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2 | 9 | def get_uri(self) -> str:
conn = self.get_connection(getattr(self, self.conn_name_attr))
conn.schema = self.__schema or conn.schema
return conn.get_uri()
| airflow/hooks/dbapi.py | 66 | airflow | {
"docstring": "\n Extract the URI from the connection.\n\n :return: the extracted uri.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 10,
"vocab_size": 8
} | 15 | Python | 13 | 59c450ee5425a2d23ef813dbf219cde14df7c85c | dbapi.py | 45,221 | 9 | 40 | get_uri | https://github.com/apache/airflow.git | Make DbApiHook use get_uri from Connection (#21764)
DBApi has its own get_uri method which does not deal
with quoting properly and neither with empty passwords.
Connection also has a get_uri method that deals properly
with the above issues.
This also fixes issues with RFC compliancy. | 43 | 0 | 8,509 | 11 |
|
1 | 22 | def test_reading_post_data_raises_unreadable_post_error(self):
req = self._get_POST_request_with_token()
mw = CsrfViewMiddleware(post_form_view)
mw.process_request(req)
resp = mw.process_view(req, post_form_view, (), {})
self.assertIsNone(resp)
req = self._get_POST_request_with_token(request_class=PostErrorRequest)
req.post_error = UnreadablePostError("Error reading input data.")
mw.process_request(req)
with self.assertLogs("django.security.csrf", "WARNING") as cm:
resp = mw.process_view(req, post_form_view, (), {})
self.assertEqual(resp.status_code, 403)
self.assertEqual(
cm.records[0].getMessage(),
"Forbidden (%s): " % REASON_CSRF_TOKEN_MISSING,
)
| tests/csrf_tests/tests.py | 214 | django | {
"docstring": "\n An UnreadablePostError raised while reading the POST data should be\n handled by the middleware.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 14,
"vocab_size": 13
} | 47 | Python | 35 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | tests.py | 202,376 | 16 | 129 | test_reading_post_data_raises_unreadable_post_error | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 171 | 0 | 50,092 | 11 |
|
2 | 5 | def broadcast_shapes(*shapes):
# NOTE: We have both cached and uncached versions to handle Tracers in shapes.
try:
return _broadcast_shapes_cached(*shapes)
except:
return _broadcast_shapes_uncached(*shapes)
@cache() | jax/_src/lax/lax.py | 52 | @cache() | jax | {
"docstring": "Returns the shape that results from NumPy broadcasting of `shapes`.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 23 | Python | 22 | 78ed03c4c2970e5e0d11f14a8d4fc968a4efbca2 | lax.py | 122,213 | 5 | 23 | broadcast_shapes | https://github.com/google/jax.git | [typing] add annotations to jax.numpy.linalg | 32 | 1 | 27,122 | 11 |
1 | 3 | def fit(self) -> Any:
return None
| freqtrade/freqai/freqai_interface.py | 19 | freqtrade | {
"docstring": "\n Most regressors use the same function names and arguments e.g. user \n can drop in LGBMRegressor in place of CatBoostRegressor and all data\n management will be properly handled by Freqai.\n :params:\n :data_dictionary: the dictionary constructed by DataHandler to hold \n all the training and test data/labels.\n ",
"language": "en",
"n_whitespaces": 96,
"n_words": 44,
"vocab_size": 37
} | 6 | Python | 6 | fc837c4daa27a18ff0e86128f4d52089b88fa5fb | freqai_interface.py | 149,761 | 10 | 10 | fit | https://github.com/freqtrade/freqtrade.git | add freqao backend machinery, user interface, documentation | 24 | 0 | 34,519 | 6 |
|
1 | 2 | def sliders(self):
return self["sliders"]
| packages/python/plotly/plotly/graph_objs/_layout.py | 22 | plotly.py | {
"docstring": "\n The 'sliders' property is a tuple of instances of\n Slider that may be specified as:\n - A list or tuple of instances of plotly.graph_objs.layout.Slider\n - A list or tuple of dicts of string/value properties that\n will be passed to the Slider constructor\n\n Supported dict properties:\n\n active\n Determines which button (by index starting from\n 0) is considered active.\n activebgcolor\n Sets the background color of the slider grip\n while dragging.\n bgcolor\n Sets the background color of the slider.\n bordercolor\n Sets the color of the border enclosing the\n slider.\n borderwidth\n Sets the width (in px) of the border enclosing\n the slider.\n currentvalue\n :class:`plotly.graph_objects.layout.slider.Curr\n entvalue` instance or dict with compatible\n properties\n font\n Sets the font of the slider step labels.\n len\n Sets the length of the slider This measure\n excludes the padding of both ends. That is, the\n slider's length is this length minus the\n padding on both ends.\n lenmode\n Determines whether this slider length is set in\n units of plot \"fraction\" or in *pixels. Use\n `len` to set the value.\n minorticklen\n Sets the length in pixels of minor step tick\n marks\n name\n When used in a template, named items are\n created in the output figure in addition to any\n items the figure already has in this array. You\n can modify these items in the output figure by\n making your own item with `templateitemname`\n matching this `name` alongside your\n modifications (including `visible: false` or\n `enabled: false` to hide it). Has no effect\n outside of a template.\n pad\n Set the padding of the slider component along\n each side.\n steps\n A tuple of :class:`plotly.graph_objects.layout.\n slider.Step` instances or dicts with compatible\n properties\n stepdefaults\n When used in a template (as\n layout.template.layout.slider.stepdefaults),\n sets the default property values to use for\n elements of layout.slider.steps\n templateitemname\n Used to refer to a named item in this array in\n the template. Named items from the template\n will be created even without a matching item in\n the input figure, but you can modify one by\n making an item with `templateitemname` matching\n its `name`, alongside your modifications\n (including `visible: false` or `enabled: false`\n to hide it). If there is no template or no\n matching item, this item will be hidden unless\n you explicitly show it with `visible: true`.\n tickcolor\n Sets the color of the border enclosing the\n slider.\n ticklen\n Sets the length in pixels of step tick marks\n tickwidth\n Sets the tick width (in px).\n transition\n :class:`plotly.graph_objects.layout.slider.Tran\n sition` instance or dict with compatible\n properties\n visible\n Determines whether or not the slider is\n visible.\n x\n Sets the x position (in normalized coordinates)\n of the slider.\n xanchor\n Sets the slider's horizontal position anchor.\n This anchor binds the `x` position to the\n \"left\", \"center\" or \"right\" of the range\n selector.\n y\n Sets the y position (in normalized coordinates)\n of the slider.\n yanchor\n Sets the slider's vertical position anchor This\n anchor binds the `y` position to the \"top\",\n \"middle\" or \"bottom\" of the range selector.\n\n Returns\n -------\n tuple[plotly.graph_objs.layout.Slider]\n ",
"language": "en",
"n_whitespaces": 2252,
"n_words": 479,
"vocab_size": 216
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _layout.py | 227,351 | 2 | 11 | sliders | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 59,024 | 7 |
|
15 | 34 | def get_dataset_path(path, annotation, image_dir):
if _dataset_exists(path, annotation, image_dir):
return path
data_name = os.path.split(path.strip().lower())[-1]
if data_name not in DOWNLOAD_DATASETS_LIST:
raise ValueError(
"Dataset {} is not valid for reason above, please check again.".
format(osp.realpath(path)))
else:
logger.WARNING(
"Dataset {} is not valid for reason above, try searching {} or "
"downloading dataset...".format(osp.realpath(path), DATASET_HOME))
for name, dataset in DATASETS.items():
if data_name == name:
logger.debug("Parse dataset_dir {} as dataset "
"{}".format(path, name))
data_dir = osp.join(DATASET_HOME, name)
if name == "spine_coco":
if _dataset_exists(data_dir, annotation, image_dir):
return data_dir
# For voc, only check dir VOCdevkit/VOC2012, VOCdevkit/VOC2007
if name in ['voc', 'fruit', 'roadsign_voc']:
exists = True
for sub_dir in dataset[1]:
check_dir = osp.join(data_dir, sub_dir)
if osp.exists(check_dir):
logger.info("Found {}".format(check_dir))
else:
exists = False
if exists:
return data_dir
# voc exist is checked above, voc is not exist here
check_exist = name != 'voc' and name != 'fruit' and name != 'roadsign_voc'
for url, md5sum in dataset[0]:
get_path(url, data_dir, md5sum, check_exist)
# voc should create list after download
if name == 'voc':
create_voc_list(data_dir)
return data_dir
raise ValueError("Dataset automaticly downloading Error.")
| ppdet/utils/download.py | 424 | PaddleDetection | {
"docstring": "\n If path exists, return path.\n Otherwise, get dataset path from DATASET_HOME, if not exists,\n download it.\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 16,
"vocab_size": 14
} | 170 | Python | 102 | 630304e0b66c0528ecaa3bf2e88b44a14b7f3383 | download.py | 211,846 | 37 | 253 | get_dataset_path | https://github.com/PaddlePaddle/PaddleDetection.git | fix auto download logger info (#7550) | 639 | 0 | 53,148 | 20 |
|
1 | 4 | def is_package(self, fullname):
raise ImportError
| python3.10.4/Lib/importlib/abc.py | 18 | XX-Net | {
"docstring": "Optional method which when implemented should return whether the\n module is a package. The fullname is a str. Returns a bool.\n\n Raises ImportError if the module cannot be found.\n ",
"language": "en",
"n_whitespaces": 52,
"n_words": 29,
"vocab_size": 24
} | 5 | Python | 5 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | abc.py | 218,183 | 2 | 10 | is_package | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 19 | 0 | 55,185 | 6 |
|
3 | 18 | def test_connect_and_rollback(self):
new_connection = connection.copy()
try:
# Ensure the database default time zone is different than
# the time zone in new_connection.settings_dict. We can
# get the default time zone by reset & show.
with new_connection.cursor() as cursor:
cursor.execute("RESET TIMEZONE")
cursor.execute("SHOW TIMEZONE")
db_default_tz = cursor.fetchone()[0]
new_tz = "Europe/Paris" if db_default_tz == "UTC" else "UTC"
new_connection.close()
# Invalidate timezone name cache, because the setting_changed
# handler cannot know about new_connection.
del new_connection.timezone_name
# Fetch a new connection with the new_tz as default
# time zone, run a query and rollback.
with self.settings(TIME_ZONE=new_tz):
new_connection.set_autocommit(False)
new_connection.rollback()
# Now let's see if the rollback rolled back the SET TIME ZONE.
with new_connection.cursor() as cursor:
cursor.execute("SHOW TIMEZONE")
tz = cursor.fetchone()[0]
self.assertEqual(new_tz, tz)
finally:
new_connection.close()
| tests/backends/postgresql/tests.py | 237 | django | {
"docstring": "\n PostgreSQL shouldn't roll back SET TIME ZONE, even if the first\n transaction is rolled back (#17062).\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 16,
"vocab_size": 15
} | 119 | Python | 79 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | tests.py | 201,749 | 19 | 125 | test_connect_and_rollback | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 448 | 0 | 49,987 | 15 |
|
3 | 10 | def _get_container_name(self) -> Optional[str]:
# Must match `/?[a-zA-Z0-9][a-zA-Z0-9_.-]+` in the end
if not self.name:
return None
return (
slugify(
self.name,
lowercase=False,
# Docker does not limit length but URL limits apply eventually so
# limit the length for safety
max_length=250,
# Docker allows these characters for container names
regex_pattern=r"[^a-zA-Z0-9_.-]+",
).lstrip(
# Docker does not allow leading underscore, dash, or period
"_-."
)
# Docker does not allow 0 character names so cast to null if the name is
# empty after slufification
or None
)
| src/prefect/infrastructure/docker.py | 85 | prefect | {
"docstring": "\n Generates a container name to match the configured name, ensuring it is Docker\n compatible.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 14,
"vocab_size": 14
} | 85 | Python | 58 | 2bff1047c0c183ec79e606b9a1c4ac966e23c8d1 | docker.py | 57,431 | 18 | 49 | _get_container_name | https://github.com/PrefectHQ/prefect.git | Add tests for docker container | 332 | 0 | 11,652 | 13 |
|
6 | 24 | def ray_dask_get_sync(dsk, keys, **kwargs):
ray_callbacks = kwargs.pop("ray_callbacks", None)
persist = kwargs.pop("ray_persist", False)
with local_ray_callbacks(ray_callbacks) as ray_callbacks:
# Unpack the Ray-specific callbacks.
(
ray_presubmit_cbs,
ray_postsubmit_cbs,
ray_pretask_cbs,
ray_posttask_cbs,
ray_postsubmit_all_cbs,
ray_finish_cbs,
) = unpack_ray_callbacks(ray_callbacks)
# NOTE: We hijack Dask's `get_async` function, injecting a different
# task executor.
object_refs = get_async(
_apply_async_wrapper(
apply_sync,
_rayify_task_wrapper,
ray_presubmit_cbs,
ray_postsubmit_cbs,
ray_pretask_cbs,
ray_posttask_cbs,
),
1,
dsk,
keys,
**kwargs,
)
if ray_postsubmit_all_cbs is not None:
for cb in ray_postsubmit_all_cbs:
cb(object_refs, dsk)
# NOTE: We explicitly delete the Dask graph here so object references
# are garbage-collected before this function returns, i.e. before all
# Ray tasks are done. Otherwise, no intermediate objects will be
# cleaned up until all Ray tasks are done.
del dsk
if persist:
result = object_refs
else:
result = ray_get_unpack(object_refs)
if ray_finish_cbs is not None:
for cb in ray_finish_cbs:
cb(result)
return result
@dataclass | python/ray/util/dask/scheduler.py | 219 | @dataclass | ray | {
"docstring": "\n A synchronous Dask-Ray scheduler. This scheduler will send top-level\n (non-inlined) Dask tasks to a Ray cluster for execution. The scheduler will\n wait for the tasks to finish executing, fetch the results, and repackage\n them into the appropriate Dask collections. This particular scheduler\n submits Ray tasks synchronously, which can be useful for debugging.\n\n This can be passed directly to `dask.compute()`, as the scheduler:\n\n >>> dask.compute(obj, scheduler=ray_dask_get_sync)\n\n You can override the currently active global Dask-Ray callbacks (e.g.\n supplied via a context manager):\n\n >>> dask.compute(\n obj,\n scheduler=ray_dask_get_sync,\n ray_callbacks=some_ray_dask_callbacks,\n )\n\n Args:\n dsk: Dask graph, represented as a task DAG dictionary.\n keys (List[str]): List of Dask graph keys whose values we wish to\n compute and return.\n\n Returns:\n Computed values corresponding to the provided keys.\n ",
"language": "en",
"n_whitespaces": 231,
"n_words": 119,
"vocab_size": 86
} | 137 | Python | 99 | 905258dbc19753c81039f993477e7ab027960729 | scheduler.py | 140,551 | 38 | 138 | ray_dask_get_sync | https://github.com/ray-project/ray.git | Clean up docstyle in python modules and add LINT rule (#25272) | 563 | 1 | 32,022 | 13 |
2 | 6 | async def async_refresh_providers(self) -> None:
old_state = self._rtsp_to_webrtc
self._rtsp_to_webrtc = await self._async_use_rtsp_to_webrtc()
if old_state != self._rtsp_to_webrtc:
self.async_write_ha_state()
| homeassistant/components/camera/__init__.py | 62 | core | {
"docstring": "Determine if any of the registered providers are suitable for this entity.\n\n This affects state attributes, so it should be invoked any time the registered\n providers or inputs to the state attributes change.\n\n Returns True if any state was updated (and needs to be written)\n ",
"language": "en",
"n_whitespaces": 73,
"n_words": 45,
"vocab_size": 34
} | 17 | Python | 14 | 81aff973ea421e848d2f3e084f123bf108bd808e | __init__.py | 308,346 | 12 | 35 | async_refresh_providers | https://github.com/home-assistant/core.git | Keep entity state management within entity (#63183)
Simplify the entity state management for webrtc providers, incurring
extra state writes on startup. Followup post-review comments for PR #62962 | 56 | 0 | 107,106 | 9 |
|
1 | 14 | def test_runtime_install_error_message(call_ray_start):
with pytest.raises(ConnectionAbortedError) as excinfo:
ray.client("localhost:25031").env({"pip": ["ray-this-doesnt-exist"]}).connect()
assert "No matching distribution found for ray-this-doesnt-exist" in str(
excinfo.value
), str(excinfo.value)
ray.util.disconnect()
| python/ray/tests/test_client_proxy.py | 110 | ray | {
"docstring": "\n Check that an error while preparing the runtime environment for the client\n server yields an actionable, clear error on the *client side*.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 22,
"vocab_size": 18
} | 21 | Python | 21 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | test_client_proxy.py | 131,426 | 7 | 60 | test_runtime_install_error_message | https://github.com/ray-project/ray.git | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 50 | 0 | 29,523 | 15 |
|
1 | 8 | def add_handler(self, handler):
sympy_deprecation_warning(
,
deprecated_since_version="1.8",
active_deprecations_target='deprecated-askhandler',
)
self.handlers.append(handler)
| sympy/assumptions/assume.py | 48 | sympy | {
"docstring": "\n The AskHandler system is deprecated. Predicate.add_handler()\n should be replaced with the multipledispatch handler of Predicate.\n ",
"language": "en",
"n_whitespaces": 49,
"n_words": 15,
"vocab_size": 15
} | 9 | Python | 9 | ad766d1c02943e86f50559abfd0c72e582c9ca6a | assume.py | 196,757 | 10 | 28 | add_handler | https://github.com/sympy/sympy.git | Update the AskHandler deprecation warnings
n.b., the issue number in the original warning message was wrong. It should
have been #20837. | 62 | 0 | 48,153 | 9 |
|
2 | 34 | def deserialize(config, custom_objects=None):
# 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 # pylint: disable=g-import-not-at-top
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.optimizers.get') | keras/optimizers/__init__.py | 271 | @keras_export('keras.optimizers.get') | keras | {
"docstring": "Inverse of the `serialize` function.\n\n Args:\n config: Optimizer configuration dictionary.\n custom_objects: Optional dictionary mapping names (strings) to custom\n objects (classes and functions) to be considered during deserialization.\n\n Returns:\n A Keras Optimizer instance.\n ",
"language": "en",
"n_whitespaces": 57,
"n_words": 32,
"vocab_size": 30
} | 103 | Python | 89 | 8ecef127f70db723c158dbe9ed3268b3d610ab55 | __init__.py | 269,012 | 26 | 152 | deserialize | https://github.com/keras-team/keras.git | Remove experimental Keras mixed precision API.
The non-experimental mixed precision API was added in TensorFlow 2.4, and since then the experimental API has been deprecated. This change removes the experimental API.
Deserializing the now-removed PolicyV1 and LossScaleOptimizerV1 classes is still supported, if they were serialized with get_config() prior to this change. These classes are deserialized into the non-experimental Policy and LossScaleOptimizer classes, which has been the case since TensorFlow 2.4. Eventually, support for deserializing these classes may be removed.
PiperOrigin-RevId: 429410341 | 220 | 1 | 79,830 | 12 |
2 | 10 | def construct_edit_url(self, instance):
if self.edit_url_name is None:
raise ImproperlyConfigured(
"%r must define edit_url_name or override construct_edit_url"
% type(self)
)
return reverse(self.edit_url_name, args=(quote(instance.pk),))
| wagtail/admin/admin_url_finder.py | 72 | wagtail | {
"docstring": "\n Return the edit URL for the given instance - regardless of whether the user can access it -\n or None if no edit URL is available.\n ",
"language": "en",
"n_whitespaces": 48,
"n_words": 26,
"vocab_size": 21
} | 22 | Python | 22 | d10f15e55806c6944827d801cd9c2d53f5da4186 | admin_url_finder.py | 71,045 | 7 | 44 | construct_edit_url | https://github.com/wagtail/wagtail.git | Reformat with black | 95 | 0 | 15,608 | 12 |
|
1 | 14 | def test_pagination_offset_without_orderby(self):
response = self.get_response(
self.organization.slug,
field="count(sentry.transactions.measurements.lcp)",
datasource="snuba",
groupBy="transaction",
cursor=Cursor(0, 1),
)
assert response.status_code == 400
print(response.json())
assert response.json()["detail"] == (
"'cursor' is only supported in combination with 'orderBy'"
)
| tests/sentry/api/endpoints/test_organization_metrics.py | 113 | sentry | {
"docstring": "\n Test that ensures an exception is raised when pagination `per_page` parameter is sent\n without order by being set\n ",
"language": "en",
"n_whitespaces": 40,
"n_words": 18,
"vocab_size": 17
} | 30 | Python | 27 | 6b29955072b4fbed6d8843ae193d65509e288f8f | test_organization_metrics.py | 95,747 | 13 | 67 | test_pagination_offset_without_orderby | https://github.com/getsentry/sentry.git | feat(metrics): Add pagination to OrganizationMetricsDataEndpoint [INGEST-851] (#31181)
* feat(metrics): Add pagination to OrganizationMetricsDataEndpoint
Adds new paginator class `MetricsDataSeriesPaginator`
to add pagination to the response of api requests made
to `OrganizationMetricsDataEndpoint` | 145 | 0 | 19,228 | 11 |
|
13 | 92 | def test_copy_page_nested_plugin(self):
with self.settings(CMS_PERMISSION=False):
# setup page 1
page_one = create_page(
"Three Placeholder", "col_three.html", "en",
position="last-child", published=True, in_navigation=True
)
page_one_ph_one = page_one.placeholders.get(slot="col_sidebar")
page_one_ph_two = page_one.placeholders.get(slot="col_left")
page_one.placeholders.get(slot="col_right")
# add the text plugin to placeholder one
text_plugin_en = add_plugin(page_one_ph_one, "TextPlugin", "en", body="Hello World")
self.assertEqual(text_plugin_en.id, CMSPlugin.objects.all()[0].id)
self.assertEqual(text_plugin_en.get_children().count(), 0)
pre_add_plugin_count = CMSPlugin.objects.count()
self.assertEqual(pre_add_plugin_count, 1)
###
# add a plugin to placeholder two
###
pre_nesting_body = "<p>the nested text plugin with a link inside</p>"
text_plugin_two = add_plugin(page_one_ph_two, "TextPlugin", "en", body=pre_nesting_body)
text_plugin_two = self.reload(text_plugin_two)
# prepare nesting plugin
page_one_ph_two = self.reload(page_one_ph_two)
text_plugin_two = self.reload(text_plugin_two)
link_plugin = add_plugin(page_one_ph_two, "LinkPlugin", "en", target=text_plugin_two)
link_plugin.name = "django-cms Link"
link_plugin.external_link = "https://www.django-cms.org"
link_plugin.parent = text_plugin_two
link_plugin.save()
link_plugin = self.reload(link_plugin)
text_plugin_two = self.reload(text_plugin_two)
in_txt =
nesting_body = f"{text_plugin_two.body}<p>{(in_txt % (link_plugin.id))}</p>"
# emulate the editor in admin that adds some txt for the nested plugin
text_plugin_two.body = nesting_body
text_plugin_two.save()
text_plugin_two = self.reload(text_plugin_two)
# the link is attached as a child?
self.assertEqual(text_plugin_two.get_children().count(), 1)
post_add_plugin_count = CMSPlugin.objects.filter(placeholder__page__publisher_is_draft=True).count()
self.assertEqual(post_add_plugin_count, 3)
page_one.save()
# get the plugins from the original page
page_one = self.reload(page_one)
page_one_ph_one = page_one.placeholders.get(slot="col_sidebar")
page_one_ph_two = page_one.placeholders.get(slot="col_left")
page_one_ph_three = page_one.placeholders.get(slot="col_right")
# verify that the plugins got created
org_placeholder_one_plugins = page_one_ph_one.get_plugins()
self.assertEqual(len(org_placeholder_one_plugins), 1)
org_placeholder_two_plugins = page_one_ph_two.get_plugins()
self.assertEqual(len(org_placeholder_two_plugins), 2)
org_placeholder_three_plugins = page_one_ph_three.get_plugins()
self.assertEqual(len(org_placeholder_three_plugins), 0)
self.assertEqual(page_one.placeholders.count(), 3)
placeholder_count = Placeholder.objects.filter(page__publisher_is_draft=True).count()
self.assertEqual(placeholder_count, 3)
self.assertEqual(CMSPlugin.objects.filter(placeholder__page__publisher_is_draft=True).count(), 3)
##
# setup page_copy_target page
##
page_copy_target = create_page(
"Three Placeholder - page copy target", "col_three.html", "en",
position="last-child", published=True, in_navigation=True
)
all_page_count = Page.objects.drafts().count()
pre_copy_placeholder_count = Placeholder.objects.filter(page__publisher_is_draft=True).count()
self.assertEqual(pre_copy_placeholder_count, 6)
# copy the page
superuser = self.get_superuser()
with self.login_user_context(superuser):
page_two = self.copy_page(page_one, page_copy_target)
# validate the expected pages,placeholders,plugins,pluginbodies
after_copy_page_plugin_count = CMSPlugin.objects.filter(
placeholder__page__publisher_is_draft=True
).count()
self.assertEqual(after_copy_page_plugin_count, 6)
# check the amount of copied stuff
after_copy_page_count = Page.objects.drafts().count()
after_copy_placeholder_count = Placeholder.objects.filter(
page__publisher_is_draft=True
).count()
self.assertGreater(after_copy_page_count, all_page_count, "no new page after copy")
self.assertGreater(after_copy_page_plugin_count, post_add_plugin_count, "plugin count is not grown")
self.assertGreater(
after_copy_placeholder_count, pre_copy_placeholder_count,
"placeholder count is not grown"
)
self.assertEqual(after_copy_page_count, 3, "no new page after copy")
# original placeholder
page_one = self.reload(page_one)
page_one_ph_one = page_one.placeholders.get(slot="col_sidebar")
page_one_ph_two = page_one.placeholders.get(slot="col_left")
page_one_ph_three = page_one.placeholders.get(slot="col_right")
# check if there are multiple pages assigned to this placeholders
found_page = page_one_ph_one.page if page_one_ph_one else None
self.assertEqual(found_page, page_one)
found_page = page_one_ph_two.page if page_one_ph_two else None
self.assertEqual(found_page, page_one)
found_page = page_one_ph_three.page if page_one_ph_three else None
self.assertEqual(found_page, page_one)
page_two = self.reload(page_two)
page_two_ph_one = page_two.placeholders.get(slot="col_sidebar")
page_two_ph_two = page_two.placeholders.get(slot="col_left")
page_two_ph_three = page_two.placeholders.get(slot="col_right")
# check if there are multiple pages assigned to this placeholders
found_page = page_two_ph_one.page if page_two_ph_one else None
self.assertEqual(found_page, page_two)
found_page = page_two_ph_two.page if page_two_ph_two else None
self.assertEqual(found_page, page_two)
found_page = page_two_ph_three.page if page_two_ph_three else None
self.assertEqual(found_page, page_two)
# check the stored placeholders org vs copy
msg = 'placehoder ids copy:{} org:{} copied page {} are identical - tree broken'.format(
page_two_ph_one.pk, page_one_ph_one.pk, page_two.pk
)
self.assertNotEqual(page_two_ph_one.pk, page_one_ph_one.pk, msg)
msg = 'placehoder ids copy:{} org:{} copied page {} are identical - tree broken'.format(
page_two_ph_two.pk, page_one_ph_two.pk, page_two.pk
)
self.assertNotEqual(page_two_ph_two.pk, page_one_ph_two.pk, msg)
msg = 'placehoder ids copy:{} org:{} copied page {} are identical - tree broken'.format(
page_two_ph_three.pk, page_one_ph_three.pk, page_two.pk
)
self.assertNotEqual(page_two_ph_three.pk, page_one_ph_three.pk, msg)
# get the plugins from the original page
org_placeholder_one_plugins = page_one_ph_one.get_plugins()
self.assertEqual(len(org_placeholder_one_plugins), 1)
org_placeholder_two_plugins = page_one_ph_two.get_plugins()
self.assertEqual(len(org_placeholder_two_plugins), 2)
org_placeholder_three_plugins = page_one_ph_three.get_plugins()
self.assertEqual(len(org_placeholder_three_plugins), 0)
# get the plugins from the copied page
copied_placeholder_one_plugins = page_two_ph_one.get_plugins()
self.assertEqual(len(copied_placeholder_one_plugins), 1)
copied_placeholder_two_plugins = page_two_ph_two.get_plugins()
self.assertEqual(len(copied_placeholder_two_plugins), 2)
copied_placeholder_three_plugins = page_two_ph_three.get_plugins()
self.assertEqual(len(copied_placeholder_three_plugins), 0)
# verify the plugins got copied
# placeholder 1
count_plugins_copied = len(copied_placeholder_one_plugins)
count_plugins_org = len(org_placeholder_one_plugins)
msg = f"plugin count {count_plugins_copied} {count_plugins_org} for placeholder one not equal"
self.assertEqual(count_plugins_copied, count_plugins_org, msg)
# placeholder 2
count_plugins_copied = len(copied_placeholder_two_plugins)
count_plugins_org = len(org_placeholder_two_plugins)
msg = f"plugin count {count_plugins_copied} {count_plugins_org} for placeholder two not equal"
self.assertEqual(count_plugins_copied, count_plugins_org, msg)
# placeholder 3
count_plugins_copied = len(copied_placeholder_three_plugins)
count_plugins_org = len(org_placeholder_three_plugins)
msg = f"plugin count {count_plugins_copied} {count_plugins_org} for placeholder three not equal"
self.assertEqual(count_plugins_copied, count_plugins_org, msg)
# verify the body of text plugin with nested link plugin
# org to copied
org_nested_text_plugin = None
# do this iteration to find the real text plugin with the attached link
# the inheritance mechanism for the cmsplugins works through
# (tuple)get_plugin_instance()
for x in org_placeholder_two_plugins:
if x.plugin_type == "TextPlugin":
instance = x.get_plugin_instance()[0]
if instance.body.startswith(pre_nesting_body):
org_nested_text_plugin = instance
break
copied_nested_text_plugin = None
for x in copied_placeholder_two_plugins:
if x.plugin_type == "TextPlugin":
instance = x.get_plugin_instance()[0]
if instance.body.startswith(pre_nesting_body):
copied_nested_text_plugin = instance
break
msg = "original nested text plugin not found"
self.assertNotEqual(org_nested_text_plugin, None, msg=msg)
msg = "copied nested text plugin not found"
self.assertNotEqual(copied_nested_text_plugin, None, msg=msg)
# get the children ids of the texplugin with a nested link
# to check if the body of the text is generated correctly
org_link_child_plugin = org_nested_text_plugin.get_children()[0]
copied_link_child_plugin = copied_nested_text_plugin.get_children()[0]
# validate the textplugin body texts
msg = "org plugin and copied plugin are the same"
self.assertTrue(org_link_child_plugin.id != copied_link_child_plugin.id, msg)
needle = "%s"
msg = "child plugin id differs to parent in body"
# linked child is in body
self.assertTrue(org_nested_text_plugin.body.find(needle % (org_link_child_plugin.id)) != -1, msg)
msg = "copy: child plugin id differs to parent in body"
self.assertTrue(copied_nested_text_plugin.body.find(needle % (copied_link_child_plugin.id)) != -1, msg)
# really nothing else
msg = "child link plugin id differs to parent body"
self.assertTrue(org_nested_text_plugin.body.find(needle % (copied_link_child_plugin.id)) == -1, msg)
msg = "copy: child link plugin id differs to parent body"
self.assertTrue(copied_nested_text_plugin.body.find(needle % (org_link_child_plugin.id)) == -1, msg)
# now reverse lookup the placeholders from the plugins
org_placeholder = org_link_child_plugin.placeholder
copied_placeholder = copied_link_child_plugin.placeholder
msg = "placeholder of the original plugin and copied plugin are the same"
ok = (org_placeholder.id != copied_placeholder.id)
self.assertTrue(ok, msg)
| cms/tests/test_nested_plugins.py | 2,353 | django-cms | {
"docstring": "\n Test to verify that page copy with a nested plugin works\n page one - 3 placeholder\n col_sidebar: 1 text plugin\n col_left: 1 text plugin with nested link plugin\n col_right: no plugin\n page two (copy target)\n Verify copied page, placeholders, plugins and body text\n <cms-plugin id=\"%s\" title=\"Link\" alt=\"Link\"></cms-plugin>",
"language": "en",
"n_whitespaces": 139,
"n_words": 47,
"vocab_size": 36
} | 866 | Python | 323 | c1290c9ff89cb00caa5469129fd527e9d82cd820 | test_nested_plugins.py | 82,407 | 166 | 1,395 | test_copy_page_nested_plugin | https://github.com/django-cms/django-cms.git | ci: Added codespell (#7355)
Co-authored-by: Christian Clauss <[email protected]>
* ci: codespell config taken from #7292 | 3,232 | 0 | 17,382 | 15 |
|
3 | 8 | def tick_bottom(self):
label = True
if 'label1On' in self._major_tick_kw:
label = (self._major_tick_kw['label1On']
or self._major_tick_kw['label2On'])
self.set_ticks_position('bottom')
# If labels were turned off before this was called, leave them off.
self.set_tick_params(which='both', labelbottom=label)
| lib/matplotlib/axis.py | 93 | matplotlib | {
"docstring": "\n Move ticks and ticklabels (if present) to the bottom of the Axes.\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 12,
"vocab_size": 11
} | 30 | Python | 28 | f156db08eee54d285ab0fb4e031e48d078ba6aa3 | axis.py | 107,482 | 7 | 51 | tick_bottom | https://github.com/matplotlib/matplotlib.git | DOC: More cleanup axes -> Axes | 103 | 0 | 22,771 | 12 |
|
1 | 15 | def test_sample_weights_validation():
# scalar value but not positive
X = [[1]]
y = [1]
weights = 0
glm = _GeneralizedLinearRegressor()
# Positive weights are accepted
glm.fit(X, y, sample_weight=1)
# 2d array
weights = [[0]]
with pytest.raises(ValueError, match="must be 1D array or scalar"):
glm.fit(X, y, weights)
# 1d but wrong length
weights = [1, 0]
msg = r"sample_weight.shape == \(2,\), expected \(1,\)!"
with pytest.raises(ValueError, match=msg):
glm.fit(X, y, weights)
@pytest.mark.parametrize("fit_intercept", ["not bool", 1, 0, [True]]) | sklearn/linear_model/_glm/tests/test_glm.py | 197 | @pytest.mark.parametrize("fit_intercept", ["not bool", 1, 0, [True]]) | scikit-learn | {
"docstring": "Test the raised errors in the validation of sample_weight.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 8
} | 73 | Python | 52 | 75a94f518f7bd7d0bf581ffb67d9f961e3c4efbc | test_glm.py | 259,456 | 13 | 99 | test_sample_weights_validation | https://github.com/scikit-learn/scikit-learn.git | ENH migrate GLMs / TweedieRegressor to linear loss (#22548)
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]> | 131 | 1 | 75,786 | 11 |
1 | 6 | def getoffset(self, text):
deprecate("getoffset", 10, "getbbox")
return self.font.getsize(text)[1]
| src/PIL/ImageFont.py | 48 | Pillow | {
"docstring": "\n Returns the offset of given text. This is the gap between the\n starting coordinate and the first marking. Note that this gap is\n included in the result of :py:func:`~PIL.ImageFont.FreeTypeFont.getsize`.\n\n :param text: Text to measure.\n\n :return: A tuple of the x and y offset\n ",
"language": "en",
"n_whitespaces": 86,
"n_words": 43,
"vocab_size": 32
} | 8 | Python | 8 | 406fe59242ad288bcd9f9fe663b227620eacd344 | ImageFont.py | 243,047 | 3 | 28 | getoffset | https://github.com/python-pillow/Pillow.git | deprecate font.getsize and related functions | 29 | 0 | 69,961 | 9 |
|
11 | 28 | def build_index(cls, path, partition_ids, index_columns, storage_options):
from pyarrow.parquet import read_table
range_index = True
column_names_to_read = []
for column in index_columns:
# According to https://arrow.apache.org/docs/python/generated/pyarrow.Schema.html,
# only RangeIndex will be stored as metadata. Otherwise, the default behavior is
# to store the index as a column.
if isinstance(column, str):
column_names_to_read.append(column)
range_index = False
elif column["name"] is not None:
column_names_to_read.append(column["name"])
# For the second check, let us consider the case where we have an empty dataframe,
# that has a valid index.
if range_index or (len(partition_ids) == 0 and len(column_names_to_read) != 0):
fs, fs_path = cls._get_fs_and_fs_path(path, storage_options)
complete_index = (
read_table(fs_path, columns=column_names_to_read, filesystem=fs)
.to_pandas()
.index
)
# Empty DataFrame case
elif len(partition_ids) == 0:
return [], False
else:
index_ids = [part_id[0][1] for part_id in partition_ids if len(part_id) > 0]
index_objs = cls.materialize(index_ids)
complete_index = index_objs[0].append(index_objs[1:])
return complete_index, range_index or (len(index_columns) == 0)
| modin/core/io/column_stores/parquet_dispatcher.py | 307 | modin | {
"docstring": "\n Compute index and its split sizes of resulting Modin DataFrame.\n\n Parameters\n ----------\n path : Pathlike\n Path to dataset.\n partition_ids : list\n Array with references to the partitions data.\n index_columns : list\n List of index columns specified by pandas metadata.\n storage_options : dict\n Parameters for specific storage engine.\n\n Returns\n -------\n index : pandas.Index\n Index of resulting Modin DataFrame.\n needs_index_sync : bool\n Whether the partition indices need to be synced with frame\n index because there's no index column, or at least one\n index column is a RangeIndex.\n\n Notes\n -----\n See `build_partition` for more detail on the contents of partitions_ids.\n ",
"language": "en",
"n_whitespaces": 291,
"n_words": 97,
"vocab_size": 73
} | 140 | Python | 106 | 4548012a6372b8ce79d7e07c9ae13fd7444a91c8 | parquet_dispatcher.py | 154,144 | 24 | 193 | build_index | https://github.com/modin-project/modin.git | FIX-#4756: Correctly propagate `storage_options` in `read_parquet` (#4764)
Co-authored-by: Yaroslav Igoshev <[email protected]>
Co-authored-by: Alexey Prutskov <[email protected]>
Signed-off-by: Karthik Velayutham <[email protected]> | 446 | 0 | 35,809 | 15 |
|
4 | 22 | def test_grabclipboard(self):
if sys.platform == "darwin":
subprocess.call(["screencapture", "-cx"])
elif sys.platform == "win32":
p = subprocess.Popen(["powershell", "-command", "-"], stdin=subprocess.PIPE)
p.stdin.write(
b
)
p.communicate()
else:
if not shutil.which("wl-paste"):
with pytest.raises(NotImplementedError) as e:
ImageGrab.grabclipboard()
assert (
str(e.value)
== "wl-paste is required for ImageGrab.grabclipboard() on Linux"
)
return
ImageGrab.grabclipboard()
| Tests/test_imagegrab.py | 191 | Pillow | {
"docstring": "[Reflection.Assembly]::LoadWithPartialName(\"System.Drawing\")\n[Reflection.Assembly]::LoadWithPartialName(\"System.Windows.Forms\")\n$bmp = New-Object Drawing.Bitmap 200, 200\n[Windows.Forms.Clipboard]::SetImage($bmp)",
"language": "en",
"n_whitespaces": 5,
"n_words": 9,
"vocab_size": 9
} | 45 | Python | 38 | ccac8540771120bdeb570ec5b7bbfc4e3e9a38dd | test_imagegrab.py | 243,602 | 22 | 106 | test_grabclipboard | https://github.com/python-pillow/Pillow.git | If available, use wl-paste for grabclipboard() on Linux | 266 | 0 | 70,057 | 15 |
|
2 | 15 | def test_symlink_exists_different_force(file, source):
dif_source = source.parent / "dif_source.txt"
target = source.parent / "symlink.lnk"
target.symlink_to(dif_source)
try:
file.symlink(source, target, force=True)
assert salt.utils.path.readlink(target) == str(source)
finally:
target.unlink()
| tests/pytests/functional/modules/file/test_symlink.py | 110 | salt | {
"docstring": "\n Test symlink with an existing symlink to a different file with force=True\n Should destroy the existing symlink and generate a new one to the correct\n location\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 26,
"vocab_size": 19
} | 24 | Python | 21 | a35b29b2651bf33c5d5b45e64bc7765ffde4aff4 | test_symlink.py | 215,810 | 9 | 65 | test_symlink_exists_different_force | https://github.com/saltstack/salt.git | Add some funtional tests
Add functional tests for the following:
- file.readlink
- file.replace
- file.symlink
Remove unit tests for file.replace as they are duplicated in the added
functional test | 63 | 0 | 54,183 | 12 |
|
1 | 4 | def _reset_logging_mixin():
global __logging_mixin
__logging_mixin = LoggingMixin(logger)
| freqtrade/exchange/common.py | 25 | freqtrade | {
"docstring": "\n Reset global logging mixin - used in tests only.\n ",
"language": "en",
"n_whitespaces": 16,
"n_words": 9,
"vocab_size": 9
} | 7 | Python | 6 | 682daa4e941abf2235e60d9ecd1ad029eec5d3c4 | common.py | 149,904 | 3 | 13 | _reset_logging_mixin | https://github.com/freqtrade/freqtrade.git | Reset logging mixin to avoid random test failure | 16 | 0 | 34,583 | 8 |
|
2 | 8 | def partial_fit(self, X, y):
if not hasattr(self, "coefs_"):
self._validate_params()
return self._fit(X, y, incremental=True)
| sklearn/neural_network/_multilayer_perceptron.py | 60 | scikit-learn | {
"docstring": "Update the model with a single iteration over the given data.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input data.\n\n y : ndarray of shape (n_samples,)\n The target values.\n\n Returns\n -------\n self : object\n Trained MLP model.\n ",
"language": "en",
"n_whitespaces": 131,
"n_words": 42,
"vocab_size": 35
} | 13 | Python | 13 | 0206d3e08c0f0917ba2f1c65cb55569b97d9a9ba | _multilayer_perceptron.py | 260,443 | 4 | 37 | partial_fit | https://github.com/scikit-learn/scikit-learn.git | MAINT validate parameters for MLPRregressor and MLPClassifier (#23789)
Co-authored-by: jeremie du boisberranger <[email protected]> | 45 | 0 | 76,253 | 9 |
|
3 | 7 | def unk_token(self) -> str:
if self._unk_token is None:
if self.verbose:
logger.error("Using unk_token, but it is not set yet.")
return None
return str(self._unk_token)
| src/transformers/tokenization_utils_base.py | 61 | transformers | {
"docstring": "\n `str`: Unknown token. Log an error if used while not having been set.\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 13,
"vocab_size": 13
} | 22 | Python | 19 | 3eed5530ec74bb60ad9f8f612717d0f6ccf820f2 | tokenization_utils_base.py | 31,489 | 9 | 35 | unk_token | https://github.com/huggingface/transformers.git | Fix properties of unset special tokens in non verbose mode (#17797)
Co-authored-by: SaulLu <[email protected]> | 80 | 0 | 5,764 | 12 |
|
1 | 2 | def sizemin(self):
return self["sizemin"]
| packages/python/plotly/plotly/graph_objs/pointcloud/_marker.py | 22 | plotly.py | {
"docstring": "\n Sets the minimum size (in px) of the rendered marker points,\n effective when the `pointcloud` shows a million or more points.\n\n The 'sizemin' property is a number and may be specified as:\n - An int or float in the interval [0.1, 2]\n\n Returns\n -------\n int|float\n ",
"language": "en",
"n_whitespaces": 104,
"n_words": 45,
"vocab_size": 40
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _marker.py | 233,259 | 2 | 11 | sizemin | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 64,703 | 7 |
|
3 | 17 | def show_file(self, path=None, **options):
if path is None:
if "file" in options:
warnings.warn(
"The 'file' argument is deprecated and will be removed in Pillow "
"10 (2023-07-01). Use 'path' instead.",
DeprecationWarning,
)
path = options.pop("file")
else:
raise TypeError("Missing required argument: 'path'")
subprocess.call(["open", "-a", "Preview.app", path])
subprocess.Popen(
[
sys.executable,
"-c",
"import os, sys, time;time.sleep(20);os.remove(sys.argv[1])",
path,
]
)
return 1
if sys.platform == "darwin":
register(MacViewer)
| src/PIL/ImageShow.py | 163 | Pillow | {
"docstring": "\n Display given file.\n\n Before Pillow 9.1.0, the first argument was ``file``. This is now deprecated,\n and will be removed in Pillow 10.0.0 (2023-07-01). ``path`` should be used\n instead.\n ",
"language": "en",
"n_whitespaces": 64,
"n_words": 28,
"vocab_size": 26
} | 63 | Python | 57 | 8da80130dbc747f3954b4904247d26289fe722f9 | ImageShow.py | 242,309 | 21 | 81 | show_file | https://github.com/python-pillow/Pillow.git | In show_file, use os.remove to remove temporary images | 328 | 0 | 69,823 | 13 |
|
12 | 57 | def confirm(self):
args = request.args
dag_id = args.get('dag_id')
task_id = args.get('task_id')
dag_run_id = args.get('dag_run_id')
state = args.get('state')
origin = args.get('origin')
if 'map_index' not in args:
map_indexes: Optional[List[int]] = None
else:
map_indexes = args.getlist('map_index', type=int)
upstream = to_boolean(args.get('upstream'))
downstream = to_boolean(args.get('downstream'))
future = to_boolean(args.get('future'))
past = to_boolean(args.get('past'))
origin = origin or url_for('Airflow.index')
dag = get_airflow_app().dag_bag.get_dag(dag_id)
if not dag:
msg = f'DAG {dag_id} not found'
return redirect_or_json(origin, msg, status='error', status_code=404)
try:
task = dag.get_task(task_id)
except airflow.exceptions.TaskNotFound:
msg = f"Task {task_id} not found"
return redirect_or_json(origin, msg, status='error', status_code=404)
task.dag = dag
if state not in (
'success',
'failed',
):
msg = f"Invalid state {state}, must be either 'success' or 'failed'"
return redirect_or_json(origin, msg, status='error', status_code=400)
latest_execution_date = dag.get_latest_execution_date()
if not latest_execution_date:
msg = f"Cannot mark tasks as {state}, seem that dag {dag_id} has never run"
return redirect_or_json(origin, msg, status='error', status_code=400)
if map_indexes is None:
tasks: Union[List[Operator], List[Tuple[Operator, int]]] = [task]
else:
tasks = [(task, map_index) for map_index in map_indexes]
to_be_altered = set_state(
tasks=tasks,
run_id=dag_run_id,
upstream=upstream,
downstream=downstream,
future=future,
past=past,
state=state,
commit=False,
)
if request.headers.get('Accept') == 'application/json':
details = [str(t) for t in to_be_altered]
return htmlsafe_json_dumps(details, separators=(',', ':'))
details = "\n".join(str(t) for t in to_be_altered)
response = self.render_template(
"airflow/confirm.html",
endpoint=url_for(f'Airflow.{state}'),
message=f"Task instances you are about to mark as {state}:",
details=details,
)
return response
| airflow/www/views.py | 729 | airflow | {
"docstring": "Show confirmation page for marking tasks as success or failed.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 208 | Python | 129 | e2f19505bf3622935480e80bee55bf5b6d80097b | views.py | 43,407 | 61 | 430 | confirm | https://github.com/apache/airflow.git | Upgrade FAB to 4.1.1 (#24399)
* Upgrade FAB to 4.1.1
The Flask Application Builder have been updated recently to
support a number of newer dependencies. This PR is the
attempt to migrate FAB to newer version.
This includes:
* update setup.py and setup.cfg upper and lower bounds to
account for proper version of dependencies that
FAB < 4.0.0 was blocking from upgrade
* added typed Flask application retrieval with a custom
application fields available for MyPy typing checks.
* fix typing to account for typing hints added in multiple
upgraded libraries optional values and content of request
returned as Mapping
* switch to PyJWT 2.* by using non-deprecated "required" claim as
list rather than separate fields
* add possibiliyt to install providers without constraints
so that we could avoid errors on conflicting constraints when
upgrade-to-newer-dependencies is used
* add pre-commit to check that 2.4+ only get_airflow_app is not
used in providers
* avoid Bad Request in case the request sent to Flask 2.0 is not
JSon content type
* switch imports of internal classes to direct packages
where classes are available rather than from "airflow.models" to
satisfy MyPY
* synchronize changes of FAB Security Manager 4.1.1 with our copy
of the Security Manager.
* add error handling for a few "None" cases detected by MyPY
* corrected test cases that were broken by immutability of
Flask 2 objects and better escaping done by Flask 2
* updated test cases to account for redirection to "path" rather
than full URL by Flask2
Fixes: #22397
* fixup! Upgrade FAB to 4.1.1 | 751 | 0 | 7,960 | 13 |
|
1 | 6 | def collection_name(self) -> t.Optional[str]:
return self.config.collection_name
| test/lib/ansible_test/_util/controller/sanity/pylint/plugins/deprecated.py | 32 | ansible | {
"docstring": "Return the collection name, or None if ansible-core is being tested.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 6 | Python | 6 | 89862fda3b4a427894061d90e2a96ad6efaf251c | deprecated.py | 268,190 | 3 | 19 | collection_name | https://github.com/ansible/ansible.git | ansible-test - Sanity test code cleanup. (#78497) | 20 | 0 | 79,442 | 7 |
|
5 | 13 | def inference_timer(self, do='start'):
if do == 'start':
self.pair_it += 1
self.begin_time = time.time()
elif do == 'stop':
end = time.time()
self.inference_time += (end - self.begin_time)
if self.pair_it == self.total_pairs:
logger.info(
f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
if self.inference_time > 0.25 * self.base_tf_seconds:
logger.warning('Inference took over 25/% of the candle time. Reduce pairlist to'
' avoid blinding open trades and degrading performance.')
self.pair_it = 0
self.inference_time = 0
return
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
| freqtrade/freqai/freqai_interface.py | 180 | freqtrade | {
"docstring": "\n Timer designed to track the cumulative time spent in FreqAI for one pass through\n the whitelist. This will check if the time spent is more than 1/4 the time\n of a single candle, and if so, it will warn the user of degraded performance\n ",
"language": "en",
"n_whitespaces": 73,
"n_words": 44,
"vocab_size": 34
} | 86 | Python | 69 | 8961b8d56042545b566d2ef5fea1cb34e2ebdb35 | freqai_interface.py | 150,371 | 16 | 99 | inference_timer | https://github.com/freqtrade/freqtrade.git | merge in inference timer and historic predictions handling improvements. | 307 | 0 | 34,720 | 16 |
|
3 | 10 | def fromkeys(cls, iterable, value="", mutable=False, encoding=None):
q = cls("", mutable=True, encoding=encoding)
for key in iterable:
q.appendlist(key, value)
if not mutable:
q._mutable = False
return q
| django/http/request.py | 91 | django | {
"docstring": "\n Return a new QueryDict with keys (may be repeated) from an iterable and\n values from value.\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 16,
"vocab_size": 15
} | 25 | Python | 23 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | request.py | 206,091 | 7 | 58 | fromkeys | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 82 | 0 | 51,360 | 9 |
|
13 | 40 | def get_rootwise_opening_balances(filters, report_type):
additional_conditions = ""
if not filters.show_unclosed_fy_pl_balances:
additional_conditions = (
" and posting_date >= %(year_start_date)s" if report_type == "Profit and Loss" else ""
)
if not flt(filters.with_period_closing_entry):
additional_conditions += " and ifnull(voucher_type, '')!='Period Closing Voucher'"
if filters.cost_center:
lft, rgt = frappe.db.get_value("Cost Center", filters.cost_center, ["lft", "rgt"])
additional_conditions += % (
lft,
rgt,
)
if filters.project:
additional_conditions += " and project = %(project)s"
if filters.finance_book:
fb_conditions = " AND finance_book = %(finance_book)s"
if filters.include_default_book_entries:
fb_conditions = (
" AND (finance_book in (%(finance_book)s, %(company_fb)s, '') OR finance_book IS NULL)"
)
additional_conditions += fb_conditions
accounting_dimensions = get_accounting_dimensions(as_list=False)
query_filters = {
"company": filters.company,
"from_date": filters.from_date,
"report_type": report_type,
"year_start_date": filters.year_start_date,
"project": filters.project,
"finance_book": filters.finance_book,
"company_fb": frappe.db.get_value("Company", filters.company, "default_finance_book"),
}
if accounting_dimensions:
for dimension in accounting_dimensions:
if filters.get(dimension.fieldname):
if frappe.get_cached_value("DocType", dimension.document_type, "is_tree"):
filters[dimension.fieldname] = get_dimension_with_children(
dimension.document_type, filters.get(dimension.fieldname)
)
additional_conditions += "and {0} in %({0})s".format(dimension.fieldname)
else:
additional_conditions += "and {0} in (%({0})s)".format(dimension.fieldname)
query_filters.update({dimension.fieldname: filters.get(dimension.fieldname)})
gle = frappe.db.sql(
.format(
additional_conditions=additional_conditions
),
query_filters,
as_dict=True,
)
opening = frappe._dict()
for d in gle:
opening.setdefault(d.account, d)
return opening
| erpnext/accounts/report/trial_balance/trial_balance.py | 521 | erpnext | {
"docstring": " and cost_center in (select name from `tabCost Center`\n\t\t\twhere lft >= %s and rgt <= %s)\n\t\tselect\n\t\t\taccount, sum(debit) as opening_debit, sum(credit) as opening_credit\n\t\tfrom `tabGL Entry`\n\t\twhere\n\t\t\tcompany=%(company)s\n\t\t\t{additional_conditions}\n\t\t\tand (posting_date < %(from_date)s or ifnull(is_opening, 'No') = 'Yes')\n\t\t\tand account in (select name from `tabAccount` where report_type=%(report_type)s)\n\t\t\tand is_cancelled = 0\n\t\tgroup by account",
"language": "en",
"n_whitespaces": 44,
"n_words": 55,
"vocab_size": 41
} | 168 | Python | 105 | 494bd9ef78313436f0424b918f200dab8fc7c20b | trial_balance.py | 65,378 | 66 | 311 | get_rootwise_opening_balances | https://github.com/frappe/erpnext.git | style: format code with black | 114 | 0 | 13,873 | 19 |
|
2 | 4 | def addIncludedDataFilesFromFileOptions():
for included_datafile in _addIncludedDataFilesFromFileOptions():
addIncludedDataFile(included_datafile)
| nuitka/freezer/IncludedDataFiles.py | 30 | Nuitka | {
"docstring": "Early data files, from user options that work with file system.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 7 | Python | 7 | abfb99b0a05dd76d2ecc6ebc20732a271857c6c8 | IncludedDataFiles.py | 178,909 | 3 | 16 | addIncludedDataFilesFromFileOptions | https://github.com/Nuitka/Nuitka.git | Plugins: Massive cleanup of data file handling
* Move data file handling out of standalone only, allowing support
for other modes as well.
* Attach logger and tags to data file objects. | 20 | 0 | 42,857 | 9 |
|
3 | 6 | def _check_and_raise_error(self) -> bool:
for plugin in self._active_plugins:
if plugin.check_and_raise_error():
return True
return False
| plugins/extract/pipeline.py | 44 | faceswap | {
"docstring": " Check all threads for errors and raise if one occurs ",
"language": "en",
"n_whitespaces": 11,
"n_words": 10,
"vocab_size": 10
} | 14 | Python | 13 | 13cfb3f39e72e9ca181f173b7b3db2a048db0d08 | pipeline.py | 101,459 | 6 | 26 | _check_and_raise_error | https://github.com/deepfakes/faceswap.git | extract: Add batch processing mode | 61 | 0 | 20,872 | 9 |
|
1 | 3 | def async_remove(self) -> None:
@callback | homeassistant/data_entry_flow.py | 20 | @callback | core | {
"docstring": "Notification that the config flow has been removed.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 5 | Python | 5 | 2224d0f43a048052cfc4572df95c7afcccdf3a57 | data_entry_flow.py | 305,039 | 2 | 8 | async_remove | https://github.com/home-assistant/core.git | Add a callback for data flow handler removal (#77394)
* Add a callback for when data flows are removed
* Call `async_remove` at the very end
* Handle and log exceptions caught during flow removal
* Log the error as an exception, with a traceback
* Adjust test's expected logging output to match updated format specifier | 11 | 1 | 103,832 | 6 |
1 | 10 | def test_is_unique_interval(self, closed):
# unique overlapping - distinct endpoints
idx = IntervalIndex.from_tuples([(0, 1), (0.5, 1.5)], inclusive=closed)
assert idx.is_unique is True
# unique overlapping - shared endpoints
idx = IntervalIndex.from_tuples([(1, 2), (1, 3), (2, 3)], inclusive=closed)
assert idx.is_unique is True
# unique nested
idx = IntervalIndex.from_tuples([(-1, 1), (-2, 2)], inclusive=closed)
assert idx.is_unique is True
# unique NaN
idx = IntervalIndex.from_tuples([(np.NaN, np.NaN)], inclusive=closed)
assert idx.is_unique is True
# non-unique NaN
idx = IntervalIndex.from_tuples(
[(np.NaN, np.NaN), (np.NaN, np.NaN)], inclusive=closed
)
assert idx.is_unique is False
| pandas/tests/indexes/interval/test_interval.py | 252 | pandas | {
"docstring": "\n Interval specific tests for is_unique in addition to base class tests\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 10
} | 81 | Python | 42 | 7e23a37e1c5bda81234801a6584563e2880769eb | test_interval.py | 166,762 | 13 | 176 | test_is_unique_interval | https://github.com/pandas-dev/pandas.git | ENH: consistency of input args for boundaries - Interval (#46522) | 211 | 0 | 39,858 | 11 |
|
1 | 5 | def preprocess_func(cls, func):
return unidist.put(func)
| modin/core/execution/unidist/implementations/pandas_on_unidist/partitioning/partition.py | 26 | modin | {
"docstring": "\n Put a function into the object store to use in ``apply``.\n\n Parameters\n ----------\n func : callable\n A function to preprocess.\n\n Returns\n -------\n unidist.ObjectRef\n A reference to `func`.\n ",
"language": "en",
"n_whitespaces": 106,
"n_words": 27,
"vocab_size": 23
} | 5 | Python | 5 | 193505fdf0c984743397ba3df56262f30aee13a8 | partition.py | 155,177 | 2 | 15 | preprocess_func | https://github.com/modin-project/modin.git | FEAT-#5053: Add pandas on unidist execution with MPI backend (#5059)
Signed-off-by: Igoshev, Iaroslav <[email protected]> | 19 | 0 | 36,269 | 7 |
|
1 | 4 | def on_page_read_source(self, page, config):
return None
| mkdocs/plugins.py | 20 | mkdocs | {
"docstring": "\n The `on_page_read_source` event can replace the default mechanism to read\n the contents of a page's source from the filesystem.\n\n Parameters:\n page: `mkdocs.nav.Page` instance\n config: global configuration object\n\n Returns:\n The raw source for a page as unicode string. If `None` is returned, the\n default loading from a file will be performed.\n ",
"language": "en",
"n_whitespaces": 134,
"n_words": 50,
"vocab_size": 41
} | 6 | Python | 6 | f79b34d174e41084391868e7b503f5c61b8b1bdf | plugins.py | 224,450 | 2 | 12 | on_page_read_source | https://github.com/mkdocs/mkdocs.git | Move plugin events docs into source code + refactor
* Create real (no-op) methods for each event in the base class.
* Refactor event dispatcher to not check for methods' existence, instead just call them.
* Move documentation from Markdown into docstrings of these methods.
* Activate the 'mkdocstrings' plugin.
* Use 'mkdocstrings' to insert documentation from those docstrings into the site. | 20 | 0 | 57,295 | 6 |
|
1 | 9 | def mixed_type_frame() -> DataFrame:
return DataFrame(
{
"a": 1.0,
"b": 2,
"c": "foo",
"float32": np.array([1.0] * 10, dtype="float32"),
"int32": np.array([1] * 10, dtype="int32"),
},
index=np.arange(10),
)
@pytest.fixture | pandas/conftest.py | 125 | @pytest.fixture | pandas | {
"docstring": "\n Fixture for DataFrame of float/int/string columns with RangeIndex\n Columns are ['a', 'b', 'c', 'float32', 'int32'].\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 15,
"vocab_size": 15
} | 27 | Python | 25 | f538568afc2c76c2d738d32e3544cf9fe6742960 | conftest.py | 167,605 | 15 | 73 | mixed_type_frame | https://github.com/pandas-dev/pandas.git | TYP: misc return type annotations (#47558) | 111 | 1 | 40,057 | 13 |
1 | 3 | def _grad(f, argnums=0):
| keras/integration_test/forwardprop_test.py | 18 | keras | {
"docstring": "Return a function which computes the gradient of `f`.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 3 | Python | 3 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | forwardprop_test.py | 272,185 | 3 | 14 | _grad | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 6 | 0 | 80,972 | 6 |
|
4 | 23 | def axvline(self, x=0, ymin=0, ymax=1, **kwargs):
self._check_no_units([ymin, ymax], ['ymin', 'ymax'])
if "transform" in kwargs:
raise ValueError("'transform' is not allowed as a keyword "
"argument; axvline generates its own transform.")
xmin, xmax = self.get_xbound()
# Strip away the units for comparison with non-unitized bounds.
xx, = self._process_unit_info([("x", x)], kwargs)
scalex = (xx < xmin) or (xx > xmax)
trans = self.get_xaxis_transform(which='grid')
l = mlines.Line2D([x, x], [ymin, ymax], transform=trans, **kwargs)
self.add_line(l)
if scalex:
self._request_autoscale_view("x")
return l
| lib/matplotlib/axes/_axes.py | 227 | matplotlib | {
"docstring": "\n Add a vertical line across the Axes.\n\n Parameters\n ----------\n x : float, default: 0\n x position in data coordinates of the vertical line.\n\n ymin : float, default: 0\n Should be between 0 and 1, 0 being the bottom of the plot, 1 the\n top of the plot.\n\n ymax : float, default: 1\n Should be between 0 and 1, 0 being the bottom of the plot, 1 the\n top of the plot.\n\n Returns\n -------\n `~matplotlib.lines.Line2D`\n\n Other Parameters\n ----------------\n **kwargs\n Valid keyword arguments are `.Line2D` properties, except for\n 'transform':\n\n %(Line2D:kwdoc)s\n\n See Also\n --------\n vlines : Add vertical lines in data coordinates.\n axvspan : Add a vertical span (rectangle) across the axis.\n axline : Add a line with an arbitrary slope.\n\n Examples\n --------\n * draw a thick red vline at *x* = 0 that spans the yrange::\n\n >>> axvline(linewidth=4, color='r')\n\n * draw a default vline at *x* = 1 that spans the yrange::\n\n >>> axvline(x=1)\n\n * draw a default vline at *x* = .5 that spans the middle half of\n the yrange::\n\n >>> axvline(x=.5, ymin=0.25, ymax=0.75)\n ",
"language": "en",
"n_whitespaces": 465,
"n_words": 173,
"vocab_size": 87
} | 74 | Python | 66 | 383de519505964ed879c40b23ef36e90c17ebe0d | _axes.py | 110,316 | 14 | 139 | axvline | https://github.com/matplotlib/matplotlib.git | [Doc] fix more spelling and grammar | 208 | 0 | 24,055 | 11 |
|
1 | 4 | def DeprecatedModule(mod, deprecated_attributes=None, is_module_deprecated=True):
| haystack/__init__.py | 23 | haystack | {
"docstring": "\n Return a wrapped object that warns about deprecated accesses at import\n ",
"language": "en",
"n_whitespaces": 18,
"n_words": 11,
"vocab_size": 11
} | 4 | Python | 4 | a59bca366174d9c692fa19750c24d65f47660ef7 | __init__.py | 256,180 | 5 | 30 | DeprecatedModule | https://github.com/deepset-ai/haystack.git | Apply black formatting (#2115)
* Testing black on ui/
* Applying black on docstores
* Add latest docstring and tutorial changes
* Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too
* Remove comments
* Relax constraints on pydoc-markdown
* Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade
* Fix a couple of bugs
* Add a type: ignore that was missing somehow
* Give path to black
* Apply Black
* Apply Black
* Relocate a couple of type: ignore
* Update documentation
* Make Linux CI run after applying Black
* Triggering Black
* Apply Black
* Remove dependency, does not work well
* Remove manually double trailing commas
* Update documentation
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | 7 | 0 | 74,787 | 6 |
|
1 | 14 | def test_cli_log_level_debug_used():
configure_logging_and_warnings(logging.DEBUG)
rasa_logger = logging.getLogger("rasa")
rasa_logger.level == logging.DEBUG
matplotlib_logger = logging.getLogger("matplotlib")
# Default log level for libraries is currently ERROR
matplotlib_logger.level == logging.ERROR
@mock.patch.dict(os.environ, {"LOG_LEVEL": "WARNING"}) | tests/utils/test_common.py | 105 | @mock.patch.dict(os.environ, {"LOG_LEVEL": "WARNING"}) | rasa | {
"docstring": "Test CLI with log level uses for rasa logger whereas libraries stay default.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | 27 | Python | 25 | f00148b089d326c952880a0e5e6bd4b2dcb98ce5 | test_common.py | 159,095 | 6 | 41 | test_cli_log_level_debug_used | https://github.com/RasaHQ/rasa.git | Configurable logging for libraries (#10614)
* Make library level logging to be configurable
Fixes https://github.com/RasaHQ/rasa/issues/10203
* Create log level documentation under cheatsheet in Rasa docs
* Add log docs to `rasa shell --debug` (and others) | 47 | 1 | 38,121 | 9 |
2 | 10 | def partition_suite_by_case(suite):
suite_class = type(suite)
all_tests = iter_test_cases(suite)
return [suite_class(tests) for _, tests in itertools.groupby(all_tests, type)]
| django/test/runner.py | 62 | django | {
"docstring": "Partition a test suite by test case, preserving the order of tests.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 11
} | 16 | Python | 15 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | runner.py | 206,410 | 4 | 38 | partition_suite_by_case | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 28 | 0 | 51,517 | 9 |
|
1 | 11 | def test_get_invalid_filter_spec(self):
# Get the image
response = self.client.get(
reverse("wagtailimages:preview", args=(self.image.id, "bad-filter-spec"))
)
# Check response
self.assertEqual(response.status_code, 400)
| wagtail/images/tests/test_admin_views.py | 71 | wagtail | {
"docstring": "\n Test that an invalid filter spec returns a 400 response\n\n This is very unlikely to happen in reality. A user would have\n to create signature for the invalid filter spec which can't be\n done with Wagtails built in URL generator. We should test it\n anyway though.\n ",
"language": "en",
"n_whitespaces": 89,
"n_words": 46,
"vocab_size": 41
} | 18 | Python | 16 | d10f15e55806c6944827d801cd9c2d53f5da4186 | test_admin_views.py | 75,149 | 5 | 41 | test_get_invalid_filter_spec | https://github.com/wagtail/wagtail.git | Reformat with black | 71 | 0 | 16,368 | 14 |
|
3 | 13 | def test_knn_imputer_keep_empty_features(keep_empty_features):
X = np.array([[1, np.nan, 2], [3, np.nan, np.nan]])
imputer = KNNImputer(keep_empty_features=keep_empty_features)
for method in ["fit_transform", "transform"]:
X_imputed = getattr(imputer, method)(X)
if keep_empty_features:
assert X_imputed.shape == X.shape
assert_array_equal(X_imputed[:, 1], 0)
else:
assert X_imputed.shape == (X.shape[0], X.shape[1] - 1)
| sklearn/impute/tests/test_impute.py | 168 | scikit-learn | {
"docstring": "Check the behaviour of `keep_empty_features` for `KNNImputer`.",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 7
} | 39 | Python | 33 | d8fa96c29828e3ca79ddd5d7466521ac4d95213c | test_impute.py | 261,584 | 10 | 110 | test_knn_imputer_keep_empty_features | https://github.com/scikit-learn/scikit-learn.git | ENH keep features with all missing values during imputation (#24770)
Co-authored-by: Chiara Marmo <[email protected]>
Co-authored-by: Julien Jerphanion <[email protected]>
Co-authored-by: Jérémie du Boisberranger <[email protected]>
Co-authored-by: Vitor SRG <[email protected]>
Fixes https://github.com/scikit-learn/scikit-learn/pull/16695
Fixes https://github.com/scikit-learn/scikit-learn/issues/16426
Fixes https://github.com/scikit-learn/scikit-learn/issues/16977 | 105 | 0 | 76,878 | 15 |
|
3 | 12 | def requires(self, extras=()):
dm = self._dep_map
deps = []
deps.extend(dm.get(None, ()))
for ext in extras:
try:
deps.extend(dm[safe_extra(ext)])
except KeyError:
raise UnknownExtra(
"%s has no such extra feature %r" % (self, ext)
)
return deps
| .venv/lib/python3.8/site-packages/pip/_vendor/pkg_resources/__init__.py | 113 | transferlearning | {
"docstring": "List of Requirements needed for this distro if `extras` are used",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 34 | Python | 32 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | __init__.py | 63,049 | 13 | 69 | requires | https://github.com/jindongwang/transferlearning.git | upd; format | 162 | 0 | 13,115 | 14 |
|
8 | 35 | def mode(a, axis=0, nan_policy='propagate'):
a, axis = _chk_asarray(a, axis)
if a.size == 0:
return ModeResult(np.array([]), np.array([]))
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.mode(a, axis)
if a.dtype == object and np.nan in set(a.ravel()):
# Fall back to a slower method since np.unique does not work with NaN
scores = set(np.ravel(a)) # get ALL unique values
testshape = list(a.shape)
testshape[axis] = 1
oldmostfreq = np.zeros(testshape, dtype=a.dtype)
oldcounts = np.zeros(testshape, dtype=int)
for score in scores:
template = (a == score)
counts = np.sum(template, axis, keepdims=True)
mostfrequent = np.where(counts > oldcounts, score, oldmostfreq)
oldcounts = np.maximum(counts, oldcounts)
oldmostfreq = mostfrequent
return ModeResult(mostfrequent, oldcounts)
| scipy/stats/_stats_py.py | 336 | scipy | {
"docstring": "Return an array of the modal (most common) value in the passed array.\n\n If there is more than one such value, only the smallest is returned.\n The bin-count for the modal bins is also returned.\n\n Parameters\n ----------\n a : array_like\n n-dimensional array of which to find mode(s).\n axis : int or None, optional\n Axis along which to operate. Default is 0. If None, compute over\n the whole array `a`.\n nan_policy : {'propagate', 'raise', 'omit'}, optional\n Defines how to handle when input contains nan.\n The following options are available (default is 'propagate'):\n\n * 'propagate': returns nan\n * 'raise': throws an error\n * 'omit': performs the calculations ignoring nan values\n\n Returns\n -------\n mode : ndarray\n Array of modal values.\n count : ndarray\n Array of counts for each mode.\n\n Examples\n --------\n >>> a = np.array([[6, 8, 3, 0],\n ... [3, 2, 1, 7],\n ... [8, 1, 8, 4],\n ... [5, 3, 0, 5],\n ... [4, 7, 5, 9]])\n >>> from scipy import stats\n >>> stats.mode(a)\n ModeResult(mode=array([[3, 1, 0, 0]]), count=array([[1, 1, 1, 1]]))\n\n To get mode of whole array, specify ``axis=None``:\n\n >>> stats.mode(a, axis=None)\n ModeResult(mode=array([3]), count=array([3]))\n\n ",
"language": "en",
"n_whitespaces": 390,
"n_words": 183,
"vocab_size": 131
} | 108 | Python | 78 | 7438fe5edfb565ff341fa6ab054461fcdd504aa2 | _stats_py.py | 241,885 | 31 | 340 | mode | https://github.com/scipy/scipy.git | MAINT: stats: mode: fix negative axis issue with np.moveaxis instead of custom code (#15421) | 259 | 0 | 69,724 | 13 |
|
1 | 8 | def score(self, X, y, **fit_params):
check_is_fitted(self)
return self.estimator_.score(self.transform(X), y, **fit_params)
| sklearn/feature_selection/_rfe.py | 56 | scikit-learn | {
"docstring": "Reduce X to the selected features and return the score of the estimator.\n\n Parameters\n ----------\n X : array of shape [n_samples, n_features]\n The input samples.\n\n y : array of shape [n_samples]\n The target values.\n\n **fit_params : dict\n Parameters to pass to the `score` method of the underlying\n estimator.\n\n .. versionadded:: 1.0\n\n Returns\n -------\n score : float\n Score of the underlying base estimator computed with the selected\n features returned by `rfe.transform(X)` and `y`.\n ",
"language": "en",
"n_whitespaces": 212,
"n_words": 72,
"vocab_size": 46
} | 10 | Python | 9 | 6e5ef2e9b8c64e6788428610ae884b9bf3d298a2 | _rfe.py | 260,641 | 3 | 36 | score | https://github.com/scikit-learn/scikit-learn.git | MAINT solve long line reported by flake8 (#24065) | 31 | 0 | 76,391 | 9 |
|
2 | 11 | def finalize(self) -> None:
if self._warn_autoconfig:
desc = configexc.ConfigErrorDesc(
"autoconfig loading not specified",
("Your config.py should call either `config.load_autoconfig()`"
" (to load settings configured via the GUI) or "
"`config.load_autoconfig(False)` (to not do so)"))
self.errors.append(desc)
with self._handle_error("updating mutated values"):
self._config.update_mutables()
| qutebrowser/config/configfiles.py | 95 | qutebrowser | {
"docstring": "Do work which needs to be done after reading config.py.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 40 | Python | 37 | d9e20f6b3071b86b479f281fe27d621e0b3ae7e5 | configfiles.py | 321,041 | 11 | 50 | finalize | https://github.com/qutebrowser/qutebrowser.git | config: Handle config.py errors while updating mutables
Fixes #3580 | 156 | 0 | 117,491 | 13 |
|
8 | 24 | def predict_on_batch(self, x):
self._check_call_args("predict_on_batch")
if (
self._distribution_strategy
and tf.distribute.in_cross_replica_context()
):
raise NotImplementedError(
"`predict_on_batch` is not supported for models distributed with"
" tf.distribute.Strategy."
)
# Validate and standardize user data.
inputs, _, _ = self._standardize_user_data(
x, extract_tensors_from_dataset=True
)
# If `self._distribution_strategy` is True, then we are in a replica context
# at this point.
if self.run_eagerly or self._distribution_strategy:
inputs = training_utils_v1.cast_if_floating_dtype(inputs)
if isinstance(inputs, collections.abc.Sequence):
# Unwrap lists with only one input, as we do when training on batch
if len(inputs) == 1:
inputs = inputs[0]
return self(inputs) # pylint: disable=not-callable
self._make_predict_function()
outputs = self.predict_function(inputs)
if len(outputs) == 1:
return outputs[0]
return outputs
| keras/engine/training_v1.py | 213 | keras | {
"docstring": "Returns predictions for a single batch of samples.\n\n Args:\n x: Input data. It could be:\n - A Numpy array (or array-like), or a list of arrays\n (in case the model has multiple inputs).\n - A TensorFlow tensor, or a list of tensors\n (in case the model has multiple inputs).\n - A `tf.data` dataset.\n\n Returns:\n Numpy array(s) of predictions.\n\n Raises:\n ValueError: In case of mismatch between given number of inputs and\n expectations of the model.\n ",
"language": "en",
"n_whitespaces": 217,
"n_words": 74,
"vocab_size": 50
} | 101 | Python | 80 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | training_v1.py | 271,940 | 24 | 127 | predict_on_batch | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 378 | 0 | 80,906 | 13 |
|
2 | 9 | def flatten(self) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature.")
return {
"bytes": Value("binary"),
"path": Value("string"),
}
| src/datasets/features/audio.py | 93 | datasets | {
"docstring": "If in the decodable state, raise an error, otherwise flatten the feature into a dictionary.",
"language": "en",
"n_whitespaces": 14,
"n_words": 15,
"vocab_size": 14
} | 26 | Python | 26 | 3804442bb7cfcb9d52044d92688115cfdc69c2da | audio.py | 104,575 | 9 | 50 | flatten | https://github.com/huggingface/datasets.git | Fix flatten of complex feature types (#3723)
* Flatten Translation and TranslationVariableLanguages
* Add tests
* Style
* Flatten for decodable features
* Fix flatten for non-dict types
* Add test
* Descriptive message in flatten for Audio feature
* Small refactor
* Add flatten to features
* Update table_flatten
* Revert changes in Dataset.flatten_/flatten
* Apply Quentin's suggestions from code review
Co-authored-by: Quentin Lhoest <[email protected]>
* Improve table_flatten docstring
* Fix tests
* Add nested test
* Minor fix
* Remove comment
Co-authored-by: Quentin Lhoest <[email protected]> | 94 | 0 | 21,901 | 10 |
|
5 | 25 | def gmean(a, axis=0, dtype=None, weights=None):
r
if not isinstance(a, np.ndarray):
# if not an ndarray object attempt to convert it
log_a = np.log(np.array(a, dtype=dtype))
elif dtype:
# Must change the default dtype allowing array type
if isinstance(a, np.ma.MaskedArray):
log_a = np.log(np.ma.asarray(a, dtype=dtype))
else:
log_a = np.log(np.asarray(a, dtype=dtype))
else:
log_a = np.log(a)
if weights is not None:
weights = np.asanyarray(weights, dtype=dtype)
return np.exp(np.average(log_a, axis=axis, weights=weights))
@_axis_nan_policy_factory(
lambda x: x, n_samples=1, n_outputs=1, too_small=0, paired=True,
result_unpacker=lambda x: (x,), kwd_samples=['weights']) | scipy/stats/_stats_py.py | 286 | @_axis_nan_policy_factory(
lambda x: x, n_samples=1, n_outputs=1, too_small=0, paired=True,
result_unpacker=lambda x: (x,), kwd_samples=['weights']) | scipy | {
"docstring": "Compute the weighted geometric mean along the specified axis.\n\n The weighted geometric mean of the array :math:`a_i` associated to weights\n :math:`w_i` is:\n\n .. math::\n\n \\exp \\left( \\frac{ \\sum_{i=1}^n w_i \\log a_i }{ \\sum_{i=1}^n w_i }\n \\right) \\, ,\n\n and, with equal weights, it falls backs to:\n\n .. math::\n\n \\sqrt[n]{ \\prod_{i=1}^n a_i } \\, .\n\n Parameters\n ----------\n a : array_like\n Input array or object that can be converted to an array.\n axis : int or None, optional\n Axis along which the geometric mean is computed. Default is 0.\n If None, compute over the whole array `a`.\n dtype : dtype, optional\n Type of the returned array and of the accumulator in which the\n elements are summed. If dtype is not specified, it defaults to the\n dtype of a, unless a has an integer dtype with a precision less than\n that of the default platform integer. In that case, the default\n platform integer is used.\n weights : array_like, optional\n The `weights` array must be broadcastable to the same shape as `a`.\n Default is None, which gives each value a weight of 1.0.\n\n Returns\n -------\n gmean : ndarray\n See `dtype` parameter above.\n\n See Also\n --------\n numpy.mean : Arithmetic average\n numpy.average : Weighted average\n hmean : Harmonic mean\n\n Notes\n -----\n The geometric average is computed over a single dimension of the input\n array, axis=0 by default, or all values in the array if axis=None.\n float64 intermediate and return values are used for integer inputs.\n\n References\n ----------\n .. [1] \"Weighted Geometric Mean\", *Wikipedia*,\n https://en.wikipedia.org/wiki/Weighted_geometric_mean.\n\n Examples\n --------\n >>> from scipy.stats import gmean\n >>> gmean([1, 4])\n 2.0\n >>> gmean([1, 2, 3, 4, 5, 6, 7])\n 3.3800151591412964\n >>> gmean([1, 4, 7], weights=[3, 1, 3])\n 2.80668351922014\n\n ",
"language": "en",
"n_whitespaces": 506,
"n_words": 276,
"vocab_size": 173
} | 76 | Python | 57 | 56869131c8e0a0d6e1af86cc1a000c61e83efcf6 | _stats_py.py | 242,047 | 79 | 148 | gmean | https://github.com/scipy/scipy.git | DOC: stats: correct doc display | 177 | 1 | 69,761 | 17 |
7 | 21 | def split_filename(filename, project_name=None):
result = None
pyver = None
filename = unquote(filename).replace(' ', '-')
m = PYTHON_VERSION.search(filename)
if m:
pyver = m.group(1)
filename = filename[:m.start()]
if project_name and len(filename) > len(project_name) + 1:
m = re.match(re.escape(project_name) + r'\b', filename)
if m:
n = m.end()
result = filename[:n], filename[n + 1:], pyver
if result is None:
m = PROJECT_NAME_AND_VERSION.match(filename)
if m:
result = m.group(1), m.group(3), pyver
return result
# Allow spaces in name because of legacy dists like "Twisted Core"
NAME_VERSION_RE = re.compile(r'(?P<name>[\w .-]+)\s*'
r'\(\s*(?P<ver>[^\s)]+)\)$')
| .venv/lib/python3.8/site-packages/pip/_vendor/distlib/util.py | 270 | transferlearning | {
"docstring": "\n Extract name, version, python version from a filename (no extension)\n\n Return name, version, pyver or None\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 16,
"vocab_size": 14
} | 84 | Python | 54 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | util.py | 62,169 | 18 | 154 | split_filename | https://github.com/jindongwang/transferlearning.git | upd; format | 212 | 0 | 12,890 | 14 |
|
1 | 7 | def test_mapped_literal_to_xcom_arg_verify_integrity(dag_maker, session):
with dag_maker(session=session) as dag:
t1 = BaseOperator(task_id='task_1')
| tests/models/test_dagrun.py | 49 | airflow | {
"docstring": "Test that when we change from literal to a XComArg the TIs are removed",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 14
} | 10 | Python | 10 | 91832a42d8124b040073481fd93c54e9e64c2609 | test_dagrun.py | 46,887 | 25 | 185 | test_mapped_literal_to_xcom_arg_verify_integrity | https://github.com/apache/airflow.git | Expand mapped tasks at DagRun.Veriy_integrity (#22679)
Create the necessary task instances for a mapped task at dagrun.verify_integrity
Co-authored-by: Ash Berlin-Taylor <[email protected]> | 23 | 0 | 9,034 | 12 |
|
1 | 3 | def raise_on_http_errors(self) -> bool:
| airbyte-cdk/python/airbyte_cdk/sources/declarative/requesters/requester.py | 16 | airbyte | {
"docstring": "\n If set to False, allows opting-out of raising HTTP code exception.\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 11
} | 4 | Python | 4 | 150ab593f8ca1f1aa960a0811aece26c46ba6c75 | requester.py | 5,320 | 4 | 8 | raise_on_http_errors | https://github.com/airbytehq/airbyte.git | Low code connectors: core structure (#12850)
* checkout from alex/cac
* doc
* doc
* remove broken test
* rename
* rename file
* delete unused file
* rename
* abstract property
* isort
* update state
* Update comment
* remove incremental mixin
* delete comment
* update comments
* update comments
* remove no_state
* rename package
* pass parameters through kwargs
* update interface to pass source in interface
* update interface to pass source in interface
* rename to stream_slicer
* Low code connectors: string interpolation with jinja (#12852)
* checkout from alex/cac
* Add missing tests
* Add missing files
* missing file
* rename
* jinja dependency
* Add comment
* comment
* comment
* Revert "delete unused file"
This reverts commit 758e939367775ddbefcd52c6e1e832976d3ba9fe.
* delete unused field
* delete unused field
* rename
* pass kwargs directly
* isort
* Revert "isort"
This reverts commit 4a792239440bc9950813ccc6ed368641ce2a96e4.
* format
* decoder
* better error handling
* remove nostate
* isort
* delete dead code
* Update mapping type to [str, Any]
* add comment
* Add comment
* pass parameters through kwargs
* move test to right module
* Add missing test
* Use authbase instead of deprecated class
* leverage generator
* rename to declarative
* rename the classes too | 11 | 0 | 753 | 6 |
|
1 | 8 | def _mask_lengths(mel_lens, log_c, log_alpha_scaled):
mask_log_c = sequence_mask(mel_lens)
log_c = log_c * mask_log_c
mask_log_alpha_scaled = mask_log_c.unsqueeze(2)
log_alpha_scaled = log_alpha_scaled * mask_log_alpha_scaled
return log_c, log_alpha_scaled
| TTS/tts/layers/overflow/neural_hmm.py | 62 | TTS | {
"docstring": "\n Mask the lengths of the forward variables so that the variable lenghts\n do not contribute in the loss calculation\n Args:\n mel_inputs (torch.FloatTensor): (batch, T, frame_channels)\n mel_inputs_lengths (torch.IntTensor): (batch)\n log_c (torch.FloatTensor): (batch, T)\n Returns:\n log_c (torch.FloatTensor) : scaled probabilities (batch, T)\n log_alpha_scaled (torch.FloatTensor): forward probabilities (batch, T, N)\n ",
"language": "en",
"n_whitespaces": 138,
"n_words": 47,
"vocab_size": 34
} | 23 | Python | 13 | 3b8b105b0d6539ac12972de94e0b2a5077fa1ce2 | neural_hmm.py | 262,688 | 6 | 38 | _mask_lengths | https://github.com/coqui-ai/TTS.git | Adding OverFlow (#2183)
* Adding encoder
* currently modifying hmm
* Adding hmm
* Adding overflow
* Adding overflow setting up flat start
* Removing runs
* adding normalization parameters
* Fixing models on same device
* Training overflow and plotting evaluations
* Adding inference
* At the end of epoch the test sentences are coming on cpu instead of gpu
* Adding figures from model during training to monitor
* reverting tacotron2 training recipe
* fixing inference on gpu for test sentences on config
* moving helpers and texts within overflows source code
* renaming to overflow
* moving loss to the model file
* Fixing the rename
* Model training but not plotting the test config sentences's audios
* Formatting logs
* Changing model name to camelcase
* Fixing test log
* Fixing plotting bug
* Adding some tests
* Adding more tests to overflow
* Adding all tests for overflow
* making changes to camel case in config
* Adding information about parameters and docstring
* removing compute_mel_statistics moved statistic computation to the model instead
* Added overflow in readme
* Adding more test cases, now it doesn't saves transition_p like tensor and can be dumped as json | 65 | 0 | 77,320 | 8 |
|
4 | 9 | def get_default_frameworks():
frameworks = []
if is_torch_available():
frameworks.append("pt")
if is_tf_available():
frameworks.append("tf")
if is_flax_available():
frameworks.append("flax")
return frameworks
_re_model_mapping = re.compile("MODEL_([A-Z_]*)MAPPING_NAMES")
| src/transformers/commands/add_new_model_like.py | 100 | transformers | {
"docstring": "\n Returns the list of frameworks (PyTorch, TensorFlow, Flax) that are installed in the environment.\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 14,
"vocab_size": 13
} | 19 | Python | 15 | 0a5ef036e6c2d5093ed348c5fd706634f6ed5e38 | add_new_model_like.py | 36,274 | 9 | 44 | get_default_frameworks | https://github.com/huggingface/transformers.git | Make `add-new-model-like` work in an env without all frameworks (#16239)
* Make add-new-model-like work without all frameworks installed
* A few fixes
* Last default frameworks | 57 | 0 | 6,594 | 10 |
|
1 | 19 | def test_limited_api(tmp_path):
# Based in part on test_cython from random.tests.test_extending
here = os.path.dirname(__file__)
ext_dir = os.path.join(here, "examples", "limited_api")
cytest = str(tmp_path / "limited_api")
shutil.copytree(ext_dir, cytest)
# build the examples and "install" them into a temporary directory
install_log = str(tmp_path / "tmp_install_log.txt")
subprocess.check_call(
[
sys.executable,
"setup.py",
"build",
"install",
"--prefix", str(tmp_path / "installdir"),
"--single-version-externally-managed",
"--record",
install_log,
],
cwd=cytest,
)
| numpy/core/tests/test_limited_api.py | 158 | numpy | {
"docstring": "Test building a third-party C extension with the limited API.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 57 | Python | 48 | 1edd6407d008fcaa282a6058ae355025c26517fd | test_limited_api.py | 159,732 | 19 | 91 | test_limited_api | https://github.com/numpy/numpy.git | TST: Split example package, skip limited API test for debug | 196 | 0 | 38,413 | 12 |
|
1 | 16 | def test_drange():
start = datetime.datetime(2011, 1, 1, tzinfo=mdates.UTC)
end = datetime.datetime(2011, 1, 2, tzinfo=mdates.UTC)
delta = datetime.timedelta(hours=1)
# We expect 24 values in drange(start, end, delta), because drange returns
# dates from an half open interval [start, end)
assert len(mdates.drange(start, end, delta)) == 24
# Same if interval ends slightly earlier
end = end - datetime.timedelta(microseconds=1)
assert len(mdates.drange(start, end, delta)) == 24
# if end is a little bit later, we expect the range to contain one element
# more
end = end + datetime.timedelta(microseconds=2)
assert len(mdates.drange(start, end, delta)) == 25
# reset end
end = datetime.datetime(2011, 1, 2, tzinfo=mdates.UTC)
# and tst drange with "complicated" floats:
# 4 hours = 1/6 day, this is an "dangerous" float
delta = datetime.timedelta(hours=4)
daterange = mdates.drange(start, end, delta)
assert len(daterange) == 6
assert mdates.num2date(daterange[-1]) == (end - delta)
@_new_epoch_decorator | lib/matplotlib/tests/test_dates.py | 292 | @_new_epoch_decorator | matplotlib | {
"docstring": "\n This test should check if drange works as expected, and if all the\n rounding errors are fixed\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 17,
"vocab_size": 16
} | 137 | Python | 80 | 9a03cb3b8c7253271054f146724c230eca96274b | test_dates.py | 108,602 | 14 | 187 | test_drange | https://github.com/matplotlib/matplotlib.git | Add tests for date module | 202 | 1 | 23,271 | 10 |
2 | 9 | def get_default_executor(cls) -> "BaseExecutor":
if cls._default_executor is not None:
return cls._default_executor
from airflow.configuration import conf
executor_name = conf.get('core', 'EXECUTOR')
cls._default_executor = cls.load_executor(executor_name)
return cls._default_executor
| airflow/executors/executor_loader.py | 86 | airflow | {
"docstring": "Creates a new instance of the configured executor if none exists and returns it",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 14
} | 24 | Python | 19 | 1a8a897120762692ca98ac5ce4da881678c073aa | executor_loader.py | 44,553 | 8 | 50 | get_default_executor | https://github.com/apache/airflow.git | Improve speed to run `airflow` by 6x (#21438)
By delaying expensive/slow imports to where they are needed, this gets
`airflow` printing it's usage information in under 0.8s, down from almost
3s which makes it feel much much snappier.
By not loading BaseExecutor we can get down to <0.5s | 77 | 0 | 8,295 | 9 |
|
1 | 15 | def test_create_profile_from_existing():
save_profiles(
ProfilesCollection(
profiles=[
Profile(name="foo", settings={PREFECT_API_KEY: "foo"}),
],
active=None,
)
)
invoke_and_assert(
["profile", "create", "bar", "--from", "foo"],
expected_output=(
f
),
)
profiles = load_profiles()
assert profiles["foo"].settings == {PREFECT_API_KEY: "foo"}, "Foo is unchanged"
assert profiles["bar"] == Profile(
name="bar",
settings={PREFECT_API_KEY: "foo"},
source=PREFECT_PROFILES_PATH.value(),
)
| tests/cli/test_profile.py | 179 | prefect | {
"docstring": "\n Created profile 'bar' matching 'foo'.\n\n Switch to your new profile with:\n\n prefect profile use 'bar'\n\n Or, to use it for a single command, include the `-p` option:\n\n prefect -p 'bar' config view\n ",
"language": "en",
"n_whitespaces": 107,
"n_words": 32,
"vocab_size": 25
} | 42 | Python | 35 | 808660dd04465fc796a34e835467e8ae1f2449b3 | test_profile.py | 55,084 | 32 | 105 | test_create_profile_from_existing | https://github.com/PrefectHQ/prefect.git | Add tests for profile CLI | 180 | 0 | 11,204 | 17 |
|
1 | 6 | def test_device_stats_gpu_from_nvidia(tmpdir):
model = BoringModel()
device_stats = DeviceStatsMonitor()
| tests/callbacks/test_device_stats_monitor.py | 31 | lightning | {
"docstring": "Test GPU stats are logged using a logger with Pytorch < 1.8.0.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | 8 | Python | 7 | b56d8677ad0ff8513e566334f4a78a24b88480c3 | test_device_stats_monitor.py | 241,714 | 19 | 82 | test_device_stats_gpu_from_nvidia | https://github.com/Lightning-AI/lightning.git | Update test_pruning.py to use `devices` instead of `gpus` or `ipus` (#11339) | 17 | 0 | 69,667 | 8 |
|
3 | 13 | def register_model_view(model, name, view_path, tab_label=None, tab_badge=None, tab_permission=None, kwargs=None):
app_label = model._meta.app_label
model_name = model._meta.model_name
if model_name not in registry['views'][app_label]:
registry['views'][app_label][model_name] = []
registry['views'][app_label][model_name].append({
'name': name,
'path': view_path,
'tab_label': tab_label,
'tab_badge': tab_badge,
'tab_permission': tab_permission,
'kwargs': kwargs or {},
})
| netbox/utilities/views.py | 172 | netbox | {
"docstring": "\n Register a subview for a core model.\n\n Args:\n model: The Django model class with which this view will be associated\n name: The name to register when creating a URL path\n view_path: A dotted path to the view class or function (e.g. 'myplugin.views.FooView')\n tab_label: The label to display for the view's tab under the model view (optional)\n tab_badge: A static value or callable to display a badge within the view's tab (optional). If a callable is\n specified, it must accept the current object as its single positional argument.\n tab_permission: The name of the permission required to display the tab (optional)\n kwargs: A dictionary of keyword arguments to send to the view (optional)\n ",
"language": "en",
"n_whitespaces": 181,
"n_words": 111,
"vocab_size": 71
} | 38 | Python | 33 | 0d7851ed9de2792ea6d9ed223c315c235290ddd7 | views.py | 265,787 | 13 | 108 | register_model_view | https://github.com/netbox-community/netbox.git | #9072: Implement a mechanism for dynamically registering model detail views | 105 | 0 | 78,196 | 12 |
|
1 | 4 | def done_adding(self) -> bool:
raise NotImplementedError()
| python/ray/data/_internal/batcher.py | 23 | ray | {
"docstring": "Indicate to the batcher that no more blocks will be added to the buffer.",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 12
} | 6 | Python | 6 | 864af14f410ab12c7553332dd3a62e716f24a667 | batcher.py | 125,025 | 3 | 12 | done_adding | https://github.com/ray-project/ray.git | [Datasets] [Local Shuffle - 1/N] Add local shuffling option. (#26094)
Co-authored-by: Eric Liang <[email protected]>
Co-authored-by: matthewdeng <[email protected]>
Co-authored-by: Matthew Deng <[email protected]>
Co-authored-by: Richard Liaw <[email protected]> | 20 | 0 | 27,753 | 7 |
|
1 | 3 | async def test_subscribe_deprecated_async(hass, mqtt_mock):
| tests/components/mqtt/test_init.py | 16 | core | {
"docstring": "Test the subscription of a topic using deprecated coroutine signature.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 4 | Python | 4 | 845bf80e725af8c921915906b0f796c7a8164d11 | test_init.py | 292,660 | 27 | 184 | test_subscribe_deprecated_async | https://github.com/home-assistant/core.git | Mqtt improve test coverage (#66279)
Co-authored-by: Martin Hjelmare <[email protected]> | 7 | 0 | 91,734 | 6 |
|
14 | 65 | def validate(self, parameters, *args, **kwargs):
result = ValidationResult(parameters)
result._no_log_values.update(set_fallbacks(self.argument_spec, result._validated_parameters))
alias_warnings = []
alias_deprecations = []
try:
result._aliases.update(_handle_aliases(self.argument_spec, result._validated_parameters, alias_warnings, alias_deprecations))
except (TypeError, ValueError) as e:
result.errors.append(AliasError(to_native(e)))
legal_inputs = _get_legal_inputs(self.argument_spec, result._validated_parameters, result._aliases)
for option, alias in alias_warnings:
result._warnings.append({'option': option, 'alias': alias})
for deprecation in alias_deprecations:
result._deprecations.append({
'name': deprecation['name'],
'version': deprecation.get('version'),
'date': deprecation.get('date'),
'collection_name': deprecation.get('collection_name'),
})
try:
result._no_log_values.update(_list_no_log_values(self.argument_spec, result._validated_parameters))
except TypeError as te:
result.errors.append(NoLogError(to_native(te)))
try:
result._unsupported_parameters.update(_get_unsupported_parameters(self.argument_spec, result._validated_parameters, legal_inputs))
except TypeError as te:
result.errors.append(RequiredDefaultError(to_native(te)))
except ValueError as ve:
result.errors.append(AliasError(to_native(ve)))
try:
check_mutually_exclusive(self._mutually_exclusive, result._validated_parameters)
except TypeError as te:
result.errors.append(MutuallyExclusiveError(to_native(te)))
result._no_log_values.update(_set_defaults(self.argument_spec, result._validated_parameters, False))
try:
check_required_arguments(self.argument_spec, result._validated_parameters)
except TypeError as e:
result.errors.append(RequiredError(to_native(e)))
_validate_argument_types(self.argument_spec, result._validated_parameters, errors=result.errors)
_validate_argument_values(self.argument_spec, result._validated_parameters, errors=result.errors)
for check in _ADDITIONAL_CHECKS:
try:
check['func'](getattr(self, "_{attr}".format(attr=check['attr'])), result._validated_parameters)
except TypeError as te:
result.errors.append(check['err'](to_native(te)))
result._no_log_values.update(_set_defaults(self.argument_spec, result._validated_parameters))
_validate_sub_spec(self.argument_spec, result._validated_parameters,
errors=result.errors,
no_log_values=result._no_log_values,
unsupported_parameters=result._unsupported_parameters)
if result._unsupported_parameters:
flattened_names = []
for item in result._unsupported_parameters:
if isinstance(item, tuple):
flattened_names.append(".".join(item))
else:
flattened_names.append(item)
unsupported_string = ", ".join(sorted(list(flattened_names)))
supported_string = ", ".join(self._valid_parameter_names)
result.errors.append(
UnsupportedError("{0}. Supported parameters include: {1}.".format(unsupported_string, supported_string)))
return result
| lib/ansible/module_utils/common/arg_spec.py | 930 | ansible | {
"docstring": "Validate ``parameters`` against argument spec.\n\n Error messages in the :class:`ValidationResult` may contain no_log values and should be\n sanitized with :func:`~ansible.module_utils.common.parameters.sanitize_keys` before logging or displaying.\n\n :arg parameters: Parameters to validate against the argument spec\n :type parameters: dict[str, dict]\n\n :return: :class:`ValidationResult` containing validated parameters.\n\n :Simple Example:\n\n .. code-block:: text\n\n argument_spec = {\n 'name': {'type': 'str'},\n 'age': {'type': 'int'},\n }\n\n parameters = {\n 'name': 'bo',\n 'age': '42',\n }\n\n validator = ArgumentSpecValidator(argument_spec)\n result = validator.validate(parameters)\n\n if result.error_messages:\n sys.exit(\"Validation failed: {0}\".format(\", \".join(result.error_messages))\n\n valid_params = result.validated_parameters\n ",
"language": "en",
"n_whitespaces": 355,
"n_words": 80,
"vocab_size": 66
} | 154 | Python | 98 | 1b947eaf92b6833d2a4fd019a30d7b85406f1778 | arg_spec.py | 267,055 | 62 | 575 | validate | https://github.com/ansible/ansible.git | arg_spec - Return aliases in validation result and update aliases (#77576)
When looking up the `no_log` setting for a parameter that is an alias in
`AnsibleModule._log_invocation()`, the alias value will always be an
empty dictionary since `self.aliases` on the `AnsibleModule` instance is
never updated after initialization. Since the `no_log` setting is on the
canonical parameter not the alias, an incorrect warning is issued if the
parameter matches `PASSWORD_MATCH`.
This PR returns the aliases dictionary as an attribute of the
`ValidationResult` and updates the `aliases` attribute on the
`AnsibleModule` instance. | 825 | 0 | 78,708 | 18 |
|
1 | 9 | def test_avatar_constraints_file_size(self):
self._setup_local_files(
{
"small": {"size": 40},
"big": {"size": 60},
}
)
res = self.get_success(
self.handler.check_avatar_size_and_mime_type("mxc://test/small")
)
self.assertTrue(res)
res = self.get_success(
self.handler.check_avatar_size_and_mime_type("mxc://test/big")
)
self.assertFalse(res)
| tests/handlers/test_profile.py | 127 | synapse | {
"docstring": "Tests that a file that's above the allowed file size is forbidden but one\n that's below it is allowed.\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 19,
"vocab_size": 16
} | 24 | Python | 18 | bf60da1a60096fac5fb778b732ff2214862ac808 | test_profile.py | 246,128 | 15 | 71 | test_avatar_constraints_file_size | https://github.com/matrix-org/synapse.git | Configurable limits on avatars (#11846)
Only allow files which file size and content types match configured
limits to be set as avatar.
Most of the inspiration from the non-test code comes from matrix-org/synapse-dinsic#19 | 161 | 0 | 71,029 | 12 |
|
6 | 20 | def get_source_segment(source, node, *, padded=False):
try:
if node.end_lineno is None or node.end_col_offset is None:
return None
lineno = node.lineno - 1
end_lineno = node.end_lineno - 1
col_offset = node.col_offset
end_col_offset = node.end_col_offset
except AttributeError:
return None
lines = _splitlines_no_ff(source)
if end_lineno == lineno:
return lines[lineno].encode()[col_offset:end_col_offset].decode()
if padded:
padding = _pad_whitespace(lines[lineno].encode()[:col_offset].decode())
else:
padding = ''
first = padding + lines[lineno].encode()[col_offset:].decode()
last = lines[end_lineno].encode()[:end_col_offset].decode()
lines = lines[lineno+1:end_lineno]
lines.insert(0, first)
lines.append(last)
return ''.join(lines)
| python3.10.4/Lib/ast.py | 303 | XX-Net | {
"docstring": "Get source code segment of the *source* that generated *node*.\n\n If some location information (`lineno`, `end_lineno`, `col_offset`,\n or `end_col_offset`) is missing, return None.\n\n If *padded* is `True`, the first line of a multi-line statement will\n be padded with spaces to match its original position.\n ",
"language": "en",
"n_whitespaces": 59,
"n_words": 44,
"vocab_size": 40
} | 70 | Python | 45 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | ast.py | 220,161 | 23 | 187 | get_source_segment | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 183 | 0 | 55,928 | 16 |
|
1 | 11 | def _replace_cwa_config_variables(self) -> Dict[str, Any]:
cwa_config = self._load_config_file("agent")
self._replace_all_config_variables(
cwa_config,
self.node_id,
self.cluster_name,
self.provider_config["region"],
)
return cwa_config
| python/ray/autoscaler/_private/aws/cloudwatch/cloudwatch_helper.py | 72 | ray | {
"docstring": "\n replace known variable occurrences in\n Unified Cloudwatch Agent config file\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 10,
"vocab_size": 10
} | 16 | Python | 15 | 71fae21e8e86c75bc58b53dccae563d15691610f | cloudwatch_helper.py | 129,126 | 13 | 45 | _replace_cwa_config_variables | https://github.com/ray-project/ray.git | [autoscaler] AWS Autoscaler CloudWatch Dashboard support (#20266)
These changes add a set of improvements to enable automatic creation and update of CloudWatch dashboards when provisioning AWS Autoscaling clusters. Successful implementation of these improvements will allow AWS Autoscaler users to:
1. Get rapid insights into their cluster state via CloudWatch dashboards.
2. Allow users to update their CloudWatch dashboard JSON configuration files during Ray up execution time.
Notes:
1. This PR is a follow-up PR for #18619, adds dashboard support. | 95 | 0 | 28,902 | 9 |
|
4 | 20 | def get_batch(self) -> Tuple[List[List[np.ndarray]], ...]:
model_inputs: List[List[np.ndarray]] = []
model_targets: List[List[np.ndarray]] = []
for side in ("a", "b"):
side_feed, side_targets = next(self._feeds[side])
if self._model.config["learn_mask"]: # Add the face mask as it's own target
side_targets += [side_targets[-1][..., 3][..., None]]
logger.trace("side: %s, input_shapes: %s, target_shapes: %s", # type: ignore
side, side_feed.shape, [i.shape for i in side_targets])
model_inputs.append([side_feed])
model_targets.append(side_targets)
return model_inputs, model_targets
| plugins/train/trainer/_base.py | 217 | faceswap | {
"docstring": " Get the feed data and the targets for each training side for feeding into the model's\n train function.\n\n Returns\n -------\n model_inputs: list\n The inputs to the model for each side A and B\n model_targets: list\n The targets for the model for each side A and B\n ",
"language": "en",
"n_whitespaces": 111,
"n_words": 46,
"vocab_size": 26
} | 59 | Python | 50 | 2beceffad9b15c1fd78f06b9b272563321c5a41e | _base.py | 101,297 | 22 | 140 | get_batch | https://github.com/deepfakes/faceswap.git | Data Augmentation update (#1263)
- lib.detected_face
- Subclass Masks for Landmark based masks
- Add training mask propery + methods to DetectedFace
- lib.training_training
- subclass TrainingDataGenerator for training and preview data
- Split cache into own module
- Reduce thread count to 1 to prevent image corruption + data re-use
- Process on largest model input/output size rather than stored image size
- Size and crop masks during caching stage
- Implement ring buffer for data flow
- Fix preview reload bug
- augmentation
- typing
- switch color aug order
- better initialization
- Fix warp + landmark warp to correctly apply at different image scales
- Slightly improved warp caching
- Don't store whether image is_preview. Handle all data as training images implicitly
- plugins.trainer: Typing and fixes to work with trainingdata refactor | 190 | 0 | 20,716 | 15 |
|
9 | 39 | def optgroups(self, name, value, attr=None):
default = (None, [], 0)
groups = [default]
has_selected = False
selected_choices = {
str(v) for v in value if str(v) not in self.choices.field.empty_values
}
if not self.is_required and not self.allow_multiple_selected:
default[1].append(self.create_option(name, "", "", False, 0))
remote_model_opts = self.field.remote_field.model._meta
to_field_name = getattr(
self.field.remote_field, "field_name", remote_model_opts.pk.attname
)
to_field_name = remote_model_opts.get_field(to_field_name).attname
choices = (
(getattr(obj, to_field_name), self.choices.field.label_from_instance(obj))
for obj in self.choices.queryset.using(self.db).filter(
**{"%s__in" % to_field_name: selected_choices}
)
)
for option_value, option_label in choices:
selected = str(option_value) in value and (
has_selected is False or self.allow_multiple_selected
)
has_selected |= selected
index = len(default[1])
subgroup = default[1]
subgroup.append(
self.create_option(
name, option_value, option_label, selected_choices, index
)
)
return groups
| django/contrib/admin/widgets.py | 373 | django | {
"docstring": "Return selected options based on the ModelChoiceIterator.",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 7
} | 108 | Python | 70 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | widgets.py | 203,557 | 33 | 244 | optgroups | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 435 | 0 | 50,447 | 15 |
|
2 | 1 | async def test_focused_child_widget_with_movement_bindings_on_screen() -> None:
| tests/test_binding_inheritance.py | 16 | textual | {
"docstring": "A focused child widget, with movement bindings in the screen, should trigger screen actions.",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 14
} | 5 | Python | 5 | e75f784b2c788f95e398821266fcaab0f79aa12f | test_binding_inheritance.py | 186,093 | 5 | 53 | test_focused_child_widget_with_movement_bindings_on_screen | https://github.com/Textualize/textual.git | Add a test for a screen binding movement, wrapping a focusable widget
This is the heart of the issue introduced by
https://github.com/Textualize/textual/pull/1170/commits/b48a1402b8103ca16d5e338538620e9e08fb2c0e
and which is being investigated in
https://github.com/Textualize/textual/issues/1343 -- the child widget can be
focused, but (as far as the author of the code is concerned) it has no
bindings. Bindings for movement-oriented keys exist on the screen which
composes up the widget into it. Up until 0.5.0 this worked just fine. As of
0.6.0, because binding inheritance was introduced, the bindings for movement
that live at the `Widget` level cause the widget that has no bindings to
appear to have bindings.
While this can potentially be worked around with the use of
inherit_bindings, this isn't a very satisfying solution and also breaks the
rule of least astonishment.
This test is going to be key to all of this. This is the test that should be
made to work without breaking any of the other currently-passing tests. | 8 | 0 | 45,329 | 6 |
|
4 | 12 | def attributes_icon(self):
if self._attributes:
return self._attributes.get(ATTR_ICON)
result = ICON_JSON_EXTRACT.search(
self._row.shared_attrs or self._row.attributes
)
return result and result.group(1)
| homeassistant/components/logbook/__init__.py | 77 | core | {
"docstring": "Extract the icon from the decoded attributes or json.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 8
} | 17 | Python | 15 | 9215702388eef03c7c3ed9f756ea0db533d5beec | __init__.py | 293,739 | 7 | 47 | attributes_icon | https://github.com/home-assistant/core.git | Separate attrs into another table (reduces database size) (#68224) | 74 | 0 | 92,792 | 11 |
|
10 | 26 | def get_default_bank_cash_account(company, account_type=None, mode_of_payment=None, account=None):
from erpnext.accounts.doctype.sales_invoice.sales_invoice import get_bank_cash_account
if mode_of_payment:
account = get_bank_cash_account(mode_of_payment, company).get("account")
if not account:
if account_type == "Bank":
account = frappe.get_cached_value("Company", company, "default_bank_account")
if not account:
account_list = frappe.get_all(
"Account", filters={"company": company, "account_type": "Bank", "is_group": 0}
)
if len(account_list) == 1:
account = account_list[0].name
elif account_type == "Cash":
account = frappe.get_cached_value("Company", company, "default_cash_account")
if not account:
account_list = frappe.get_all(
"Account", filters={"company": company, "account_type": "Cash", "is_group": 0}
)
if len(account_list) == 1:
account = account_list[0].name
if account:
account_details = frappe.db.get_value(
"Account", account, ["account_currency", "account_type"], as_dict=1
)
return frappe._dict(
{
"account": account,
"balance": get_balance_on(account),
"account_currency": account_details.account_currency,
"account_type": account_details.account_type,
}
)
else:
return frappe._dict()
@frappe.whitelist() | erpnext/accounts/doctype/journal_entry/journal_entry.py | 409 | @frappe.whitelist() | erpnext | {
"docstring": "\n\t\tSet the default account first. If the user hasn't set any default account then, he doesn't\n\t\twant us to set any random account. In this case set the account only if there is single\n\t\taccount (of that type), otherwise return empty dict.\n\t\t",
"language": "en",
"n_whitespaces": 39,
"n_words": 42,
"vocab_size": 33
} | 106 | Python | 58 | 494bd9ef78313436f0424b918f200dab8fc7c20b | journal_entry.py | 64,875 | 40 | 236 | get_default_bank_cash_account | https://github.com/frappe/erpnext.git | style: format code with black | 70 | 1 | 13,744 | 19 |
1 | 5 | def destroy_if_owned(self) -> int:
raise NotImplementedError
@dataclass | python/ray/data/_internal/execution/interfaces.py | 23 | @dataclass | ray | {
"docstring": "Clears the object store memory for these blocks if owned.\n\n Returns:\n The number of bytes freed.\n ",
"language": "en",
"n_whitespaces": 41,
"n_words": 16,
"vocab_size": 16
} | 7 | Python | 7 | 2cd4637521a0c75e0375266ff52d3dfca2084c2d | interfaces.py | 138,197 | 7 | 10 | destroy_if_owned | https://github.com/ray-project/ray.git | [data] New executor backend [1/n]--- Add basic interfaces (#31216)
This PR adds the basic interfaces and feature flags; split out from https://github.com/ray-project/ray/pull/30903/files
See REP ray-project/enhancements#18 for more details. | 20 | 1 | 31,352 | 6 |
1 | 4 | def subtract(inputs, **kwargs):
return Subtract(**kwargs)(inputs)
| keras/layers/merging/subtract.py | 32 | keras | {
"docstring": "Functional interface to the `Subtract` layer.\n\n Args:\n inputs: A list of input tensors (exactly 2).\n **kwargs: Standard layer keyword arguments.\n\n Returns:\n A tensor, the difference of the inputs.\n\n Examples:\n\n ```python\n import keras\n\n input1 = keras.layers.Input(shape=(16,))\n x1 = keras.layers.Dense(8, activation='relu')(input1)\n input2 = keras.layers.Input(shape=(32,))\n x2 = keras.layers.Dense(8, activation='relu')(input2)\n subtracted = keras.layers.subtract([x1, x2])\n\n out = keras.layers.Dense(4)(subtracted)\n model = keras.models.Model(inputs=[input1, input2], outputs=out)\n ```\n ",
"language": "en",
"n_whitespaces": 154,
"n_words": 59,
"vocab_size": 48
} | 5 | Python | 5 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | subtract.py | 272,697 | 2 | 18 | subtract | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 11 | 0 | 81,039 | 9 |
|
7 | 26 | def pick_config_ids(device_type, os, navigator):
if os is None:
default_dev_types = ["desktop"]
else:
default_dev_types = list(DEVICE_TYPE_OS.keys())
dev_type_choices = get_option_choices(
"device_type", device_type, default_dev_types, list(DEVICE_TYPE_OS.keys())
)
os_choices = get_option_choices(
"os", os, list(OS_NAVIGATOR.keys()), list(OS_NAVIGATOR.keys())
)
nav_choices = get_option_choices(
"navigator", navigator, list(NAVIGATOR_OS.keys()), list(NAVIGATOR_OS.keys())
)
variants = []
for dev, os, nav in product(dev_type_choices, os_choices, nav_choices):
if (
os in DEVICE_TYPE_OS[dev]
and nav in DEVICE_TYPE_NAVIGATOR[dev]
and nav in OS_NAVIGATOR[os]
):
variants.append((dev, os, nav))
if not variants:
raise InvalidOption(
"Options device_type, os and navigator" " conflicts with each other"
)
device_type, os_id, navigator_id = randomizer.choice(variants)
assert os_id in OS_PLATFORM
assert navigator_id in NAVIGATOR_OS
assert device_type in DEVICE_TYPE_OS
return device_type, os_id, navigator_id
| build/pyinstaller/user_agent/base.py | 315 | OpenBBTerminal | {
"docstring": "\n Select one random pair (device_type, os_id, navigator_id) from\n all possible combinations matching the given os and\n navigator filters.\n\n :param os: allowed os(es)\n :type os: string or list/tuple or None\n :param navigator: allowed browser engine(s)\n :type navigator: string or list/tuple or None\n :param device_type: limit possible oses by device type\n :type device_type: list/tuple or None, possible values:\n \"desktop\", \"smartphone\", \"tablet\", \"all\"\n ",
"language": "en",
"n_whitespaces": 98,
"n_words": 60,
"vocab_size": 42
} | 104 | Python | 66 | ab4de1dd70fba866930150e440a03e461a6ca6a8 | base.py | 283,195 | 31 | 199 | pick_config_ids | https://github.com/OpenBB-finance/OpenBBTerminal.git | Create a packaged app bundle with Pyinstaller (#1525)
* Add dashboard widget assets
* Add ipywidgets and ipyflex to project
* Add currencies dashboard notebook
* Update docs and docstrings
* Add pyinstaller to project deps
* Add pyinstaller artifacts to gitignore
* Fix linter errors in terminal.py
* Update cspell hook and action with a pyinstaller specific word
* Add pyinstaller specfile and artifacts
* Add splashscreen image
* Add app icon
* adding splash screen support to terminal.spec and terminal.py
* Restore the conda env build files
* Sync deps
* Add border to the splashscreen image
* Clean up terminal launcher
* Add support for default feature flags in packages apps
* Fix types and linting
* Add splashscreen management to app bootup
* Check prediction feature flag when entering crypto/pred
* Update pyinstaller spec file
* fix .spec file to work for splash and icon - removed the ".."
* Allows to export when using installer (#1568)
* fix export for packaged apps
* fix filename
* Git : replace commit_hash when it is set in config_terminal
* Add update of the git commit hash in gtff default during build
* Add packaged app name and feature flag to logs
* Add platform specific icon assignment
* Add macOS build assets
* Add tensorflow to hidden imports
* Move LOGGING_COMMIT_HASH to gtff
* Adding files/folders needed to .spec and pyinstaller folder. This will make certain commands work again.
* Linting
* Workflow : ignore ./build/pyinstaller from codespell
* Workflow : exclude ./build/pyinstaller from flake8
* Poetry + Workflow : add types-six
* Pyinstaller : remove property_cached, user_agent and vaderSentiment
* Revert "Pyinstaller : remove property_cached, user_agent and vaderSentiment"
This reverts commit dbb3e2b81086f97819ebd21457148c7160a4d703.
* Clean up local paths in specfile
* Validate deps have correct Jinja version (they do)
* Fix logging commit hash to be set correctly for the logger to see it
Co-authored-by: Andrew <[email protected]>
Co-authored-by: didierlopes.eth <[email protected]>
Co-authored-by: Chavithra PARANA <[email protected]> | 273 | 0 | 84,461 | 13 |
|
1 | 32 | def name_scope(name):
| keras/backend.py | 64 | """A context manager for use when defining a Python op.
This context manager pushes a name scope, which will make the name of all
operations added within it have a prefix.
For example, to define a new Python op called `my_op`:use when defining a Python op.
This context manager pushes a namewhich will make the name of all
operations added within it have a prefix.
Forto define a new Python op called | keras | {
"docstring": "A context manager for use when defining a Python op.\n\n This context manager pushes a name scope, which will make the name of all\n operations added within it have a prefix.\n\n For example, to define a new Python op called `my_op`:\n\n",
"language": "en",
"n_whitespaces": 49,
"n_words": 41,
"vocab_size": 34
} | 2 | Python | 2 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | backend.py | 269,444 | 2 | 13 | name_scope | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 5 | 4 | 80,083 | 6 |
15 | 6 | def get_loc(self, key, method=None):
if method is not None:
raise NotImplementedError(
"only the default get_loc method is "
"currently supported for MultiIndex"
)
self._check_indexing_error(key)
| pandas/core/indexes/multi.py | 53 | pandas | {
"docstring": "\n Get location for a label or a tuple of labels.\n\n The location is returned as an integer/slice or boolean\n mask.\n\n Parameters\n ----------\n key : label or tuple of labels (one for each level)\n method : None\n\n Returns\n -------\n loc : int, slice object or boolean mask\n If the key is past the lexsort depth, the return may be a\n boolean mask array, otherwise it is always a slice or int.\n\n See Also\n --------\n Index.get_loc : The get_loc method for (single-level) index.\n MultiIndex.slice_locs : Get slice location given start label(s) and\n end label(s).\n MultiIndex.get_locs : Get location for a label/slice/list/mask or a\n sequence of such.\n\n Notes\n -----\n The key cannot be a slice, list of same-level labels, a boolean mask,\n or a sequence of such. If you want to use those, use\n :meth:`MultiIndex.get_locs` instead.\n\n Examples\n --------\n >>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])\n\n >>> mi.get_loc('b')\n slice(1, 3, None)\n\n >>> mi.get_loc(('b', 'e'))\n 1\n ",
"language": "en",
"n_whitespaces": 428,
"n_words": 149,
"vocab_size": 93
} | 24 | Python | 22 | 46ddb8ef882940fa3da58813e0b7a2df1061031e | multi.py | 163,113 | 51 | 324 | get_loc | https://github.com/pandas-dev/pandas.git | BUG: Index.get_loc always raise InvalidIndexError on listlike (#45181) | 97 | 0 | 39,371 | 11 |
|
2 | 5 | def setter(self, attr):
if attr not in self._options:
raise KeyError("No such option: %s" % attr)
| mitmproxy/optmanager.py | 40 | mitmproxy | {
"docstring": "\n Generate a setter for a given attribute. This returns a callable\n taking a single argument.\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 15,
"vocab_size": 12
} | 15 | Python | 15 | b3587b52b25077f68116b9852b041d33e7fc6601 | optmanager.py | 251,421 | 5 | 27 | setter | https://github.com/mitmproxy/mitmproxy.git | make it black! | 40 | 0 | 73,730 | 11 |
|
3 | 8 | def sqrtdenest(expr, max_iter=3):
expr = expand_mul(expr)
for i in range(max_iter):
z = _sqrtdenest0(expr)
if expr == z:
return expr
expr = z
return expr
| sympy/simplify/sqrtdenest.py | 69 | sympy | {
"docstring": "Denests sqrts in an expression that contain other square roots\n if possible, otherwise returns the expr unchanged. This is based on the\n algorithms of [1].\n\n Examples\n ========\n\n >>> from sympy.simplify.sqrtdenest import sqrtdenest\n >>> from sympy import sqrt\n >>> sqrtdenest(sqrt(5 + 2 * sqrt(6)))\n sqrt(2) + sqrt(3)\n\n See Also\n ========\n\n sympy.solvers.solvers.unrad\n\n References\n ==========\n\n .. [1] http://researcher.watson.ibm.com/researcher/files/us-fagin/symb85.pdf\n\n .. [2] D. J. Jeffrey and A. D. Rich, 'Symplifying Square Roots of Square Roots\n by Denesting' (available at http://www.cybertester.com/data/denest.pdf)\n\n ",
"language": "en",
"n_whitespaces": 133,
"n_words": 75,
"vocab_size": 63
} | 24 | Python | 16 | 2a1afca9477eb781f16d5d6b63fa37abed7740a3 | sqrtdenest.py | 198,306 | 8 | 42 | sqrtdenest | https://github.com/sympy/sympy.git | Use sympify less | 68 | 0 | 48,866 | 10 |
|
1 | 5 | def as_hashes(self) -> Hashes:
return Hashes({self.name: [self.value]})
| src/pip/_internal/models/link.py | 39 | pip | {
"docstring": "Return a Hashes instance which checks only for the current hash.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 7 | Python | 7 | bad03ef931d9b3ff4f9e75f35f9c41f45839e2a1 | link.py | 174,923 | 3 | 23 | as_hashes | https://github.com/pypa/pip.git | Use data-dist-info-metadata (PEP 658) to decouple resolution from downloading (#11111)
Co-authored-by: Tzu-ping Chung <[email protected]> | 21 | 0 | 41,518 | 11 |
|
1 | 6 | def plextv_resources_two_servers_fixture():
return load_fixture("plex/plextv_resources_two_servers.xml")
@pytest.fixture(name="plextv_shared_users", scope="session") | tests/components/plex/conftest.py | 46 | @pytest.fixture(name="plextv_shared_users", scope="session") | core | {
"docstring": "Load two-server payload for plex.tv resources and return it.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 6 | Python | 6 | 10195dc700770cdfdeaff79c53cf5d1d763f20c6 | conftest.py | 308,798 | 2 | 10 | plextv_resources_two_servers_fixture | https://github.com/home-assistant/core.git | Improve server selection for Plex config flows (#63408) | 11 | 1 | 107,536 | 8 |
2 | 15 | def remove_signature_from_binary(filename):
logger.debug("Removing signature from file %r", filename)
cmd_args = ['codesign', '--remove', '--all-architectures', filename]
p = subprocess.run(cmd_args, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
if p.returncode:
raise SystemError(f"codesign command ({cmd_args}) failed with error code {p.returncode}!\noutput: {p.stdout}")
| PyInstaller/utils/osx.py | 118 | pyinstaller | {
"docstring": "\n Remove the signature from all architecture slices of the given binary file using the codesign utility.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 16,
"vocab_size": 14
} | 32 | Python | 31 | 1cd3b73e2939052271a0bc26cf204eebee4dcd15 | osx.py | 262,773 | 6 | 60 | remove_signature_from_binary | https://github.com/pyinstaller/pyinstaller.git | macOS: Remove the timeouts for codesigning/signature stripping/lipo. (#6644) | 54 | 0 | 77,359 | 12 |
|
5 | 24 | def _get_child_layer_node_ids(self, node_id):
# Sequential and Functional track layers with names following the format
# "layer-N". Use this to generate the list of layers.
num_layers = 0
child_layers = {}
pattern = re.compile("layer-(\\d+)")
for child in self._proto.nodes[node_id].children:
m = pattern.match(child.local_name)
if m is None:
continue
layer_n = int(m.group(1))
num_layers = max(layer_n + 1, num_layers)
child_layers[layer_n] = child.node_id
ordered = []
for n in range(num_layers):
child = child_layers.get(n)
if child is None:
break
ordered.append(child)
return ordered
| keras/saving/saved_model/load.py | 191 | keras | {
"docstring": "Returns the node ids of each layer in a Sequential/Functional model.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 75 | Python | 55 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | load.py | 276,014 | 18 | 116 | _get_child_layer_node_ids | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 263 | 0 | 81,524 | 12 |
|
1 | 16 | def test_token_node_empty_csrf_cookie(self):
req = self._get_request(cookie="")
mw = CsrfViewMiddleware(token_view)
mw.process_view(req, token_view, (), {})
resp = token_view(req)
token = get_token(req)
self.assertIsNotNone(token)
csrf_secret = _unmask_cipher_token(token)
self._check_token_present(resp, csrf_secret)
| tests/csrf_tests/tests.py | 113 | django | {
"docstring": "\n A new token is sent if the csrf_cookie is the empty string.\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 12,
"vocab_size": 10
} | 24 | Python | 20 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | tests.py | 202,366 | 9 | 68 | test_token_node_empty_csrf_cookie | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 87 | 0 | 50,084 | 10 |
|
15 | 25 | def parse_tag(self, tag):
subtags = tag.split("-")
lang = {}
labels = ["language", "extlang", "script", "region", "variant", "variant"]
while subtags and labels:
subtag = subtags.pop(0)
found = False
while labels:
label = labels.pop(0)
subtag = self.casing[label](subtag)
if self.format[label].fullmatch(subtag):
if subtag in self.db[label]:
found = True
valstr = self.val2str(self.db[label][subtag]["Description"])
if label == "variant" and label in lang:
lang[label] += ": " + valstr
else:
lang[label] = valstr
break
elif subtag in self.db["deprecated"][label]:
found = True
note = f"The {subtag!r} {label} code is deprecated"
if "Preferred-Value" in self.db["deprecated"][label][subtag]:
prefer = self.db["deprecated"][label][subtag][
"Preferred-Value"
]
note += f"', prefer '{self.val2str(prefer)}'"
lang[label] = self.val2str(
self.db["deprecated"][label][subtag]["Description"]
)
warn(note)
break
if not found:
if subtag == "u" and subtags[0] == "sd": # CLDR regional subdivisions
sd = subtags[1]
if sd in self.subdiv:
ext = self.subdiv[sd]
else:
ext = f"<Unknown subdivision: {ext}>"
else: # other extension subtags are not supported yet
ext = f"{subtag}{''.join(['-'+ext for ext in subtags])}".lower()
if not self.format["singleton"].fullmatch(subtag):
ext = f"<Invalid extension: {ext}>"
warn(ext)
lang["extension"] = ext
subtags = []
return lang
| nltk/corpus/reader/bcp47.py | 598 | nltk | {
"docstring": "Convert a BCP-47 tag to a dictionary of labelled subtags",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | 166 | Python | 94 | f019fbedb3d2b6a2e6b58ec1b38db612b106568b | bcp47.py | 42,591 | 47 | 318 | parse_tag | https://github.com/nltk/nltk.git | Support both iso639-3 codes and BCP-47 language tags (#3060)
* Add support for iso639-3 language codes
* Add support for retired language codes
* Move langnames.py to the top-level
* Add langcode() function
* Add iso639retired dictionary
* Improve wrapper functions
* Add module docstring with doctest
* Add 2-letter language codes
* Add regular expression check
* Improve inverse lookup of retired codes
* Support BCP-47
* Avoid deprecated langcodes
* Set stack level for warnings to warn on the langname call
Now it throws e.g.
```
...\nltk_3060.py:9: UserWarning: Shortening 'smo' to 'sm'
print(f"{lang}: {langname(code)}")
```
Rather than
```
...\nltk\langnames.py:64: UserWarning: Shortening zha to za
warn(f"Shortening {code} to {code2}")
```
* Dict key membership is equivalent to dict membership
* Resolve bug: subtag -> tag
* Capitalize BCP47 in CorpusReader name
* Reimplement removed type hint changes from #3081
Co-authored-by: Tom Aarsen <[email protected]> | 1,053 | 0 | 7,646 | 22 |
|
3 | 21 | def test_recurrent_unroll_and_filter(self):
inputs = TensorDict(
{
"in": torch.arange(B * T * 2).reshape(B, T, 2),
"bork": torch.arange(5 * 4).reshape(5, 4),
}
)
states = TensorDict(
{
"in": torch.arange(B * 4).reshape(B, 4),
"bork": torch.arange(5 * 4).reshape(5, 4),
}
)
outputs, out_states = SimpleRecurrentModel(ModelConfig()).unroll(inputs, states)
desired = TensorDict({"out": torch.arange(B * T * 3).reshape(B, T, 3)})
desired_states = TensorDict({"out": torch.arange(B * 5).reshape(B, 5)})
for k in outputs.flatten().keys() | desired.flatten().keys():
check(outputs[k], desired[k])
for k in out_states.flatten().keys() | desired_states.flatten().keys():
check(out_states[k], desired_states[k])
| rllib/models/tests/test_torch_model.py | 374 | ray | {
"docstring": "Ensures unused inputs are filtered out before _unroll and that\n outputs are correct.",
"language": "en",
"n_whitespaces": 19,
"n_words": 13,
"vocab_size": 12
} | 75 | Python | 44 | b0945548e874642287b81514b71432a2330de1d3 | test_torch_model.py | 128,542 | 20 | 235 | test_recurrent_unroll_and_filter | https://github.com/ray-project/ray.git | [RLlib] Add torch models (#29043)
1. converted class attributes to setters
2. use override decorator
3. SimpleModel should not have any T dimension, it can confuse folks. So I removed it.
4. I merged all the unittests under one class and separated them by methods names. It will be easier to use -k filter to run pytests later if we don't allow repetative method names.
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
Signed-off-by: Steven Morad <[email protected]> | 271 | 0 | 28,737 | 16 |
|
8 | 18 | def _shard(self, num_shards=None, index=None, contiguous=False):
if num_shards is None:
num_shards = dist.get_world_size()
if index is None:
index = dist.get_rank()
if contiguous:
div = len(self) // num_shards
mod = len(self) % num_shards
start = div * index + min(index, mod)
end = start + div + (1 if index < mod else 0)
new_data = [self.new_data[idx] for idx in range(start, end)]
else:
new_data = [
self.new_data[idx] for idx in range(len(self.new_data))
if idx % num_shards == index
]
return MapDataset(new_data)
| paddlenlp/datasets/dataset.py | 220 | PaddleNLP | {
"docstring": "\n Split the dataset into `num_shards` pieces. Note that the size of each\n shard might be different because the original dataset may not be evenly\n divisible.\n\n Args:\n num_shards (int, optional): An integer representing the number of\n data shards. If None, `num_shards` would be number of trainers.\n Defaults to `None`.\n index (int, optional): An integer representing the index of the\n current shard. If None, `index` would be the current trainer rank\n id. Defaults to `None`.\n contiguous: (bool, optional): If true, contiguous chunks of data \n will be select for sharding. And total number of examples will \n be the same. Otherwise each shard will contain all examples of \n dataset whose index mod `num_shards` = `index`. Defaults to `False`.\n ",
"language": "en",
"n_whitespaces": 291,
"n_words": 114,
"vocab_size": 66
} | 78 | Python | 44 | 1c10abadb7c960e58ce44813f6197dfca9cbd28d | dataset.py | 322,238 | 17 | 141 | _shard | https://github.com/PaddlePaddle/PaddleNLP.git | Integrate HF Datasets and add DatasetTuple (#1612)
* fix bart perf
* update fastergeneration doc
* add img
* add img
* change img
* update img
* fix img
* update docs
* fix readme
* update readme
* fix perf
* fix perf
* fix modelling
* fix perf and sample code
* fix perf
* fix perf
* fix seq_len for gpt_sample
* add forced eos token id for faster
* upgrade perf and add forced eos token id
* chenge stack to gather
* add auto perf
* minor fix
* remove encoder change
* Update bart_perf.py
* Update bart_perf.py
* 1. Integrate HF Datasets
2. return all splits by default
3. load_dataset returns DatasetTuple now
* add HF Dataset example
* add kwargs for HF load_dataset
* change datasets to alternative
* remove experimental | 249 | 0 | 118,103 | 16 |
|
3 | 19 | def _register_arrow_data_serializer(serialization_context):
import pyarrow as pa
if os.environ.get(RAY_DISABLE_CUSTOM_ARROW_DATA_SERIALIZATION, "0") == "1":
return
# Register custom reducer for Arrow Arrays.
array_types = _get_arrow_array_types()
for array_type in array_types:
serialization_context._register_cloudpickle_reducer(
array_type, _arrow_array_reduce
)
# Register custom reducer for Arrow ChunkedArrays.
serialization_context._register_cloudpickle_reducer(
pa.ChunkedArray, _arrow_chunkedarray_reduce
)
# Register custom reducer for Arrow RecordBatches.
serialization_context._register_cloudpickle_reducer(
pa.RecordBatch, _arrow_recordbatch_reduce
)
# Register custom reducer for Arrow Tables.
serialization_context._register_cloudpickle_reducer(pa.Table, _arrow_table_reduce)
| python/ray/data/_internal/arrow_serialization.py | 124 | ray | {
"docstring": "Custom reducer for Arrow data that works around a zero-copy slicing pickling\n bug by using the Arrow IPC format for the underlying serialization.\n\n Background:\n Arrow has both array-level slicing and buffer-level slicing; both are zero-copy,\n but the former has a serialization bug where the entire buffer is serialized\n instead of just the slice, while the latter's serialization works as expected\n and only serializes the slice of the buffer. I.e., array-level slicing doesn't\n propagate the slice down to the buffer when serializing the array.\n\n All that these copy methods do is, at serialization time, take the array-level\n slicing and translate them to buffer-level slicing, so only the buffer slice is\n sent over the wire instead of the entire buffer.\n\n See https://issues.apache.org/jira/browse/ARROW-10739.\n ",
"language": "en",
"n_whitespaces": 188,
"n_words": 120,
"vocab_size": 75
} | 61 | Python | 38 | c1d62d46495f0157faf3168aa87eed350802e10f | arrow_serialization.py | 128,560 | 16 | 73 | _register_arrow_data_serializer | https://github.com/ray-project/ray.git | [Datasets] Arrow 7.0.0+ Support: Use Arrow IPC format for pickling Arrow data to circumvent slice view buffer truncation bug. (#29055)
This PR registers a custom serializer for Array arrays, chunked arrays, record batches, and tables that works around an Arrow serialization bug that serializes the entire underlying data buffer when serializing zero-copy slice views. The custom serializer uses the Arrow IPC format as the underlying pickled representation. | 149 | 0 | 28,745 | 9 |
|
1 | 2 | def csrc(self):
return self["csrc"]
| packages/python/plotly/plotly/graph_objs/_scatterternary.py | 22 | plotly.py | {
"docstring": "\n Sets the source reference on Chart Studio Cloud for `c`.\n\n The 'csrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n ",
"language": "en",
"n_whitespaces": 77,
"n_words": 27,
"vocab_size": 25
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _scatterternary.py | 228,160 | 2 | 11 | csrc | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 59,833 | 7 |
|
1 | 12 | def test_size_hint(view):
view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test1'))
height1 = view.sizeHint().height()
assert height1 > 0
view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test2'))
height2 = view.sizeHint().height()
assert height2 == height1 * 2
| tests/unit/mainwindow/test_messageview.py | 122 | qutebrowser | {
"docstring": "The message height should increase with more messages.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 22 | Python | 15 | 5616a99eff34f7074641d1391ed77d6b4b743529 | test_messageview.py | 320,927 | 7 | 72 | test_size_hint | https://github.com/qutebrowser/qutebrowser.git | Add a MessageInfo data class
Preparation for #7246 | 43 | 0 | 117,440 | 11 |
|
6 | 47 | def handler(request, operation, current_url):
if operation != QNetworkAccessManager.Operation.GetOperation:
return networkreply.ErrorNetworkReply(
request, "Unsupported request type",
QNetworkReply.NetworkError.ContentOperationNotPermittedError)
url = request.url()
if ((url.scheme(), url.host(), url.path()) ==
('qute', 'settings', '/set')):
if current_url != QUrl('qute://settings/'):
log.network.warning("Blocking malicious request from {} to {}"
.format(current_url.toDisplayString(),
url.toDisplayString()))
return networkreply.ErrorNetworkReply(
request, "Invalid qute://settings request",
QNetworkReply.NetworkError.ContentAccessDenied)
try:
mimetype, data = qutescheme.data_for_url(url)
except qutescheme.Error as e:
errors = {
qutescheme.NotFoundError:
QNetworkReply.NetworkError.ContentNotFoundError,
qutescheme.UrlInvalidError:
QNetworkReply.NetworkError.ContentOperationNotPermittedError,
qutescheme.RequestDeniedError:
QNetworkReply.NetworkError.ContentAccessDenied,
qutescheme.SchemeOSError:
QNetworkReply.NetworkError.ContentNotFoundError,
qutescheme.Error:
QNetworkReply.NetworkError.InternalServerError,
}
exctype = type(e)
log.misc.error("{} while handling qute://* URL".format(
exctype.__name__))
return networkreply.ErrorNetworkReply(request, str(e), errors[exctype])
except qutescheme.Redirect as e:
qtutils.ensure_valid(e.url)
return networkreply.RedirectNetworkReply(e.url)
return networkreply.FixedDataNetworkReply(request, data, mimetype)
| qutebrowser/browser/webkit/network/webkitqutescheme.py | 418 | qutebrowser | {
"docstring": "Scheme handler for qute:// URLs.\n\n Args:\n request: QNetworkRequest to answer to.\n operation: The HTTP operation being done.\n current_url: The page we're on currently.\n\n Return:\n A QNetworkReply.\n ",
"language": "en",
"n_whitespaces": 63,
"n_words": 26,
"vocab_size": 25
} | 93 | Python | 76 | 0877fb0d78635692e481c8bde224fac5ad0dd430 | webkitqutescheme.py | 321,172 | 38 | 264 | handler | https://github.com/qutebrowser/qutebrowser.git | Run scripts/dev/rewrite_enums.py | 483 | 0 | 117,576 | 15 |
|
2 | 4 | def get(self, key, default_value=None):
if key in self:
return self[key]
else:
return default_value
| pipenv/patched/notpip/_vendor/pyparsing/results.py | 42 | pipenv | {
"docstring": "\n Returns named result matching the given key, or if there is no\n such name, then returns the given ``default_value`` or ``None`` if no\n ``default_value`` is specified.\n\n Similar to ``dict.get()``.\n\n Example::\n\n integer = Word(nums)\n date_str = integer(\"year\") + '/' + integer(\"month\") + '/' + integer(\"day\")\n\n result = date_str.parse_string(\"1999/12/31\")\n print(result.get(\"year\")) # -> '1999'\n print(result.get(\"hour\", \"not specified\")) # -> 'not specified'\n print(result.get(\"hour\")) # -> None\n ",
"language": "en",
"n_whitespaces": 171,
"n_words": 62,
"vocab_size": 44
} | 13 | Python | 12 | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | results.py | 20,619 | 5 | 26 | get | https://github.com/pypa/pipenv.git | 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 | 56 | 0 | 3,457 | 8 |
|
1 | 23 | def huber(y_true, y_pred, delta=1.0):
y_pred = tf.cast(y_pred, dtype=backend.floatx())
y_true = tf.cast(y_true, dtype=backend.floatx())
delta = tf.cast(delta, dtype=backend.floatx())
error = tf.subtract(y_pred, y_true)
abs_error = tf.abs(error)
half = tf.convert_to_tensor(0.5, dtype=abs_error.dtype)
return backend.mean(
tf.where(
abs_error <= delta,
half * tf.square(error),
delta * abs_error - half * tf.square(delta),
),
axis=-1,
)
@keras_export(
"keras.losses.log_cosh",
"keras.losses.logcosh",
"keras.metrics.log_cosh",
"keras.metrics.logcosh",
)
@tf.__internal__.dispatch.add_dispatch_support | keras/losses.py | 243 | @keras_export(
"keras.losses.log_cosh",
"keras.losses.logcosh",
"keras.metrics.log_cosh",
"keras.metrics.logcosh",
)
@tf.__internal__.dispatch.add_dispatch_support | keras | {
"docstring": "Computes Huber loss value.\n\n For each value x in `error = y_true - y_pred`:\n\n ```\n loss = 0.5 * x^2 if |x| <= d\n loss = d * |x| - 0.5 * d^2 if |x| > d\n ```\n where d is `delta`. See: https://en.wikipedia.org/wiki/Huber_loss\n\n Args:\n y_true: tensor of true targets.\n y_pred: tensor of predicted targets.\n delta: A float, the point where the Huber loss function changes from a\n quadratic to linear.\n\n Returns:\n Tensor with one scalar loss entry per sample.\n ",
"language": "en",
"n_whitespaces": 158,
"n_words": 80,
"vocab_size": 57
} | 53 | Python | 38 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | losses.py | 274,548 | 15 | 139 | huber | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 143 | 1 | 81,226 | 13 |
2 | 12 | def print_help(self):
id_string = ""
for s_id, sub_dict in self.current_series.items():
id_string += f" [cyan]{s_id.upper()}[/cyan] : {sub_dict['title']}"
help_string = f
t_console.print(help_string)
| gamestonk_terminal/economy/fred/prediction/pred_controller.py | 96 | OpenBBTerminal | {
"docstring": "Print help\nPrediction Techniques Menu:\n\n load load new series\n\nSelected Series (starting from [green]{self.start_date}[/green]):\n{id_string}\n\nModels:\n ets exponential smoothing (e.g. Holt-Winters)\n knn k-Nearest Neighbors\n regression polynomial regression\n arima autoregressive integrated moving average\n mlp MultiLayer Perceptron\n rnn Recurrent Neural Network\n lstm Long-Short Term Memory\n conv1d 1D Convolutional Neural Network\n mc Monte-Carlo simulations\n ",
"language": "en",
"n_whitespaces": 151,
"n_words": 51,
"vocab_size": 47
} | 20 | Python | 18 | f5b0dc8e7b5ae7ed3a4b175ba48aba0d5ea9d2db | pred_controller.py | 281,032 | 24 | 36 | print_help | https://github.com/OpenBB-finance/OpenBBTerminal.git | Add prediction to economy/fred (#1133) | 69 | 0 | 83,477 | 12 |
|
4 | 20 | def __call__(self, _metric=None, **kwargs):
assert self._last_report_time is not None, (
"StatusReporter._start() must be called before the first "
"report __call__ is made to ensure correct runtime metrics."
)
if _metric:
kwargs[DEFAULT_METRIC] = _metric
# time per iteration is recorded directly in the reporter to ensure
# any delays in logging results aren't counted
report_time = time.time()
if TIME_THIS_ITER_S not in kwargs:
kwargs[TIME_THIS_ITER_S] = report_time - self._last_report_time
self._last_report_time = report_time
# add results to a thread-safe queue
self._queue.put(kwargs.copy(), block=True)
# This blocks until notification from the FunctionRunner that the last
# result has been returned to Tune and that the function is safe to
# resume training.
self._continue_semaphore.acquire()
# If the trial should be terminated, exit gracefully.
if self._end_event.is_set():
self._end_event.clear()
sys.exit(0)
| python/ray/tune/function_runner.py | 182 | ray | {
"docstring": "Report updated training status.\n\n Pass in `done=True` when the training job is completed.\n\n Args:\n kwargs: Latest training result status.\n\n Example:\n >>> reporter(mean_accuracy=1, training_iteration=4)\n >>> reporter(mean_accuracy=1, training_iteration=4, done=True)\n\n Raises:\n StopIteration: A StopIteration exception is raised if the trial has\n been signaled to stop.\n ",
"language": "en",
"n_whitespaces": 136,
"n_words": 42,
"vocab_size": 35
} | 120 | Python | 86 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | function_runner.py | 132,209 | 16 | 107 | __call__ | https://github.com/ray-project/ray.git | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 305 | 0 | 29,699 | 10 |
|
2 | 14 | def register_plotting_backend_cb(key) -> None:
if key == "matplotlib":
# We defer matplotlib validation, since it's the default
return
from pandas.plotting._core import _get_plot_backend
_get_plot_backend(key)
with cf.config_prefix("plotting"):
cf.register_option(
"backend",
defval="matplotlib",
doc=plotting_backend_doc,
validator=register_plotting_backend_cb,
)
register_converter_doc =
| pandas/core/config_init.py | 97 | pandas | {
"docstring": "\n: bool or 'auto'.\n Whether to register converters with matplotlib's units registry for\n dates, times, datetimes, and Periods. Toggling to False will remove\n the converters, restoring any converters that pandas overwrote.\n",
"language": "en",
"n_whitespaces": 39,
"n_words": 31,
"vocab_size": 29
} | 33 | Python | 33 | 9612375ca28ade056f15d4338f1bfde5d045c9fc | config_init.py | 167,696 | 5 | 25 | register_plotting_backend_cb | https://github.com/pandas-dev/pandas.git | TYP: return values in core/*.py (#47587)
* TYP: return values in core/*.py
* fix test
* to_html
* to_html part 2
* DataFrame.query
* more overloads
* fix query?
* increase stacklevel by one
* fix rename_axis
* and an overload for DataFrame.eval
* address comments
* fix typevar | 88 | 0 | 40,080 | 9 |
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