Datasets:

semeru

/

Galeras-3k_deduped

like 0

python/ray/train/gbdt_trainer.py

[air] remove unnecessary logs + improve repr for result (#26906)

ray

Python

gbdt_trainer.py

def preprocess_datasets(self) -> None: super().preprocess_datasets() # XGBoost/LightGBM-Ray requires each dataset to have at least as many # blocks as there are workers. # TODO: Move this logic to the respective libraries for dataset_key, dataset in self.datasets.items(): if dataset.num_blocks() < self._ray_params.num_actors: if dataset.size_bytes() > _WARN_REPARTITION_THRESHOLD: warnings.warn( f"Dataset '{dataset_key}' has {dataset.num_blocks()} blocks, " f"which is less than the `num_workers` " f"{self._ray_params.num_actors}. " f"This dataset will be automatically repartitioned to " f"{self._ray_params.num_actors} blocks. You can disable " "this error message by partitioning the dataset " "to have blocks >= number of workers via " "`dataset.repartition(num_workers)`." ) self.datasets[dataset_key] = dataset.repartition( self._ray_params.num_actors )

d79431e32cffbf3f86da5f7417697dc8edd1da3f

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

def preprocess_datasets(self) -> None: super().preprocess_datasets() # XGBoost/LightGBM-Ray requires each dataset to have at least as many # blocks as there are workers. # TODO: Move this logic to the respective libraries for dataset_key, dataset in self.datasets.items(): if dataset.num_blocks() < self._ray_params.num_actors: if dataset.size_bytes() > _WARN_REPARTITION_THRESHOLD: warnings.warn( f"Dataset '{dataset_key}' has {dataset.num_blocks()} blocks, " f"which is less than the `num_workers` " f"{self._ray_para

preprocess_datasets

pandas/tests/io/test_sql.py

ENH: to_sql returns rowcount (#45137)

pandas

Python

test_sql.py

def test_datetime_NaT(self): df = DataFrame( {"A": date_range("2013-01-01 09:00:00", periods=3), "B": np.arange(3.0)} ) df.loc[1, "A"] = np.nan assert df.to_sql("test_datetime", self.conn, index=False) == 3 # with read_table -> type information from schema used result = sql.read_sql_table("test_datetime", self.conn) tm.assert_frame_equal(result, df) # with read_sql -> no type information -> sqlite has no native result = sql.read_sql_query("SELECT * FROM test_datetime", self.conn) if self.flavor == "sqlite": assert isinstance(result.loc[0, "A"], str) result["A"] = to_datetime(result["A"], errors="coerce") tm.assert_frame_equal(result, df) else: tm.assert_frame_equal(result, df)

3dfed3fcd552dcbf4daf7f78c82a87638f896512

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

def test_datetime_NaT(self): df = DataFrame( {"A": date_range("2013-01-01 09:00:00", periods=3),

test_datetime_NaT

tests/strategy/test_interface.py

Enhance hyperoptable strategy to test instance parameters

freqtrade

Python

test_interface.py

def test_auto_hyperopt_interface(default_conf): default_conf.update({'strategy': 'HyperoptableStrategy'}) PairLocks.timeframe = default_conf['timeframe'] strategy = StrategyResolver.load_strategy(default_conf) strategy.ft_bot_start() with pytest.raises(OperationalException): next(strategy.enumerate_parameters('deadBeef')) assert strategy.buy_rsi.value == strategy.buy_params['buy_rsi'] # PlusDI is NOT in the buy-params, so default should be used assert strategy.buy_plusdi.value == 0.5 assert strategy.sell_rsi.value == strategy.sell_params['sell_rsi'] assert repr(strategy.sell_rsi) == 'IntParameter(74)' # Parameter is disabled - so value from sell_param dict will NOT be used. assert strategy.sell_minusdi.value == 0.5 all_params = strategy.detect_all_parameters() assert isinstance(all_params, dict) assert len(all_params['buy']) == 2 assert len(all_params['sell']) == 2 # Number of Hyperoptable parameters assert all_params['count'] == 6 strategy.__class__.sell_rsi = IntParameter([0, 10], default=5, space='buy') with pytest.raises(OperationalException, match=r"Inconclusive parameter.*"): [x for x in strategy.detect_parameters('sell')]

5bf021be2e8f1479753e66573575fa7cde00a2b6

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

def test_auto_hyperopt_interface(default_conf): default_conf.update({'strategy': 'HyperoptableStrategy'}) PairLocks.timeframe = default_conf['timeframe'] strategy = StrategyResolver.load_strategy(default_conf) strategy.ft_bot_start() with pytest.raises(OperationalException): next(strategy.enumerate_parameters('deadBeef')) assert strategy.buy_rsi.value == strategy.buy_params['buy_rsi'] # PlusDI is NOT in the buy-params, so default should be use

test_auto_hyperopt_interface

fastai/general_optimizer.py

Upgrading to support latest Pytorch version

DeOldify

Python

general_optimizer.py

def _get_val3(self, state, val, param): v = val.view(val.size(0), -1).mean(1) return state.add_(1-param, v) if self.decay else state.add_(v)

4fc3616712edb19179b17dd270ad6cf63abf99c2

https://github.com/jantic/DeOldify.git

def _get_val3(self, state, val, param): v = val.view(val.size(0), -1).

_get_val3

tests/admin_views/tests.py

Refs #33476 -- Reformatted code with Black.

django

Python

tests.py

def test_pk_hidden_fields(self): story1 = Story.objects.create( title="The adventures of Guido", content="Once upon a time in Djangoland..." ) story2 = Story.objects.create( title="Crouching Tiger, Hidden Python", content="The Python was sneaking into...", ) response = self.client.get(reverse("admin:admin_views_story_changelist")) # Only one hidden field, in a separate place than the table. self.assertContains(response, 'id="id_form-0-id"', 1) self.assertContains(response, 'id="id_form-1-id"', 1) self.assertContains( response, '<div class="hiddenfields">\n' '<input type="hidden" name="form-0-id" value="%d" id="id_form-0-id">' '<input type="hidden" name="form-1-id" value="%d" id="id_form-1-id">\n</div>' % (story2.id, story1.id), html=True, ) self.assertContains(response, '<td class="field-id">%d</td>' % story1.id, 1) self.assertContains(response, '<td class="field-id">%d</td>' % story2.id, 1)

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def test_pk_hidden_fields(self): story1 = Story.objects.create( title="The adventures of Guido", content="Once upon a time in Djangoland..." ) story2 = Story.object

test_pk_hidden_fields

py/visdom/__init__.py

apply black py to all python files

visdom

Python

__init__.py

def get_window_data(self, win=None, env=None): return self._send( msg={"win": win, "eid": env}, endpoint="win_data", create=False, )

5b8b7f267cfaf76a2a39a727ef31a62b3909a093

https://github.com/fossasia/visdom.git

def get_window_data(self, win=None, env=None):

get_window_data

nuitka/Caching.py

Optimization: Make experimental caching bytecode demotion work.

Nuitka

Python

Caching.py

def getModuleImportableFilesHash(full_name): package_name = full_name.getPackageName() paths = getPackageSearchPath(None) if package_name is not None: paths += getPackageSearchPath(package_name) all_suffixes = getAllModuleSuffixes() result_hash = Hash() for path in paths: if not os.path.isdir(path): continue for fullname, filename in listDir(path): if isPackageDir(fullname) or filename.endswith(all_suffixes): result_hash.updateFromValues(filename, b"\0") return result_hash.asHexDigest()

840959fbec6d897aa7e51f63e1c34e46402ced8b

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

def getModuleImportableFilesHash(full_name): package_name = full_name.getPackageName() paths = getPackageSearchPath(None) if package_name is not None: paths += getPackageSearchPath(package_name) all_suffixes = getAllModuleSuffixes() result_hash = Hash() for path in paths: if not os.path.isdir(path): continue for fullname, filename in listDir(path): if isPackageDir(fullname) or filename.endswith(all_suffixes): result_hash.updateFromValues(filename, b"\0") return result_hash.asHexDig

getModuleImportableFilesHash

homeassistant/components/lg_netcast/media_player.py

Improve entity type hints [l] (#77655)

core

Python

media_player.py

def media_pause(self) -> None: self._playing = False self._state = STATE_PAUSED self.send_command(34)

d1ecd74a1a153b85b829acf45b5c6a5ea79df5c1

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

def media_pause(self) -> None: self._playing = False self._state = STATE_PAUSED self

media_pause

timm/models/vision_transformer.py

Move DeiT to own file, vit getting crowded. Working towards fixing #1029, make pooling interface for transformers and mlp closer to convnets. Still working through some details...

pytorch-image-models

Python

vision_transformer.py

def _reset_representation(self, representation_size): self.representation_size = representation_size if self.representation_size: self.pre_logits = nn.Sequential(OrderedDict([ ('fc', nn.Linear(self.embed_dim, self.representation_size)), ('act', nn.Tanh()) ])) else: self.pre_logits = nn.Identity()

5f81d4de234f579bdc988e8346da14b37a3af160

https://github.com/huggingface/pytorch-image-models.git

def _reset_representation(self, representation_size): self.representation_size = representation_size if self.representation_size: self.pre_logits = nn.Sequential(OrderedDict([

_reset_representation

tests/unit/bokeh/core/property/test_instance.py

Add Init signatures to Bokeh models (#12035) * Add signatures to Bokeh Model initializers * use explicit type for override default * move InstanceDefault to bokeh.core.properties * enable assertions

bokeh

Python

test_instance.py

def test___repr__(self) -> None: m = bcpi.InstanceDefault(_TestModel, x=10, z=[10]) assert repr(m) == "<Instance: _TestModel(x=10, z=[10])>"

1b3e6acd6eebd352106cc5ecf5e12dbf90e0607c

https://github.com/bokeh/bokeh.git

def test___repr__(self) -> None: m =

test___repr__

dash/testing/plugin.py

Revert "Remove percynofinalize." This reverts commit 8d2b1d3f5eab35f88eba46f6e96de5f484857513.

dash

Python

plugin.py

def pytest_addoption(parser): dash = parser.getgroup("Dash", "Dash Integration Tests") dash.addoption( "--webdriver", choices=("Chrome", "Firefox"), default="Chrome", help="Name of the selenium driver to use", ) dash.addoption( "--remote", action="store_true", help="instruct pytest to use selenium grid" ) dash.addoption( "--remote-url", action="store", default=SELENIUM_GRID_DEFAULT, help="set a different selenium grid remote url if other than default", ) dash.addoption( "--headless", action="store_true", help="set this flag to run in headless mode" ) dash.addoption( "--percy-assets", action="store", default="tests/assets", help="configure how Percy will discover your app's assets", ) dash.addoption( "--nopercyfinalize", action="store_false", help="set this flag to control percy finalize at CI level", ) dash.addoption( "--pause", action="store_true", help="pause using pdb after opening the test app, so you can interact with it", ) @pytest.mark.tryfirst

d2bd5aa361a1fdff7e8fa3e29568fc295c40489e

@pytest.mark.tryfirst

https://github.com/plotly/dash.git

def pytest_addoption(parser): dash = parser.getgroup("Dash", "Dash Integration Tests") dash.addoption( "--webdriver", choices=("Chrome", "Firefox"), default="Chrome", help="Name of the selenium driver to use", ) dash.addoption( "--remote", action="store_true", help="instruct pytest to use selenium grid" ) dash.addoption( "--remote-url", action="store", default=SELENIUM_GRID_DEFAULT, help="set a different selenium grid remote url if other than default", ) dash.addoption( "--headless", action="store_true", help="set this flag to run in headless mode" ) dash.addoption( "--percy-assets", action="store", default="tests/assets", help="configure how Percy will

pytest_addoption

bokeh/colors/color.py

Combine alpha values by multiplying together (#12283) * Combine multiple alpha in color2rgba * Visual integration tests * Update varying_alpha_palette to combine alpha * Include alpha in RGB.to_hex() * Improvements for mypy * More improvements for mypy * Remove unused combine_alpha() from bokehjs

bokeh

Python

color.py

def to_hex(self) -> str: if self.a < 1.0: return "#%02X%02X%02X%02X" % (self.r, self.g, self.b, round(self.a*255)) else: return "#%02X%02X%02X" % (self.r, self.g, self.b)

91fbc852c5ed7245e661e3075310f79246aac09a

https://github.com/bokeh/bokeh.git

def to_hex(self) -> str: if self.a < 1.0: return "#%02X%02X%02X%02X" % (self.r, self.g, self.b, round(self.a*255)

to_hex

tests/aggregation/tests.py

Refs #33476 -- Reformatted code with Black.

django

Python

tests.py

def test_sum_distinct_aggregate(self): authors = Author.objects.filter(book__in=[self.b5, self.b6]) self.assertEqual(authors.count(), 3) distinct_authors = authors.distinct() self.assertEqual(distinct_authors.count(), 2) # Selected author ages are 57 and 46 age_sum = distinct_authors.aggregate(Sum("age")) self.assertEqual(age_sum["age__sum"], 103)

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def test_sum_distinct_aggregate(self): authors = Author.objects.filter(book__in=[self.b5, self.b6]) self.assertEqual(authors.count(), 3)

test_sum_distinct_aggregate

src/emailservice/demo_pb2_grpc.py

chore(deps): manual upgrade of dependencies (#1396) * chore(deps): manual upgrade of dependencies accumulated upgrades from renovate PRs #997, #1094, #1095, #1193, #1379, #1384, #1387, #1388, #1389, * chore(deps): fix dependencies due to requests constraint the charset-normalizer is rolled back to lates of major version 2 (2.1.1). add importlib-metadata dependency. update opentelemetry-* to the version supported by 1.15.0 of the SDK. rollback to docker 3.10 for email & recommendation cause profiler package does not support python 3.11 regenerate email and recommendation svs protobuf * chore(deps): adding changes from #1402 and #1403

microservices-demo

Python

demo_pb2_grpc.py

def add_CurrencyServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'GetSupportedCurrencies': grpc.unary_unary_rpc_method_handler( servicer.GetSupportedCurrencies, request_deserializer=demo__pb2.Empty.FromString, response_serializer=demo__pb2.GetSupportedCurrenciesResponse.SerializeToString, ), 'Convert': grpc.unary_unary_rpc_method_handler( servicer.Convert, request_deserializer=demo__pb2.CurrencyConversionRequest.FromString, response_serializer=demo__pb2.Money.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'hipstershop.CurrencyService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) # This class is part of an EXPERIMENTAL API.

c6105dbd7210286dde07aa0a09381bf99840acca

https://github.com/GoogleCloudPlatform/microservices-demo.git

def add_CurrencyServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'GetSupportedCurrencies': grpc.unary_unary_rpc_method_handler( servicer.GetSupportedCurrencies, request_deserializer=demo__pb2.Empty.FromString, response_serializer=demo__pb2.GetSupported

add_CurrencyServiceServicer_to_server

mindsdb/integrations/mysql/mysql.py

fix dtypes casting

mindsdb

Python

mysql.py

def _to_mysql_table(self, dtype_dict, predicted_cols, columns): subtype_map = { dtype.integer: 'int', dtype.float: 'double', dtype.binary: 'bool', dtype.date: 'Date', dtype.datetime: 'Datetime', dtype.binary: 'VARCHAR(500)', dtype.categorical: 'VARCHAR(500)', dtype.tags: 'VARCHAR(500)', dtype.image: 'VARCHAR(500)', dtype.video: 'VARCHAR(500)', dtype.audio: 'VARCHAR(500)', dtype.short_text: 'VARCHAR(500)', dtype.rich_text: 'VARCHAR(500)', dtype.quantity: 'VARCHAR(500)', dtype.num_array: 'VARCHAR(500)', dtype.cat_array: 'VARCHAR(500)', dtype.num_tsarray: 'VARCHAR(500)', dtype.cat_tsarray: 'VARCHAR(500)', 'default': 'VARCHAR(500)' } column_declaration = [] for name in columns: try: col_subtype = dtype_dict[name] new_type = subtype_map.get(col_subtype, subtype_map.get('default')) column_declaration.append(f' `{name}` {new_type} ') if name in predicted_cols: column_declaration.append(f' `{name}_original` {new_type} ') except Exception as e: log.error(f'Error: can not determine mysql data type for column {name}: {e}') return column_declaration

80d671fae9c05521009615a2bfff1b760a98debd

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

def _to_mysql_table(self, dtype_dict, predicted_cols, columns): subtype_map = { dtype.integer: 'int', dtype.float: 'double', dtype.binary: 'bool', dtype.date: 'Date', dtype.datetime: 'Datetime', dtype.binary: 'VARCHAR(500)', dtype.categorical: 'VARCHAR(500)', dtype.tags: 'VARCHAR(500)', dtype.image: 'VARCHAR(500)', dtype.video: 'VARCHAR(500)', dtype.audio: 'VARCHAR(500)', dtype.short_text: 'VARCHAR(500)', dtype.rich_text: 'VARCHAR(500)', dtype.quantity: 'VARCHAR(500)', dtype.num_array: 'VARCHAR(500)', dtype.cat_array: 'VARCHAR(500)', dtype.num_tsarray: 'VARCHAR(500)', dtype.cat_tsarray: 'VARCHAR(500)', 'default': 'VARCHAR(500)' } column_declaration = [] for name in columns: try: col_subtype = dtype_dict[name] new_type = subtype_map.get(col_subtype, subtype_map.get('default')) column_declaration.append(f' `{name}` {new_type} ') if name in predicted_cols: column_declaration.append(f' `{name}_

_to_mysql_table

saleor/webhook/observability/tests/test_obfuscation.py

Observability reporter (#9803) * Initial commit * Add observability celery beat task * Add observability_reporter_task and observability_send_events * Convert payload to camel case * Add fakeredis to dev dependencies * Add redis buffer tests * Refactor buffer * Update * Optimize buffer * Add tests * Add types-redis to dev dependencies * Refactor * Fix after rebase * Refactor opentracing * Add opentracing to observability tasks * Add more tests * Fix buffer fixtures * Report dropped events * Fix buffer tests * Refactor get_buffer * Refactor unit tests * Set Redis connection client_name * Refactor redis tests * Fix test_get_or_create_connection_pool * Fix JsonTruncText comparison * Add more generate_event_delivery_attempt_payload tests

saleor

Python

test_obfuscation.py

def test_anonymize_gql_operation_response_with_fragment_spread(gql_operation_factory): query = result = {"data": "result"} sensitive_fields = {"Product": {"name"}} operation_result = gql_operation_factory(query, result=result) anonymize_gql_operation_response(operation_result, sensitive_fields) assert operation_result.result["data"] == MASK @pytest.mark.parametrize( "sensitive_fields", [ {"NonExistingType": {}}, {"Product": {"nonExistingField"}}, {"Node": {"id"}}, ], )

7ea7916c65357741c3911e307acb58d547a5e91a

@pytest.mark.parametrize( "sensitive_fields", [ {"NonExistingType": {}}, {"Product": {"nonExistingField"}}, {"Node": {"id"}}, ], )

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

def test_anonymize_gql_operation_response_with_fragment_spread(gql_operation_factory): query = result = {"data": "result"} sensitive_fields = {"Product": {"name"}} operation_result = gql_operation_factory(query, result=result) anonymize_gql_operation_response(operation_result, sensitive_fields) assert operation_result.result["data"] == MASK @pytest.mark.parametrize( "sensitive_fields", [ {"NonExistingType": {}}, {"Product": {"nonExistingField"}}, {"Node": {"id"}}, ], )

test_anonymize_gql_operation_response_with_fragment_spread

awx/main/analytics/subsystem_metrics.py

Add subsystem metrics for task manager

awx

Python

subsystem_metrics.py

def observe(self, field, value): self.METRICS[field].observe(value) self.metrics_have_changed = True if self.auto_pipe_execute is True: self.pipe_execute()

2f82b757483cf67829a8c0ed843b51d126ec658e

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

def observe(self, field, value): self.METRICS[field].observe(value) self.metrics_have_changed = True if self.auto_pipe_execute is True: self.pipe_execute()

observe

ppocr/postprocess/table_postprocess.py

add TableMaster

PaddleOCR

Python

table_postprocess.py

def decode_label(self, batch): structure_idx = batch[1] gt_bbox_list = batch[2] shape_list = batch[-1] ignored_tokens = self.get_ignored_tokens() end_idx = self.dict[self.end_str] structure_batch_list = [] bbox_batch_list = [] batch_size = len(structure_idx) for batch_idx in range(batch_size): structure_list = [] bbox_list = [] for idx in range(len(structure_idx[batch_idx])): char_idx = int(structure_idx[batch_idx][idx]) if idx > 0 and char_idx == end_idx: break if char_idx in ignored_tokens: continue structure_list.append(self.character[char_idx]) bbox = gt_bbox_list[batch_idx][idx] if bbox.sum() != 0: bbox = self._bbox_decode(bbox, shape_list[batch_idx]) bbox_list.append(bbox) structure_batch_list.append(structure_list) bbox_batch_list.append(bbox_list) result = { 'bbox_batch_list': bbox_batch_list, 'structure_batch_list': structure_batch_list, } return result

a0c33908d500fe893d8e79e11399a5ab665f330b

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

def decode_label(self, batch): structure_idx = batch[1] gt_bbox_list = batch[2] shape_list = batch[-1] ignored_tokens = self.get_ignored_tokens() end_idx = self.dict[self.end_str] structure_batch_list = [] bbox_batch_list = [] batch_size = len(structure_idx) for batch_idx in range(batch_size): structure_list = [] bbox_list = [] for idx in range(len(structure_idx[batch_idx])): char_idx = int(structure_idx[batch_idx][idx]) if idx > 0 and char_idx == end_idx: break if char_idx in ignored_tokens: continue structure_list.append(self.character[char_idx]) bbox = gt_bbox_list[batch_idx][idx] if bbox.sum() != 0: bbox = self._bbox_decode(bbox, shape_list[batch_idx]) bbox_list.append(bbox) structure_batch_list.append(structure_list) bbox_batch_list.append(bbox_list) result = { 'bbox_batch_list': bbox_batch_list, 'structure_batch_list': structure_batch_list, } return result

decode_label

src/sentry/search/events/datasets/metrics.py

feat(mep): Add the team-key-transaction alias (#32593) - This moves some of our aliases to their own files, the intention is to eventually migrate more of the class methods there this gives us a few benefits - We can unit test the resolverse now - They're easier to re-use across datasets

sentry

Python

metrics.py

def field_alias_converter(self) -> Mapping[str, Callable[[str], SelectType]]: return { constants.PROJECT_ALIAS: self._resolve_project_slug_alias, constants.PROJECT_NAME_ALIAS: self._resolve_project_slug_alias, constants.TEAM_KEY_TRANSACTION_ALIAS: self._resolve_team_key_transaction_alias, }

ebf2d415cbbbfc4473bcc304f0032f805969a0f9

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

def field_alias_converter(self) -> Mapping[str, Callable[[str], SelectType]]: return { constants.PROJECT_ALIAS: self._resolve_project_slug_alias, constants.PROJECT_NAME_ALIAS: self._resolve_project_slug_alias, constants.TEAM_KEY_TRANSACTION_ALIAS: self._resolve_t

field_alias_converter

tests/snapshot_tests/test_snapshots.py

more pauses for demo?

textual

Python

test_snapshots.py

def test_demo(snap_compare): assert snap_compare( Path("../../src/textual/demo.py"), press=["down", "down", "down", "_", "_"], terminal_size=(100, 30), )

91e23ff34c2f34da291c7cadf26dcb5fbde4e439

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

def test_demo(snap_compare):

test_demo

deploy/pphuman/pipeline.py

ppvehicle plate license

PaddleDetection

Python

pipeline.py

def predict_video(self, video_file): # mot # mot -> attr # mot -> pose -> action capture = cv2.VideoCapture(video_file) video_out_name = 'output.mp4' if self.file_name is None else self.file_name # Get Video info : resolution, fps, frame count width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = int(capture.get(cv2.CAP_PROP_FPS)) frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) print("video fps: %d, frame_count: %d" % (fps, frame_count)) if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) out_path = os.path.join(self.output_dir, video_out_name) fourcc = cv2.VideoWriter_fourcc(* 'mp4v') writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height)) frame_id = 0 entrance, records, center_traj = None, None, None if self.draw_center_traj: center_traj = [{}] id_set = set() interval_id_set = set() in_id_list = list() out_id_list = list() prev_center = dict() records = list() entrance = [0, height / 2., width, height / 2.] video_fps = fps video_action_imgs = [] if self.with_video_action: short_size = self.cfg["VIDEO_ACTION"]["short_size"] scale = ShortSizeScale(short_size) while (1): if frame_id % 10 == 0: print('frame id: ', frame_id) ret, frame = capture.read() if not ret: break if self.modebase["idbased"] or self.modebase["skeletonbased"]: if frame_id > self.warmup_frame: self.pipe_timer.total_time.start() self.pipe_timer.module_time['mot'].start() res = self.mot_predictor.predict_image( [copy.deepcopy(frame)], visual=False) if frame_id > self.warmup_frame: self.pipe_timer.module_time['mot'].end() # mot output format: id, class, score, xmin, ymin, xmax, ymax mot_res = parse_mot_res(res) # flow_statistic only support single class MOT boxes, scores, ids = res[0] # batch size = 1 in MOT mot_result = (frame_id + 1, boxes[0], scores[0], ids[0]) # single class statistic = flow_statistic( mot_result, self.secs_interval, self.do_entrance_counting, video_fps, entrance, id_set, interval_id_set, in_id_list, out_id_list, prev_center, records) records = statistic['records'] # nothing detected if len(mot_res['boxes']) == 0: frame_id += 1 if frame_id > self.warmup_frame: self.pipe_timer.img_num += 1 self.pipe_timer.total_time.end() if self.cfg['visual']: _, _, fps = self.pipe_timer.get_total_time() im = self.visualize_video(frame, mot_res, frame_id, fps, entrance, records, center_traj) # visualize writer.write(im) if self.file_name is None: # use camera_id cv2.imshow('PPHuman&&PPVehicle', im) if cv2.waitKey(1) & 0xFF == ord('q'): break continue self.pipeline_res.update(mot_res, 'mot') crop_input, new_bboxes, ori_bboxes = crop_image_with_mot( frame, mot_res) if self.with_vehicleplate: platelicense = self.vehicleplate_detector.get_platelicense( crop_input) self.pipeline_res.update(platelicense, 'vehicleplate') if self.with_attr: if frame_id > self.warmup_frame: self.pipe_timer.module_time['attr'].start() attr_res = self.attr_predictor.predict_image( crop_input, visual=False) if frame_id > self.warmup_frame: self.pipe_timer.module_time['attr'].end() self.pipeline_res.update(attr_res, 'attr') if self.with_idbased_detaction: if frame_id > self.warmup_frame: self.pipe_timer.module_time['det_action'].start() det_action_res = self.det_action_predictor.predict( crop_input, mot_res) if frame_id > self.warmup_frame: self.pipe_timer.module_time['det_action'].end() self.pipeline_res.update(det_action_res, 'det_action') if self.cfg['visual']: self.det_action_visual_helper.update(det_action_res) if self.with_idbased_clsaction: if frame_id > self.warmup_frame: self.pipe_timer.module_time['cls_action'].start() cls_action_res = self.cls_action_predictor.predict_with_mot( crop_input, mot_res) if frame_id > self.warmup_frame: self.pipe_timer.module_time['cls_action'].end() self.pipeline_res.update(cls_action_res, 'cls_action') if self.cfg['visual']: self.cls_action_visual_helper.update(cls_action_res) if self.with_skeleton_action: if frame_id > self.warmup_frame: self.pipe_timer.module_time['kpt'].start() kpt_pred = self.kpt_predictor.predict_image( crop_input, visual=False) keypoint_vector, score_vector = translate_to_ori_images( kpt_pred, np.array(new_bboxes)) kpt_res = {} kpt_res['keypoint'] = [ keypoint_vector.tolist(), score_vector.tolist() ] if len(keypoint_vector) > 0 else [[], []] kpt_res['bbox'] = ori_bboxes if frame_id > self.warmup_frame: self.pipe_timer.module_time['kpt'].end() self.pipeline_res.update(kpt_res, 'kpt') self.kpt_buff.update(kpt_res, mot_res) # collect kpt output state = self.kpt_buff.get_state( ) # whether frame num is enough or lost tracker skeleton_action_res = {} if state: if frame_id > self.warmup_frame: self.pipe_timer.module_time[ 'skeleton_action'].start() collected_keypoint = self.kpt_buff.get_collected_keypoint( ) # reoragnize kpt output with ID skeleton_action_input = parse_mot_keypoint( collected_keypoint, self.coord_size) skeleton_action_res = self.skeleton_action_predictor.predict_skeleton_with_mot( skeleton_action_input) if frame_id > self.warmup_frame: self.pipe_timer.module_time['skeleton_action'].end() self.pipeline_res.update(skeleton_action_res, 'skeleton_action') if self.cfg['visual']: self.skeleton_action_visual_helper.update( skeleton_action_res) if self.with_mtmct and frame_id % 10 == 0: crop_input, img_qualities, rects = self.reid_predictor.crop_image_with_mot( frame, mot_res) if frame_id > self.warmup_frame: self.pipe_timer.module_time['reid'].start() reid_res = self.reid_predictor.predict_batch(crop_input) if frame_id > self.warmup_frame: self.pipe_timer.module_time['reid'].end() reid_res_dict = { 'features': reid_res, "qualities": img_qualities, "rects": rects } self.pipeline_res.update(reid_res_dict, 'reid') else: self.pipeline_res.clear('reid') if self.with_video_action: # get the params frame_len = self.cfg["VIDEO_ACTION"]["frame_len"] sample_freq = self.cfg["VIDEO_ACTION"]["sample_freq"] if sample_freq * frame_len > frame_count: # video is too short sample_freq = int(frame_count / frame_len) # filter the warmup frames if frame_id > self.warmup_frame: self.pipe_timer.module_time['video_action'].start() # collect frames if frame_id % sample_freq == 0: # Scale image scaled_img = scale(frame) video_action_imgs.append(scaled_img) # the number of collected frames is enough to predict video action if len(video_action_imgs) == frame_len: classes, scores = self.video_action_predictor.predict( video_action_imgs) if frame_id > self.warmup_frame: self.pipe_timer.module_time['video_action'].end() video_action_res = {"class": classes[0], "score": scores[0]} self.pipeline_res.update(video_action_res, 'video_action') print("video_action_res:", video_action_res) video_action_imgs.clear() # next clip self.collector.append(frame_id, self.pipeline_res) if frame_id > self.warmup_frame: self.pipe_timer.img_num += 1 self.pipe_timer.total_time.end() frame_id += 1 if self.cfg['visual']: _, _, fps = self.pipe_timer.get_total_time() im = self.visualize_video(frame, self.pipeline_res, frame_id, fps, entrance, records, center_traj) # visualize writer.write(im) if self.file_name is None: # use camera_id cv2.imshow('PPHuman', im) if cv2.waitKey(1) & 0xFF == ord('q'): break writer.release() print('save result to {}'.format(out_path))

f2a883edd26c4630672c32415d9c5334846a4b5c

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

def predict_video(self, video_file): # mot # mot -> attr # mot -> pose -> action capture = cv2.VideoCapture(video_file) video_out_name = 'output.mp4' if self.file_name is None else self.file_name # Get Video info : resolution, fps, frame count width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = int(capture.get(cv2.CAP_PROP_FPS)) frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) print("video fps: %d, frame_count: %d" % (fps, frame_count)) if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) out_path = os.path.join(self.output_dir, video_out_name) fourcc = cv2.VideoWriter_fourcc(* 'mp4v') writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height)) frame_id = 0 entrance, records, center_traj = None, None, None if self.draw_center_traj: center_traj = [{}] id_set = set() interval_id_set = set() in_id_list = list() out_id_list = list() prev_center = dict() records = list() entrance = [0, height / 2., width, height / 2.] video_fps = fps video_action_imgs = [] if self.with_video_action: short_size = self.cfg["VIDEO_ACTION"]["short_size"] scale = ShortSizeScale(short_size) while (1): if frame_id % 10 == 0: print('frame id: ', frame_id) ret, frame = capture.read() if not ret: break if self.modebase["idbased"] or self.modebase["skeletonbased"]: if frame_id > self.warmup_frame: self.pipe_timer.total_time.start() self.pipe_timer.module_time['mot'].start() res = self.mot_predictor.predict_image( [copy.deepcopy(frame)], visual=False) if frame_id > self.warmup_frame: self.pipe_timer.module_time['mot'].end() # mot output format: id, class, score, xmin, ymin, xmax, ymax mot_res = parse_mot_res(res) # flow_statistic only support single class MOT boxes, scores, ids = res[0] # batch size = 1 in MOT mot_result = (frame_id + 1, boxes[0], scores[0], ids[0]) # single class statistic = flow_statistic( mot_result, self.secs_interval, self.do_entrance_counting, video_fps, entrance, id_set, interval_id_set, in_id_list, out_id_list, prev_center, records) records = statistic['records'] # nothing detected if len(mot_res['boxes']) == 0: frame_id += 1 if frame_id > self.warmup_frame: self.pipe_timer.img_num += 1 self.pipe_timer.total_time.end() if self.cfg['visual']: _, _, fps = self.pipe_timer.get_total_time() im = self.visualize_video(frame, mot_res, frame_id, fps, entrance, records, center_traj) # visualize writer.write(im) if self.file_name is None: # use camera_id cv2.imshow('PPHuman&&PPVehicle', im) if cv2.waitKey(1) & 0xFF == ord('q'): break continue self.pipeline_res.update(mot_res, 'mot') crop_input, new_bboxes, ori_bboxes = crop_image_with_mot( frame, mot_res) if self.with_vehicleplate: platelicense = self.vehicleplate_detector.get_platelicense( crop_input) self.pipeline_res.update(platelicense, 'vehicleplate') if self.with_attr: if frame_id > self.warmup_frame: self.pipe_timer.module_time['attr'].start() attr_res = self.attr_predictor.predict_image( crop_input, visual=False) if frame_id > self.warmup_frame: self.pipe_timer.module_time['attr'].end() self.pipeline_res.update(attr_res, 'attr') if self.with_idbased_detaction: if frame_id > self.warmup_frame: self.pipe_timer.module_time['det_action'].start() det_action_res = self.det_action_predictor.predict( crop_input, mot_res) if frame_id > self.warmup_frame: self.pipe_timer.module_time['det_action'].end() self.pipeline_res.update(det_action_res, 'det_action') if self.cfg['visual']: self.det_action_visual_helper.update(det_action_res) if self.with_idbased_clsaction: if frame_id > self.warmup_frame: self.pipe_timer.module_time['cls_action'].start() cls_action_res = self.cls_action_predictor.predict_with_mot( crop_input, mot_res) if frame_id > self.warmup_frame: self.pipe_timer.module_time['cls_action'].end() self.pipeline_res.update(cls_action_res, 'cls_action') if self.cfg['visual']: self.cls_action_visual_helper.update(cls_action_res) if self.with_skeleton_action: if frame_id > self.warmup_frame: self.pipe_timer.module_time['kpt'].start() kpt_pred = self.kpt_predictor.predict_image( crop_input, visual=False) keypoint_vector, score_vector = translate_to_ori_images( kpt_pred, np.array(new_bboxes)) kpt_res = {} kpt_res['keypoint'] = [ keypoint_vector.tolist(), score_vector.tolist() ] if len(keypoint_vector) > 0 else [[], []] kpt_res['bbox'] = ori_bboxes if frame_id > self.warmup_frame: self.pipe_timer.module_time['kpt'].end() self.pipeline_res.update(kpt_res, 'kpt') self.kpt_buff.update(kpt_res, mot_res) # collect kpt output state = self.kpt_buff.get_state( ) # whether frame num is enough or lost tracker skeleton_action_res = {} if state: if frame_id > self.warmup_frame: self.pipe_timer.module_time[ 'skeleton_action'].start() collected_keypoint = self.kpt_buff.get_collected_keypoint( ) # reoragnize kpt output with ID skeleton_action_input = parse_mot_keypoint( collected_keypoint, self.coord_size) skeleton_action_res = self.skeleton_action_predictor.predict_skeleton_with_mot( skeleton_action_input) if frame_id > self.warmup_frame: self.pipe_timer.module_time['skeleton_action'].end() self.pipeline_res.update(skeleton_action_res, 'skeleton_action') if self.cfg['visual']: self.skeleton_action_visual_helper.update( skeleton_action_res) if self.with_mtmct and frame_id % 10 == 0: crop_input, img_qualities, rects = self.reid_predictor.crop_image_with_mot( frame, mot_res) if frame_id > self.warmup_frame: self.pipe_timer.module_time['reid'].start() reid_res = self.reid_predictor.predict_batch(crop_input) if frame_id > self.warmup_frame: self.pipe_timer.module_time['reid'].end() reid_res_dict = { 'features': reid_res, "qualities": img_qualities, "rects": rects } self.pipeline_res.update(reid_res_dict, 'reid') else: self.pipeline_res.clear('reid') if self.with_video_action: # get the params frame_len = self.cfg["VIDEO_ACTION"]["frame_len"] sample_freq = self.cfg["VIDEO_ACTION"]["sample_freq"] if sample_freq * frame_len > frame_count: # video is too short sample_freq = int(frame_count / frame_len) # filter the warmup frames if frame_id > self.warmup_frame: self.pipe_timer.module_time['video_action'].start() # collect frames if frame_id % sample_freq == 0: # Scale image scaled_img = scale(frame) video_action_imgs.append(scaled_img) # the number of collected frames is enough to predict video action if len(video_action_imgs) == frame_len: classes, scores = self.video_action_predictor.predict( video_action_imgs) if frame_id > self.warmup_frame: self.pipe_timer.module_time['video_action'].end() video_action_res = {"class": classes[0], "score": scores[0]} self.pipeline_res.update(video_action_res, 'video_action') print("video_action_res:", video_action_res) video_action_imgs.clear() # next clip self.collector.append(frame_id, self.pipeline_res) if frame_id > self.warmup_frame: self.pipe_timer.img_num += 1 self.pipe_timer.total_time.end() frame_id += 1 if self.cfg['visual']: _, _, fps = self.pipe_timer.get_total_time() im = self.visualize_video(frame, self.pipeline_res, frame_id, fps, entrance, records, center_traj) # visualize writer.write(im) if self.file_name is None: # use camera_id

predict_video

mindsdb/interfaces/database/integrations.py

better dependencies installation

mindsdb

Python

integrations.py

def _get_handler_meta(self, module): handler_dir = Path(module.__path__[0]) handler_folder_name = handler_dir.name handler_name = handler_folder_name if handler_name.endswith('_handler'): handler_name = handler_name[:-8] dependencies = self._read_dependencies(handler_dir) self.handler_modules[module.name] = module import_error = None if hasattr(module, 'import_error'): import_error = module.import_error handler_meta = { 'import': { 'success': import_error is None, 'folder': handler_folder_name, 'dependencies': dependencies }, 'version': module.version } if import_error is not None: handler_meta['import']['error_message'] = str(import_error) for attr in ('connection_args_example', 'connection_args', 'description', 'name', 'type', 'title'): if hasattr(module, attr): handler_meta[attr] = getattr(module, attr) # region icon if hasattr(module, 'icon_path'): icon_path = handler_dir.joinpath(module.icon_path) handler_meta['icon'] = { 'name': icon_path.name, 'type': icon_path.name[icon_path.name.rfind('.') + 1:].lower() } if handler_meta['icon']['type'] == 'svg': with open(str(icon_path), 'rt') as f: handler_meta['icon']['data'] = f.read() else: with open(str(icon_path), 'rb') as f: handler_meta['icon']['data'] = base64.b64encode(f.read()).decode('utf-8') # endregion if handler_meta.get('name') in ('files', 'views', 'lightwood'): handler_meta['permanent'] = True else: handler_meta['permanent'] = False return handler_meta

f8e08f9509befc7b7ddfda5fccbd1b9b72c9b5f9

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

def _get_handler_meta(self, module): handler_dir = Path(module.__path__[0]) handler_folder_name = handler_dir.name handler_name = handler_folder_name if handler_name.endswith('_handler'): handler_name = handler_name[:-8] dependencies = s

_get_handler_meta

python/ray/tune/examples/mnist_pytorch.py

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

ray

Python

mnist_pytorch.py

def get_data_loaders(): mnist_transforms = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] ) # We add FileLock here because multiple workers will want to # download data, and this may cause overwrites since # DataLoader is not threadsafe. with FileLock(os.path.expanduser("~/data.lock")): train_loader = torch.utils.data.DataLoader( datasets.MNIST( "~/data", train=True, download=True, transform=mnist_transforms ), batch_size=64, shuffle=True, ) test_loader = torch.utils.data.DataLoader( datasets.MNIST( "~/data", train=False, download=True, transform=mnist_transforms ), batch_size=64, shuffle=True, ) return train_loader, test_loader

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

def get_data_loaders(): mnist_transforms = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] ) # We add FileLock here because multiple workers will want to # download data, and this may cause overwrites since # DataLoader is not threadsafe. with FileLock(os.path.expanduser("~/data.lock")): train_loader = torch.utils.data.DataLoader( datasets.MNIST( "~/data", train=True, download=True, transform=mnist_transforms ), batch_size=64, shuffle=True,

get_data_loaders

packages/syft/src/syft/core/adp/vm_private_scalar_manager.py

Fix serialize

PySyft

Python

vm_private_scalar_manager.py

def get(self, index) -> int: while index > len(self.prime_numbers)-1: self.exp += 1 self.prime_numbers = primes(10**self.exp) return self.prime_numbers[index]

8436a4bbdd900476b4f85cad7024ef4e2e964352

https://github.com/OpenMined/PySyft.git

def get(self, index) -> int: while index > len(self.prime_numbers)-1: self.exp += 1 self.prime_numbers = primes(10**self.exp) return

get

modules/image/text_recognition/ch_pp-ocrv3_det/test.py

update ch_pp-ocrv3_det (#2173) * update ch_pp-ocrv3_det * update

PaddleHub

Python

test.py

def test_detect_text4(self): results = self.module.detect_text( images=[cv2.imread('tests/test.jpg')], use_gpu=False, visualization=True, ) self.assertEqual( results[0]['data'], [[[261, 202], [376, 202], [376, 239], [261, 239]], [[283, 162], [352, 162], [352, 202], [283, 202]]])

4382eee60dbee6cb153822a4cb839693e59091bf

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

def test_detect_text4(self): results = self.module.detect_text( images=[cv2.imread('tes

test_detect_text4

nni/compression/pytorch/utils/config_validation_v1.py

[Compression] remove pruning v1 & refactor directory (#5228)

nni

Python

config_validation_v1.py

def validate_op_types(model, op_types, logger): found_types = set(['default']) | set(map(lambda x: type(x[1]).__name__, model.named_modules())) not_found_op_types = list(set(op_types) - found_types) if not_found_op_types: logger.warning('op_types %s not found in model', not_found_op_types) return True

d68c786ff81bad19c04619d6a999ff34aaa724e7

https://github.com/microsoft/nni.git

def validate_op_types(model, op_types, logger): found_types = set(['default']) | set(map(lambda x: type(x[1]).__name__, model.named_modules())) not_found_op_types = list(set(op_types) - found_types) if not_found_op_types: logger.warning('op_types %s not found in model', not_found_op_types) return True

validate_op_types

test/test_inputs.py

inputs

gradio

Python

test_inputs.py

def test_as_component(self): bool_input = gr.inputs.Checkbox() self.assertEqual(bool_input.preprocess(True), True) self.assertEqual(bool_input.preprocess_example(True), True) self.assertEqual(bool_input.serialize(True, True), True) with tempfile.TemporaryDirectory() as tmpdirname: to_save = bool_input.save_flagged(tmpdirname, "bool_input", True, None) self.assertEqual(to_save, True) restored = bool_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, True) self.assertIsInstance(bool_input.generate_sample(), bool) bool_input = gr.inputs.Checkbox(default=True, label="Check Your Input") self.assertEqual( bool_input.get_template_context(), { "default_value": True, "name": "checkbox", "label": "Check Your Input", "css": {}, }, )

04b6b80b3361a14eaee4a064bccc25494332e83c

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

def test_as_component(self): bool_input = gr.

test_as_component

tests/queries/test_qs_combinators.py

Fixed #29338 -- Allowed using combined queryset in Subquery. Thanks Eugene Kovalev for the initial patch, Simon Charette for the review, and Chetan Khanna for help.

django

Python

test_qs_combinators.py

def test_union_in_subquery(self): ReservedName.objects.bulk_create([ ReservedName(name='rn1', order=8), ReservedName(name='rn2', order=1), ReservedName(name='rn3', order=5), ]) qs1 = Number.objects.filter(num__gt=7, num=OuterRef('order')) qs2 = Number.objects.filter(num__lt=2, num=OuterRef('order')) self.assertCountEqual( ReservedName.objects.annotate( number=Subquery(qs1.union(qs2).values('num')), ).filter(number__isnull=False).values_list('order', flat=True), [8, 1], )

30a01441347d5a2146af2944b29778fa0834d4be

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

def test_union_in_subquery(self): ReservedName.objects.bulk_create([ ReservedName(name='rn1', order=8), ReservedName(name='rn2', order=1), ReservedName(name='rn3', or

test_union_in_subquery

homeassistant/components/motion_blinds/cover.py

Motion request update till stop (#68580) * update untill stop * fixes * fix spelling

core

Python

cover.py

def set_absolute_position(self, **kwargs): position = kwargs[ATTR_ABSOLUTE_POSITION] self._blind.Set_position(100 - position) self.request_position_till_stop()

83983bc875445d7147cb98e70f1214c6ed270da9

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

def set_absolute_position(self, **kwargs): position = kwargs

set_absolute_position

tests/unit/javascript/test_greasemonkey.py

greasemonkey: Don't implicitly load scripts Needed for #7245 and also seems like cleaner code.

qutebrowser

Python

test_greasemonkey.py

def test_all(gm_manager): _save_script(test_gm_script, 'test.user.js') gm_manager.load_scripts() assert (gm_manager.all_scripts()[0].name == "qutebrowser test userscript") @pytest.mark.parametrize("url, expected_matches", [ # included ('http://trolol.com/', 1), # neither included nor excluded ('http://aaaaaaaaaa.com/', 0), # excluded ('https://badhost.xxx/', 0), ])

21419c9ef5a90ea36a27afaf2503a57f8f9f8536

@pytest.mark.parametrize("url, expected_matches", [ # included ('http://trolol.com/', 1), # neither included nor excluded ('http://aaaaaaaaaa.com/', 0), # excluded ('https://badhost.xxx/', 0), ])

https://github.com/qutebrowser/qutebrowser.git

def test_all(gm_manager): _save_script(test_gm_script, 'test.user.js') gm_manager.load_scripts() assert (gm_manager.all_scripts()[0].name == "qutebrowser test userscript") @pytest.mark.parametrize("url, expected

test_all

erpnext/regional/india/e_invoice/utils.py

test: Add test for einvoice discounts

erpnext

Python

utils.py

def get_invoice_value_details(invoice): invoice_value_details = frappe._dict(dict()) invoice_value_details.base_total = abs(sum([i.taxable_value for i in invoice.get("items")])) if ( invoice.apply_discount_on == "Grand Total" and invoice.discount_amount and invoice.get("is_cash_or_non_trade_discount") ): invoice_value_details.invoice_discount_amt = invoice.discount_amount else: invoice_value_details.invoice_discount_amt = 0 invoice_value_details.round_off = invoice.base_rounding_adjustment invoice_value_details.base_grand_total = abs(invoice.base_rounded_total) or abs( invoice.base_grand_total ) invoice_value_details.grand_total = abs(invoice.rounded_total) or abs(invoice.grand_total) invoice_value_details = update_invoice_taxes(invoice, invoice_value_details) return invoice_value_details

38352b3e46fb18435c780e5775bbc886491eac96

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

def get_invoice_value_details(invoice): invoice_value_details = frappe._dict(dict()) invoice_value_details.base_total = abs(sum([i.taxable_value for i in invoice.get("items")])) if ( invoice.apply_discount_on == "Grand Total" and invoice.discount_amount and invoice.get("is_cash_or_non_trade_discount") ): invoice_value_details.invoice_discount_amt = invoice.discount_amount else: invoice_value_details.invoice_discount_a

get_invoice_value_details

python/ccxt/async_support/currencycom.py

1.73.50 [ci skip]

ccxt

Python

currencycom.py

async def fetch_balance(self, params={}): await self.load_markets() response = await self.privateGetV2Account(params) # # { # "makerCommission": "0.20", # "takerCommission": "0.20", # "buyerCommission": "0.20", # "sellerCommission": "0.20", # "canTrade": True, # "canWithdraw": True, # "canDeposit": True, # "updateTime": "1645266330", # "userId": "644722", # "balances": [ # { # "accountId": "120702016179403605", # "collateralCurrency": False, # "asset": "CAKE", # "free": "1.784", # "locked": "0.0", # "default": False, # }, # { # "accountId": "109698017413175316", # "collateralCurrency": True, # "asset": "USD", # "free": "7.58632", # "locked": "0.0", # "default": True, # } # ] # } # return self.parse_balance(response)

72c00f3d959baa1e355e8d8231e60561abe62eea

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

async def fetch_balance(self, params={}): await self.load_markets() response = await self.privateGetV2Account(params) # # { # "makerCommission": "0.20", # "takerCommission": "0.20", # "buyerCommission": "0.20", # "sellerCommission": "0.20", # "canTrade": True, # "canWithdraw": True, # "canDeposit": True, # "updateTime": "1645266330", # "userId": "644722", # "balances": [ # { # "accountId": "120702016179403605", # "collateralCurrency": False, # "asset": "CAKE", # "free": "1.784", # "locked": "0.0", # "default": False, # }, # { # "accountId": "109698017413175316", # "collateralCurrency": True, # "asset": "USD", # "free": "7.58632", # "locked": "0.0", #

fetch_balance

modules/image/semantic_segmentation/ace2p/data_feed.py

update ace2p (#2003) * update ace2p * add clean func * update ace2p

PaddleHub

Python

data_feed.py

def preprocess(org_im, scale, rotation): image = org_im.copy() image_height, image_width, _ = image.shape aspect_ratio = scale[1] * 1.0 / scale[0] image_center, image_scale = _box2cs([0, 0, image_width - 1, image_height - 1], aspect_ratio) trans = get_affine_transform(image_center, image_scale, rotation, scale) image = cv2.warpAffine( image, trans, (int(scale[1]), int(scale[0])), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0)) img_mean = np.array([0.406, 0.456, 0.485]).reshape((1, 1, 3)) img_std = np.array([0.225, 0.224, 0.229]).reshape((1, 1, 3)) image = image.astype(np.float32) image = (image / 255.0 - img_mean) / img_std image = image.transpose(2, 0, 1).astype(np.float32) image_info = { 'image_center': image_center, 'image_height': image_height, 'image_width': image_width, 'image_scale': image_scale, 'rotation': rotation, 'scale': scale } return image, image_info

000473594a0d7c7d27795d017abe961902251869

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

def preprocess(org_im, scale, rotation): image = org_im.copy() image_height, image_width, _ = image.shape aspect_ratio = scale[1] * 1.0 / scale[0] image_center, image_scale = _box2cs([0, 0, image_width - 1, image_height - 1], aspect_ratio) trans = get_affine_transform(image_center, image_scale, rotation, scale) image = cv2.warpAffine( image, trans, (int(scale[1]), int(scale[0])), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0)) img_mean = np.array([0.406, 0.456, 0.485]).reshape((1, 1, 3)) img_std = np.array([0.225, 0.224, 0.229]).reshape((1, 1, 3)) image = image.astype(np.float32) image = (image / 255.0 - img_mean) / img_std image = image.transpose(2, 0, 1).astype(np.float32) image_info = { 'image_center': image_center, 'image_height': image_height, 'image_width': image_width, 'image_scale': image_scale, 'rotation': rotation, 'scale': scale } return image, image_info

preprocess

python/ray/tune/trial_runner.py

[tune] De-clutter log outputs in trial runner (#24257) There are currently some debug logs left logging to INFO scope. This PR demotes them to DEBUG and cleans up the messages.

ray

Python

trial_runner.py

def _process_trial_result(self, trial, result): result.update(trial_id=trial.trial_id) is_duplicate = RESULT_DUPLICATE in result force_checkpoint = result.get(SHOULD_CHECKPOINT, False) # TrialScheduler and SearchAlgorithm still receive a # notification because there may be special handling for # the `on_trial_complete` hook. if is_duplicate: logger.debug("Trial finished without logging 'done'.") result = trial.last_result result.update(done=True) self._total_time += result.get(TIME_THIS_ITER_S, 0) flat_result = flatten_dict(result) self._validate_result_metrics(flat_result) if self._stopper(trial.trial_id, result) or trial.should_stop(flat_result): decision = TrialScheduler.STOP else: with warn_if_slow("scheduler.on_trial_result"): decision = self._scheduler_alg.on_trial_result(self, trial, flat_result) if decision == TrialScheduler.STOP: result.update(done=True) else: # Only updating search alg if the trial is not to be stopped. with warn_if_slow("search_alg.on_trial_result"): self._search_alg.on_trial_result(trial.trial_id, flat_result) # If this is not a duplicate result, the callbacks should # be informed about the result. if not is_duplicate: with warn_if_slow("callbacks.on_trial_result"): self._callbacks.on_trial_result( iteration=self._iteration, trials=self._trials, trial=trial, result=result.copy(), ) trial.update_last_result(result) # Include in next experiment checkpoint self.trial_executor.mark_trial_to_checkpoint(trial) # Checkpoints to disk. This should be checked even if # the scheduler decision is STOP or PAUSE. Note that # PAUSE only checkpoints to memory and does not update # the global checkpoint state. self._checkpoint_trial_if_needed(trial, force=force_checkpoint) if trial.is_saving: logger.debug(f"Caching trial decision for trial {trial}: {decision}") # Cache decision to execute on after the save is processed. # This prevents changing the trial's state or kicking off # another training step prematurely. self._cached_trial_decisions[trial.trial_id] = decision return None else: self._queue_decision(trial, decision) return decision

4a30ae0ab65b6f4e966aa9bd9b50720889c8458e

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

def _process_trial_result(self, trial, result): result.update(trial_id=trial.trial_id) is_duplicate =

_process_trial_result

tests/test_engine.py

Fix tests

prefect

Python

test_engine.py

async def test_non_prefect_types_return_completed_state(self): result_state = await return_value_to_state("foo") assert result_state.is_completed() assert result_state.data.decode() == "foo"

b5836927c71ed0448b674a89efeba64133b586cc

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

async def test_non_prefect_types_return_completed_state(self): result_state = await return_value_to_state("foo") assert result_state.is_completed() assert result_state.data.decode() == "foo"

test_non_prefect_types_return_completed_state

homeassistant/components/sonos/speaker.py

Make Sonos typing more complete (#68072)

core

Python

speaker.py

def subscription_address(self) -> str: assert len(self._subscriptions) > 0 addr, port = self._subscriptions[0].event_listener.address return ":".join([addr, str(port)]) # # Subscription handling and event dispatchers #

73ba7a989b0cae6fba3564947d819e1eeb423f54

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

def subscription_address(self) -> str: assert len(self._subscriptions) > 0 addr, port = self._subscriptions[0].event_listener.address return ":".jo

subscription_address

tests/css/test_tokenize.py

Parsing variable values as individual tokens

textual

Python

test_tokenize.py

def test_variable_declaration_no_semicolon(): css = "$x: 1\n$y: 2" assert list(tokenize(css, "")) == [ Token(name="variable_name", value="$x:", code=css, path="", location=(0, 0)), Token(name="whitespace", value=" ", code=css, path="", location=(0, 3)), Token(name="number", value="1", code=css, path="", location=(0, 4)), Token(name="variable_value_end", value="\n", code=css, path="", location=(0, 5)), Token(name="variable_name", value="$y:", code=css, path="", location=(1, 0)), Token(name="whitespace", value=" ", code=css, path="", location=(1, 3)), Token(name="number", value="2", code=css, path="", location=(1, 4)), ]

6587ba257fc5ea07968752aeaf818b1002cdae0f

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

def test_variable_declaration_no_semicolon(): css = "$x: 1\n$y: 2" assert list(tokenize(css, "")) == [ Token(name="variable_name", value="$x:", code=css, path="", location=(0, 0)), Token(name="whitespace", value=" ", code=css, path="", location=(0, 3)), Token(name="number", value="1", code=css, path="", location=(0, 4)), Token(name="variable_value_end", value="\n", code=css, path="", location=(0, 5)), Token(name="variable_name", value="$y:", code=css, path="", location=(1, 0)), Token(name="whitespace", value=" ", code=css, path="", lo

test_variable_declaration_no_semicolon

lib/matplotlib/tests/test_ticker.py

Remove *math* parameter of various mathtext internal APIs. The *math* parameter is passed through many layers of the call stack but is ultimately only used for a single purpose: deciding whether to replace the ASCII hyphen by a (longer) unicode minus. Instead of doing that, just do the substitution at the parsing stage. In particular, this fixes problematic unicode minus support with the "cm" fontset. This patch also reverts a significant part of 52003e4, as LogFormatters no longer need to pass unicode minuses in mathtext -- everything gets converted by mathtext. Likewise, this change also invalidates the test_log_scales baseline image (old, buggy wrt. unicode minus); replace it by a test that the drawn ticks are as expected (which was the intent in 90c1aa3).

matplotlib

Python

test_ticker.py

def logit_deformatter(string): r match = re.match( r"[^\d]*" r"(?P<comp>1-)?" r"(?P<mant>\d*\.?\d*)?" r"(?:\\cdot)?" r"(?:10\^\{(?P<expo>-?\d*)})?" r"[^\d]*$", string, ) if match: comp = match["comp"] is not None mantissa = float(match["mant"]) if match["mant"] else 1 expo = int(match["expo"]) if match["expo"] is not None else 0 value = mantissa * 10 ** expo if match["mant"] or match["expo"] is not None: if comp: return 1 - value return value match = re.match( r"[^\d]*\\frac\{(?P<num>\d+)\}\{(?P<deno>\d+)\}[^\d]*$", string ) if match: num, deno = float(match["num"]), float(match["deno"]) return num / deno raise ValueError("Not formatted by LogitFormatter")

85f30cbd485eddc93e3c9ff115ac21c0886909d5

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

def logit_deformatter(string): r match = re.match( r"[^\d]*" r"(?P<comp>1-)?" r"(?P<mant>\d*\.?\d*)?" r"(?:\\

logit_deformatter

keras/optimizers/__init__.py

Flip the default optimizer to experimental optimizer when deserializing optimizer. PiperOrigin-RevId: 465336057

keras

Python

__init__.py

def deserialize(config, custom_objects=None, **kwargs): # loss_scale_optimizer has a direct dependency of optimizer, import here # rather than top to avoid the cyclic dependency. from keras.mixed_precision import ( loss_scale_optimizer, ) use_legacy_optimizer = kwargs.pop("use_legacy_optimizer", True) if len(config["config"]) > 0: # If the optimizer config is not empty, then we use the value of # `is_legacy_optimizer` to override `use_legacy_optimizer`. If # `is_legacy_optimizer` does not exist in config, it means we are # using the legacy optimzier. use_legacy_optimizer = config["config"].get("is_legacy_optimizer", True) if ( tf.__internal__.tf2.enabled() and tf.executing_eagerly() and not use_legacy_optimizer ): all_classes = { "adadelta": adadelta_experimental.Adadelta, "adagrad": adagrad_experimental.Adagrad, "adam": adam_experimental.Adam, "adamax": adamax_experimental.Adamax, "experimentaladadelta": adadelta_experimental.Adadelta, "experimentaladagrad": adagrad_experimental.Adagrad, "experimentaladam": adam_experimental.Adam, "experimentalsgd": sgd_experimental.SGD, "nadam": nadam_experimental.Nadam, "rmsprop": rmsprop_experimental.RMSprop, "sgd": sgd_experimental.SGD, "ftrl": ftrl_experimental.Ftrl, "lossscaleoptimizer": loss_scale_optimizer.LossScaleOptimizerV3, "lossscaleoptimizerv3": loss_scale_optimizer.LossScaleOptimizerV3, # LossScaleOptimizerV1 was an old version of LSO that was removed. # Deserializing it turns it into a LossScaleOptimizer "lossscaleoptimizerv1": loss_scale_optimizer.LossScaleOptimizer, } else: all_classes = { "adadelta": adadelta_v2.Adadelta, "adagrad": adagrad_v2.Adagrad, "adam": adam_v2.Adam, "adamax": adamax_v2.Adamax, "experimentaladadelta": adadelta_experimental.Adadelta, "experimentaladagrad": adagrad_experimental.Adagrad, "experimentaladam": adam_experimental.Adam, "experimentalsgd": sgd_experimental.SGD, "nadam": nadam_v2.Nadam, "rmsprop": rmsprop_v2.RMSprop, "sgd": gradient_descent_v2.SGD, "ftrl": ftrl.Ftrl, "lossscaleoptimizer": loss_scale_optimizer.LossScaleOptimizer, "lossscaleoptimizerv3": loss_scale_optimizer.LossScaleOptimizerV3, # LossScaleOptimizerV1 was an old version of LSO that was removed. # Deserializing it turns it into a LossScaleOptimizer "lossscaleoptimizerv1": loss_scale_optimizer.LossScaleOptimizer, } # Make deserialization case-insensitive for built-in optimizers. if config["class_name"].lower() in all_classes: config["class_name"] = config["class_name"].lower() return deserialize_keras_object( config, module_objects=all_classes, custom_objects=custom_objects, printable_module_name="optimizer", ) @keras_export( "keras.__internal__.optimizers.convert_to_legacy_optimizer", v1=[] )

30bf872258415cd4a83ac1a33b031cc804981a9c

@keras_export( "keras.__internal__.optimizers.convert_to_legacy_optimizer", v1=[] )

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

def deserialize(config, custom_objects=None, **kwargs): # loss_scale_optimizer has a direct dependency of optimizer, import here # rather than top to avoid the cyclic dependency. from keras.mixed_precision import ( loss_scale_optimizer, ) use_legacy_optimizer = kwargs.pop("use_legacy_optimizer", True) if len(config["config"]) > 0: # If the optimizer config is not empty, then we use the value of #

deserialize

tests/sentry/api/endpoints/test_organization_member_team_details.py

ref(tests): DRY OrganizationMember tests. (#32715)

sentry

Python

test_organization_member_team_details.py

def test_member_can_leave(self): self.login_as(self.team_member.user) self.get_success_response( self.org.slug, self.team_member.id, self.team.slug, status_code=status.HTTP_200_OK ) assert not OrganizationMemberTeam.objects.filter( team=self.team, organizationmember=self.team_member ).exists()

e676b34aea4b38ee33ee0dd2de5e0cc8e546ae1a

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

def test_member_can_leave(self): self.login_as(self.team_member.user) self.get_success_response( self.org.slug, self.team_member.id, self.team.slug, status_code=status.HTTP_200_OK ) assert not OrganizationMemberTeam.objects.filter( team=self.team, organizationmember=self.team_member ).exists()

test_member_can_leave

tests/test_api_validate.py

plugin.api.validate: turn module into package Turn module into package with multiple logical sub-modules: - Define a public interface in the package's `__init__` module - Split validation schemas, validators and validate logic - schemas: classes which register attributes used by their respective `validate` implementations - validators: functions which can internally call `validate` and which return something that can be validated - validate: singledispatch functions which implement the validation logic for schemas and various other types - Rename validation schemas for better internal references - Rename singledispatch methods Other clean-up work: - Update comments and fix grammar - Add type annotations - Use f-strings - Use `str` instead of the `text` alias - Simplify some code blocks - Rearrange classes and functions - Rephrase certain error messages - Add a few more tests for better code coverage

streamlink

Python

test_api_validate.py

def test_url(self): url_ = "https://google.se/path" assert validate(url(), url_) assert validate(url(scheme="http"), url_) assert validate(url(path="/path"), url_) with self.assertRaises(ValueError) as cm: validate(url(), "foo") assert str(cm.exception) == "'foo' is not a valid URL" with self.assertRaises(ValueError) as cm: validate(url(foo="bar"), "https://foo") assert str(cm.exception) == "Invalid URL attribute 'foo'" with self.assertRaises(ValueError) as cm: validate(url(path=endswith(".m3u8")), "https://foo/bar.mpd") assert str(cm.exception) == "Unable to validate URL attribute 'path': '/bar.mpd' does not end with '.m3u8'"

120c10302381600abb4044083ce0a106b31df8f0

https://github.com/streamlink/streamlink.git

def test_url(self): url_ = "https://google.se/path" assert validate(url(), url_) assert validate(url(scheme="http"), url

test_url

tests/test_filesystems.py

Added Sync API for public interface methods. (#6511) * Added sync api * Removed trailing Spaces * filesystem tests for sync methods * sync tests * added more tests * adding test to resolve conflict * resolving git conflict * removed redundant/unneccessary tests * removed redundant/unneccessary tests Co-authored-by: Michael Adkins <michael@prefect.io> Co-authored-by: Bada-S <jamessopkin@gmail.com> Co-authored-by: James Bada Sopkin <69161193+Bada-S@users.noreply.github.com>

prefect

Python

test_filesystems.py

def test_read_write_roundtrip_sync(self): fs = RemoteFileSystem(basepath="memory://root") fs.write_path("test.txt", content=b"hello") assert fs.read_path("test.txt") == b"hello"

6e0a171ae169f4db1cfdd5ad9e0a576ff4962386

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

def test_read_write_roundtrip_sync(self): fs = RemoteFileSystem(basepath="memory://root") fs.write_path("test.txt", content=b"hello")

test_read_write_roundtrip_sync

mitmproxy/tls.py

`pyupgrade --py39-plus **/*.py`

mitmproxy

Python

tls.py

def alpn_protocols(self) -> list[bytes]: if self._client_hello.extensions: for extension in self._client_hello.extensions.extensions: if extension.type == 0x10: return list(x.name for x in extension.body.alpn_protocols) return []

e83ec8390ad6be6a86cfcfc57bce14cb8861bf32

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

def alpn_protocols(self) -> list[bytes]: if self._client_hello.extensions: for extension in self._client_hello.extensions.extensions: if extension.type == 0x10: return list(x.name for x in extension.body.alpn_protocols) return []

alpn_protocols

cps/kobo.py

Fixes for kobosync with multiple users (#2230)

calibre-web

Python

kobo.py

def sync_shelves(sync_token, sync_results, only_kobo_shelves=False): new_tags_last_modified = sync_token.tags_last_modified # transmit all archived shelfs independent of last sync (why should this matter?) for shelf in ub.session.query(ub.ShelfArchive).filter(ub.ShelfArchive.user_id == current_user.id): new_tags_last_modified = max(shelf.last_modified, new_tags_last_modified) sync_results.append({ "DeletedTag": { "Tag": { "Id": shelf.uuid, "LastModified": convert_to_kobo_timestamp_string(shelf.last_modified) } } }) ub.session.delete(shelf) ub.session_commit() extra_filters = [] if only_kobo_shelves: for shelf in ub.session.query(ub.Shelf).filter( func.datetime(ub.Shelf.last_modified) > sync_token.tags_last_modified, ub.Shelf.user_id == current_user.id, not ub.Shelf.kobo_sync ): sync_results.append({ "DeletedTag": { "Tag": { "Id": shelf.uuid, "LastModified": convert_to_kobo_timestamp_string(shelf.last_modified) } } }) extra_filters.append(ub.Shelf.kobo_sync) if sqlalchemy_version2: shelflist = ub.session.execute(select(ub.Shelf).outerjoin(ub.BookShelf).filter( or_(func.datetime(ub.Shelf.last_modified) > sync_token.tags_last_modified, func.datetime(ub.BookShelf.date_added) > sync_token.tags_last_modified), ub.Shelf.user_id == current_user.id, *extra_filters ).distinct().order_by(func.datetime(ub.Shelf.last_modified).asc())).columns(ub.Shelf) else: shelflist = ub.session.query(ub.Shelf).outerjoin(ub.BookShelf).filter( or_(func.datetime(ub.Shelf.last_modified) > sync_token.tags_last_modified, func.datetime(ub.BookShelf.date_added) > sync_token.tags_last_modified), ub.Shelf.user_id == current_user.id, *extra_filters ).distinct().order_by(func.datetime(ub.Shelf.last_modified).asc()) for shelf in shelflist: if not shelf_lib.check_shelf_view_permissions(shelf): continue new_tags_last_modified = max(shelf.last_modified, new_tags_last_modified) tag = create_kobo_tag(shelf) if not tag: continue if shelf.created > sync_token.tags_last_modified: sync_results.append({ "NewTag": tag }) else: sync_results.append({ "ChangedTag": tag }) sync_token.tags_last_modified = new_tags_last_modified ub.session_commit() # Creates a Kobo "Tag" object from a ub.Shelf object

df670795731e7568462869b815f4eb39da1bb41a

https://github.com/janeczku/calibre-web.git

def sync_shelves(sync_token, sync_results, only_kobo_shelves=False): new_tags_last_modified = sync_token.tags_last_modified # transmit all archived shelfs independent of last sync (why should this matter?) for shelf in ub.session.query(ub.ShelfArchive).filter(ub.ShelfArchive.user_id == current_user.id): new_tags_last_modified = max(shelf.last_modified, new_tags_last_modified) sync_results.append({ "DeletedTag": { "Tag": { "Id": shelf.uuid, "LastModified": convert_to_kobo_timestamp_string(shelf.last_modified) } } }) ub.session.delete(shelf) ub.session_commit() extra_filters = [] if only_kobo_shelves: for shelf in ub.session.query(ub.Shelf).filter( func.datetime(ub.Shelf.last_modified) > sync_token.tags_last_modified, ub.Shelf.user_id == current_user.id, not ub.Shelf.kobo_sync ): sync_results.append({ "DeletedTag": { "Tag": { "Id": shelf.uuid, "LastModified": convert_to_kobo_timestamp_string(shelf.last_modified) } } }) extra_filters.append(ub.Shelf.kobo_sync) if sqlalchemy_version2: shelflist = ub.sessio

sync_shelves

python/ray/data/tests/test_dataset_formats.py

Split test_dataset.py into two (#22303)

ray

Python

test_dataset_formats.py

def test_json_roundtrip(ray_start_regular_shared, fs, data_path): # Single block. df = pd.DataFrame({"one": [1, 2, 3], "two": ["a", "b", "c"]}) ds = ray.data.from_pandas([df]) ds._set_uuid("data") ds.write_json(data_path, filesystem=fs) file_path = os.path.join(data_path, "data_000000.json") ds2 = ray.data.read_json([file_path], filesystem=fs) ds2df = ds2.to_pandas() assert ds2df.equals(df) # Test metadata ops. for block, meta in ds2._blocks.get_blocks_with_metadata(): BlockAccessor.for_block(ray.get(block)).size_bytes() == meta.size_bytes if fs is None: os.remove(file_path) else: fs.delete_file(_unwrap_protocol(file_path)) # Two blocks. df2 = pd.DataFrame({"one": [4, 5, 6], "two": ["e", "f", "g"]}) ds = ray.data.from_pandas([df, df2]) ds._set_uuid("data") ds.write_json(data_path, filesystem=fs) ds2 = ray.data.read_json(data_path, parallelism=2, filesystem=fs) ds2df = ds2.to_pandas() assert pd.concat([df, df2], ignore_index=True).equals(ds2df) # Test metadata ops. for block, meta in ds2._blocks.get_blocks_with_metadata(): BlockAccessor.for_block(ray.get(block)).size_bytes() == meta.size_bytes @pytest.mark.parametrize( "fs,data_path,endpoint_url", [ (None, lazy_fixture("local_path"), None), (lazy_fixture("local_fs"), lazy_fixture("local_path"), None), (lazy_fixture("s3_fs"), lazy_fixture("s3_path"), lazy_fixture("s3_server")), ], )

85d6946c9524d8544e69262f737018151efb1567

@pytest.mark.parametrize( "fs,data_path,endpoint_url", [ (None, lazy_fixture("local_path"), None), (lazy_fixture("local_fs"), lazy_fixture("local_path"), None), (lazy_fixture("s3_fs"), lazy_fixture("s3_path"), lazy_fixture("s3_server")), ], )

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

def test_json_roundtrip(ray_start_regular_shared, fs, data_path): # Single block. df = pd.DataFrame({"one": [1, 2, 3], "two": ["a", "b", "c"]}) ds = ray.data.from_pandas([df]) ds._set_uuid("data") ds.write_json(data_path, filesystem=fs) file_path = os.path.join(data_path, "data_000000.json") ds2 = ray.data.read_json([file_path], filesystem=fs) ds2df = ds2.to_pandas() assert ds2df.equals(df) # Test metadata ops. for block, meta in ds2._blocks.get_blocks_with_metadata(): BlockAccessor.for_block(ray.get(block)).size_bytes() == meta.size_bytes if fs is None: os.remove(file_path) else: fs.delete_file(_unwrap_protocol(file_path)) # Two blocks. df2 = pd.DataFrame({"one": [4, 5, 6], "two": ["e", "f", "g"]}) ds = ray.data.from_pandas([df, df2]) ds._set_uuid("data") ds.write_json(data_path, filesystem=fs) ds2 = ray.data.read_json(data_path, parallelism=2, filesystem=fs) ds2df = ds2.to_pandas() assert pd.concat([df, df2], ignore_index=True).equals(ds2df) # Test metadata ops. for b

test_json_roundtrip

homeassistant/components/usgs_earthquakes_feed/geo_location.py

Migrate usgs_earthquakes_feed to async library (#68370) * use new async integration library * migrate to new async integration library * updated unit tests * updated logger * fix tests and improve test coverage * fix test * fix requirements * time control to fix tests

core

Python

geo_location.py

async def _remove_entity(self, external_id): async_dispatcher_send(self._hass, SIGNAL_DELETE_ENTITY.format(external_id))

21d28dd35629a7f4fc086bf9ff4f65ee9270873b

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

async def _remove_entity(self, external_id): async_dispatcher_send(self._hass, SIGNAL_DELETE_ENTITY.format(ext

_remove_entity

metrics/mahalanobis/mahalanobis.py

Add Mahalanobis distance metric (#3794) * Add Mahalanobis class metric * reformat code * try to fix tests * reformat file with black * fix metric example * reformat with black * running isort * fix flake8 * change assert to ValueError * change metric's features * Update metrics/mahalanobis/mahalanobis.py Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> * PR feedback * Update metrics/mahalanobis/mahalanobis.py Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>

datasets

Python

mahalanobis.py

def _info(self): return datasets.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features( { "X": datasets.Sequence(datasets.Value("float", id="sequence"), id="X"), } ), )

52b3ba8a3173adb0a4d6411f2b4978551672e450

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

def _info(self): return datasets.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Fe

_info

tests/unit/recommenders/evaluation/test_python_evaluation_time_performance.py

Improved time of generating synthedic data, updated pytest fixtures, removed unused lines, updated benchmark results

recommenders

Python

test_python_evaluation_time_performance.py

def test_python_map_at_k(rating_true, rating_pred): with Timer() as t: map_at_k( rating_true=rating_true, rating_pred=rating_pred, col_prediction=DEFAULT_PREDICTION_COL, k=10, ) assert t.interval < 29.90376154

9d7d8212292b05605bb2b7c5c425c107d4266e8c

https://github.com/microsoft/recommenders.git

def test_python_map_at_k(rating_true, rating_pred): with Timer() as t: map_at_k(

test_python_map_at_k

demo/kinematics_blocks/run.py

kinematics block

gradio

Python

run.py

def plot(v, a): g = 9.81 theta = a/180*3.14 tmax = ((2 * v) * np.sin(theta)) / g timemat = tmax*np.linspace(0,1,40)[:,None] x = ((v * timemat) * np.cos(theta)) y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat ** 2)) fig = plt.figure() plt.scatter(x=x, y=y, marker='.') plt.xlim(0, 100) plt.ylim(0, 60) return fig block = gr.Blocks() with block: gr.Markdown("Let's do some kinematics! Choose the speed and angle to see the trajectory.") with gr.Row(): speed = gr.Slider(25, min=1, max=30,label="Speed") angle = gr.Slider(45, min=0, max=90, label="Angle") output = gr.Image(type="plot") btn = gr.Button("Run") btn.click(plot, [speed, angle], output) block.launch()

ef533b23321de5a37f81f9d5b5c60f8ea8e620a7

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

def plot(v, a): g = 9.81 theta = a/180*3.14 tmax = ((2 * v) * np.sin(theta)) / g timemat = tmax*np.linspace(0,1,40)[:,None] x = ((v * timemat) * np.cos(theta)) y = ((v * timemat) * np.sin(theta))

plot

homeassistant/components/twinkly/light.py

Add Twinkly effects (#82861) * Add Twinkly effects * Remove spurious comment

core

Python

light.py

async def async_update_movies(self) -> None: movies = await self._client.get_saved_movies() _LOGGER.debug("Movies: %s", movies) if "movies" in movies: self._movies = movies["movies"]

33cd59d3c2e1f945c16b39d929349e3eeb4cfb9a

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

async def async_update_movies(self) -> None: movies = await self._client.get_saved_movies() _LOGGER.debug("Movies: %s", movies) if "movie

async_update_movies

pipenv/patched/notpip/_internal/cli/req_command.py

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

pipenv

Python

req_command.py

def warn_if_run_as_root() -> None: if running_under_virtualenv(): return if not hasattr(os, "getuid"): return # On Windows, there are no "system managed" Python packages. Installing as # Administrator via pip is the correct way of updating system environments. # # We choose sys.platform over utils.compat.WINDOWS here to enable Mypy platform # checks: https://mypy.readthedocs.io/en/stable/common_issues.html if sys.platform == "win32" or sys.platform == "cygwin": return if os.getuid() != 0: return logger.warning( "Running pip as the 'root' user can result in broken permissions and " "conflicting behaviour with the system package manager. " "It is recommended to use a virtual environment instead: " "https://pip.pypa.io/warnings/venv" )

f3166e673fe8d40277b804d35d77dcdb760fc3b3

https://github.com/pypa/pipenv.git

def warn_if_run_as_root() -> None: if running_under_virtualenv(): return if not hasattr(os, "getuid"): return # On Windows, there are no "system managed" Python packages. Installing as # Administrator via pip is the correct way of updating system environments. # # We choose sys.platform over utils.compat.WINDOWS here to enable Mypy platform # checks: https://mypy.readthedocs.io/en/stable/common_issues.html if sys.platform == "win32" or sys.platform == "cygwin": return

warn_if_run_as_root

terminal.py

Updating some names (#1575) * quick econ fix * black * keys and feature flags * terminal name :eyes: * some more replacements * some more replacements * edit pyproject * gst -> openbb * add example portfolios back to git * Update api from gst * sorry. skipping some tests * another round of names * another round of test edits * Missed some .gst refs and update timezone * water mark stuff * Fixing Names in terminal.spec and name of GTFF_DEFAULTS to OBBFF_DEFAULTS * fix more GST to OpenBB Terminal * Logging : merge conflicts with main * Revert wrong files Co-authored-by: Andrew <andrew.kenreich@gmail.com> Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>

OpenBBTerminal

Python

terminal.py

def call_etf(self, _): from openbb_terminal.etf.etf_controller import ETFController self.queue = self.load_class(ETFController, self.queue)

b71abcfbf4d7e8ac1855522aff0378e13c8b5362

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

def call_etf(self, _): from openbb_terminal.etf.etf_controller import ETFController self.queue = self.load

call_etf

scripts/dev/update_3rdparty.py

Use legacy PDF.js build for macOS/Windows releases Fixes #7108

qutebrowser

Python

update_3rdparty.py

def find_pdfjs_asset(assets, legacy): for asset in assets: name = asset["name"] if ( name.startswith("pdfjs-") and name.endswith("-dist.zip") and name.endswith("-legacy-dist.zip") == legacy ): return asset raise Exception(f"No pdfjs found in {assets}")

f6a365172afe127a4ba770e14569f2d3cd7569b4

https://github.com/qutebrowser/qutebrowser.git

def find_pdfjs_asset(assets, legacy): for asset in assets: name = asset["name"] if ( name.startswith("pdfjs-") and name.endswith("-dist.zip") and name.endswith("-legacy-dist.zip") == legacy ): return a

find_pdfjs_asset

tests/admin_views/tests.py

Refs #33476 -- Reformatted code with Black.

django

Python

tests.py

def test_extended_bodyclass_template_delete_selected_confirmation(self): group = Group.objects.create(name="foogroup") post_data = { "action": "delete_selected", "selected_across": "0", "index": "0", "_selected_action": group.id, } response = self.client.post(reverse("admin:auth_group_changelist"), post_data) self.assertEqual(response.context["site_header"], "Django administration") self.assertContains(response, "bodyclass_consistency_check ")

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def test_extended_bodyclass_template_delete_selected_confirmation(self): group = Group.objects.create(name="foogroup") post_data = { "action": "delete_selected",

test_extended_bodyclass_template_delete_selected_confirmation

tests/test_arrow_dataset.py

Optimize contiguous shard and select (#4466) * optimize contiguous shard and select * minor * support iterators (and therefore generators) * comments + docstrings

datasets

Python

test_arrow_dataset.py

def test_concatenate_with_indices_from_disk(self, in_memory): data1, data2, data3 = {"id": [0, 1, 2] * 2}, {"id": [3, 4, 5] * 2}, {"id": [6, 7]} info1 = DatasetInfo(description="Dataset1") info2 = DatasetInfo(description="Dataset2") with tempfile.TemporaryDirectory() as tmp_dir: dset1, dset2, dset3 = ( Dataset.from_dict(data1, info=info1), Dataset.from_dict(data2, info=info2), Dataset.from_dict(data3), ) dset1, dset2, dset3 = self._to(in_memory, tmp_dir, dset1, dset2, dset3) dset1, dset2, dset3 = ( dset1.select([2, 1, 0], indices_cache_file_name=os.path.join(tmp_dir, "i1.arrow")), dset2.select([2, 1, 0], indices_cache_file_name=os.path.join(tmp_dir, "i2.arrow")), dset3.select([1, 0], indices_cache_file_name=os.path.join(tmp_dir, "i3.arrow")), ) with concatenate_datasets([dset3, dset2, dset1]) as dset_concat: self.assertEqual((len(dset1), len(dset2), len(dset3)), (3, 3, 2)) self.assertEqual(len(dset_concat), len(dset1) + len(dset2) + len(dset3)) self.assertListEqual(dset_concat["id"], [7, 6, 5, 4, 3, 2, 1, 0]) # in_memory = False: # 3 cache files for the dset_concat._data table, and 1 for the dset_concat._indices_table # There is only 1 for the indices tables (i1.arrow) # Indeed, the others are brought to memory since an offset is applied to them. # in_memory = True: # 1 cache file for i1.arrow since both dset_concat._data and dset_concat._indices are in memory self.assertEqual(len(dset_concat.cache_files), 1 if in_memory else 3 + 1) self.assertEqual(dset_concat.info.description, "Dataset2\n\nDataset1") del dset1, dset2, dset3

599403601739e7a73e8ebbc8653d246e07207265

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

def test_concatenate_with_indices_from_disk(self, in_memory): data1, data2, data3 = {"id": [0, 1, 2] * 2}, {"id": [3, 4, 5] * 2}, {"id": [6, 7]} info1 = DatasetInfo(description="Dataset1") info2 = DatasetInfo(description="Dataset2") with tempfile.TemporaryDirectory() as tmp_dir: dset1, dset2, dset3 = ( Dataset.from_dict(data1, info=info1), Dataset.from_dict(data2, info=info2), Dataset.from_dict(data3), ) dset1, dset2, dset3 = self._to(in_memory, tmp_dir, dset1, dset2, dset3) dset1, dset2, dset3 = ( dset1.select([2,

test_concatenate_with_indices_from_disk

freqtrade/rpc/api_server/ws/channel.py

log warning if channel too far behind, add docstrings to message stream

freqtrade

Python

channel.py

def _calc_send_limit(self): # Only update if we have enough data if len(self._send_times) == self._send_times.maxlen: # At least 1s or twice the average of send times, with a # maximum of 3 seconds per message self._send_high_limit = min(max(self.avg_send_time * 2, 1), 3)

afc00bc30a94abd64fee000535e66287fd91595f

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

def _calc_send_limit(self): # Only update if we have enough data if len(self._send_times) == self._send_times.maxlen: # At least 1s or twice the average of send times, with a # maximum of 3 seconds per message self._send_hig

_calc_send_limit

gamestonk_terminal/portfolio/portfolio_analysis/pa_controller.py

Terminal Wide Rich (#1161) * My idea for how we handle Rich moving forward * remove independent consoles * FIxed pylint issues * add a few vars * Switched print to console * More transitions * Changed more prints * Replaced all prints * Fixing tabulate * Finished replace tabulate * Finished removing rich from Tabulate * add Panel around menu * add GST watermark under feature flag * Fixed 46 tests * Delete test_screener[False].yaml * Delete test_screener[True].yaml * Fixed the rest of the tests * add help and source color vars and use rgb * rich on stocks/options * update rich on disc, dps, sia * rich in gov, ins and scr menus * ba and ca menus with rich * Fixed import issue * Fixed some tests * removed termcolor * Removed prettytable * add rich to remaining stocks menus * FIxed linting issue * Added James' changes * Updated dependencies * Add rich to cryptocurrency menu * refactor economy and forex * refactor etf with rich * refactor mfunds * refactor rich rest * not specify style so default color works well on any background * Fixing mypy issues * Updated tests * More test fixes * James' test fixes * Updating tests : stocks/screener - fix cassettes using BR * Updating tests : crypto * Updating tests : disable DEBUG_MODE * Updating tests : stocks/fa/yfinance * minor fixes that escape * Improve the rich table function (that replaces tabulate :D ) * Fixed bad code * delete rogue file + dcf fix + NoConsole * sia mypy * fuck you linter * fuck you linter pt 2 * skip hehe * i hate the black linter * ubuntu mypy attempt * Update : rich_config + gtff * Updating tests : conftest * Updating tests : stocks * Update : rich_config * Updating : rich_config * make panel configurable for Theodore :b * colors update * Merged * Updating : rich_config + feature_flags * Updating : rich_config * Updating tests : stocks * Updating : feature_flags Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt> Co-authored-by: Chavithra PARANA <chavithra@gmail.com> Co-authored-by: james <jmaslek11@gmail.com> Co-authored-by: jose-donato <zmcdonato@gmail.com>

OpenBBTerminal

Python

pa_controller.py

def call_group(self, other_args): parser = argparse.ArgumentParser( prog="group", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, description="Displays portfolio grouped by a given column", ) if other_args and "-" not in other_args[0][0]: other_args.insert(0, "-g") parser.add_argument( "-g", "--group", type=str, dest="group", default="Ticker", choices=self.portfolio.columns, help="Column to group by", ) parser.add_argument( "-a", "--allocation", action="store_true", default=False, help="Add allocation column in % to dataframe", dest="allocation", ) # The following arguments will be used in a later PR for customizable 'reports' # The --func flag will need to be tested that it exists for pandas groupby # parser.add_argument("-f", # "--func", # type=str, # dest="function", # help="Aggregate function to apply to groups" # ) # parser.add_argument("-d", # "--display", # default = None, # help = "Columns to display", # dest="cols") ns_parser = parse_known_args_and_warn(parser, other_args) if ns_parser: if "value" in self.portfolio.columns: portfolio_view.display_group_holdings( portfolio=self.portfolio, group_column=ns_parser.group, allocation=ns_parser.allocation, ) else: console.print( "'value' column not in portfolio. " "Either add manually or load without --no_last_price flag\n" )

82747072c511beb1b2672846ae2ee4aec53eb562

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

def call_group(self, other_args): parser = argparse.ArgumentParser( prog="group", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter,

call_group

pipenv/patched/notpip/_vendor/pyparsing/testing.py

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

pipenv

Python

testing.py

def assertRaisesParseException(self, exc_type=ParseException, msg=None): with self.assertRaises(exc_type, msg=msg): yield

f3166e673fe8d40277b804d35d77dcdb760fc3b3

https://github.com/pypa/pipenv.git

def assertRaisesParseException(self, exc_type=ParseException, msg=None): with self.assertRaises(exc_type, msg=msg): yield

assertRaisesParseException

django/contrib/humanize/templatetags/humanize.py

Refs #33476 -- Reformatted code with Black.

django

Python

humanize.py

def apnumber(value): try: value = int(value) except (TypeError, ValueError): return value if not 0 < value < 10: return value return ( _("one"), _("two"), _("three"), _("four"), _("five"), _("six"), _("seven"), _("eight"), _("nine"), )[value - 1] # Perform the comparison in the default time zone when USE_TZ = True # (unless a specific time zone has been applied with the |timezone filter). @register.filter(expects_localtime=True)

9c19aff7c7561e3a82978a272ecdaad40dda5c00

@register.filter(expects_localtime=True)

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

def apnumber(value): try: value = int(value) except (TypeError, ValueError): return value if not 0 < value < 10: return value return ( _("one"), _("two"), _("three"), _("four"), _("five"), _("six"), _("seven"), _("eight"), _("nine"), )[value - 1] # Perform the comparison in the default time zone

apnumber

src/textual/_terminal_modes.py

[terminal buffering] Address PR feedback

textual

Python

_terminal_modes.py

def mode_is_supported(self) -> bool: return self.report_parameter in MODE_REPORTS_PARAMETERS_INDICATING_SUPPORT MODE_REPORTS_PARAMETERS_INDICATING_SUPPORT = frozenset( { ModeReportParameter.Set, ModeReportParameter.Reset, ModeReportParameter.PermanentlySet, ModeReportParameter.PermanentlyReset, } )

7f27e70440c177b2a047b7f74a78ed5cd5b4b596

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

def mode_is_supported(self) -> bool: return self.report_parameter in MODE_REPORTS_PARAMETERS_INDICATING_SUPPORT MODE_REPOR

mode_is_supported

python3.10.4/Lib/datetime.py

add python 3.10.4 for windows

XX-Net

Python

datetime.py

def _days_before_year(year): "year -> number of days before January 1st of year." y = year - 1 return y*365 + y//4 - y//100 + y//400

8198943edd73a363c266633e1aa5b2a9e9c9f526

https://github.com/XX-net/XX-Net.git

def _days_before_year(year): "year -> number of days before January 1st of year." y = year - 1 return y*365 + y//4 - y//100 + y//400

_days_before_year

tests/components/matter/test_init.py

Fix matter websocket reconnect (#84192)

core

Python

test_init.py

def listen_ready_timeout_fixture() -> Generator[int, None, None]: with patch( "homeassistant.components.matter.LISTEN_READY_TIMEOUT", new=0 ) as timeout: yield timeout

6a8d9a91cb3fd5a55f13de54ea5db23125e72632

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

def listen_ready_timeout_fixture() -> Generator[int, None, None]: with patch( "homeassistant.components.matter.LISTEN_READY_TIMEOUT", new=0 ) as timeout: yield timeout

listen_ready_timeout_fixture

tests/sentry/models/test_groupmeta.py

ref: replace self.assertRaises with pytest.raises (#35685) * add flake8 plugin to detect assertRaises * ref: replace self.assertRaises with pytest.raises * non-sed fixes

sentry

Python

test_groupmeta.py

def test_get_value_bulk(self): with pytest.raises(GroupMeta.CacheNotPopulated): GroupMeta.objects.get_value_bulk([self.group], "foo") GroupMeta.objects.create(group=self.group, key="foo", value="bar") with pytest.raises(GroupMeta.CacheNotPopulated): GroupMeta.objects.get_value_bulk([self.group], "foo") GroupMeta.objects.populate_cache([self.group]) result = GroupMeta.objects.get_value_bulk([self.group], "foo") assert result == {self.group: "bar"}

284e980df0018f8baee659999268bdd4c7d08255

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

def test_get_value_bulk(self): with pytest.raises(GroupMeta.CacheNotPopulated): GroupMeta.objects.get_value_bulk([self.group], "foo") GroupMeta.objects.create(group=self.group, key="foo", value="bar") with pytest.raises(GroupMeta.CacheNotPopulated): GroupMet

test_get_value_bulk

tests/conftest.py

Updated uninstall brotli wording (#1333)

OpenBBTerminal

Python

conftest.py

def brotli_check(): installed_packages = pkg_resources.working_set for item in list(installed_packages): if "brotli" in str(item).lower(): pytest.exit("Uninstall brotli and brotlipy before running tests")

f8fc7d00ffe6b22b2e2a951fb887f1312644d32f

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

def brotli_check(): installed_packages = pkg_resources.working_set for item in list(installed_packages): if "brotli" in str(item).lower(): pytest.exit("Uninstall brotli and brotlipy before running tests")

brotli_check

TTS/tts/utils/text/phonemizers/espeak_wrapper.py

Handle espeak 1.48.15 (#2203)

TTS

Python

espeak_wrapper.py

def backend(self, backend): if backend not in ["espeak", "espeak-ng"]: raise Exception("Unknown backend: %s" % backend) self._ESPEAK_LIB = backend self._ESPEAK_VER = get_espeakng_version() if backend == "espeak-ng" else get_espeak_version()

fdeefcc6126dfe1382696d9105992295883be0a7

https://github.com/coqui-ai/TTS.git

def backend(self, backend): if backend not in ["espeak", "espeak-ng"]: raise Exception("Unknown backend: %s" % backend) self._ESPEAK_LIB = backend

backend

erpnext/selling/report/customer_acquisition_and_loyalty/customer_acquisition_and_loyalty.py

fix: bulk fix (~330) missing translations

erpnext

Python

customer_acquisition_and_loyalty.py

def get_data_by_territory(filters, common_columns): columns = [ { "label": _("Territory"), "fieldname": "territory", "fieldtype": "Link", "options": "Territory", "width": 150, } ] columns += common_columns customers_in = get_customer_stats(filters, tree_view=True) territory_dict = {} for t in frappe.db.sql( , as_dict=1 ): territory_dict.update({t.name: {"parent": t.parent_territory, "is_group": t.is_group}}) depth_map = frappe._dict() for name, info in territory_dict.items(): default = depth_map.get(info["parent"]) + 1 if info["parent"] else 0 depth_map.setdefault(name, default) data = [] for name, indent in depth_map.items(): condition = customers_in.get(name) new = customers_in[name]["new"] if condition else [0, 0.0] repeat = customers_in[name]["repeat"] if condition else [0, 0.0] temp = { "territory": name, "parent_territory": territory_dict[name]["parent"], "indent": indent, "new_customers": new[0], "repeat_customers": repeat[0], "total": new[0] + repeat[0], "new_customer_revenue": new[1], "repeat_customer_revenue": repeat[1], "total_revenue": new[1] + repeat[1], "bold": 0 if indent else 1, } data.append(temp) loop_data = sorted(data, key=lambda k: k["indent"], reverse=True) for ld in loop_data: if ld["parent_territory"]: parent_data = [x for x in data if x["territory"] == ld["parent_territory"]][0] for key in parent_data.keys(): if key not in ["indent", "territory", "parent_territory", "bold"]: parent_data[key] += ld[key] return columns, data, None, None, None, 1

a896895a9e76a68ab055ce7871bb9d181d3fac15

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

def get_data_by_territory(filters, common_columns): columns = [ { "label": _("Territory"), "fieldname": "territory", "fieldtype": "Link", "options": "Territory", "width": 150, } ] columns += common_columns customers_in = get_customer_stats(filters, tree_view=True) territory_dict = {} for t in frappe.db.sql( , as_dict=1 ): territory_dict.update({t.name: {"parent": t.parent_territory, "is_group": t.is_group}}) depth_map = frappe._dict() for name, info in te

get_data_by_territory

tests/unit/test_helper.py

fix: random port assignment (#4139) * fix: assign only unique ports * fix: check for none ports * fix: use port as int * fix: change debug out * fix: add more debug out * fix: protect partiald port finding * fix: track partiald ports * fix: move partial ports up * fix: lock as cls var * fix: more debug stuff * fix: more log output * fix: remove get * fix: try again on docker fail * Revert "fix: try again on docker fail" This reverts commit c2947ee5c824fb8133319c26be4eb3de36ae7925. * fix: add more debug * fix: try connect with socket * style: fix overload and cli autocomplete * fix: set min port env in ci * fix: set min port in jinad * fix: port helper test * fix: keep track of port in * fix: clean up * fix: remove connect check * fix: remove psutil * style: fix overload and cli autocomplete * fix: seperate jinad port range * fix: use asyncio to run jinad pea * fix: kill jinad process with fire * fix: remove codecov * fix: docker compose tests * Revert "fix: remove codecov" This reverts commit 31d0d41e882699656f5b109ff7d747bf74b47971. * fix: upgrade codecov action * fix: clean up * fix: remove codecov * fix: readd code cov * fix: increase timeout for k8s test * fix: wrong cov tag * Revert "fix: wrong cov tag" This reverts commit 00ce072dd1eb5a84b19c0d3f3eafb5ebf8c1ae53. * Revert "fix: increase timeout for k8s test" This reverts commit 9b0e3134489fc90953beed0c2ff1393e0abcf26d. * fix: reset ci file * fix: readd port config * fix: use run_async helper again * fix: dont touch import Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>

jina

Python

test_helper.py

def test_random_port_unique(config): reset_ports() assert os.environ['JINA_RANDOM_PORT_MIN'] generated_ports = set() for i in range(1000): port = random_port() assert port not in generated_ports assert int(os.environ['JINA_RANDOM_PORT_MIN']) <= port <= 65535 generated_ports.add(port) @pytest.fixture

f4f8f314481dcdec3bc8d322012a1942303d768f

@pytest.fixture

https://github.com/jina-ai/jina.git

def test_random_port_unique(config): reset_ports() assert os.environ['JINA_RANDOM_PORT_MIN'] generated_ports = set() for i in range(1000): port = random_port() assert port not in generated_ports assert int(os.environ['JINA_RANDOM_PORT_MIN']) <= port <= 65535 generated_ports.add(port) @pyte

test_random_port_unique

modin/test/exchange/dataframe_protocol/pandas/test_protocol.py

FIX-#4652: Support categorical data in `from_dataframe` (#4737) Signed-off-by: Karthik Velayutham <vkarthik@ponder.io>

modin

Python

test_protocol.py

def test_simple_import(): modin_df = pd.DataFrame(test_data["int_data"]) eval_df_protocol(modin_df)

8521bbe63f15fbfc6c86a9d5a3c99112738ce7fd

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

def test_simple_import(): modin_df = pd.DataFrame(test_data["int_data"]) eval_df_protocol(modin_df

test_simple_import

Tests/test_image_point.py

Support more affine expression forms in Image.point In modes I and F, Image.point only supported affine expressions of the forms (lambda x:) x * a, x + a, and x * a + b. Expressions like 1 - x had to be written x * -1 + 1. This rewrite, though still limited to affine transformations, supports far more expression forms, including 1 - x, (2 * x + 1) / 3, etc.

Pillow

Python

test_image_point.py

def test_sanity(): im = hopper() with pytest.raises(ValueError): im.point(list(range(256))) im.point(list(range(256)) * 3) im.point(lambda x: x) im.point(lambda x: x * 1.2) im = im.convert("I") with pytest.raises(ValueError): im.point(list(range(256))) im.point(lambda x: x * 1) im.point(lambda x: x + 1) im.point(lambda x: x * 1 + 1) im.point(lambda x: 0.1 + 0.2 * x) im.point(lambda x: -x) im.point(lambda x: x - 0.5) im.point(lambda x: 1 - x / 2) im.point(lambda x: (2 + x) / 3) im.point(lambda x: 0.5) with pytest.raises(TypeError): im.point(lambda x: x * x) with pytest.raises(TypeError): im.point(lambda x: 1 / x) with pytest.raises(TypeError): im.point(lambda x: x // 2)

4e12ccc63e40a9b567af3b2e1ac821f5157cddc6

https://github.com/python-pillow/Pillow.git

def test_sanity(): im = hopper() with pytest.raises(ValueError): im.point(list(range(256))) im.point(list(range(256)) * 3) im.point(lambda x: x) im.point(lambda x: x * 1.2) im = im.convert("I") with pytest.raises(ValueE

test_sanity

tests/test_deployments.py

Update tests

prefect

Python

test_deployments.py

async def test_multiple_specs_from_yaml(self): specs = deployment_specs_from_yaml(TEST_FILES_DIR / "multiple-deployments.yaml") assert len(specs) == 2 specs_by_name = {spec.name: spec for spec in specs} assert set(specs_by_name.keys()) == { "hello-sun-deployment", "hello-moon-deployment", } sun_deploy = specs_by_name["hello-sun-deployment"] moon_deploy = specs_by_name["hello-moon-deployment"] assert sun_deploy.flow_location == str(TEST_FILES_DIR / "multiple_flows.py") assert sun_deploy.flow_name == "hello-sun" assert moon_deploy.flow_location == str(TEST_FILES_DIR / "multiple_flows.py") assert moon_deploy.flow_name == "hello-moon" sun_src = specs[sun_deploy] moon_src = specs[moon_deploy] assert sun_src["file"] == str(TEST_FILES_DIR / "multiple-deployments.yaml") assert moon_src["file"] == str(TEST_FILES_DIR / "multiple-deployments.yaml") assert sun_src["line"] == 1 assert moon_src["line"] == 5 for spec in specs: await spec.validate()

262f05bece5560d0e8cfc36daa6403a67239f825

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

async def test_multiple_specs_from_yaml(self): specs = deployment_specs_from_yaml(TEST_FILES_DIR / "multiple-deployments.yaml")

test_multiple_specs_from_yaml

networkx/algorithms/community/tests/test_louvain.py

Add weights to karate club graph (#5285) Add weights to the karate_club_graph. Modifies `non_randomness` and `naive_greedy_modularity_communities` to accept a `weight` parameter and modifies tests that use the kcg accordingly Co-authored-by: Kevin Berry <kevin.berry@worthix.com> Co-authored-by: Dan Schult <dschult@colgate.edu>

networkx

Python

test_louvain.py

def test_none_weight_param(): G = nx.karate_club_graph() nx.set_edge_attributes( G, {edge: i * i for i, edge in enumerate(G.edges)}, name="foo" ) part = [ {0, 1, 2, 3, 7, 9, 11, 12, 13, 17, 19, 21}, {16, 4, 5, 6, 10}, {23, 25, 27, 28, 24, 31}, {32, 33, 8, 14, 15, 18, 20, 22, 26, 29, 30}, ] partition1 = louvain_communities(G, weight=None, seed=2) partition2 = louvain_communities(G, weight="foo", seed=2) partition3 = louvain_communities(G, weight="weight", seed=2) assert part == partition1 assert part != partition2 assert part != partition3 assert partition2 != partition3

290ebce534b84f9db20ec58b98cbb170e65a0ba1

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

def test_none_weight_param(): G = nx.karate_club_graph() nx.set_edge_attributes( G, {edge: i * i for i, edge in enumerate(G.edges)}, name="foo" ) part = [ {0, 1, 2, 3, 7, 9, 11, 12, 13, 17, 19, 21}, {16, 4, 5, 6, 10}, {23, 25, 27, 28, 24, 31}, {32, 33, 8, 14, 15, 18, 20, 22, 26, 29, 30}, ] partition1 = louvain_communities(G, weight=None, seed=2) partition2 = louvain_communities(G, weight="foo", seed=2) partition3 = louvain_communities(G, weight="weight", seed=2

test_none_weight_param

pipenv/utils/shell.py

Issue 4993 Add standard pre commit hooks and apply linting. (#4994) * Add .pre-commit-config.yaml to the project and exclude tests (for now). This does not include the MyPy linting that pip does but does include everything else.

pipenv

Python

shell.py

def find_requirements(max_depth=3): i = 0 for c, _, _ in walk_up(os.getcwd()): i += 1 if i < max_depth: r = os.path.join(c, "requirements.txt") if os.path.isfile(r): return r raise RuntimeError("No requirements.txt found!") # Borrowed from Pew. # See https://github.com/berdario/pew/blob/master/pew/_utils.py#L82 @contextmanager

9a3b3ce70621af6f9adaa9eeac9cf83fa149319c

@contextmanager

https://github.com/pypa/pipenv.git

def find_requirements(max_depth=3): i = 0 for c, _, _ in walk_u

find_requirements

tests/db_functions/math/test_sin.py

Refs #33476 -- Reformatted code with Black.

django

Python

test_sin.py

def test_integer(self): IntegerModel.objects.create(small=-20, normal=15, big=-1) obj = IntegerModel.objects.annotate( small_sin=Sin("small"), normal_sin=Sin("normal"), big_sin=Sin("big"), ).first() self.assertIsInstance(obj.small_sin, float) self.assertIsInstance(obj.normal_sin, float) self.assertIsInstance(obj.big_sin, float) self.assertAlmostEqual(obj.small_sin, math.sin(obj.small)) self.assertAlmostEqual(obj.normal_sin, math.sin(obj.normal)) self.assertAlmostEqual(obj.big_sin, math.sin(obj.big))

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def test_integer(self): IntegerModel.objects.create(small=-20, normal=15, big=-1) obj = IntegerModel.objects.annotate( small_sin=Sin("small"), normal_sin=Sin("normal"), big_sin=Sin("big"), ).first() self.assertIsInstance(obj.small_sin, float) self.assertIsInstance(obj.normal_sin, float) self.assertIsInstance(obj.big_sin, float) self.assertAlmostEqual(obj.small_sin, math.sin(obj.sma

test_integer

saleor/core/tests/test_dataloaders.py

Add plugin manager promise (#11414)

saleor

Python

test_dataloaders.py

def test_plugins_manager_loader_loads_requestor_in_plugin(rf, customer_user, settings): settings.PLUGINS = ["saleor.plugins.tests.sample_plugins.ActivePlugin"] request = rf.request() request.user = customer_user request.app = None handler = BaseHandler() handler.load_middleware() handler.get_response(request) manager = get_plugin_manager_promise(request).get() plugin = manager.all_plugins.pop() assert isinstance(plugin.requestor, type(customer_user)) assert plugin.requestor.id == customer_user.id

decd505f55d02c616ce5b804c06a71e120d15c15

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

def test_plugins_manager_loader_loads_requestor_in_plugin(rf, customer_user, settings): settings.PLUGINS = ["saleor.plugins.tests.sample_plugins.ActivePlugin"] request = rf.request() request.user = customer_user request.app = None handler = BaseHandler() handler.load_middleware() handler.get_response(request) manager = get_plugin_manager_pro

test_plugins_manager_loader_loads_requestor_in_plugin

cps/comic.py

Bugfix for cbr support without comicapi

calibre-web

Python

comic.py

def _extract_Cover_from_archive(original_file_extension, tmp_file_name, rarExecutable): cover_data = extension = None if original_file_extension.upper() == '.CBZ': cf = zipfile.ZipFile(tmp_file_name) for name in cf.namelist(): ext = os.path.splitext(name) if len(ext) > 1: extension = ext[1].lower() if extension in COVER_EXTENSIONS: cover_data = cf.read(name) break elif original_file_extension.upper() == '.CBT': cf = tarfile.TarFile(tmp_file_name) for name in cf.getnames(): ext = os.path.splitext(name) if len(ext) > 1: extension = ext[1].lower() if extension in COVER_EXTENSIONS: cover_data = cf.extractfile(name).read() break elif original_file_extension.upper() == '.CBR' and use_rarfile: try: rarfile.UNRAR_TOOL = rarExecutable cf = rarfile.RarFile(tmp_file_name) for name in cf.namelist(): ext = os.path.splitext(name) if len(ext) > 1: extension = ext[1].lower() if extension in COVER_EXTENSIONS: cover_data = cf.read(name) break except Exception as ex: log.debug('Rarfile failed with error: %s', ex) return cover_data, extension

8007e450b3178f517b83b0989744c6df38867932

https://github.com/janeczku/calibre-web.git

def _extract_Cover_from_archive(original_file_extension, tmp_file_name, rarExecutable): cover_data = extension = None if original_file_extension.upper() == '.CBZ': cf = zipfile.ZipFile(tmp_file_name) for name in cf.namelist(): ext = os.path.splitext(name) if len(ext) > 1: extension = ext[1].lower() if extension in COVER_EXTENSIONS: cover_data =

_extract_Cover_from_archive

modin/config/__main__.py

REFACTOR-#4629: Add type annotations to `modin/config` (#4685) Signed-off-by: Karthik Velayutham <vkarthik@ponder.io>

modin

Python

__main__.py

def print_config_help() -> None: for objname in sorted(globals()): obj = globals()[objname] if isinstance(obj, type) and issubclass(obj, Parameter) and not obj.is_abstract: print(f"{obj.get_help()}\n\tCurrent value: {obj.get()}") # noqa: T201

02363589aa5105e091fa3d790b29cddf94cc8118

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

def print_config_help() -> None: for objname in sorted(globals()): obj = globals()[objname] if isinstance(obj, type) and issubcla

print_config_help

gamestonk_terminal/cryptocurrency/due_diligence/dd_controller.py

reset different coin from dd controller on crypto (#1118)

OpenBBTerminal

Python

dd_controller.py

def call_reset(self, _): self.queue.insert(0, "dd") if self.current_coin: self.queue.insert(0, f"load {self.current_coin} --source {self.source}") self.queue.insert(0, "crypto") self.queue.insert(0, "reset") self.queue.insert(0, "quit") self.queue.insert(0, "quit")

041a6cd5a06af3809419d043c2410d5317799d1a

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

def call_reset(self, _):

call_reset

sklearn/metrics/tests/test_pairwise_distances_reduction.py

FEA Add support for float32 on `PairwiseDistancesReduction` using Tempita (#23865) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

scikit-learn

Python

test_pairwise_distances_reduction.py

def relative_rounding(scalar, n_significant_digits): if scalar == 0: return 0.0 magnitude = int(floor(log10(abs(scalar)))) + 1 return round(scalar, n_significant_digits - magnitude)

b7d01716216042dda9663f1732d8419e62858a1e

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

def relative_rounding(scalar, n_significant_digits): if scalar == 0: return 0.0 magnitude = int(floor(log10(abs(scalar)))) + 1 return round(scalar, n_signifi

relative_rounding

kittens/ask/main.py

ask kitten: allow having hidden text in the message

kitty

Python

main.py

def draw_screen(self) -> None: self.cmd.clear_screen() msg_lines: List[str] = [] if self.message: for line in self.message.splitlines(): msg_lines.extend(self.draw_long_text(line)) y = self.screen_size.rows - len(msg_lines) y = max(0, (y // 2) - 2) self.print(end='\r\n'*y) for line in msg_lines: if self.replacement_text in line: idx = line.find(self.replacement_text) x = wcswidth(line[:idx]) self.replacement_range = Range(x, x + wcswidth(self.replacement_text), y) self.print(line) y += 1 if self.screen_size.rows > 2: self.print() y += 1 if self.cli_opts.type == 'yesno': self.draw_yesno(y) else: self.draw_choice(y)

bfcd3249930a46c3ba5c53e48e182136809fb6e8

https://github.com/kovidgoyal/kitty.git

def draw_screen(self) -> None: self.cmd.clear_screen() msg_lines: List[str] = [] if self.message: for line in self.message.splitlines(): msg_lines.extend(self.draw_long_text(line)) y = self.screen_size.rows - len(msg_lines) y = max(0, (y // 2) - 2) self.print(end='\r\n'*y) for line in msg_lines: if self.replacement_text in line: idx = line.find(self.replacement_text) x = wcswidth(line[:idx]) self.replacement_range = Range(x

draw_screen

python/ccxt/huobi.py

1.68.88 [ci skip]

ccxt

Python

huobi.py

def cancel_orders(self, ids, symbol=None, params={}): self.load_markets() marketType = None marketType, params = self.handle_market_type_and_params('cancelOrder', None, params) request = { # spot ----------------------------------------------------------- # 'order-ids': ids.jsoin(','), # max 50 # 'client-order-ids': ','.join(ids), # max 50 # contracts ------------------------------------------------------ # 'order_id': id, # comma separated, max 10 # 'client_order_id': clientOrderId, # comma separated, max 10 # 'contract_code': market['id'], # 'symbol': market['settleId'], } method = None if marketType == 'spot': clientOrderIds = self.safe_value_2(params, 'client-order-id', 'clientOrderId') clientOrderIds = self.safe_value_2(params, 'client-order-ids', 'clientOrderIds', clientOrderIds) if clientOrderIds is None: if isinstance(clientOrderIds, basestring): request['order-ids'] = ids else: request['order-ids'] = ','.join(ids) else: if isinstance(clientOrderIds, basestring): request['client-order-ids'] = clientOrderIds else: request['client-order-ids'] = ','.join(clientOrderIds) params = self.omit(params, ['client-order-id', 'client-order-ids', 'clientOrderId', 'clientOrderIds']) method = 'spotPrivatePostV1OrderOrdersBatchcancel' else: if symbol is None: raise ArgumentsRequired(self.id + ' cancelOrders() requires a symbol for ' + marketType + ' orders') market = self.market(symbol) request['contract_code'] = market['id'] if market['linear']: defaultMargin = 'cross' if market['future'] else 'isolated' marginType = self.safe_string_2(self.options, 'defaultMarginType', 'marginType', defaultMargin) if marginType == 'isolated': method = 'contractPrivatePostLinearSwapApiV1SwapCancel' elif marginType == 'cross': method = 'contractPrivatePostLinearSwapApiV1SwapCrossCancel' elif market['inverse']: if market['future']: method = 'contractPrivatePostApiV1ContractCancel' request['symbol'] = market['settleId'] elif market['swap']: method = 'contractPrivatePostSwapApiV1SwapCancel' else: raise NotSupported(self.id + ' cancelOrders() does not support ' + marketType + ' markets') clientOrderIds = self.safe_string_2(params, 'client_order_id', 'clientOrderId') clientOrderIds = self.safe_string_2(params, 'client_order_ids', 'clientOrderIds', clientOrderIds) if clientOrderIds is None: request['order_id'] = ','.join(ids) else: request['client_order_id'] = clientOrderIds params = self.omit(params, ['client_order_id', 'client_order_ids', 'clientOrderId', 'clientOrderIds']) response = getattr(self, method)(self.extend(request, params)) # # spot # # { # "status": "ok", # "data": { # "success": [ # "5983466" # ], # "failed": [ # { # "err-msg": "Incorrect order state", # "order-state": 7, # "order-id": "", # "err-code": "order-orderstate-error", # "client-order-id": "first" # }, # { # "err-msg": "Incorrect order state", # "order-state": 7, # "order-id": "", # "err-code": "order-orderstate-error", # "client-order-id": "second" # }, # { # "err-msg": "The record is not found.", # "order-id": "", # "err-code": "base-not-found", # "client-order-id": "third" # } # ] # } # } # # contracts # # { # "status": "ok", # "data": { # "errors": [ # { # "order_id": "769206471845261312", # "err_code": 1061, # "err_msg": "This order doesnt exist." # } # ], # "successes": "773120304138219520" # }, # "ts": 1604367997451 # } # return response

7a8a00ecacc1eb719ced2f95c06a472a8b55892c

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

def cancel_orders(self, ids, symbol=None, params={}): self.load_markets() marketType = None marketType, params = self.handle_market_type_and_params('cancelOrder', None, params) request = { # spot ----------------------------------------------------------- # 'order-ids': ids.jsoin(','), # max 50 # 'client-order-ids': ','.join(ids), # max 50 # contracts ------------------------------------------------------ # 'order_id': id, # comma separated, max 10 # 'client_order_id': clientOrderId, # comma separated, max 10 # 'contract_code': market['id'], # 'symbol': market['settleId'], } method = None if marketType == 'spot': clientOrderIds = self.safe_value_2(params, 'client-order-id', 'clientOrderId') clientOrderIds = self.safe_value_2(params, 'client-order-ids', 'clientOrderIds', clientOrderIds) if clientOrderIds is None: if isinstance(clientOrderIds, basestring): request['order-ids'] = ids else: request['order-ids'] = ','.join(ids) else: if isinstance(clientOrderIds, basestring): request['client-order-ids'] = clientOrderIds else: request['client-order-ids'] = ','.join(clientOrderIds) params = self.omit(params, ['client-order-id', 'client-order-ids', 'clientOrderId', 'clientOrderIds']) method = 'spotPrivatePostV1OrderOrdersBatchcancel' else: if symbol is None: raise ArgumentsRequired(self.id + ' cancelOrders() requires a symbol for ' + marketType + ' orders') market = self.market(symbol) request['contract_code'] = market['id'] if market['linear']: defaultMargin = 'cross' if market['future'] else 'isolated' marginType = self.safe_string_2(self.options, 'defaultMarginType', 'marginType', defaultMargin) if marginType == 'isolated': method = 'contractPrivatePostLinearSwapApiV1SwapCancel' elif marginType == 'cross': method = 'contractPrivatePostLinearSwapApiV1SwapCrossCancel' elif market['inverse']: if market['future']: method = 'contractPrivatePostApiV1ContractCancel' request['symbol'] = market['settleId'] elif market['swap']: method = 'contractPrivatePostSwapApiV1SwapCancel' else: raise NotSupported(self.id + ' cancelOrders() does not support ' + marketType + ' markets') clientOrderIds = self.safe_string_2(params, 'client_order_id', 'clientOrderId') clientOrderIds = self.safe_string_2(params, 'client_order_ids', 'clientOrderIds', clientOrderIds) if clientOrderIds is None: request['order_id'] = ','.join(ids) else: request['client_order_id'] = clientOrderIds params = self.omit(params, ['client_order_id', 'client_order_ids', 'clientOrderId', 'clientOrderIds']) response = getattr(self, method)(self.extend(request, params)) # # spot # # { # "status": "ok", # "data": { # "success": [ # "5983466" # ], # "failed": [ # { # "err-msg": "Incorrect order state", # "order-state": 7, # "order-id": "", # "err-code": "order-orderstate-error", # "client-order-id": "first" # }, # { # "err-msg": "Incorrect order state", # "order-state": 7, # "order-id": "", # "err-code": "order-orderstate-error", #

cancel_orders

zerver/tests/test_auth_backends.py

test_auth_backends: Extract external_auth_backends. Signed-off-by: Zixuan James Li <p359101898@gmail.com>

zulip

Python

test_auth_backends.py

def test_get_external_method_dicts_correctly_sorted(self) -> None: with self.settings( AUTHENTICATION_BACKENDS=( "zproject.backends.EmailAuthBackend", "zproject.backends.GitHubAuthBackend", "zproject.backends.GoogleAuthBackend", "zproject.backends.ZulipRemoteUserBackend", "zproject.backends.SAMLAuthBackend", "zproject.backends.AzureADAuthBackend", ), ): external_auth_methods = get_external_method_dicts() external_auth_backends: List[Type[ExternalAuthMethod]] = [ ZulipRemoteUserBackend, GitHubAuthBackend, AzureADAuthBackend, GoogleAuthBackend, ] # First backends in the list should be SAML: self.assertIn("saml:", external_auth_methods[0]["name"]) self.assertEqual( [social_backend["name"] for social_backend in external_auth_methods[1:]], [ social_backend.name for social_backend in sorted( external_auth_backends, key=lambda x: x.sort_order, reverse=True, ) ], )

51df4031ac45ae6fcc6d2ccea22eed897116c582

https://github.com/zulip/zulip.git

def test_get_external_method_dicts_correctly_sorted(self) -> None: with self.settings( AUTHENTICATION_BACKENDS=( "zproject.backends.EmailAuthBackend", "zproject.backends.GitHubAuthBackend", "zproject.backends.GoogleAuthBackend", "zproject.backends.ZulipRemoteUserBackend", "zproject.backends.SAMLAuthBackend", "zproject.backends.AzureADAuthBackend", ), ): external_auth_methods = get_external_method_dicts() external_auth_backends: List[Type[ExternalAuthMethod]] = [ ZulipRemoteUserBackend, GitHubAuthBackend, AzureADAuthBackend,

test_get_external_method_dicts_correctly_sorted

homeassistant/components/tado/sensor.py

Improve entity type hints [t] (#77883)

core

Python

sensor.py

async def async_added_to_hass(self) -> None: self.async_on_remove( async_dispatcher_connect( self.hass, SIGNAL_TADO_UPDATE_RECEIVED.format( self._tado.home_id, "weather", "data" ), self._async_update_callback, ) ) self._async_update_home_data()

458001a06e7678273ea43c33e55b833adadced9e

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

async def async_added_to_hass(self) -> None: self.async_on_remove( async_dispatcher_connect( self.hass, SIGNAL_TADO_UPDATE_RECEIVED.format( self._tado.home_id, "weather", "data" ), self._async_update_callback, ) ) self._async_update_home_data()

async_added_to_hass

.venv/lib/python3.8/site-packages/pip/_vendor/pkg_resources/__init__.py

upd; format

transferlearning

Python

__init__.py

def obtain(self, requirement, installer=None): if installer is not None: return installer(requirement)

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def obtain(self, requirement, installer=None): if installer is not None: return installer(requirement)

obtain

dashboard/modules/job/common.py

[jobs] Rename JobData -> JobInfo (#22499) `JobData` could be confused with the actual output data of a job, `JobInfo` makes it more clear that this is status information + metadata.

ray

Python

common.py

def get_all_jobs(self) -> Dict[str, JobInfo]: raw_job_ids_with_prefixes = _internal_kv_list( self.JOB_DATA_KEY_PREFIX, namespace=ray_constants.KV_NAMESPACE_JOB ) job_ids_with_prefixes = [ job_id.decode() for job_id in raw_job_ids_with_prefixes ] job_ids = [] for job_id_with_prefix in job_ids_with_prefixes: assert job_id_with_prefix.startswith( self.JOB_DATA_KEY_PREFIX ), "Unexpected format for internal_kv key for Job submission" job_ids.append(job_id_with_prefix[len(self.JOB_DATA_KEY_PREFIX) :]) return {job_id: self.get_info(job_id) for job_id in job_ids}

58e5f0140d247059ca45b249446614929930c126

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

def get_all_jobs(self) -> Dict[str, JobInfo]: raw_job_ids_with_prefixes = _internal_kv_list( self.JOB_DATA_KEY_PREFIX, namespace=ray_constants.KV_NAMESPACE_JOB ) job_ids_with_prefixe

get_all_jobs

autokeras/tasks/image_test.py

mvoing tests (#1664) Co-authored-by: Haifeng Jin <haifeng-jin@users.noreply.github.com>

autokeras

Python

image_test.py

def test_img_seg_fit_call_auto_model_fit(fit, tmp_path): auto_model = ak.tasks.image.ImageSegmenter( directory=tmp_path, seed=test_utils.SEED ) auto_model.fit( x=test_utils.generate_data(num_instances=100, shape=(32, 32, 3)), y=test_utils.generate_data(num_instances=100, shape=(32, 32)), ) assert fit.is_called @mock.patch("autokeras.AutoModel.fit")

b349e06f3b6e80ba527347b2b0463bcc403ae8c5

@mock.patch("autokeras.AutoModel.fit")

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

def test_img_seg_fit_call_auto_model_fit(fit, tmp_path): auto_model = ak.tasks.image.ImageSegmenter( directory=tmp_path, seed=test_utils.SEED ) auto_model.fit( x=test_utils.generate_data(num_instances=100, shape=(32, 32, 3)), y=test_utils.generate_data(num_instances=100, shape=(32, 32)), ) assert fit.is_called

test_img_seg_fit_call_auto_model_fit

tests/integration_tests/test_visualization.py

Renamed "training" to "trainer" and "numerical" to "number" (#1743)

ludwig

Python

test_visualization.py

def test_visualization_binary_threshold_vs_metric_output_saved(csv_filename): input_features = [ text_feature(vocab_size=10, min_len=1, encoder="stacked_cnn"), number_feature(), category_feature(vocab_size=10, embedding_size=5), set_feature(), sequence_feature(vocab_size=10, max_len=10, encoder="embed"), ] output_features = [category_feature(vocab_size=4, reduce_input="sum")] # Generate test data rel_path = generate_data(input_features, output_features, csv_filename) input_features[0]["encoder"] = "parallel_cnn" exp_dir_name = run_experiment_with_visualization(input_features, output_features, dataset=rel_path) vis_output_pattern_pdf = os.path.join(exp_dir_name, "*.pdf") vis_output_pattern_png = os.path.join(exp_dir_name, "*.png") output_feature_name = get_output_feature_name(exp_dir_name) probability = os.path.join(exp_dir_name, PREDICTIONS_PARQUET_FILE_NAME) experiment_source_data_name = csv_filename.split(".")[0] ground_truth = experiment_source_data_name + ".csv" split_file = experiment_source_data_name + ".split.csv" test_cmd_pdf = [ "python", "-m", "ludwig.visualize", "--visualization", "binary_threshold_vs_metric", "--positive_label", "2", "--metrics", "accuracy", "--ground_truth", ground_truth, "--output_feature_name", output_feature_name, "--split_file", split_file, "--ground_truth_metadata", exp_dir_name + "/model/training_set_metadata.json", "--probabilities", probability, probability, "--model_names", "Model1", "Model2", "-od", exp_dir_name, ] test_cmd_png = test_cmd_pdf.copy() + ["-ff", "png"] commands = [test_cmd_pdf, test_cmd_png] vis_patterns = [vis_output_pattern_pdf, vis_output_pattern_png] for command, viz_pattern in zip(commands, vis_patterns): result = subprocess.run(command) figure_cnt = glob.glob(viz_pattern) assert 0 == result.returncode assert 1 == len(figure_cnt)

6ec371f2201c8b0a13b5719d59a19da94aaa09de

https://github.com/ludwig-ai/ludwig.git

def test_visualization_binary_threshold_vs_metric_output_saved(csv_filename): input_features = [ text_feature(vocab_size=10, min_len=1, encoder="stacked_cnn"), number_feature(), category_feature(vocab_size=10, embedding_size=5), set_feature(), sequence_feature(vocab_size=10, max_len=10, encoder="embed"), ] output_features = [category_feature(vocab_size=4, reduce_input="sum")] # Generate test data rel_path = generate_data(input_features, output_features, csv_filename) input_features[0]["encoder"] = "parallel_cnn" exp_dir_name = run_experiment_with_visualization(input_features, output_features, dataset=rel_path) vis_output_pattern_pdf = os.path.join(exp_dir_name, "*.pdf") vis_output_pattern_png = os.path.join(exp_dir_name, "*.png") output_feature_name = get_output_feature_name(exp_dir_name) probability = os.path.join(exp_dir_name, PREDICTIONS_PARQUET_FILE_NAME) experiment_source_data_name = csv_filename.split(".")[0] ground_truth = experiment_source_data_name + ".csv" split_file =

test_visualization_binary_threshold_vs_metric_output_saved

wagtail/contrib/settings/tests/generic/test_model.py

Add generic settings to compliment site-specific settings (#8327)

wagtail

Python

test_model.py

def test_get_page_url_returns_empty_string_if_attribute_value_not_a_page(self): settings = self._create_importantpagesgenericsetting_object() for value in (None, self.default_site): with self.subTest(attribute_value=value): settings.test_attribute = value # when called directly self.assertEqual(settings.get_page_url("test_attribute"), "") # when called indirectly via shortcut self.assertEqual(settings.page_url.test_attribute, "")

d967eccef28ce47f60d26be1c28f2d83a25f40b0

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

def test_get_page_url_returns_empty_string_if_attribute_value_not_a_page(self): settings = self._create_importantpagesgenericsetting_object() for value in (None, self.default_site): with self.subTest(attribute_value=value): settings.test

test_get_page_url_returns_empty_string_if_attribute_value_not_a_page

pandas/io/excel/_odfreader.py

REF change to_datetime -> Timestamp for in odf reader (#49712) change to_datetime -> Timestamp for single value Co-authored-by: MarcoGorelli <>

pandas

Python

_odfreader.py

def _get_cell_value(self, cell) -> Scalar | NaTType: from odf.namespaces import OFFICENS if str(cell) == "#N/A": return np.nan cell_type = cell.attributes.get((OFFICENS, "value-type")) if cell_type == "boolean": if str(cell) == "TRUE": return True return False if cell_type is None: return self.empty_value elif cell_type == "float": # GH5394 cell_value = float(cell.attributes.get((OFFICENS, "value"))) val = int(cell_value) if val == cell_value: return val return cell_value elif cell_type == "percentage": cell_value = cell.attributes.get((OFFICENS, "value")) return float(cell_value) elif cell_type == "string": return self._get_cell_string_value(cell) elif cell_type == "currency": cell_value = cell.attributes.get((OFFICENS, "value")) return float(cell_value) elif cell_type == "date": cell_value = cell.attributes.get((OFFICENS, "date-value")) return pd.Timestamp(cell_value) elif cell_type == "time": stamp = pd.Timestamp(str(cell)) # cast needed here because Scalar doesn't include datetime.time return cast(Scalar, stamp.time()) else: self.close() raise ValueError(f"Unrecognized type {cell_type}")

86f182829d2cfe2f4c380d7f2ecd6ea27d6e0f1d

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

def _get_cell_value(self, cell) -> Scalar | NaTType: from odf.namespaces import OFFICENS if str(cell) == "#N/A": return np.nan cell_type = cell.attributes.get((OFFICENS, "value-type")) if cell_type == "boolean": if str(cell) == "TRUE": return True return False if cell_type is N

_get_cell_value

airbyte-integrations/connectors/source-github/unit_tests/test_stream.py

🐛 Source Github: test coverage more than 90% (#10967) Signed-off-by: Sergey Chvalyuk <grubberr@gmail.com>

airbyte

Python

test_stream.py

def test_stream_organizations_read(): organization_args = {"organizations": ["org1", "org2"]} stream = Organizations(**organization_args) responses.add("GET", "https://api.github.com/orgs/org1", json={"id": 1}) responses.add("GET", "https://api.github.com/orgs/org2", json={"id": 2}) records = read_full_refresh(stream) assert records == [{"id": 1}, {"id": 2}] @responses.activate

0f475ce6ff95c4fc1e6793edd8d88861c1e8f60a

@responses.activate

https://github.com/airbytehq/airbyte.git

def test_stream_organizations_read(): organization_args = {"organizations": ["org1", "org2"]} stream = Organizations(**organization_args) responses.add("GET", "https://api.github.com/orgs/org1", json={"id": 1}) responses.add("GET", "https://api.github.com/or

test_stream_organizations_read

tests/federation/transport/test_server.py

Tests: replace mocked Authenticator with the real thing (#11913) If we prepopulate the test homeserver with a key for a remote homeserver, we can make federation requests to it without having to stub out the authenticator. This has two advantages: * means that what we are testing is closer to reality (ie, we now have complete tests for the incoming-request-authorisation flow) * some tests require that other objects be signed by the remote server (eg, the event in `/send_join`), and doing that would require a whole separate set of mocking out. It's much simpler just to use real keys.

synapse

Python

test_server.py

def test_open_public_room_list_over_federation(self): channel = self.make_signed_federation_request( "GET", "/_matrix/federation/v1/publicRooms", ) self.assertEquals(200, channel.code)

c3db7a0b59d48b8872bc24096f9a2467ef35f703

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

def test_open_public_room_list_over_federation(self): channel = self.make_signed_federation_request( "GET", "/

test_open_public_room_list_over_federation

tests/pytests/unit/utils/win_dacl/test_get_name.py

Add tests, migrate some tests to pytest

salt

Python

test_get_name.py

def test_get_name_normal_name(): result = salt.utils.win_dacl.get_name("Administrators") expected = "Administrators" assert result == expected

3bb43882e727b1d36abe2e501759c9c5e9048ecf

https://github.com/saltstack/salt.git

def test_get_name_normal_name(): result = salt.utils.win_dacl.get_name("Administrators") expected = "Administrators" assert result == expected

test_get_name_normal_name

lib/matplotlib/tests/test_axes.py

Curved polar errorbars - uses _interpolation_steps - prefers transform MarkerStyle in init over _transform property - adjusted what's new - added more tests for overlapping, asymmetric and long errorbars - combine all tests to a single figure - remove overlappnig since it does not work same on all platforms - rework test figure, add overlapping, might work by avoiding grid - update what's new with image and link to example

matplotlib

Python

test_axes.py

def test_mixed_errorbar_polar_caps(): fig = plt.figure() ax = plt.subplot(111, projection='polar') # symmetric errorbars th_sym = [1, 2, 3] r_sym = [0.9]*3 ax.errorbar(th_sym, r_sym, xerr=0.35, yerr=0.2, fmt="o") # long errorbars th_long = [np.pi/2 + .1, np.pi + .1] r_long = [1.8, 2.2] ax.errorbar(th_long, r_long, xerr=0.8 * np.pi, yerr=0.15, fmt="o") # asymmetric errorbars th_asym = [4*np.pi/3 + .1, 5*np.pi/3 + .1, 2*np.pi-0.1] r_asym = [1.1]*3 xerr = [[.3, .3, .2], [.2, .3, .3]] yerr = [[.35, .5, .5], [.5, .35, .5]] ax.errorbar(th_asym, r_asym, xerr=xerr, yerr=yerr, fmt="o") # overlapping errorbar th_over = [2.1] r_over = [3.1] ax.errorbar(th_over, r_over, xerr=10, yerr=.2, fmt="o")

907f78dbf959c0609ab484c59e840eea3eafee31

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

def test_mixed_errorbar_polar_caps(): fig = plt.figure() ax = plt.subplot(111, projection='polar') # symmetric errorbars th_sym = [1, 2, 3] r_sym = [0.9]*3 ax.errorbar(th_sym, r_sym, xerr=0.35, yerr=0.2, fmt="o") # long errorbars th_long = [np.pi/2 + .1, np.pi + .1] r_long = [1.8, 2.2] ax.errorbar(th_long, r_long, xerr=0.8 * np.pi, yerr=0.15, fmt="o") # asymmetric errorbars th_asym = [4*np.pi/3 + .1, 5*np.pi/3 + .1, 2*np.pi-0.1] r

test_mixed_errorbar_polar_caps

tests/components/risco/util.py

Upgrade pyrisco to 0.5.0 (#75648) * Upgrade to pyrisco 0.4.0 * Parametrized error tests in config flow * Inline error parameters * Switch to RiscoCloud

core

Python

util.py

def two_zone_alarm(): zone_mocks = {0: _zone_mock(), 1: _zone_mock()} alarm_mock = MagicMock() with patch.object( zone_mocks[0], "id", new_callable=PropertyMock(return_value=0) ), patch.object( zone_mocks[0], "name", new_callable=PropertyMock(return_value="Zone 0") ), patch.object( zone_mocks[1], "id", new_callable=PropertyMock(return_value=1) ), patch.object( zone_mocks[1], "name", new_callable=PropertyMock(return_value="Zone 1") ), patch.object( alarm_mock, "zones", new_callable=PropertyMock(return_value=zone_mocks), ), patch( "homeassistant.components.risco.RiscoCloud.get_state", return_value=alarm_mock, ): yield alarm_mock

7cf2d1759dde088105f77ca61dba8e58e3474b83

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

def two_zone_alarm(): zone_mocks = {0: _zone_mock(), 1: _zone_mock()} alarm_mock = MagicMock() with patch.object( zone_mocks[0], "id", new_callable=PropertyMock(return_value=0) ), patch.object( zone_mocks[0], "name", new_callable=PropertyMock(return_value="Zone 0") ), patch.object( zone_mocks[1], "id", new_callable=PropertyMock(return_value=1) ), patch.object( zone_mocks[1], "name", new_callable=PropertyMock(return_value="Zone 1") ), patch.object( alarm_mock, "zones", new_callable=PropertyMock(return_value=zone_mocks), ), patch( "homeassistant.components.risco.RiscoCloud.get_state", return_v

two_zone_alarm

networkx/readwrite/nx_shp.py

Minor improvements from general code readthrough (#5414) * Add deprecated directive to reversed docstring. * Add missing dep directives to shpfiles. * Remove defn of INF sentinel. * typo. * str -> comment in forloop. * STY: appropriate casing for var name.

networkx

Python

nx_shp.py

def read_shp(path, simplify=True, geom_attrs=True, strict=True): msg = ( "read_shp is deprecated and will be removed in 3.0." "See https://networkx.org/documentation/latest/auto_examples/index.html#geospatial." ) warnings.warn(msg, DeprecationWarning, stacklevel=2) try: from osgeo import ogr except ImportError as err: raise ImportError("read_shp requires OGR: http://www.gdal.org/") from err if not isinstance(path, str): return net = nx.DiGraph() shp = ogr.Open(path) if shp is None: raise RuntimeError(f"Unable to open {path}") for lyr in shp: fields = [x.GetName() for x in lyr.schema] for f in lyr: g = f.geometry() if g is None: if strict: raise nx.NetworkXError("Bad data: feature missing geometry") else: continue flddata = [f.GetField(f.GetFieldIndex(x)) for x in fields] attributes = dict(zip(fields, flddata)) attributes["ShpName"] = lyr.GetName() # Note: Using layer level geometry type if g.GetGeometryType() == ogr.wkbPoint: net.add_node((g.GetPoint_2D(0)), **attributes) elif g.GetGeometryType() in (ogr.wkbLineString, ogr.wkbMultiLineString): for edge in edges_from_line(g, attributes, simplify, geom_attrs): e1, e2, attr = edge net.add_edge(e1, e2) net[e1][e2].update(attr) else: if strict: raise nx.NetworkXError( f"GeometryType {g.GetGeometryType()} not supported" ) return net

cc1db275efc709cb964ce88abbfa877798d58c10

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

def read_shp(path, simplify=True, geom_attrs=True, strict=True): msg = ( "read_shp is deprecated and will be removed in 3.0." "See https://networkx.org/documentation/latest/auto_examples/index.html#geospatial." ) warnings.warn(msg, DeprecationWarning, stacklevel=2) try: from osgeo import ogr except ImportError as err: raise ImportError("read_shp requires OGR: http://www.gdal.org/") from e

read_shp

seaborn/tests/_core/test_plot.py

Implement prototype Plot.pair behavior

seaborn

Python

test_plot.py

def test_col_wrapping(self): cols = list("abcd") wrap = 3 p = Plot().facet(col=cols, wrap=wrap).plot() gridspec = p._figure.axes[0].get_gridspec() assert len(p._figure.axes) == 4 assert gridspec.ncols == 3 assert gridspec.nrows == 2 # TODO test axis labels and titles

9cfdd4c5ed60562f3429386d25758476c5c57998

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

def test_col_wrapping(self): cols = list("abcd") wrap = 3 p = Plot().facet(col=cols, wrap=wrap).plot() gridspec = p._figure.axes[0].get_gridspec() assert len(p._figure.axes) == 4 assert gridspec.ncols == 3 assert gridspec.nrows == 2 # TODO test axis labels and titles

test_col_wrapping

tests/providers/ssh/operators/test_ssh.py

Refactor SSH tests to not use SSH server in operator tests (#21326) This required a slight refactor to the SSHOperator (moving `exec_ssh_client_command` "down" in to the Hook) but the SSH _Operator_ tests now just use stubbing, and the only place that connects to a real SSH server is the one test of `test_exec_ssh_client_command` in SSHHook. This is both better structured, and hopefully produces less (or ideally no) random failures in our tests

airflow

Python

test_ssh.py

def test_command_errored(self): # Test that run_ssh_client_command works on invalid commands command = "not_a_real_command" task = SSHOperator( task_id="test", ssh_hook=self.hook, command=command, ) self.exec_ssh_client_command.return_value = (1, b'', b'Error here') with pytest.raises(AirflowException, match=f"error running cmd: {command}, error: Error here"): task.execute(None)

ab762a5a8ae147ae33500ee3c7e7a73d25d03ad7

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

def test_command_errored(self): # Test that run_ssh_client_command works on invalid commands command = "not_a_real_command" task = SSHOperator( task_id="test", ssh_hook=self.hook, command=command, ) self.exec_ssh_client_comman

test_command_errored

release/nightly_tests/dataset/pipelined_training.py

Round robin during spread scheduling (#21303) - Separate spread scheduling and default hydra scheduling (i.e. SpreadScheduling != HybridScheduling(threshold=0)): they are already separated in the API layer and they have the different end goals so it makes sense to separate their implementations and evolve them independently. - Simple round robin for spread scheduling: this is just a starting implementation, can be optimized later. - Prefer not to spill back tasks that are waiting for args since the pull is already in progress.

ray

Python

pipelined_training.py

def create_dataset(files, num_workers=4, epochs=50, num_windows=1): if num_windows > 1: num_rows = ray.data.read_parquet( files ).count() # This should only read Parquet metadata. file_splits = np.array_split(files, num_windows)

baa14d695aafd0abb817026ddd4b4182f68f6b61

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

def create_dataset(files, num_workers=4, epochs=50, num_windows=1): if num_windows > 1: num_rows = ray.data.read_parquet( files ).count() # This should only read Parquet metadata. file_splits = np.array_split(files, num_windows)

create_dataset

tests/freqai/test_freqai_datakitchen.py

Fix arm test failure

freqtrade

Python

test_freqai_datakitchen.py

def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog): freqai = make_data_dictionary(mocker, freqai_conf) # freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1}) freqai.dk.use_DBSCAN_to_remove_outliers(predict=False) assert log_has_re(r"DBSCAN found eps of 2\.3\d\.", caplog)

041258a5499534fae6de843a7b9d10dde02c7659

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

def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog): freqai = make_data_dictionary(mocker, freqai_conf) # freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1}) freqai.dk.use_DBSCAN_to_remove_outliers(predict=False) assert log_has_re(r"DBSCAN found eps of 2\.

test_use_DBSCAN_to_remove_outliers

python/ray/tests/perf_integration_tests/test_perf_integration.py

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

ray

Python

test_perf_integration.py

def warmup(): x = np.zeros(10 ** 6, dtype=np.uint8) for _ in range(5): for _ in range(5): ray.put(x) for _ in range(5): ray.get([dummy_task.remote(0) for _ in range(1000)]) @pytest.mark.benchmark @pytest.mark.parametrize("num_tasks", num_tasks_submitted, ids=num_tasks_ids)

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

@pytest.mark.benchmark @pytest.mark.parametrize("num_tasks", num_tasks_submitted, ids=num_tasks_ids)

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

def warmup(): x = np.zeros(10 ** 6, dtype=np.uint8) for _ in range(5): for _ in range(5): ray.put(x) for _ in range(5): ray.ge

warmup

homeassistant/components/zha/climate.py

Adjust type hints for ClimateEntityFeature (#82206)

core

Python

climate.py

def supported_features(self) -> ClimateEntityFeature: features = self._supported_flags if HVACMode.HEAT_COOL in self.hvac_modes: features |= ClimateEntityFeature.TARGET_TEMPERATURE_RANGE if self._fan is not None: self._supported_flags |= ClimateEntityFeature.FAN_MODE return features

39ac2c11017f84276cb23d15843dcccae5b104f4

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

def supported_features(self) -> ClimateEntityFeature: features = self._supported_flags if HVACMode.HEAT_COOL in self.hvac_modes:

supported_features