .serializers import * # Create your views here. class AccountLCAPI(generics.ListCreateAPIView): queryset = get_user_model().objects.all() serializer_class = Account

26.875

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null

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null

0

1

0

1

0

4

2c1c9277947e14ab23b6c2966b1322b29eb10062

162

py

Python

problem0427.py

kmarcini/Project-Euler-Python

d644e8e1ec4fac70a9ab407ad5e1f0a75547c8d3

[ "BSD-3-Clause" ]

null

problem0427.py

kmarcini/Project-Euler-Python

d644e8e1ec4fac70a9ab407ad5e1f0a75547c8d3

[ "BSD-3-Clause" ]

null

problem0427.py

kmarcini/Project-Euler-Python

d644e8e1ec4fac70a9ab407ad5e1f0a75547c8d3

[ "BSD-3-Clause" ]

null

########################### # # #427 n-sequences - Project Euler # https://projecteuler.net/problem=427 # # Code by Kevin Marciniak # ###########################

18

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0.041667

0.111111

162

8

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null

0

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4

2c294b7cb6cd4e6c15ae5e7c5d1cf3badafff066

189

py

Python

comments/non_persistent_models.py

taufactor/tau-factor

d7883156bd9502742e0ad5c798fa1b2c38c7ff60

[ "MIT" ]

null

comments/non_persistent_models.py

taufactor/tau-factor

d7883156bd9502742e0ad5c798fa1b2c38c7ff60

[ "MIT" ]

null

comments/non_persistent_models.py

taufactor/tau-factor

d7883156bd9502742e0ad5c798fa1b2c38c7ff60

[ "MIT" ]

1

2021-05-18T19:01:14.000Z

import typing from courses import models as courses_models class CreateCourseCommentParams(typing.NamedTuple): course: courses_models.Course title: str content: str

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null

0

1

0

1

0

4

257c374a83306162a9cc67c4c3e292c420eab766

9,511

py

Python

rlkit/samplers/rollout_functions.py

AndrewPaulChester/rlkit

0743c713d60250013803f7f158a38b431f6c9fa9

[ "MIT" ]

null

rlkit/samplers/rollout_functions.py

AndrewPaulChester/rlkit

0743c713d60250013803f7f158a38b431f6c9fa9

[ "MIT" ]

null

rlkit/samplers/rollout_functions.py

AndrewPaulChester/rlkit

0743c713d60250013803f7f158a38b431f6c9fa9

[ "MIT" ]

null

import numpy as np from gym_craft.utils.representations import json_to_screen def multitask_rollout( env, agent, max_path_length=np.inf, render=False, render_kwargs=None, observation_key=None, desired_goal_key=None, get_action_kwargs=None, return_dict_obs=False, ): if render_kwargs is None: render_kwargs = {} if get_action_kwargs is None: get_action_kwargs = {} dict_obs = [] dict_next_obs = [] observations = [] actions = [] rewards = [] terminals = [] agent_infos = [] env_infos = [] next_observations = [] path_length = 0 agent.reset() o = env.reset() if render: env.render(**render_kwargs) goal = o[desired_goal_key] while path_length < max_path_length: dict_obs.append(o) if observation_key: o = o[observation_key] new_obs = np.hstack((o, goal)) a, agent_info = agent.get_action(new_obs, **get_action_kwargs) next_o, r, d, env_info = env.step(a) if render: env.render(**render_kwargs) observations.append(o) rewards.append(r) terminals.append(d) actions.append(a) next_observations.append(next_o) dict_next_obs.append(next_o) agent_infos.append(agent_info) env_infos.append(env_info) path_length += 1 if d: break o = next_o actions = np.array(actions) if len(actions.shape) == 1: actions = np.expand_dims(actions, 1) observations = np.array(observations) next_observations = np.array(next_observations) if return_dict_obs: observations = dict_obs next_observations = dict_next_obs return dict( observations=observations, actions=actions, rewards=np.array(rewards).reshape(-1, 1), next_observations=next_observations, terminals=np.array(terminals).reshape(-1, 1), agent_infos=agent_infos, env_infos=env_infos, goals=np.repeat(goal[None], path_length, 0), full_observations=dict_obs, ) def rollout(env, agent, max_path_length=np.inf, render=False, render_kwargs=None): """ The following value for the following keys will be a 2D array, with the first dimension corresponding to the time dimension. - observations - actions - rewards - next_observations - terminals The next two elements will be lists of dictionaries, with the index into the list being the index into the time - agent_infos - env_infos """ if render_kwargs is None: render_kwargs = {} observations = [] actions = [] explored = [] rewards = [] terminals = [] agent_infos = [] env_infos = [] o = env.reset() agent.reset() next_o = None path_length = 0 if render: env.render(**render_kwargs) while path_length < max_path_length: (a, e), agent_info = agent.get_action(o) next_o, r, d, env_info = env.step(a) observations.append(o) rewards.append(r) terminals.append(d) actions.append(a) explored.append(e) agent_infos.append(agent_info) env_infos.append(env_info) # ADDED THIS SECTION TO HANDLE INTERMEDIATE EXPERIENCE if "intermediate_experience" in env_info: path_length += len(env_info["intermediate_experience"]) path_length += 1 if d: break o = next_o if render: env.render(**render_kwargs) actions = np.array(actions) if len(actions.shape) == 1: actions = np.expand_dims(actions, 1) observations = np.array(observations) if len(observations.shape) == 1: observations = np.expand_dims(observations, 1) next_o = np.array([next_o]) next_observations = np.vstack((observations[1:, :], np.expand_dims(next_o, 0))) return dict( observations=observations, actions=actions, explored=np.array(explored).reshape(-1, 1), rewards=np.array(rewards, dtype=np.float32).reshape(-1, 1), next_observations=next_observations, terminals=np.array(terminals).reshape(-1, 1), agent_infos=agent_infos, env_infos=env_infos, ) def intermediate_rollout( env, agent, restart=True, starting_obs=None, max_path_length=np.inf, render=False, render_kwargs=None, experience_interval=1, ): """ The following value for the following keys will be a 2D array, with the first dimension corresponding to the time dimension. - observations - actions - rewards - next_observations - terminals The next two elements will be lists of dictionaries, with the index into the list being the index into the time - agent_infos - env_infos """ if render_kwargs is None: render_kwargs = {} observations = [] actions = [] explored = [] rewards = [] terminals = [] agent_infos = [] env_infos = [] if restart: o = env.reset() agent.reset() else: o = starting_obs next_o = None path_length = 0 i = 0 if render: env.render(**render_kwargs) while path_length < max_path_length: (a, e), agent_info = agent.get_action(o) try: a = a.item() except AttributeError: pass if isinstance(o, str): o = env.observation_space.converter(o) next_o, r, d, env_info = env.step(a) observations.append(o) rewards.append(r) terminals.append(d) actions.append(a) explored.append(e) agent_infos.append(agent_info) env_infos.append(env_info) if i % experience_interval == 0: path_length += 1 i += 1 step_timeout, step_complete, plan_ended = agent.check_action_status([next_o]) if d or step_timeout[0] or plan_ended[0]: break o = next_o if render: env.render(**render_kwargs) if isinstance(next_o, str): next_o_converted = env.observation_space.converter(next_o) actions = np.array(actions) if len(actions.shape) == 1: actions = np.expand_dims(actions, 1) observations = np.array(observations) if len(observations.shape) == 1: observations = np.expand_dims(observations, 1) next_o_converted = np.array([next_o_converted]) next_observations = np.vstack( (observations[1:, :], np.expand_dims(next_o_converted, 0)) ) return ( dict( observations=observations, actions=actions, explored=np.array(explored).reshape(-1, 1), rewards=np.array(rewards, dtype=np.float32).reshape(-1, 1), next_observations=next_observations, terminals=np.array(terminals).reshape(-1, 1), agent_infos=agent_infos, env_infos=env_infos, ), (d, next_o), ) def hierarchical_rollout( env, agent, max_path_length=np.inf, render=False, render_kwargs=None ): """ The following value for the following keys will be a 2D array, with the first dimension corresponding to the time dimension. - observations - actions - rewards - next_observations - terminals The next two elements will be lists of dictionaries, with the index into the list being the index into the time - agent_infos - env_infos """ if render_kwargs is None: render_kwargs = {} observations = [] actions = [] explored = [] rewards = [] terminals = [] agent_infos = [] env_infos = [] o = env.reset() agent.reset() next_o = None path_length = 0 cumulative_reward = 0 first_time = True if render: env.render(**render_kwargs) while path_length < max_path_length: (a, e), agent_info = agent.get_action(o, [0]) next_o, r, d, env_info = env.step(a) if agent_info.get("subgoal") is not None: img = json_to_screen(o) observations.append(img) actions.append(agent_info["subgoal"]) explored.append(e) agent_infos.append(agent_info) env_infos.append(env_info) path_length += 1 if not first_time: rewards.append(cumulative_reward) terminals.append(d) first_time = False cumulative_reward = 0 cumulative_reward += r if d: break o = next_o if render: env.render(**render_kwargs) rewards.append(cumulative_reward) terminals.append(d) actions = np.array(actions) if len(actions.shape) == 1: actions = np.expand_dims(actions, 1) observations = np.array(observations) if len(observations.shape) == 1: observations = np.expand_dims(observations, 1) next_o = np.array([next_o]) next_observations = np.vstack( (observations[1:, :], np.expand_dims(json_to_screen(next_o), 0)) ) return dict( observations=observations, actions=actions, explored=np.array(explored).reshape(-1, 1), rewards=np.array(rewards).reshape(-1, 1), next_observations=next_observations, terminals=np.array(terminals).reshape(-1, 1), agent_infos=agent_infos, env_infos=env_infos, )

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null

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4

259ab6bb6481700cf4ae5e2a5b3a42d34a274d4f

1,212

py

Python

tests/_math/test_validations.py

antalszava/piquasso

7ebff83145cfab44929114437c250852dff5f9a5

[ "Apache-2.0" ]

12

2021-09-12T15:51:45.000Z

2022-03-05T22:25:47.000Z

tests/_math/test_validations.py

antalszava/piquasso

7ebff83145cfab44929114437c250852dff5f9a5

[ "Apache-2.0" ]

36

2021-09-13T08:01:27.000Z

2022-03-21T11:53:30.000Z

tests/_math/test_validations.py

antalszava/piquasso

7ebff83145cfab44929114437c250852dff5f9a5

[ "Apache-2.0" ]

null

# # Copyright 2021 Budapest Quantum Computing Group # # 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 piquasso._math.validations import is_natural, all_natural def test_zero_is_natural(): assert is_natural(0) def test_positive_integers_are_natural(): assert is_natural(2) def test_negative_integers_are_natural(): assert not is_natural(-2) def test_floats_close_to_integers_count_as_natural(): assert is_natural(2.0) def test_floats_NOT_close_to_integers_do_NOT_count_as_natural(): assert not is_natural(2.5) def test_all_natural_positive_case(): assert all_natural([1, 1.0, 0.0, 2.0]) def test_all_natural_negative_case(): assert not all_natural([1, 1.0, 0.0, -2.0])

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0

1

0

1

0

4

25ca9531414896379d7eab5ac76c01a0db18d4d7

209

py

Python

back.py

winius/chat-bot

7d44745c4544413e612e5cffb740890d248dd030

[ "Apache-2.0" ]

null

back.py

winius/chat-bot

7d44745c4544413e612e5cffb740890d248dd030

[ "Apache-2.0" ]

null

back.py

winius/chat-bot

7d44745c4544413e612e5cffb740890d248dd030

[ "Apache-2.0" ]

null

import random def main(vk, peer_id): vk.messages.send(peer_id=peer_id, random_id=random.getrandbits(32), message='🌐 Главное меню', keyboard=open("keyboards/default.json", "r", encoding="UTF-8").read())

52.25

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209

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1

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1

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4

25f1b3c5db5a9ef4e305a00f822a09c5477cee27

3,341

py

Python

slaid/commons/ecvl.py

mdrio/slaid

67c85f0d1702bced1c089bfb3c20ba1cfbc9c225

[ "MIT" ]

null

slaid/commons/ecvl.py

mdrio/slaid

67c85f0d1702bced1c089bfb3c20ba1cfbc9c225

[ "MIT" ]

null

slaid/commons/ecvl.py

mdrio/slaid

67c85f0d1702bced1c089bfb3c20ba1cfbc9c225

[ "MIT" ]

1

2022-02-11T15:54:47.000Z

# NAPARI LAZY OPENSLIDE # Copyright (c) 2020, Trevor Manz # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of napari-lazy-openslide nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import logging from typing import List, Tuple import numpy as np from pyecvl.ecvl import Image as EcvlImage from pyecvl.ecvl import OpenSlideImage from slaid.commons import Image as BaseImage from slaid.commons import ImageInfo import slaid.commons.base as base logger = logging.getLogger('ecvl') class Image(BaseImage): IMAGE_INFO = ImageInfo.create('rgb', 'yx', 'first') def __init__(self, image: EcvlImage): self._image = image def to_array(self, image_info: ImageInfo = None): # FIXME array = np.array(self._image) array = array.transpose(0, 2, 1) if image_info is not None: array = self.IMAGE_INFO.convert(array, image_info) return array @property def dimensions(self) -> Tuple[int, int]: return self._image.dims_ class BasicSlide(base.BasicSlide): IMAGE_INFO = Image.IMAGE_INFO def __init__(self, filename: str): super().__init__(filename) self._slide = OpenSlideImage(filename) @property def dimensions(self) -> Tuple[int, int]: return tuple(self._slide.GetLevelsDimensions()[0]) def read_region(self, location: Tuple[int, int], level, size: Tuple[int, int]) -> Image: return Image(self._slide.ReadRegion(level, location + size)) def get_best_level_for_downsample(self, downsample: int): return self._slide.GetBestLevelForDownsample(downsample) @property def level_dimensions(self) -> List[Tuple[int, int]]: return [tuple(d) for d in self._slide.GetLevelsDimensions()] @property def level_downsamples(self): return self._slide.GetLevelDownsamples() def load(filename: str): slide = BasicSlide(filename) return slide

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1

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d3421ebfdadd50a661ab3ff3c339c3f73135af13

53

py

Python

ch02/__init__.py

laszlokiraly/LearningAlgorithms

032a3cc409546619cf41220821d081cde54bbcce

[ "MIT" ]

74

2021-05-06T22:03:18.000Z

2022-03-25T04:37:51.000Z

ch02/__init__.py

laszlokiraly/LearningAlgorithms

032a3cc409546619cf41220821d081cde54bbcce

[ "MIT" ]

null

ch02/__init__.py

laszlokiraly/LearningAlgorithms

032a3cc409546619cf41220821d081cde54bbcce

[ "MIT" ]

19

2021-07-16T11:42:00.000Z

2022-03-22T00:25:49.000Z

""" Module containing Python code for Chapter 2. """

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null

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d359f498acf6ccc425859c61a4c4bd9b13cd05f5

72

py

Python

Python/InvalidType.py

udaypandey/BubblyCode

675fbcdd32c80f685ddb10ed4a5a92e17d139795

[ "MIT" ]

null

Python/InvalidType.py

udaypandey/BubblyCode

675fbcdd32c80f685ddb10ed4a5a92e17d139795

[ "MIT" ]

null

Python/InvalidType.py

udaypandey/BubblyCode

675fbcdd32c80f685ddb10ed4a5a92e17d139795

[ "MIT" ]

null

def say(message, foobar): print(message * foobar) say("hello", 3)

12

27

0.638889

10

72

4.6

0.7

0.565217

0

0.017241

0.194444

72

5

28

14.4

0.775862

0

0.069444

0

1

0.333333

false

0

0.333333

1

0

null

1

0

1

0

null

0

1

0

4

d368c91efbc90357720da91ab2b372a4d28b51b8

393

py

Python

log.py

ViggAlm/PasswordKit

b92f67f7b5b9623f1c63646003b684eee9e8912a

[ "BSD-2-Clause" ]

1

2022-02-22T19:51:56.000Z

log.py

ViggAlm/PasswordKit

b92f67f7b5b9623f1c63646003b684eee9e8912a

[ "BSD-2-Clause" ]

null

log.py

ViggAlm/PasswordKit

b92f67f7b5b9623f1c63646003b684eee9e8912a

[ "BSD-2-Clause" ]

null

from colorama import Fore prefix = "[" + Fore.YELLOW + "PasswordKit" + Fore.WHITE + "]" def result(text): print(Fore.WHITE + f"{prefix}" + Fore.GREEN + f"{text}") def error(text): print(Fore.WHITE + f"{prefix}" + Fore.RED + f"{text}") def general(text): print(Fore.WHITE + f"{prefix}{text}") def question(text): print(Fore.WHITE + f"{prefix}" + Fore.CYAN + f"{text}")

19.65

61

0.608142

55

393

4.345455

0.345455

0.188285

0.217573

0.301255

0.468619

0.364017

0

0.183206

393

19

62

20.684211

0.744548

0

0.175573

0

1

0.4

false

0.1

0

0.5

0.4

0

null

0

1

0

null

0

1

0

1

0

4

d37cdc52c145b2070b33a144edb7a4a7f04e5af4

641

py

Python

pomodorr/projects/apps.py

kamil559/pomodorr

232e6e98ff3481561dd1235794b3960066713210

[ "MIT" ]

null

pomodorr/projects/apps.py

kamil559/pomodorr

232e6e98ff3481561dd1235794b3960066713210

[ "MIT" ]

15

2020-04-11T18:30:57.000Z

2020-07-05T09:37:43.000Z

pomodorr/projects/apps.py

kamil559/pomodorr

232e6e98ff3481561dd1235794b3960066713210

[ "MIT" ]

null

from django.apps import AppConfig from django.utils.translation import gettext_lazy as _ class ProjectsConfig(AppConfig): name = 'pomodorr.projects' verbose = _('Projects') def ready(self): try: from pomodorr.projects.signals.dispatchers import notify_force_finish from pomodorr.projects.signals.handlers import task_completed_notify_channel notify_force_finish.connect(receiver=task_completed_notify_channel, dispatch_uid='pomodorr.projects.signals.task_completed_notify_channel') except ImportError: pass # noqa F401

33.736842

111

0.692668

67

641

6.373134

0.567164

0.149883

0.161593

0.18267

0

0.006224

0.24805

641

18

112

35.611111

0.879668

0.014041

0

0.126984

0.087302

0

1

0.076923

false

0.076923

0.384615

0

0.692308

0

null

0

1

0

null

0

1

0

1

0

4

d383cd85a741d1d27af48818777ba3ae5e9dea05

537

py

Python

tests/pymath/test_visible_cubes.py

JASTYN/pythonmaster

46638ab09d28b65ce5431cd0759fe6df272fb85d

[ "Apache-2.0", "MIT" ]

3

2017-05-02T10:28:13.000Z

2019-02-06T09:10:11.000Z

tests/pymath/test_visible_cubes.py

JASTYN/pythonmaster

46638ab09d28b65ce5431cd0759fe6df272fb85d

[ "Apache-2.0", "MIT" ]

2

2017-06-21T20:39:14.000Z

2020-02-25T10:28:57.000Z

tests/pymath/test_visible_cubes.py

JASTYN/pythonmaster

46638ab09d28b65ce5431cd0759fe6df272fb85d

[ "Apache-2.0", "MIT" ]

2

2016-07-29T04:35:22.000Z

2017-01-18T17:05:36.000Z

import unittest from pymath.visible_cubes import VisibleCubes class VisibleCubesTest(unittest.TestCase): def test_1(self): self.assertEqual(VisibleCubes.not_visible_cubes(0), 0) def test_2(self): self.assertEqual(VisibleCubes.not_visible_cubes(1), 0) def test_3(self): self.assertEqual(VisibleCubes.not_visible_cubes(2), 0) def test_4(self): self.assertEqual(VisibleCubes.not_visible_cubes(3), 1) def test_5(self): self.assertEqual(VisibleCubes.not_visible_cubes(4), 8)

25.571429

62

0.72067

72

537

5.152778

0.305556

0.19407

0.256065

0.41779

0.619946

0

0.033937

0.176909

537

20

63

26.85

0.80543

0

0.384615

1

0.384615

false

0

0.153846

0

0.615385

0

null

0

1

0

null

0

1

0

1

0

4

d3909bb5f50aa6a1cd42a85aa5f53b8ef59eb532

161

py

Python

m3gnet/trainers/__init__.py

materialsvirtuallab/m3gnet

94fb01d0c90d3b2bffcdc4514f7eb3cb8fab6c90

[ "BSD-3-Clause" ]

1

2022-03-31T14:47:24.000Z

m3gnet/trainers/__init__.py

materialsvirtuallab/m3gnet

94fb01d0c90d3b2bffcdc4514f7eb3cb8fab6c90

[ "BSD-3-Clause" ]

null

m3gnet/trainers/__init__.py

materialsvirtuallab/m3gnet

94fb01d0c90d3b2bffcdc4514f7eb3cb8fab6c90

[ "BSD-3-Clause" ]

null

"""M3GNet trainers""" # -*- coding: utf-8 -*- from ._potential import PotentialTrainer from ._property import Trainer __all__ = ["Trainer", "PotentialTrainer"]

23

41

0.720497

16

161

6.875

0.75

0

0.014184

0.124224

161

6

42

26.833333

0.765957

0.236025

0

0.196581

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

6c96573e2a86c4d06cd5e786e6a9c65c803e3b54

486

py

Python

tests/__init__.py

pedromtorres/TigerShark

2790a7c03905a094b126b48387c7919c09cce238

[ "BSD-3-Clause" ]

24

2015-03-18T10:15:20.000Z

2022-03-18T13:38:34.000Z

tests/__init__.py

tspannhw/TigerShark

5081641f1b189a43e9eab4813256598cc0a79f6f

[ "BSD-3-Clause" ]

6

2015-03-27T12:36:57.000Z

2021-04-13T15:01:24.000Z

tests/__init__.py

tspannhw/TigerShark

5081641f1b189a43e9eab4813256598cc0a79f6f

[ "BSD-3-Clause" ]

21

2015-11-21T09:19:47.000Z

2020-09-17T16:52:50.000Z

#!/usr/bin/env python """The test package contains test data files as well as unit tests. run_tests =========== .. automodule:: test.run_tests :members: test_navigation =============== .. automodule:: test.test_navigation :members: test_parse =========== .. automodule:: test.test_parse :members: test_wsClaims ============== .. automodule:: test.test_wsClaims :members: test_convert ============= .. automodule:: test.test_convert :members: """

13.885714

67

0.596708

51

486

5.490196

0.411765

0.25

0.257143

0

0.174897

486

34

68

14.294118

0.698254

0.979424

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

1

0

1

0

null

0

1

0

4

6c9da72f1e7cd9a44b07213708fdc729bbcbffaf

33

py

Python

Boolean/Boolean1.py

liyuanyuan11/Python

d94cc7ab39e56c6e24bfc741a30da77590d1d220

[ "MIT" ]

null

Boolean/Boolean1.py

liyuanyuan11/Python

d94cc7ab39e56c6e24bfc741a30da77590d1d220

[ "MIT" ]

null

Boolean/Boolean1.py

liyuanyuan11/Python

d94cc7ab39e56c6e24bfc741a30da77590d1d220

[ "MIT" ]

null

thisIsBool=True print(thisIsBool)

16.5

17

0.878788

4

33

7.25

0.75

0

0.030303

33

2

17

16.5

0.90625

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

4

6c9ee4047b3a967591fcf09f72904d1bcbe40877

272

py

Python

Python POO/Getters e Setters/exemplo03/alarm.py

luccasocastro/Curso-Python

7ad2b980bb2f95f833811291273d6ca1beb0fe77

[ "MIT" ]

null

Python POO/Getters e Setters/exemplo03/alarm.py

luccasocastro/Curso-Python

7ad2b980bb2f95f833811291273d6ca1beb0fe77

[ "MIT" ]

null

Python POO/Getters e Setters/exemplo03/alarm.py

luccasocastro/Curso-Python

7ad2b980bb2f95f833811291273d6ca1beb0fe77

[ "MIT" ]

null

class Alarme: def __init__(self, estado: bool) -> None: self.__estado = estado def getEstado(self) -> bool: return self.__estado def setEstado(self, valor: bool) -> None: if isinstance(valor, bool): self.__estado = valor

22.666667

45

0.599265

31

272

4.935484

0.451613

0.261438

0

0.294118

272

11

46

24.727273

0.796875

0

1

0.375

false

0

0.125

0.625

0

null

1

0

null

0

1

0

1

0

4

6cc0464d548268e9ae806041a26f69d2278a7df3

1,559

py

Python

qiskit/_openquantumcompiler.py

christians94/qiskit-sdk-py

5c1c68a5aa3dcccdf5c10f9eb307383ebb40826b

[ "Apache-2.0" ]

null

qiskit/_openquantumcompiler.py

christians94/qiskit-sdk-py

5c1c68a5aa3dcccdf5c10f9eb307383ebb40826b

[ "Apache-2.0" ]

1

2018-08-08T17:56:06.000Z

qiskit/_openquantumcompiler.py

christians94/qiskit-sdk-py

5c1c68a5aa3dcccdf5c10f9eb307383ebb40826b

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # pylint: disable=redefined-builtin # Copyright 2017 IBM RESEARCH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= """Tools for compiling Quantum Programs.""" from qiskit.unroll import DagUnroller, JsonBackend # TODO: This is here for backward compatibility with QISKit Developer Challenge # Once the challenge is finished, we have to remove this entire module. def dag2json(dag_circuit, basis_gates='u1,u2,u3,cx,id'): """Make a Json representation of the circuit. Takes a circuit dag and returns json circuit obj. This is an internal function. Args: dag_circuit (QuantumCircuit): a dag representation of the circuit. basis_gates (str): a comma seperated string and are the base gates, which by default are: u1,u2,u3,cx,id Returns: json: the json version of the dag """ return DagUnroller(dag_circuit, JsonBackend(basis_gates.split(","))).execute()

42.135135

82

0.686337

214

1,559

4.971963

0.593458

0.056391

0.024436

0.030075

0.018797

0

0.012658

0.189224

1,559

36

83

43.305556

0.829114

0.817832

0

0.068493

0

0.027778

0

1

0.333333

false

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

6cd7b3f78c6ea020b669ed58c3c399d8c2e66207

590

py

Python

letter/models.py

HilmiZul/epkl3

63df215eb1676cf5ab2f36f2f20436b19b540b9a

[ "MIT" ]

6

2019-02-15T07:15:33.000Z

2021-01-05T12:18:21.000Z

letter/models.py

HilmiZul/epkl3

63df215eb1676cf5ab2f36f2f20436b19b540b9a

[ "MIT" ]

6

2019-09-14T14:47:48.000Z

2022-03-12T00:56:51.000Z

letter/models.py

HilmiZul/epkl3

63df215eb1676cf5ab2f36f2f20436b19b540b9a

[ "MIT" ]

null

from django.db import models from master.models import Siswa, Instansi # Create your models here. class Permohonan(models.Model): nama_siswa = models.ForeignKey(Siswa, on_delete=models.CASCADE) nama_instansi = models.ForeignKey(Instansi, on_delete=models.CASCADE) def __str__(self): return self.nama_instansi.nama # class PermohonanTKJ(models.Model): # nama_siswa = models.ForeignKey(Siswa, on_delete=models.CASCADE) # nama_instansi = models.ForeignKey(InstansiTKJ, on_delete=models.CASCADE) # def __str__(self): # return self.nama_instansi.nama

32.777778

78

0.750847

75

590

5.666667

0.333333

0.150588

0.131765

0.197647

0.691765

0

0.154237

590

17

79

34.705882

0.851703

0.450847

0

1

0.142857

false

0

0.285714

0.142857

1

0

null

0

1

0

1

0

null

0

1

0

4

6ce67f80393f8088cf44c387c9e1c5262727136a

581

py

Python

model/digamma.py

OpenXAIProject/Beta-Bernoulli-Dependent-Dropout

723df2d2392ec16eca3452d4afb81d54c4a2f841

[ "Apache-2.0" ]

13

2018-11-29T05:56:11.000Z

2018-12-05T02:47:23.000Z

model/digamma.py

OpenXAIProject/Beta-Bernoulli-Dependent-Dropout

723df2d2392ec16eca3452d4afb81d54c4a2f841

[ "Apache-2.0" ]

null

model/digamma.py

OpenXAIProject/Beta-Bernoulli-Dependent-Dropout

723df2d2392ec16eca3452d4afb81d54c4a2f841

[ "Apache-2.0" ]

8

2018-11-30T00:42:27.000Z

2018-12-04T10:11:08.000Z

import tensorflow as tf # @MISC {1446110, # TITLE = {Approximating the Digamma function}, # AUTHOR = {njuffa (https://math.stackexchange.com/users/114200/njuffa)}, # HOWPUBLISHED = {Mathematics Stack Exchange}, # NOTE = {URL:https://math.stackexchange.com/q/1446110 (version: 2015-09-22)}, # EPRINT = {https://math.stackexchange.com/q/1446110}, # URL = {https://math.stackexchange.com/q/1446110}} def digamma_approx(x): def digamma_over_one(x): return tf.log(x + 0.4849142940227510) \ - 1/(1.0271785180163817*x) return digamma_over_one(x+1) - 1./x

38.733333

78

0.686747

77

581

5.116883

0.545455

0.091371

0.22335

0.253807

0.266497

0.182741

0

0.159919

0.149742

581

14

79

41.5

0.637652

0.616179

0

1

0.333333

false

0

0.166667

0.833333

0

null

0

1

0

null

0

1

0

1

0

4

6cf35020ab985340d7a20cd326ad9113518513ba

91

py

Python

_downloads/plot_cubist_lena.py

dolfly/scipy-lectures.github.com

0e5babdf839754b075ded5d986f767be35fbbe65

[ "CC-BY-3.0" ]

null

_downloads/plot_cubist_lena.py

dolfly/scipy-lectures.github.com

0e5babdf839754b075ded5d986f767be35fbbe65

[ "CC-BY-3.0" ]

null

_downloads/plot_cubist_lena.py

dolfly/scipy-lectures.github.com

0e5babdf839754b075ded5d986f767be35fbbe65

[ "CC-BY-3.0" ]

null

import numpy as np from scipy import misc import matplotlib.pyplot as plt l = misc.lena()

15.166667

31

0.769231

16

91

4.375

0.75

0

0.175824

91

5

32

18.2

0.933333

0

1

0

false

0

0.75

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

9f03534c24da605d0a327352c085084a1edd3ca6

484

py

Python

odk2stata/dataset/__init__.py

PMA-2020/odk2stata

5178736eeedd4bad93bd35044a57cf071f282b4b

[ "MIT" ]

2

2019-12-04T10:30:04.000Z

2022-03-23T11:07:07.000Z

odk2stata/dataset/__init__.py

PMA-2020/odk2stata

5178736eeedd4bad93bd35044a57cf071f282b4b

[ "MIT" ]

3

2019-10-11T17:28:51.000Z

2022-01-09T06:07:08.000Z

odk2stata/dataset/__init__.py

jkpr/odk2stata

361b88d7fdd5751f16cd3ed5ceb7acdbde3bb82c

[ "MIT" ]

3

2019-07-10T23:33:44.000Z

2021-12-18T06:25:53.000Z

"""A module to describe the dataset based on an ODK file. This module describes three primary abstractions: - DatasetCollection - Dataset - Column An ODK file can have repeat groups. When the data are exported to CSV, then those repeat groups become their own datasets. Therefore, the top level is the DatasetCollection, which comprises of the primary Dataset and repeat group Datasets. Each Dataset has Columns. """ from .dataset_collection import DatasetCollection

28.470588

70

0.778926

68

484

5.529412

0.705882

0.026596

0.047872

0

0.181818

484

16

71

30.25

0.949495

0.876033

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

9f1f74457ba290ef289d78ba38eca46ee30f0a62

533

py

Python

utils/app_decorators.py

Omeramsc/hakesh-trom

bede21b96ea807ed78d5169ac5b4a917c15ce286

[ "MIT" ]

null

utils/app_decorators.py

Omeramsc/hakesh-trom

bede21b96ea807ed78d5169ac5b4a917c15ce286

[ "MIT" ]

4

2020-06-19T09:58:55.000Z

2022-02-13T16:20:28.000Z

utils/app_decorators.py

Omeramsc/hakesh-trom

bede21b96ea807ed78d5169ac5b4a917c15ce286

[ "MIT" ]

1

2020-03-18T18:38:49.000Z

from functools import wraps from flask import redirect, abort from flask_login import current_user def admin_access(f): @wraps(f) def decorated_function(*args, **kwargs): if not current_user.is_admin: abort(403) return f(*args, **kwargs) return decorated_function def user_access(f): @wraps(f) def decorated_function(*args, **kwargs): if current_user.is_admin: abort(403) return f(*args, **kwargs) return decorated_function

22.208333

45

0.628518

66

533

4.893939

0.348485

0.210526

0.074303

0.080495

0.687307

0

0.015707

0.283302

533

23

46

23.173913

0.829843

0

0.588235

0

1

0.235294

false

0

0.176471

0

0.647059

0

null

1

0

1

0

null

0

1

0

1

0

4

9f2f032fefbb445ef3a91589293fd83015bc8ef0

1,641

py

Python

Apartment_Renting_App/backend/views/about_page.py

sushilkplassar/csc848_LiveGator

e95d861679f1dfb8f547d5b9f7d9b7a4fa530c8d

[ "MIT" ]

null

Apartment_Renting_App/backend/views/about_page.py

sushilkplassar/csc848_LiveGator

e95d861679f1dfb8f547d5b9f7d9b7a4fa530c8d

[ "MIT" ]

1

2019-11-24T06:30:44.000Z

Apartment_Renting_App/backend/views/about_page.py

pancreaspinch/LiveGator

680592aaf7a6c1603c0ae798a8094ca5f3ff250f

[ "MIT" ]

null

#################################### # File name: about_page.py # # Description: # Author: Team-13 # # Submission: Spring-2019 # # Instructor: Dragutin Petkovic # #################################### from flask import Flask, Blueprint, request, flash, url_for, redirect, render_template from flask_login import login_user, logout_user, current_user , login_required from werkzeug.security import check_password_hash, generate_password_hash about_page_endpoints = Blueprint('about_page_endpoints', __name__) @about_page_endpoints.route('/about', methods=['GET', 'POST']) def about(): return render_template('about.html') @about_page_endpoints.route('/about/AmarisAboutMe', methods=['GET', 'POST']) def aboutAmarisAboutMe(): return render_template('about_AmarisAboutMe.html') @about_page_endpoints.route('/about/kim', methods=['GET', 'POST']) def aboutKim(): return render_template('about_Kim.html') @about_page_endpoints.route('/about/sushil', methods=['GET', 'POST']) def aboutSushil(): return render_template('about_sushil.html') @about_page_endpoints.route('/about/Kurtis', methods=['GET', 'POST']) def aboutKurtis(): return render_template('about_Kurtis.html') @about_page_endpoints.route('/about/Adeel', methods=['GET', 'POST']) def aboutAdeel(): return render_template('about_Adeel.html') @about_page_endpoints.route('/about/simon', methods=['GET', 'POST']) def aboutSimon(): return render_template('about_simon.html') @about_page_endpoints.route('/about/brian', methods=['GET', 'POST']) def aboutBrian(): return render_template('about_brian.html')

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null

0

1

0

1

0

4

9f4c781ed13aa5ab9d4df3bec8dc6e44debfb804

51

py

Python

model/efficientNet/__init__.py

ztt0810/general_img_cls_template

2ae164d14e1abca1cdcf327acf306dd0415fd3ac

[ "MIT" ]

1

2021-03-02T15:05:24.000Z

model/efficientNet/__init__.py

ztt0810/general_img_cls_template

2ae164d14e1abca1cdcf327acf306dd0415fd3ac

[ "MIT" ]

null

model/efficientNet/__init__.py

ztt0810/general_img_cls_template

2ae164d14e1abca1cdcf327acf306dd0415fd3ac

[ "MIT" ]

null

from .efficientNet import * from .utils import *

17

28

0.72549

6

51

6.166667

0.666667

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0.196078

51

2

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25.5

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null

0

1

0

null

0

1

0

1

0

4

9f558eee486a98a8b26462c909884d369ed07d81

170

py

Python

FacebookChatPhisher/secretDirectory/secret.py

AshrafTaifour/Private-Facebook-Scraper

0fc72414792ec7a04770364cb036a0f14767069c

[ "MIT" ]

2

2021-04-05T05:17:57.000Z

2021-06-27T07:46:32.000Z

FacebookChatPhisher/secretDirectory/secret.py

AshrafTaifour/Private-Facebook-Scraper

0fc72414792ec7a04770364cb036a0f14767069c

[ "MIT" ]

2

2021-05-16T21:16:36.000Z

2021-07-30T14:37:38.000Z

FacebookChatPhisher/secretDirectory/secret.py

AshrafTaifour/Private-Facebook-Scraper

0fc72414792ec7a04770364cb036a0f14767069c

[ "MIT" ]

null

EMAIL = "YOUR_FACEBOOK_EMAIL" UNAME = "YOUR_FB_USER_NAME" passw = r"YOUR_FACBOOK_PASSWORD" torBrowserPath = 'PATH_TO_TORBROWSERDIRECTORY' exePath = 'PATH_TO_DRIVER_EXE'

24.285714

46

0.817647

23

170

5.521739

0.782609

0.094488

0

0.094118

170

6

47

28.333333

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0.60355

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0.2

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null

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1

0

1

0

null

0

1

0

4

9f56a63924ec5c746a985d2d77f5a5ae47777582

231

py

Python

rename.py

samlarkin/bin

cc2dcf22b37e28f19a237ce4f073aabb539114cd

[ "BSD-3-Clause" ]

null

rename.py

samlarkin/bin

cc2dcf22b37e28f19a237ce4f073aabb539114cd

[ "BSD-3-Clause" ]

null

rename.py

samlarkin/bin

cc2dcf22b37e28f19a237ce4f073aabb539114cd

[ "BSD-3-Clause" ]

null

import os for fn in os.listdir(): if '.wiki' in fn: new_fn = fn[:] new_fn = new_fn.replace('.wiki', '') new_fn = new_fn.replace('.', '') new_fn = new_fn + '.wiki' os.replace(fn, new_fn)

23.1

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0.506494

35

231

3.114286

0.285714

0.366972

0.385321

0.275229

0.311927

0

0.316017

231

9

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25.666667

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false

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null

1

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null

0

4

9f69d1c4d6e28dbe64a9d4ed81782a9a6530a5e0

29

py

Python

connection_notifier/__init__.py

NNRepos/connection_alerter

463a4225618cc4155cd97d27f84b28930f68a175

[ "MIT" ]

null

connection_notifier/__init__.py

NNRepos/connection_alerter

463a4225618cc4155cd97d27f84b28930f68a175

[ "MIT" ]

1

2019-11-01T19:33:48.000Z

2021-11-14T19:36:43.000Z

connection_notifier/__init__.py

NNRepos/connection_alerter

463a4225618cc4155cd97d27f84b28930f68a175

[ "MIT" ]

null

name = "connection_notifier"

14.5

28

0.793103

3

29

7.333333

1

0

0.103448

29

1

29

0.846154

0

0.655172

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0

1

0

null

0

1

0

1

0

null

0

4

9f8184849b20efb42772fe5c324d73b990a92874

265

py

Python

mindsdb/libs/data_types/tester_response.py

aykuttasil/mindsdb

2c36b6f75f13d7104fe4d3dbb7ca307fa84f45ad

[ "MIT" ]

1

2022-03-14T00:32:53.000Z

mindsdb/libs/data_types/tester_response.py

aykuttasil/mindsdb

2c36b6f75f13d7104fe4d3dbb7ca307fa84f45ad

[ "MIT" ]

null

mindsdb/libs/data_types/tester_response.py

aykuttasil/mindsdb

2c36b6f75f13d7104fe4d3dbb7ca307fa84f45ad

[ "MIT" ]

null

class TesterResponse(): def __init__(self, error=0, accuracy =0 , predicted_targets={}, real_targets={}): self.error = error self.accuracy = accuracy self.predicted_targets = predicted_targets self.real_targets = real_targets

26.5

85

0.671698

29

265

5.793103

0.37931

0.285714

0.214286

0

0.009804

0.230189

265

9

86

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0.813725

0

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0.333333

0

1

0

null

1

0

1

0

null

0

4

9f823af966072fa5ca7e3673da51ceeb153a05cf

624

py

Python

src/metrics/abstract_scorer.py

Hazoom/sede

2bf261248feb330889a9e84f74e33ff7df0d6a86

[ "Apache-2.0" ]

65

2021-06-06T09:54:43.000Z

2022-02-28T08:15:02.000Z

src/metrics/abstract_scorer.py

Hazoom/sede

2bf261248feb330889a9e84f74e33ff7df0d6a86

[ "Apache-2.0" ]

null

src/metrics/abstract_scorer.py

Hazoom/sede

2bf261248feb330889a9e84f74e33ff7df0d6a86

[ "Apache-2.0" ]

10

2021-06-29T11:04:50.000Z

2022-02-12T08:15:49.000Z

from typing import List, Dict from abc import ABC, abstractmethod class AbstractScorer(ABC): @abstractmethod def get_name(self) -> str: raise NotImplementedError() @abstractmethod def __call__(self, pred_lns: List[str], tgt_lns: List[str]) -> None: raise NotImplementedError() @abstractmethod def get_metric(self, reset: bool = False) -> Dict[str, float]: raise NotImplementedError() @abstractmethod def reset(self) -> None: raise NotImplementedError() @abstractmethod def get_metric_names(self) -> List[str]: raise NotImplementedError()

24.96

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0.674679

66

624

6.227273

0.409091

0.206813

0.36983

0.399027

0.262774

0

0.227564

624

24

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0.111111

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0

null

1

0

null

0

1

0

4

4c7d156f26e5e8a4d0712c00077b28beeea31a16

39

py

Python

pycotacao/__init__.py

CaioWzy/PyCotacao

6a536f35fad4c38db8ae116d29eb7a4ab4735778

[ "MIT" ]

3

2020-02-08T05:44:39.000Z

2020-10-29T14:10:22.000Z

pycotacao/__init__.py

CaioWzy/PyCotacao

6a536f35fad4c38db8ae116d29eb7a4ab4735778

[ "MIT" ]

null

pycotacao/__init__.py

CaioWzy/PyCotacao

6a536f35fad4c38db8ae116d29eb7a4ab4735778

[ "MIT" ]

1

2021-03-16T01:48:59.000Z

from .api import * __version__ = "1.1"

13

19

0.666667

6

39

3.666667

0.833333

0

0.0625

0.179487

39

3

19

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1

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false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

4

4c82fabe7b2f200f060085e375884907e7a22229

54

py

Python

riscv_mini/const.py

cdonovick/magma_riscv_mini

b7f39e09df28c6dde26ec427aae54aa9b88f1d11

[ "BSD-3-Clause" ]

3

2021-04-13T18:52:09.000Z

2022-01-05T07:18:03.000Z

riscv_mini/const.py

cdonovick/magma_riscv_mini

b7f39e09df28c6dde26ec427aae54aa9b88f1d11

[ "BSD-3-Clause" ]

1

2020-09-01T23:46:05.000Z

2020-09-09T19:13:08.000Z

riscv_mini/const.py

cdonovick/magma_riscv_mini

b7f39e09df28c6dde26ec427aae54aa9b88f1d11

[ "BSD-3-Clause" ]

2

2021-04-16T17:15:18.000Z

2021-09-17T21:09:37.000Z

class Const: PC_START = 0x200 PC_EVEC = 0x100

13.5

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8

54

4.125

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0.296296

54

3

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null

0

1

0

1

0

null

0

1

0

4

4c94bef0d115389da735a1e29841834c3e9e1548

29,691

py

Python

test/test_socks.py

glowatsk/txtorcon

db8de75a4568561b118be11299bda61f3fb84719

[ "MIT" ]

180

2015-01-12T23:57:06.000Z

2022-03-17T00:24:35.000Z

test/test_socks.py

glowatsk/txtorcon

db8de75a4568561b118be11299bda61f3fb84719

[ "MIT" ]

262

2015-01-16T21:14:50.000Z

2022-02-25T01:33:42.000Z

test/test_socks.py

glowatsk/txtorcon

db8de75a4568561b118be11299bda61f3fb84719

[ "MIT" ]

61

2015-01-05T01:10:57.000Z

2022-01-04T08:13:39.000Z

from six import BytesIO, text_type from mock import Mock, patch from twisted.trial import unittest from twisted.internet import defer from twisted.internet.address import IPv4Address from twisted.internet.protocol import Protocol from twisted.internet.interfaces import IStreamClientEndpoint from twisted.test import proto_helpers from twisted.test.iosim import connect, FakeTransport from zope.interface import directlyProvides from txtorcon import socks class SocksStateMachine(unittest.TestCase): def test_illegal_request(self): with self.assertRaises(ValueError) as ctx: socks._SocksMachine('FOO_RESOLVE', u'meejah.ca', 443) self.assertTrue( 'Unknown request type' in str(ctx.exception) ) def test_illegal_host(self): with self.assertRaises(ValueError) as ctx: socks._SocksMachine('RESOLVE', 1234, 443) self.assertTrue( "'host' must be" in str(ctx.exception) ) def test_illegal_ip_addr(self): with self.assertRaises(ValueError) as ctx: socks._create_ip_address(1234, 443) self.assertTrue( "'host' must be" in str(ctx.exception) ) def test_connect_but_no_creator(self): with self.assertRaises(ValueError) as ctx: socks._SocksMachine( 'CONNECT', u'foo.bar', ) self.assertTrue( "create_connection function required" in str(ctx.exception) ) @defer.inlineCallbacks def test_connect_socks_illegal_packet(self): class BadSocksServer(Protocol): def __init__(self): self._buffer = b'' def dataReceived(self, data): self._buffer += data if len(self._buffer) == 3: assert self._buffer == b'\x05\x01\x00' self._buffer = b'' self.transport.write(b'\x05\x01\x01') factory = socks._TorSocksFactory(u'meejah.ca', 1234, 'CONNECT', Mock()) server_proto = BadSocksServer() server_transport = FakeTransport(server_proto, isServer=True) client_proto = factory.buildProtocol('ignored') client_transport = FakeTransport(client_proto, isServer=False) pump = yield connect( server_proto, server_transport, client_proto, client_transport, ) self.assertTrue(server_proto.transport.disconnected) self.assertTrue(client_proto.transport.disconnected) pump.flush() @defer.inlineCallbacks def test_connect_socks_unknown_version(self): class BadSocksServer(Protocol): def __init__(self): self._buffer = b'' self._recv_stack = [ (b'\x05\x01\x00', b'\x05\xff'), ] def dataReceived(self, data): self._buffer += data if len(self._recv_stack) == 0: assert "not expecting any more data, got {}".format(repr(self._buffer)) return expecting, to_send = self._recv_stack.pop(0) got = self._buffer[:len(expecting)] self._buffer = self._buffer[len(expecting):] assert got == expecting, "wanted {} but got {}".format(repr(expecting), repr(got)) self.transport.write(to_send) factory = socks._TorSocksFactory(u'1.2.3.4', 1234, 'CONNECT', Mock()) server_proto = BadSocksServer() server_transport = FakeTransport(server_proto, isServer=True) client_proto = factory.buildProtocol('ignored') client_transport = FakeTransport(client_proto, isServer=False) # returns IOPump yield connect( server_proto, server_transport, client_proto, client_transport, ) self.assertTrue(server_proto.transport.disconnected) self.assertTrue(client_proto.transport.disconnected) @defer.inlineCallbacks def test_connect_socks_unknown_reply_code(self): class BadSocksServer(Protocol): def __init__(self): self._buffer = b'' self._recv_stack = [ (b'\x05\x01\x00', b'\x05\x00'), # the \xff is an invalid reply-code (b'\x05\x01\x00\x01\x01\x02\x03\x04\x04\xd2', b'\x05\xff\x00\x04\x01\x01\x01\x01'), ] def dataReceived(self, data): self._buffer += data if len(self._recv_stack) == 0: assert "not expecting any more data, got {}".format(repr(self._buffer)) return expecting, to_send = self._recv_stack.pop(0) got = self._buffer[:len(expecting)] self._buffer = self._buffer[len(expecting):] assert got == expecting, "wanted {} but got {}".format(repr(expecting), repr(got)) self.transport.write(to_send) factory = socks._TorSocksFactory(u'1.2.3.4', 1234, 'CONNECT', Mock()) server_proto = BadSocksServer() server_transport = FakeTransport(server_proto, isServer=True) client_proto = factory.buildProtocol('ignored') client_transport = FakeTransport(client_proto, isServer=False) d = client_proto._machine.when_done() # returns IOPump yield connect( server_proto, server_transport, client_proto, client_transport, ) with self.assertRaises(Exception) as ctx: yield d self.assertIn('Unknown SOCKS error-code', str(ctx.exception)) @defer.inlineCallbacks def test_socks_relay_data(self): class BadSocksServer(Protocol): def __init__(self): self._buffer = b'' self._recv_stack = [ (b'\x05\x01\x00', b'\x05\x00'), (b'\x05\x01\x00\x01\x01\x02\x03\x04\x04\xd2', b'\x05\x00\x00\x01\x01\x02\x03\x04\x12\x34'), ] def dataReceived(self, data): self._buffer += data if len(self._recv_stack) == 0: assert "not expecting any more data, got {}".format(repr(self._buffer)) return expecting, to_send = self._recv_stack.pop(0) got = self._buffer[:len(expecting)] self._buffer = self._buffer[len(expecting):] assert got == expecting, "wanted {} but got {}".format(repr(expecting), repr(got)) self.transport.write(to_send) factory = socks._TorSocksFactory(u'1.2.3.4', 1234, 'CONNECT', Mock()) server_proto = BadSocksServer() server_transport = FakeTransport(server_proto, isServer=True) client_proto = factory.buildProtocol('ignored') client_transport = FakeTransport(client_proto, isServer=False) pump = yield connect( server_proto, server_transport, client_proto, client_transport, ) # should be relaying now, try sending some datas client_proto.transport.write(b'abcdef') pump.flush() self.assertEqual(b'abcdef', server_proto._buffer) @defer.inlineCallbacks def test_socks_ipv6(self): class BadSocksServer(Protocol): def __init__(self): self._buffer = b'' self._recv_stack = [ (b'\x05\x01\x00', b'\x05\x00'), (b'\x05\x01\x00\x04\x20\x02\x44\x93\x04\xd2', b'\x05\x00\x00\x04' + (b'\x00' * 16) + b'\xbe\xef'), ] def dataReceived(self, data): self._buffer += data if len(self._recv_stack) == 0: assert "not expecting any more data, got {}".format(repr(self._buffer)) return expecting, to_send = self._recv_stack.pop(0) got = self._buffer[:len(expecting)] self._buffer = self._buffer[len(expecting):] assert got == expecting, "wanted {} but got {}".format(repr(expecting), repr(got)) self.transport.write(to_send) factory = socks._TorSocksFactory(u'2002:4493:5105::a299:9bff:fe0e:4471', 1234, 'CONNECT', Mock()) server_proto = BadSocksServer() expected_address = object() server_transport = FakeTransport(server_proto, isServer=True) client_proto = factory.buildProtocol(u'ignored') client_transport = FakeTransport(client_proto, isServer=False, hostAddress=expected_address) pump = yield connect( server_proto, server_transport, client_proto, client_transport, ) # should be relaying now, try sending some datas client_proto.transport.write(b'abcdef') addr = yield factory._get_address() # FIXME how shall we test for IPv6-ness? assert addr is expected_address pump.flush() self.assertEqual(b'abcdef', server_proto._buffer) def test_end_to_end_wrong_method(self): dis = [] def on_disconnect(error_message): dis.append(error_message) sm = socks._SocksMachine('RESOLVE', u'meejah.ca', 443, on_disconnect=on_disconnect) sm.connection() sm.feed_data(b'\x05') sm.feed_data(b'\x01') # we should have sent the request to the server, and nothing # else (because we disconnected) data = BytesIO() sm.send_data(data.write) self.assertEqual( b'\x05\x01\x00', data.getvalue(), ) self.assertEqual(1, len(dis)) self.assertEqual("Wanted method 0 or 2, got 1", dis[0]) def test_end_to_end_wrong_version(self): dis = [] def on_disconnect(error_message): dis.append(error_message) sm = socks._SocksMachine('RESOLVE', u'meejah.ca', 443, on_disconnect=on_disconnect) sm.connection() sm.feed_data(b'\x06') sm.feed_data(b'\x00') # we should have sent the request to the server, and nothing # else (because we disconnected) data = BytesIO() sm.send_data(data.write) self.assertEqual( b'\x05\x01\x00', data.getvalue(), ) self.assertEqual(1, len(dis)) self.assertEqual("Expected version 5, got 6", dis[0]) def test_end_to_end_connection_refused(self): dis = [] def on_disconnect(error_message): dis.append(error_message) sm = socks._SocksMachine( 'CONNECT', u'1.2.3.4', 443, on_disconnect=on_disconnect, create_connection=lambda a, p: None, ) sm.connection() sm.feed_data(b'\x05') sm.feed_data(b'\x00') # reply with 'connection refused' sm.feed_data(b'\x05\x05\x00\x01\x00\x00\x00\x00\xff\xff') self.assertEqual(1, len(dis)) self.assertEqual(socks.ConnectionRefusedError.message, dis[0]) def test_end_to_end_successful_relay(self): class Proto(object): data = b'' lost = [] def dataReceived(self, d): self.data = self.data + d def connectionLost(self, reason): self.lost.append(reason) the_proto = Proto() dis = [] def on_disconnect(error_message): dis.append(error_message) sm = socks._SocksMachine( 'CONNECT', u'1.2.3.4', 443, on_disconnect=on_disconnect, create_connection=lambda a, p: the_proto, ) sm.connection() sm.feed_data(b'\x05') sm.feed_data(b'\x00') # reply with success, port 0x1234 sm.feed_data(b'\x05\x00\x00\x01\x00\x00\x00\x00\x12\x34') # now some data that should get relayed sm.feed_data(b'this is some relayed data') # should *not* have disconnected self.assertEqual(0, len(dis)) self.assertTrue(the_proto.data, b"this is some relayed data") sm.disconnected(socks.SocksError("it's fine")) self.assertEqual(1, len(Proto.lost)) self.assertTrue("it's fine" in str(Proto.lost[0])) def test_end_to_end_success(self): sm = socks._SocksMachine('RESOLVE', u'meejah.ca', 443) sm.connection() sm.feed_data(b'\x05') sm.feed_data(b'\x00') # now we check we got the right bytes out the other side data = BytesIO() sm.send_data(data.write) self.assertEqual( b'\x05\x01\x00' b'\x05\xf0\x00\x03\tmeejah.ca\x00\x00', data.getvalue(), ) def test_end_to_end_connect_and_relay(self): sm = socks._SocksMachine( 'CONNECT', u'1.2.3.4', 443, create_connection=lambda a, p: None, ) sm.connection() sm.feed_data(b'\x05') sm.feed_data(b'\x00') sm.feed_data(b'some relayed data') # now we check we got the right bytes out the other side data = BytesIO() sm.send_data(data.write) self.assertEqual( b'\x05\x01\x00' b'\x05\x01\x00\x01\x01\x02\x03\x04\x01\xbb', data.getvalue(), ) def test_resolve(self): # kurt: most things use (hsot, port) tuples, this probably # should too sm = socks._SocksMachine('RESOLVE', u'meejah.ca', 443) sm.connection() sm.version_reply(0x00) data = BytesIO() sm.send_data(data.write) self.assertEqual( b'\x05\x01\x00' b'\x05\xf0\x00\x03\tmeejah.ca\x00\x00', data.getvalue(), ) @defer.inlineCallbacks def test_resolve_with_reply(self): # kurt: most things use (hsot, port) tuples, this probably # should too sm = socks._SocksMachine('RESOLVE', u'meejah.ca', 443) sm.connection() sm.version_reply(0x00) # make sure the state-machine wanted to send out the correct # request. data = BytesIO() sm.send_data(data.write) self.assertEqual( b'\x05\x01\x00' b'\x05\xf0\x00\x03\tmeejah.ca\x00\x00', data.getvalue(), ) # now feed it a reply (but not enough to parse it yet!) d = sm.when_done() # ...we have to send at least 8 bytes, but NOT the entire hostname sm.feed_data(b'\x05\x00\x00\x03') sm.feed_data(b'\x06meeja') self.assertTrue(not d.called) # now send the rest, checking the buffering in _parse_domain_name_reply sm.feed_data(b'h\x00\x00') self.assertTrue(d.called) answer = yield d # XXX answer *should* be not-bytes, though I think self.assertEqual(b'meejah', answer) @defer.inlineCallbacks def test_unknown_response_type(self): # kurt: most things use (hsot, port) tuples, this probably # should too sm = socks._SocksMachine('RESOLVE', u'meejah.ca', 443) sm.connection() # don't actually support username/password (which is version 0x02) yet # sm.version_reply(0x02) sm.version_reply(0) # make sure the state-machine wanted to send out the correct # request. data = BytesIO() sm.send_data(data.write) self.assertEqual( b'\x05\x01\x00' b'\x05\xf0\x00\x03\tmeejah.ca\x00\x00', data.getvalue(), ) sm.feed_data(b'\x05\x00\x00\xaf\x00\x00\x00\x00') with self.assertRaises(socks.SocksError) as ctx: yield sm.when_done() self.assertTrue('Unexpected response type 175' in str(ctx.exception)) @defer.inlineCallbacks def test_resolve_ptr(self): sm = socks._SocksMachine('RESOLVE_PTR', u'1.2.3.4', 443) sm.connection() sm.version_reply(0x00) data = BytesIO() sm.send_data(data.write) self.assertEqual( b'\x05\x01\x00' b'\x05\xf1\x00\x01\x01\x02\x03\x04\x00\x00', data.getvalue(), ) sm.feed_data( b'\x05\x00\x00\x01\x00\x01\x02\xff\x12\x34' ) addr = yield sm.when_done() self.assertEqual('0.1.2.255', addr) def test_connect(self): sm = socks._SocksMachine( 'CONNECT', u'1.2.3.4', 443, create_connection=lambda a, p: None, ) sm.connection() sm.version_reply(0x00) data = BytesIO() sm.send_data(data.write) self.assertEqual( b'\x05\x01\x00' b'\x05\x01\x00\x01\x01\x02\x03\x04\x01\xbb', data.getvalue(), ) # XXX should re-write (at LEAST) these to use Twisted's IOPump class SocksConnectTests(unittest.TestCase): @defer.inlineCallbacks def test_connect_no_tls(self): socks_ep = Mock() transport = proto_helpers.StringTransport() def connect(factory): factory.startFactory() proto = factory.buildProtocol("addr") proto.makeConnection(transport) self.assertEqual(b'\x05\x01\x00', transport.value()) proto.dataReceived(b'\x05\x00') proto.dataReceived(b'\x05\x00\x00\x01\x00\x00\x00\x00\x00\x00') return proto socks_ep.connect = connect protocol = Mock() factory = Mock() factory.buildProtocol = Mock(return_value=protocol) ep = socks.TorSocksEndpoint(socks_ep, u'meejah.ca', 443) proto = yield ep.connect(factory) self.assertEqual(proto, protocol) @defer.inlineCallbacks def test_connect_deferred_proxy(self): socks_ep = Mock() directlyProvides(socks_ep, IStreamClientEndpoint) transport = proto_helpers.StringTransport() def connect(factory): factory.startFactory() proto = factory.buildProtocol("addr") proto.makeConnection(transport) self.assertEqual(b'\x05\x01\x00', transport.value()) proto.dataReceived(b'\x05\x00') proto.dataReceived(b'\x05\x00\x00\x01\x00\x00\x00\x00\x00\x00') return proto socks_ep.connect = connect protocol = Mock() factory = Mock() factory.buildProtocol = Mock(return_value=protocol) ep = socks.TorSocksEndpoint( socks_endpoint=defer.succeed(socks_ep), host=u'meejah.ca', port=443, ) proto = yield ep.connect(factory) self.assertEqual(proto, protocol) @defer.inlineCallbacks def test_connect_deferred_proxy_wrong_return(self): class NotAnEndpoint(object): "definitely doesn't implement IStreamClientEndpoint" not_an_endpoint = NotAnEndpoint() factory = Mock() ep = socks.TorSocksEndpoint( socks_endpoint=defer.succeed(not_an_endpoint), host=u'meejah.ca', port=443, ) with self.assertRaises(ValueError) as ctx: yield ep.connect(factory) self.assertIn( "must resolve to an IStreamClientEndpoint provider", str(ctx.exception), ) @defer.inlineCallbacks def test_connect_tls(self): socks_ep = Mock() transport = proto_helpers.StringTransport() def connect(factory): factory.startFactory() proto = factory.buildProtocol("addr") proto.makeConnection(transport) self.assertEqual(b'\x05\x01\x00', transport.value()) proto.dataReceived(b'\x05\x00') proto.dataReceived(b'\x05\x00\x00\x01\x00\x00\x00\x00\x00\x00') return proto socks_ep.connect = connect protocol = Mock() factory = Mock() factory.buildProtocol = Mock(return_value=protocol) ep = socks.TorSocksEndpoint(socks_ep, u'meejah.ca', 443, tls=True) proto = yield ep.connect(factory) self.assertEqual(proto, protocol) @defer.inlineCallbacks def test_connect_tls_context(self): socks_ep = Mock() transport = proto_helpers.StringTransport() def connect(factory): factory.startFactory() proto = factory.buildProtocol("addr") proto.makeConnection(transport) self.assertEqual(b'\x05\x01\x00', transport.value()) proto.dataReceived(b'\x05\x00') proto.dataReceived(b'\x05\x00\x00\x01\x00\x00\x00\x00\x00\x00') return proto socks_ep.connect = connect protocol = Mock() factory = Mock() factory.buildProtocol = Mock(return_value=protocol) from OpenSSL import SSL class CertificateOptions(object): def getContext(self, *args, **kw): return SSL.Context(SSL.TLSv1_METHOD) ep = socks.TorSocksEndpoint(socks_ep, u'meejah.ca', 443, tls=CertificateOptions()) proto = yield ep.connect(factory) self.assertEqual(proto, protocol) @defer.inlineCallbacks def test_connect_socks_error(self): socks_ep = Mock() transport = proto_helpers.StringTransport() def connect(factory): factory.startFactory() proto = factory.buildProtocol("addr") proto.makeConnection(transport) self.assertEqual(b'\x05\x01\x00', transport.value()) proto.dataReceived(b'\x05\x00') proto.dataReceived(b'\x05\x01\x00\x01\x00\x00\x00\x00') return proto socks_ep.connect = connect protocol = Mock() factory = Mock() factory.buildProtocol = Mock(return_value=protocol) ep = socks.TorSocksEndpoint(socks_ep, u'meejah.ca', 443, tls=True) with self.assertRaises(Exception) as ctx: yield ep.connect(factory) self.assertTrue(isinstance(ctx.exception, socks.GeneralServerFailureError)) @defer.inlineCallbacks def test_connect_socks_error_unknown(self): socks_ep = Mock() transport = proto_helpers.StringTransport() def connect(factory): factory.startFactory() proto = factory.buildProtocol("addr") proto.makeConnection(transport) self.assertEqual(b'\x05\x01\x00', transport.value()) proto.dataReceived(b'\x05\x00') proto.dataReceived(b'\x05\xff\x00\x01\x00\x00\x00\x00') return proto socks_ep.connect = connect protocol = Mock() factory = Mock() factory.buildProtocol = Mock(return_value=protocol) ep = socks.TorSocksEndpoint(socks_ep, u'meejah.ca', 443, tls=True) with self.assertRaises(Exception) as ctx: yield ep.connect(factory) self.assertTrue('Unknown SOCKS error-code' in str(ctx.exception)) @defer.inlineCallbacks def test_connect_socks_illegal_byte(self): socks_ep = Mock() transport = proto_helpers.StringTransport() def connect(factory): factory.startFactory() proto = factory.buildProtocol("addr") proto.makeConnection(transport) self.assertEqual(b'\x05\x01\x00', transport.value()) proto.dataReceived(b'\x05\x00') proto.dataReceived(b'\x05\x01\x00\x01\x00\x00\x00\x00') return proto socks_ep.connect = connect protocol = Mock() factory = Mock() factory.buildProtocol = Mock(return_value=protocol) ep = socks.TorSocksEndpoint(socks_ep, u'meejah.ca', 443, tls=True) with self.assertRaises(Exception) as ctx: yield ep.connect(factory) self.assertTrue(isinstance(ctx.exception, socks.GeneralServerFailureError)) @defer.inlineCallbacks def test_get_address_endpoint(self): socks_ep = Mock() transport = proto_helpers.StringTransport() delayed_addr = [] def connect(factory): delayed_addr.append(factory._get_address()) delayed_addr.append(factory._get_address()) factory.startFactory() proto = factory.buildProtocol("addr") proto.makeConnection(transport) self.assertEqual(b'\x05\x01\x00', transport.value()) proto.dataReceived(b'\x05\x00') proto.dataReceived(b'\x05\x00\x00\x01\x00\x00\x00\x00\x00\x00') return proto socks_ep.connect = connect protocol = Mock() factory = Mock() factory.buildProtocol = Mock(return_value=protocol) ep = socks.TorSocksEndpoint(socks_ep, u'meejah.ca', 443, tls=True) yield ep.connect(factory) addr = yield ep._get_address() self.assertEqual(addr, IPv4Address('TCP', '10.0.0.1', 12345)) self.assertEqual(2, len(delayed_addr)) self.assertTrue(delayed_addr[0] is not delayed_addr[1]) self.assertTrue(all([d.called for d in delayed_addr])) @defer.inlineCallbacks def test_get_address(self): # normally, ._get_address is only called via the # attach_stream() method on Circuit addr = object() factory = socks._TorSocksFactory() d = factory._get_address() self.assertFalse(d.called) factory._did_connect(addr) maybe_addr = yield d self.assertEqual(addr, maybe_addr) # if we do it a second time, should be immediate d = factory._get_address() self.assertTrue(d.called) self.assertEqual(d.result, addr) class SocksResolveTests(unittest.TestCase): @defer.inlineCallbacks def test_resolve(self): socks_ep = Mock() transport = proto_helpers.StringTransport() def connect(factory): factory.startFactory() proto = factory.buildProtocol("addr") proto.makeConnection(transport) # XXX sadness: we probably "should" just feed the right # bytes to the protocol to convince it a connection is # made ... *or* we can cheat and just do the callback # directly... proto._machine._when_done.fire("the dns answer") return proto socks_ep.connect = connect hn = yield socks.resolve(socks_ep, u'meejah.ca') self.assertEqual(hn, "the dns answer") @defer.inlineCallbacks def test_resolve_ptr(self): socks_ep = Mock() transport = proto_helpers.StringTransport() def connect(factory): factory.startFactory() proto = factory.buildProtocol("addr") proto.makeConnection(transport) # XXX sadness: we probably "should" just feed the right # bytes to the protocol to convince it a connection is # made ... *or* we can cheat and just do the callback # directly... proto._machine._when_done.fire(u"the dns answer") return proto socks_ep.connect = connect hn = yield socks.resolve_ptr(socks_ep, u'meejah.ca') self.assertEqual(hn, "the dns answer") @patch('txtorcon.socks._TorSocksFactory') def test_resolve_ptr_str(self, fac): socks_ep = Mock() d = socks.resolve_ptr(socks_ep, 'meejah.ca') self.assertEqual(1, len(fac.mock_calls)) self.assertTrue( isinstance(fac.mock_calls[0][1][0], text_type) ) return d @patch('txtorcon.socks._TorSocksFactory') def test_resolve_str(self, fac): socks_ep = Mock() d = socks.resolve(socks_ep, 'meejah.ca') self.assertEqual(1, len(fac.mock_calls)) self.assertTrue( isinstance(fac.mock_calls[0][1][0], text_type) ) return d @patch('txtorcon.socks._TorSocksFactory') def test_resolve_ptr_bytes(self, fac): socks_ep = Mock() d = socks.resolve_ptr(socks_ep, b'meejah.ca') self.assertEqual(1, len(fac.mock_calls)) self.assertTrue( isinstance(fac.mock_calls[0][1][0], text_type) ) return d @patch('txtorcon.socks._TorSocksFactory') def test_resolve_bytes(self, fac): socks_ep = Mock() d = socks.resolve(socks_ep, b'meejah.ca') self.assertEqual(1, len(fac.mock_calls)) self.assertTrue( isinstance(fac.mock_calls[0][1][0], text_type) ) return d class SocksErrorTests(unittest.TestCase): def _check_error(self, error, cls_, code, message): self.assertTrue(isinstance(error, cls_)) self.assertEqual(error.code, code) self.assertEqual(error.message, message) self.assertEqual(str(error), message) def test_error_factory(self): for cls in socks.SocksError.__subclasses__(): error = socks._create_socks_error(cls.code) self._check_error(error, cls, cls.code, cls.message) def test_custom_error(self): code = 0xFF message = 'Custom error message' self._check_error(socks.SocksError(message), socks.SocksError, None, message) self._check_error(socks.SocksError(message=message), socks.SocksError, None, message) self._check_error(socks.SocksError(code=code), socks.SocksError, code, '') self._check_error(socks.SocksError(message, code=code), socks.SocksError, code, message) self._check_error(socks.SocksError(message=message, code=code), socks.SocksError, code, message)

35.686298

111

0.596376

3,414

29,691

5.039836

0.099004

0.015343

0.016738

0.016855

0.79141

0.772696

0.746019

0.707834

0.687086

0.66843

0

0.041999

0.294298

29,691

831

112

35.729242

0.779172

0.063049

0

0.689498

0

0.099475

0.039567

0

0.000719

0.001203

0.141553

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0.100457

false

0

0.018265

0.001522

0.165906

0

null

0

1

0

1

0

null

0

4

4cb04dfc726d4ac1800ff0dd0d01f5c28f942726

364

py

Python

python/testData/inspections/PyDataclassInspection/comparisonForManuallyOrdered.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/inspections/PyDataclassInspection/comparisonForManuallyOrdered.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/inspections/PyDataclassInspection/comparisonForManuallyOrdered.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from dataclasses import dataclass @dataclass(order=False) class Test1: def __gt__(self, other): pass @dataclass class Test2: def __gt__(self, other): pass print(Test1() < Test1()) print(Test2() < Test2()) print(Test1() > Test1()) print(Test2() > Test2()) print(Test1 < Test1) print(Test2 < Test2) print(Test1 > Test1) print(Test2 > Test2)

15.826087

33

0.664835

46

364

5.086957

0.326087

0.17094

0.25641

0.34188

0.666667

0.512821

0

0.060606

0.184066

364

23

34

15.826087

0.727273

0

0.235294

0

1

0.117647

false

0.117647

0.058824

0

0.294118

0.470588

0

null

0

1

0

null

0

1

0

1

0

4

4cb8191cdc92d1a5f9c909f5adfaf89d0827d41c

93

py

Python

test/test_getfiles.py

Zhu-Jianwei/LaTeX-helper

322bf686d1aee32804013d813d46bd27a4a8325f

[ "MIT" ]

4

2022-03-13T12:02:38.000Z

2022-03-13T15:30:20.000Z

test/test_getfiles.py

Zhu-Jianwei/LaTeX-helper

322bf686d1aee32804013d813d46bd27a4a8325f

[ "MIT" ]

null

test/test_getfiles.py

Zhu-Jianwei/LaTeX-helper

322bf686d1aee32804013d813d46bd27a4a8325f

[ "MIT" ]

3

2022-02-06T08:05:37.000Z

2022-02-07T08:26:48.000Z

from utils.fileio import * if __name__ == '__main__': print(get_tex_list_recursive('.'))

23.25

38

0.709677

12

93

4.583333

1

0

0.139785

93

4

38

23.25

0.6875

0

0.095745

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

4cc547610eb48bfe2850a07303a37c73843e903c

17

py

Python

src/passpredict/_version.py

samtx/pass-predictor

6577f75cd7d64bd3c12a9512880d4b29c2682b4c

[ "MIT" ]

4

2017-01-31T07:12:48.000Z

2018-12-02T21:30:14.000Z

layer-atlas/version.gradle

netguru/Atlas-Android

7a3ca807c8d641346bcb73811cb0d308dca16efe

[ "Apache-2.0" ]

null

layer-atlas/version.gradle

netguru/Atlas-Android

7a3ca807c8d641346bcb73811cb0d308dca16efe

[ "Apache-2.0" ]

null

version = '0.5.0'

17

0.588235

4

17

2.5

0.75

0

0.2

0.117647

17

1

17

0.466667

0

0.277778

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

4cee5af5f6e26e8e12d1a1f706296d1d58e7b45d

162

py

Python

checkers/5Go/client/__init__.py

C4T-BuT-S4D/stay-home-ctf-2022

7084f53a69e3e25d62a1aaad8b7dfe1cd663a4ec

[ "WTFPL" ]

4

2022-02-06T19:56:46.000Z

2022-02-12T15:21:47.000Z

services/5Go/client/__init__.py

C4T-BuT-S4D/stay-home-ctf-2022

7084f53a69e3e25d62a1aaad8b7dfe1cd663a4ec

[ "WTFPL" ]

null

services/5Go/client/__init__.py

C4T-BuT-S4D/stay-home-ctf-2022

7084f53a69e3e25d62a1aaad8b7dfe1cd663a4ec

[ "WTFPL" ]

2

2022-02-07T09:59:47.000Z

2022-02-07T10:22:20.000Z

from pathlib import Path import sys sys.path.insert(0, str(Path(__file__).absolute().parent / 'proto')) from .daeh5 import Daeh5 # noqa __all__ = ('Daeh5',)

16.2

67

0.703704

23

162

4.608696

0.652174

0

0.028986

0.148148

162

9

68

18

0.73913

0.024691

0

0.064103

0

1

0

false

0

0.6

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

4

4cef4c98ae09635815699f63ee6fc37238126027

165

py

Python

tests/extmod/ure_debug.py

TG-Techie/circuitpython

390295dd218fb705fe652de77132dea472adf1ed

[ "MIT", "BSD-3-Clause", "MIT-0", "Unlicense" ]

3

2020-01-09T21:50:22.000Z

2020-01-15T08:27:48.000Z

tests/extmod/ure_debug.py

TG-Techie/circuitpython

390295dd218fb705fe652de77132dea472adf1ed

[ "MIT", "BSD-3-Clause", "MIT-0", "Unlicense" ]

null

tests/extmod/ure_debug.py

TG-Techie/circuitpython

390295dd218fb705fe652de77132dea472adf1ed

[ "MIT", "BSD-3-Clause", "MIT-0", "Unlicense" ]

1

2020-01-11T12:42:41.000Z

# test printing debugging info when compiling try: import ure except ImportError: print("SKIP") raise SystemExit ure.compile("^a|b[0-9]\w$", ure.DEBUG)

18.333333

45

0.690909

24

165

4.75

0.916667

0

0.014815

0.181818

165

8

46

20.625

0.82963

0.260606

0

0.133333

0

1

0

true

0

0.333333

0

0.333333

0.166667

1

0

null

0

1

0

null

0

1

0

1

0

4

4cf5f417e64e0bebe31c66283110f507586b44ab

444

py

Python

Handlers/BaseHandler.py

Nuit-De-L-Info-2016-STRI-DL/Backend

418f8411cd556c6fc96c7f19c976560e773e35f0

[ "MIT" ]

null

Handlers/BaseHandler.py

Nuit-De-L-Info-2016-STRI-DL/Backend

418f8411cd556c6fc96c7f19c976560e773e35f0

[ "MIT" ]

3

2016-12-01T20:37:56.000Z

2017-09-28T11:02:52.000Z

Handlers/BaseHandler.py

Nuit-De-L-Info-2016-STRI-DL/Backend

418f8411cd556c6fc96c7f19c976560e773e35f0

[ "MIT" ]

null

import tornado class BaseHandler(tornado.web.RequestHandler): """Superclass for Handlers which require a connected user """ @property def redis_client(self): return self.application.redis_client def get_current_user(self): """Get current connected user :return: current connected user """ return self.get_secure_cookie("user") def get(self): self.render('404.html')

21.142857

61

0.653153

51

444

5.568627

0.54902

0.137324

0.140845

0.183099

0

0.009036

0.252252

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980ae279fc6ff666cb86472b0e41e07b61f192a0

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py

Python

master/routers/spiders.py

sdulsj/spider_platform

8850fdfc1bb12817ef47da89856da68a7a52fd2e

[ "MIT" ]

7

2018-08-17T09:04:18.000Z

2021-10-05T17:02:28.000Z

master/routers/spiders.py

parker-pu/spider_platform

8850fdfc1bb12817ef47da89856da68a7a52fd2e

[ "MIT" ]

1

2021-06-04T05:50:43.000Z

master/routers/spiders.py

parker-pu/spider_platform

8850fdfc1bb12817ef47da89856da68a7a52fd2e

[ "MIT" ]

3

2019-03-21T09:40:45.000Z

2020-05-09T13:27:59.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ # @Date : 2018/8/15 # @Author: lsj # @File : views.py # @Desc : 默认Python版本支持：3.6 """ from app.routers import blueprint_spider from flask_login import login_required

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e24923e9e8b668ee62513b6705dcec9a67c54140

138

py

Python

ldndc2nc/__main__.py

deekaey/ldndc2nc

43dcf4ec1175fe9535c0182cefc1553db18f7472

[ "BSD-3-Clause" ]

null

ldndc2nc/__main__.py

deekaey/ldndc2nc

43dcf4ec1175fe9535c0182cefc1553db18f7472

[ "BSD-3-Clause" ]

null

ldndc2nc/__main__.py

deekaey/ldndc2nc

43dcf4ec1175fe9535c0182cefc1553db18f7472

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ldndc2nc.__main__: executed when ldndc2nc directory is called as script.""" from .ldndc2nc import main main()

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e24ae2ac9ca6b814c6d97818e0f21f67872cd6dc

81

py

Python

blog_embed/__init__.py

insaction-dev/markdown-blog-embed

a45fd7641d600a203b778c9d4d6e7b07460bbfe3

[ "MIT" ]

null

blog_embed/__init__.py

insaction-dev/markdown-blog-embed

a45fd7641d600a203b778c9d4d6e7b07460bbfe3

[ "MIT" ]

null

blog_embed/__init__.py

insaction-dev/markdown-blog-embed

a45fd7641d600a203b778c9d4d6e7b07460bbfe3

[ "MIT" ]

null

"""Allows custom embeds of other websites' data through simple link copy-paste"""

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e286c0d52f3f7354d99fd6d4b897ffa7143497f8

87

py

Python

apps/zabbix/apps.py

ykyk1229/TurtleDove

074ed03396d603a920bc382ee916fc0f3adab6ea

[ "MIT" ]

3

2020-06-11T10:57:22.000Z

2021-03-25T02:45:05.000Z

apps/zabbix/apps.py

ykyk1229/TurtleDove

074ed03396d603a920bc382ee916fc0f3adab6ea

[ "MIT" ]

2

2020-08-05T07:59:45.000Z

2020-08-05T08:00:48.000Z

apps/zabbix/apps.py

sunfan666/cmdb

71722a8dddf5e337d7658328cfcac0c9108b067c

[ "MIT" ]

null

from django.apps import AppConfig class ZabbixConfig(AppConfig): name = 'zabbix'

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2c33286bf46b60906134beaba1829ed0697c48cd

2,426

py

Python

libs/python/test/test_imaged_object_api.py

Scripta-Qumranica-Electronica/SQE_API_Connectors

aaa9b9eb8709d4257c32ea57321a179c6b1e041a

[ "MIT" ]

null

libs/python/test/test_imaged_object_api.py

Scripta-Qumranica-Electronica/SQE_API_Connectors

aaa9b9eb8709d4257c32ea57321a179c6b1e041a

[ "MIT" ]

null

libs/python/test/test_imaged_object_api.py

Scripta-Qumranica-Electronica/SQE_API_Connectors

aaa9b9eb8709d4257c32ea57321a179c6b1e041a

[ "MIT" ]

null

# coding: utf-8 """ SQE API No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 The version of the OpenAPI document: v1 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import unittest import qumranica_api_connector from qumranica_api_connector.api.imaged_object_api import ImagedObjectApi # noqa: E501 from qumranica_api_connector.rest import ApiException class TestImagedObjectApi(unittest.TestCase): """ImagedObjectApi unit test stubs""" def setUp(self): self.api = qumranica_api_connector.api.imaged_object_api.ImagedObjectApi() # noqa: E501 def tearDown(self): pass def test_v1_editions_edition_id_imaged_objects_get(self): """Test case for v1_editions_edition_id_imaged_objects_get Provides a listing of imaged objects related to the specified edition, can include images and also their masks with optional. # noqa: E501 """ pass def test_v1_editions_edition_id_imaged_objects_imaged_object_id_get(self): """Test case for v1_editions_edition_id_imaged_objects_imaged_object_id_get Provides information for the specified imaged object related to the specified edition, can include images and also their masks with optional. # noqa: E501 """ pass def test_v1_imaged_objects_imaged_object_id_get(self): """Test case for v1_imaged_objects_imaged_object_id_get Provides information for the specified imaged object. # noqa: E501 """ pass def test_v1_imaged_objects_imaged_object_id_text_fragments_get(self): """Test case for v1_imaged_objects_imaged_object_id_text_fragments_get Provides a list of all text fragments that should correspond to the imaged object. # noqa: E501 """ pass def test_v1_imaged_objects_institutions_get(self): """Test case for v1_imaged_objects_institutions_get Provides a list of all institutional image providers. # noqa: E501 """ pass def test_v1_imaged_objects_institutions_institution_name_get(self): """Test case for v1_imaged_objects_institutions_institution_name_get Provides a list of all institutional image providers. # noqa: E501 """ pass if __name__ == '__main__': unittest.main()

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2c4e3c128a7b528636324830f29b2f4c8c54768f

138

py

Python

training/unsupervised/ndim/operations.py

purijs/Sentinel-Training-Data-Generation

6d707cf197b7c822076c9cb58ca9b79c5ffdb297

[ "MIT" ]

null

training/unsupervised/ndim/operations.py

purijs/Sentinel-Training-Data-Generation

6d707cf197b7c822076c9cb58ca9b79c5ffdb297

[ "MIT" ]

null

training/unsupervised/ndim/operations.py

purijs/Sentinel-Training-Data-Generation

6d707cf197b7c822076c9cb58ca9b79c5ffdb297

[ "MIT" ]

null

class NdComputations(object): def __init__(self, lerp, nd_distance): self.lerp = lerp self.nd_distance = nd_distance

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4

2c71c76dbc5cf3746ed57a3d539725bfb3b8bd30

60

py

Python

python/testData/resolve/TypeDunderDocNewStyleClass.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2018-12-29T09:53:39.000Z

2018-12-29T09:53:42.000Z

python/testData/resolve/TypeDunderDocNewStyleClass.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/resolve/TypeDunderDocNewStyleClass.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

class A(object): pass print(A.__doc__) # <ref>

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2ca3c7bc970d88cb2f150a75cd389256ab450ff4

383

py

Python

0-python-tutorial/02-comments01.py

luis2ra/py3-00-w3schools

6bb851837f8ef9520491d13fa2c909047c9b18cf

[ "MIT" ]

null

0-python-tutorial/02-comments01.py

luis2ra/py3-00-w3schools

6bb851837f8ef9520491d13fa2c909047c9b18cf

[ "MIT" ]

null

0-python-tutorial/02-comments01.py

luis2ra/py3-00-w3schools

6bb851837f8ef9520491d13fa2c909047c9b18cf

[ "MIT" ]

null

# @author: https://github.com/luis2ra from https://www.w3schools.com/python/python_comments.asp ''' Python Comments Comments can be used to explain Python code. Comments can be used to make the code more readable. Comments can be used to prevent execution when testing code. Comments starts with a #, and Python will ignore them. ''' # This is a comment. print("Hello, World!")

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e2c836e650ec6b90ea6e834a95a542a893d342de

27

py

Python

src/pydictionaria/__init__.py

clld/pydictionaria

e86f849ee732e11c82830dd10cf29fcbc455ced3

[ "Apache-2.0" ]

1

2022-02-23T10:35:21.000Z

src/pydictionaria/__init__.py

dictionaria/pydictionaria

4c18edc3b3bb95bf44dd3b2b910aaff3fcd14045

[ "Apache-2.0" ]

17

2019-05-10T07:47:25.000Z

2022-03-05T23:53:11.000Z

src/pydictionaria/__init__.py

clld/pydictionaria

e86f849ee732e11c82830dd10cf29fcbc455ced3

[ "Apache-2.0" ]

null

# __version__ = '2.2.dev0'

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e2e6e109535f8bb2e122bb20dd4f01afe3c28c67

10,248

py

Python

papertalk/utils/mendeley/example.py

karissa/papertalk

79d77383b1a6fb41e9fef8b9c4f619df97cc8a7c

[ "MIT" ]

null

papertalk/utils/mendeley/example.py

karissa/papertalk

79d77383b1a6fb41e9fef8b9c4f619df97cc8a7c

[ "MIT" ]

2

2016-02-04T23:59:58.000Z

papertalk/utils/mendeley/example.py

karissa/papertalk

79d77383b1a6fb41e9fef8b9c4f619df97cc8a7c

[ "MIT" ]

null

#!/usr/bin/env python """ Mendeley Open API Example Client Copyright (c) 2010, Mendeley Ltd. <copyright@mendeley.com> Permission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies. THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. For details of the Mendeley Open API see http://dev.mendeley.com/ Example usage: python example.py """ from pprint import pprint from mendeley_client import * import os import sys # edit config.json first mendeley = create_client() ######################################## ######## Public Resources Tests ######## ######################################## print """ ----------------------------------------------------- Canonical document details -----------------------------------------------------""" response = mendeley.details('cbcca150-6cff-11df-a2b2-0026b95e3eb7') pprint(response) print """ ----------------------------------------------------- Canonical document details DOI look up -----------------------------------------------------""" response = mendeley.details('10.1371%2Fjournal.ppat.1000281', type='doi') pprint(response) print """ ----------------------------------------------------- Canonical document details PubMed Id look up -----------------------------------------------------""" response = mendeley.details('19910365', type='pmid') pprint(response) print """ ----------------------------------------------------- Categories -----------------------------------------------------""" response = mendeley.categories() pprint(response) print """ ----------------------------------------------------- Subcategories -----------------------------------------------------""" response = mendeley.subcategories(3) pprint(response) print """ ----------------------------------------------------- Search -----------------------------------------------------""" response = mendeley.search('phiC31', items=10) pprint(response) print """ ----------------------------------------------------- Tagged 'modularity' -----------------------------------------------------""" response = mendeley.tagged('modularity', items=5) pprint(response) print """ ----------------------------------------------------- Tagged 'test' in category 14 -----------------------------------------------------""" response = mendeley.tagged('test', cat=14) pprint(response) print """ ----------------------------------------------------- Tagged 'modularity' in subcategory 'Bioinformatics' -----------------------------------------------------""" response = mendeley.tagged('modularity', subcat=455) pprint(response) print """ ----------------------------------------------------- Related -----------------------------------------------------""" response = mendeley.related('91df2740-6d01-11df-a2b2-0026b95e3eb7') pprint(response) print """ ----------------------------------------------------- Authored by 'Ann Cowan' -----------------------------------------------------""" response = mendeley.authored('Ann Cowan', items=5) pprint(response) print """ ----------------------------------------------------- Public groups -----------------------------------------------------""" response = mendeley.public_groups() pprint(response) groupId = '536181' print """ ----------------------------------------------------- Public group details -----------------------------------------------------""" response = mendeley.public_group_details(groupId) pprint(response) print """ ----------------------------------------------------- Public group documents -----------------------------------------------------""" response = mendeley.public_group_docs(groupId) pprint(response) print """ ----------------------------------------------------- Public group people -----------------------------------------------------""" response = mendeley.public_group_people(groupId) pprint(response) print """ ----------------------------------------------------- Author statistics -----------------------------------------------------""" response = mendeley.author_stats() pprint(response) print """ ----------------------------------------------------- Papers statistics -----------------------------------------------------""" response = mendeley.paper_stats() pprint(response) print """ ----------------------------------------------------- Publications outlets statistics -----------------------------------------------------""" response = mendeley.publication_stats() pprint(response) ############################################### ######## User Specific Resources Tests ######## ############################################### print """ ----------------------------------------------------- My Library authors statistics -----------------------------------------------------""" response = mendeley.library_author_stats() pprint(response) print """ ----------------------------------------------------- My Library tag statistics -----------------------------------------------------""" response = mendeley.library_tag_stats() pprint(response) print """ ----------------------------------------------------- My Library publication statistics -----------------------------------------------------""" response = mendeley.library_publication_stats() pprint(response) ### Library ### print 'Library' print """ ----------------------------------------------------- My Library documents -----------------------------------------------------""" documents = mendeley.library() pprint(documents) print """ ----------------------------------------------------- Create a new library document -----------------------------------------------------""" response = mendeley.create_document(document={'type' : 'Book','title': 'Document creation test', 'year': 2008}) pprint(response) documentId = response['document_id'] print """ ----------------------------------------------------- Document details -----------------------------------------------------""" response = mendeley.document_details(documentId) pprint(response) print """ ----------------------------------------------------- Delete library document -----------------------------------------------------""" response = mendeley.delete_library_document(documentId) pprint(response) print """ ----------------------------------------------------- Documents authored -----------------------------------------------------""" response = mendeley.documents_authored() pprint(response) print """ ----------------------------------------------------- Create new folder -----------------------------------------------------""" response = mendeley.create_folder(folder={'name': 'Test folder creation'}) pprint(response) folderId = response['folder_id'] print """ ----------------------------------------------------- Create new child folder -----------------------------------------------------""" response = mendeley.create_folder(folder={'name': 'Test child folder creation', 'parent':folderId}) pprint(response) print """ ----------------------------------------------------- List folders -----------------------------------------------------""" folders = mendeley.folders() pprint(folders) print """ ----------------------------------------------------- Delete folder -----------------------------------------------------""" response = mendeley.delete_folder(folderId) pprint(response) print """ ----------------------------------------------------- Create public open group -----------------------------------------------------""" response = mendeley.create_group(group={'name':'My awesome public group', 'type': 'open'}) pprint(response) groupId = response["group_id"] print """ ----------------------------------------------------- Delete public group -----------------------------------------------------""" response = mendeley.delete_group(groupId) pprint(response) print """ ----------------------------------------------------- Create private group -----------------------------------------------------""" response = mendeley.create_group(group={'name':'Private group test', 'type': 'private'}) pprint(response) groupId = response['group_id'] print """ ----------------------------------------------------- Create new group folder -----------------------------------------------------""" response = mendeley.create_group_folder(groupId, folder={'name': 'Test folder creation'}) pprint(response) folderId = response['folder_id'] print """ ----------------------------------------------------- Create new child group folder -----------------------------------------------------""" response = mendeley.create_group_folder(groupId, folder={'name': 'Test child folder creation', 'parent':folderId}) pprint(response) print """ ----------------------------------------------------- List group folders -----------------------------------------------------""" folders = mendeley.group_folders(groupId) pprint(folders) print """ ----------------------------------------------------- Delete group folder -----------------------------------------------------""" response = mendeley.delete_group_folder(groupId, folderId) pprint(response) print """ ----------------------------------------------------- Delete private group -----------------------------------------------------""" response = mendeley.delete_group(groupId) pprint(response) print """ ----------------------------------------------------- Current user's profile info -----------------------------------------------------""" response = mendeley.my_profile_info() pprint(response) print """ ----------------------------------------------------- Current user's contacts -----------------------------------------------------""" response = mendeley.contacts() pprint(response)

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e2e9fd5c53d5066b87e3c2911fa1333d6225513f

253

py

Python

lstchain/visualization/tests/test_plot_dl2.py

labsaha/cta-lstchain

7e65e2cd9c42fbc827b0dcf1bccc7141203ebd22

[ "BSD-3-Clause" ]

null

lstchain/visualization/tests/test_plot_dl2.py

labsaha/cta-lstchain

7e65e2cd9c42fbc827b0dcf1bccc7141203ebd22

[ "BSD-3-Clause" ]

null

lstchain/visualization/tests/test_plot_dl2.py

labsaha/cta-lstchain

7e65e2cd9c42fbc827b0dcf1bccc7141203ebd22

[ "BSD-3-Clause" ]

3

2021-06-25T14:20:17.000Z

2021-06-25T16:01:33.000Z

import pandas as pd from lstchain.visualization import plot_dl2 from lstchain.tests.test_lstchain import dl2_file, dl2_params_lstcam_key def test_plot_disp(): dl2_df = pd.read_hdf(dl2_file, key=dl2_params_lstcam_key) plot_dl2.plot_disp(dl2_df)

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3949f89dbcad477050d6fca188d02fd75c7d5286

917

py

Python

importio2/extractor_data.py

import-io/import-io-api-python

5c838a357742233e714b2ccfd19d25c18531cfa3

[ "Apache-2.0" ]

1

2021-08-18T03:27:40.000Z

importio2/extractor_data.py

import-io/import-io-api-python

5c838a357742233e714b2ccfd19d25c18531cfa3

[ "Apache-2.0" ]

null

importio2/extractor_data.py

import-io/import-io-api-python

5c838a357742233e714b2ccfd19d25c18531cfa3

[ "Apache-2.0" ]

2

2021-09-13T14:28:50.000Z

2021-09-27T17:56:21.000Z

# # Copyright 2016 Import.io # # 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. # class ExtractorData(object): pass class CSVData(ExtractorData): def __init__(self, header=None, data=None): self._header = header self._data = data def __getitem__(self, item): return self._data[item] def __len__(self): return len(self._data) class LogData(ExtractorData): pass

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1a3c439ef6960c58744d8611e7ba305dc3cc1c62

202

py

Python

images/python_analysis/src/venv/lib/python3.7/site-packages/elasticsearch/_async/__init__.py

Jael24/TB_ElasticStack

f9aad11eda69045140a90f28739b558bf077d877

[ "MIT" ]

2

2021-05-01T05:40:55.000Z

2021-06-25T13:34:46.000Z

images/python_analysis/src/venv/lib/python3.7/site-packages/elasticsearch/_async/__init__.py

Jael24/TB_ElasticStack

f9aad11eda69045140a90f28739b558bf077d877

[ "MIT" ]

2

2021-02-22T14:55:01.000Z

2021-03-23T12:42:33.000Z

images/python_analysis/src/venv/lib/python3.7/site-packages/elasticsearch/_async/__init__.py

Jael24/TB_ElasticStack

f9aad11eda69045140a90f28739b558bf077d877

[ "MIT" ]

1

2020-05-06T01:31:18.000Z

# Licensed to Elasticsearch B.V under one or more agreements. # Elasticsearch B.V licenses this file to you under the Apache 2.0 License. # See the LICENSE file in the project root for more information

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py

Python

eddrit/routes/api/__init__.py

corenting/eddrit

640db842d48afa8f8b379f7412c90ad9216312df

[ "MIT" ]

9

2020-10-16T20:29:05.000Z

2022-03-06T08:07:06.000Z

eddrit/routes/api/__init__.py

corenting/eddrit

640db842d48afa8f8b379f7412c90ad9216312df

[ "MIT" ]

7

2020-10-16T16:34:57.000Z

2022-01-19T17:30:29.000Z

eddrit/routes/api/__init__.py

corenting/eddrit

640db842d48afa8f8b379f7412c90ad9216312df

[ "MIT" ]

null

from starlette.routing import Mount from eddrit.routes.api.comments import routes as comments_routes routes = [ Mount("/comments", routes=comments_routes), ]

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py

Python

Language Skills/Python/Unit 08 Loops/02 Practice makes perfect/Fun with Numbers/4-digit_sum.py

rhyep/Python_tutorials

f5c8a64b91802b005dfe7dd9035f8d8daae8c3e3

[ "MIT" ]

346

2016-02-22T20:21:10.000Z

2022-01-27T20:55:53.000Z

Language Skills/Python/Unit 8/2-Practice makes perfect/Fun with Numbers/4-digit_sum.py

vpstudios/Codecademy-Exercise-Answers

ebd0ee8197a8001465636f52c69592ea6745aa0c

[ "MIT" ]

55

2016-04-07T13:58:44.000Z

2020-06-25T12:20:24.000Z

Language Skills/Python/Unit 8/2-Practice makes perfect/Fun with Numbers/4-digit_sum.py

vpstudios/Codecademy-Exercise-Answers

ebd0ee8197a8001465636f52c69592ea6745aa0c

[ "MIT" ]

477

2016-02-21T06:17:02.000Z

2021-12-22T10:08:01.000Z

def digit_sum(n): b = [] n = str(n) for a in n: a = int(a) b.append(a) return sum(b) print sum(b)

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46d41f15f46cf2b1d80352d88c0c09e2a4750a52

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py

Python

test/test_flow_class.py

RViMLab/oflibpytorch

5a6573fe25e174a6d3bd678320b6c20cccfc9b0a

[ "MIT" ]

5

2021-08-08T21:03:44.000Z

2022-02-23T21:50:08.000Z

test/test_flow_class.py

RViMLab/oflibpytorch

5a6573fe25e174a6d3bd678320b6c20cccfc9b0a

[ "MIT" ]

null

test/test_flow_class.py

RViMLab/oflibpytorch

5a6573fe25e174a6d3bd678320b6c20cccfc9b0a

[ "MIT" ]

1

2021-08-08T21:03:46.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright: 2021, Claudio S. Ravasio # License: MIT (https://opensource.org/licenses/MIT) # Author: Claudio S. Ravasio, PhD student at University College London (UCL), research assistant at King's College # London (KCL), supervised by: # Dr Christos Bergeles, PI of the Robotics and Vision in Medicine (RViM) lab in the School of Biomedical Engineering & # Imaging Sciences (BMEIS) at King's College London (KCL) # Prof Lyndon Da Cruz, consultant ophthalmic surgeon, Moorfields Eye Hospital, London UK # # This file is part of oflibpytorch import unittest import torch import cv2 import numpy as np import math import sys sys.path.append('..') from src.oflibpytorch.flow_class import Flow from src.oflibpytorch.utils import to_numpy, apply_flow, matrix_from_transforms, resize_flow class FlowTest(unittest.TestCase): def test_flow(self): if torch.cuda.is_available(): expected_device_list = ['cpu', 'cuda', 'cpu'] else: expected_device_list = ['cpu', 'cpu', 'cpu'] vecs_np_2hw = np.zeros((2, 100, 200)) vecs_np_hw2 = np.zeros((100, 200, 2)) vecs_pt_2hw = torch.zeros((2, 100, 200)) vecs_pt_hw2 = torch.zeros((100, 200, 2)) mask_empty = None mask_np = np.ones((100, 200), 'bool') mask_pt = torch.ones(100, 200).to(torch.bool) for vecs in [vecs_np_2hw, vecs_np_hw2, vecs_pt_2hw, vecs_pt_hw2]: for ref, ref_expected in zip(['t', 's', None], ['t', 's', 't']): for mask in [mask_empty, mask_np, mask_pt]: for device, device_expected in zip(['cpu', 'cuda', None], expected_device_list): flow = Flow(vecs, ref=ref, mask=mask, device=device) self.assertIsNone(np.testing.assert_equal(to_numpy(flow.vecs), vecs_np_2hw)) self.assertIsNone(np.testing.assert_equal(flow.vecs_numpy, vecs_np_hw2)) self.assertEqual(flow.ref, ref_expected) self.assertIsNone(np.testing.assert_equal(to_numpy(flow.mask), mask_np)) self.assertEqual(flow.device, device_expected) self.assertEqual(flow.vecs.device.type, device_expected) self.assertEqual(flow.mask.device.type, device_expected) # tensor to cuda, test cuda if torch.cuda.is_available(): expected_device_list = ['cpu', 'cuda', 'cuda'] else: expected_device_list = ['cpu', 'cpu', 'cpu'] vecs_pt_cuda = torch.zeros((2, 100, 200)).to('cuda') for ref, ref_expected in zip(['t', 's', None], ['t', 's', 't']): for mask in [mask_empty, mask_np, mask_pt]: for device, device_expected in zip(['cpu', 'cuda', None], expected_device_list): flow = Flow(vecs_pt_cuda, ref=ref, mask=mask, device=device) self.assertIsNone(np.testing.assert_equal(to_numpy(flow.vecs), vecs_np_2hw)) self.assertIsNone(np.testing.assert_equal(flow.vecs_numpy, vecs_np_hw2)) self.assertEqual(flow.ref, ref_expected) self.assertIsNone(np.testing.assert_equal(to_numpy(flow.mask), mask_np)) self.assertEqual(flow.device, device_expected) self.assertEqual(flow.vecs.device.type, device_expected) self.assertEqual(flow.mask.device.type, device_expected) # Wrong flow vector type or shape with self.assertRaises(TypeError): Flow('test') with self.assertRaises(ValueError): Flow(np.zeros((2, 100, 200, 1))) with self.assertRaises(ValueError): Flow(torch.ones(2, 100, 200, 1)) with self.assertRaises(ValueError): Flow(np.zeros((3, 100, 200))) with self.assertRaises(ValueError): Flow(torch.ones(3, 100, 200)) # Invalid flow vector values vectors = np.random.rand(100, 200, 2) vectors[10, 10] = np.NaN vectors[20, 20] = np.Inf vectors[30, 30] = -np.Inf with self.assertRaises(ValueError): Flow(vectors) vectors = torch.tensor(vectors) with self.assertRaises(ValueError): Flow(vectors) # Wrong mask shape vecs = torch.zeros((2, 100, 200)) with self.assertRaises(TypeError): Flow(vecs, mask='test') with self.assertRaises(ValueError): Flow(vecs, mask=np.zeros((2, 100, 200))) with self.assertRaises(ValueError): Flow(vecs, mask=torch.ones(2, 100, 200)) with self.assertRaises(ValueError): Flow(vecs, mask=np.zeros((101, 200))) with self.assertRaises(ValueError): Flow(vecs, mask=torch.ones(100, 201)) with self.assertRaises(ValueError): Flow(vecs, mask=np.ones((100, 200)) * 20) with self.assertRaises(ValueError): Flow(vecs, mask=torch.ones(100, 200) * 10) def test_zero(self): if torch.cuda.is_available(): expected_device_list = ['cpu', 'cuda'] else: expected_device_list = ['cpu', 'cpu'] shape = [200, 300] zero_flow = Flow.zero(shape) self.assertIsNone(np.testing.assert_equal(zero_flow.shape, shape)) self.assertIsNone(np.testing.assert_equal(zero_flow.vecs_numpy, 0)) self.assertIs(zero_flow.ref, 't') zero_flow = Flow.zero(shape, 's') self.assertIs(zero_flow.ref, 's') for device, expected_device in zip(['cpu', 'cuda'], expected_device_list): flow = Flow.zero(shape, device=device) self.assertEqual(flow.vecs.device.type, expected_device) self.assertEqual(flow.mask.device.type, expected_device) def test_from_matrix(self): # With reference 's', this simply corresponds to using flow_from_matrix, tested in test_utils. # With reference 't': # Rotation of 30 degrees clockwise around point [10, 50] (hor, ver) matrix_np = np.array([[math.sqrt(3) / 2, -.5, 26.3397459622], [.5, math.sqrt(3) / 2, 1.69872981078], [0, 0, 1]]) matrix_pt = torch.tensor(matrix_np) shape = [200, 300] matrix_device_list = ['cpu', 'cuda'] flow_device_list = ['cpu', 'cuda', None] if torch.cuda.is_available(): flow_expected_device_list = ['cpu', 'cuda', None] else: flow_expected_device_list = ['cpu', 'cpu', 'cpu'] for matrix in [matrix_pt, matrix_np]: for matrix_device in matrix_device_list: for flow_device, flow_expected_device in zip(flow_device_list, flow_expected_device_list): if isinstance(matrix, torch.Tensor): matrix = matrix.to(matrix_device) flow = Flow.from_matrix(matrix, shape, 't', device=flow_device) if flow_expected_device is None: # If no device passed, expect same device as the matrix passed in flow_expected_device = matrix.device.type if isinstance(matrix, torch.Tensor) else 'cpu' self.assertEqual(flow.device, flow_expected_device) self.assertIsNone(np.testing.assert_allclose(flow.vecs_numpy[50, 10], [0, 0], atol=1e-4)) self.assertIsNone(np.testing.assert_allclose(flow.vecs_numpy[50, 299], [38.7186583063, 144.5], atol=1e-4, rtol=1e-4)) self.assertIsNone(np.testing.assert_allclose(flow.vecs_numpy[199, 10], [-74.5, 19.9622148361], atol=1e-4, rtol=1e-4)) self.assertIsNone(np.testing.assert_equal(flow.shape, shape)) def test_from_transforms(self): shape = [10, 20] transforms = [['rotation', 5, 10, -30]] for device in ['cpu', 'cuda', None]: flow = Flow.from_transforms(transforms, shape, device=device) expected_device = device if torch.cuda.is_available() and device is not None else 'cpu' self.assertEqual(flow.device, expected_device) def test_from_kitti(self): path = 'kitti.png' f = Flow.from_kitti(path, load_valid=True) desired_flow = np.arange(0, 10)[:, np.newaxis] * np.arange(0, 20)[np.newaxis, :] self.assertIsNone(np.testing.assert_equal(f.vecs_numpy[..., 0], desired_flow)) self.assertIsNone(np.testing.assert_equal(f.vecs_numpy[..., 1], 0)) self.assertIsNone(np.testing.assert_equal(f.mask_numpy[:, 0], True)) self.assertIsNone(np.testing.assert_equal(f.mask_numpy[:, 10], False)) f = Flow.from_kitti(path, load_valid=False) self.assertIsNone(np.testing.assert_equal(f.mask_numpy, True)) with self.assertRaises(TypeError): # Wrong load_valid type Flow.from_kitti(path, load_valid='test') with self.assertRaises(ValueError): # Wrong path Flow.from_kitti('test') with self.assertRaises(ValueError): # Wrong flow shape Flow.from_kitti('kitti_wrong.png') def test_from_sintel(self): path = 'sintel.flo' f = Flow.from_sintel(path) desired_flow = np.arange(0, 10)[:, np.newaxis] * np.arange(0, 20)[np.newaxis, :] self.assertIsNone(np.testing.assert_equal(f.vecs_numpy[..., 0], desired_flow)) self.assertIsNone(np.testing.assert_equal(f.mask_numpy, True)) f = Flow.from_sintel(path, 'sintel_invalid.png') self.assertIsNone(np.testing.assert_equal(f.mask_numpy[:, 0], True)) self.assertIsNone(np.testing.assert_equal(f.mask_numpy[:, 10], False)) with self.assertRaises(ValueError): # Wrong tag Flow.from_sintel('sintel_wrong.flo') with self.assertRaises(ValueError): # Wrong mask path Flow.from_sintel(path, 'test.png') with self.assertRaises(ValueError): # Wrong mask shape Flow.from_sintel(path, 'sintel_invalid_wrong.png') def test_copy(self): vectors = np.random.rand(200, 200, 2) mask = np.random.rand(200, 200) > 0.5 for ref in ['t', 's']: for device in ['cpu', 'cuda']: flow = Flow(vectors, ref, mask, device) flow_copy = flow.copy() self.assertIsNone(np.testing.assert_equal(flow.vecs_numpy, flow_copy.vecs_numpy)) self.assertIsNone(np.testing.assert_equal(flow.mask_numpy, flow_copy.mask_numpy)) self.assertEqual(flow.ref, flow_copy.ref) self.assertEqual(flow.device, flow_copy.device) self.assertNotEqual(id(flow), id(flow_copy)) def test_to_device(self): vectors = np.random.rand(200, 200, 2) mask = np.random.rand(200, 200) > 0.5 for ref in ['t', 's']: for start_device in ['cpu', 'cuda']: for target_device in ['cpu', 'cuda']: flow = Flow(vectors, ref, mask, start_device) f = flow.to_device(target_device) self.assertIsNone(np.testing.assert_equal(flow.vecs_numpy, f.vecs_numpy)) self.assertIsNone(np.testing.assert_equal(flow.mask_numpy, f.mask_numpy)) self.assertEqual(flow.ref, f.ref) self.assertEqual(f.device, target_device) def test_str(self): flow = Flow.zero(shape=(100, 200), ref='s', device='cuda') self.assertEqual(str(flow)[:54], "Flow object, reference s, shape 100*200, device cuda; ") def test_getitem(self): vectors = np.random.rand(200, 200, 2) flow = Flow(vectors) indices = np.random.randint(0, 150, size=(20, 2)) for i in indices: # Cutting a number of elements self.assertIsNone(np.testing.assert_allclose(flow[i].vecs_numpy, vectors[i])) # Cutting a specific item self.assertIsNone(np.testing.assert_allclose(flow[i[0]:i[0] + 1, i[1]:i[1] + 1].vecs_numpy, vectors[i[0]:i[0] + 1, i[1]:i[1] + 1])) # Cutting an area self.assertIsNone(np.testing.assert_allclose(flow[i[0]:i[0] + 40, i[1]:i[1] + 40].vecs_numpy, vectors[i[0]:i[0] + 40, i[1]:i[1] + 40])) # Make sure the device hasn't changed for device in ['cpu', 'cuda']: flow = Flow(vectors, device=device) expected_device = device if torch.cuda.is_available() else 'cpu' self.assertEqual(flow[10:20].device, expected_device) def test_add(self): mask1 = np.ones((100, 200), 'bool') mask1[:40] = 0 mask2 = np.ones((100, 200), 'bool') mask2[60:] = 0 vecs1 = np.random.rand(100, 200, 2) vecs2 = np.random.rand(100, 200, 2) vecs2_np_2hw = np.random.rand(2, 100, 200) vecs2_pt_2hw = torch.rand(2, 100, 200) vecs2_pt_hw2 = torch.rand(100, 200, 2) vec_list = [vecs2, vecs2_np_2hw, vecs2_pt_2hw, vecs2_pt_hw2] if torch.cuda.is_available(): vec_list.append(torch.rand(2, 100, 200).to('cuda')) vec_list.append(torch.rand(100, 200, 2).to('cuda')) vecs3 = np.random.rand(200, 200, 2) flow1 = Flow(vecs1, mask=mask1) flow2 = Flow(vecs2, mask=mask2) flow3 = Flow(vecs3) # Addition for vecs in vec_list: if isinstance(vecs, torch.Tensor): v = to_numpy(vecs) else: v = vecs if v.shape[0] == 2: v = np.moveaxis(v, 0, -1) self.assertIsNone(np.testing.assert_allclose((flow1 + vecs).vecs_numpy, vecs1 + v, rtol=1e-6, atol=1e-6)) self.assertEqual((flow1 + vecs).device, flow1.vecs.device.type) self.assertEqual((flow1 + vecs).device, flow1.mask.device.type) self.assertIsNone(np.testing.assert_allclose((flow1 + flow2).vecs_numpy, vecs1 + vecs2, rtol=1e-6, atol=1e-6)) self.assertIsNone(np.testing.assert_equal(np.sum(to_numpy((flow1 + flow2).mask)), (60 - 40) * 200)) with self.assertRaises(TypeError): flow1 + 'test' with self.assertRaises(ValueError): flow1 + flow3 with self.assertRaises(ValueError): flow1 + vecs3 def test_sub(self): mask1 = np.ones((100, 200), 'bool') mask1[:40] = 0 mask2 = np.ones((100, 200), 'bool') mask2[60:] = 0 vecs1 = np.random.rand(100, 200, 2) vecs2 = np.random.rand(100, 200, 2) vecs2_np_2hw = np.random.rand(2, 100, 200) vecs2_pt_2hw = torch.rand(2, 100, 200) vecs2_pt_hw2 = torch.rand(100, 200, 2) vec_list = [vecs2, vecs2_np_2hw, vecs2_pt_2hw, vecs2_pt_hw2] if torch.cuda.is_available(): vec_list.append(torch.rand(2, 100, 200).to('cuda')) vec_list.append(torch.rand(100, 200, 2).to('cuda')) vecs3 = np.random.rand(200, 200, 2) flow1 = Flow(vecs1, mask=mask1) flow2 = Flow(vecs2, mask=mask2) flow3 = Flow(vecs3) # Subtraction for vecs in vec_list: if isinstance(vecs, torch.Tensor): v = to_numpy(vecs) else: v = vecs if v.shape[0] == 2: v = np.moveaxis(v, 0, -1) self.assertIsNone(np.testing.assert_allclose((flow1 - vecs).vecs_numpy, vecs1 - v, rtol=1e-6, atol=1e-6)) self.assertEqual((flow1 + vecs).device, flow1.vecs.device.type) self.assertEqual((flow1 + vecs).device, flow1.mask.device.type) self.assertIsNone(np.testing.assert_allclose((flow1 - flow2).vecs_numpy, vecs1 - vecs2, rtol=1e-6, atol=1e-6)) self.assertIsNone(np.testing.assert_equal(np.sum(to_numpy((flow1 - flow2).mask)), (60 - 40) * 200)) with self.assertRaises(TypeError): flow1 - 'test' with self.assertRaises(ValueError): flow1 - flow3 with self.assertRaises(ValueError): flow1 - vecs3 def test_mul(self): vecs1 = np.random.rand(100, 200, 2) vecs2 = np.random.rand(100, 200, 2) flow1 = Flow(vecs1) # Multiplication ints = np.random.randint(-10, 10, 100) floats = (np.random.rand(100) - .5) * 20 # ... using ints and floats for i, f in zip(ints, floats): self.assertIsNone(np.testing.assert_allclose((flow1 * i).vecs_numpy, vecs1 * i, rtol=1e-6, atol=1e-6)) self.assertIsNone(np.testing.assert_allclose((flow1 * f).vecs_numpy, vecs1 * f, rtol=1e-6, atol=1e-6)) # ... using a list of length 2 int_list = np.random.randint(-10, 10, (100, 2)) for li in int_list: v = vecs1.astype('f') v[..., 0] *= li[0] v[..., 1] *= li[1] self.assertIsNone(np.testing.assert_allclose((flow1 * list(li)).vecs_numpy, v, rtol=1e-6, atol=1e-6)) # ... using a numpy array of size 2 int_list = np.random.randint(-10, 10, (100, 2)) for li in int_list: v = vecs1.astype('f') v[..., 0] *= li[0] v[..., 1] *= li[1] self.assertIsNone(np.testing.assert_allclose((flow1 * li).vecs_numpy, v, rtol=1e-6, atol=1e-6)) # ... using a numpy array and torch tensor of the same shape as the flow vecs2_pt_hw2 = torch.rand(100, 200, 2) vecs_list = [vecs2, vecs2_pt_hw2] if torch.cuda.is_available(): vecs_list.append(torch.rand(100, 200, 2).to('cuda')) for vecs in vecs_list: if isinstance(vecs, torch.Tensor): v = to_numpy(vecs) else: v = vecs self.assertIsNone(np.testing.assert_allclose((flow1 * vecs[..., 0]).vecs_numpy, vecs1 * v[..., :1], rtol=1e-6, atol=1e-6)) self.assertEqual((flow1 * vecs[..., 0]).device, flow1.vecs.device.type) self.assertEqual((flow1 * vecs[..., 0]).device, flow1.mask.device.type) # ... using numpy arrays and torch tensors of the same shape as the flow vectors vecs2_np_2hw = np.random.rand(2, 100, 200) vecs2_pt_2hw = torch.rand(2, 100, 200) vecs2_pt_hw2 = torch.rand(100, 200, 2) vecs_list = [vecs2, vecs2_np_2hw, vecs2_pt_2hw, vecs2_pt_hw2] if torch.cuda.is_available(): vecs_list.append(torch.rand(2, 100, 200).to('cuda')) vecs_list.append(torch.rand(100, 200, 2).to('cuda')) for vecs in vecs_list: if isinstance(vecs, torch.Tensor): v = to_numpy(vecs) else: v = vecs if v.shape[0] == 2: v = np.moveaxis(v, 0, -1) self.assertIsNone(np.testing.assert_allclose((flow1 * vecs).vecs_numpy, vecs1 * v, rtol=1e-6, atol=1e-6)) self.assertEqual((flow1 * vecs).device, flow1.vecs.device.type) self.assertEqual((flow1 * vecs).device, flow1.mask.device.type) # ... using a list of the wrong length with self.assertRaises(ValueError): flow1 * [0, 1, 2] # ... using a numpy array of the wrong size with self.assertRaises(ValueError): flow1 * np.array([0, 1, 2]) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 * np.random.rand(200, 200) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 * np.random.rand(200, 200, 2) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 * np.random.rand(100, 200, 3) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 * np.random.rand(200, 200, 2, 1) def test_div(self): vecs1 = np.random.rand(100, 200, 2) + .5 vecs2 = -np.random.rand(100, 200, 2) - .5 flow1 = Flow(vecs1) # Division ints = np.random.randint(-10, 10, 100) floats = (np.random.rand(100) - .5) * 20 # ... using ints and floats for i, f in zip(ints, floats): if i < -1e-5 or i > 1e-5: self.assertIsNone(np.testing.assert_allclose((flow1 / i).vecs_numpy, vecs1 / i, rtol=1e-6, atol=1e-6)) if f < -1e-5 or f > 1e-5: self.assertIsNone(np.testing.assert_allclose((flow1 / f).vecs_numpy, vecs1 / f, rtol=1e-6, atol=1e-6)) # ... using a list of length 2 int_list = np.random.randint(-10, 10, (100, 2)) for li in int_list: if li[0] != 0 and li[1] != 0: v = vecs1.astype('f') v[..., 0] /= li[0] v[..., 1] /= li[1] self.assertIsNone(np.testing.assert_allclose((flow1 / list(li)).vecs_numpy, v, rtol=1e-6, atol=1e-6)) # ... using a numpy array of size 2 int_list = np.random.randint(-10, 10, (100, 2)) for li in int_list: if li[0] != 0 and li[1] != 0: v = vecs1.astype('f') v[..., 0] /= li[0] v[..., 1] /= li[1] self.assertIsNone(np.testing.assert_allclose((flow1 / li).vecs_numpy, v, rtol=1e-6, atol=1e-6)) # ... using a numpy array and torch tensor of the same shape as the flow vecs2_pt_hw2 = torch.rand(100, 200, 2) + .5 vecs_list = [vecs2, vecs2_pt_hw2] if torch.cuda.is_available(): vecs_list.append(torch.rand(100, 200, 2).to('cuda') + .5) for vecs in vecs_list: if isinstance(vecs, torch.Tensor): v = to_numpy(vecs) else: v = vecs self.assertIsNone(np.testing.assert_allclose((flow1 / vecs[..., 0]).vecs_numpy, vecs1 / v[..., :1], rtol=1e-6, atol=1e-6)) self.assertEqual((flow1 / vecs[..., 0]).device, flow1.vecs.device.type) self.assertEqual((flow1 / vecs[..., 0]).device, flow1.mask.device.type) # ... using numpy arrays and torch tensors of the same shape as the flow vectors vecs2_np_2hw = np.random.rand(2, 100, 200) + .5 vecs2_pt_2hw = torch.rand(2, 100, 200) + .5 vecs2_pt_hw2 = torch.rand(100, 200, 2) + .5 vecs_list = [vecs2, vecs2_np_2hw, vecs2_pt_2hw, vecs2_pt_hw2] if torch.cuda.is_available(): vecs_list.append(torch.rand(2, 100, 200).to('cuda') + .5) vecs_list.append(torch.rand(100, 200, 2).to('cuda') + .5) for vecs in vecs_list: if isinstance(vecs, torch.Tensor): v = to_numpy(vecs) else: v = vecs if v.shape[0] == 2: v = np.moveaxis(v, 0, -1) self.assertIsNone(np.testing.assert_allclose((flow1 / vecs).vecs_numpy, vecs1 / v, rtol=1e-6, atol=1e-6)) self.assertEqual((flow1 / vecs).device, flow1.vecs.device.type) self.assertEqual((flow1 / vecs).device, flow1.mask.device.type) # ... using a list of the wrong length with self.assertRaises(ValueError): flow1 / [1, 2, 3] # ... using a numpy array of the wrong size with self.assertRaises(ValueError): flow1 / np.array([1, 2, 3]) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 / np.ones((200, 200)) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 / np.ones((200, 200, 2)) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 / np.ones((100, 200, 3)) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 / np.ones((200, 200, 2, 1)) def test_pow(self): vecs1 = np.random.rand(100, 200, 2) vecs2 = np.random.rand(100, 200, 2) flow1 = Flow(vecs1) # Exponentiation ints = np.random.randint(-2, 2, 100) floats = (np.random.rand(100) - .5) * 4 # ... using ints and floats for i, f in zip(ints, floats): self.assertIsNone(np.testing.assert_allclose((flow1 ** i).vecs_numpy, vecs1 ** i, rtol=1e-6, atol=1e-6)) self.assertIsNone(np.testing.assert_allclose((flow1 ** f).vecs_numpy, vecs1 ** f, rtol=1e-6, atol=1e-6)) # ... using a list of length 2 int_list = np.random.randint(-5, 5, (100, 2)) for li in int_list: v = vecs1.astype('f') v[..., 0] **= li[0] v[..., 1] **= li[1] self.assertIsNone(np.testing.assert_allclose((flow1 ** list(li)).vecs_numpy, v, rtol=1e-6, atol=1e-6)) # ... using a numpy array of size 2 int_list = np.random.randint(-5, 5, (100, 2)) for li in int_list: v = vecs1.astype('f') v[..., 0] **= li[0] v[..., 1] **= li[1] self.assertIsNone(np.testing.assert_allclose((flow1 ** li).vecs_numpy, v, rtol=1e-6, atol=1e-6)) # ... using a numpy array and torch tensor of the same shape as the flow vecs2_pt_hw2 = torch.rand(100, 200, 2) vecs_list = [vecs2, vecs2_pt_hw2] if torch.cuda.is_available(): vecs_list.append(torch.rand(100, 200, 2).to('cuda')) for vecs in vecs_list: if isinstance(vecs, torch.Tensor): v = to_numpy(vecs) else: v = vecs self.assertIsNone(np.testing.assert_allclose((flow1 ** vecs[..., 0]).vecs_numpy, vecs1 ** v[..., :1], rtol=1e-6, atol=1e-6)) self.assertEqual((flow1 ** vecs[..., 0]).device, flow1.vecs.device.type) self.assertEqual((flow1 ** vecs[..., 0]).device, flow1.mask.device.type) # ... using numpy arrays and torch tensors of the same shape as the flow vectors vecs2_np_2hw = np.random.rand(2, 100, 200) vecs2_pt_2hw = torch.rand(2, 100, 200) vecs2_pt_hw2 = torch.rand(100, 200, 2) vecs_list = [vecs2, vecs2_np_2hw, vecs2_pt_2hw, vecs2_pt_hw2] if torch.cuda.is_available(): vecs_list.append(torch.rand(2, 100, 200).to('cuda')) vecs_list.append(torch.rand(100, 200, 2).to('cuda')) for vecs in vecs_list: if isinstance(vecs, torch.Tensor): v = to_numpy(vecs) else: v = vecs if v.shape[0] == 2: v = np.moveaxis(v, 0, -1) self.assertIsNone(np.testing.assert_allclose((flow1 ** vecs).vecs_numpy, vecs1 ** v, rtol=1e-6, atol=1e-6)) self.assertEqual((flow1 ** vecs).device, flow1.vecs.device.type) self.assertEqual((flow1 ** vecs).device, flow1.mask.device.type) # ... using a list of the wrong length with self.assertRaises(ValueError): flow1 ** [0, 1, 2] # ... using a numpy array of the wrong size with self.assertRaises(ValueError): flow1 ** np.array([0, 1, 2]) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 ** np.random.rand(200, 200) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 ** np.random.rand(200, 200, 2) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 ** np.random.rand(100, 200, 3) # ... using a numpy array of the wrong shape with self.assertRaises(ValueError): flow1 ** np.random.rand(200, 200, 2, 1) def test_neg(self): vecs1 = np.random.rand(100, 200, 2) flow1 = Flow(vecs1) self.assertIsNone(np.testing.assert_allclose((-flow1).vecs_numpy, -vecs1)) def test_resize(self): shape = [20, 10] ref = 's' flow = Flow.from_transforms([['rotation', 30, 50, 30]], shape, ref) # Different scales scales = [.2, .5, 1, 1.5, 2, 10] for scale in scales: self.assertIsNone(np.testing.assert_equal(flow.resize(scale).vecs_numpy, to_numpy(resize_flow(flow.vecs, scale), switch_channels=True))) # Scale mask shape_small = (20, 40) shape_large = (30, 80) mask_small = np.ones(shape_small, 'bool') mask_small[:6, :20] = 0 mask_large = np.ones(shape_large, 'bool') mask_large[:9, :40] = 0 flow_small = Flow.from_transforms([['rotation', 0, 0, 30]], shape_small, 't', mask_small) flow_large = flow_small.resize((1.5, 2)) self.assertIsNone(np.testing.assert_equal(to_numpy(flow_large.mask), mask_large)) # Check scaling is performed correctly based on the actual flow field ref = 't' flow_small = Flow.from_transforms([['rotation', 0, 0, 30]], (50, 80), ref) flow_large = Flow.from_transforms([['rotation', 0, 0, 30]], (150, 240), ref) flow_resized = flow_large.resize(1/3) self.assertIsNone(np.testing.assert_allclose(flow_resized.vecs_numpy, flow_small.vecs_numpy, atol=1, rtol=.1)) def test_pad(self): shape = [100, 80] for ref in ['t', 's']: flow = Flow.zero(shape, ref, np.ones(shape, 'bool')) flow = flow.pad([10, 20, 30, 40]) self.assertIsNone(np.testing.assert_equal(flow.shape[:2], [shape[0] + 10 + 20, shape[1] + 30 + 40])) self.assertIsNone(np.testing.assert_equal(flow.vecs_numpy, 0)) self.assertIsNone(np.testing.assert_equal(to_numpy(flow[10:-20, 30:-40].mask), 1)) flow.mask[10:-20, 30:-40] = 0 self.assertIsNone(np.testing.assert_equal(to_numpy(flow.mask), 0)) self.assertIs(flow.ref, ref) # 'Replicate' padding flow = Flow.from_transforms([['rotation', 30, 50, 30]], shape, ref) padded_flow = flow.pad([10, 10, 20, 20], mode='replicate') self.assertIsNone(np.testing.assert_equal(padded_flow.vecs_numpy[0, 20:-20], flow.vecs_numpy[0])) self.assertIsNone(np.testing.assert_equal(padded_flow.vecs_numpy[10:-10, 0], flow.vecs_numpy[:, 0])) # 'Reflect' padding padded_flow = flow.pad([10, 10, 20, 20], mode='reflect') self.assertIsNone(np.testing.assert_equal(padded_flow.vecs_numpy[0, 20:-20], flow.vecs_numpy[10])) self.assertIsNone(np.testing.assert_equal(padded_flow.vecs_numpy[10:-10, 0], flow.vecs_numpy[:, 20])) # Invalid padding mode with self.assertRaises(ValueError): flow.pad([10, 10, 20, 20], mode='test') def test_apply(self): img_np = np.moveaxis(cv2.imread('smudge.png'), -1, 0) img_pt = torch.tensor(img_np) # Check flow.apply results in the same as using apply_flow directly for ref in ['t', 's']: for consider_mask in [True, False]: for device in ['cpu', 'cuda']: for img in [img_pt.to('cpu'), img_pt.to('cuda')]: mask = torch.ones(img_pt.shape[1:], dtype=torch.bool) mask[400:] = False flow = Flow.from_transforms([['rotation', 30, 50, 30]], img.shape[1:], ref, mask, device) # Target is a 3D torch tensor warped_img_desired = apply_flow(flow.vecs, img, ref, mask if consider_mask else None) warped_img_actual = flow.apply(img, consider_mask=consider_mask) self.assertEqual(flow.device, warped_img_actual.device.type) self.assertIsNone(np.testing.assert_equal(to_numpy(warped_img_actual), to_numpy(warped_img_desired))) warped_img_actual, _ = flow.apply(img, mask, True, consider_mask=consider_mask) self.assertIsNone(np.testing.assert_equal(to_numpy(warped_img_actual), to_numpy(warped_img_desired))) # Target is a 2D torch tensor warped_img_desired = apply_flow(flow.vecs, img[0], ref, mask if consider_mask else None) warped_img_actual = flow.apply(img[0], consider_mask=consider_mask) self.assertEqual(flow.device, warped_img_actual.device.type) self.assertIsNone(np.testing.assert_equal(to_numpy(warped_img_actual), to_numpy(warped_img_desired))) warped_img_actual, _ = flow.apply(img[0], mask, True, consider_mask=consider_mask) self.assertIsNone(np.testing.assert_equal(to_numpy(warped_img_actual), to_numpy(warped_img_desired))) for f_device in ['cpu', 'cuda']: f = flow.to_device(f_device) # Target is a flow object warped_flow_desired = apply_flow(flow.vecs, f.vecs, ref, mask if consider_mask else None) warped_flow_actual = flow.apply(f, consider_mask=consider_mask) self.assertEqual(flow.device, warped_flow_actual.device) self.assertIsNone(np.testing.assert_equal(to_numpy(warped_flow_actual.vecs), to_numpy(warped_flow_desired))) # Check using a smaller flow field on a larger target works the same as a full flow field on the same target img = img_pt ref = 't' flow = Flow.from_transforms([['rotation', 30, 50, 30]], img.shape[1:], ref) warped_img_desired = apply_flow(flow.vecs, img, ref) shape = [img.shape[1] - 90, img.shape[2] - 110] padding = [50, 40, 30, 80] cut_flow = Flow.from_transforms([['rotation', 0, 0, 30]], shape, ref) # # ... not cutting (target torch tensor) # warped_img_actual = cut_flow.apply(img, padding=padding, cut=False) # self.assertIsNone(np.testing.assert_equal(to_numpy(warped_img_actual[padding[0]:-padding[1], # padding[2]:-padding[3]]), # to_numpy(warped_img_desired[padding[0]:-padding[1], # padding[2]:-padding[3]]))) # ... cutting (target torch tensor) warped_img_actual = cut_flow.apply(img, padding=padding, cut=True) self.assertIsNone(np.testing.assert_allclose(to_numpy(warped_img_actual).astype('f'), to_numpy(warped_img_desired[:, padding[0]:-padding[1], padding[2]:-padding[3]]).astype('f'), atol=1)) # result rounded (uint8), so errors can be 1 # ... not cutting (target flow object) target_flow = Flow.from_transforms([['rotation', 30, 50, 30]], img.shape[1:], ref) warped_flow_desired = apply_flow(flow.vecs, target_flow.vecs, ref) warped_flow_actual = cut_flow.apply(target_flow, padding=padding, cut=False) self.assertIsNone(np.testing.assert_allclose(to_numpy(warped_flow_actual.vecs[:, padding[0]:-padding[1], padding[2]:-padding[3]]), to_numpy(warped_flow_desired[:, padding[0]:-padding[1], padding[2]:-padding[3]]), atol=1e-1)) # ... cutting (target flow object) warped_flow_actual = cut_flow.apply(target_flow, padding=padding, cut=True) self.assertIsNone(np.testing.assert_allclose(to_numpy(warped_flow_actual.vecs), to_numpy(warped_flow_desired[:, padding[0]:-padding[1], padding[2]:-padding[3]]), atol=1e-1)) # Non-valid padding values for ref in ['t', 's']: flow = Flow.from_transforms([['rotation', 0, 0, 30]], shape, ref) with self.assertRaises(TypeError): flow.apply(target_flow, return_valid_area='test') with self.assertRaises(TypeError): flow.apply(target_flow, consider_mask='test') with self.assertRaises(TypeError): flow.apply(target_flow, padding=100, cut=True) with self.assertRaises(ValueError): flow.apply(target_flow, padding=[10, 20, 30, 40, 50], cut=True) with self.assertRaises(ValueError): flow.apply(target_flow, padding=[10., 20, 30, 40], cut=True) with self.assertRaises(ValueError): flow.apply(target_flow, padding=[-10, 10, 10, 10], cut=True) with self.assertRaises(TypeError): flow.apply(target_flow, padding=[10, 20, 30, 40, 50], cut=2) with self.assertRaises(TypeError): flow.apply(target_flow, padding=[10, 20, 30, 40, 50], cut='true') def test_switch_ref(self): shape = (200, 300) # Mode 'invalid' for refs in [['t', 's'], ['s', 't']]: flow = Flow.from_transforms([['rotation', 30, 50, 30]], shape, refs[0]) flow = flow.switch_ref(mode='invalid') self.assertEqual(flow.ref, refs[1]) # Mode 'valid' transforms = [['rotation', 256, 256, 30]] flow_s = Flow.from_transforms(transforms, shape, 's') flow_t = Flow.from_transforms(transforms, shape, 't') switched_s = flow_t.switch_ref() self.assertIsNone(np.testing.assert_allclose(switched_s.vecs_numpy[switched_s.mask_numpy], flow_s.vecs_numpy[switched_s.mask_numpy], rtol=1e-3, atol=1e-3)) switched_t = flow_s.switch_ref() self.assertIsNone(np.testing.assert_allclose(switched_t.vecs_numpy[switched_t.mask_numpy], flow_t.vecs_numpy[switched_t.mask_numpy], rtol=1e-3, atol=1e-3)) # Invalid mode passed flow = Flow.from_transforms([['rotation', 30, 50, 30]], shape, 't') with self.assertRaises(ValueError): flow.switch_ref('test') with self.assertRaises(ValueError): flow.switch_ref(1) def test_invert(self): f_s = Flow.from_transforms([['rotation', 256, 256, 30]], (512, 512), 's') # Forwards f_t = Flow.from_transforms([['rotation', 256, 256, 30]], (512, 512), 't') # Forwards b_s = Flow.from_transforms([['rotation', 256, 256, -30]], (512, 512), 's') # Backwards b_t = Flow.from_transforms([['rotation', 256, 256, -30]], (512, 512), 't') # Backwards # Inverting s to s b_s_inv = f_s.invert() self.assertIsNone(np.testing.assert_allclose(b_s_inv.vecs_numpy[b_s_inv.mask_numpy], b_s.vecs_numpy[b_s_inv.mask_numpy], rtol=1e-3, atol=1e-3)) f_s_inv = b_s.invert() self.assertIsNone(np.testing.assert_allclose(f_s_inv.vecs_numpy[f_s_inv.mask_numpy], f_s.vecs_numpy[f_s_inv.mask_numpy], rtol=1e-3, atol=1e-3)) # Inverting s to t b_t_inv = f_s.invert('t') self.assertIsNone(np.testing.assert_allclose(b_t_inv.vecs_numpy[b_t_inv.mask_numpy], b_t.vecs_numpy[b_t_inv.mask_numpy], rtol=1e-3, atol=1e-3)) f_t_inv = b_s.invert('t') self.assertIsNone(np.testing.assert_allclose(f_t_inv.vecs_numpy[f_t_inv.mask_numpy], f_t.vecs_numpy[f_t_inv.mask_numpy], rtol=1e-3, atol=1e-3)) # Inverting t to t b_t_inv = f_t.invert() self.assertIsNone(np.testing.assert_allclose(b_t_inv.vecs_numpy[b_t_inv.mask_numpy], b_t.vecs_numpy[b_t_inv.mask_numpy], rtol=1e-3, atol=1e-3)) f_t_inv = b_t.invert() self.assertIsNone(np.testing.assert_allclose(f_t_inv.vecs_numpy[f_t_inv.mask_numpy], f_t.vecs_numpy[f_t_inv.mask_numpy], rtol=1e-3, atol=1e-3)) # Inverting t to s b_s_inv = f_t.invert('s') self.assertIsNone(np.testing.assert_allclose(b_s_inv.vecs_numpy[b_s_inv.mask_numpy], b_s.vecs_numpy[b_s_inv.mask_numpy], rtol=1e-3, atol=1e-3)) f_s_inv = b_t.invert('s') self.assertIsNone(np.testing.assert_allclose(f_s_inv.vecs_numpy[f_s_inv.mask_numpy], f_s.vecs_numpy[f_s_inv.mask_numpy], rtol=1e-3, atol=1e-3)) def test_track(self): f_s = Flow.from_transforms([['rotation', 0, 0, 30]], (512, 512), 's') f_t = Flow.from_transforms([['rotation', 0, 0, 30]], (512, 512), 't') # Test valid status for 't' flow f_t.mask[:, 200:] = False pts = torch.tensor([ [0, 50], # Moved out of bounds by a valid flow vector [0, 500], # Moved out of bounds by an invalid flow vector [8.3, 7.2], # Moved normally by valid flow vector [120.4, 160.2], # Moved normally by valid flow vector [300, 200] # Moved normally by invalid flow vector ]) desired_valid_status = [False, False, True, True, False] _, tracked = f_t.track(pts, get_valid_status=True) self.assertIsNone(np.testing.assert_equal(to_numpy(tracked), desired_valid_status)) # Test valid status for 's' flow f_s.mask[:, 200:] = False pts = torch.tensor([ [0, 50], # Moved out of bounds by a valid flow vector [0, 500], # Moved out of bounds by an invalid flow vector [8.3, 7.2], # Moved normally by valid flow vector [120.4, 160.2], # Moved normally by valid flow vector [300, 200] # Moved normally by invalid flow vector ]) desired_valid_status = [False, False, True, True, False] _, tracked = f_s.track(pts, get_valid_status=True) self.assertIsNone(np.testing.assert_equal(to_numpy(tracked), desired_valid_status)) # Invalid inputs with self.assertRaises(TypeError): f_s.track(pts, True, get_valid_status='test') def test_valid_target(self): transforms = [['rotation', 0, 0, 45]] shape = (7, 7) mask = np.ones(shape, 'bool') mask[4:, :3] = False f_s_masked = Flow.from_transforms(transforms, shape, 's', mask) mask = np.ones(shape, 'bool') mask[:3, 4:] = False f_t_masked = Flow.from_transforms(transforms, shape, 't', mask) f_s = Flow.from_transforms(transforms, shape, 's') f_t = Flow.from_transforms(transforms, shape, 't') desired_area_s = np.array([ [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0] ]).astype('bool') desired_area_t = np.array([ [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0] ]).astype('bool') desired_area_s_masked_consider_mask = np.array([ [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0] ]).astype('bool') desired_area_s_masked = np.array([ [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0] ]).astype('bool') desired_area_t_masked = np.array([ [1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0] ]).astype('bool') self.assertIsNone(np.testing.assert_equal(to_numpy(f_s.valid_target()), desired_area_s)) self.assertIsNone(np.testing.assert_equal(to_numpy(f_t.valid_target()), desired_area_t)) self.assertIsNone(np.testing.assert_equal(f_s_masked.valid_target(), desired_area_s_masked_consider_mask)) self.assertIsNone(np.testing.assert_equal(to_numpy(f_s_masked.valid_target(False)), desired_area_s_masked)) self.assertIsNone(np.testing.assert_equal(to_numpy(f_t_masked.valid_target()), desired_area_t_masked)) def test_valid_source(self): transforms = [['rotation', 0, 0, 45]] shape = (7, 7) mask = np.ones(shape, 'bool') mask[4:, :3] = False f_s_masked = Flow.from_transforms(transforms, shape, 's', mask) mask = np.ones(shape, 'bool') mask[:3, 4:] = False f_t_masked = Flow.from_transforms(transforms, shape, 't', mask) f_s = Flow.from_transforms(transforms, shape, 's') f_t = Flow.from_transforms(transforms, shape, 't') desired_area_s = np.array([ [1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0] ]).astype('bool') desired_area_t = np.array([ [1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0] ]).astype('bool') desired_area_s_masked = np.array([ [1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0] ]).astype('bool') desired_area_t_masked_consider_mask = np.array([ [1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0] ]).astype('bool') desired_area_t_masked = np.array([ [1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0] ]).astype('bool') self.assertIsNone(np.testing.assert_equal(to_numpy(f_s.valid_source()), desired_area_s)) self.assertIsNone(np.testing.assert_equal(to_numpy(f_t.valid_source()), desired_area_t)) self.assertIsNone(np.testing.assert_equal(to_numpy(f_s_masked.valid_source()), desired_area_s_masked)) self.assertIsNone(np.testing.assert_equal(f_t_masked.valid_source(), desired_area_t_masked_consider_mask)) self.assertIsNone(np.testing.assert_equal(f_t_masked.valid_source(False), desired_area_t_masked)) def test_get_padding(self): transforms = [['rotation', 0, 0, 45]] shape = (7, 7) mask = np.ones(shape, 'bool') mask[:, 4:] = False f_s_masked = Flow.from_transforms(transforms, shape, 's', mask) mask = np.ones(shape, 'bool') mask[4:] = False f_t_masked = Flow.from_transforms(transforms, shape, 't', mask) f_s = Flow.from_transforms(transforms, shape, 's') f_t = Flow.from_transforms(transforms, shape, 't') f_s_desired = [5, 0, 0, 3] f_t_desired = [0, 3, 5, 0] f_s_masked_desired = [3, 0, 0, 1] f_t_masked_desired = [0, 1, 3, 0] self.assertIsNone(np.testing.assert_equal(f_s.get_padding(), f_s_desired)) self.assertIsNone(np.testing.assert_equal(f_t.get_padding(), f_t_desired)) self.assertIsNone(np.testing.assert_equal(f_s_masked.get_padding(), f_s_masked_desired)) self.assertIsNone(np.testing.assert_equal(f_t_masked.get_padding(), f_t_masked_desired)) f = Flow.zero(shape) f._vecs[0] = torch.rand(*shape) * 1e-4 self.assertIsNone(np.testing.assert_equal(f.get_padding(), [0, 0, 0, 0])) def test_is_zero(self): shape = (10, 10) mask = np.ones(shape, 'bool') mask[0, 0] = False flow = np.zeros(shape + (2,)) flow[0, 0] = 10 flow = Flow(flow, mask=mask) self.assertEqual(flow.is_zero(), True) self.assertEqual(flow.is_zero(masked=True), True) self.assertEqual(flow.is_zero(masked=False), False) with self.assertRaises(TypeError): # Masked wrong type flow.is_zero(masked='test') def test_visualise(self): # Correct values for the different modes # Horizontal flow towards the right is red flow = Flow.from_transforms([['translation', 1, 0]], [200, 300]) desired_img = np.tile(np.array([0, 0, 255]).reshape((1, 1, 3)), (200, 300, 1)) self.assertIsNone(np.testing.assert_equal(flow.visualise('bgr', return_tensor=False), desired_img)) self.assertIsNone(np.testing.assert_equal(flow.visualise('rgb', return_tensor=False), desired_img[..., ::-1])) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', return_tensor=False)[..., 0], 0)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', return_tensor=False)[..., 1], 255)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', return_tensor=False)[..., 2], 255)) # Flow outwards at the angle of 240 degrees (counter-clockwise) is green flow = Flow.from_transforms([['translation', -1, math.sqrt(3)]], [200, 300]) desired_img = np.tile(np.array([0, 255, 0]).reshape((1, 1, 3)), (200, 300, 1)) self.assertIsNone(np.testing.assert_equal(flow.visualise('bgr', return_tensor=False), desired_img)) self.assertIsNone(np.testing.assert_equal(flow.visualise('rgb', return_tensor=False), desired_img)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', return_tensor=False)[..., 0], 60)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', return_tensor=False)[..., 1], 255)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', return_tensor=False)[..., 2], 255)) # Flow outwards at the angle of 240 degrees (counter-clockwise) is blue flow = Flow.from_transforms([['translation', -1, -math.sqrt(3)]], [200, 300]) desired_img = np.tile(np.array([255, 0, 0]).reshape((1, 1, 3)), (200, 300, 1)) self.assertIsNone(np.testing.assert_equal(flow.visualise('bgr', return_tensor=False), desired_img)) self.assertIsNone(np.testing.assert_equal(flow.visualise('rgb', return_tensor=False), desired_img[..., ::-1])) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', return_tensor=False)[..., 0], 120)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', return_tensor=False)[..., 1], 255)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', return_tensor=False)[..., 2], 255)) # Show the flow mask mask = np.zeros((200, 300)) mask[30:-30, 40:-40] = 1 flow = Flow.from_transforms([['translation', 1, 0]], (200, 300), 't', mask) self.assertIsNone(np.testing.assert_equal(flow.visualise('bgr', True, return_tensor=False)[10, 10], [0, 0, 180])) self.assertIsNone(np.testing.assert_equal(flow.visualise('rgb', True, return_tensor=False)[10, 10], [180, 0, 0])) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', True, return_tensor=False)[..., 0], 0)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', True, return_tensor=False)[..., 1], 255)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', True, return_tensor=False)[10, 10, 2], 180)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', True, return_tensor=False)[100, 100, 2], 255)) # Show the flow mask border mask = np.zeros((200, 300)) mask[30:-30, 40:-40] = 1 flow = Flow.from_transforms([['translation', 1, 0]], (200, 300), 't', mask) self.assertIsNone(np.testing.assert_equal(flow.visualise('bgr', True, True, return_tensor=False)[30, 40], [0, 0, 0])) self.assertIsNone(np.testing.assert_equal(flow.visualise('rgb', True, True, return_tensor=False)[30, 40], [0, 0, 0])) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', True, True, return_tensor=False)[..., 0], 0)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', True, True, return_tensor=False)[30, 40, 1], 0)) self.assertIsNone(np.testing.assert_equal(flow.visualise('hsv', True, True, return_tensor=False)[30, 40, 2], 0)) # Output is tensor if required mask = np.zeros((200, 300)) mask[30:-30, 40:-40] = 1 flow = Flow.from_transforms([['translation', 1, 0]], (200, 300), 't', mask) self.assertIsInstance(flow.visualise('bgr', True, True), torch.Tensor) # Invalid arguments flow = Flow.zero([10, 10]) with self.assertRaises(ValueError): flow.visualise(mode=3) with self.assertRaises(ValueError): flow.visualise(mode='test') with self.assertRaises(TypeError): flow.visualise('rgb', show_mask=2) with self.assertRaises(TypeError): flow.visualise('rgb', show_mask_borders=2) with self.assertRaises(TypeError): flow.visualise('rgb', return_tensor=2) with self.assertRaises(TypeError): flow.visualise('rgb', range_max='2') with self.assertRaises(ValueError): flow.visualise('rgb', range_max=-1) def test_visualise_arrows(self): img = cv2.imread('smudge.png') mask = np.zeros(img.shape[:2]) mask[50:-50, 20:-20] = 1 flow = Flow.from_transforms([['rotation', 256, 256, 30]], img.shape[:2], 's', mask) for scaling in [0.1, 1, 2]: for show_mask in [True, False]: for show_mask_border in [True, False]: for return_tensor in [True, False]: img = flow.visualise_arrows( grid_dist=10, scaling=scaling, show_mask=show_mask, show_mask_borders=show_mask_border, return_tensor=return_tensor ) if return_tensor: self.assertIsInstance(img, torch.Tensor) else: self.assertIsInstance(img, np.ndarray) with self.assertRaises(TypeError): flow.visualise_arrows(grid_dist='test') with self.assertRaises(ValueError): flow.visualise_arrows(grid_dist=-1) with self.assertRaises(TypeError): flow.visualise_arrows(10, img='test') with self.assertRaises(ValueError): flow.visualise_arrows(10, img=mask) with self.assertRaises(ValueError): flow.visualise_arrows(10, img=mask[10:]) with self.assertRaises(ValueError): flow.visualise_arrows(10, img=img[..., :2]) with self.assertRaises(TypeError): flow.visualise_arrows(10, img, scaling='test') with self.assertRaises(ValueError): flow.visualise_arrows(10, img, scaling=-1) with self.assertRaises(TypeError): flow.visualise_arrows(10, img, None, show_mask='test') with self.assertRaises(TypeError): flow.visualise_arrows(10, img, None, True, show_mask_borders='test') with self.assertRaises(TypeError): flow.visualise_arrows(10, img, None, True, True, colour='test') with self.assertRaises(ValueError): flow.visualise_arrows(10, img, None, True, True, colour=(0, 0)) with self.assertRaises(TypeError): flow.visualise_arrows(10, img, None, True, True, colour=(0, 0, 0), return_tensor='test') def test_show(self): flow = Flow.zero([200, 300]) with self.assertRaises(TypeError): flow.show('test') with self.assertRaises(ValueError): flow.show(-1) def test_show_arrows(self): flow = Flow.zero([200, 300]) with self.assertRaises(TypeError): flow.show_arrows('test') with self.assertRaises(ValueError): flow.show_arrows(-1) def test_matrix(self): # Partial affine transform, test reconstruction with all methods transforms = [ ['translation', 2, 1], ['rotation', 20, 20, 30], ['scaling', 10, 10, 1.1] ] matrix = matrix_from_transforms(transforms) flow_s = Flow.from_matrix(matrix, (100, 200), 's') flow_t = Flow.from_matrix(matrix, (100, 200), 't') actual_matrix_s = flow_s.matrix(dof=4, method='ransac') actual_matrix_t = flow_t.matrix(dof=4, method='ransac') self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_s), matrix, rtol=1e-6)) self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_t), matrix, rtol=1e-3)) actual_matrix_s = flow_s.matrix(dof=4, method='lmeds') actual_matrix_t = flow_t.matrix(dof=4, method='lmeds') self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_s), matrix, rtol=1e-6)) self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_t), matrix, rtol=1e-3)) actual_matrix_s = flow_s.matrix(dof=6, method='ransac') actual_matrix_t = flow_t.matrix(dof=6, method='ransac') self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_s), matrix, rtol=1e-6)) self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_t), matrix, rtol=1e-3)) actual_matrix_s = flow_s.matrix(dof=6, method='lmeds') actual_matrix_t = flow_t.matrix(dof=6, method='lmeds') self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_s), matrix, rtol=1e-6)) self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_t), matrix, rtol=1e-3)) actual_matrix_s = flow_s.matrix(dof=8, method='lms') actual_matrix_t = flow_t.matrix(dof=8, method='lms') self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_s), matrix, rtol=1e-6, atol=1e-4)) self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_t), matrix, rtol=1e-6, atol=1e-4)) actual_matrix_s = flow_s.matrix(dof=8, method='ransac') actual_matrix_t = flow_t.matrix(dof=8, method='ransac') self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_s), matrix, rtol=1e-6, atol=1e-4)) self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_t), matrix, rtol=1e-6, atol=1e-4)) actual_matrix_s = flow_s.matrix(dof=8, method='lmeds') actual_matrix_t = flow_t.matrix(dof=8, method='lmeds') self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_s), matrix, rtol=1e-6, atol=1e-4)) self.assertIsNone(np.testing.assert_allclose(to_numpy(actual_matrix_t), matrix, rtol=1e-6, atol=1e-4)) # Random matrix, check to see how often an approximate 'reconstruction' fails, target is <5% of cases failed = 0 for i in range(1000): matrix = (np.random.rand(3, 3) - .5) * 20 if -1e-4 < matrix[2, 2] < 1e-4: matrix[2, 2] = 0 else: matrix /= matrix[2, 2] flow_s = Flow.from_matrix(matrix, (50, 100), 's') try: np.testing.assert_allclose(flow_s.matrix(8, 'lms'), matrix, atol=1e-2, rtol=1e-2) np.testing.assert_allclose(flow_s.matrix(8, 'ransac'), matrix, atol=1e-2, rtol=1e-2) np.testing.assert_allclose(flow_s.matrix(8, 'lmeds'), matrix, atol=1e-2, rtol=1e-2) except AssertionError: failed += 1 self.assertTrue(failed <= 50) # Partial affine transform reconstruction in the presence of noise, only check first 4 values matrix = matrix_from_transforms(transforms) flow_s = Flow.from_matrix(matrix, (100, 200), 's') flow_noise = (np.random.rand(100, 200, 2) - .5) * 5 actual_matrix_4_ransac = (flow_s + flow_noise).matrix(4, 'ransac') actual_matrix_4_lmeds = (flow_s + flow_noise).matrix(4, 'lmeds') actual_matrix_6_ransac = (flow_s + flow_noise).matrix(6, 'ransac') actual_matrix_6_lmeds = (flow_s + flow_noise).matrix(6, 'lmeds') actual_matrix_8_lms = (flow_s + flow_noise).matrix(8, 'lms') actual_matrix_8_ransac = (flow_s + flow_noise).matrix(8, 'ransac') actual_matrix_8_lmeds = (flow_s + flow_noise).matrix(8, 'lmeds') for actual_matrix in [actual_matrix_4_ransac, actual_matrix_4_lmeds, actual_matrix_6_ransac, actual_matrix_6_lmeds, actual_matrix_8_lms, actual_matrix_8_ransac, actual_matrix_8_lmeds]: self.assertIsNone(np.testing.assert_allclose(actual_matrix[:2, :2], matrix[:2, :2], atol=1e-2, rtol=1e-1)) # Masked vs non-masked matrix fitting matrix = matrix_from_transforms(transforms) mask = np.zeros((100, 200), 'bool') mask[:50, :50] = 1 # upper left corner will contain the real values flow = Flow.from_matrix(matrix, (100, 200), 's', mask) random_vecs = (np.random.rand(2, 100, 200) - 0.5) * 200 random_vecs[:, :50, :50] = flow.vecs[:, :50, :50] flow.vecs = random_vecs # Make sure this fails with the 'lmeds' method: with self.assertRaises(AssertionError): np.testing.assert_allclose(flow.matrix(4, 'lmeds', False), matrix) # Test that it does NOT fail when the invalid flow elements are masked out self.assertIsNone(np.testing.assert_allclose(flow.matrix(4, 'lmeds', True), matrix)) # Fallback of 'lms' to 'ransac' when dof == 4 or dof == 6 matrix = matrix_from_transforms(transforms) flow_s = Flow.from_matrix(matrix, (100, 200), 's') actual_matrix_s_lms = flow_s.matrix(dof=4, method='lms') actual_matrix_s_ransac = flow_s.matrix(dof=4, method='ransac') self.assertIsNone(np.testing.assert_equal(to_numpy(actual_matrix_s_lms), to_numpy(actual_matrix_s_ransac))) # Invalid inputs matrix = matrix_from_transforms(transforms) flow_s = Flow.from_matrix(matrix, (100, 200), 's') with self.assertRaises(ValueError): flow_s.matrix(dof='test') with self.assertRaises(ValueError): flow_s.matrix(dof=5) with self.assertRaises(ValueError): flow_s.matrix(dof=4, method='test') with self.assertRaises(TypeError): flow_s.matrix(dof=4, method='lms', masked='test') def test_combine_with(self): img = cv2.imread('smudge.png') shape = img.shape[:2] transforms = [ ['rotation', 255.5, 255.5, -30], ['scaling', 100, 100, 0.8], ] for ref in ['s', 't']: f1 = Flow.from_transforms(transforms[0:1], shape, ref) f2 = Flow.from_transforms(transforms[1:2], shape, ref) f3 = Flow.from_transforms(transforms, shape, ref) # Mode 1 f1_actual = f2.combine_with(f3, 1) # f1.show(500, show_mask=True, show_mask_borders=True) # f1_actual.show(show_mask=True, show_mask_borders=True) self.assertIsInstance(f1_actual, Flow) self.assertEqual(f1_actual.ref, ref) comb_mask = f1_actual.mask_numpy & f1.mask_numpy self.assertIsNone(np.testing.assert_allclose(f1_actual.vecs_numpy[comb_mask], f1.vecs_numpy[comb_mask], atol=5e-2)) # Mode 2 f2_actual = f1.combine_with(f3, 2) # f2.show(500, show_mask=True, show_mask_borders=True) # f2_actual.show(show_mask=True, show_mask_borders=True) self.assertIsInstance(f2_actual, Flow) self.assertEqual(f2_actual.ref, ref) comb_mask = f2_actual.mask_numpy & f2.mask_numpy self.assertIsNone(np.testing.assert_allclose(f2_actual.vecs_numpy[comb_mask], f2.vecs_numpy[comb_mask], atol=5e-2)) # Mode 3 f3_actual = f1.combine_with(f2, 3) # f3.show(500, show_mask=True, show_mask_borders=True) # f3_actual.show(show_mask=True, show_mask_borders=True) self.assertIsInstance(f3_actual, Flow) self.assertEqual(f3_actual.ref, ref) comb_mask = f3_actual.mask_numpy & f3.mask_numpy self.assertIsNone(np.testing.assert_allclose(f3_actual.vecs_numpy[comb_mask], f3.vecs_numpy[comb_mask], atol=5e-2)) # Invalid inputs fs = Flow.from_transforms(transforms[0:1], [20, 20], 's') ft = Flow.from_transforms(transforms[1:2], [20, 20], 't') fs2 = Flow.from_transforms(transforms[0:1], [20, 30], 's') with self.assertRaises(TypeError): # Flow not a Flow object fs.combine_with(fs.vecs, 1) with self.assertRaises(ValueError): # Flow not the same shape fs.combine_with(fs2, 1) with self.assertRaises(ValueError): # Flow not the same reference fs.combine_with(ft, 1) with self.assertRaises(ValueError): # Mode not 1, 2 or 3 fs.combine_with(fs, mode=0) with self.assertRaises(TypeError): # Thresholded not boolean fs.combine_with(fs, 1, thresholded='test') if __name__ == '__main__': unittest.main()

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46fab5fe057959b5d24e56de0f96ac30c03704cc

245

py

Python

B-CNA-410-LYN-4-1-groundhog/src/r.py

Neotoxic-off/Epitech2024

8b3dd04fa9ac2b7019c0b5b1651975a7252d929b

[ "Apache-2.0" ]

2

2022-02-07T12:44:51.000Z

2022-02-08T12:04:08.000Z

B-CNA-410-LYN-4-1-groundhog/src/r.py

Neotoxic-off/Epitech2024

8b3dd04fa9ac2b7019c0b5b1651975a7252d929b

[ "Apache-2.0" ]

null

B-CNA-410-LYN-4-1-groundhog/src/r.py

Neotoxic-off/Epitech2024

8b3dd04fa9ac2b7019c0b5b1651975a7252d929b

[ "Apache-2.0" ]

1

2022-01-23T21:26:06.000Z

#!/usr/bin/env python from math import sqrt def r(inputs, period): if (len(inputs) > period): if (inputs[-(1 + period)] - 1 > 0): return (round((inputs[-1] / inputs[-(1 + period)] - 1) * 100)) return ("nan")

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0.220472

0

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