Datasets:

semeru

/

Galeras-3k_deduped

like 0

tests/basic/tests.py

Refs #33476 -- Reformatted code with Black.

django

Python

tests.py

def test_does_not_exist(self): # Django raises an Article.DoesNotExist exception for get() if the # parameters don't match any object. with self.assertRaisesMessage( ObjectDoesNotExist, "Article matching query does not exist." ): Article.objects.get( id__exact=2000, ) # To avoid dict-ordering related errors check only one lookup # in single assert. with self.assertRaises(ObjectDoesNotExist): Article.objects.get(pub_date__year=2005, pub_date__month=8) with self.assertRaisesMessage( ObjectDoesNotExist, "Article matching query does not exist." ): Article.objects.get( pub_date__week_day=6, )

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def test_does_not_exist(self): # Django raises an Article.DoesNotExist exception for get() if the # parameters don't match any object. with self.assertRaisesMessage( ObjectDoesNotExist, "Article matching query does not exist." ): Article.objects.get( id__exact=2000, ) # To avoid dict-ordering related errors check only one lookup # in single assert. with self.assertRaises(ObjectDoesNotExist): Article.objects.get(pub_date__year=2005, pub_date__month=8) with self.assertRaisesMessage( ObjectDoesNotExist, "Article matching query does not exist." ):

test_does_not_exist

scripts/extract.py

Data Augmentation update (#1263) - lib.detected_face - Subclass Masks for Landmark based masks - Add training mask propery + methods to DetectedFace - lib.training_training - subclass TrainingDataGenerator for training and preview data - Split cache into own module - Reduce thread count to 1 to prevent image corruption + data re-use - Process on largest model input/output size rather than stored image size - Size and crop masks during caching stage - Implement ring buffer for data flow - Fix preview reload bug - augmentation - typing - switch color aug order - better initialization - Fix warp + landmark warp to correctly apply at different image scales - Slightly improved warp caching - Don't store whether image is_preview. Handle all data as training images implicitly - plugins.trainer: Typing and fixes to work with trainingdata refactor

faceswap

Python

extract.py

def _set_skip_list(self) -> None: if self._skip_num == 1 and not self._alignments.data: logger.debug("No frames to be skipped") return skip_list = [] for idx, filename in enumerate(self._images.file_list): if idx % self._skip_num != 0: logger.trace("Adding image '%s' to skip list due to " # type: ignore "extract_every_n = %s", filename, self._skip_num) skip_list.append(idx) # Items may be in the alignments file if skip-existing[-faces] is selected elif os.path.basename(filename) in self._alignments.data: self._existing_count += 1 logger.trace("Removing image: '%s' due to previously existing", # type: ignore filename) skip_list.append(idx) if self._existing_count != 0: logger.info("Skipping %s frames due to skip_existing/skip_existing_faces.", self._existing_count) logger.debug("Adding skip list: %s", skip_list) self._images.add_skip_list(skip_list)

2beceffad9b15c1fd78f06b9b272563321c5a41e

https://github.com/deepfakes/faceswap.git

def _set_skip_list(self) -> None: if self._skip_num == 1 and not self._al

_set_skip_list

tests/server.py

Use `getClientAddress` instead of `getClientIP`. (#12599) getClientIP was deprecated in Twisted 18.4.0, which also added getClientAddress. The Synapse minimum version for Twisted is currently 18.9.0, so all supported versions have the new API.

synapse

Python

server.py

def getPeer(self): # We give an address so that getClientAddress/getClientIP returns a non null entry, # causing us to record the MAU return address.IPv4Address("TCP", self._ip, 3423)

7fbf42499d92ec3c9a05d9f36ec5fecd1ab1f18c

https://github.com/matrix-org/synapse.git

def getPeer(self): # We give an address so that getClientAddress/getClientIP returns a non null entry, # causing us to record the MAU return address.IPv4Addre

getPeer

mkdocs/tests/config/config_options_tests.py

Refactor: use config_options module through a short alias 'c'

mkdocs

Python

config_options_tests.py

def test_incorrect_type_error(self): for cls in c.Dir, c.File, c.FilesystemObject: with self.subTest(cls):

f1da904a7fae401c5f96ef6494bfd2bbfcb8c29e

https://github.com/mkdocs/mkdocs.git

def test_incorrect_type_error(self): for cls in c.Dir, c.File, c.FilesystemObject: with self.subTest(cls):

test_incorrect_type_error

keras/saving/saved_model/save_impl.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

save_impl.py

def _replace_child_layer_functions(layer, serialization_cache): # pylint: disable=protected-access original_fns = {}

84afc5193d38057e2e2badf9c889ea87d80d8fbf

https://github.com/keras-team/keras.git

def _replace_child_layer_functions(layer, serialization_cache): # pylint: disable=protected-access original_fns = {}

_replace_child_layer_functions

keras/mixed_precision/layer_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

layer_test.py

def test_gradient(self, strategy_fn): x = tf.constant([1.0]) with strategy_fn().scope() as strategy: with policy.policy_scope("mixed_float16"): layer = mp_test_util.MultiplyLayer(assert_type=tf.float16) # Learning rate is small enough that if applied to a float16 variable, # the variable will not change. So this tests the learning rate is not # applied to a float16 value, but instead the float32 variable. opt = gradient_descent.SGD(2**-14)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

https://github.com/keras-team/keras.git

def test_gradient(self, strategy_fn): x = tf.constant([1.0]) with strategy_fn().scope() as strategy: with policy.policy_scope("mixed_float16"): layer = mp_test_util.MultiplyLayer(assert_type=tf.float16) # Learning rate is small enough that if applied to a float16 variable, # the variable will not change. So this tests the learning rate is not # applied to a float16 value, but instead

test_gradient

examples/mixture/plot_gmm_selection.py

DOC Rework Gaussian Mixture example (#24721) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

scikit-learn

Python

plot_gmm_selection.py

def gmm_bic_score(estimator, X): # Make it negative since GridSearchCV expects a score to maximize return -estimator.bic(X) param_grid = { "n_components": range(1, 7), "covariance_type": ["spherical", "tied", "diag", "full"], } grid_search = GridSearchCV( GaussianMixture(), param_grid=param_grid, scoring=gmm_bic_score ) grid_search.fit(X) # %% # Plot the BIC scores # ------------------- # # To ease the plotting we can create a `pandas.DataFrame` from the results of # the cross-validation done by the grid search. We re-inverse the sign of the # BIC score to show the effect of minimizing it. import pandas as pd df = pd.DataFrame(grid_search.cv_results_)[ ["param_n_components", "param_covariance_type", "mean_test_score"] ] df["mean_test_score"] = -df["mean_test_score"] df = df.rename( columns={ "param_n_components": "Number of components", "param_covariance_type": "Type of covariance", "mean_test_score": "BIC score", } ) df.sort_values(by="BIC score").head() # %% import seaborn as sns sns.catplot( data=df, kind="bar", x="Number of components", y="BIC score", hue="Type of covariance", ) plt.show() # %% # In the present case, the model with 2 components and full covariance (which # corresponds to the true generative model) has the lowest BIC score and is # therefore selected by the grid search. # # Plot the best model # ------------------- # # We plot an ellipse to show each Gaussian component of the selected model. For # such purpose, one needs to find the eigenvalues of the covariance matrices as # returned by the `covariances_` attribute. The shape of such matrices depends # on the `covariance_type`: # # - `"full"`: (`n_components`, `n_features`, `n_features`) # - `"tied"`: (`n_features`, `n_features`) # - `"diag"`: (`n_components`, `n_features`) # - `"spherical"`: (`n_components`,) from matplotlib.patches import Ellipse from scipy import linalg color_iter = sns.color_palette("tab10", 2)[::-1] Y_ = grid_search.predict(X) fig, ax = plt.subplots() for i, (mean, cov, color) in enumerate( zip( grid_search.best_estimator_.means_, grid_search.best_estimator_.covariances_, color_iter, ) ): v, w = linalg.eigh(cov) if not np.any(Y_ == i): continue plt.scatter(X[Y_ == i, 0], X[Y_ == i, 1], 0.8, color=color) angle = np.arctan2(w[0][1], w[0][0]) angle = 180.0 * angle / np.pi # convert to degrees v = 2.0 * np.sqrt(2.0) * np.sqrt(v) ellipse = Ellipse(mean, v[0], v[1], angle=180.0 + angle, color=color) ellipse.set_clip_box(fig.bbox) ellipse.set_alpha(0.5) ax.add_artist(ellipse) plt.title( f"Selected GMM: {grid_search.best_params_['covariance_type']} model, " f"{grid_search.best_params_['n_components']} components" ) plt.axis("equal") plt.show()

fa4376a5815ce8b15f48f220bc353de0e06aa259

https://github.com/scikit-learn/scikit-learn.git

def gmm_bic_score(estimator, X): # Make it negative since GridSearchCV expects a score to maximize return -estimator.bic(X) param_grid = { "n_components": range(1, 7), "covariance_type": ["spherical", "tied", "diag", "full"], } grid_search = GridSearchCV( GaussianMixture(), param_grid=param_grid, scoring=gmm_bic_score ) grid_search.fit(X) # %% # Plot the BIC scores # ------------------- # # To ease the plotting we can create a `pandas.DataFrame` from the results of # the cross-validation done by the grid search. We re-inverse the sign of the # BIC score to show the effect of minimizing it. import pandas as pd df = pd.DataFrame(grid_search.cv_results_)[ ["param_n_components", "param_covariance_type", "mean_test_score"] ] df["mean_test_score"] = -df["mean_test_score"] df = df.rename( columns={ "param_n_components": "Number of components", "param_covariance_type": "Type of covariance", "mean_test_score": "BIC score", } ) df.sort_values(by="BIC score").head() # %% import seaborn as sns sns.catplot( data=df, kind="bar", x="Number of components", y="BIC score", hue="Type of covariance", ) plt.show() # %% # In the present case, the model with 2 components and full covariance (which # corresponds to the true generative model) has the lowest BIC score and is # therefore selected by the grid search. # # Plot the best model # ------------------- # # We plot an ellipse to show each Gaussian component of the selected model. For # such purpose, one needs to find the eigenvalues of the covariance matrices as # returned by the `covariances_` attribute. The shape of such matrices depends # on the `covariance_type`: # # - `"full"`: (`n_components`, `n_features`, `n_features`) # - `"tied"`: (`n_features`, `n_features`) # - `"diag"`: (`n_components`, `n_features`) # - `"spherical"`: (`n_components`,) from matplotlib.patches import Ellipse from scipy import linalg color_iter = sns.color_palette("tab10", 2)[::-1] Y_ = grid_search.predict(X) fig, ax = plt.subplots() for i, (mean, cov, color) in enumerate( zip( grid_search.best_estimator_.means_, grid_search.best_estimator_.covariances_, color_iter, ) ): v, w = linalg.eigh(cov) if not np.any(Y_ == i): continue plt.scatter(X[Y_ == i, 0], X[Y_ == i, 1], 0.8, color=color) angle = np.arctan2(w[0][1], w[0][0]) angle = 180.0 * angle / np.pi # convert to degrees v = 2.0 * np.sqrt(2.0) * np.sqrt(v) ellipse = Ellipse(mean, v[0], v[1], angle=180.0 + angle, color=c

gmm_bic_score

python/ray/tune/tests/test_logger.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

Python

test_logger.py

def testCSV(self): config = {"a": 2, "b": 5, "c": {"c": {"D": 123}, "e": None}} t = Trial(evaluated_params=config, trial_id="csv", logdir=self.test_dir) logger = CSVLoggerCallback() logger.on_trial_result(0, [], t, result(0, 4)) logger.on_trial_result(1, [], t, result(1, 5)) logger.on_trial_result( 2, [], t, result(2, 6, score=[1, 2, 3], hello={"world": 1}) ) logger.on_trial_complete(3, [], t) self._validate_csv_result()

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

def testCSV(self): config = {"a": 2, "b": 5, "c": {"c": {"D": 123}, "e": None}}

testCSV

tests/data/test_btanalysis.py

Update bt_results filename to new.json

freqtrade

Python

test_btanalysis.py

def test_analyze_trade_parallelism(testdatadir): filename = testdatadir / "backtest-result_new.json" bt_data = load_backtest_data(filename) res = analyze_trade_parallelism(bt_data, "5m") assert isinstance(res, DataFrame) assert 'open_trades' in res.columns assert res['open_trades'].max() == 3 assert res['open_trades'].min() == 0

28011a39076d41e6f1f2182215cbcb420bcb3fa5

https://github.com/freqtrade/freqtrade.git

def test_analyze_trade_parallelism(testdatadir): filename = testdatadir / "backtest-result_new.json" bt_data = load_backtest_data(filename) res = analyze_trade_parallelism(bt_data, "5m") assert isinstance(res, DataFrame) assert 'open_trades' in res.columns assert

test_analyze_trade_parallelism

src/diffusers/utils/logging.py

changes comments and env vars in `utils/logging` removes mentions of 🤗Transformers with 🤗Diffusers equivalent.

diffusers

Python

logging.py

def _get_default_logging_level(): env_level_str = os.getenv("DIFFUSERS_VERBOSITY", None) if env_level_str: if env_level_str in log_levels: return log_levels[env_level_str] else: logging.getLogger().warning( f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, " f"has to be one of: { ', '.join(log_levels.keys()) }" ) return _default_log_level

c3cc8eb23c8095217388d350409b454ea396c12b

https://github.com/huggingface/diffusers.git

def _get_default_logging_level(): env_level_str = os.getenv("DIFFUSERS_VERBOSITY", None) if env_level_str:

_get_default_logging_level

saleor/graphql/meta/tests/test_meta_mutations.py

Move checkout metadata to separate model (#11264) * seperate checkout matadata to CheckoutMetadata model * change naming of checkout metadata class field * fix tests after rebase * add dataloaders,move resolving to checkout type instead of meta type, fix review remarks * fix tests * clen up migrations * add missing migration, add missing metdata filter for checkout * fixes for cases when checkout has no metadata_storage, cosmetic changes after review * fixes for cases when checkout has no metadata_storage, cosmetic changes after review * update changelog * move comment to proper place * delete index from state * add using helper to ensure metadata exists * fix migration, add get_or_create fo metadata calls * fix tests after rebase, fixes after review * fix in migration queryset

saleor

Python

test_meta_mutations.py

def test_delete_public_metadata_for_checkout_by_token(api_client, checkout): # given checkout.metadata_storage.store_value_in_metadata({PUBLIC_KEY: PUBLIC_VALUE}) checkout.metadata_storage.save(update_fields=["metadata"]) checkout_id = graphene.Node.to_global_id("Checkout", checkout.pk) # when response = execute_clear_public_metadata_for_item( api_client, None, checkout.token, "Checkout" ) # then assert item_without_public_metadata( response["data"]["deleteMetadata"]["item"], checkout.metadata_storage, checkout_id, )

10356eb2845766684a13cb3adcbde2ad0e5e07ec

https://github.com/saleor/saleor.git

def test_delete_public_metadata_for_checkout_by_token(api_client, checkout): # given checkout.metadata_storage.store_value_in_metadata({PUBLIC_KEY: PUBLIC_VALUE}) checkout.metadata_storage.save(update_fields=["metadata"]) checkout_id = graphene.Node.to_global_id("Checkout", checkout.pk) # when response = execute_clear_public_metadata_for_item( api_client, None, checkout.token, "Checkout" ) # then assert item_without_public_metadata( response["data"]["deleteMetadata"]["item"], checkout.metadata_storage, checkout_id, )

test_delete_public_metadata_for_checkout_by_token

keras/legacy_tf_layers/convolutional_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

convolutional_test.py

def testFunctionalConv1DNoReuse(self): with tf.Graph().as_default(): length = 10 data = tf.random.uniform((5, length, 3), seed=1) conv_layers.separable_conv1d(data, 32, 3) self.assertEqual(len(tf.compat.v1.trainable_variables()), 3) conv_layers.separable_conv1d(data, 32, 3) self.assertEqual(len(tf.compat.v1.trainable_variables()), 6)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

https://github.com/keras-team/keras.git

def testFunctionalConv1DNoReuse(self): with tf.Graph().as_defaul

testFunctionalConv1DNoReuse

keras/layers/normalization/batch_normalization_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

batch_normalization_test.py

def test_batchnorm_convnet(self): if tf.test.is_gpu_available(cuda_only=True): with self.session(): model = keras.models.Sequential() norm = keras.layers.BatchNormalization( axis=1, input_shape=(3, 4, 4), momentum=0.8 ) model.add(norm) model.compile( loss="mse", optimizer=tf.compat.v1.train.GradientDescentOptimizer(0.01), run_eagerly=test_utils.should_run_eagerly(), ) # centered on 5.0, variance 10.0 x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4)) model.fit(x, x, epochs=4, verbose=0) out = model.predict(x) out -= np.reshape(keras.backend.eval(norm.beta), (1, 3, 1, 1)) out /= np.reshape(keras.backend.eval(norm.gamma), (1, 3, 1, 1)) np.testing.assert_allclose( np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1 ) np.testing.assert_allclose( np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1 )

84afc5193d38057e2e2badf9c889ea87d80d8fbf

https://github.com/keras-team/keras.git

def test_batchnorm_convnet(self): if tf.test.is_gpu_available(cuda_only=True): with self.session(): model = keras.models.Sequential() norm = keras.layers.BatchNormalization( axis=1, input_shape=(3, 4, 4), momentum=0.8 ) model.add(norm) model.compile( loss="mse", optimizer=tf

test_batchnorm_convnet

yt_dlp/extractor/musicdex.py

[Musicdex] Add extractors (#2421) Closes #2204 Authored by: Ashish0804

yt-dlp

Python

musicdex.py

def _return_info(self, track_json, album_json, id): return { 'id': str(id), 'title': track_json.get('name'), 'track': track_json.get('name'), 'description': track_json.get('description'), 'track_number': track_json.get('number'), 'url': format_field(track_json, 'url', 'https://www.musicdex.org/%s'), 'duration': track_json.get('duration'), 'genre': [genre.get('name') for genre in track_json.get('genres') or []], 'like_count': track_json.get('likes_count'), 'view_count': track_json.get('plays'), 'artist': [artist.get('name') for artist in track_json.get('artists') or []], 'album_artist': [artist.get('name') for artist in album_json.get('artists') or []], 'thumbnail': format_field(album_json, 'image', 'https://www.musicdex.org/%s'), 'album': album_json.get('name'), 'release_year': try_get(album_json, lambda x: date_from_str(unified_strdate(x['release_date'])).year), 'extractor_key': MusicdexSongIE.ie_key(), 'extractor': 'MusicdexSong', }

fb62afd6f047aea7e88a6b0df00b49f78ba16e84

https://github.com/yt-dlp/yt-dlp.git

def _return_info(self, track_json, album_json, id): return { 'id': str(id), 'title': track_json.get('name'), 'track': track_json.get('name'), 'description': track_json.get('description'), 'track_number': track_json.get('number'), 'url': format_field(track_json, 'url', 'https://www.musicdex.org/%s'), 'duration': track_json.get('duration'), 'genre': [genre.get('name') for genre in track_json.get('genres') or []], 'like_count': track_json.get('likes_count'), 'view_count': track_json.get('plays'), 'artist': [artist.get('name') for artist in track_json.get('artists') or []], 'album_artist': [artist.get('name') for artist in album_json.get('artists') or []], 'thumbnail': format_field(album_json, 'image', 'https://www.musicdex.org/%s'), 'album': album_json.get('name'),

_return_info

tests/sentry/api/endpoints/test_user_roles.py

feat: Add endpoints for managing user roles (#30993) - Add endpoints for creating, updating, deleting user roles. - Add endpoints for assigning and unassigning roles from users. - Add permission validation to endpoints (permissions now must be known). - Add sudo/superuser requirements to various endpoints involving permissions. - Add standard audit logs to all permission related endpoints. Additionally this cleans up the various permission endpoints, improving testing, adding various security (sudo, superuser-only).

sentry

Python

test_user_roles.py

def test_lookup_self(self): role = UserRole.objects.create(name="support", permissions=["broadcasts.admin"]) role.users.add(self.user) role2 = UserRole.objects.create(name="admin", permissions=["users.admin"]) role2.users.add(self.user) UserRole.objects.create(name="other", permissions=["users.edit"]) resp = self.get_response("me") assert resp.status_code == 200 assert len(resp.data) == 2, resp.data role_names = [r["name"] for r in resp.data] assert "support" in role_names assert "admin" in role_names

45750ab53007ebae64d7a82d5020e65ab94b6da7

https://github.com/getsentry/sentry.git

def test_lookup_self(self): role = UserRole.objects.create(name="support", permissions=["broadcasts.admin"]) role.users.add(self.user) role2 = UserRole.objects.create(name="admin", permissions=["users.admin"]) role2.users.add(self.user) UserRole.objects.create(name="other", permissions=["users.edit"]) resp = self.get_response("me") assert resp.status_code == 200 assert len(resp.data) == 2, resp.data role_names = [r

test_lookup_self

tests/orion/database/test_queries.py

Return a work queue ID

prefect

Python

test_queries.py

async def test_get_runs_in_queue_limit(self, session, db, fr_1, fr_2, fr_3): query = db.queries.get_scheduled_flow_runs_from_work_queues( db=db, limit_per_queue=1 ) result = await session.execute(query) runs = result.all() assert [r[0].id for r in runs] == [fr_1.id, fr_3.id]

da5115381e62b084922641a8b1270806f695055f

https://github.com/PrefectHQ/prefect.git

async def test_get_runs_in_queue_limit(self, session, db, fr_1, fr_2, fr_3): query = db.queries.get_scheduled_flow_runs_from_work_queues( db=db, limit_per_queue=1 ) result = await session.execute(query) runs = result.all() assert

test_get_runs_in_queue_limit

airflow/cli/commands/dag_command.py

Add `list-import-errors` to `airflow dags` command (#22084) This will help users to see the dags with import error and enable scripts process the output

airflow

Python

dag_command.py

def dag_list_import_errors(args): dagbag = DagBag(process_subdir(args.subdir)) data = [] for filename, errors in dagbag.import_errors.items(): data.append({"filepath": filename, "error": errors}) AirflowConsole().print_as( data=data, output=args.output, ) @cli_utils.action_cli @suppress_logs_and_warning

e1134590973355549272b1f3a213dbfa29698df7

@cli_utils.action_cli @suppress_logs_and_warning

https://github.com/apache/airflow.git

def dag_list_import_errors(args): dagbag = DagBag(process_subd

dag_list_import_errors

mindsdb/api/mysql/mysql_proxy/mysql_proxy.py

before integration tests

mindsdb

Python

mysql_proxy.py

def process_query(self, sql): executor = Executor( session=self.session, sqlserver=self ) executor.query_execute(sql) if executor.error is not None: resp = SQLAnswer( answer_type = ANSWER_TYPE.ERROR, error_code=executor.error['code'], error_message=executor.error['message'] ) elif executor.data is None: resp = SQLAnswer( answer_type = ANSWER_TYPE.OK, state_track=executor.state_track, ) else: resp = SQLAnswer( answer_type=ANSWER_TYPE.TABLE, state_track=executor.state_track, columns=self.to_mysql_columns(executor.columns), data=executor.data, status=executor.server_status ) return resp

e8a8d9b71deae2c291efb49ff11573285f3aec35

https://github.com/mindsdb/mindsdb.git

def process_query(self, sql): executor = Executor( session=self.session, sqlserver=self ) executor.query_execute(sql) if executor.error is not None: resp = SQLAnswer( answer_type = ANSWER_TYPE.ERROR, error_code=executor.error['code'], error_message=executor.error['message'] ) elif executor.data is None: resp = SQLAnswer( answer_type = ANSWER_TYPE.OK, state_track=executor.state_track, ) else: resp = SQLAnswer( answer_type=ANSWER_TYPE.TABLE, state_track=executor.state_track, columns=self.to_mysql_columns(executor.columns), data=executor.data, stat

process_query

pandas/tests/frame/methods/test_to_csv.py

BUG/API: Indexes on empty frames/series should be RangeIndex (#49637) * BUG/API: ndexes on empty frames/series should be RangeIndex, are Index[object] * fix black * fix window stuff * Add docs * double ticks * unneeded line * update thatsnew text * update whatsnew text * fix rst * Update doc/source/whatsnew/v2.0.0.rst Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com> Co-authored-by: Terji Petersen <terjipetersen@Terjis-Air.fritz.box> Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>

pandas

Python

test_to_csv.py

def test_to_csv_empty(self): df = DataFrame(index=np.arange(10)) result, expected = self._return_result_expected(df, 1000) tm.assert_frame_equal(result, expected, check_column_type=False)

e93ee07729afe0bc7661655755df6adad657c23b

https://github.com/pandas-dev/pandas.git

def test_to_csv_empty(self):

test_to_csv_empty

src/textual/_terminal_features.py

[terminal buffering] Add support for the "mode 2026" That task is definitely way more complicated that it seemed to be 😅

textual

Python

_terminal_features.py

def from_autodetect(cls) -> TerminalSupportedFeatures: # Using macOS, but not using the default terminal: let's assume we're on iTerm2 iterm2_synchronized_update = ( platform.system() == "Darwin" and os.environ.get("TERM_PROGRAM", "") != "Apple_Terminal" ) # Detecting "mode2026" is more complicated, as we have to use an async request/response # machinery with the terminal emulator - for now we should just assume it's not supported. # See the use of the Mode and ModeReportParameter classes in the Textual code to check this machinery. mode2026_synchronized_update = False return cls( iterm2_synchronized_update=iterm2_synchronized_update, mode2026_synchronized_update=mode2026_synchronized_update, )

d14659c1a3760eade2dd3479b66eb8b2e7711db0

https://github.com/Textualize/textual.git

def from_autodetect(cls) -> TerminalSupportedFeatures: # Using macOS, but not using the default terminal: let's assume we're on iTerm2 iterm2_synchronized_update = ( platform.system() == "Darwin" and os.environ.get("TERM_PROGRAM", "") != "Apple_Terminal" ) # Detecting "mode2026" is more complicated, as we have to use an async request/response # machinery with the terminal emulator - for now we should just assume it's not supported. # See the use of the Mode and ModeReportParameter classes in the Textual code to check this machinery. mode2026_synchronized_update = False return cls( iterm2_synchronized_update=iterm2_synchronized_update, mode2026_synchronized_update=mode2026_synchronized_update, )

from_autodetect

python/ray/autoscaler/_private/gcp/config.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

Python

config.py

def _get_service_account(account, config, iam): project_id = config["provider"]["project_id"] full_name = "projects/{project_id}/serviceAccounts/{account}" "".format( project_id=project_id, account=account ) try: service_account = iam.projects().serviceAccounts().get(name=full_name).execute() except errors.HttpError as e: if e.resp.status != 404: raise service_account = None return service_account

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

def _get_service_account(account, config, iam): project_id = config["provider"]["project_id"] full_name = "projects/{project_id}/serviceAccounts/{account}" "".format( project_id=project_id, account=account ) try: service_account = iam.projects().serviceAccounts().get(name=full_name).execute() except errors.HttpError as e: if e.resp.status != 404: raise service_account = None return service_account

_get_service_account

pandas/tests/indexes/categorical/test_reindex.py

DEPR: Index.reindex with non-unique Index (#49485)

pandas

Python

test_reindex.py

def test_reindex_list_non_unique_unused_category(self): msg = "cannot reindex on an axis with duplicate labels" ci = CategoricalIndex(["a", "b", "c", "a"], categories=["a", "b", "c", "d"]) with pytest.raises(ValueError, match=msg): ci.reindex(["a", "c"])

a215264d472e79c48433fa3a04fa492abc41e38d

https://github.com/pandas-dev/pandas.git

def test_reindex_list_non_unique_unused_category(self): msg = "cannot reindex on an axis with duplicate labels" ci = CategoricalIndex(["a", "b", "c", "a"], categorie

test_reindex_list_non_unique_unused_category

networkx/utils/misc.py

make lazy_import private and remove its internal use (#5878) * make lazy_import private and remove its internal use * add back the non-lazy imports of numpy to utils.misc

networkx

Python

misc.py

def choice(self, seq): import numpy as np if isinstance(self._rng, np.random.Generator): idx = self._rng.integers(0, len(seq)) else: idx = self._rng.randint(0, len(seq)) return seq[idx]

afef7ebde11cbe9d92d6a98319fe431a219d9f8c

https://github.com/networkx/networkx.git

def choice(self, seq): import numpy as np if isinstance(self._rng, np.random.Generator): idx = self.

choice

tests/sentry/api/endpoints/test_accept_organization_invite.py

chore(hybrid-cloud): use organization_slug in AcceptOrganizationInvite API (#42138)

sentry

Python

test_accept_organization_invite.py

def test_not_needs_authentication(self): self.login_as(self.user) om = OrganizationMember.objects.create( email="newuser@example.com", token="abc", organization=self.organization ) for path in self._get_paths([om.id, om.token]): resp = self.client.get(path) assert resp.status_code == 200 assert not resp.data["needsAuthentication"]

e94d7cd092d813d88c2216fca3ca6bd48e0747a3

https://github.com/getsentry/sentry.git

def test_not_needs_authentication(self): self.login_as(self.user)

test_not_needs_authentication

wagtail/documents/widgets.py

Reformat with black

wagtail

Python

widgets.py

def media(self): return forms.Media( js=[ versioned_static("wagtaildocs/js/document-chooser-modal.js"), versioned_static("wagtaildocs/js/document-chooser.js"), ] )

d10f15e55806c6944827d801cd9c2d53f5da4186

https://github.com/wagtail/wagtail.git

def media(self): return forms.Media( js=[ versioned_static("wagtaildocs/js/document-chooser-modal.js"), versioned_static("wagtaildocs/js/document-choose

media

glances/globals.py

Improve code quality

glances

Python

globals.py

def json_dumps_dictlist(data, item): if isinstance(data, dict): try: return json_dumps({item: data[item]}) except: return None elif isinstance(data, list): try: # Source: # http://stackoverflow.com/questions/4573875/python-get-index-of-dictionary-item-in-list return json_dumps({item: map(itemgetter(item), data)}) except: return None else: return None

004288eac5b4ffbcced7149113150d7cc42df28e

https://github.com/nicolargo/glances.git

def json_dumps_dictlist(data, item): if isinstance(

json_dumps_dictlist

pandas/tests/indexing/test_loc.py

BUG: can_hold_element size checks on ints/floats (#45273)

pandas

Python

test_loc.py

def test_loc_setitem_uint8_upcast(): # GH#26049 df = DataFrame([1, 2, 3, 4], columns=["col1"], dtype="uint8") df.loc[2, "col1"] = 300 # value that can't be held in uint8 # TODO: would be better to get uint16? expected = DataFrame([1, 2, 300, 4], columns=["col1"], dtype="int64") tm.assert_frame_equal(df, expected)

37c33438837cbb7e41a949b44a20c82b82289498

https://github.com/pandas-dev/pandas.git

def test_loc_setitem_uint8_upcast(): # GH#26049

test_loc_setitem_uint8_upcast

homeassistant/helpers/template.py

Make `this` variable available in template entities (#65201) * feat: make this variable available in template entities This makes the variable `this` available in template entities. It will simplify the use of self-referencing template entities. Because, without this, we have to repeat the entity id every time. If we can solve this without explicitly spelling the entity id, code can be re-used much better. As a side-effect, this will allow to use `variables`-like patterns, where attributes can be used as variables to calculate subsequent attributes or state. Example: ```yaml template: sensor: - name: test state: "{{ this.attributes.test }}" # not: "{{ state_attr('sensor.test', 'test' }}" attributes: test: "{{ now() }}" ``` * expose entity_id instead of this * add test * Refactor to expose this variable * Tweak repr dunder Co-authored-by: Erik <erik@montnemery.com>

core

Python

template.py

def __getitem__(self, item): if item in _COLLECTABLE_STATE_ATTRIBUTES: # _collect_state inlined here for performance if self._collect and _RENDER_INFO in self._hass.data: self._hass.data[_RENDER_INFO].entities.add(self._entity_id) return getattr(self._state, item) if item == "entity_id": return self._entity_id if item == "state_with_unit": return self.state_with_unit raise KeyError

d20a620590bc2df74eb2f9c0d35e1b7f12be5ded

https://github.com/home-assistant/core.git

def __getitem__(self, item): if item in _COLLECTABLE_STATE_ATTRIBUTES: # _collect_state inlined here for performance if self._collect and _RENDER_INFO in self._hass.data: self._hass.data[_RENDER_INFO].entities.add(self._entity_id) return getattr(self._state, item) if item == "entity_id": return self._entity_id if item == "state_with_unit": return self.state_with_

__getitem__

tests/components/hassio/test_sensor.py

Add auto_update property to supervisor and addon update entities (#69055)

core

Python

test_sensor.py

def mock_all(aioclient_mock, request): aioclient_mock.post("http://127.0.0.1/homeassistant/options", json={"result": "ok"}) aioclient_mock.get("http://127.0.0.1/supervisor/ping", json={"result": "ok"}) aioclient_mock.post("http://127.0.0.1/supervisor/options", json={"result": "ok"}) aioclient_mock.get( "http://127.0.0.1/info", json={ "result": "ok", "data": {"supervisor": "222", "homeassistant": "0.110.0", "hassos": None}, }, ) aioclient_mock.get( "http://127.0.0.1/store", json={ "result": "ok", "data": {"addons": [], "repositories": []}, }, ) aioclient_mock.get( "http://127.0.0.1/host/info", json={ "result": "ok", "data": { "result": "ok", "data": { "chassis": "vm", "operating_system": "Debian GNU/Linux 10 (buster)", "kernel": "4.19.0-6-amd64", }, }, }, ) aioclient_mock.get( "http://127.0.0.1/core/info", json={"result": "ok", "data": {"version_latest": "1.0.0", "version": "1.0.0"}}, ) aioclient_mock.get( "http://127.0.0.1/os/info", json={"result": "ok", "data": {"version_latest": "1.0.0", "version": "1.0.0"}}, ) aioclient_mock.get( "http://127.0.0.1/supervisor/info", json={ "result": "ok", "data": { "result": "ok", "version": "1.0.0", "version_latest": "1.0.0", "addons": [ { "name": "test", "state": "started", "slug": "test", "installed": True, "update_available": False, "version": "2.0.0", "version_latest": "2.0.1", "repository": "core", "url": "https://github.com/home-assistant/addons/test", }, { "name": "test2", "state": "stopped", "slug": "test2", "installed": True, "update_available": False, "version": "3.1.0", "version_latest": "3.2.0", "repository": "core", "url": "https://github.com", }, ], }, }, ) aioclient_mock.get( "http://127.0.0.1/addons/test/stats", json={ "result": "ok", "data": { "cpu_percent": 0.99, "memory_usage": 182611968, "memory_limit": 3977146368, "memory_percent": 4.59, "network_rx": 362570232, "network_tx": 82374138, "blk_read": 46010945536, "blk_write": 15051526144, }, }, ) aioclient_mock.get("http://127.0.0.1/addons/test/changelog", text="") aioclient_mock.get( "http://127.0.0.1/addons/test/info", json={"result": "ok", "data": {"auto_update": True}}, ) aioclient_mock.get("http://127.0.0.1/addons/test2/changelog", text="") aioclient_mock.get( "http://127.0.0.1/addons/test2/info", json={"result": "ok", "data": {"auto_update": False}}, ) aioclient_mock.get( "http://127.0.0.1/ingress/panels", json={"result": "ok", "data": {"panels": {}}} ) @pytest.mark.parametrize( "entity_id,expected", [ ("sensor.home_assistant_operating_system_version", "1.0.0"), ("sensor.home_assistant_operating_system_newest_version", "1.0.0"), ("sensor.test_version", "2.0.0"), ("sensor.test_newest_version", "2.0.1"), ("sensor.test2_version", "3.1.0"), ("sensor.test2_newest_version", "3.2.0"), ("sensor.test_cpu_percent", "0.99"), ("sensor.test2_cpu_percent", "unavailable"), ("sensor.test_memory_percent", "4.59"), ("sensor.test2_memory_percent", "unavailable"), ], )

78e4d7e1ca8f49068d8f63f6c80bb3048f5ad8e8

@pytest.mark.parametrize( "entity_id,expected", [ ("sensor.home_assistant_operating_system_version", "1.0.0"), ("sensor.home_assistant_operating_system_newest_version", "1.0.0"), ("sensor.test_version", "2.0.0"), ("sensor.test_newest_version", "2.0.1"), ("sensor.test2_version", "3.1.0"), ("sensor.test2_newest_version", "3.2.0"), ("sensor.test_cpu_percent", "0.99"), ("sensor.test2_cpu_percent", "unavailable"), ("sensor.test_memory_percent", "4.59"), ("sensor.test2_memory_percent", "unavailable"), ], )

https://github.com/home-assistant/core.git

def mock_all(aioclient_mock, request): aioclient_mock.post("http://127.0.0.1/homeassistant/options", json={"result": "ok"}) aioclient_mock.get("http://127.0.0.1/supervisor/ping", json={"result": "ok"}) aioclient_mock.post("http://127.0.0.1/supervisor/options", json={"result": "ok"}) aioclient_mock.get( "http://127.0.0.1/info", json={ "result": "ok", "data": {"supervisor": "222", "homeassistant": "0.110.0", "hassos": None}, }, ) aioclient_mock.get( "http://127.0.0.1/store", json={ "result": "ok", "data": {"addons": [], "repositories": []}, }, ) aioclient_mock.get( "http://127.0.0.1/host/info", json={ "result": "ok", "data": { "result": "ok", "data": { "chassis": "vm", "operating_system": "Debian GNU/Linux 10 (buster)", "kernel": "4.19.0-6-amd64", }, }, }, ) aioclient_mock.get( "http://127.0.0.1/core/info", json={"result": "ok", "data": {"version_latest": "1.0.0", "version": "1.0.0"}}, ) aioclient_mock.get( "http://127.0.0.1/os/info", json={"result": "ok", "data": {"version_latest": "1.0.0", "version": "1.0.0"}}, ) aioclient_mock.get( "http://127.0.0.1/supervisor/info", json={ "result": "ok", "data": { "result": "ok", "version": "1.0.0",

mock_all

pandas/_testing/asserters.py

TYP: misc return type annotations (#47558)

pandas

Python

asserters.py

def assert_is_sorted(seq) -> None: if isinstance(seq, (Index, Series)): seq = seq.values # sorting does not change precisions assert_numpy_array_equal(seq, np.sort(np.array(seq)))

f538568afc2c76c2d738d32e3544cf9fe6742960

https://github.com/pandas-dev/pandas.git

def assert_is_sorted(seq) -> None: if isinstance(seq, (Index, Series)): seq = seq.values # sorting does not change precisions

assert_is_sorted

django/db/backends/postgresql/operations.py

Refs #33476 -- Reformatted code with Black.

django

Python

operations.py

def subtract_temporals(self, internal_type, lhs, rhs): if internal_type == "DateField": lhs_sql, lhs_params = lhs rhs_sql, rhs_params = rhs params = (*lhs_params, *rhs_params) return "(interval '1 day' * (%s - %s))" % (lhs_sql, rhs_sql), params return super().subtract_temporals(internal_type, lhs, rhs)

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def subtract_temporals(self, internal_type, lhs, rhs):

subtract_temporals

src/transformers/training_args.py

update smddp api to v1.4.0 (#16371) * update smddp api to v1.4.0 * Update src/transformers/trainer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/trainer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * address comments * fix style * remove unused import * fix indent * disable style check for import * fix space Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

transformers

Python

training_args.py

def local_process_index(self): if is_torch_tpu_available(): return xm.get_local_ordinal() elif is_sagemaker_mp_enabled(): return smp.local_rank() elif is_sagemaker_dp_enabled(): return dist.get_rank() elif self.local_rank != -1: return self.local_rank return 0

81ac45f85c35244831f11f73c09ea10eee4f953a

https://github.com/huggingface/transformers.git

def local_process_index(self): if is_torch_tpu_available(): return xm.get_local_ordinal() elif is_sagemaker_mp_enabled(): return smp.local_rank()

local_process_index

nuitka/freezer/Standalone.py

macOS: Massive improvements for dependency scans * Was not recursively scanning dependencies and therefore could miss some of them. * Made internal functions private. * Make sure to pass proper "package" value to DLL scans, so it can include the needed directories. * Do not mutate information of DLL map, it is used later for other things and we now detect errors in that.

Nuitka

Python

Standalone.py

def getScanDirectories(package_name, original_dir): # Many cases, pylint: disable=too-many-branches cache_key = package_name, original_dir if cache_key in _scan_dir_cache: return _scan_dir_cache[cache_key] scan_dirs = [sys.prefix] if package_name is not None: scan_dirs.extend(_getPackageSpecificDLLDirectories(package_name)) if original_dir is not None: scan_dirs.append(original_dir) scan_dirs.extend(getSubDirectories(original_dir)) if ( Utils.isWin32Windows() and package_name is not None and package_name.isBelowNamespace("win32com") ): pywin32_dir = getPyWin32Dir() if pywin32_dir is not None: scan_dirs.append(pywin32_dir) for path_dir in os.environ["PATH"].split(";"): if not os.path.isdir(path_dir): continue if areSamePaths(path_dir, os.path.join(os.environ["SYSTEMROOT"])): continue if areSamePaths(path_dir, os.path.join(os.environ["SYSTEMROOT"], "System32")): continue if areSamePaths(path_dir, os.path.join(os.environ["SYSTEMROOT"], "SysWOW64")): continue scan_dirs.append(path_dir) result = [] # Remove directories that hold no DLLs. for scan_dir in scan_dirs: sys.stdout.flush() # These are useless, but plenty. if os.path.basename(scan_dir) == "__pycache__": continue scan_dir = getDirectoryRealPath(scan_dir) # No DLLs, no use. if not any(entry[1].lower().endswith(".dll") for entry in listDir(scan_dir)): continue result.append(os.path.realpath(scan_dir)) _scan_dir_cache[cache_key] = result return result

a470b75c8e045312ea22dbfb6c5fc6702835b31c

https://github.com/Nuitka/Nuitka.git

def getScanDirectories(package_name, original_dir): # Many cases, pylint: disable=too-many-branches cache_key = package_name, original_dir if cache_key in _scan_dir_cache: return _scan_dir_cache[cache_key] scan_dirs = [sys.prefix] if package_name is not None: scan_dirs.extend(_getPackageSpecificDLLDirectories(package_name)) if original_dir is not None: scan_dirs.append(original_dir) scan_dirs.extend(getSubDirectories(original_dir)) if ( Utils.isWin32Windows() and package_name is not None and package_name.isBelowNamespace("win32com") ): pywin32_dir = getPyWin32Dir() if pywin32_dir is not None: scan_dirs.append(pywin32_dir) for path_dir in os.environ["PATH"].split(";"): if not os.path.isdir(path_dir): continue if areSamePaths(path_dir, os.path.join(os.environ["SYSTEMROOT"])): continue if areSamePaths(path_dir, os.path.join(os.environ["SYSTEMROOT"], "System32")): continue if areSamePaths(path_dir, os.path.join(os.environ["SYSTEMROOT"], "SysWOW64")): continue scan_dirs.append(path_dir) result = [] # Remove directories that hold no DLLs. for scan_dir in scan_dirs: sys.stdout.flush() # These are useless, but plenty. if os.path.basename(scan_dir) == "__pycache__": continue scan_dir = getDirectoryRealPath(scan_dir) # No DLLs, no use. if not any(en

getScanDirectories

pytorch_lightning/plugins/environments/lsf_environment.py

Modify LSFEnvironment to use more reliable environment variable (#10825) Co-authored-by: thomas chaton <thomas@grid.ai> Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

lightning

Python

lsf_environment.py

def _get_node_rank(self) -> int: hosts = self._read_hosts() count: Dict[str, int] = {} for host in hosts: if host not in count: count[host] = len(count) return count[socket.gethostname()]

dbf1acd5a553ffc1546734be164cc89cef2b741d

https://github.com/Lightning-AI/lightning.git

def _get_node_rank(self) -> int: hosts = self._read_hosts() count: Dict[str, int] = {} for host in hosts: if host not in count: count[host] = len(count)

_get_node_rank

dask/dataframe/tests/test_dataframe.py

Add `DataFrame` and `Series` `median` method (#9483)

dask

Python

test_dataframe.py

def test_median_approximate(method): df = pd.DataFrame({"x": range(100), "y": range(100, 200)}) ddf = dd.from_pandas(df, npartitions=10) if PANDAS_GT_110: assert_eq( ddf.median_approximate(method=method), df.median(), atol=1, ) else: result = ddf.median_approximate(method=method) expected = df.median() assert ((result - expected).abs() < 1).all().compute()

142de2608df2494bf11e08038aadddb544b4500c

https://github.com/dask/dask.git

def test_median_approximate(method): df = pd.DataFrame({"x": range(100), "y": range(100, 200)}) ddf = dd.from_pandas(d

test_median_approximate

pandas/tests/series/test_constructors.py

BUG: Series(floatlike, dtype=intlike) inconsistent with non-ndarray data (#45142)

pandas

Python

test_constructors.py

def test_constructor_coerce_float_fail(self, any_int_numpy_dtype): # see gh-15832 # Updated: make sure we treat this list the same as we would treat # the equivalent ndarray vals = [1, 2, 3.5] res = Series(vals, dtype=any_int_numpy_dtype) expected = Series(np.array(vals), dtype=any_int_numpy_dtype) tm.assert_series_equal(res, expected) alt = Series(np.array(vals)) # i.e. we ignore the dtype kwd tm.assert_series_equal(alt, expected)

ad9d42a4c847eb9f341dd6743466a4bed70a0a6e

https://github.com/pandas-dev/pandas.git

def test_constructor_coerce_float_fail(self, any_int_numpy_dtype): # see gh-15832 # Updated: make sure we treat this list the same as we would treat # the equivalent ndarray vals = [1, 2, 3.5] res = Series(vals, dtype=any_in

test_constructor_coerce_float_fail

seaborn/tests/_core/test_subplots.py

Begin removal of data/layers as Plotter attributes

seaborn

Python

test_subplots.py

def test_row_faceted_x_paired(self): x = ["f", "s"] key = "a" order = list("abc") facet_spec = {"variables": {"row": key}, "row_order": order} s = Subplots({}, facet_spec, {"x": x}) assert s.n_subplots == len(order) * len(x) assert s.subplot_spec["ncols"] == len(x) assert s.subplot_spec["nrows"] == len(order) assert s.subplot_spec["sharex"] == "col" assert s.subplot_spec["sharey"] is True

6b61a26a462effaea1c80518e98185abb12174ed

https://github.com/mwaskom/seaborn.git

def test_row_faceted_x_paired(self): x = ["f", "s"] key = "a" order = list("abc") facet_spec = {"variables": {"row": key}, "row_order": order} s = Subplots({}, facet_spec, {"x": x}) assert s.n_subplots == len(order) * le

test_row_faceted_x_paired

python/ccxt/async_support/bittrex.py

1.72.42 [ci skip]

ccxt

Python

bittrex.py

async def fetch_markets(self, params={}): response = await self.publicGetMarkets(params) # # [ # { # "symbol":"LTC-BTC", # "baseCurrencySymbol":"LTC", # "quoteCurrencySymbol":"BTC", # "minTradeSize":"0.01686767", # "precision":8, # "status":"ONLINE", # "OFFLINE" # "createdAt":"2014-02-13T00:00:00Z" # }, # { # "symbol":"VDX-USDT", # "baseCurrencySymbol":"VDX", # "quoteCurrencySymbol":"USDT", # "minTradeSize":"300.00000000", # "precision":8, # "status":"ONLINE", # "OFFLINE" # "createdAt":"2019-05-23T00:41:21.843Z", # "notice":"USDT has swapped to an ERC20-based token as of August 5, 2019." # } # ] # result = [] for i in range(0, len(response)): market = response[i] baseId = self.safe_string(market, 'baseCurrencySymbol') quoteId = self.safe_string(market, 'quoteCurrencySymbol') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) status = self.safe_string(market, 'status') result.append({ 'id': self.safe_string(market, 'symbol'), 'symbol': base + '/' + quote, 'base': base, 'quote': quote, 'settle': None, 'baseId': baseId, 'quoteId': quoteId, 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': (status == 'ONLINE'), 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': { 'price': self.safe_integer(market, 'precision', 8), 'amount': int('8'), }, 'limits': { 'leverage': { 'min': None, 'max': None, }, 'amount': { 'min': self.safe_number(market, 'minTradeSize'), 'max': None, }, 'price': { 'min': None, 'max': None, }, 'cost': { 'min': None, 'max': None, }, }, 'info': market, }) return result

101076f6f891ddfbcc5ba99a7858decf58565537

https://github.com/ccxt/ccxt.git

async def fetch_markets(self, params={}): response = await self.publicGetMarkets(params) # # [ # { # "symbol":"LTC-BTC", # "baseCurrencySymbol":"LTC", # "quot

fetch_markets

django/db/models/base.py

Refs #33476 -- Reformatted code with Black.

django

Python

base.py

def _get_next_or_previous_by_FIELD(self, field, is_next, **kwargs): if not self.pk: raise ValueError("get_next/get_previous cannot be used on unsaved objects.") op = "gt" if is_next else "lt" order = "" if is_next else "-" param = getattr(self, field.attname) q = Q((field.name, param), (f"pk__{op}", self.pk), _connector=Q.AND) q = Q(q, (f"{field.name}__{op}", param), _connector=Q.OR) qs = ( self.__class__._default_manager.using(self._state.db) .filter(**kwargs) .filter(q) .order_by("%s%s" % (order, field.name), "%spk" % order) ) try: return qs[0] except IndexError: raise self.DoesNotExist( "%s matching query does not exist." % self.__class__._meta.object_name )

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def _get_next_or_previous_by_FIELD(self, field, is_next, **kwargs): if not self.pk: raise ValueError("get_next/get_previous cannot be used on unsaved objects.") op = "gt" if is_next else "lt" order = "" if is_next else "-" param = getatt

_get_next_or_previous_by_FIELD

lib/gui/analysis/event_reader.py

Update code to support Tensorflow versions up to 2.8 (#1213) * Update maximum tf version in setup + requirements * - bump max version of tf version in launcher - standardise tf version check * update keras get_custom_objects for tf>2.6 * bugfix: force black text in GUI file dialogs (linux) * dssim loss - Move to stock tf.ssim function * Update optimizer imports for compatibility * fix logging for tf2.8 * Fix GUI graphing for TF2.8 * update tests * bump requirements.txt versions * Remove limit on nvidia-ml-py * Graphing bugfixes - Prevent live graph from displaying if data not yet available * bugfix: Live graph. Collect loss labels correctly * fix: live graph - swallow inconsistent loss errors * Bugfix: Prevent live graph from clearing during training * Fix graphing for AMD

faceswap

Python

event_reader.py

def _parse_outputs(self, event): serializer = get_serializer("json") struct = event.summary.value[0].tensor.string_val[0] config = serializer.unmarshal(struct)["config"] model_outputs = self._get_outputs(config) for side_outputs, side in zip(model_outputs, ("a", "b")): logger.debug("side: '%s', outputs: '%s'", side, side_outputs) layer_name = side_outputs[0][0] output_config = next(layer for layer in config["layers"] if layer["name"] == layer_name)["config"] layer_outputs = self._get_outputs(output_config) for output in layer_outputs: # Drill into sub-model to get the actual output names loss_name = output[0][0] if loss_name[-2:] not in ("_a", "_b"): # Rename losses to reflect the side output new_name = f"{loss_name.replace('_both', '')}_{side}" logger.debug("Renaming loss output from '%s' to '%s'", loss_name, new_name) loss_name = new_name if loss_name not in self._loss_labels: logger.debug("Adding loss name: '%s'", loss_name) self._loss_labels.append(loss_name) logger.debug("Collated loss labels: %s", self._loss_labels)

c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf

https://github.com/deepfakes/faceswap.git

def _parse_outputs(self, event): serializer = get_serializer("json") struct = event.summary.value[0].tensor.string_val[0] config = serializer.unmarshal(struct)["config"] model_outputs = self._get_outputs(config) for side_outputs, side in zip(model_outputs, ("a", "b")): logger.debug("side: '%s', outputs: '%s'", side, side_outputs) layer_name = side_outputs[0][0] output_config = next(layer for layer in config["layers"] if layer["name"] == layer_name)["config"] layer_outputs = self._get_outputs(output_config) for output in layer_outputs: # Drill into sub-model to get the actual output names loss_name = output[0][0] if loss_name[-2:] not in ("_a", "_b"): # Rename losses to reflect the side output new_name = f"{loss_nam

_parse_outputs

airflow/models/baseoperator.py

Add `--map-index` parameter to task CLI commands (#20980)

airflow

Python

baseoperator.py

def get_serialized_fields(cls): if cls.__serialized_fields is None: fields_dict = attr.fields_dict(cls) cls.__serialized_fields = frozenset( fields_dict.keys() - { 'dag', 'deps', 'inherits_from_dummy_operator', 'is_mapped', 'operator_extra_links', 'upstream_task_ids', 'task_type', } | {'template_fields'} ) return cls.__serialized_fields # TODO: Deprecate for Airflow 3.0 Chainable = Union[DependencyMixin, Sequence[DependencyMixin]]

8dabce8887f02216c1037be35e80c214edcbadfe

https://github.com/apache/airflow.git

def get_serialized_fields(cls): if cls.__serialized_fields is None: fields_dict = attr.fields_dict(cls) cls.__serialized_fields = frozenset( fields_dict.keys() - { 'dag', 'deps', 'inherits_from_dummy_operator', 'is_ma

get_serialized_fields

test/test_components.py

Create fewer temp files and make them consistently-named (#2758) * tmp files * components * changes * temp_file_sets * TempFileManager class * added file manager * internal functions * tests * formatting * changes * video tests * added tests for File * cheetah image * formatting * tests for upload button * temp files * formatting * changelog * fixed audio * tmp files * tmp files * gallery * deprecated type=file * fixing tests * patch os.path.exists * fixed test_video_postprocess_converts_to_playable_format * fixed tests * changelog * fix tests * formatting * added a download_if_needed * formatting * fixed download * fixed gallery demo * fix tests * version * fix for mac * consolidate

gradio

Python

test_components.py

def test_video_postprocess_converts_to_playable_format(self): test_file_dir = pathlib.Path(pathlib.Path(__file__).parent, "test_files") # This file has a playable container but not playable codec with tempfile.NamedTemporaryFile( suffix="bad_video.mp4", delete=False ) as tmp_not_playable_vid: bad_vid = str(test_file_dir / "bad_video_sample.mp4") assert not processing_utils.video_is_playable(bad_vid) shutil.copy(bad_vid, tmp_not_playable_vid.name) _ = gr.Video().postprocess(tmp_not_playable_vid.name) # The original video gets converted to .mp4 format full_path_to_output = pathlib.Path(tmp_not_playable_vid.name).with_suffix( ".mp4" ) assert processing_utils.video_is_playable(str(full_path_to_output)) # This file has a playable codec but not a playable container with tempfile.NamedTemporaryFile( suffix="playable_but_bad_container.mkv", delete=False ) as tmp_not_playable_vid: bad_vid = str(test_file_dir / "playable_but_bad_container.mkv") assert not processing_utils.video_is_playable(bad_vid) shutil.copy(bad_vid, tmp_not_playable_vid.name) _ = gr.Video().postprocess(tmp_not_playable_vid.name) full_path_to_output = pathlib.Path(tmp_not_playable_vid.name).with_suffix( ".mp4" ) assert processing_utils.video_is_playable(str(full_path_to_output))

20057aa946b6711ae2928eb0a81c8f00c3b0a7a9

https://github.com/gradio-app/gradio.git

def test_video_postprocess_converts_to_playable_format(self): test_file_dir = pathlib.Path(pathlib.Path(__file__).parent, "test_files") # This file has a playable container but not playable codec with tempfile.NamedTemporaryFile( suffix="bad_video.mp4", delete=False ) as tmp_not_playable_vid: bad_vid = str(test_file_dir / "bad_video_sample.mp4") assert not processing_utils.video_is_playable(bad_vid) shutil.copy(bad_vid, tmp_not_playable_vid.name) _ = gr.Video().postprocess(tmp_not_playable_vid.name) # The original video gets converted to .mp4 format full_path_to_output = pathlib.Path(tmp_not_playable_vid.name).with_suffix( ".mp4" ) assert processing_utils.video_is_playable(str(full_path_to_output)) # This file has a playable codec but not a playable container with tempfile.NamedTemporaryFile( suffix="playable_but_bad_container.mkv", delete=False ) as tmp_not_playable_vid: bad_vid = str(test_file_dir / "playable_but_bad_container.mkv") assert not processing_utils.video_is_playable(bad_vid) shutil.copy(bad_vid, tmp_not_playable_vid.name) _ = gr.Video().postprocess(tmp_not

test_video_postprocess_converts_to_playable_format

pandas/tests/io/parser/test_python_parser_only.py

REGR: index_col False and header=None inferring index names in some cases (#47139)

pandas

Python

test_python_parser_only.py

def test_index_col_false_and_header_none(python_parser_only): # GH#46955 parser = python_parser_only data = result = parser.read_csv(StringIO(data), sep=",", header=None, index_col=False) expected = DataFrame({0: [0.5, 0.1], 1: [0.03, 0.2]}) tm.assert_frame_equal(result, expected)

c50b745a99e644e7c7552f196f49f6269b79e258

https://github.com/pandas-dev/pandas.git

def test_index_col_false_and_header_none(python_parser_only): # GH#46955 parser = python

test_index_col_false_and_header_none

rllib/utils/replay_buffers/tests/test_prioritized_replay_buffer_replay_buffer_api.py

[RLlib] gymnasium support (new `Env.reset()/step()/seed()/render()` APIs). (#28369)

ray

Python

test_prioritized_replay_buffer_replay_buffer_api.py

def _generate_data(self): return SampleBatch( { SampleBatch.T: [np.random.random((4,))], SampleBatch.ACTIONS: [np.random.choice([0, 1])], SampleBatch.REWARDS: [np.random.rand()], SampleBatch.OBS: [np.random.random((4,))], SampleBatch.NEXT_OBS: [np.random.random((4,))], SampleBatch.TERMINATEDS: [np.random.choice([False, True])], SampleBatch.TRUNCATEDS: [np.random.choice([False, False])], } )

8e680c483ce326cefc62e44f68ab1a6948b1c3d2

https://github.com/ray-project/ray.git

def _generate_data(self): return SampleBatch( { SampleBatch.T: [np.random.random((4,))], SampleBatch.ACTIONS: [np.random.choice([0, 1])], SampleBatch.REWARDS: [np.random.rand()], SampleBatch.OBS: [np.random.random((4,))], SampleBatch.NEXT_OBS: [np.random.random((4,))], SampleBatch.TERMINATEDS: [np.random.choice([False, True])], SampleBatch.TRUNCATEDS: [np.random.choice([False, False])], }

_generate_data

sklearn/metrics/cluster/_unsupervised.py

FIX Support integers in silhouette_score for precomputed distances (#22108) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

scikit-learn

Python

_unsupervised.py

def silhouette_samples(X, labels, *, metric="euclidean", **kwds): X, labels = check_X_y(X, labels, accept_sparse=["csc", "csr"]) # Check for non-zero diagonal entries in precomputed distance matrix if metric == "precomputed": error_msg = ValueError( "The precomputed distance matrix contains non-zero " "elements on the diagonal. Use np.fill_diagonal(X, 0)." ) if X.dtype.kind == "f": atol = np.finfo(X.dtype).eps * 100 if np.any(np.abs(np.diagonal(X)) > atol): raise ValueError(error_msg) elif np.any(np.diagonal(X) != 0): # integral dtype raise ValueError(error_msg) le = LabelEncoder() labels = le.fit_transform(labels) n_samples = len(labels) label_freqs = np.bincount(labels) check_number_of_labels(len(le.classes_), n_samples) kwds["metric"] = metric reduce_func = functools.partial( _silhouette_reduce, labels=labels, label_freqs=label_freqs ) results = zip(*pairwise_distances_chunked(X, reduce_func=reduce_func, **kwds)) intra_clust_dists, inter_clust_dists = results intra_clust_dists = np.concatenate(intra_clust_dists) inter_clust_dists = np.concatenate(inter_clust_dists) denom = (label_freqs - 1).take(labels, mode="clip") with np.errstate(divide="ignore", invalid="ignore"): intra_clust_dists /= denom sil_samples = inter_clust_dists - intra_clust_dists with np.errstate(divide="ignore", invalid="ignore"): sil_samples /= np.maximum(intra_clust_dists, inter_clust_dists) # nan values are for clusters of size 1, and should be 0 return np.nan_to_num(sil_samples)

e4015289e0eeb390190ce0d051cee756bc5ecb33

https://github.com/scikit-learn/scikit-learn.git

def silhouette_samples(X, labels, *, metric="euclidean", **kwds): X, labels = check_X_y(X, labels, accept_sparse=["csc", "csr"]) # Check for non-zero diagonal entries in precomputed distance matrix if metric == "precomputed": error_msg = ValueError( "The precomputed distance matrix contains non-zero " "elements on the diagonal. Use np.fill_diagonal(X, 0)." ) if X.dtype.kind == "f": atol = np.finfo(X.dtype).eps * 100 if np.any(np.abs(np.diagonal(X)) > atol): raise ValueError(error_msg) elif np.any(np.diagonal(X) != 0): # integral dtype raise ValueError(error_msg) le = LabelEncoder() labels

silhouette_samples

tests/speech_encoder_decoder/test_modeling_flax_speech_encoder_decoder.py

use scale=1.0 in floats_tensor called in speech model testers (#17007) Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

transformers

Python

test_modeling_flax_speech_encoder_decoder.py

def get_pretrained_model_and_inputs(self): model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained( "facebook/wav2vec2-large-lv60", "gpt2-medium" ) batch_size = 13 input_values = floats_tensor([batch_size, 512], scale=1.0) attention_mask = random_attention_mask([batch_size, 512]) decoder_input_ids = ids_tensor([batch_size, 4], model.config.decoder.vocab_size) decoder_attention_mask = random_attention_mask([batch_size, 4]) inputs = { "inputs": input_values, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, } return model, inputs

e952e049b4fbb5d3e2ba6a140f10fb4049dd8654

https://github.com/huggingface/transformers.git

def get_pretrained_model_and_inputs(self): model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained( "facebook/wav2vec2-large-lv60", "gpt2-medium" ) batch_size = 13 input_values = floats_tensor([batch_size, 512], scale=1.0)

get_pretrained_model_and_inputs

tests/sentry/relay/test_config.py

test: Add missing tests to sentry/relay/config/__init__.py [TET-504] (#41058) This PR increase code coverage from ~82% upto 98% in sentry/relay/config/__init__.py. codecov [report](https://app.codecov.io/gh/getsentry/sentry/pull/41058): <img width="1060" alt="image" src="https://user-images.githubusercontent.com/1374633/200516881-ed23da43-37df-4fc2-b291-310fc13f0ff5.png">

sentry

Python

test_config.py

def test_project_config_setattr(default_project): project_cfg = ProjectConfig(default_project) with pytest.raises(Exception) as exc_info: project_cfg.foo = "bar" assert str(exc_info.value) == "Trying to change read only ProjectConfig object" @pytest.mark.django_db

4821e6846b007cce0092f43141e4b436beb2bedc

@pytest.mark.django_db

https://github.com/getsentry/sentry.git

def test_project_config_setattr(default_project): project_cfg = ProjectConfig(default_project) with pytest.raises(Ex

test_project_config_setattr

keras/distribute/distribute_coordinator_utils.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

distribute_coordinator_utils.py

def _is_chief(self): if not self._cluster_spec or self._task_type in [ _TaskType.CHIEF, _TaskType.EVALUATOR, None, ]: return True # If not local and chief not in the cluster_spec, use the first worker as # chief. if ( _TaskType.CHIEF not in self._cluster_spec.jobs and self._task_type == _TaskType.WORKER and self._task_id == 0 ): return True return False

84afc5193d38057e2e2badf9c889ea87d80d8fbf

https://github.com/keras-team/keras.git

def _is_chief(self): if not self._cluster_spec or self._task_type in [

_is_chief

erpnext/patches/v14_0/remove_india_localisation.py

fix: unlink custom fields in patch

erpnext

Python

remove_india_localisation.py

def unlink_custom_fields(): frappe.db.set_value( "Custom Field", {"dt": "Item", "fieldname": "gst_hsn_code"}, {"fieldtype": "Data", "options": ""}, )

70c4117c22df5ab3efda0be842452cabc2f9aab9

https://github.com/frappe/erpnext.git

def unlink_custom_fields(): frappe.db.set_value( "Custom Field", {"dt": "Item", "fieldname": "gst_hsn_code"}, {"fieldtype": "Data", "options": ""}, )

unlink_custom_fields

sklearn/mixture/tests/test_gaussian_mixture.py

MNT Update black to stable version (#22474)

scikit-learn

Python

test_gaussian_mixture.py

def test_gaussian_suffstat_sk_spherical(): # computing spherical covariance equals to the variance of one-dimension # data after flattening, n_components=1 rng = np.random.RandomState(0) n_samples, n_features = 500, 2 X = rng.rand(n_samples, n_features) X = X - X.mean() resp = np.ones((n_samples, 1)) nk = np.array([n_samples]) xk = X.mean() covars_pred_spherical = _estimate_gaussian_covariances_spherical(resp, X, nk, xk, 0) covars_pred_spherical2 = np.dot(X.flatten().T, X.flatten()) / ( n_features * n_samples ) assert_almost_equal(covars_pred_spherical, covars_pred_spherical2) # check the precision computation precs_chol_pred = _compute_precision_cholesky(covars_pred_spherical, "spherical") assert_almost_equal(covars_pred_spherical, 1.0 / precs_chol_pred**2)

1fc86b6aacd89da44a3b4e8abf7c3e2ba4336ffe

https://github.com/scikit-learn/scikit-learn.git

def test_gaussian_suffstat_sk_spherical(): # computing spherical covariance equals to the variance of one-dimension # data after flattening, n_components=1 rng = np.random.RandomState(0) n_samples, n_features = 500, 2 X = rng.rand(n_samples, n_features) X = X - X.mean() resp = np.ones((n_samples, 1)) nk = np.array([n_samples]) xk = X.mean() covars_pred_spherical = _estimate_gaussian_covariances_spherical(resp, X, nk, xk, 0) covars_pred_spherical2 = np.dot(X.flatten().T, X.flatten()) / ( n_features * n_samples ) assert_almost_equal(covars_pred_spherical, covars_pred_spherical2) # check the precisio

test_gaussian_suffstat_sk_spherical

tests/utils/test_db.py

Enhance `db upgrade` args (#22102) Make `db upgrade` args more like `db downgrade`. ``` usage: airflow db upgrade [-h] [--from-revision FROM_REVISION] [--from-version FROM_VERSION] [-r REVISION] [-s] [-n VERSION] Upgrade the schema of the metadata database. To print but not execute commands, use option ``--show-sql-only``. If using options ``--from-revision`` or ``--from-version``, you must also use ``--show-sql-only``, because if actually *running* migrations, we should only migrate from the *current* revision. optional arguments: -h, --help show this help message and exit --from-revision FROM_REVISION (Optional) If generating sql, may supply a *from* revision --from-version FROM_VERSION (Optional) If generating sql, may supply a *from* version -r REVISION, --revision REVISION (Optional) The airflow revision to upgrade to. Note: must provide either `--revision` or `--version`. -s, --show-sql-only Don't actually run migrations; just print out sql scripts for offline migration. Required if using either `--from-version` or `--from-version`. -n VERSION, --version VERSION (Optional) The airflow version to upgrade to. Note: must provide either `--revision` or `--version`. ```

airflow

Python

test_db.py

def test_offline_upgrade_revision(self, from_revision, to_revision): with mock.patch('airflow.utils.db.settings.engine.dialect'): with mock.patch('alembic.command.upgrade') as mock_alembic_upgrade: upgradedb(from_revision=from_revision, to_revision=to_revision, show_sql_only=True) mock_alembic_upgrade.assert_called_once_with(mock.ANY, f"{from_revision}:{to_revision}", sql=True)

3452f7ce45607af04bade5e5edebaa18fdc13819

https://github.com/apache/airflow.git

def test_offline_upgrade_revision(self, from_revision, to_revision): with mock.p

test_offline_upgrade_revision

python/ray/tune/trial.py

[Tune] Logging of bad results dict keys (#23954) [User complains](https://discuss.ray.io/t/which-attributes-can-be-used-in-checkpoint-score-attr-when-using-tune-run/5826) about logging on failure of locating `checkpoint_score_attr ` in results dict not being informative. I propose that we log the actual results dict keys and extended stopping criteria, which imho should not log the whole result dict as this might contain tensors. Maybe there are other similar cases in tune library, in which I don't know my way around that good.

ray

Python

trial.py

def should_stop(self, result): if result.get(DONE): return True for criteria, stop_value in self.stopping_criterion.items(): if criteria not in result: raise TuneError( "Stopping criteria {} not provided in result dict. Keys " "are {}.".format(criteria, list(result.keys())) ) elif isinstance(criteria, dict): raise ValueError( "Stopping criteria is now flattened by default. " "Use forward slashes to nest values `key1/key2/key3`." ) elif result[criteria] >= stop_value: return True return False

bc8742792cde5be62e22add01686a9c539e0f465

https://github.com/ray-project/ray.git

def should_stop(self, result): if result.get(DONE): return True for criteria, stop_value in self.stopping_criterion.items(): if criteria not in result: raise TuneError( "Stopping criteria {} not provided in result dict. Keys " "are {}.".format(criteria, list(result.keys()))

should_stop

mindsdb/migrations/versions/2022-08-25_6a54ba55872e_view_integration.py

removed integration_id from view (is already absent in db.py)

mindsdb

Python

2022-08-25_6a54ba55872e_view_integration.py

def downgrade(): # ### commands auto generated by Alembic - please adjust! ### with op.batch_alter_table('view', schema=None) as batch_op: batch_op.add_column(sa.Column('integration_id', sa.INTEGER(), autoincrement=False, nullable=False)) batch_op.create_foreign_key('fk_integration_id', 'integration', ['integration_id'], ['id']) # ### end Alembic commands ###

12c18196c71dee5b16b7c8ddcfe9a0bdffbf8440

https://github.com/mindsdb/mindsdb.git

def downgrade(): # ### commands auto generated by Alembic - please adjust! ### with op.batch_alter_table('view', schema=None) as batch_op: batch_op.add_column(sa.Column('integration_id', sa.INTEGER(), autoincrement=False, nullable=False)) batch_op.create_foreign_key('fk_integrati

downgrade

python/ray/tune/tests/test_sync.py

[tune] Fast path for `sync_dir_between_nodes` (#24958) This PR adds a fast path for `sync_dir_between_nodes` that gets triggered if both source IP and target IP are the same. It uses simple `shutil` operations instead of packing and unpacking to improve performance.

ray

Python

test_sync.py

def _prepareDirForTestSyncRemoteTask(self): temp_source = tempfile.mkdtemp() temp_up_target = tempfile.mkdtemp() temp_down_target = tempfile.mkdtemp() self.addCleanup(shutil.rmtree, temp_source) self.addCleanup(shutil.rmtree, temp_up_target) self.addCleanup(shutil.rmtree, temp_down_target) os.makedirs(os.path.join(temp_source, "A", "a1")) os.makedirs(os.path.join(temp_source, "A", "a2")) os.makedirs(os.path.join(temp_source, "B", "b1")) with open(os.path.join(temp_source, "level_0.txt"), "wt") as fp: fp.write("Level 0\n") with open(os.path.join(temp_source, "A", "level_a1.txt"), "wt") as fp: fp.write("Level A1\n") with open(os.path.join(temp_source, "A", "a1", "level_a2.txt"), "wt") as fp: fp.write("Level A2\n") with open(os.path.join(temp_source, "B", "level_b1.txt"), "wt") as fp: fp.write("Level B1\n") return temp_source, temp_up_target, temp_down_target

a25235a2c2d8882b7793cb7e5010764aa4adc999

https://github.com/ray-project/ray.git

def _prepareDirForTestSyncRemoteTask(self): temp_source = tempfile.mkdtemp() temp_up_target = tempfile.mkdtemp() temp_down_target = tempfile.mkdtemp() self.addCleanup(shutil.rmtree, temp_source) self.addCleanup(shutil.rmtree, temp_up_target) self.addCleanup(shutil.rmtree, temp_down_target) os.makedirs(os.path.join(temp_source, "A", "a1"))

_prepareDirForTestSyncRemoteTask

sympy/printing/tests/test_julia.py

Add extra spaces in julia_code() printing.

sympy

Python

test_julia.py

def test_1_over_x_and_sqrt(): # 1.0 and 0.5 would do something different in regular StrPrinter, # but these are exact in IEEE floating point so no different here. assert julia_code(1/x) == '1 ./ x' assert julia_code(x**-1) == julia_code(x**-1.0) == '1 ./ x' assert julia_code(1/sqrt(x)) == '1 ./ sqrt(x)' assert julia_code(x**-S.Half) == julia_code(x**-0.5) == '1 ./ sqrt(x)' assert julia_code(sqrt(x)) == 'sqrt(x)' assert julia_code(x**S.Half) == julia_code(x**0.5) == 'sqrt(x)' assert julia_code(1/pi) == '1 / pi' assert julia_code(pi**-1) == julia_code(pi**-1.0) == '1 / pi' assert julia_code(pi**-0.5) == '1 / sqrt(pi)'

4c22fad1d280711c2a868cabb7d2dbd90c1ac052

https://github.com/sympy/sympy.git

def test_1_over_x_and_sqrt(): # 1.0 and 0.5 would do something different in regular StrPrinter, # but these are exact in IEEE floating point so no different here. assert julia_code(1/x) == '1 ./ x' assert julia_code(x**-1) == julia_code(x**-1.0) == '1 ./ x' assert julia_code(1/sqrt(x)) == '1 ./ sqrt(x)' assert julia_code(x**-S.Half) == julia_code(x**-0.5) == '1 ./ sqrt(x)' assert julia_code(sqrt(x)) == 'sqrt(x)' assert julia_code(x**S.Half) == julia_code(x**0.

test_1_over_x_and_sqrt

tests/components/homekit_controller/test_lock.py

Improve homekit_controller tests (#65266)

core

Python

test_lock.py

async def test_switch_change_lock_state(hass, utcnow): helper = await setup_test_component(hass, create_lock_service) await hass.services.async_call( "lock", "lock", {"entity_id": "lock.testdevice"}, blocking=True ) helper.async_assert_service_values( ServicesTypes.LOCK_MECHANISM, { CharacteristicsTypes.LOCK_MECHANISM_TARGET_STATE: 1, }, ) await hass.services.async_call( "lock", "unlock", {"entity_id": "lock.testdevice"}, blocking=True ) helper.async_assert_service_values( ServicesTypes.LOCK_MECHANISM, { CharacteristicsTypes.LOCK_MECHANISM_TARGET_STATE: 0, }, )

58b8c30221a6f6e5acbbe98b7e3298b03fb741f5

https://github.com/home-assistant/core.git

async def test_switch_change_lock_state(hass, utcnow): helper = await setup_test_component(hass, create_lock_service) await hass.services.async_call( "lock", "lock", {"entity_id": "lock.testdevice"}, blocking=True ) helper.async_assert_service_values(

test_switch_change_lock_state

tests/openbb_terminal/stocks/dark_pool_shorts/test_finra_model.py

Here we merge all API Refactor related branches (#2236) * Update api.py * Updated forex menu * refactor ycrv command * refactor ycrv command black * refactor ecocal command * Minh changes * Adding space to test pushing * title fix ecocal df * get economic calendar annotation * fix investingcom tests * refactor index command * refactor overview command * give defaults to wsj view function args * rename date args investincom * refacto bigmac command * fix ecocal typo * refactor rtps command * alphavantage gdp * alphavantage gdp per capita * alphavantage cpi * alphavantage tyld * alphavantage inf * refactor macro command * refactor macro command w helpers * refactor treasury command * fix macro on terminal * treasury labels * refactor maturities * update treasury maturities doc strings * refactor get economic calendar finhub * refactor map command api * display map filter choices * route economy api to performance map * route economy api to performance map * display group choices on valuation command * refactor performance and valuation commands * refactor spectrum model and view * add choices to spectrum controller * delete image after view * fix model tests finviz * fix finciz view tests * refactor futures * fix some tests * fix more tests * fix controller test * refactor fred series notes * update fred notes docstring * refacto fred series ids * fix pred and qa when empty datasets * refactor fred * uncomment stuff * refacto get series data * fix some tests * set defaults on args * refactor fred yield curve * black * fix spell and remove ecocal names * fix linting * linting * pylint fix * change dangerous defaults * Working through crypto fixes (#2256) * Working through crypto fixes * Continued adding crypto stuff * Added crypto overview * Added test fixes * Added fixtures * Fixed tests * Fixed charting issue * Removed broken APIs * Final adjustments * Added test fixes * map get groups and get ycrv countries into old api * exposed econdb helper funcs * remove helpers * refactor search indices * linting * refactor arg currency * pylint from currency * Started switching crpyto ascending to ascend * Merging * Portfolio model arguements, params, and docstring * Refactored for etf commands (#2292) * Refactored for etf commands * Fixed tests * Added load command * Fixed menu * Portfolio logic fixes * Added econometrics (#2260) * Added econometrics * Fixed tests * Simplified API * Added test fixes * Added test csv * Allowed examples to be loaded * Fund refactor (#2291) * Fund refactor * Changed fund_name and fund to name * Changed ascending to ascend * Stock menu refactoring for easier API usage (#2194) * Stocks refactoring for easier API usage * Linting * Refactor newly added features * Linting * Fixing tests * Refactor common files used by stocks menu * Fixing flake8 * Fix linting and tests * Linting * Fix flake8 * refactor insider_data * refactor mentions * refactor watchlist * refactor sentiment * refactor sentiment * fix yahoofinance tests * refactor load and candle * refactor get_news and display_news * refactor stocks.ins.act * candle default matplotlib * fix yahoofinance_view tests * fix ark model tests * fix ark view tests * fix business insider model * fix business insider view * refactor csimarket model * fix tests csi market model * update dd controller * fix get suppliers tests * fix dd controller tests * fix finhub tests * fix finviz tests * fix fmp tests * fix marketwatch tests * corrected argument keywords in test_bt_model * corrected argument keywords in test_bt_view * refactor fa controller * refactor marketwatch view * refactor gov controller * fix tests fa av * fix tests elect * fix dcf tests * fix polygon tests * fix fmp tests * fix quiverquant tests * fix yahoofinance fa tests * fix more fa tests * fix insider tests * fix more tests * fix more tests * fix options tests * fix stock gov tests * fix tests test_ba_controller * fix tests for test_finviz_compare_model.py * fixed 2 tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fix final tests * fixed tests * fixed tests * Fix tests * black * forgot to black tests * fixed tests * fixed tests * fixed tests * fixed tests * flakefix * Tests + code : Stocks / Discovery * fix tests * added recorder * fixed tests * fixed tests * black * black * remove unused imports * refactor display raw * sia dicts fix * pylint * linting * remove dangerous default * fix tests * fix beta model test * black * skip screener qa test * change sector path to sectors * update tests readme * fix metric defaults * black * substitute lost ticker * defaults cpic * another round on sia * refactor cramer * reduce default tweets on sentiment * refactor yf hist, corr, volume * arkorders default * refactor income, balance, cashflow * refacto scorr, screener, getfinnhub * refactor stockgrid * ibkr refactor * another round on stockgrid * add dividens end point * refactor discovery endpoints * update docstrings with similar input * refactor messages * refactor ba * refactor regioons * refactor twitter sentiment * refactor hist * refactor regions * give default to timeframe * refactor bunch of defaults and arg names * remove leftover imports * refactor vwap * let tests run * fix tests * fix stock tests * fix stockanalysis tests * flake * MYPY * Made important changes * added fixes * Fixed big issue * Added fixes to tests * fix qa tests * fix tests * fix 1 more test * last stocks failing * fix crypto test Co-authored-by: Chavithra PARANA <chavithra@gmail.com> Co-authored-by: montezdesousa <montezdesousa@gmail.com> Co-authored-by: hjoaquim <h.joaquim@campus.fct.unl.pt> Co-authored-by: montezdesousa <79287829+montezdesousa@users.noreply.github.com> Co-authored-by: colin99d <colin99delahunty@gmail.com> * fix portfolio tests * change period to window * update ca docstrings * refactor get_similar_companies func * Fixed * Update CI * Update CI 2 * Update CI 3 * Update dependencies Co-authored-by: colin99d <colin99delahunty@gmail.com> Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com> Co-authored-by: montezdesousa <montezdesousa@gmail.com> Co-authored-by: James Simmons <simmonsj330@gmail.com> Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com> Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com> Co-authored-by: montezdesousa <79287829+montezdesousa@users.noreply.github.com> Co-authored-by: northern-64bit <75195383+northern-64bit@users.noreply.github.com> Co-authored-by: hjoaquim <h.joaquim@campus.fct.unl.pt>

OpenBBTerminal

Python

test_finra_model.py

def test_getATSdata(recorder): df_ats, d_ats_reg = finra_model.getATSdata( limit=2, tier_ats="T1", ) d_ats_reg = {k: round(v, 9) for k, v in d_ats_reg.items()} recorder.capture_list([df_ats, d_ats_reg])

9e1a58e2dbedec4e4a9f9c2e32ddf091776c606b

https://github.com/OpenBB-finance/OpenBBTerminal.git

def test_getATSdata(recorder): df_ats, d_ats_reg = finra_model.getATSdata( limit=2, tier_ats="T1", ) d_ats_reg = {k: round(v, 9) for k, v in d_ats_reg.items()} recorder.capture_list([df

test_getATSdata

jax/_src/lax/linalg.py

Add support for Hessenberg and tridiagonal matrix reductions on CPU. * Implement jax.scipy.linalg.hessenberg and jax.lax.linalg.hessenberg. * Export what was previously jax._src.lax.linalg.orgqr as jax.lax.linalg.householder_product, since it can be used with some minor tweaks to compute the unitary matrix of a Hessenberg reduction. * Implement jax.lax.linalg.tridiagonal, which is the symmetric (Hermitian) equivalent of Hessenberg reduction. None of these primitives are differentiable at the moment. PiperOrigin-RevId: 487224934

jax

Python

linalg.py

def _triangular_solve_shape_rule(a, b, *, left_side=False, **unused_kwargs): if a.ndim < 2: msg = "triangular_solve requires a.ndim to be at least 2, got {}." raise TypeError(msg.format(a.ndim)) if b.ndim < 2: msg = "triangular_solve requires b.ndim to be at least 2, got {}." raise TypeError(msg.format(b.ndim)) if a.shape[-1] != a.shape[-2]: msg = ("triangular_solve requires the last two dimensions of a to be equal " "in size, got a.shape of {}.") raise TypeError(msg.format(a.shape)) if a.shape[:-2] != b.shape[:-2]: msg = ("triangular_solve requires both arguments to have the same number " "of dimensions and equal batch dimensions, got {} and {}.") raise TypeError(msg.format(a.shape, b.shape)) common_dim = -2 if left_side else -1 if a.shape[-1] != b.shape[common_dim]: msg = "Incompatible shapes for arguments to triangular_solve: {} and {}." raise TypeError(msg.format(a.shape, b.shape)) return b.shape

1cead779a3abd18066195919fc5693a15cfa9070

https://github.com/google/jax.git

def _triangular_solve_shape_rule(a, b, *, left_side=False, **unused_kwargs): if a.ndim < 2: msg = "triangular_solve requires a.ndim to be at least 2, got {}." raise TypeError(msg.format(a.ndim)) if b.ndim < 2: msg = "triangular_solve requires b.ndim to be at least 2, got {}." raise TypeError(msg.format(b.ndim)) if a.shape[-1] != a.shape[-2]: msg = ("triangular_solve requires the last two dimensions of a to be equal " "in size, got a.shape of {}.") raise TypeError(msg.format(a.shape)) if a.shape[:-2] != b.shape[:-2]: msg = ("triangular_solve requires both arguments to have the same number " "of dimensions and equal batch dimensions, got {} and {}.") raise TypeError(msg.format(a.shape, b.shape)) common_dim = -2 if left_side else -1 if a.shape[-1] != b.shape[common_dim]: msg = "Incompatible shapes for arguments to triangular_solve: {} and {}." raise TypeError(msg.format(a.shape, b.shape)) return b.shape

triangular_solve_shape_rule

dask/array/percentile.py

absolufy-imports - No relative - PEP8 (#8796) Conversation in https://github.com/dask/distributed/issues/5889

dask

Python

percentile.py

def merge_percentiles(finalq, qs, vals, method="lower", Ns=None, raise_on_nan=True): from dask.array.utils import array_safe if isinstance(finalq, Iterator): finalq = list(finalq) finalq = array_safe(finalq, like=finalq) qs = list(map(list, qs)) vals = list(vals) if Ns is None: vals, Ns = zip(*vals) Ns = list(Ns) L = list(zip(*[(q, val, N) for q, val, N in zip(qs, vals, Ns) if N])) if not L: if raise_on_nan: raise ValueError("No non-trivial arrays found") return np.full(len(qs[0]) - 2, np.nan) qs, vals, Ns = L # TODO: Perform this check above in percentile once dtype checking is easy # Here we silently change meaning if vals[0].dtype.name == "category": result = merge_percentiles( finalq, qs, [v.codes for v in vals], method, Ns, raise_on_nan ) import pandas as pd return pd.Categorical.from_codes(result, vals[0].categories, vals[0].ordered) if not np.issubdtype(vals[0].dtype, np.number): method = "nearest" if len(vals) != len(qs) or len(Ns) != len(qs): raise ValueError("qs, vals, and Ns parameters must be the same length") # transform qs and Ns into number of observations between percentiles counts = [] for q, N in zip(qs, Ns): count = np.empty_like(finalq, shape=len(q)) count[1:] = np.diff(array_safe(q, like=q[0])) count[0] = q[0] count *= N counts.append(count) # Sort by calculated percentile values, then number of observations. combined_vals = np.concatenate(vals) combined_counts = array_safe(np.concatenate(counts), like=combined_vals) sort_order = np.argsort(combined_vals) combined_vals = np.take(combined_vals, sort_order) combined_counts = np.take(combined_counts, sort_order) # percentile-like, but scaled by total number of observations combined_q = np.cumsum(combined_counts) # rescale finalq percentiles to match combined_q finalq = array_safe(finalq, like=combined_vals) desired_q = finalq * sum(Ns) # the behavior of different interpolation methods should be # investigated further. if method == "linear": rv = np.interp(desired_q, combined_q, combined_vals) else: left = np.searchsorted(combined_q, desired_q, side="left") right = np.searchsorted(combined_q, desired_q, side="right") - 1 np.minimum(left, len(combined_vals) - 1, left) # don't exceed max index lower = np.minimum(left, right) upper = np.maximum(left, right) if method == "lower": rv = combined_vals[lower] elif method == "higher": rv = combined_vals[upper] elif method == "midpoint": rv = 0.5 * (combined_vals[lower] + combined_vals[upper]) elif method == "nearest": lower_residual = np.abs(combined_q[lower] - desired_q) upper_residual = np.abs(combined_q[upper] - desired_q) mask = lower_residual > upper_residual index = lower # alias; we no longer need lower index[mask] = upper[mask] rv = combined_vals[index] else: raise ValueError( "interpolation method can only be 'linear', 'lower', " "'higher', 'midpoint', or 'nearest'" ) return rv

cccb9d8d8e33a891396b1275c2448c352ef40c27

https://github.com/dask/dask.git

def merge_percentiles(finalq, qs, vals, method="lower", Ns=None, raise_on_nan=True): from dask.array.utils import array_safe if isinstance(finalq, Iterator): finalq = list(finalq) finalq = array_safe(finalq, like=finalq) qs = list(map(list, qs)) vals = list(vals) if Ns is None: vals, Ns = zip(*vals) Ns = list(Ns) L = list(zip(*[(q, val, N) for q, val, N in zip(qs, vals, Ns) if N])) if not L: if raise_on_nan: raise ValueError("No non-trivial arrays found") return np.full(len(qs[0]) - 2, np.nan) qs, vals, Ns = L # TODO: Perform this check above in percentile once dtype checking is easy # Here we silently change meaning if vals[0].dtype.name == "category": result = merge_percentiles( finalq, qs, [v.codes for v in vals], method, Ns, raise_on_nan

merge_percentiles

awx/main/tasks/receptor.py

Refactored tasks.py to a package --- Added 3 new sub-package : awx.main.tasks.system , awx.main.tasks.jobs , awx.main.tasks.receptor --- Modified the functional tests and unit tests accordingly

awx

Python

receptor.py

def get_receptor_sockfile(): with open(__RECEPTOR_CONF, 'r') as f: data = yaml.safe_load(f) for section in data: for entry_name, entry_data in section.items(): if entry_name == 'control-service': if 'filename' in entry_data: return entry_data['filename'] else: raise RuntimeError(f'Receptor conf {__RECEPTOR_CONF} control-service entry does not have a filename parameter') else: raise RuntimeError(f'Receptor conf {__RECEPTOR_CONF} does not have control-service entry needed to get sockfile')

a4a3ba65d736045733cb49430d7076b73aec23bb

https://github.com/ansible/awx.git

def get_receptor_sockfile(): with open(__RECEPTOR_CONF, 'r') as f: data = yaml.safe_load(f) for section in data: for entry_name, entry_data in sectio

get_receptor_sockfile

psutil/tests/test_linux.py

[Linux] Speedup `Process.full_memory_info()` (#2108) `Process.memory_full_info()` (reporting proecss USS/PSS/Swap memory) now reads ``/proc/pid/smaps_rollup`` instead of ``/proc/pids/smaps`` which makes it 5 times faster. Without patch: ``` ~/svn/psutil {linux-smaps-rollup}$ python3 -m timeit -s "import psutil; p = psutil.Process()" "p.memory_full_info()" 500 loops, best of 5: 518 usec per loop ``` With patch (5 times faster): ``` ~/svn/psutil {linux-smaps-rollup}$ python3 -m timeit -s "import psutil; p = psutil.Process()" "p.memory_full_info()" 2000 loops, best of 5: 111 usec per loop ``` ---- `make test-memleaks` suite, who heavily rely on `Process.memory_full_info()`, also received a nice speedup: Before patch: ``` $ make test-memleaks ---------------------------------------------------------------------- Ran 99 tests in 1.646s OK (skipped=9) SUCCESS ``` After patch: ``` $ make test-memleaks ---------------------------------------------------------------------- Ran 99 tests in 1.195s OK (skipped=9) SUCCESS ```

psutil

Python

test_linux.py

def test_parse_smaps_mocked(self): # See: https://github.com/giampaolo/psutil/issues/1222 with mock_open_content( "/proc/%s/smaps" % os.getpid(), textwrap.dedent().encode()) as m: p = psutil._pslinux.Process(os.getpid()) uss, pss, swap = p._parse_smaps() assert m.called self.assertEqual(uss, (6 + 7 + 14) * 1024) self.assertEqual(pss, 3 * 1024) self.assertEqual(swap, 15 * 1024) # On PYPY file descriptors are not closed fast enough.

f1f299527634a425cb34b621d6201fa9172d3529

https://github.com/giampaolo/psutil.git

def test_parse_smaps_mocked(self): # See: https://github.com/giampaolo/psutil/issues/1222 with mock_open_content( "/proc/%s/smaps" % os.getpid(), textwrap.dedent().encode()) as m: p = psutil._pslinux.Process(os.getpid()) uss, pss, swap = p._parse_smaps() assert m.called

test_parse_smaps_mocked

rllib/algorithms/algorithm_config.py

[RLlib] AlgorithmConfig: Next steps (volume 01); Algos, RolloutWorker, PolicyMap, WorkerSet use AlgorithmConfig objects under the hood. (#29395)

ray

Python

algorithm_config.py

def __getitem__(self, item): # TODO: Uncomment this once all algorithms use AlgorithmConfigs under the # hood (as well as Ray Tune). # if log_once("algo_config_getitem"): # logger.warning( # "AlgorithmConfig objects should NOT be used as dict! " # f"Try accessing `{item}` directly as a property." # ) item = self._translate_special_keys(item) return getattr(self, item)

182744bbd151c166b8028355eae12a5da63fb3cc

https://github.com/ray-project/ray.git

def __getitem__(self, item): # TODO: Uncomment this once all algorithms use AlgorithmConfigs under the # hood (as well as Ray Tune). # if log_once("algo_config_getitem"): # logger.warning( # "AlgorithmConfig

__getitem__

keras/saving/experimental/serialization_lib_test.py

Support lambdas in new serialization. PiperOrigin-RevId: 491075544

keras

Python

serialization_lib_test.py

def test_custom_fn(self): obj = {"activation": custom_fn} serialized, _, reserialized = self.roundtrip( obj, custom_objects={"custom_fn": custom_fn} ) self.assertEqual(serialized, reserialized) # Test inside layer dense = keras.layers.Dense(1, activation=custom_fn) dense.build((None, 2)) _, new_dense, _ = self.roundtrip( dense, custom_objects={"custom_fn": custom_fn} ) x = tf.random.normal((2, 2)) y1 = dense(x) _ = new_dense(x) new_dense.set_weights(dense.get_weights()) y2 = new_dense(x) self.assertAllClose(y1, y2, atol=1e-5)

b3f12f1acc0a599e9aa61349c7e1b4e3afcdd932

https://github.com/keras-team/keras.git

def test_custom_fn(self): obj = {"activation": custom_fn} serialized, _, reserialized = self.roun

test_custom_fn

lib/matplotlib/axes/_base.py

DOC: More cleanup axes -> Axes

matplotlib

Python

_base.py

def tick_params(self, axis='both', **kwargs): _api.check_in_list(['x', 'y', 'both'], axis=axis) if axis in ['x', 'both']: xkw = dict(kwargs) xkw.pop('left', None) xkw.pop('right', None) xkw.pop('labelleft', None) xkw.pop('labelright', None) self.xaxis.set_tick_params(**xkw) if axis in ['y', 'both']: ykw = dict(kwargs) ykw.pop('top', None) ykw.pop('bottom', None) ykw.pop('labeltop', None) ykw.pop('labelbottom', None) self.yaxis.set_tick_params(**ykw)

f156db08eee54d285ab0fb4e031e48d078ba6aa3

https://github.com/matplotlib/matplotlib.git

def tick_params(self, axis='both', **kwargs): _api.check_in_list(['x', 'y', 'both'], axis=axis) if axis in ['x', 'both']: xkw = dict(kwargs) xkw.pop('left', None) xkw.pop('right', None) xkw.pop('labelleft', None) xkw.pop('labelright', None) self.xaxis.set_tick_params(**xkw) if axis in ['y', 'both']: ykw = dict(kwargs) ykw.pop('top', None) ykw.pop('bottom', None) ykw.pop('labeltop', None) ykw.pop('labelbottom', None) self.yaxis.set_tick_params(**ykw)

tick_params

netbox/dcim/views.py

Check that device has a platform set before rendering napalm tab

netbox

Python

views.py

def render(self, instance): # Display NAPALM tabs only for devices which meet certain requirements if not ( instance.status == 'active' and instance.primary_ip and instance.platform and instance.platform.napalm_driver ): return None return super().render(instance) @register_model_view(Device, 'status')

977b79ecee4d1d8054c0fd9528c563376fe3bcd9

@register_model_view(Device, 'status')

https://github.com/netbox-community/netbox.git

def render(self, instance): # Display NAPALM tabs only for devices which meet certain requirements if not ( instance.status == 'active' and instance.primary_ip and instance.platform and instance.platform.napalm_driver

render

tests/sentry/api/endpoints/test_organization_metric_details.py

ref(metrics-indexer): Change bulk_record, record signatures (#32811) * ref(metrics-indexer): Change bulk_record, record signatures

sentry

Python

test_organization_metric_details.py

def test_same_entity_multiple_metric_ids(self): self.store_session( self.build_session( project_id=self.project.id, started=(time.time() // 60) * 60, status="ok", release="foobar@2.0", errors=2, ) ) response = self.get_response( self.organization.slug, "derived_metric.multiple_metrics", ) assert response.status_code == 404 assert response.json()["detail"] == ( "Not all the requested metrics or the constituent metrics in " "['derived_metric.multiple_metrics'] have data in the dataset" ) org_id = self.organization.id self._send_buckets( [ { "org_id": org_id, "project_id": self.project.id, "metric_id": indexer.record(org_id, "metric_foo_doe"), "timestamp": int(time.time()), "tags": { resolve_weak("release"): indexer.record(org_id, "foo"), }, "type": "c", "value": 1, "retention_days": 90, }, ], entity="metrics_counters", ) response = self.get_success_response( self.organization.slug, "derived_metric.multiple_metrics", ) assert response.data == { "name": "derived_metric.multiple_metrics", "type": "numeric", "operations": [], "unit": "percentage", "tags": [{"key": "release"}], }

f9dcd325304b37e3bff3869c1589354755e9300e

https://github.com/getsentry/sentry.git

def test_same_entity_multiple_metric_ids(self): self.store_session( self.build_session( project_id=self.project.id, started=(time.time() // 60) * 60, status="ok", release="foobar@2.0", errors=2, ) ) response = self.get_response( self.organization.slug, "derived_metric.multiple_metrics", ) assert response.status_code == 404 assert response.json()["detail"] == ( "Not all the requested m

test_same_entity_multiple_metric_ids

python/ray/tune/tests/test_integration_mlflow.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

Python

test_integration_mlflow.py

def testMlFlowMixinConfig(self): clear_env_vars() trial_config = {"par1": 4, "par2": 9.0}

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

def testMlFlowMixinConfig(self): clear_env_vars() trial_config = {"par1": 4, "par2": 9.0}

testMlFlowMixinConfig

manim/mobject/text/code_mobject.py

Always expand user when validating file-paths (#2885) The users are expanded when providing a file-path for the following mobjects: - CodeMobjects - ImageMobjects - SVGMobjects

manim

Python

code_mobject.py

def _ensure_valid_file(self): if self.file_name is None: raise Exception("Must specify file for Code") possible_paths = [ os.path.join(os.path.join("assets", "codes"), self.file_name), os.path.expanduser(self.file_name), ] for path in possible_paths: if os.path.exists(path): self.file_path = path return error = ( f"From: {os.getcwd()}, could not find {self.file_name} at either " + f"of these locations: {possible_paths}" ) raise OSError(error)

7a801707b6dc373fae21af192214620ce0a53380

https://github.com/ManimCommunity/manim.git

def _ensure_valid_file(self): if self.file_name is None: raise Exception("Must specify file for Code") possible_paths = [ os.path.join(os.path.join("assets", "codes"), self.file_name), os.path.expanduser(self.file_name), ] for path in possible_paths: if os.path.exists(path): self.file_path = path return

_ensure_valid_file

mitmproxy/tools/web/master.py

clean up initialization mess We now manage the eventloop ourselves no matter which tool.

mitmproxy

Python

master.py

async def running(self): # Register tornado with the current event loop tornado.ioloop.IOLoop.current() # Add our web app. http_server = tornado.httpserver.HTTPServer(self.app) http_server.listen(self.options.web_port, self.options.web_host) self.log.info( f"Web server listening at http://{self.options.web_host}:{self.options.web_port}/", ) return await super().running()

bbc65e5f375693ec32d50b805cdb369a607f0b67

https://github.com/mitmproxy/mitmproxy.git

async def running(self): # Register tornado with the current event loop tornado.ioloop.IOLoop.current() # Add our web app. http_server = tornado.httpserver.HTTPServer(self.app) http_server.listen(self.options.web_port, self.options.web_host) self.log.info( f"Web server listening at http://{self.options.web_host}:{self.options.web_port}/", ) r

running

dask/dataframe/io/tests/test_parquet.py

Change `to_parquet` default to `write_metadata_file=None` (#8988) * Refactor to_parquet A bit of refactoring before changing the default of `write_metadata_file` to `None` in `to_parquet`. - Simplify implementation - Don't include file metadata in `write_partition` calls if it's not needed - Everything needed to support implementing `write_metadata_file=None` as default *except* changing the value (to ensure tests pass). * Fixup failing parquet tests Most of the failures are due to divisions not being known by default anymore, since they're only known by default if a `_metadata` file is present. * Respond to feedback

dask

Python

test_parquet.py

def test_pathlib_path(tmpdir, engine): import pathlib df = pd.DataFrame({"x": [4, 5, 6, 1, 2, 3]}) df.index.name = "index" ddf = dd.from_pandas(df, npartitions=2) path = pathlib.Path(str(tmpdir)) ddf.to_parquet(path, engine=engine) ddf2 = dd.read_parquet(path, engine=engine) assert_eq(ddf, ddf2, check_divisions=False) @FASTPARQUET_MARK

00572071d15e7e8cfc20d8342b00aabadf0d2102

@FASTPARQUET_MARK

https://github.com/dask/dask.git

def test_pathlib_path(tmpdir, engine): import pathlib df = pd.DataFrame({"x": [4, 5, 6, 1, 2, 3]}) df.index.name = "index" ddf = dd.from_pan

test_pathlib_path

mindsdb/integrations/handlers/lightwood_handler/lightwood_handler/lightwood_handler.py

file storage factory

mindsdb

Python

lightwood_handler.py

def _retrain(self, statement): model_name = statement.name.parts[-1] base_predictor_record = get_model_record( name=model_name, ml_handler_name='lightwood', company_id=self.company_id, active=True ) if base_predictor_record is None: return Response( RESPONSE_TYPE.ERROR, error_message=f"Error: model '{model_name}' does not exists!" ) new_predictor_record = db.Predictor( company_id=self.company_id, name=base_predictor_record.name, integration_id=base_predictor_record.integration_id, data_integration_id=base_predictor_record.data_integration_id, fetch_data_query=base_predictor_record.fetch_data_query, mindsdb_version=mindsdb_version, lightwood_version=lightwood_version, to_predict=base_predictor_record.to_predict, learn_args=base_predictor_record.learn_args, data={'name': base_predictor_record.name}, active=False, status=PREDICTOR_STATUS.GENERATING ) db.session.add(new_predictor_record) db.session.commit() data_handler_meta = self.handler_controller.get_by_id(base_predictor_record.data_integration_id) data_handler = self.handler_controller.get_handler(data_handler_meta['name']) ast = self.parser(base_predictor_record.fetch_data_query, dialect=self.dialect) response = data_handler.query(ast) if response.type == RESPONSE_TYPE.ERROR: return response new_predictor_record.training_data_columns_count = len(response.data_frame.columns) new_predictor_record.training_data_rows_count = len(response.data_frame) db.session.commit() predictor_storage = self.storage_factory(new_predictor_record.id) p = HandlerProcess( run_update, new_predictor_record.id, response.data_frame, self.company_id, str(predictor_storage.folder_path) ) p.start() return Response(RESPONSE_TYPE.OK)

5f7345439b9317659e36eaa296aa7f7607ef7e79

https://github.com/mindsdb/mindsdb.git

def _retrain(self, statement): model_name = statement.name.parts[-1] base_predictor_record = get_model_record( name=model_name, ml_handler_name='lightwood', company_id=self.company_id, active=True ) if base_predictor_record is None: return Response( RESPONSE_TYPE.ERROR, error_message=f"Error: model '{model_name}' does not exists!" ) new_predictor_record = db.Predictor( company_id=self.company_id, name=base_predictor_record.name, integration_id=base_predictor_record.integration_id, data_integration_id=base_predictor_record.data_integration_id, fetch_data_query=base_predictor_record.fetch_data_query, mindsdb_version=mindsdb_version, lightwood_version=lightwood_version, to_predict=base_predictor_record.to_predict, learn_args=base_predictor_record.learn_args, data={'name': base_predictor_record.name}, active=False, status=PREDICTOR_STATUS.GENERATING ) db.session.add(new_predictor_record) db.session.commit() data_handler_meta = self.handler_controller.get_by_id(base_predictor_record.data_integration_id) data_handler = self.handler_controller.get_handler(data_handler_meta['name']) ast = self.parser(base_predictor_record.fetch_data_query, dialect=self.dialect) response = data_handler.query(ast) if response.type == RESPONSE_TYPE.ERROR: return response new_predictor_record.training_data_columns_count = len(response.data_frame.columns) new_predictor_record.training_data_rows_count = len(response.data_frame) db.session.commit() predictor_storage = self.storage_factory(new_predictor_record.id) p = HandlerProcess( run_update, new_predictor_record.id, response.data_frame, self.company_id, str(predictor_storage.folder_path) ) p.start()

_retrain

tests/sentry/issues/test_issue_occurrence.py

feat(issue_platform): Create a function to support saving an issue occurrence and related data (#42120) This introduces a function for saving an `IssueOccurrence` based on `IssueOccurrenceData`, as well as an related event data. In future prs it will also handle creating/updating the related issue and passing this information to eventstream.

sentry

Python

test_issue_occurrence.py

def test(self) -> None: occurrence = self.build_occurrence() occurrence.save(self.project.id) fetched_occurrence = IssueOccurrence.fetch(occurrence.id, self.project.id) assert fetched_occurrence is not None self.assert_occurrences_identical(occurrence, fetched_occurrence) @region_silo_test

0f6923d5dc11e8552e6620dd14596f1d4efe630c

@region_silo_test

https://github.com/getsentry/sentry.git

def test(self) -> None: occurrence = self.build_occurrence() occurrence.save(self.project.id) fetched_occurrence = IssueOccurrence.fetch(occurrence.id, self.project.id) assert fetched_occurrence is not None self.assert_occurrences_identical(occurrence, fetched

test

wagtail/admin/panels.py

Deprecate EditHandler.bind_to

wagtail

Python

panels.py

def bind_to(self, model=None, instance=None, request=None, form=None): warn( "The %s.bind_to() method has been replaced by bind_to_model(model) and get_bound_panel(instance=instance, request=request, form=form)" % type(self).__name__, category=RemovedInWagtail219Warning, stacklevel=2, ) return self.get_bound_panel(instance=instance, request=request, form=form)

defa7f8ed248354e57e90f3f5d31466de43c73f9

https://github.com/wagtail/wagtail.git

def bind_to(self, model=None, instance=None, request=None, form=None): warn( "The %s.bind_to() method has been replaced by bind_to_model(model) and get_bound_panel(instance=instance, request=request, form=form)" % type(self).__name__, category=RemovedInWagtail219Warning, stacklevel=2, ) return self.get_bound_panel(instance=insta

bind_to

onnx/backend/test/case/node/det.py

Use Python type annotations rather than comments (#3962) * These have been supported since Python 3.5. ONNX doesn't support Python < 3.6, so we can use the annotations. Diffs generated by https://pypi.org/project/com2ann/. Signed-off-by: Gary Miguel <garymiguel@microsoft.com> * Remove MYPY conditional logic in gen_proto.py It breaks the type annotations and shouldn't be needed. Signed-off-by: Gary Miguel <garymiguel@microsoft.com> * Get rid of MYPY bool from more scripts Signed-off-by: Gary Miguel <garymiguel@microsoft.com> * move Descriptors class above where its referenced in type annotation Signed-off-by: Gary Miguel <garymiguel@microsoft.com> * fixes Signed-off-by: Gary Miguel <garymiguel@microsoft.com> * remove extra blank line Signed-off-by: Gary Miguel <garymiguel@microsoft.com> * fix type annotations Signed-off-by: Gary Miguel <garymiguel@microsoft.com> * fix type annotation in gen_docs Signed-off-by: Gary Miguel <garymiguel@microsoft.com> * fix Operators.md Signed-off-by: Gary Miguel <garymiguel@microsoft.com> * fix TestCoverage.md Signed-off-by: Gary Miguel <garymiguel@microsoft.com> * fix protoc-gen-mypy.py Signed-off-by: Gary Miguel <garymiguel@microsoft.com>

onnx

Python

det.py

def export_2d() -> None: node = onnx.helper.make_node( 'Det', inputs=['x'], outputs=['y'], ) x = np.arange(4).reshape(2, 2).astype(np.float32) y = np.linalg.det(x) # expect -2 expect(node, inputs=[x], outputs=[y], name='test_det_2d')

83fa57c74edfd13ddac9548b8a12f9e3e2ed05bd

https://github.com/onnx/onnx.git

def export_2d() -> None: node = onnx.helper.make_node( 'Det',

export_2d

tests/acceptance/test_project_settings_sampling.py

ref(sampling): Remove skip from acceptance tests - (#38440)

sentry

Python

test_project_settings_sampling.py

def store_outcomes(self, outcome, num_times=1): outcomes = [] for _ in range(num_times): outcome_copy = outcome.copy() outcome_copy["timestamp"] = outcome_copy["timestamp"].strftime("%Y-%m-%dT%H:%M:%S.%fZ") outcomes.append(outcome_copy) assert ( requests.post( settings.SENTRY_SNUBA + "/tests/outcomes/insert", data=json.dumps(outcomes) ).status_code == 200 )

d6bc97675d194a4d336ed4444d48c4a4fb349255

https://github.com/getsentry/sentry.git

def store_outcomes(self, outcome, num_times=1): outcomes = [] for _ in range(num_times): outcome_copy = outcome.copy() outcome_copy["timestamp"] = outcome_copy["timestamp"].strftime("%Y-%m-%dT%H:%M:%S.%fZ") outcomes.append(outcome_copy) assert ( re

store_outcomes

ppocr/data/imaug/rec_img_aug.py

add rec vitstr algorithm.

PaddleOCR

Python

rec_img_aug.py

def __call__(self, data): img = data['image'] img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) image_shape = self.image_shape if self.padding: imgC, imgH, imgW = image_shape # todo: change to 0 and modified image shape h = img.shape[0] w = img.shape[1] ratio = w / float(h) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) resized_image = cv2.resize(img, (resized_w, imgH)) norm_img = np.expand_dims(resized_image, -1) norm_img = norm_img.transpose((2, 0, 1)) resized_image = norm_img.astype(np.float32) / 128. - 1. padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) padding_im[:, :, 0:resized_w] = resized_image data['image'] = padding_im return data if self.resize_type == 'PIL': image_pil = Image.fromarray(np.uint8(img)) img = image_pil.resize(self.image_shape, self.inter_type) img = np.array(img) if self.resize_type == 'OpenCV': img = cv2.resize(img, self.image_shape) norm_img = np.expand_dims(img, -1) norm_img = norm_img.transpose((2, 0, 1)) if self.scale: data['image'] = norm_img.astype(np.float32) / 128. - 1. else: data['image'] = norm_img.astype(np.float32) / 255. return data

9816aebdb078ec14ca3141560f2431981c8948f5

https://github.com/PaddlePaddle/PaddleOCR.git

def __call__(self, data): img = data['image'] img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) image_shape = self.image_shape if self.padding: imgC, imgH, imgW = image_shape # todo: change to 0 and modified image shape h = img.shape[0] w = img.shape[1] ratio = w / float(h) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) resized_image = cv2.resize(img, (resized_w, imgH)) norm_img = np.expand_dims(resized_image, -1) norm_img = norm_img.transpose((2, 0, 1)) resized_image = norm_img.astype(np.float32) / 128. - 1. padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) padding_im[:, :, 0:resized_w] = resized_image data['image'] = padding_im return data if self.resize_type == 'PIL': image_pil = Image.fromarray(np.uint8(img)) img = image_pil.resize(self.image_shape, self.inter_type)

__call__

lib/matplotlib/offsetbox.py

Don't pass unused xdescent to _get_packed_offsets. Instead of passing a list of (widths, xdescents) where xdescent is unused, just pass a list of widths. This helper is private so we just need to adjust the call sites and tests with no deprecation. This patch is preliminary work for some further cleanup on the offsetbox module.

matplotlib

Python

offsetbox.py

def _get_packed_offsets(widths, total, sep, mode="fixed"): r _api.check_in_list(["fixed", "expand", "equal"], mode=mode) if mode == "fixed": offsets_ = np.cumsum([0] + [w + sep for w in widths]) offsets = offsets_[:-1] if total is None: total = offsets_[-1] - sep return total, offsets elif mode == "expand": # This is a bit of a hack to avoid a TypeError when *total* # is None and used in conjugation with tight layout. if total is None: total = 1 if len(widths) > 1: sep = (total - sum(widths)) / (len(widths) - 1) else: sep = 0 offsets_ = np.cumsum([0] + [w + sep for w in widths]) offsets = offsets_[:-1] return total, offsets elif mode == "equal": maxh = max(widths) if total is None: if sep is None: raise ValueError("total and sep cannot both be None when " "using layout mode 'equal'") total = (maxh + sep) * len(widths) else: sep = total / len(widths) - maxh offsets = (maxh + sep) * np.arange(len(widths)) return total, offsets

8ef4e017f8a95db8704728a5fffd2c0384afc525

https://github.com/matplotlib/matplotlib.git

def _get_packed_offsets(widths, total, sep, mode="fixed"): r _api.check_in_list(["fixed", "expand", "equal"], mode=mode) if mode == "fixed": offsets_ = np.cumsum([0] + [w + sep for w in widths]) offsets = offsets_[:-1] if total is None: total = offsets_[-1] - sep return total, offsets elif mode == "expand": # This is a bit of a hack to avoid a TypeError when *total* # is None and used in conjugation with tight layout. if total is None: total = 1 if len(widths) > 1: sep = (total - sum(widths)) / (len(widths) - 1) else: sep = 0 offsets_ = np.cumsum([0] + [w + sep for w in widths]) offsets = offsets_[:-1] return total, offsets elif mode == "equal": maxh = max(widths) if total is None: if sep is None: raise ValueError("total and sep cannot both be None when " "using la

_get_packed_offsets

tests/tpot_tests.py

Revert "Deployed 7ccda9a with MkDocs version: 1.3.0" This reverts commit bd9629c40e01241766197119b581a99409b07068.

tpot

Python

tpot_tests.py

def test_fit_7(): tpot_obj = TPOTRegressor( random_state=42, population_size=1, offspring_size=2, generations=1, verbosity=0 ) tpot_obj.fit(pretest_X_reg, pretest_y_reg) assert isinstance(tpot_obj._optimized_pipeline, creator.Individual) assert not (tpot_obj._start_datetime is None)

388616b6247ca4ea8de4e2f340d6206aee523541

https://github.com/EpistasisLab/tpot.git

def test_fit_7(): tpot_obj = TPOTRegressor( random_state=42, population_size=1, offspring_size=2, generations=1, verbosity=0 ) tpot_obj.fit(pretest_X_reg, pretest_y_reg) assert isinstance(tpot_obj._optimized_pipeline, creator.Individual) assert not (tpot_obj._start_datetime is None)

test_fit_7

tests/engines/test_gif.py

Reformat to 80 chars and mypy.ini

thumbor

Python

test_gif.py

def test_errors_on_gifsicle_should_not_raises_errors_when_output(self): engine = Engine(self.context) with open( join(STORAGE_PATH, "SmallFullColourGIF.gif"), "rb" ) as image_file: buffer = image_file.read() engine.load(buffer, ".gif") result = engine.run_gifsicle("--some-invalid-opt") expect(result).Not.to_be_null()

301124c5b377fa56b940d298900dbc5816dbc24e

https://github.com/thumbor/thumbor.git

def test_errors_on_gifsicle_should_not_raises_errors_when_output(self): engine = Engine(self.context) with open( join(STORAGE_PATH, "SmallFullColourGIF.gif"), "rb" ) as image_file: buffer = image_file.read() engine.load(buffer, ".gif") result = engine.run_gifsicle("--some-invalid-o

test_errors_on_gifsicle_should_not_raises_errors_when_output

scapy/contrib/http2.py

E275 - Missing whitespace after keyword (#3711) Co-authored-by: Alexander Aring <alex.aring@gmail.com> Co-authored-by: Anmol Sarma <me@anmolsarma.in> Co-authored-by: antoine.torre <torreantoine1@gmail.com> Co-authored-by: Antoine Vacher <devel@tigre-bleu.net> Co-authored-by: Arnaud Ebalard <arno@natisbad.org> Co-authored-by: atlowl <86038305+atlowl@users.noreply.github.com> Co-authored-by: Brian Bienvenu <brian@bienvenu.id.au> Co-authored-by: Chris Packham <chris.packham@alliedtelesis.co.nz> Co-authored-by: CQ <cq674350529@163.com> Co-authored-by: Daniel Collins <kinap@users.noreply.github.com> Co-authored-by: Federico Maggi <federico.maggi@gmail.com> Co-authored-by: Florian Maury <florian.maury@ssi.gouv.fr> Co-authored-by: _Frky <3105926+Frky@users.noreply.github.com> Co-authored-by: g-mahieux <37588339+g-mahieux@users.noreply.github.com> Co-authored-by: gpotter2 <gabriel@potter.fr> Co-authored-by: Guillaume Valadon <guillaume@valadon.net> Co-authored-by: Hao Zheng <haozheng10@gmail.com> Co-authored-by: Haresh Khandelwal <hareshkhandelwal@gmail.com> Co-authored-by: Harri Hämäläinen <hhamalai@iki.fi> Co-authored-by: hecke <hecke@naberius.de> Co-authored-by: Jan Romann <jan.romann@gmail.com> Co-authored-by: Jan Sebechlebsky <sebechlebskyjan@gmail.com> Co-authored-by: jdiog0 <43411724+jdiog0@users.noreply.github.com> Co-authored-by: jockque <38525640+jockque@users.noreply.github.com> Co-authored-by: Julien Bedel <30991560+JulienBedel@users.noreply.github.com> Co-authored-by: Keith Scott <kscott@mitre.org> Co-authored-by: Kfir Gollan <kfir@drivenets.com> Co-authored-by: Lars Munch <lars@segv.dk> Co-authored-by: ldp77 <52221370+ldp77@users.noreply.github.com> Co-authored-by: Leonard Crestez <cdleonard@gmail.com> Co-authored-by: Marcel Patzlaff <mpatzlaff@benocs.com> Co-authored-by: Martijn Thé <martijnthe@users.noreply.github.com> Co-authored-by: Martine Lenders <authmillenon@gmail.com> Co-authored-by: Michael Farrell <micolous+git@gmail.com> Co-authored-by: Michał Mirosław <mirq-linux@rere.qmqm.pl> Co-authored-by: mkaliszan <mkaliszan@benocs.com> Co-authored-by: mtury <maxence.tury@ssi.gouv.fr> Co-authored-by: Neale Ranns <nranns@cisco.com> Co-authored-by: Octavian Toader <Octavian.Toader@belden.com> Co-authored-by: Peter Eisenlohr <peter@eisenlohr.org> Co-authored-by: Phil <phil@secdev.org> Co-authored-by: Pierre Lalet <pierre@droids-corp.org> Co-authored-by: Pierre Lorinquer <pierre.lorinquer@ssi.gouv.fr> Co-authored-by: piersoh <42040737+piersoh@users.noreply.github.com> Co-authored-by: plorinquer <pierre.lorinquer@ssi.gouv.fr> Co-authored-by: pvinci <pvinci@users.noreply.github.com> Co-authored-by: Rahul Jadhav <nyrahul@gmail.com> Co-authored-by: Robin Jarry <robin.jarry@6wind.com> Co-authored-by: romain-perez <51962832+romain-perez@users.noreply.github.com> Co-authored-by: rperez <rperez@debian> Co-authored-by: Sabrina Dubroca <sd@queasysnail.net> Co-authored-by: Sebastian Baar <sebastian.baar@gmx.de> Co-authored-by: sebastien mainand <sebastien.mainand@ssi.gouv.fr> Co-authored-by: smehner1 <smehner1@gmail.com> Co-authored-by: speakinghedge <hecke@naberius.de> Co-authored-by: Steven Van Acker <steven@singularity.be> Co-authored-by: Thomas Faivre <thomas.faivre@6wind.com> Co-authored-by: Tran Tien Dat <peter.trantiendat@gmail.com> Co-authored-by: Wael Mahlous <wael.mahlous@gmail.com> Co-authored-by: waeva <74464394+waeva@users.noreply.github.com> Co-authored-by: Alexander Aring <alex.aring@gmail.com> Co-authored-by: Anmol Sarma <me@anmolsarma.in> Co-authored-by: antoine.torre <torreantoine1@gmail.com> Co-authored-by: Antoine Vacher <devel@tigre-bleu.net> Co-authored-by: Arnaud Ebalard <arno@natisbad.org> Co-authored-by: atlowl <86038305+atlowl@users.noreply.github.com> Co-authored-by: Brian Bienvenu <brian@bienvenu.id.au> Co-authored-by: Chris Packham <chris.packham@alliedtelesis.co.nz> Co-authored-by: CQ <cq674350529@163.com> Co-authored-by: Daniel Collins <kinap@users.noreply.github.com> Co-authored-by: Federico Maggi <federico.maggi@gmail.com> Co-authored-by: Florian Maury <florian.maury@ssi.gouv.fr> Co-authored-by: _Frky <3105926+Frky@users.noreply.github.com> Co-authored-by: g-mahieux <37588339+g-mahieux@users.noreply.github.com> Co-authored-by: gpotter2 <gabriel@potter.fr> Co-authored-by: Guillaume Valadon <guillaume@valadon.net> Co-authored-by: Hao Zheng <haozheng10@gmail.com> Co-authored-by: Haresh Khandelwal <hareshkhandelwal@gmail.com> Co-authored-by: Harri Hämäläinen <hhamalai@iki.fi> Co-authored-by: hecke <hecke@naberius.de> Co-authored-by: Jan Romann <jan.romann@gmail.com> Co-authored-by: Jan Sebechlebsky <sebechlebskyjan@gmail.com> Co-authored-by: jdiog0 <43411724+jdiog0@users.noreply.github.com> Co-authored-by: jockque <38525640+jockque@users.noreply.github.com> Co-authored-by: Julien Bedel <30991560+JulienBedel@users.noreply.github.com> Co-authored-by: Keith Scott <kscott@mitre.org> Co-authored-by: Kfir Gollan <kfir@drivenets.com> Co-authored-by: Lars Munch <lars@segv.dk> Co-authored-by: ldp77 <52221370+ldp77@users.noreply.github.com> Co-authored-by: Leonard Crestez <cdleonard@gmail.com> Co-authored-by: Marcel Patzlaff <mpatzlaff@benocs.com> Co-authored-by: Martijn Thé <martijnthe@users.noreply.github.com> Co-authored-by: Martine Lenders <authmillenon@gmail.com> Co-authored-by: Michael Farrell <micolous+git@gmail.com> Co-authored-by: Michał Mirosław <mirq-linux@rere.qmqm.pl> Co-authored-by: mkaliszan <mkaliszan@benocs.com> Co-authored-by: mtury <maxence.tury@ssi.gouv.fr> Co-authored-by: Neale Ranns <nranns@cisco.com> Co-authored-by: Octavian Toader <Octavian.Toader@belden.com> Co-authored-by: Peter Eisenlohr <peter@eisenlohr.org> Co-authored-by: Phil <phil@secdev.org> Co-authored-by: Pierre Lalet <pierre@droids-corp.org> Co-authored-by: Pierre Lorinquer <pierre.lorinquer@ssi.gouv.fr> Co-authored-by: piersoh <42040737+piersoh@users.noreply.github.com> Co-authored-by: pvinci <pvinci@users.noreply.github.com> Co-authored-by: Rahul Jadhav <nyrahul@gmail.com> Co-authored-by: Robin Jarry <robin.jarry@6wind.com> Co-authored-by: romain-perez <51962832+romain-perez@users.noreply.github.com> Co-authored-by: rperez <rperez@debian> Co-authored-by: Sabrina Dubroca <sd@queasysnail.net> Co-authored-by: Sebastian Baar <sebastian.baar@gmx.de> Co-authored-by: sebastien mainand <sebastien.mainand@ssi.gouv.fr> Co-authored-by: smehner1 <smehner1@gmail.com> Co-authored-by: Steven Van Acker <steven@singularity.be> Co-authored-by: Thomas Faivre <thomas.faivre@6wind.com> Co-authored-by: Tran Tien Dat <peter.trantiendat@gmail.com> Co-authored-by: Wael Mahlous <wael.mahlous@gmail.com> Co-authored-by: waeva <74464394+waeva@users.noreply.github.com>

scapy

Python

http2.py

def h2i(self, pkt, x): # type: (Optional[packet.Packet], Optional[int]) -> Optional[int] assert not isinstance(x, six.integer_types) or x >= 0 return x

08b1f9d67c8e716fd44036a027bdc90dcb9fcfdf

https://github.com/secdev/scapy.git

def h2i(self, pkt, x): # type: (Optional[packet.Packet], Optional[int]) -> Optional[int] assert not isinstance(x, six.integer_types) or x >= 0 return

h2i

mindsdb/integrations/handlers/crate_handler/crate_handler.py

ML handler supbrocess (#3377) * log -> logger dividing components: app initialize parse args set env.MINDSDB_CONFIG_PATH config requiers env.MINDSDB_CONFIG_PATH sets env.MINDSDB_DB_CON Config() - makes initialization log uses config initialize_log - makes initialization database uses env.MINDSDB_DB_CON have init() method file storage uses config * partial sync for model storage interfaces * ml handler in subprocess interface * fix delete model * fix: model with error in success status * fix: remove hf predictor * fix pg handler * MLHandlerPersistWrapper keeps wrapper process opened * predictor with error keeps 'success' status #3362 * lock for sending tasks to subprocess one by one * check target of predictor before run learn in subproccess * fix check target * fix: json_ai override and problem definition generation * fix None case * folder for ml handler tests * fix: add timeseries_settings key to learn_args * fixes in lw_handler * fix: del join_learn_process * tests for LW handler * finish unit test for LW * changes in tests: - set-project -> to base class - return of ml handler is dataframe - draft for project structure test * merge from staging * create_validation method to check learn params before send to subprocess fixes of HF fixed version of transformers in HF requirements Co-authored-by: Patricio Cerda Mardini <pcerdam@live.com>

mindsdb

Python

crate_handler.py

def disconnect(self): if self.is_connected is False: return try: self.connection.close() self.is_connected=False except Exception as e: log.logger.error(f"Error while disconnecting to CrateDB, {e}") return

9ce5a21dd6359fd7e8ebf78051ce9e97bd195ec9

https://github.com/mindsdb/mindsdb.git

def disconnect(self): if self.is_connected is False: return try: self.connection.close() self.is_connected=False except Exception as e: log.logger.error(f"Error while disconnecting to CrateDB, {e}")

disconnect

saleor/graphql/product/tests/queries/test_products_query.py

Split test_product.py and test_variant.py into multiple files (#11173) * Split test_product.py into multiple files * Split test_variant.py into multiple files

saleor

Python

test_products_query.py

def test_sort_products(user_api_client, product, channel_USD): product.updated_at = datetime.utcnow() product.save() product.pk = None product.slug = "second-product" product.updated_at = datetime.utcnow() product.save() ProductChannelListing.objects.create( product=product, channel=channel_USD, is_published=True, visible_in_listings=True, ) variant = ProductVariant.objects.create(product=product, sku="1234") ProductVariantChannelListing.objects.create( variant=variant, channel=channel_USD, price_amount=Decimal(20), cost_price_amount=Decimal(2), currency=channel_USD.currency_code, ) product.pk = None product.slug = "third-product" product.updated_at = datetime.utcnow() product.save() ProductChannelListing.objects.create( product=product, channel=channel_USD, is_published=True, visible_in_listings=True, ) variant_second = ProductVariant.objects.create(product=product, sku="12345") ProductVariantChannelListing.objects.create( variant=variant_second, channel=channel_USD, currency=channel_USD.currency_code, ) variables = {"channel": channel_USD.slug} query = SORT_PRODUCTS_QUERY # Test sorting by PRICE, ascending sort_by = "{field: PRICE, direction: ASC}" asc_price_query = query % {"sort_by_product_order": sort_by} response = user_api_client.post_graphql(asc_price_query, variables) content = get_graphql_content(response) edges = content["data"]["products"]["edges"] assert len(edges) == 2 price1 = edges[0]["node"]["pricing"]["priceRangeUndiscounted"]["start"]["gross"][ "amount" ] price2 = edges[1]["node"]["pricing"]["priceRangeUndiscounted"]["start"]["gross"][ "amount" ] assert price1 < price2 # Test sorting by PRICE, descending sort_by = "{field: PRICE, direction:DESC}" desc_price_query = query % {"sort_by_product_order": sort_by} response = user_api_client.post_graphql(desc_price_query, variables) content = get_graphql_content(response) edges = content["data"]["products"]["edges"] price1 = edges[0]["node"]["pricing"]["priceRangeUndiscounted"]["start"]["gross"][ "amount" ] price2 = edges[1]["node"]["pricing"]["priceRangeUndiscounted"]["start"]["gross"][ "amount" ] assert price1 > price2 # Test sorting by MINIMAL_PRICE, ascending sort_by = "{field: MINIMAL_PRICE, direction:ASC}" asc_price_query = query % {"sort_by_product_order": sort_by} response = user_api_client.post_graphql(asc_price_query, variables) content = get_graphql_content(response) edges = content["data"]["products"]["edges"] price1 = edges[0]["node"]["pricing"]["priceRange"]["start"]["gross"]["amount"] price2 = edges[1]["node"]["pricing"]["priceRange"]["start"]["gross"]["amount"] assert price1 < price2 # Test sorting by MINIMAL_PRICE, descending sort_by = "{field: MINIMAL_PRICE, direction:DESC}" desc_price_query = query % {"sort_by_product_order": sort_by} response = user_api_client.post_graphql(desc_price_query, variables) content = get_graphql_content(response) edges = content["data"]["products"]["edges"] price1 = edges[0]["node"]["pricing"]["priceRange"]["start"]["gross"]["amount"] price2 = edges[1]["node"]["pricing"]["priceRange"]["start"]["gross"]["amount"] assert price1 > price2 # Test sorting by DATE, ascending asc_date_query = query % {"sort_by_product_order": "{field: DATE, direction:ASC}"} response = user_api_client.post_graphql(asc_date_query, variables) content = get_graphql_content(response) date_0 = content["data"]["products"]["edges"][0]["node"]["updatedAt"] date_1 = content["data"]["products"]["edges"][1]["node"]["updatedAt"] assert parse_datetime(date_0) < parse_datetime(date_1) # Test sorting by DATE, descending desc_date_query = query % {"sort_by_product_order": "{field: DATE, direction:DESC}"} response = user_api_client.post_graphql(desc_date_query, variables) content = get_graphql_content(response) date_0 = content["data"]["products"]["edges"][0]["node"]["updatedAt"] date_1 = content["data"]["products"]["edges"][1]["node"]["updatedAt"] assert parse_datetime(date_0) > parse_datetime(date_1)

d90be220d6b687d08153934a51354011a3cb5ca1

https://github.com/saleor/saleor.git

def test_sort_products(user_api_client, product, channel_USD): product.updated_at = datetime.utcnow() product.save() product.pk = None product.slug = "second-product" product.updated_at = datetime.utcnow() product.save() ProductChannelListing.objects.create( product=product, channel=channel_USD, is_published=True, visible_in_listings=True, ) variant = ProductVariant.objects.create(product=product, sku="1234") ProductVariantChannelListing.objects.create( variant=variant, channel=channel_USD, price_amount=Decimal(20), cost_price_amount=Decimal(2), currency=channel_USD.currency_code, ) product.pk = None product.slug = "third-product" product.updated_at = datetime.utcnow() product.save() ProductChannelListing.objects.create( product=product, channel=channel_USD, is_published=True, visible_in_listings=True, ) variant_second = ProductVariant.objects.create(product=product, sku="12345") ProductVariantChannelListing.objects.create( variant=variant_second, channel=channel_USD, currency=channel_USD.currency_code, ) variables = {"channel": channel_USD.slug} query = SORT_PRODUCTS_QUERY # Test sorting by PRICE, ascending sort_by = "{field: PRICE, direction: ASC}" asc_price_query = query % {"sort_by_product_order": sort_by} response = user_api_client.post_graphql(asc_price_query, variables) content = get_graphql_content(response) edges = content["data"]["products"]["edges"] assert len(edges) == 2 price1 = edges[0]["node"]["pricing"]["priceRangeUndiscounted"]["start"]["gross"][ "amount" ] price2 = edges[1]["node"]["pricing"]["priceRangeUndiscounted"]["start"]["gross"][ "amount" ] assert price1 < price2 # Test sorting by PRICE, descending sort_by = "{field: PRICE, direction:DESC}" desc_price_query = query % {"sort_by_product_order": sort_by} response = user_api_client.post_graphql(desc_price_query, variables) content = get_graphql_content(response) edges = content["data"]["products"]["edges"] price1 = edges[0]["node"]["pricing"]["priceRangeUndiscounted"]["start"]["gross"][ "amount" ] price2 = edges[1]["node"]["pricing"]["priceRangeUndiscounted"]["start"]["gross"][ "amount" ] assert price1 > price2 # Test sorting by MINIMAL_PRICE, ascending sort_by = "{field: MINIMAL_PRICE, direction:ASC}" asc_price_query = query % {"sort_by_product_order": sort_by} response = user_api_client.post_graphql(asc_price_query, variables) content = get_graphql_content(response) edges = content["data"]["products"]["edges"] price1 = edges[0]["node"]["pricing"]["priceRange"]["start"]["gross"]["amount"] price2 = edges[1]["node"]["pricing"]["priceRange"]["start"]["gross"]["amount"] assert price1 < price2 # Test sorting by MINIMAL_PRICE, descending sort_by = "{field: MINIMAL_PRICE, direction:DESC}" desc_price_query = query % {"sort_by_product_order": sort_by} response = user_api_client.post_graphql(desc_price_query, variables) content = get_graphql_content(response) edges = content["data"]["products"]["edges"] price1 = edges[0]["node"]["pricing"]["priceRange"]["start"]["gross"]["amount"] price2 = edges[1]["node"]["pricing"]["priceRange"]["start"]["gross"]["amount"] assert price1 > price2 # Test sorting by DATE, ascending asc_date_query = query % {"sort_by_product_order": "{field: DATE, direction:ASC}"} response = user_api_client.post_graphql(asc_date_query, variables) content = get_graphql_content(response) date_0 = conten

test_sort_products

tests/rest/client/test_relations.py

Refactor relations tests (#12232) * Moves the relation pagination tests to a separate class. * Move the assertion of the response code into the `_send_relation` helper. * Moves some helpers into the base-class.

synapse

Python

test_relations.py

def test_edit_thread(self) -> None: # Create a thread and edit the last event. channel = self._send_relation( RelationTypes.THREAD, "m.room.message", content={"msgtype": "m.text", "body": "A threaded reply!"}, ) threaded_event_id = channel.json_body["event_id"] new_body = {"msgtype": "m.text", "body": "I've been edited!"} channel = self._send_relation( RelationTypes.REPLACE, "m.room.message", content={"msgtype": "m.text", "body": "foo", "m.new_content": new_body}, parent_id=threaded_event_id, ) # Fetch the thread root, to get the bundled aggregation for the thread. channel = self.make_request( "GET", f"/rooms/{self.room}/event/{self.parent_id}", access_token=self.user_token, ) self.assertEqual(200, channel.code, channel.json_body) # We expect that the edit message appears in the thread summary in the # unsigned relations section. relations_dict = channel.json_body["unsigned"].get("m.relations") self.assertIn(RelationTypes.THREAD, relations_dict) thread_summary = relations_dict[RelationTypes.THREAD] self.assertIn("latest_event", thread_summary) latest_event_in_thread = thread_summary["latest_event"] self.assertEqual(latest_event_in_thread["content"]["body"], "I've been edited!")

1da0f79d5455b594f2aa989106a672786f5b990f

https://github.com/matrix-org/synapse.git

def test_edit_thread(self) -> None: # Create a thread and edit the last event. channel = self._send_relation( RelationTypes.THREAD, "m.room.message", content={"msgtype": "m.text", "body": "A threaded reply!"}, ) threaded_event_id = channel.json_body["event_id"] new_body = {"msgtype": "m.text", "body": "I've been edited!"} channel = self._send_relation( RelationTypes.REPLACE, "m.room.message", content={"msgtype": "m.text", "body": "foo", "m.new_content": new_body}, parent_id=threaded_event_id, ) # Fetch the thread root, to get the bundled aggregation for the thread. channel = self.make_request( "GET", f"/rooms/{self.room}/event/{self.parent_id}", access_token=self.user_token, ) self.assertEqual(200, channel.code, channel.json_body) # We expect that the edit message appears in the thread summary in the # unsigned relations section. relations_dict = channel.json_body["unsigned"].get("m.r

test_edit_thread

keras/engine/training_distributed_v1.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

training_distributed_v1.py

def fit(self, *args, **kwargs): return _train_with_multi_worker(self._single_worker_loop.fit)( *args, **kwargs )

84afc5193d38057e2e2badf9c889ea87d80d8fbf

https://github.com/keras-team/keras.git

def fit(self, *args, **kwargs): return _train_with_multi_worker(self._single_worker_loop.fit)( *args, **kwarg

fit

keras/activations.py

Move serialization-related logic in utils/generic_utils.py to saving/legacy/serialization.py. PiperOrigin-RevId: 479688207

keras

Python

activations.py

def deserialize(name, custom_objects=None): activation_functions = {} current_module = sys.modules[__name__] # we put 'current_module' after 'activation_layers' to prefer the local one # if there is a collision generic_utils.populate_dict_with_module_objects( activation_functions, (activation_layers, current_module), obj_filter=callable, ) return serialization.deserialize_keras_object( name, module_objects=activation_functions, custom_objects=custom_objects, printable_module_name="activation function", ) @keras_export("keras.activations.get") @tf.__internal__.dispatch.add_dispatch_support

c269e3cd8fed713fb54d2971319df0bfe6e1bf10

@keras_export("keras.activations.get") @tf.__internal__.dispatch.add_dispatch_support

https://github.com/keras-team/keras.git

def deserialize(name, custom_objects=None): activation_functions = {} current_module = sys.modules[__name__] # we put 'current_module' after 'activation_layers' to prefer the local one # if there is a collision generic_utils.populate_dict_with_module_objects( activation_functions, (activation_layers, current_module), obj_filter=callable, ) return serialization.deserialize_keras_object( name, module_objects=activation_functions, custom_objects=custom_objects, printable_module_name="activation function", ) @keras_export("keras.activations.get") @tf.__internal__.dispatch.add_dispatch_support

deserialize

keras/backend.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

backend.py

def cumprod(x, axis=0): return tf.math.cumprod(x, axis=axis) @keras_export("keras.backend.var") @doc_controls.do_not_generate_docs

84afc5193d38057e2e2badf9c889ea87d80d8fbf

@keras_export("keras.backend.var") @doc_controls.do_not_generate_docs

https://github.com/keras-team/keras.git

def cumprod(x, axis=0): return tf.math.cumprod(x, axis

cumprod

python/ray/train/examples/train_fashion_mnist_example.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

Python

train_fashion_mnist_example.py

def validate_epoch(dataloader, model, loss_fn): size = len(dataloader.dataset) // train.world_size() num_batches = len(dataloader) model.eval() test_loss, correct = 0, 0 with torch.no_grad(): for X, y in dataloader: pred = model(X) test_loss += loss_fn(pred, y).item() correct += (pred.argmax(1) == y).type(torch.float).sum().item() test_loss /= num_batches correct /= size print( f"Test Error: \n " f"Accuracy: {(100 * correct):>0.1f}%, " f"Avg loss: {test_loss:>8f} \n" ) return test_loss

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

def validate_epoch(dataloader, model, loss_fn): size = len(dataloader.dataset) // train.world_size() num_batches = len(dataloader) model.eval() test_loss, correct = 0, 0 with torch.no_grad(): for X, y in dataloader: pred = model(X) test_loss += loss_fn(pred, y).item() correct += (pred.argmax(1) == y).type(torch.float).sum().item() test_loss /= num_batches correct /= size print( f"Test Error: \n " f"Accuracy: {(100 * correct):>0.1f}%,

validate_epoch

wagtail/search/tests/test_queries.py

Reformat with black

wagtail

Python

test_queries.py

def test_only_query(self): filters, query = separate_filters_from_query("hello world") self.assertDictEqual(filters, {}) self.assertEqual(query, "hello world")

d10f15e55806c6944827d801cd9c2d53f5da4186

https://github.com/wagtail/wagtail.git

def test_only_query(self): filters, query = separate_filters_from_query("hello world") self.assertDictEqual(filters, {}) sel

test_only_query

.venv/lib/python3.8/site-packages/pip/_vendor/html5lib/_tokenizer.py

upd; format

transferlearning

Python

_tokenizer.py

def rcdataState(self): data = self.stream.char() if data == "&": self.state = self.characterReferenceInRcdata elif data == "<": self.state = self.rcdataLessThanSignState elif data == EOF: # Tokenization ends. return False elif data == "\u0000": self.tokenQueue.append({"type": tokenTypes["ParseError"], "data": "invalid-codepoint"}) self.tokenQueue.append({"type": tokenTypes["Characters"], "data": "\uFFFD"}) elif data in spaceCharacters: # Directly after emitting a token you switch back to the "data # state". At that point spaceCharacters are important so they are # emitted separately. self.tokenQueue.append({"type": tokenTypes["SpaceCharacters"], "data": data + self.stream.charsUntil(spaceCharacters, True)}) # No need to update lastFourChars here, since the first space will # have already been appended to lastFourChars and will have broken # any <!-- or --> sequences else: chars = self.stream.charsUntil(("&", "<", "\u0000")) self.tokenQueue.append({"type": tokenTypes["Characters"], "data": data + chars}) return True

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

https://github.com/jindongwang/transferlearning.git

def rcdataState(self): data = self.stream.char() if data == "&": self.state = self.characterReferenceInRcdata elif data == "<": self.state = self.rcdataLessThanSignState elif data == EOF:

rcdataState

wagtail/images/rect.py

Reformat with black

wagtail

Python

rect.py

def transform(self, transform): # Transform each corner of the rect tl_transformed = transform.transform_vector(Vector(self.left, self.top)) tr_transformed = transform.transform_vector(Vector(self.right, self.top)) bl_transformed = transform.transform_vector(Vector(self.left, self.bottom)) br_transformed = transform.transform_vector(Vector(self.right, self.bottom)) # Find extents of the transformed corners left = min( [tl_transformed.x, tr_transformed.x, bl_transformed.x, br_transformed.x] ) right = max( [tl_transformed.x, tr_transformed.x, bl_transformed.x, br_transformed.x] ) top = min( [tl_transformed.y, tr_transformed.y, bl_transformed.y, br_transformed.y] ) bottom = max( [tl_transformed.y, tr_transformed.y, bl_transformed.y, br_transformed.y] ) return Rect(left, top, right, bottom)

d10f15e55806c6944827d801cd9c2d53f5da4186

https://github.com/wagtail/wagtail.git

def transform(self, transform): # Transform each corner of the rect tl_transformed = transform.transform_vector(Vector(self.left, self.top)) tr_transformed = transform.transform_vector(Vector(self.right, self.top)) bl_transformed = transform.transform_vector(Vector(self.left, self.bottom)) br_transformed = transform.transform_vector(Vector(self.right, self.bottom)) # Find extents of the transformed corners left = min( [tl_transformed.x, tr_transformed.x, bl_transformed.x, br_transformed.x] ) right = max( [tl_transformed.x, tr_transformed.x, bl_transformed.x, br_transformed.x] ) top = min( [tl_transformed.y, tr_transformed.y, bl_transformed.y, br_transformed.y] ) bottom = max( [tl_transformed.y, tr_transformed.y, bl_transformed.y, br_transformed.y] ) re

transform

homeassistant/components/flux/switch.py

String formatting and max line length - Part 2 (#84393)

core

Python

switch.py

async def async_flux_update(self, utcnow=None): if utcnow is None: utcnow = dt_utcnow() now = as_local(utcnow) sunset = get_astral_event_date(self.hass, SUN_EVENT_SUNSET, now.date()) start_time = self.find_start_time(now) stop_time = self.find_stop_time(now) if stop_time <= start_time: # stop_time does not happen in the same day as start_time if start_time < now: # stop time is tomorrow stop_time += datetime.timedelta(days=1) elif now < start_time: # stop_time was yesterday since the new start_time is not reached stop_time -= datetime.timedelta(days=1) if start_time < now < sunset: # Daytime time_state = "day" temp_range = abs(self._start_colortemp - self._sunset_colortemp) day_length = int(sunset.timestamp() - start_time.timestamp()) seconds_from_start = int(now.timestamp() - start_time.timestamp()) percentage_complete = seconds_from_start / day_length temp_offset = temp_range * percentage_complete if self._start_colortemp > self._sunset_colortemp: temp = self._start_colortemp - temp_offset else: temp = self._start_colortemp + temp_offset else: # Night time time_state = "night" if now < stop_time: if stop_time < start_time and stop_time.day == sunset.day: # we need to use yesterday's sunset time sunset_time = sunset - datetime.timedelta(days=1) else: sunset_time = sunset night_length = int(stop_time.timestamp() - sunset_time.timestamp()) seconds_from_sunset = int(now.timestamp() - sunset_time.timestamp()) percentage_complete = seconds_from_sunset / night_length else: percentage_complete = 1 temp_range = abs(self._sunset_colortemp - self._stop_colortemp) temp_offset = temp_range * percentage_complete if self._sunset_colortemp > self._stop_colortemp: temp = self._sunset_colortemp - temp_offset else: temp = self._sunset_colortemp + temp_offset rgb = color_temperature_to_rgb(temp) x_val, y_val, b_val = color_RGB_to_xy_brightness(*rgb) brightness = self._brightness if self._brightness else b_val if self._disable_brightness_adjust: brightness = None if self._mode == MODE_XY: await async_set_lights_xy( self.hass, self._lights, x_val, y_val, brightness, self._transition ) _LOGGER.debug( ( "Lights updated to x:%s y:%s brightness:%s, %s%% " "of %s cycle complete at %s" ), x_val, y_val, brightness, round(percentage_complete * 100), time_state, now, ) elif self._mode == MODE_RGB: await async_set_lights_rgb(self.hass, self._lights, rgb, self._transition) _LOGGER.debug( "Lights updated to rgb:%s, %s%% of %s cycle complete at %s", rgb, round(percentage_complete * 100), time_state, now, ) else: # Convert to mired and clamp to allowed values mired = color_temperature_kelvin_to_mired(temp) await async_set_lights_temp( self.hass, self._lights, mired, brightness, self._transition ) _LOGGER.debug( ( "Lights updated to mired:%s brightness:%s, %s%% " "of %s cycle complete at %s" ), mired, brightness, round(percentage_complete * 100), time_state, now, )

cb13418babd21a1e9584978b0c523f1b1e4e1cb0

https://github.com/home-assistant/core.git

async def async_flux_update(self, utcnow=None): if utcnow is None: utcnow = dt_utcnow() now = as_local(utcnow) sunset = get_astral_event_date(self.hass, SUN_EVENT_SUNSET, now.date()) start_time = self.find_start_time(now) stop_time = self.find_stop_time(now) if stop_time <= start_time: # stop_time does not happen in the same day as start_time if start_time < now: # stop time is tomorrow stop_time += datetime.timedelta(days=1) elif now < start_time: # stop_time was yesterday since the new start_time is not reached stop_time -= datetime.timedelta(days=1) if start_time < now < sunset: # Daytime time_state = "day" temp_range = abs(self._start_colortemp - self._sunset_colortemp) day_length = int(sunset.timestamp() - start_time.timestamp(

async_flux_update

tests/providers/slack/hooks/test_slack.py

Return slack api call response in slack_hook (#21107)

airflow

Python

test_slack.py

def test_api_call(self, slack_client_class_mock): slack_client_mock = mock.Mock() slack_client_class_mock.return_value = slack_client_mock slack_client_mock.api_call.return_value = {'ok': True} slack_hook = SlackHook(token='test_token') test_api_json = {'channel': 'test_channel'} slack_hook.call("chat.postMessage", json=test_api_json) slack_client_mock.api_call.assert_called_with("chat.postMessage", json=test_api_json)

0ac3b8c3dd749c59e60cf0169580b9e7c5049d9e

https://github.com/apache/airflow.git

def test_api_call(self, slack_client_class_mock): slack_client_mock = mock.Mock() slack_client_class_mock.return_value = slack_client_mock slack_client_mock.api_call.return_value = {'ok': True} slack_hook = SlackHook(token='test_token') test_api_json = {'channel': 'test_channel'} slack_hook.call(

test_api_call

lib/matplotlib/figure.py

ENH: add ability to remove layout engine This also adds a "place holder" layout engine to ensure that users can not "go through zero" and change to an incompatible layout engine. Co-authored-by: Jody Klymak <jklymak@gmail.com>

matplotlib

Python

figure.py

def _check_layout_engines_compat(self, old, new): if old is None or new is None: return True if old.colorbar_gridspec == new.colorbar_gridspec: return True # colorbar layout different, so check if any colorbars are on the # figure... for ax in self.axes: if hasattr(ax, '_colorbar'): # colorbars list themselves as a colorbar. return False return True

f7f3bb6079048506613c513231e1bd2a87ebc7d3

https://github.com/matplotlib/matplotlib.git

def _check_layout_engines_compat(self, old, new): if old is None or ne

_check_layout_engines_compat

rllib/algorithms/marwil/marwil.py

[RLlib] Make Dataset reader default reader and enable CRR to use dataset (#26304) Co-authored-by: avnish <avnish@avnishs-MBP.local.meter>

ray

Python

marwil.py

def training_step(self) -> ResultDict: # Collect SampleBatches from sample workers. with self._timers[SAMPLE_TIMER]: batch = synchronous_parallel_sample(worker_set=self.workers) batch = batch.as_multi_agent() self._counters[NUM_AGENT_STEPS_SAMPLED] += batch.agent_steps() self._counters[NUM_ENV_STEPS_SAMPLED] += batch.env_steps() # Add batch to replay buffer. self.local_replay_buffer.add(batch) # Pull batch from replay buffer and train on it. train_batch = sample_min_n_steps_from_buffer( self.local_replay_buffer, self.config["train_batch_size"], count_by_agent_steps=self._by_agent_steps, ) # Train. if self.config["simple_optimizer"]: train_results = train_one_step(self, train_batch) else: train_results = multi_gpu_train_one_step(self, train_batch) # TODO: Move training steps counter update outside of `train_one_step()` method. # # Update train step counters. # self._counters[NUM_ENV_STEPS_TRAINED] += train_batch.env_steps() # self._counters[NUM_AGENT_STEPS_TRAINED] += train_batch.agent_steps() global_vars = { "timestep": self._counters[NUM_AGENT_STEPS_SAMPLED], } # Update weights - after learning on the local worker - on all remote # workers. if self.workers.remote_workers(): with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]: self.workers.sync_weights(global_vars=global_vars) # Update global vars on local worker as well. self.workers.local_worker().set_global_vars(global_vars) return train_results # Deprecated: Use ray.rllib.algorithms.marwil.MARWILConfig instead!

1243ed62bf4121c83881c3ddc095bc6a873a09f3

https://github.com/ray-project/ray.git

def training_step(self) -> ResultDict: # Collect SampleBatches from sample workers. with self._timers[SAMPLE_TIMER]: batch = synchronous_parallel_sample(worker_set=self.workers) batch = batch.as_multi_agent() self._counters[NUM_AGENT_STEPS_SAMPLED] += batch.agent_steps() self._counters[NUM_ENV_STEPS_SAMPLED] += batch.env_steps() # Add batch to replay buffer. self.local_replay_buffer.add(batch) # Pull batch from replay buffer and train on it. train_batch = sample_min_n_steps_from_buffer( self.local_replay_buffer, self.config["train_batch_size"], count_by_agent_steps=self._by_agent_steps, ) # Train. if self.config["simple_optimizer"]: train_results = train_one_step(self, train_batch) else: train_results = multi_gpu_train_one_step(self, train_batch) # TODO: Move training steps counter update outside of `train_one_step()` method. # # Update train step counters. # self._counters[NUM_ENV_STEPS_TRAINED] += train_batch.env_steps() # self._counters[NUM_AGENT_STEPS_TRAINED] += train_batch.agent_steps() global_vars = { "timestep": self._counters[NUM_AGENT_STEPS_SAMPLED], } # Update weights - after learning on the local worker - on all remote # workers. if self.workers.remote_workers(): with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]: self.workers.sync_weights(global_vars=global_vars) # Update global vars on local worker as well. self.w

training_step

tests/models/test_default_evaluator.py

Evaluation Default evaluator (#5092) * init Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * rename module Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * revert black change Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * change module path Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * refactor Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * lazy load pyspark Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * revert export Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix curcit import Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix conftest.py Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * Revert "fix conftest.py" This reverts commit 2ea29c62bfffc5461bf77f3da15b5c00f51de19b. * fix tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update doc Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * default evaluator Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix doc Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix doc Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update import Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix doc Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update hash algo Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update import Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comment Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix lint Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add more tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix lint Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update shap explainer Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * remove scikitplot dep Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add pr curve Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add shap.summary_plot Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * log explainer Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * improve explainer code Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update shap init Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update explainer creating Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update predict_proba Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * refactor Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add multi-class metrics artifacts Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update doc Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add log_loss metric Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * lazy load pyspark Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address ben comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * prevent show shap logo, add tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * support spark model Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add shap version check Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update docs, loose classifier label limit Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * multiclass classifier merge metrics/plots Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * zfill feature name Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update doc Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add config max_num_classes_threshold_logging_roc_pr_curve_for_multiclass_classifier Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * refactor Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * improve label handling Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * refactor Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * black Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * increase plot dpi Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix test fixture Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix pylint Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update doc Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * use matplot rc_context Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix shap import Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * refactor EvaluationDataset Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * limit user specify shap algos Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * clean Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update evaluation dataset Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * use svg fig Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * revert svg Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * curve dashline, legend display ap/roc, legend move out Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * linewidth 1 Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * keyword arguments for evaluate, fix tests Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * mark abc.abstractmethod, kw args for ModelEvaluator methods Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix pylint Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix pylint Signed-off-by: Weichen Xu <weichen.xu@databricks.com>

mlflow

Python

test_default_evaluator.py

def test_gen_multiclass_roc_curve(): y = [0, 1, 2, 1, 2] y_probs = [ [0.7, 0.1, 0.2], [0.2, 0.3, 0.5], [0.25, 0.4, 0.35], [0.3, 0.4, 0.3], [0.8, 0.1, 0.1], ] results = _gen_classifier_curve( is_binomial=False, y=y, y_probs=y_probs, labels=[0, 1, 2], curve_type="roc" ) print(results) expected_x_data_list = [ [0.0, 0.25, 0.25, 1.0], [0.0, 0.33333333, 0.33333333, 1.0], [0.0, 0.33333333, 0.33333333, 1.0, 1.0], ] expected_y_data_list = [[0.0, 0.0, 1.0, 1.0], [0.0, 0.5, 1.0, 1.0], [0.0, 0.0, 0.5, 0.5, 1.0]] line_labels = ["label=0,AUC=0.750", "label=1,AUC=0.750", "label=2,AUC=0.333"] for index, (name, x_data, y_data) in enumerate(results.plot_fn_args["data_series"]): assert name == line_labels[index] assert np.allclose(x_data, expected_x_data_list[index], rtol=1e-3) assert np.allclose(y_data, expected_y_data_list[index], rtol=1e-3) assert results.plot_fn_args["xlabel"] == "False Positive Rate" assert results.plot_fn_args["ylabel"] == "True Positive Rate" assert results.plot_fn_args["line_kwargs"] == {"drawstyle": "steps-post", "linewidth": 1} expected_auc = [0.75, 0.75, 0.3333] assert np.allclose(results.auc, expected_auc, rtol=1e-3)

964f5ab75098c55f028f8acfeeae05df35ea68d5

https://github.com/mlflow/mlflow.git

def test_gen_multiclass_roc_curve(): y = [0, 1, 2, 1, 2] y_probs = [ [0.7, 0.1, 0.2], [0.2, 0.3, 0.5], [0.25, 0.4, 0.35], [0.3, 0.4, 0.3], [0.8, 0.1, 0.1], ] results = _gen_classifier_curve( is_binomial=False, y=y, y_probs=y_probs, labels=[0, 1, 2], curve_type="roc" ) print(results) expected_x_data_list = [ [0.0, 0.25, 0.25, 1.0], [0.0, 0.33333333, 0.33333333, 1.0], [0.0, 0.33333333, 0.33333333, 1.0, 1.0], ] expected_y_data_list = [[0.0, 0.0, 1.0, 1.0], [0.0, 0.5, 1.0, 1.0], [0.0, 0.0, 0.5, 0.5, 1.0]] line_labels = ["label=0,AUC=0.750", "label=1,AUC=0.750", "label=2,AUC=0.333"] for index, (name, x_data, y_data) in enumerate(results.plot_fn_args["data_series"]): assert name == line_labels[index] assert np.allclose(x_data, expected_x_data_list[index], rtol=1e-3) assert np.allclose(y_data, expected_y_data_list[index], rtol=1e-3) assert results.plot_fn_args["xlabel"] == "False Positive Rate" assert results.plot_fn_args["ylabel"] ==

test_gen_multiclass_roc_curve

tests/components/homekit_controller/specific_devices/test_eve_energy.py

Migrate HomeKit Controller to use stable identifiers (#80064)

core

Python

test_eve_energy.py

async def test_eve_energy_setup(hass): accessories = await setup_accessories_from_file(hass, "eve_energy.json") await setup_test_accessories(hass, accessories) await assert_devices_and_entities_created( hass, DeviceTestInfo( unique_id=HUB_TEST_ACCESSORY_ID, name="Eve Energy 50FF", model="Eve Energy 20EAO8601", manufacturer="Elgato", sw_version="1.2.9", hw_version="1.0.0", serial_number="AA00A0A00000", devices=[], entities=[ EntityTestInfo( entity_id="switch.eve_energy_50ff", unique_id="00:00:00:00:00:00_1_28", friendly_name="Eve Energy 50FF", state="off", ), EntityTestInfo( entity_id="sensor.eve_energy_50ff_amps", unique_id="00:00:00:00:00:00_1_28_33", friendly_name="Eve Energy 50FF Amps", unit_of_measurement=ELECTRIC_CURRENT_AMPERE, capabilities={"state_class": SensorStateClass.MEASUREMENT}, state="0", ), EntityTestInfo( entity_id="sensor.eve_energy_50ff_volts", unique_id="00:00:00:00:00:00_1_28_32", friendly_name="Eve Energy 50FF Volts", unit_of_measurement=ELECTRIC_POTENTIAL_VOLT, capabilities={"state_class": SensorStateClass.MEASUREMENT}, state="0.400000005960464", ), EntityTestInfo( entity_id="sensor.eve_energy_50ff_power", unique_id="00:00:00:00:00:00_1_28_34", friendly_name="Eve Energy 50FF Power", unit_of_measurement=POWER_WATT, capabilities={"state_class": SensorStateClass.MEASUREMENT}, state="0", ), EntityTestInfo( entity_id="sensor.eve_energy_50ff_energy_kwh", unique_id="00:00:00:00:00:00_1_28_35", friendly_name="Eve Energy 50FF Energy kWh", capabilities={"state_class": SensorStateClass.MEASUREMENT}, unit_of_measurement=ENERGY_KILO_WATT_HOUR, state="0.28999999165535", ), EntityTestInfo( entity_id="switch.eve_energy_50ff_lock_physical_controls", unique_id="00:00:00:00:00:00_1_28_36", friendly_name="Eve Energy 50FF Lock Physical Controls", entity_category=EntityCategory.CONFIG, state="off", ), EntityTestInfo( entity_id="button.eve_energy_50ff_identify", unique_id="00:00:00:00:00:00_1_1_3", friendly_name="Eve Energy 50FF Identify", entity_category=EntityCategory.DIAGNOSTIC, state="unknown", ), ], ), )

f23b1750e85f07091eb896a0b12b8f95e5646338

https://github.com/home-assistant/core.git

async def test_eve_energy_setup(hass): accessories = await setup_accessories_from_file(hass, "eve_energy.json") await setup_test_accessories(hass, accessories) await assert_devices_and_entities_created( hass, DeviceTestInfo( unique_id=HUB_TEST_ACCESSORY_ID, name="Eve Energy 50FF", model="Eve Energy 20EAO8601", manufacturer="Elgato", sw_version="1.2.9", hw_version="1.0.0", serial_number="AA00A0A00000", devices=[], entities=[ EntityTestInfo( entity_id="switch.eve_energy_50ff", unique_id="00:00:00:00:00:00_1_28", friendly_name="Eve Energy 50FF", state="off", ), EntityTestInfo( entity_id="sensor.eve_energy_50ff_amps", unique_id="00:00:00:00:00:00_1_28_33", friendly_name="Eve Energy 50FF Amps", unit_of_m

test_eve_energy_setup

saleor/checkout/tasks.py

Optimize memory usage of delete_expired_checkouts task (#11175)

saleor

Python

tasks.py

def delete_expired_checkouts(): now = timezone.now() expired_anonymous_checkouts = ( Q(email__isnull=True) & Q(user__isnull=True) & Q(last_change__lt=now - settings.ANONYMOUS_CHECKOUTS_TIMEDELTA) ) expired_user_checkout = (Q(email__isnull=False) | Q(user__isnull=False)) & Q( last_change__lt=now - settings.USER_CHECKOUTS_TIMEDELTA ) empty_checkouts = Q(lines__isnull=True) & Q( last_change__lt=now - settings.EMPTY_CHECKOUTS_TIMEDELTA ) qs = Checkout.objects.filter( empty_checkouts | expired_anonymous_checkouts | expired_user_checkout ) deleted_count = 0 for tokens_batch in queryset_in_batches(qs): batch_count, _ = Checkout.objects.filter(token__in=tokens_batch).delete() deleted_count += batch_count if deleted_count: task_logger.debug("Removed %s checkouts.", deleted_count)

f6edcd7b4f7ee334245733f118d3b10cd065d671

https://github.com/saleor/saleor.git

def delete_expired_checkouts(): now = timezone.now() expired_anonymous_checkouts = ( Q(email__isnull=True) & Q(user__isnull=True) & Q(last_change__lt=now - settings.ANONYMOUS_CHECKOUTS_TIMEDELTA) ) expired_user_checkout = (Q(email__isnull=False) | Q(user__isnull=False))

delete_expired_checkouts

tests/admin_changelist/tests.py

Refs #33476 -- Reformatted code with Black.

django

Python

tests.py

def test_search_help_text(self): superuser = self._create_superuser("superuser") m = BandAdmin(Band, custom_site) # search_fields without search_help_text. m.search_fields = ["name"] request = self._mocked_authenticated_request("/band/", superuser) response = m.changelist_view(request) self.assertIsNone(response.context_data["cl"].search_help_text) self.assertNotContains(response, '<div class="help">') # search_fields with search_help_text. m.search_help_text = "Search help text" request = self._mocked_authenticated_request("/band/", superuser) response = m.changelist_view(request) self.assertEqual( response.context_data["cl"].search_help_text, "Search help text" ) self.assertContains(response, '<div class="help">Search help text</div>')

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def test_search_help_text(self): superuser = self._create_superuser("superuser") m = BandAdmin(Band, custom_site) # search_fields without search_help_text. m.search_fields = ["name"] request = self._mocked_authenticated_request("/band/", superuser) response = m.changelist_view(request) self.assertIsNone(response.context_data["cl"].search_help_text) self.assertNotContains(response, '<div class="help">') #

test_search_help_text

dask/dataframe/tests/test_dataframe.py

Avoid pytest.warns(None) (#8718) Compatibility with new pytest 7.0 Co-authored-by: Luka Sturtewagen <luka.sturtewagen@sensorfact.nl> Co-authored-by: Julia Signell <jsignell@gmail.com>

dask

Python

test_dataframe.py

def test_head_npartitions_warn(): match = "5 elements requested, only 3 elements" with pytest.warns(UserWarning, match=match): d.head(5) match = "Insufficient elements" with pytest.warns(UserWarning, match=match): d.head(100) with pytest.warns(UserWarning, match=match): d.head(7) with pytest.warns(UserWarning, match=match): d.head(7, npartitions=2) # No warn if all partitions are inspected for n in [3, -1]: with warnings.catch_warnings(record=True) as record: d.head(10, npartitions=n) assert not record # With default args, this means that a 1 partition dataframe won't warn d2 = dd.from_pandas(pd.DataFrame({"x": [1, 2, 3]}), npartitions=1) with warnings.catch_warnings(record=True) as record: d2.head() assert not record

e715a4d4b5b30374d6ea625a921b5557f0ce6efa

https://github.com/dask/dask.git

def test_head_npartitions_warn(): match = "5 elements requested, only 3 elements" with pytest.warns

test_head_npartitions_warn

mitmproxy/tools/main.py

use Python 3.9+ typing

mitmproxy

Python

main.py

def mitmweb(args=None) -> Optional[int]: # pragma: no cover from mitmproxy.tools import web run(web.master.WebMaster, cmdline.mitmweb, args) return None

fdde9ba3b3caaa2654048cec0af07bfcc3a6a3f8

https://github.com/mitmproxy/mitmproxy.git

def mitmweb(args=None) -> Optional[int]: # pragma: no cover from mitmproxy.tools import web run(web.master.WebMaster, cmdline.mitmweb, args) return None

mitmweb