ast_errors
stringlengths 0
3.2k
| d_id
int64 44
121k
| id
int64 70
338k
| n_whitespaces
int64 3
14k
| path
stringlengths 8
134
| n_words
int64 4
4.82k
| n_identifiers
int64 1
131
| random_cut
stringlengths 16
15.8k
| commit_message
stringlengths 2
15.3k
| fun_name
stringlengths 1
84
| commit_id
stringlengths 40
40
| repo
stringlengths 3
28
| file_name
stringlengths 5
79
| ast_levels
int64 6
31
| nloc
int64 1
548
| url
stringlengths 31
59
| complexity
int64 1
66
| token_counts
int64 6
2.13k
| n_ast_errors
int64 0
28
| vocab_size
int64 4
1.11k
| n_ast_nodes
int64 15
19.2k
| language
stringclasses 1
value | documentation
dict | code
stringlengths 101
62.2k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
18,905 | 92,382 | 121 | src/sentry/sentry_metrics/indexer/base.py | 33 | 13 | def get_mapped_key_strings_to_ints(self) -> MutableMapping[str, int]:
cache_ | feat(metrics_indexer): Add rate limits functionality to indexer [INGEST-1380] (#36263)
* feat(metrics_indexer): Add rate limits functionality to indexer [INGEST-1380]
The postgres string indexer now is able to rate limit writes using four
sentry options. If that happens, `None` is returned in place of an
integer, and the FetchType is RATE_LIMITED.
The kafka consumer/message processor explicitly checks for those `None`
values and throws away every message that references a rate-limited
string. It logs a Sentry error for every dropped message just because
that's already what we do for other kinds of dropped messages.
Rate limiting and quota management currently creates a ton of
dataclasses and that probably wastes time. There are a ton of
low-hanging fruits:
* the return value of _construct_quotas could be globally cached, as
long as the cache is wiped when the sentry options change.
* the same Quota object (for global limits) is referenced from multiple
RequestedQuota instances (one for each org).
`sentry.ratelimits.sliding_windows` could check the `id()` of the
quota (if there is no prefix override) to avoid computing and checking
the same quota multiple times.
An even lower hanging fruit is that we're fetching the same keys from
Redis multiple times, because multiple organizations (and therefore
multiple RequestedQuota instances) adhere to the global quota. So that's
been fixed, but as for the rest let's wait for timings from prod.
* fix typo
* fix typing
* apply review feedback
* fix typing, add test
* fix tests
* apply review feedback about logging too many msgs
* fix leaking option in test
* sike, more test failures | get_mapped_key_strings_to_ints | c4cc0467974bcfb2b3c95120bd19c337aa977183 | sentry | base.py | 13 | 18 | https://github.com/getsentry/sentry.git | 4 | 66 | 0 | 26 | 111 | Python | {
"docstring": "\n Return the results, but formatted as the following:\n {\n \"1:a\": 10,\n \"1:b\": 11,\n \"1:c\", 12,\n \"2:e\": 13\n }\n This is for when we use indexer_cache.set_many()\n ",
"language": "en",
"n_whitespaces": 129,
"n_words": 25,
"vocab_size": 24
} | def get_mapped_key_strings_to_ints(self) -> MutableMapping[str, int]:
cache_key_results: MutableMapping[str, int] = {}
for org_id, result_dict in self.results.items():
for string, id in result_dict.items():
key = f"{org_id}:{string}"
if id is not None:
cache_key_results[key] = id
return cache_key_results
|
|
56,992 | 223,585 | 71 | python3.10.4/Lib/email/_header_value_parser.py | 29 | 12 | def get_atext(value):
m = _non_atom_end_matcher(value)
if not m:
raise errors.HeaderParseError(
"expected atext but found '{}'".format(value))
atext = m.group()
value = value[len(atext):]
atext = ValueTerminal(atext, 'atext')
_validate_xtext(atext)
return atext, | add python 3.10.4 for windows | get_atext | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | _header_value_parser.py | 12 | 10 | https://github.com/XX-net/XX-Net.git | 2 | 61 | 0 | 23 | 106 | Python | {
"docstring": "atext = <matches _atext_matcher>\n\n We allow any non-ATOM_ENDS in atext, but add an InvalidATextDefect to\n the token's defects list if we find non-atext characters.\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 24,
"vocab_size": 24
} | def get_atext(value):
m = _non_atom_end_matcher(value)
if not m:
raise errors.HeaderParseError(
"expected atext but found '{}'".format(value))
atext = m.group()
value = value[len(atext):]
atext = ValueTerminal(atext, 'atext')
_validate_xtext(atext)
return atext, value
|
|
3,328 | 20,336 | 20 | pipenv/patched/notpip/_vendor/pygments/formatters/img.py | 6 | 5 | def _get_linenumber_pos(self, lineno):
retur | 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 | _get_linenumber_pos | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | img.py | 8 | 2 | https://github.com/pypa/pipenv.git | 1 | 21 | 0 | 6 | 34 | Python | {
"docstring": "\n Get the actual position for the start of a line number.\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 10
} | def _get_linenumber_pos(self, lineno):
return (self.image_pad, self._get_line_y(lineno))
|
|
15,780 | 71,919 | 1,056 | wagtail/admin/tests/test_contentstate.py | 111 | 10 | def test_image_inside_paragraph(self):
| Reformat with black | test_image_inside_paragraph | d10f15e55806c6944827d801cd9c2d53f5da4186 | wagtail | test_contentstate.py | 16 | 52 | https://github.com/wagtail/wagtail.git | 1 | 181 | 0 | 72 | 347 | Python | {
"docstring": "\n <p>before <embed embedtype=\"image\" alt=\"an image\" id=\"1\" format=\"left\" /> after</p>\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 9,
"vocab_size": 9
} | def test_image_inside_paragraph(self):
# In Draftail's data model, images are block-level elements and therefore
# split up preceding / following text into their own paragraphs
converter = ContentstateConverter(features=["image"])
result = json.loads(
converter.from_database_format(
)
)
self.assertContentStateEqual(
result,
{
"blocks": [
{
"key": "00000",
"inlineStyleRanges": [],
"entityRanges": [],
"depth": 0,
"text": "before",
"type": "unstyled",
},
{
"key": "00000",
"inlineStyleRanges": [],
"entityRanges": [{"key": 0, "offset": 0, "length": 1}],
"depth": 0,
"text": " ",
"type": "atomic",
},
{
"key": "00000",
"inlineStyleRanges": [],
"entityRanges": [],
"depth": 0,
"text": "after",
"type": "unstyled",
},
],
"entityMap": {
"0": {
"data": {
"format": "left",
"alt": "an image",
"id": "1",
"src": "/media/not-found",
},
"mutability": "IMMUTABLE",
"type": "IMAGE",
}
},
},
)
|
|
76,515 | 260,816 | 216 | sklearn/svm/_bounds.py | 93 | 26 | def l1_min_c(X, y, *, loss="squared_hinge", fit_intercept=True, intercept_scaling=1.0):
if loss not in ("squared_hinge", "log"):
raise ValueError('loss type not in ("squared_hinge", "log")')
X = check_array(X, accept_sparse="csc")
check_consistent_length(X, y)
Y = LabelBinarizer(neg_label=-1).fit_transform(y).T
# maximum absolute value over classes and features
den = np.max(np.abs(safe_sparse_dot(Y, X)))
if fit_intercept:
bias = np.full(
(np.size(y), 1), intercept_scaling, dtype=np.array(intercept_scaling).dtype
)
den = max(den, abs(np.dot(Y, bias)).max())
if den == 0.0:
raise ValueError(
"Ill-posed l1_min_c calculation: l1 will always "
"select zero coefficients for this data"
)
if loss == "squared_hinge":
return 0.5 / den
else: # loss == | DOC Ensures that l1_min_c passes numpydoc validation (#24134) | l1_min_c | 6d16698dd8ba4407e5c3c588d7b5e6a5257eddc9 | scikit-learn | _bounds.py | 16 | 21 | https://github.com/scikit-learn/scikit-learn.git | 5 | 176 | 0 | 70 | 276 | Python | {
"docstring": "Return the lowest bound for C.\n\n The lower bound for C is computed such that for C in (l1_min_C, infinity)\n the model is guaranteed not to be empty. This applies to l1 penalized\n classifiers, such as LinearSVC with penalty='l1' and\n linear_model.LogisticRegression with penalty='l1'.\n\n This value is valid if class_weight parameter in fit() is not set.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training vector, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n y : array-like of shape (n_samples,)\n Target vector relative to X.\n\n loss : {'squared_hinge', 'log'}, default='squared_hinge'\n Specifies the loss function.\n With 'squared_hinge' it is the squared hinge loss (a.k.a. L2 loss).\n With 'log' it is the loss of logistic regression models.\n\n fit_intercept : bool, default=True\n Specifies if the intercept should be fitted by the model.\n It must match the fit() method parameter.\n\n intercept_scaling : float, default=1.0\n When fit_intercept is True, instance vector x becomes\n [x, intercept_scaling],\n i.e. a \"synthetic\" feature with constant value equals to\n intercept_scaling is appended to the instance vector.\n It must match the fit() method parameter.\n\n Returns\n -------\n l1_min_c : float\n Minimum value for C.\n ",
"language": "en",
"n_whitespaces": 336,
"n_words": 190,
"vocab_size": 121
} | def l1_min_c(X, y, *, loss="squared_hinge", fit_intercept=True, intercept_scaling=1.0):
if loss not in ("squared_hinge", "log"):
raise ValueError('loss type not in ("squared_hinge", "log")')
X = check_array(X, accept_sparse="csc")
check_consistent_length(X, y)
Y = LabelBinarizer(neg_label=-1).fit_transform(y).T
# maximum absolute value over classes and features
den = np.max(np.abs(safe_sparse_dot(Y, X)))
if fit_intercept:
bias = np.full(
(np.size(y), 1), intercept_scaling, dtype=np.array(intercept_scaling).dtype
)
den = max(den, abs(np.dot(Y, bias)).max())
if den == 0.0:
raise ValueError(
"Ill-posed l1_min_c calculation: l1 will always "
"select zero coefficients for this data"
)
if loss == "squared_hinge":
return 0.5 / den
else: # loss == 'log':
return 2.0 / den
|
|
7,856 | 43,184 | 89 | airflow/migrations/versions/0111_2_3_3_add_indexes_for_cascade_deletes.py | 32 | 10 | def _mysql_tables_where_indexes_already_present(conn):
to_check = [
('xcom', 'idx_xcom_task_instance'),
('task_reschedule', 'idx_task_reschedule_dag_run'),
| Add indexes for CASCADE deletes for task_instance (#24488)
When we add foreign keys with ON DELETE CASCADE, and we delete rows in the foreign table, the database needs to join back to the referencing table. If there's no suitable index, then it can be slow to perform the deletes. | _mysql_tables_where_indexes_already_present | 677c42227c08f705142f298ab88915f133cd94e5 | airflow | 0111_2_3_3_add_indexes_for_cascade_deletes.py | 13 | 11 | https://github.com/apache/airflow.git | 3 | 61 | 0 | 29 | 115 | Python | {
"docstring": "\n If user downgraded and is upgrading again, we have to check for existing\n indexes on mysql because we can't (and don't) drop them as part of the\n downgrade.\n ",
"language": "en",
"n_whitespaces": 41,
"n_words": 28,
"vocab_size": 27
} | def _mysql_tables_where_indexes_already_present(conn):
to_check = [
('xcom', 'idx_xcom_task_instance'),
('task_reschedule', 'idx_task_reschedule_dag_run'),
('task_fail', 'idx_task_fail_task_instance'),
]
tables = set()
for tbl, idx in to_check:
if conn.execute(f"show indexes from {tbl} where Key_name = '{idx}'").first():
tables.add(tbl)
return tables
|
|
70,986 | 246,075 | 548 | tests/http/test_webclient.py | 103 | 36 | def test_webclient_resolves_with_client_resource(self):
for resource_name_order_list in [
["webclient", "client"],
["client", "webclient"],
]:
# Create a dictionary from path regex -> resource
resource_dict: Dict[str, Resource] = {}
| Add a regression test for using both webclient and client resources simultaneously (#11765) | test_webclient_resolves_with_client_resource | 121b9e2475f4d7b3bca50d81732f07db80b2264f | synapse | test_webclient.py | 18 | 30 | https://github.com/matrix-org/synapse.git | 3 | 150 | 0 | 79 | 241 | Python | {
"docstring": "\n Tests that both client and webclient resources can be accessed simultaneously.\n\n This is a regression test created in response to https://github.com/matrix-org/synapse/issues/11763.\n ",
"language": "en",
"n_whitespaces": 43,
"n_words": 21,
"vocab_size": 21
} | def test_webclient_resolves_with_client_resource(self):
for resource_name_order_list in [
["webclient", "client"],
["client", "webclient"],
]:
# Create a dictionary from path regex -> resource
resource_dict: Dict[str, Resource] = {}
for resource_name in resource_name_order_list:
resource_dict.update(
SynapseHomeServer._configure_named_resource(self.hs, resource_name)
)
# Create a root resource which ties the above resources together into one
root_resource = Resource()
create_resource_tree(resource_dict, root_resource)
# Create a site configured with this resource to make HTTP requests against
listener_config = ListenerConfig(
port=8008,
bind_addresses=["127.0.0.1"],
type="http",
http_options=HttpListenerConfig(
resources=[HttpResourceConfig(names=resource_name_order_list)]
),
)
test_site = SynapseSite(
logger_name="synapse.access.http.fake",
site_tag=self.hs.config.server.server_name,
config=listener_config,
resource=root_resource,
server_version_string="1",
max_request_body_size=1234,
reactor=self.reactor,
)
# Attempt to make requests to endpoints on both the webclient and client resources
# on test_site.
self._request_client_and_webclient_resources(test_site)
|
|
33,921 | 147,365 | 65 | python/ray/cloudpickle/cloudpickle.py | 32 | 10 | def unregister_pickle_by_value(module):
if not isinstance(module, types.ModuleType):
raise ValueError(f"Input should be a module object, got {str(module)} instead")
if module.__name__ not in _PICKLE_BY_VALUE_MODULES:
| [docs] fix doctests and activate CI (#23418) | unregister_pickle_by_value | 60054995e65304fb14e6d0ab69bdec07aa9389fe | ray | cloudpickle.py | 13 | 7 | https://github.com/ray-project/ray.git | 3 | 47 | 0 | 28 | 92 | Python | {
"docstring": "Unregister that the input module should be pickled by value.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def unregister_pickle_by_value(module):
if not isinstance(module, types.ModuleType):
raise ValueError(f"Input should be a module object, got {str(module)} instead")
if module.__name__ not in _PICKLE_BY_VALUE_MODULES:
raise ValueError(f"{module} is not registered for pickle by value")
else:
_PICKLE_BY_VALUE_MODULES.remove(module.__name__)
|
|
16,362 | 75,130 | 155 | wagtail/images/tests/test_admin_views.py | 30 | 19 | def test_get_bad_permissions(self):
# Remove privileges from user
self.user.is_superuser = False
self.user.user_permissions.a | Reformat with black | test_get_bad_permissions | d10f15e55806c6944827d801cd9c2d53f5da4186 | wagtail | test_admin_views.py | 14 | 12 | https://github.com/wagtail/wagtail.git | 1 | 78 | 0 | 24 | 135 | Python | {
"docstring": "\n This tests that the view returns a \"permission denied\" redirect if a user without correct\n permissions attempts to access it\n ",
"language": "en",
"n_whitespaces": 42,
"n_words": 20,
"vocab_size": 19
} | def test_get_bad_permissions(self):
# Remove privileges from user
self.user.is_superuser = False
self.user.user_permissions.add(
Permission.objects.get(
content_type__app_label="wagtailadmin", codename="access_admin"
)
)
self.user.save()
# Get
response = self.client.get(
reverse("wagtailimages:url_generator", args=(self.image.id,))
)
# Check response
self.assertRedirects(response, reverse("wagtailadmin_home"))
|
|
3,772 | 21,342 | 40 | pipenv/patched/notpip/_vendor/distlib/_backport/shutil.py | 14 | 7 | def get_archive_formats():
formats = [(name, registry[2]) for name, registry in
_ARCHIVE_FORMATS.items | Vendor in pip 22.1.2 | get_archive_formats | c69d55f7c82d5ae2cce542bcfb98d043ca4836a0 | pipenv | shutil.py | 10 | 5 | https://github.com/pypa/pipenv.git | 2 | 34 | 0 | 13 | 56 | Python | {
"docstring": "Returns a list of supported formats for archiving and unarchiving.\n\n Each element of the returned sequence is a tuple (name, description)\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 21,
"vocab_size": 19
} | def get_archive_formats():
formats = [(name, registry[2]) for name, registry in
_ARCHIVE_FORMATS.items()]
formats.sort()
return formats
|
|
46,070 | 189,462 | 1,007 | manim/mobject/svg/svg_mobject.py | 245 | 48 | def _handle_transforms(self, element, mobject):
if element.hasAttribute("x") and element.hasAttribute("y"):
x = self._attribute_to_float(element.getAttribute("x"))
# Flip y
y = -self._attribute_to_float(element.getAttribute("y"))
mobject.shift(x * RIGHT + y * UP)
transform_attr_value = element.getAttribute("transform")
# parse the various transforms in the attribute value
transform_names = ["matrix", "translate", "scale", "rotate", "skewX", "skewY"]
# Borrowed/Inspired from:
# https://github.com/cjlano/svg/blob/3ea3384457c9780fa7d67837c9c5fd4ebc42cb3b/svg/svg.py#L75
# match any SVG transformation with its parameter (until final parenthesis)
# [^)]* == anything but a closing parenthesis
# '|'.join == OR-list of SVG transformations
transform_regex = "|".join([x + r"[^)]*\)" for x in transform_names])
transforms = re.findall(transform_regex, transform_attr_value)
number_regex = r"[-+]?(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][-+]?\d+)?"
for t in transforms:
op | Hide more private methods from the docs. (#2468)
* hide privs from text_mobject.py
* hide privs from tex_mobject.py
* hide privs from code_mobject.py
* hide privs from svg_mobject.py
* remove SVGPath and utils from __init__.py
* don't import string_to_numbers
* hide privs from geometry.py
* hide privs from matrix.py
* hide privs from numbers.py
* hide privs from three_dimensions.py
* forgot underscore under set_stroke_width_from_length
* there were more i missed
* unhidea method that was used in docs
* forgot other text2hash
* remove svg_path from docs | handle_transforms | 902e7eb4f0147b5882a613b67467e38a1d47f01e | manim | svg_mobject.py | 18 | 48 | https://github.com/ManimCommunity/manim.git | 14 | 429 | 0 | 143 | 706 | Python | {
"docstring": "Applies the SVG transform to the specified mobject. Transforms include:\n ``matrix``, ``translate``, and ``scale``.\n\n Parameters\n ----------\n element : :class:`minidom.Element`\n The transform command to perform\n\n mobject : :class:`Mobject`\n The Mobject to transform.\n ",
"language": "en",
"n_whitespaces": 95,
"n_words": 31,
"vocab_size": 25
} | def _handle_transforms(self, element, mobject):
if element.hasAttribute("x") and element.hasAttribute("y"):
x = self._attribute_to_float(element.getAttribute("x"))
# Flip y
y = -self._attribute_to_float(element.getAttribute("y"))
mobject.shift(x * RIGHT + y * UP)
transform_attr_value = element.getAttribute("transform")
# parse the various transforms in the attribute value
transform_names = ["matrix", "translate", "scale", "rotate", "skewX", "skewY"]
# Borrowed/Inspired from:
# https://github.com/cjlano/svg/blob/3ea3384457c9780fa7d67837c9c5fd4ebc42cb3b/svg/svg.py#L75
# match any SVG transformation with its parameter (until final parenthesis)
# [^)]* == anything but a closing parenthesis
# '|'.join == OR-list of SVG transformations
transform_regex = "|".join([x + r"[^)]*\)" for x in transform_names])
transforms = re.findall(transform_regex, transform_attr_value)
number_regex = r"[-+]?(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][-+]?\d+)?"
for t in transforms:
op_name, op_args = t.split("(")
op_name = op_name.strip()
op_args = [float(x) for x in re.findall(number_regex, op_args)]
if op_name == "matrix":
transform_args = np.array(op_args).reshape([3, 2])
x = transform_args[2][0]
y = -transform_args[2][1]
matrix = np.identity(self.dim)
matrix[:2, :2] = transform_args[:2, :]
matrix[1] *= -1
matrix[:, 1] *= -1
for mob in mobject.family_members_with_points():
if config["renderer"] == "opengl":
mob.points = np.dot(mob.points, matrix)
else:
mob.points = np.dot(mob.points, matrix)
mobject.shift(x * RIGHT + y * UP)
elif op_name == "scale":
scale_values = op_args
if len(scale_values) == 2:
scale_x, scale_y = scale_values
mobject.scale(np.array([scale_x, scale_y, 1]), about_point=ORIGIN)
elif len(scale_values) == 1:
scale = scale_values[0]
mobject.scale(np.array([scale, scale, 1]), about_point=ORIGIN)
elif op_name == "translate":
if len(op_args) == 2:
x, y = op_args
else:
x = op_args
y = 0
mobject.shift(x * RIGHT + y * DOWN)
else:
# TODO: handle rotate, skewX and skewY
# for now adding a warning message
logger.warning(
"Handling of %s transform is not supported yet!",
op_name,
)
|
|
@pytest.fixture(name="pro") | 97,233 | 298,288 | 11 | tests/components/airvisual/conftest.py | 6 | 7 | def pro_data_fixture():
return json.loads(load_fixture("data.json", "airvisual_pro"))
@pytest.fixture( | Ensure AirVisual Pro migration includes device and entity customizations (#84798)
* Ensure AirVisual Pro migration includes device and entity customizations
* Update homeassistant/components/airvisual/__init__.py
Co-authored-by: Martin Hjelmare <marhje52@gmail.com>
* Code review
* Fix tests
* Fix tests FOR REAL
Co-authored-by: Martin Hjelmare <marhje52@gmail.com> | pro_data_fixture | 34dc47ad1037c6bf569f8cb2199f5933c2a0a079 | core | conftest.py | 10 | 2 | https://github.com/home-assistant/core.git | 1 | 17 | 1 | 6 | 51 | Python | {
"docstring": "Define an update coordinator data example for the Pro.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | def pro_data_fixture():
return json.loads(load_fixture("data.json", "airvisual_pro"))
@pytest.fixture(name="pro") |
76,408 | 260,671 | 549 | sklearn/datasets/_species_distributions.py | 179 | 50 | def fetch_species_distributions(*, data_home=None, download_if_missing=True):
data_home = get_data_home(data_home)
if not exists(data_home):
makedirs(data_home)
# Define parameters for the data files. These should not be changed
# unless the data model changes. They will be saved in the npz file
# with the downloaded data.
extra_params = dict(
x_left_lower_corner=-94.8,
Nx=1212,
y_left_lower_corner=-56.05,
Ny=1592,
grid_size=0.05,
)
dtype = np.int16
archive_path = _pkl_filepath(data_home, DATA_ARCHIVE_NAME)
if not exists(archive_path):
if not download_if_missing:
raise IOError("Data not found and `download_if_missing` is False")
logger.info("Downloading species data from %s to %s" % (SAMPLES.url, data_home))
samples_path = _fetch_remote(SAMPLES, dirname=data_home)
with np.load(samples_path) as X: # samples.zip is a valid npz
for f in X.files:
fhandle = BytesIO(X[f])
if "train" in f:
train = _load_csv(fhandle)
if "test" in f:
test = _load_csv(fhandle)
remove(samples_path)
logger.info(
"Downloading coverage data from %s to %s" % (COVERAGES.url, data_home)
)
coverages_path = _fetch_remote(COVERAGES, dirname=data_home)
with np.load(coverages_path) as X: # coverages.zip is a valid npz
coverages = []
for f in X.files:
fhandle = BytesIO(X[f])
logger.debug(" - converting {}".format(f) | DOC Ensures that fetch_species_distributions passes numpydoc validation (#24162)
Co-authored-by: Franck Charras <franck.charras@inria.fr> | fetch_species_distributions | fc656c2189d64a43089f514dcdedb0fae70dfe56 | scikit-learn | _species_distributions.py | 16 | 43 | https://github.com/scikit-learn/scikit-learn.git | 8 | 302 | 0 | 115 | 485 | Python | {
"docstring": "Loader for species distribution dataset from Phillips et. al. (2006).\n\n Read more in the :ref:`User Guide <datasets>`.\n\n Parameters\n ----------\n data_home : str, default=None\n Specify another download and cache folder for the datasets. By default\n all scikit-learn data is stored in '~/scikit_learn_data' subfolders.\n\n download_if_missing : bool, default=True\n If False, raise a IOError if the data is not locally available\n instead of trying to download the data from the source site.\n\n Returns\n -------\n data : :class:`~sklearn.utils.Bunch`\n Dictionary-like object, with the following attributes.\n\n coverages : array, shape = [14, 1592, 1212]\n These represent the 14 features measured\n at each point of the map grid.\n The latitude/longitude values for the grid are discussed below.\n Missing data is represented by the value -9999.\n train : record array, shape = (1624,)\n The training points for the data. Each point has three fields:\n\n - train['species'] is the species name\n - train['dd long'] is the longitude, in degrees\n - train['dd lat'] is the latitude, in degrees\n test : record array, shape = (620,)\n The test points for the data. Same format as the training data.\n Nx, Ny : integers\n The number of longitudes (x) and latitudes (y) in the grid\n x_left_lower_corner, y_left_lower_corner : floats\n The (x,y) position of the lower-left corner, in degrees\n grid_size : float\n The spacing between points of the grid, in degrees\n\n Notes\n -----\n\n This dataset represents the geographic distribution of species.\n The dataset is provided by Phillips et. al. (2006).\n\n The two species are:\n\n - `\"Bradypus variegatus\"\n <http://www.iucnredlist.org/details/3038/0>`_ ,\n the Brown-throated Sloth.\n\n - `\"Microryzomys minutus\"\n <http://www.iucnredlist.org/details/13408/0>`_ ,\n also known as the Forest Small Rice Rat, a rodent that lives in Peru,\n Colombia, Ecuador, Peru, and Venezuela.\n\n - For an example of using this dataset with scikit-learn, see\n :ref:`examples/applications/plot_species_distribution_modeling.py\n <sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.\n\n References\n ----------\n\n * `\"Maximum entropy modeling of species geographic distributions\"\n <http://rob.schapire.net/papers/ecolmod.pdf>`_\n S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,\n 190:231-259, 2006.\n ",
"language": "en",
"n_whitespaces": 631,
"n_words": 310,
"vocab_size": 197
} | def fetch_species_distributions(*, data_home=None, download_if_missing=True):
data_home = get_data_home(data_home)
if not exists(data_home):
makedirs(data_home)
# Define parameters for the data files. These should not be changed
# unless the data model changes. They will be saved in the npz file
# with the downloaded data.
extra_params = dict(
x_left_lower_corner=-94.8,
Nx=1212,
y_left_lower_corner=-56.05,
Ny=1592,
grid_size=0.05,
)
dtype = np.int16
archive_path = _pkl_filepath(data_home, DATA_ARCHIVE_NAME)
if not exists(archive_path):
if not download_if_missing:
raise IOError("Data not found and `download_if_missing` is False")
logger.info("Downloading species data from %s to %s" % (SAMPLES.url, data_home))
samples_path = _fetch_remote(SAMPLES, dirname=data_home)
with np.load(samples_path) as X: # samples.zip is a valid npz
for f in X.files:
fhandle = BytesIO(X[f])
if "train" in f:
train = _load_csv(fhandle)
if "test" in f:
test = _load_csv(fhandle)
remove(samples_path)
logger.info(
"Downloading coverage data from %s to %s" % (COVERAGES.url, data_home)
)
coverages_path = _fetch_remote(COVERAGES, dirname=data_home)
with np.load(coverages_path) as X: # coverages.zip is a valid npz
coverages = []
for f in X.files:
fhandle = BytesIO(X[f])
logger.debug(" - converting {}".format(f))
coverages.append(_load_coverage(fhandle))
coverages = np.asarray(coverages, dtype=dtype)
remove(coverages_path)
bunch = Bunch(coverages=coverages, test=test, train=train, **extra_params)
joblib.dump(bunch, archive_path, compress=9)
else:
bunch = joblib.load(archive_path)
return bunch
|
|
53,677 | 213,613 | 33 | ivy/core/device.py | 19 | 7 | def set_split_factor(factor, dev=None):
assert 0 <= factor
global split_factors
dev = ivy.default(dev, default_device())
split_f | renamed dev_str arg to dev for all methods. | set_split_factor | d743336b1f3654cd0315f380f43eed4116997c1d | ivy | device.py | 10 | 5 | https://github.com/unifyai/ivy.git | 1 | 34 | 0 | 17 | 56 | Python | {
"docstring": "\n Set the global split factor for a given device, which can be used to scale batch splitting chunk sizes for the\n device across the codebase.\n\n :param factor: The factor to set the device-specific split factor to.\n :type factor: float\n :param dev: The device to set the split factor for. Sets the default device by default.\n :type dev: str, optional\n ",
"language": "en",
"n_whitespaces": 81,
"n_words": 59,
"vocab_size": 38
} | def set_split_factor(factor, dev=None):
assert 0 <= factor
global split_factors
dev = ivy.default(dev, default_device())
split_factors[dev] = factor
# noinspection PyShadowingNames |
|
48,271 | 196,977 | 539 | sympy/testing/runtests.py | 197 | 40 | def run(self, test, compileflags=None, out=None, clear_globs=True):
self.test = test
# Remove ``` from the end of example, which may appear in Markdown
# files
for example in test.examples:
example.want = example.want.replace('```\n', '')
example.exc_msg = example.exc_msg and example.exc_msg.replace('```\n', '')
if compileflags is None:
compileflags = pdoctest._extract_future_flags(test.globs)
save_stdout = sys.stdout
if out is None:
out = save_stdout.write
sys.stdout = self._fakeout
# Patch pdb.set_trace to restore sys.stdout during interactive
# debugging (so it's not still redirected to self._fakeout).
# Note that the interactive output will go to *our*
# save_stdout, even if that's not the real sys.stdout; this
# allows us to write test cases for the set_trace behavior.
save_set_trace = pdb.set_trace
self.debugger = pdoctest._OutputRedirectingPdb( | Enable doctests in Markdown files | run | 3ebd6862a0c33fcf357d9f4ac5c2a8fd80a98675 | sympy | runtests.py | 15 | 26 | https://github.com/sympy/sympy.git | 7 | 195 | 0 | 129 | 392 | Python | {
"docstring": "\n Run the examples in ``test``, and display the results using the\n writer function ``out``.\n\n The examples are run in the namespace ``test.globs``. If\n ``clear_globs`` is true (the default), then this namespace will\n be cleared after the test runs, to help with garbage\n collection. If you would like to examine the namespace after\n the test completes, then use ``clear_globs=False``.\n\n ``compileflags`` gives the set of flags that should be used by\n the Python compiler when running the examples. If not\n specified, then it will default to the set of future-import\n flags that apply to ``globs``.\n\n The output of each example is checked using\n ``SymPyDocTestRunner.check_output``, and the results are\n formatted by the ``SymPyDocTestRunner.report_*`` methods.\n ",
"language": "en",
"n_whitespaces": 220,
"n_words": 111,
"vocab_size": 72
} | def run(self, test, compileflags=None, out=None, clear_globs=True):
self.test = test
# Remove ``` from the end of example, which may appear in Markdown
# files
for example in test.examples:
example.want = example.want.replace('```\n', '')
example.exc_msg = example.exc_msg and example.exc_msg.replace('```\n', '')
if compileflags is None:
compileflags = pdoctest._extract_future_flags(test.globs)
save_stdout = sys.stdout
if out is None:
out = save_stdout.write
sys.stdout = self._fakeout
# Patch pdb.set_trace to restore sys.stdout during interactive
# debugging (so it's not still redirected to self._fakeout).
# Note that the interactive output will go to *our*
# save_stdout, even if that's not the real sys.stdout; this
# allows us to write test cases for the set_trace behavior.
save_set_trace = pdb.set_trace
self.debugger = pdoctest._OutputRedirectingPdb(save_stdout)
self.debugger.reset()
pdb.set_trace = self.debugger.set_trace
# Patch linecache.getlines, so we can see the example's source
# when we're inside the debugger.
self.save_linecache_getlines = pdoctest.linecache.getlines
linecache.getlines = self.__patched_linecache_getlines
# Fail for deprecation warnings
with raise_on_deprecated():
try:
return self.__run(test, compileflags, out)
finally:
sys.stdout = save_stdout
pdb.set_trace = save_set_trace
linecache.getlines = self.save_linecache_getlines
if clear_globs:
test.globs.clear()
# We have to override the name mangled methods.
monkeypatched_methods = [
'patched_linecache_getlines',
'run',
'record_outcome'
]
for method in monkeypatched_methods:
oldname = '_DocTestRunner__' + method
newname = '_SymPyDocTestRunner__' + method
setattr(SymPyDocTestRunner, newname, getattr(DocTestRunner, oldname))
|
|
5,217 | 29,303 | 41 | saleor/graphql/product/tests/queries/test_product_variants_query.py | 19 | 10 | def _fetch_all_variants(client, variables={}, permissions=None):
query =
response = client.post_graphql(
query, variables, permissions=permissions, check_no_permissions=False
)
content = get_graphql_content(response)
return content["data"]["productVariants"]
| Split test_product.py and test_variant.py into multiple files (#11173)
* Split test_product.py into multiple files
* Split test_variant.py into multiple files | _fetch_all_variants | d90be220d6b687d08153934a51354011a3cb5ca1 | saleor | test_product_variants_query.py | 9 | 18 | https://github.com/saleor/saleor.git | 1 | 49 | 0 | 17 | 78 | Python | {
"docstring": "\n query fetchAllVariants($channel: String) {\n productVariants(first: 10, channel: $channel) {\n totalCount\n edges {\n node {\n id\n }\n }\n }\n }\n ",
"language": "en",
"n_whitespaces": 165,
"n_words": 19,
"vocab_size": 13
} | def _fetch_all_variants(client, variables={}, permissions=None):
query =
response = client.post_graphql(
query, variables, permissions=permissions, check_no_permissions=False
)
content = get_graphql_content(response)
return content["data"]["productVariants"]
|
|
2,940 | 19,350 | 552 | ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py | 192 | 49 | def astar_torus(grid, start_node, goal_node):
colors = ['white', 'black', 'red', 'pink', 'yellow', 'green', 'orange']
levels = [0, 1, 2, 3, 4, 5, 6, 7]
cmap, norm = from_levels_and_colors(levels, colors)
grid[start_node] = 4
grid[goal_node] = 5
parent_map = [[() for _ in range(M)] for _ in range(M)]
heuristic_map = calc_heuristic_map(M, goal_node)
explored_heuristic_map = np.full((M, M), np.inf)
distance_map = np.full((M, M), np.inf)
explored_heuristic_map[start_node] = heuristic_map[start_node]
distance_map[start_node] = 0
while True:
grid[start_node] = 4
grid[goal_node] = 5
current_node = np.unravel_index(
np.argmin(explored_heuristic_map, axis=None), explored_heuristic_map.shape)
min_distance = np.min(explored_heuristic_map)
if (current_node == goal_node) or np.isinf(min_distance):
break
grid[current_node] = 2
explored_heuristic_map[current_node] = np.inf
i, j = current_node[0], current_node[1]
neighbors = find_neighbors(i, j)
for neighbor in neighbors:
if grid[neighbor] == 0 or grid[neighbor] == 5:
distance_map[neighbor] = distance_map[current_node] + 1
explored_heuristic_map[neighbor] = heuristic_map[neighbor]
parent_map[neighbor[0]][neighbo | docs: Fix a few typos (#695)
There are small typos in:
- ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
- ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
- docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
- docs/modules/slam/ekf_slam/ekf_slam_main.rst
Fixes:
- Should read `configuration` rather than `configuation`.
- Should read `trajectory` rather than `tracjectory`.
- Should read `prediction` rather than `prediciton`.
Signed-off-by: Tim Gates <tim.gates@iress.com> | astar_torus | c6bdd48715adcbe17c4146b7cae3b0fc569f7bde | PythonRobotics | arm_obstacle_navigation.py | 17 | 47 | https://github.com/AtsushiSakai/PythonRobotics.git | 13 | 475 | 0 | 134 | 721 | Python | {
"docstring": "\n Finds a path between an initial and goal joint configuration using\n the A* Algorithm on a tororiadal grid.\n\n Args:\n grid: An occupancy grid (ndarray)\n start_node: Initial joint configuration (tuple)\n goal_node: Goal joint configuration (tuple)\n\n Returns:\n Obstacle-free route in joint space from start_node to goal_node\n ",
"language": "en",
"n_whitespaces": 88,
"n_words": 44,
"vocab_size": 37
} | def astar_torus(grid, start_node, goal_node):
colors = ['white', 'black', 'red', 'pink', 'yellow', 'green', 'orange']
levels = [0, 1, 2, 3, 4, 5, 6, 7]
cmap, norm = from_levels_and_colors(levels, colors)
grid[start_node] = 4
grid[goal_node] = 5
parent_map = [[() for _ in range(M)] for _ in range(M)]
heuristic_map = calc_heuristic_map(M, goal_node)
explored_heuristic_map = np.full((M, M), np.inf)
distance_map = np.full((M, M), np.inf)
explored_heuristic_map[start_node] = heuristic_map[start_node]
distance_map[start_node] = 0
while True:
grid[start_node] = 4
grid[goal_node] = 5
current_node = np.unravel_index(
np.argmin(explored_heuristic_map, axis=None), explored_heuristic_map.shape)
min_distance = np.min(explored_heuristic_map)
if (current_node == goal_node) or np.isinf(min_distance):
break
grid[current_node] = 2
explored_heuristic_map[current_node] = np.inf
i, j = current_node[0], current_node[1]
neighbors = find_neighbors(i, j)
for neighbor in neighbors:
if grid[neighbor] == 0 or grid[neighbor] == 5:
distance_map[neighbor] = distance_map[current_node] + 1
explored_heuristic_map[neighbor] = heuristic_map[neighbor]
parent_map[neighbor[0]][neighbor[1]] = current_node
grid[neighbor] = 3
if np.isinf(explored_heuristic_map[goal_node]):
route = []
print("No route found.")
else:
route = [goal_node]
while parent_map[route[0][0]][route[0][1]] != ():
route.insert(0, parent_map[route[0][0]][route[0][1]])
print("The route found covers %d grid cells." % len(route))
for i in range(1, len(route)):
grid[route[i]] = 6
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None)
plt.show()
plt.pause(1e-2)
return route
|
|
75,049 | 257,234 | 53 | haystack/pipelines/base.py | 20 | 8 | def root_node(self) -> Optional[str]:
if len(self.graph.nodes) < 1:
retur | Validate YAML files without loading the nodes (#2438)
* Remove BasePipeline and make a module for RayPipeline
* Can load pipelines from yaml, plenty of issues left
* Extract graph validation logic into _add_node_to_pipeline_graph & refactor load_from_config and add_node to use it
* Fix pipeline tests
* Move some tests out of test_pipeline.py and create MockDenseRetriever
* myoy and pylint (silencing too-many-public-methods)
* Fix issue found in some yaml files and in schema files
* Fix paths to YAML and fix some typos in Ray
* Fix eval tests
* Simplify MockDenseRetriever
* Fix Ray test
* Accidentally pushed merge coinflict, fixed
* Typo in schemas
* Typo in _json_schema.py
* Slightly reduce noisyness of version validation warnings
* Fix version logs tests
* Fix version logs tests again
* remove seemingly unused file
* Add check and test to avoid adding the same node to the pipeline twice
* Update Documentation & Code Style
* Revert config to pipeline_config
* Remo0ve unused import
* Complete reverting to pipeline_config
* Some more stray config=
* Update Documentation & Code Style
* Feedback
* Move back other_nodes tests into pipeline tests temporarily
* Update Documentation & Code Style
* Fixing tests
* Update Documentation & Code Style
* Fixing ray and standard pipeline tests
* Rename colliding load() methods in dense retrievers and faiss
* Update Documentation & Code Style
* Fix mypy on ray.py as well
* Add check for no root node
* Fix tests to use load_from_directory and load_index
* Try to workaround the disabled add_node of RayPipeline
* Update Documentation & Code Style
* Fix Ray test
* Fix FAISS tests
* Relax class check in _add_node_to_pipeline_graph
* Update Documentation & Code Style
* Try to fix mypy in ray.py
* unused import
* Try another fix for Ray
* Fix connector tests
* Update Documentation & Code Style
* Fix ray
* Update Documentation & Code Style
* use BaseComponent.load() in pipelines/base.py
* another round of feedback
* stray BaseComponent.load()
* Update Documentation & Code Style
* Fix FAISS tests too
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: tstadel <60758086+tstadel@users.noreply.github.com> | root_node | f8e02310bf0dfbd1ab79a1c3c73434e0aeba4f4b | haystack | base.py | 10 | 7 | https://github.com/deepset-ai/haystack.git | 2 | 37 | 0 | 19 | 61 | Python | {
"docstring": "\n Returns the root node of the pipeline's graph.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 8,
"vocab_size": 7
} | def root_node(self) -> Optional[str]:
if len(self.graph.nodes) < 1:
return None
return list(self.graph.nodes)[0] # List conversion is required, see networkx docs
|
|
48,760 | 197,989 | 352 | sympy/core/add.py | 71 | 29 | def as_coefficients_dict(self, *syms):
i | 22531: as_coefficients_dict accepts symbols | as_coefficients_dict | ea7fed2718f07bac46d4e154bd4e7ec31a4289e7 | sympy | add.py | 16 | 23 | https://github.com/sympy/sympy.git | 7 | 187 | 0 | 47 | 297 | Python | {
"docstring": "Return a dictionary mapping terms to their Rational coefficient.\n Since the dictionary is a defaultdict, inquiries about terms which\n were not present will return a coefficient of 0. If an expression is\n not an Add it is considered to have a single term.\n\n If symbols `syms` are provided, any multiplicative terms\n independent of them will be considered a coefficient and a\n regular dictionary of syms-dependent generators as keys and\n their corresponding coefficients as values will be returned.\n\n Examples\n ========\n\n >>> from sympy import exp\n >>> from sympy.abc import a, x\n >>> (3*x + a*x + 4).as_coefficients_dict()\n {1: 4, x: 3, a*x: 1}\n >>> _[a]\n 0\n >>> (3*a*x).as_coefficients_dict()\n {a*x: 3}\n\n >>> (3*exp(x)*x + a/x + 2).as_coefficients_dict(x)\n {1: 2, 1/x: a, x*exp(x): 3}\n ",
"language": "en",
"n_whitespaces": 261,
"n_words": 121,
"vocab_size": 83
} | def as_coefficients_dict(self, *syms):
if not syms:
d = defaultdict(list)
for ai in self.args:
c, m = ai.as_coeff_Mul()
d[m].append(c)
for k, v in d.items():
if len(v) == 1:
d[k] = v[0]
else:
d[k] = Add(*v)
di = defaultdict(int)
di.update(d)
return di
else:
d = defaultdict(list)
ind, dep = self.as_independent(*syms, as_Add=True)
for i in Add.make_args(dep):
c, x = i.as_independent(*syms, as_Add=False)
d[x].append(c)
d = {k: Add(*d[k]) for k in d}
d.update({S.One: ind})
return d
|
|
17,632 | 83,227 | 272 | zerver/lib/test_classes.py | 73 | 23 | def verify_emoji_code_foreign_keys(self) -> None:
dct = {}
for row in RealmEmoji.objects.all():
dct[row.id] = row
if not dct:
raise AssertionError("test needs RealmEmoji rows")
count = 0
for row in Reaction.objects.filter(reaction_type=Reaction.REALM_EMOJI):
realm_emoji_id = int(row.emoji_code)
assert realm_emoji_id in dct
self.assertEqual(dct[realm_emoji_id].name, row.emoji_name)
self.assertEqual(dct[realm_emoji_id].realm_id, row.user_profile.realm_id)
count += 1
for row in UserStatus.objects.filter(reaction_type=UserStatus.RE | docs: Fix many spelling mistakes.
Signed-off-by: Anders Kaseorg <anders@zulip.com> | verify_emoji_code_foreign_keys | b0ce4f1bce8031881addecb1e86073483517f392 | zulip | test_classes.py | 11 | 29 | https://github.com/zulip/zulip.git | 6 | 179 | 0 | 40 | 282 | Python | {
"docstring": "\n DB tables that refer to RealmEmoji use int(emoji_code) as the\n foreign key. Those tables tend to de-normalize emoji_name due\n to our inheritance-based setup. This helper makes sure those\n invariants are intact, which is particularly tricky during\n the import/export process (or during conversions from things\n like Slack/RocketChat/MatterMost/etc.).\n ",
"language": "en",
"n_whitespaces": 96,
"n_words": 46,
"vocab_size": 41
} | def verify_emoji_code_foreign_keys(self) -> None:
dct = {}
for row in RealmEmoji.objects.all():
dct[row.id] = row
if not dct:
raise AssertionError("test needs RealmEmoji rows")
count = 0
for row in Reaction.objects.filter(reaction_type=Reaction.REALM_EMOJI):
realm_emoji_id = int(row.emoji_code)
assert realm_emoji_id in dct
self.assertEqual(dct[realm_emoji_id].name, row.emoji_name)
self.assertEqual(dct[realm_emoji_id].realm_id, row.user_profile.realm_id)
count += 1
for row in UserStatus.objects.filter(reaction_type=UserStatus.REALM_EMOJI):
realm_emoji_id = int(row.emoji_code)
assert realm_emoji_id in dct
self.assertEqual(dct[realm_emoji_id].name, row.emoji_name)
self.assertEqual(dct[realm_emoji_id].realm_id, row.user_profile.realm_id)
count += 1
if count == 0:
raise AssertionError("test is meaningless without any pertinent rows")
|
|
14,080 | 65,988 | 16 | erpnext/erpnext_integrations/doctype/mpesa_settings/mpesa_settings.py | 28 | 15 | def format_string_to_json(balance_info):
Working Account|KES|481000.00|481000.00|0.00|0.00
balance_dict = frappe._dict()
for account_info in balance_info.split("&"):
account_info = account_info.split("|")
balance_dict[account_info[0]] = dict(
current_balance=fmt_money(account_info[2], currency="KES"),
available_balance=fmt_money(account_info[3], currency="KES"),
reserved_balance=fmt_money(account_info[4], currency="KES"),
uncleared_balance=fmt_money(account_info[5], currency="KES"),
)
return dumps(balance_dict)
| style: format code with black | format_string_to_json | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | mpesa_settings.py | 15 | 11 | https://github.com/frappe/erpnext.git | 2 | 103 | 0 | 22 | 166 | Python | {
"docstring": "\n\tFormat string to json.\n\n\te.g: \n\t=> {'Working Account': {'current_balance': '481000.00',\n\t 'available_balance': '481000.00',\n\t 'reserved_balance': '0.00',\n\t 'uncleared_balance': '0.00'}}\n\t",
"language": "en",
"n_whitespaces": 35,
"n_words": 16,
"vocab_size": 15
} | def format_string_to_json(balance_info):
Working Account|KES|481000.00|481000.00|0.00|0.00
balance_dict = frappe._dict()
for account_info in balance_info.split("&"):
account_info = account_info.split("|")
balance_dict[account_info[0]] = dict(
current_balance=fmt_money(account_info[2], currency="KES"),
available_balance=fmt_money(account_info[3], currency="KES"),
reserved_balance=fmt_money(account_info[4], currency="KES"),
uncleared_balance=fmt_money(account_info[5], currency="KES"),
)
return dumps(balance_dict)
|
|
40,626 | 170,943 | 495 | pandas/io/xml.py | 148 | 23 | def _validate_path(self) -> list[Any]:
msg = (
"xpath does not return any nodes or attributes. "
"Be sure to specify in `xpath` the parent nodes of | STYLE: fix pylint: no-else-raise (#49520)
* fix pylint: no-else-raise
* fix possible imbalanced tuple unpacking warning
Co-authored-by: carlotta <c.fabian@turbit.de> | _validate_path | d13c9e034ce8a1d738766c4b1cec80c76f5523be | pandas | xml.py | 13 | 35 | https://github.com/pandas-dev/pandas.git | 14 | 160 | 0 | 86 | 268 | Python | {
"docstring": "\n Notes\n -----\n `etree` supports limited XPath. If user attempts a more complex\n expression syntax error will raise.\n ",
"language": "en",
"n_whitespaces": 53,
"n_words": 17,
"vocab_size": 17
} | def _validate_path(self) -> list[Any]:
msg = (
"xpath does not return any nodes or attributes. "
"Be sure to specify in `xpath` the parent nodes of "
"children and attributes to parse. "
"If document uses namespaces denoted with "
"xmlns, be sure to define namespaces and "
"use them in xpath."
)
try:
elems = self.xml_doc.findall(self.xpath, namespaces=self.namespaces)
children = [ch for el in elems for ch in el.findall("*")]
attrs = {k: v for el in elems for k, v in el.attrib.items()}
if elems is None:
raise ValueError(msg)
if elems is not None:
if self.elems_only and children == []:
raise ValueError(msg)
if self.attrs_only and attrs == {}:
raise ValueError(msg)
if children == [] and attrs == {}:
raise ValueError(msg)
except (KeyError, SyntaxError):
raise SyntaxError(
"You have used an incorrect or unsupported XPath "
"expression for etree library or you used an "
"undeclared namespace prefix."
)
return elems
|
|
35,242 | 153,058 | 183 | modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py | 38 | 13 | def mask(self, row_labels, col_labels):
new_obj = | REFACTOR-#2656: Update modin to fit algebra (code only) (#3717)
Co-authored-by: Yaroslav Igoshev <Poolliver868@mail.ru>
Co-authored-by: Vasily Litvinov <vasilij.n.litvinov@intel.com>
Co-authored-by: Alexey Prutskov <alexey.prutskov@intel.com>
Co-authored-by: Devin Petersohn <devin-petersohn@users.noreply.github.com>
Signed-off-by: Rehan Durrani <rehan@ponder.io> | mask | 58bbcc37477866d19c8b092a0e1974a4f0baa586 | modin | partition.py | 11 | 15 | https://github.com/modin-project/modin.git | 5 | 86 | 0 | 26 | 131 | Python | {
"docstring": "\n Lazily create a mask that extracts the indices provided.\n\n Parameters\n ----------\n row_labels : list-like, slice or label\n The row labels for the rows to extract.\n col_labels : list-like, slice or label\n The column labels for the columns to extract.\n\n Returns\n -------\n PandasOnRayDataframePartition\n A new ``PandasOnRayDataframePartition`` object.\n ",
"language": "en",
"n_whitespaces": 143,
"n_words": 46,
"vocab_size": 34
} | def mask(self, row_labels, col_labels):
new_obj = super().mask(row_labels, col_labels)
if isinstance(row_labels, slice) and isinstance(
self._length_cache, ObjectIDType
):
new_obj._length_cache = compute_sliced_len.remote(
row_labels, self._length_cache
)
if isinstance(col_labels, slice) and isinstance(
self._width_cache, ObjectIDType
):
new_obj._width_cache = compute_sliced_len.remote(
col_labels, self._width_cache
)
return new_obj
|
|
10,347 | 51,540 | 102 | modules/image/classification/efficientnetb0_imagenet/processor.py | 33 | 17 | def postprocess(data_out, label_list, top_k):
output = []
for result in data_out:
result_i = softmax(result)
output_i = {}
indexs = np.argsort(result_i)[::-1][0:top_k]
for index in indexs:
label = label_list[index].split(',')[0]
output_i[label] = float(result_i[index])
output.append(output_i)
return output
| update efficientnetb0_imagenet (#2041)
* update efficientnetb0_imagenet
* remove unused print | postprocess | 7cd67aba38c19a835c3229d9b4be21798c5c8673 | PaddleHub | processor.py | 14 | 11 | https://github.com/PaddlePaddle/PaddleHub.git | 3 | 86 | 0 | 25 | 138 | Python | {
"docstring": "\n Postprocess output of network, one image at a time.\n\n Args:\n data_out (numpy.ndarray): output data of network.\n label_list (list): list of label.\n top_k (int): Return top k results.\n ",
"language": "en",
"n_whitespaces": 58,
"n_words": 27,
"vocab_size": 24
} | def postprocess(data_out, label_list, top_k):
output = []
for result in data_out:
result_i = softmax(result)
output_i = {}
indexs = np.argsort(result_i)[::-1][0:top_k]
for index in indexs:
label = label_list[index].split(',')[0]
output_i[label] = float(result_i[index])
output.append(output_i)
return output
|
|
2,635 | 13,415 | 164 | jina/serve/executors/__init__.py | 47 | 8 | def requests(self):
if hasattr(self, '_requests'):
return self._requests
else:
if not hasattr(self, 'requests_by_class'):
| fix: fix bug inheritance, requests nested dict (#5380) | requests | b44d767f22bd862cdb75926ba388c14f5db0323c | jina | __init__.py | 14 | 10 | https://github.com/jina-ai/jina.git | 4 | 83 | 0 | 34 | 137 | Python | {
"docstring": "\n Get the request dictionary corresponding to this specific class\n\n :return: Returns the requests corresponding to the specific Executor instance class\n ",
"language": "en",
"n_whitespaces": 42,
"n_words": 20,
"vocab_size": 14
} | def requests(self):
if hasattr(self, '_requests'):
return self._requests
else:
if not hasattr(self, 'requests_by_class'):
self.requests_by_class = {}
if self.__class__.__name__ not in self.requests_by_class:
self.requests_by_class[self.__class__.__name__] = {}
# we need to copy so that different instances with different (requests) in input do not disturb one another
self._requests = copy.copy(self.requests_by_class[self.__class__.__name__])
return self._requests
|
|
6,170 | 33,860 | 65 | src/transformers/pipelines/text2text_generation.py | 27 | 12 | def __call__(self, *args, **kwargs):
r
result = sup | Fixing t2t pipelines lists outputs. (#15008)
Backward compatibility broken in
https://github.com/huggingface/transformers/pull/14988 | __call__ | 8c2618e6aac3473da7757fb230690ffd4aea4c32 | transformers | text2text_generation.py | 10 | 32 | https://github.com/huggingface/transformers.git | 5 | 68 | 0 | 23 | 102 | Python | {
"docstring": "\n Generate the output text(s) using text(s) given as inputs.\n\n Args:\n args (`str` or `List[str]`):\n Input text for the encoder.\n return_tensors (`bool`, *optional*, defaults to `False`):\n Whether or not to include the tensors of predictions (as token indices) in the outputs.\n return_text (`bool`, *optional*, defaults to `True`):\n Whether or not to include the decoded texts in the outputs.\n clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):\n Whether or not to clean up the potential extra spaces in the text output.\n truncation (`TruncationStrategy`, *optional*, defaults to `TruncationStrategy.DO_NOT_TRUNCATE`):\n The truncation strategy for the tokenization within the pipeline. `TruncationStrategy.DO_NOT_TRUNCATE`\n (default) will never truncate, but it is sometimes desirable to truncate the input to fit the model's\n max_length instead of throwing an error down the line.\n generate_kwargs:\n Additional keyword arguments to pass along to the generate method of the model (see the generate method\n corresponding to your framework [here](./model#generative-models)).\n\n Return:\n A list or a list of list of `dict`: Each result comes as a dictionary with the following keys:\n\n - **generated_text** (`str`, present when `return_text=True`) -- The generated text.\n - **generated_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token\n ids of the generated text.\n ",
"language": "en",
"n_whitespaces": 464,
"n_words": 188,
"vocab_size": 114
} | def __call__(self, *args, **kwargs):
r
result = super().__call__(*args, **kwargs)
if isinstance(args[0], list) and all(isinstance(el, str) for el in args[0]):
return [res[0] for res in result]
return result
|
|
4,217 | 22,145 | 133 | pipenv/patched/pip/_vendor/requests/utils.py | 42 | 10 | def rewind_body(prepared_request):
body_seek = getattr(prepared_request.body, "seek", None)
if body_seek is not None and isinstance(
prepared_request._body_p | Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir. | rewind_body | cd5a9683be69c86c8f3adcd13385a9bc5db198ec | pipenv | utils.py | 13 | 13 | https://github.com/pypa/pipenv.git | 4 | 56 | 0 | 37 | 97 | Python | {
"docstring": "Move file pointer back to its recorded starting position\n so it can be read again on redirect.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 17,
"vocab_size": 17
} | def rewind_body(prepared_request):
body_seek = getattr(prepared_request.body, "seek", None)
if body_seek is not None and isinstance(
prepared_request._body_position, integer_types
):
try:
body_seek(prepared_request._body_position)
except OSError:
raise UnrewindableBodyError(
"An error occurred when rewinding request body for redirect."
)
else:
raise UnrewindableBodyError("Unable to rewind request body for redirect.")
|
|
118,400 | 323,181 | 192 | paddlenlp/trainer/utils/helper.py | 64 | 18 | def nested_concat(tensors, new_tensors, padding_index=-100):
assert type(tensors) == type(
new_tensors
), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_concat(
t, n, padding_index=padding_index)
| [Trainer] Add init version of paddlenlp trainer and apply finetune for ernie-1.0 pretraining. (#1761)
* add some datasets for finetune.
* support fine tune for all tastks.
* add trainer prototype.
* init verison for paddlenlp trainer.
* refine trainer.
* update for some details.
* support multi-cards training evaluation.
* support load from ckpt.
* support for export inference model.
* first version of trainer.
* seq cls support clue.
* trainer support for token classification and question answersing tasks.
* fix as reviews.
Co-authored-by: Zeyu Chen <chenzeyu01@baidu.com> | nested_concat | 44a290e94d1becd1f09fddc3d873f9e19c9d6919 | PaddleNLP | helper.py | 14 | 17 | https://github.com/PaddlePaddle/PaddleNLP.git | 5 | 116 | 0 | 50 | 200 | Python | {
"docstring": "\n Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or\n nested list/tuples of tensors.\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 25,
"vocab_size": 22
} | def nested_concat(tensors, new_tensors, padding_index=-100):
assert type(tensors) == type(
new_tensors
), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_concat(
t, n, padding_index=padding_index)
for t, n in zip(tensors, new_tensors))
elif isinstance(tensors, paddle.Tensor):
return paddle_pad_and_concatenate(
tensors, new_tensors, padding_index=padding_index)
elif isinstance(tensors, np.ndarray):
return numpy_pad_and_concatenate(
tensors, new_tensors, padding_index=padding_index)
else:
raise TypeError(
f"Unsupported type for concatenation: got {type(tensors)}")
|
|
10,764 | 53,269 | 72 | src/prefect/cli/orion.py | 22 | 10 | def kubernetes_manifest():
tem | Add kubernetes manifest commands | kubernetes_manifest | 23365cf7727c45f38ad983d610ffec5c15ceca21 | prefect | orion.py | 15 | 10 | https://github.com/PrefectHQ/prefect.git | 1 | 44 | 0 | 18 | 83 | Python | {
"docstring": "\n Generates a kubernetes manifest for to deploy Orion to a cluster.\n\n Example:\n $ prefect orion kubernetes-manifest | kubectl apply -f -\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 21,
"vocab_size": 19
} | def kubernetes_manifest():
template = Template(
(prefect.__module_path__ / "cli" / "templates" / "kubernetes.yaml").read_text()
)
manifest = template.substitute(
{
"image_name": get_prefect_image_name(),
}
)
print(manifest)
|
|
18,149 | 86,690 | 1,253 | tests/sentry/api/endpoints/test_project_dynamic_sampling.py | 183 | 43 | def test_queries_when_requested_project_is_head_of_trace(self, mock_query, mock_querybuilder):
# Case A: Head of trace project
self.login_as(self.user)
heart = self.create_project(
name="Heart", slug="heart", teams=[self.team], fire_project_created=True
)
mock_query.side_effect = [
{"data": [{"count()": 1000}]},
]
mock_querybuilder.side_effect = [
{
"data": [
{
"trace": "6503ee33b7bc43aead1facaa625a5dba",
"id": "6ddc83ee612b4e89b95b5278c8fd188f",
"random_number() AS random_number": 42 | feat(dynamic-sampling): Improve empty transaction breakdown message [TET-338] (#39539)
This PR add new attribute parentProjectBreakdown to
/api/0/projects/<organization_slug>/<project_slug>/dynamic-sampling/distribution/
api:
```
{
"projectBreakdown": null,
"sampleSize": 0,
"startTimestamp": null,
"endTimestamp": null,
"parentProjectBreakdown": [
{
"projectId": 1,
"percentage": 0.9,
"project": "sentry"
},
{
"projectId": 2,
"percentage": 0.1,
"project": "javascript"
}
]
}
```
TODO:
- [x] Update src/sentry/snuba/referrer.py
https://github.com/getsentry/sentry/blob/0fbbf1626f86399b1ca4a2781d66ef96aac69de7/src/sentry/snuba/referrer.py#L208-L210
- [x] Add missing tests
Co-authored-by: Andrii Soldatenko <andrii.soldatenko@gmail.io>
Co-authored-by: ahmedetefy <ahmed.etefy12@gmail.com> | test_queries_when_requested_project_is_head_of_trace | ceee9dfd8d6fed70d34546e7b46ebb7bf1d49745 | sentry | test_project_dynamic_sampling.py | 14 | 77 | https://github.com/getsentry/sentry.git | 1 | 384 | 0 | 103 | 644 | Python | {
"docstring": "\n Case A: Requesting for a project (bar) that is root but is a head of distributed traces\n Example of smart query response (DYNAMIC_SAMPLING_DISTRIBUTION_FETCH_PROJECT_STATS):\n |---------+-------+------|\n | project | count | root |\n |---------+-------+------|\n | bar | 100 | 100 |\n | heart | 5 | 0 |\n |---------+-------+------|\n ",
"language": "en",
"n_whitespaces": 127,
"n_words": 47,
"vocab_size": 28
} | def test_queries_when_requested_project_is_head_of_trace(self, mock_query, mock_querybuilder):
# Case A: Head of trace project
self.login_as(self.user)
heart = self.create_project(
name="Heart", slug="heart", teams=[self.team], fire_project_created=True
)
mock_query.side_effect = [
{"data": [{"count()": 1000}]},
]
mock_querybuilder.side_effect = [
{
"data": [
{
"trace": "6503ee33b7bc43aead1facaa625a5dba",
"id": "6ddc83ee612b4e89b95b5278c8fd188f",
"random_number() AS random_number": 4255299100,
"is_root": 1,
},
{
"trace": "6503ee33b7bc43aead1facaa625a5dba",
"id": "0b127a578f8440c793f9ba1de595229f",
"random_number() AS random_number": 3976019453,
"is_root": 1,
},
]
},
{
"data": [
{
"project": self.project.slug,
"project_id": self.project.id,
"count": 2,
"root_count": 2,
},
{
"project": heart.slug,
"project_id": heart.id,
"count": 1,
"root_count": 0,
},
]
},
]
end_time = timezone.now()
start_time = end_time - timedelta(hours=1)
query = "environment:dev"
requested_sample_size = 2
calls = self.generate_fetch_transactions_count_query(
query, start_time, end_time, requested_sample_size
)
snuba_query_random_transactions = random_transactions_snuba_query(
query, requested_sample_size, start_time, end_time, self.project
)
snuba_query_project_stats = project_stats_snuba_query(
query,
start_time,
end_time,
self.project,
trace_ids=["6503ee33b7bc43aead1facaa625a5dba"] * 2,
)
with Feature({"organizations:server-side-sampling": True}):
response = self.client.get(
f"{self.endpoint}?sampleSize={requested_sample_size}&query={query}"
)
assert response.status_code == 200
assert mock_query.mock_calls == calls
assert len(mock_querybuilder.call_args_list) == 2
self.assert_mocked_query_calls(
snuba_query_random_transactions, snuba_query_project_stats, mock_querybuilder
)
response_data = response.json()
assert response_data["projectBreakdown"] == [
{"project_id": self.project.id, "project": self.project.slug, "count()": 2},
{"project_id": heart.id, "project": heart.slug, "count()": 1},
]
assert response_data["parentProjectBreakdown"] == [
{"project": self.project.slug, "projectId": self.project.id, "percentage": 1.0}
]
|
|
14,129 | 66,180 | 18 | erpnext/hr/doctype/leave_ledger_entry/leave_ledger_entry.py | 29 | 15 | def validate_leave_allocation_against_leave_application(ledger):
leave_app | style: format code with black | validate_leave_allocation_against_leave_application | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | leave_ledger_entry.py | 14 | 20 | https://github.com/frappe/erpnext.git | 2 | 61 | 0 | 27 | 100 | Python | {
"docstring": "Checks that leave allocation has no leave application against it\n\t\tSELECT transaction_name\n\t\tFROM `tabLeave Ledger Entry`\n\t\tWHERE\n\t\t\temployee=%s\n\t\t\tAND leave_type=%s\n\t\t\tAND transaction_type='Leave Application'\n\t\t\tAND from_date>=%s\n\t\t\tAND to_date<=%s\n\t",
"language": "en",
"n_whitespaces": 18,
"n_words": 27,
"vocab_size": 23
} | def validate_leave_allocation_against_leave_application(ledger):
leave_application_records = frappe.db.sql_list(
,
(ledger.employee, ledger.leave_type, ledger.from_date, ledger.to_date),
)
if leave_application_records:
frappe.throw(
_("Leave allocation {0} is linked with the Leave Application {1}").format(
ledger.transaction_name, ", ".join(leave_application_records)
)
)
|
|
52,657 | 209,346 | 129 | scapy/contrib/pnio_rpc.py | 53 | 6 | def dce_rpc_endianess(pkt):
try:
endianness = pkt.underlayer.endian
except AttributeError:
# handle the case where a PN | MS-RPCE support (#3674)
* Add DCE/RPC
* Add tests to DCERPC5 / PNIO_RPC fixes
* Support for NDR fields in DCERPC
* Fully implement KRB5_GSS
* Support also RFC4121 | dce_rpc_endianess | a738a0b375a5599187626c9a9b081f7c25392f69 | scapy | pnio_rpc.py | 10 | 11 | https://github.com/secdev/scapy.git | 4 | 38 | 0 | 39 | 78 | Python | {
"docstring": "determine the symbol for the endianness of a the DCE/RPC",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 8
} | def dce_rpc_endianess(pkt):
try:
endianness = pkt.underlayer.endian
except AttributeError:
# handle the case where a PNIO class is
# built without its DCE-RPC under-layer
# i.e there is no endianness indication
return "!"
if endianness == 0: # big endian
return ">"
elif endianness == 1: # little endian
return "<"
else:
return "!"
|
|
10,853 | 53,590 | 424 | src/prefect/flow_runners.py | 108 | 17 | def _get_extra_hosts(self, docker_client) -> Dict[str, str]:
if sys.platform == "linux" and (
# Do not | Add pattern for loading CLI defaults from settings
Also, renames ORION_HOST to API_URL and adds utils to `Settings` to retrieve things by the envar key | _get_extra_hosts | b25d9d283b714c719f363176d49892188c50dffd | prefect | flow_runners.py | 14 | 25 | https://github.com/PrefectHQ/prefect.git | 5 | 99 | 0 | 87 | 188 | Python | {
"docstring": "\n A host.docker.internal -> host-gateway mapping is necessary for communicating\n with the API on Linux machines. Docker Desktop on macOS will automatically\n already have this mapping.\n ",
"language": "en",
"n_whitespaces": 54,
"n_words": 25,
"vocab_size": 24
} | def _get_extra_hosts(self, docker_client) -> Dict[str, str]:
if sys.platform == "linux" and (
# Do not warn if the user has specified a host manually that does not use
# a local address
"PREFECT_API_URL" not in self.env
or re.search(
".*(localhost)|(127.0.0.1)|(host.docker.internal).*",
self.env["PREFECT_API_URL"],
)
):
user_version = packaging.version.parse(docker_client.version()["Version"])
required_version = packaging.version.parse("20.10.0")
if user_version < required_version:
warnings.warn(
"`host.docker.internal` could not be automatically resolved to your "
"local ip address. This feature is not supported on Docker Engine "
f"v{user_version}, upgrade to v{required_version}+ if you "
"encounter issues."
)
return {}
else:
# Compatibility for linux -- https://github.com/docker/cli/issues/2290
# Only supported by Docker v20.10.0+ which is our minimum recommend version
return {"host.docker.internal": "host-gateway"}
|
|
54,254 | 215,927 | 247 | tests/pytests/unit/modules/test_win_certutil.py | 47 | 12 | def test_del_store():
with patch("salt.modules.win_certutil.get_cert_serial") as cert_serial_mock:
cmd_mock = MagicMock(
return_value=(
"CertInfo\r\n"
"================ Certificate 0 ================\r\n"
"Serial Number: 180720d39cd2db3244ba037417241e90\r\n"
"OtherStuff"
)
)
cache_mock = MagicMock(return_value="/tmp/cert.cer")
cert_serial_mock.return_value = "ABCDEF"
with patch.dict(
certutil.__salt__, {"cmd.run": cmd_mock, "cp.cache_file": cache_mock}
), patch("os.path.exists", MagicMock(return_value=True)):
certutil.del_store("salt://path/to/file", "TrustedPublisher")
cmd_mock.assert_called_once_with(
'certutil.exe -delstore TrustedPublisher "ABCDEF"'
| Add tests, fix state module | test_del_store | a8d2d1e1397cdc79b2c5f1ad7f6e3b729dcf8857 | salt | test_win_certutil.py | 14 | 20 | https://github.com/saltstack/salt.git | 1 | 93 | 0 | 42 | 188 | Python | {
"docstring": "\n Test removing a certificate to a specific store\n ",
"language": "en",
"n_whitespaces": 15,
"n_words": 8,
"vocab_size": 7
} | def test_del_store():
with patch("salt.modules.win_certutil.get_cert_serial") as cert_serial_mock:
cmd_mock = MagicMock(
return_value=(
"CertInfo\r\n"
"================ Certificate 0 ================\r\n"
"Serial Number: 180720d39cd2db3244ba037417241e90\r\n"
"OtherStuff"
)
)
cache_mock = MagicMock(return_value="/tmp/cert.cer")
cert_serial_mock.return_value = "ABCDEF"
with patch.dict(
certutil.__salt__, {"cmd.run": cmd_mock, "cp.cache_file": cache_mock}
), patch("os.path.exists", MagicMock(return_value=True)):
certutil.del_store("salt://path/to/file", "TrustedPublisher")
cmd_mock.assert_called_once_with(
'certutil.exe -delstore TrustedPublisher "ABCDEF"'
)
cache_mock.assert_called_once_with("salt://path/to/file", "base")
|
|
117,233 | 320,622 | 95 | tests/conftest.py | 44 | 15 | def _select_backend(config):
backend_arg = config.getoption('--qute-backend')
backend_env = os. | tests: Remove some unused imports | _select_backend | 9c4169c7b7d96a10012a72c70fc38c6154f7481f | qutebrowser | conftest.py | 10 | 11 | https://github.com/qutebrowser/qutebrowser.git | 5 | 62 | 0 | 31 | 113 | Python | {
"docstring": "Select the backend for running tests.\n\n The backend is auto-selected in the following manner:\n 1. Use QtWebKit if available\n 2. Otherwise use QtWebEngine as a fallback\n\n Auto-selection is overridden by either passing a backend via\n `--qute-backend=<backend>` or setting the environment variable\n `QUTE_TESTS_BACKEND=<backend>`.\n\n Args:\n config: pytest config\n\n Raises:\n ImportError if the selected backend is not available.\n\n Returns:\n The selected backend as a string (e.g. 'webkit').\n ",
"language": "en",
"n_whitespaces": 115,
"n_words": 64,
"vocab_size": 49
} | def _select_backend(config):
backend_arg = config.getoption('--qute-backend')
backend_env = os.environ.get('QUTE_TESTS_BACKEND')
backend = backend_arg or backend_env or _auto_select_backend()
# Fail early if selected backend is not available
# pylint: disable=unused-import
if backend == 'webkit':
import PyQt5.QtWebKitWidgets
elif backend == 'webengine':
import PyQt5.QtWebEngineWidgets
else:
raise utils.Unreachable(backend)
return backend
|
|
47,842 | 196,342 | 85 | sympy/logic/boolalg.py | 28 | 15 | def equals(self, other):
from sympy.logic.inference import satisfiable
from sympy.core.relational import Relational
if self.has(Relational) or other.has(Relational):
raise NotImplementedError('handling of relationals')
return self.atoms() == other.atoms() and \
not satisfiable(No | Updated import locations | equals | 498015021131af4dbb07eb110e5badaba8250c7b | sympy | boolalg.py | 13 | 7 | https://github.com/sympy/sympy.git | 4 | 71 | 0 | 26 | 113 | Python | {
"docstring": "\n Returns True if the given formulas have the same truth table.\n For two formulas to be equal they must have the same literals.\n\n Examples\n ========\n\n >>> from sympy.abc import A, B, C\n >>> from sympy import And, Or, Not\n >>> (A >> B).equals(~B >> ~A)\n True\n >>> Not(And(A, B, C)).equals(And(Not(A), Not(B), Not(C)))\n False\n >>> Not(And(A, Not(A))).equals(Or(B, Not(B)))\n False\n\n ",
"language": "en",
"n_whitespaces": 150,
"n_words": 58,
"vocab_size": 42
} | def equals(self, other):
from sympy.logic.inference import satisfiable
from sympy.core.relational import Relational
if self.has(Relational) or other.has(Relational):
raise NotImplementedError('handling of relationals')
return self.atoms() == other.atoms() and \
not satisfiable(Not(Equivalent(self, other)))
|
|
14,703 | 67,999 | 45 | erpnext/stock/utils.py | 64 | 16 | def get_latest_stock_qty(item_code, warehouse=None):
values, condition = [item_code], ""
if warehouse:
lft, rgt, is_group = frappe.db.get_value("Warehouse", warehouse, ["lft", "rgt", "is_group"])
if is_group:
values.extend([lft, rgt])
condition += "and exists (\
select | style: format code with black | get_latest_stock_qty | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | utils.py | 13 | 20 | https://github.com/frappe/erpnext.git | 3 | 98 | 0 | 52 | 165 | Python | {
"docstring": "select sum(actual_qty) from tabBin\n\t\twhere item_code=%s {0}",
"language": "en",
"n_whitespaces": 5,
"n_words": 7,
"vocab_size": 7
} | def get_latest_stock_qty(item_code, warehouse=None):
values, condition = [item_code], ""
if warehouse:
lft, rgt, is_group = frappe.db.get_value("Warehouse", warehouse, ["lft", "rgt", "is_group"])
if is_group:
values.extend([lft, rgt])
condition += "and exists (\
select name from `tabWarehouse` wh where wh.name = tabBin.warehouse\
and wh.lft >= %s and wh.rgt <= %s)"
else:
values.append(warehouse)
condition += " AND warehouse = %s"
actual_qty = frappe.db.sql(
.format(
condition
),
values,
)[0][0]
return actual_qty
|
|
75,584 | 259,125 | 138 | sklearn/kernel_approximation.py | 45 | 21 | def get_feature_names_out(self, input_features=None):
input_features = _check_feature_names_in(
self, input_features, generate_names=True
)
est_name = self.__class__.__nam | ENH Adds get_feature_names_out for AdditiveChi2Sampler (#22137)
Co-authored-by: Olivier Grisel <olivier.grisel@gmail.com>
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> | get_feature_names_out | 67a3feed2fe4e82c1cc129c34b9e223b94a8d531 | scikit-learn | kernel_approximation.py | 11 | 11 | https://github.com/scikit-learn/scikit-learn.git | 5 | 94 | 0 | 31 | 176 | Python | {
"docstring": "Get output feature names for transformation.\n\n Parameters\n ----------\n input_features : array-like of str or None, default=None\n Only used to validate feature names with the names seen in :meth:`fit`.\n\n Returns\n -------\n feature_names_out : ndarray of str objects\n Transformed feature names.\n ",
"language": "en",
"n_whitespaces": 110,
"n_words": 39,
"vocab_size": 32
} | def get_feature_names_out(self, input_features=None):
input_features = _check_feature_names_in(
self, input_features, generate_names=True
)
est_name = self.__class__.__name__.lower()
names_list = [f"{est_name}_{name}_sqrt" for name in input_features]
for j in range(1, self.sample_steps):
cos_names = [f"{est_name}_{name}_cos{j}" for name in input_features]
sin_names = [f"{est_name}_{name}_sin{j}" for name in input_features]
names_list.extend(cos_names + sin_names)
return np.asarray(names_list, dtype=object)
|
|
16,391 | 75,312 | 63 | wagtail/images/tests/test_templatetags.py | 21 | 12 | def test_render_valid_image_as_context_variable(self):
context = {"image": self.image, "image_node": "fake value"}
node = ImageNode(Variable("image"), "original", "image_node")
rendered = node.render(context) | Reformat with black | test_render_valid_image_as_context_variable | d10f15e55806c6944827d801cd9c2d53f5da4186 | wagtail | test_templatetags.py | 11 | 6 | https://github.com/wagtail/wagtail.git | 1 | 59 | 0 | 19 | 108 | Python | {
"docstring": "\n Tests that an ImageNode with a valid image and a context variable name\n renders an empty string and puts a rendition in the context variable\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 25,
"vocab_size": 19
} | def test_render_valid_image_as_context_variable(self):
context = {"image": self.image, "image_node": "fake value"}
node = ImageNode(Variable("image"), "original", "image_node")
rendered = node.render(context)
self.assertEqual(rendered, "")
self.assertIsInstance(context["image_node"], Rendition)
|
|
47,479 | 195,934 | 61 | sympy/polys/rootisolation.py | 29 | 12 | def dup_cauchy_lower_bound(f, K):
g = dup_reverse(f)
if len(g) < 2:
raise PolynomialError('Polynomial has no non-zero roots.')
if K.is_ZZ:
K = K.get_field()
b = dup_cauchy_upper_bound(g, K)
return | Add `dup_...` funcs for Cauchy bounds. | dup_cauchy_lower_bound | 4f34fcc3406452ace4a70d541064f2dfdcee9205 | sympy | rootisolation.py | 10 | 8 | https://github.com/sympy/sympy.git | 3 | 53 | 0 | 25 | 89 | Python | {
"docstring": "Compute the Cauchy lower bound on the absolute value of all non-zero\n roots of f, real or complex.",
"language": "en",
"n_whitespaces": 23,
"n_words": 18,
"vocab_size": 16
} | def dup_cauchy_lower_bound(f, K):
g = dup_reverse(f)
if len(g) < 2:
raise PolynomialError('Polynomial has no non-zero roots.')
if K.is_ZZ:
K = K.get_field()
b = dup_cauchy_upper_bound(g, K)
return K.one / b
|
|
35,807 | 154,142 | 1,287 | modin/core/io/column_stores/parquet_dispatcher.py | 327 | 50 | def call_deploy(cls, fname, col_partitions, storage_options, **kwargs):
from pyarrow.parquet import ParquetFile
from modin.core.storage_formats.pandas.parsers import ParquetFileToRead
# If we don't have any columns to read, we should just return an empty
# set of references.
if len(col_partitions) == 0:
return []
filesystem, parquet_files = cls.get_fsspec_files(fname, storage_options)
row_groups_per_file = []
num_row_groups = 0
# Count up the total number of row groups across all files and
# keep track of row groups per file to use later.
for file | FIX-#4756: Correctly propagate `storage_options` in `read_parquet` (#4764)
Co-authored-by: Yaroslav Igoshev <Poolliver868@mail.ru>
Co-authored-by: Alexey Prutskov <lehaprutskov@gmail.com>
Signed-off-by: Karthik Velayutham <vkarthik@ponder.io> | call_deploy | 4548012a6372b8ce79d7e07c9ae13fd7444a91c8 | modin | parquet_dispatcher.py | 13 | 69 | https://github.com/modin-project/modin.git | 9 | 287 | 0 | 182 | 460 | Python | {
"docstring": "\n Deploy remote tasks to the workers with passed parameters.\n\n Parameters\n ----------\n fname : str, path object or file-like object\n Name of the file to read.\n col_partitions : list\n List of arrays with columns names that should be read\n by each partition.\n storage_options : dict\n Parameters for specific storage engine.\n **kwargs : dict\n Parameters of deploying read_* function.\n\n Returns\n -------\n List\n Array with references to the task deploy result for each partition.\n ",
"language": "en",
"n_whitespaces": 215,
"n_words": 71,
"vocab_size": 52
} | def call_deploy(cls, fname, col_partitions, storage_options, **kwargs):
from pyarrow.parquet import ParquetFile
from modin.core.storage_formats.pandas.parsers import ParquetFileToRead
# If we don't have any columns to read, we should just return an empty
# set of references.
if len(col_partitions) == 0:
return []
filesystem, parquet_files = cls.get_fsspec_files(fname, storage_options)
row_groups_per_file = []
num_row_groups = 0
# Count up the total number of row groups across all files and
# keep track of row groups per file to use later.
for file in parquet_files:
with filesystem.open(file) as f:
row_groups = ParquetFile(f).num_row_groups
row_groups_per_file.append(row_groups)
num_row_groups += row_groups
# step determines how many row groups are going to be in a partition
step = compute_chunksize(
num_row_groups,
NPartitions.get(),
min_block_size=1,
)
current_partition_size = 0
file_index = 0
partition_files = [] # 2D array - each element contains list of chunks to read
row_groups_used_in_current_file = 0
total_row_groups_added = 0
# On each iteration, we add a chunk of one file. That will
# take us either to the end of a partition, or to the end
# of a file.
while total_row_groups_added < num_row_groups:
if current_partition_size == 0:
partition_files.append([])
partition_file = partition_files[-1]
file_path = parquet_files[file_index]
row_group_start = row_groups_used_in_current_file
row_groups_left_in_file = (
row_groups_per_file[file_index] - row_groups_used_in_current_file
)
row_groups_left_for_this_partition = step - current_partition_size
if row_groups_left_for_this_partition <= row_groups_left_in_file:
# File has at least what we need to finish partition
# So finish this partition and start a new one.
num_row_groups_to_add = row_groups_left_for_this_partition
current_partition_size = 0
else:
# File doesn't have enough to complete this partition. Add
# it into current partition and go to next file.
num_row_groups_to_add = row_groups_left_in_file
current_partition_size += num_row_groups_to_add
if num_row_groups_to_add == row_groups_left_in_file:
file_index += 1
row_groups_used_in_current_file = 0
else:
row_groups_used_in_current_file += num_row_groups_to_add
partition_file.append(
ParquetFileToRead(
file_path, row_group_start, row_group_start + num_row_groups_to_add
)
)
total_row_groups_added += num_row_groups_to_add
assert (
total_row_groups_added == num_row_groups
), "row groups added does not match total num of row groups across parquet files"
all_partitions = []
for files_to_read in partition_files:
all_partitions.append(
[
cls.deploy(
cls.parse,
files_for_parser=files_to_read,
columns=cols,
num_returns=3,
storage_options=storage_options,
**kwargs,
)
for cols in col_partitions
]
)
return all_partitions
|
|
35,407 | 153,459 | 98 | modin/db_conn.py | 26 | 12 | def get_connection(self):
if self.lib == _PSYCOPG_LIB_NAME:
import psycopg2
return psycopg2.connect(*self.args, **self.kwargs)
if self.lib == _SQLALCHEMY_LIB_NAME:
from sqlalchemy import create_engine
| FEAT-#979: Enable reading from SQL server. (#4279)
Co-authored-by: eavidan <eran.avidan@intel.com>
Co-authored-by: Devin Petersohn <devin-petersohn@users.noreply.github.com>
Signed-off-by: mvashishtha <mahesh@ponder.io> | get_connection | 2d40797b2b700d81d4db4a4cd023d563edf6431f | modin | db_conn.py | 13 | 8 | https://github.com/modin-project/modin.git | 3 | 63 | 0 | 21 | 106 | Python | {
"docstring": "\n Make the database connection and get it.\n\n For psycopg2, pass all arguments to psycopg2.connect() and return the\n result of psycopg2.connect(). For sqlalchemy, pass all arguments to\n sqlalchemy.create_engine() and return the result of calling connect()\n on the engine.\n\n Returns\n -------\n Any\n The open database connection.\n ",
"language": "en",
"n_whitespaces": 119,
"n_words": 44,
"vocab_size": 30
} | def get_connection(self):
if self.lib == _PSYCOPG_LIB_NAME:
import psycopg2
return psycopg2.connect(*self.args, **self.kwargs)
if self.lib == _SQLALCHEMY_LIB_NAME:
from sqlalchemy import create_engine
return create_engine(*self.args, **self.kwargs).connect()
raise UnsupportedDatabaseException("Unsupported database library")
|
|
@keras_export("keras.backend.argmin")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | 80,095 | 269,459 | 12 | keras/backend.py | 9 | 10 | def argmax(x, axis=-1):
return tf.argmax(x, axis)
@keras_export("keras.backend.argmin")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | argmax | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | backend.py | 7 | 2 | https://github.com/keras-team/keras.git | 1 | 20 | 1 | 9 | 62 | Python | {
"docstring": "Returns the index of the maximum value along an axis.\n\n Args:\n x: Tensor or variable.\n axis: axis along which to perform the reduction.\n\n Returns:\n A tensor.\n ",
"language": "en",
"n_whitespaces": 56,
"n_words": 26,
"vocab_size": 23
} | def argmax(x, axis=-1):
return tf.argmax(x, axis)
@keras_export("keras.backend.argmin")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs |
49,448 | 199,955 | 184 | sympy/core/facts.py | 51 | 8 | def print_rules(self) -> Iterator[str]:
yield from self._defined_facts_lines()
yield ''
yield ''
yield from self._full_implications_lines()
yield ''
yield ''
yield from self._prereq_lines()
yield ''
yield ''
yield from self._beta_rules_lines()
yield ''
yield ''
yield "generated_assumptions = {'defined_facts': defined_facts, 'full | refactor | print_rules | f68e8de4252200cfc74b9433d00f77c4510ac68d | sympy | facts.py | 8 | 18 | https://github.com/sympy/sympy.git | 1 | 63 | 0 | 24 | 140 | Python | {
"docstring": " Returns a generator with lines to represent the facts and rules ",
"language": "en",
"n_whitespaces": 12,
"n_words": 11,
"vocab_size": 11
} | def print_rules(self) -> Iterator[str]:
yield from self._defined_facts_lines()
yield ''
yield ''
yield from self._full_implications_lines()
yield ''
yield ''
yield from self._prereq_lines()
yield ''
yield ''
yield from self._beta_rules_lines()
yield ''
yield ''
yield "generated_assumptions = {'defined_facts': defined_facts, 'full_implications': full_implications,"
yield " 'prereq': prereq, 'beta_rules': beta_rules, 'beta_triggers': beta_triggers}"
yield ''
yield ''
|
|
56,673 | 222,610 | 21 | python3.10.4/Lib/distutils/cmd.py | 7 | 5 | def ensure_string(self, option, default=None):
self._ensure_stringlike(option, "strin | add python 3.10.4 for windows | ensure_string | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | cmd.py | 8 | 2 | https://github.com/XX-net/XX-Net.git | 1 | 22 | 0 | 7 | 36 | Python | {
"docstring": "Ensure that 'option' is a string; if not defined, set it to\n 'default'.\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 13,
"vocab_size": 13
} | def ensure_string(self, option, default=None):
self._ensure_stringlike(option, "string", default)
|
|
9,926 | 49,815 | 27 | modules/image/text_to_image/disco_diffusion_cnclip_vitb16/reverse_diffusion/model/nn.py | 14 | 11 | def update_ema(target_params, source_params, rate=0.99):
for targ, src in zip(target_params, source_params):
targ.detach().mul_(rate).add_(src, alpha=1 | add disco_diffusion_cnclip_vitb16 module | update_ema | f4d6e64cdc132ae868699a0ba442f4ab1d304a14 | PaddleHub | nn.py | 13 | 3 | https://github.com/PaddlePaddle/PaddleHub.git | 2 | 47 | 0 | 14 | 70 | Python | {
"docstring": "\n Update target parameters to be closer to those of source parameters using\n an exponential moving average.\n\n :param target_params: the target parameter sequence.\n :param source_params: the source parameter sequence.\n :param rate: the EMA rate (closer to 1 means slower).\n ",
"language": "en",
"n_whitespaces": 57,
"n_words": 38,
"vocab_size": 27
} | def update_ema(target_params, source_params, rate=0.99):
for targ, src in zip(target_params, source_params):
targ.detach().mul_(rate).add_(src, alpha=1 - rate)
|
|
@lru_cache(maxsize=1) | 3,904 | 21,526 | 188 | pipenv/patched/notpip/_vendor/platformdirs/android.py | 68 | 19 | def _android_folder() -> str | None:
try:
# First try to get path to android app via pyjnius
from jnius import autoclass
| Vendor in pip 22.1.2 | _android_folder | c69d55f7c82d5ae2cce542bcfb98d043ca4836a0 | pipenv | android.py | 17 | 15 | https://github.com/pypa/pipenv.git | 4 | 86 | 1 | 52 | 164 | Python | {
"docstring": ":return: base folder for the Android OS or None if cannot be found",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | def _android_folder() -> str | None:
try:
# First try to get path to android app via pyjnius
from jnius import autoclass
Context = autoclass("android.content.Context") # noqa: N806
result: str | None = Context.getFilesDir().getParentFile().getAbsolutePath()
except Exception:
# if fails find an android folder looking path on the sys.path
pattern = re.compile(r"/data/(data|user/\d+)/(.+)/files")
for path in sys.path:
if pattern.match(path):
result = path.split("/files")[0]
break
else:
result = None
return result
@lru_cache(maxsize=1) |
52,587 | 209,060 | 63 | scapy/volatile.py | 27 | 8 | def de_bruijn(charset, n, maxlen):
# type: (str, int, int) -> str
k = len(char | Add CyclicPattern class for generation of payload data (#3508)
* Add CyclicPattern class for generation of payload data
* minor enhancment
* fix python2
* fix python2
* use six
* fix flake | de_bruijn | e2fc7dddb40a7b80f2e65ad6593c0b10080019d0 | scapy | volatile.py | 9 | 7 | https://github.com/secdev/scapy.git | 1 | 44 | 0 | 21 | 50 | Python | {
"docstring": "\n Generate the De Bruijn Sequence up to `maxlen` characters\n for the charset `charset` and subsequences of length `n`.\n Algorithm modified from wikipedia\n https://en.wikipedia.org/wiki/De_Bruijn_sequence\n ",
"language": "en",
"n_whitespaces": 59,
"n_words": 23,
"vocab_size": 22
} | def de_bruijn(charset, n, maxlen):
# type: (str, int, int) -> str
k = len(charset)
a = [0] * k * n
sequence = [] # type: List[str]
|
|
21,283 | 101,901 | 168 | lib/gui/display_command.py | 33 | 24 | def _add_option_refresh(self) -> None:
logger.debug("Adding refresh option")
btnrefresh = ttk.Button(self.optsframe,
image=get_images().icons["reload"],
command=lambda x="update": preview_trigger().set(x)) # type:ignore
btnrefresh.pack(padx=2, side=tk.RIGHT)
Tooltip(btnrefresh,
text=_("Preview updates at every model save. Click to refresh now."),
wrap_length=200)
logger.debug("Added refresh option")
| Typing - lib.gui.display_command | _add_option_refresh | dab823a3eb7a5257cb1e0818ee10ed234d3de97f | faceswap | display_command.py | 14 | 11 | https://github.com/deepfakes/faceswap.git | 1 | 86 | 0 | 30 | 147 | Python | {
"docstring": " Add refresh button to refresh preview immediately ",
"language": "en",
"n_whitespaces": 8,
"n_words": 7,
"vocab_size": 6
} | def _add_option_refresh(self) -> None:
logger.debug("Adding refresh option")
btnrefresh = ttk.Button(self.optsframe,
image=get_images().icons["reload"],
command=lambda x="update": preview_trigger().set(x)) # type:ignore
btnrefresh.pack(padx=2, side=tk.RIGHT)
Tooltip(btnrefresh,
text=_("Preview updates at every model save. Click to refresh now."),
wrap_length=200)
logger.debug("Added refresh option")
|
|
69,929 | 242,808 | 201 | src/PIL/Image.py | 60 | 14 | def close(self):
try:
if hasattr(self, "_close__fp"):
self._close__fp()
if self.fp:
self.fp.close()
self.fp = None
except Exception as msg:
logger.debug("Error closing: %s", msg)
if getat | [Private] class names should be CamelCase | close | 7fa92c67b1471a66739c4768cdef616c27675981 | Pillow | Image.py | 12 | 12 | https://github.com/python-pillow/Pillow.git | 5 | 77 | 0 | 51 | 138 | Python | {
"docstring": "\n Closes the file pointer, if possible.\n\n This operation will destroy the image core and release its memory.\n The image data will be unusable afterward.\n\n This function is required to close images that have multiple frames or\n have not had their file read and closed by the\n :py:meth:`~PIL.Image.Image.load` method. See :ref:`file-handling` for\n more information.\n ",
"language": "en",
"n_whitespaces": 110,
"n_words": 53,
"vocab_size": 45
} | def close(self):
try:
if hasattr(self, "_close__fp"):
self._close__fp()
if self.fp:
self.fp.close()
self.fp = None
except Exception as msg:
logger.debug("Error closing: %s", msg)
if getattr(self, "map", None):
self.map = None
# Instead of simply setting to None, we're setting up a
# deferred error that will better explain that the core image
# object is gone.
self.im = DeferredError(ValueError("Operation on closed image"))
|
|
47,458 | 195,871 | 31 | sympy/solvers/diophantine/diophantine.py | 16 | 12 | def diop_general_sum_of_squares(eq, limit=1):
r
var, coeff, diop_type = classify_diop(eq, _dict=False)
if diop_type == GeneralSumOfSquares.name:
return set(GeneralSumOfSquares(eq).solve(limit=limit))
| Improved documentation formatting | diop_general_sum_of_squares | cda8dfe6f45dc5ed394c2f5cda706cd6c729f713 | sympy | diophantine.py | 13 | 37 | https://github.com/sympy/sympy.git | 2 | 47 | 0 | 15 | 73 | Python | {
"docstring": "\n Solves the equation `x_{1}^2 + x_{2}^2 + . . . + x_{n}^2 - k = 0`.\n\n Returns at most ``limit`` number of solutions.\n\n Usage\n =====\n\n ``general_sum_of_squares(eq, limit)`` : Here ``eq`` is an expression which\n is assumed to be zero. Also, ``eq`` should be in the form,\n `x_{1}^2 + x_{2}^2 + . . . + x_{n}^2 - k = 0`.\n\n Details\n =======\n\n When `n = 3` if `k = 4^a(8m + 7)` for some `a, m \\in Z` then there will be\n no solutions. Refer to [1]_ for more details.\n\n Examples\n ========\n\n >>> from sympy.solvers.diophantine.diophantine import diop_general_sum_of_squares\n >>> from sympy.abc import a, b, c, d, e\n >>> diop_general_sum_of_squares(a**2 + b**2 + c**2 + d**2 + e**2 - 2345)\n {(15, 22, 22, 24, 24)}\n\n Reference\n =========\n\n .. [1] Representing an integer as a sum of three squares, [online],\n Available:\n http://www.proofwiki.org/wiki/Integer_as_Sum_of_Three_Squares\n ",
"language": "en",
"n_whitespaces": 216,
"n_words": 138,
"vocab_size": 98
} | def diop_general_sum_of_squares(eq, limit=1):
r
var, coeff, diop_type = classify_diop(eq, _dict=False)
if diop_type == GeneralSumOfSquares.name:
return set(GeneralSumOfSquares(eq).solve(limit=limit))
|
|
@Directory.register | 45,631 | 186,806 | 97 | acme/acme/messages.py | 34 | 14 | def resolved_combinations(self) -> Tuple[Tuple[ChallengeBody, ...], ...]:
| deprecate more attributes in acme (#9369)
* deprecate more attributes in acme
* Deprecate .Authorization.combinations by renaming the field and
deprecating in getters/setters
* Silence deprecation warnings from our own imports of acme.mixins
Co-authored-by: Brad Warren <bmw@users.noreply.github.com> | resolved_combinations | f7e61edcb2ea3195c9889c407a08e6dffb7f60dc | certbot | messages.py | 11 | 11 | https://github.com/certbot/certbot.git | 3 | 50 | 1 | 31 | 87 | Python | {
"docstring": "Combinations with challenges instead of indices.\n\n .. deprecated: 1.30.0\n\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 9,
"vocab_size": 9
} | def resolved_combinations(self) -> Tuple[Tuple[ChallengeBody, ...], ...]:
warnings.warn(
"acme.messages.Authorization.resolved_combinations is deprecated and will be "
"removed in a future release.", DeprecationWarning)
return tuple(tuple(self.challenges[idx] for idx in combo)
for combo in self.combinations) # pylint: disable=not-an-iterable
@Directory.register |
75,768 | 259,434 | 383 | sklearn/_loss/tests/test_loss.py | 174 | 25 | def test_tweedie_log_identity_consistency(p):
half_tweedie_log = HalfTweedieLoss(power=p)
half_tweedie_identity = HalfTweedieLossIdentity(power=p)
n_samples = 10
y_true, raw_prediction = random_y_true_raw_prediction(
loss=half_tweedie_log, n_samples=n_samples, seed=42
)
y_pred = half_tweedie_log.link.inverse(raw_prediction) # exp(raw_prediction)
# Let's compare the loss values, up to some constant term that is dropped
# in HalfTweedieLoss but not in HalfTweedieLossIdentity.
loss_log = half_tweedie_log.loss(
y_true=y_true, raw_prediction=raw_prediction
) + half_tweedie_log.constant_to_optimal_zero(y_true)
loss_identity = half_tweedie_identity.loss(
y_true=y_true, raw_prediction=y_pred
) + half_tweedie_identity.constant_to_optimal_zero(y_true)
# Note that HalfTweedieLoss ignores different constant terms than
# HalfTweedieLos | ENH migrate GLMs / TweedieRegressor to linear loss (#22548)
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> | test_tweedie_log_identity_consistency | 75a94f518f7bd7d0bf581ffb67d9f961e3c4efbc | scikit-learn | test_loss.py | 10 | 25 | https://github.com/scikit-learn/scikit-learn.git | 1 | 155 | 0 | 109 | 255 | Python | {
"docstring": "Test for identical losses when only the link function is different.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def test_tweedie_log_identity_consistency(p):
half_tweedie_log = HalfTweedieLoss(power=p)
half_tweedie_identity = HalfTweedieLossIdentity(power=p)
n_samples = 10
y_true, raw_prediction = random_y_true_raw_prediction(
loss=half_tweedie_log, n_samples=n_samples, seed=42
)
y_pred = half_tweedie_log.link.inverse(raw_prediction) # exp(raw_prediction)
# Let's compare the loss values, up to some constant term that is dropped
# in HalfTweedieLoss but not in HalfTweedieLossIdentity.
loss_log = half_tweedie_log.loss(
y_true=y_true, raw_prediction=raw_prediction
) + half_tweedie_log.constant_to_optimal_zero(y_true)
loss_identity = half_tweedie_identity.loss(
y_true=y_true, raw_prediction=y_pred
) + half_tweedie_identity.constant_to_optimal_zero(y_true)
# Note that HalfTweedieLoss ignores different constant terms than
# HalfTweedieLossIdentity. Constant terms means terms not depending on
# raw_prediction. By adding these terms, `constant_to_optimal_zero`, both losses
# give the same values.
assert_allclose(loss_log, loss_identity)
# For gradients and hessians, the constant terms do not matter. We have, however,
# to account for the chain rule, i.e. with x=raw_prediction
# gradient_log(x) = d/dx loss_log(x)
# = d/dx loss_identity(exp(x))
# = exp(x) * gradient_identity(exp(x))
# Similarly,
# hessian_log(x) = exp(x) * gradient_identity(exp(x))
# + exp(x)**2 * hessian_identity(x)
gradient_log, hessian_log = half_tweedie_log.gradient_hessian(
y_true=y_true, raw_prediction=raw_prediction
)
gradient_identity, hessian_identity = half_tweedie_identity.gradient_hessian(
y_true=y_true, raw_prediction=y_pred
)
assert_allclose(gradient_log, y_pred * gradient_identity)
assert_allclose(
hessian_log, y_pred * gradient_identity + y_pred**2 * hessian_identity
)
|
|
@_noconds_(True) | 48,218 | 196,851 | 306 | sympy/integrals/transforms.py | 89 | 33 | def laplace_transform(f, t, s, legacy_matrix=True, **hints):
r
debug('\n***** laplace_transform(%s, %s, %s)'%(f, t, s))
if isinstance(f, MatrixBase) and hasattr(f, 'applyfunc'):
conds = not hints.get('noconds', False)
if conds and legacy_matrix:
SymPyDeprecationWarning(
feature="laplace_transform of a Matrix with noconds=False (default)",
useinstead="the option legacy_matrix=False to get the new behaviour",
issue=21504,
deprecated_since_version="1.9"
).warn()
return f.applyfunc(lambda fij: laplace_transform(fij, t, s, **hints))
else:
elements_trans = [laplace_transform(fij, t, s, **hints) for fij in f]
if c | Fix a few docstring formatting issues | laplace_transform | 1eeb01e15f06c6692a5bfd6fd2d2a3002d864a07 | sympy | transforms.py | 17 | 85 | https://github.com/sympy/sympy.git | 7 | 196 | 1 | 71 | 315 | Python | {
"docstring": "\n Compute the Laplace Transform `F(s)` of `f(t)`,\n\n .. math :: F(s) = \\int_{0^{-}}^\\infty e^{-st} f(t) \\mathrm{d}t.\n\n Explanation\n ===========\n\n For all sensible functions, this converges absolutely in a\n half-plane\n\n .. math :: a < \\operatorname{Re}(s)\n\n This function returns ``(F, a, cond)`` where ``F`` is the Laplace\n transform of ``f``, `a` is the half-plane of convergence, and `cond` are\n auxiliary convergence conditions.\n\n The implementation is rule-based, and if you are interested in which\n rules are applied, and whether integration is attemped, you can switch\n debug information on by setting ``sympy.SYMPY_DEBUG=True``.\n\n The lower bound is `0-`, meaning that this bound should be approached\n from the lower side. This is only necessary if distributions are involved.\n At present, it is only done if `f(t)` contains ``DiracDelta``, in which\n case the Laplace transform is computed implicitly as\n\n .. math :: F(s) = \\lim_{\\tau\\to 0^{-}} \\int_{\\tau}^\\infty e^{-st} f(t) \\mathrm{d}t\n\n by applying rules.\n\n If the integral cannot be fully computed in closed form, this function\n returns an unevaluated :class:`LaplaceTransform` object.\n\n For a description of possible hints, refer to the docstring of\n :func:`sympy.integrals.transforms.IntegralTransform.doit`. If ``noconds=True``,\n only `F` will be returned (i.e. not ``cond``, and also not the plane ``a``).\n\n .. deprecated:: 1.9\n Legacy behavior for matrices where ``laplace_transform`` with\n ``noconds=False`` (the default) returns a Matrix whose elements are\n tuples. The behavior of ``laplace_transform`` for matrices will change\n in a future release of SymPy to return a tuple of the transformed\n Matrix and the convergence conditions for the matrix as a whole. Use\n ``legacy_matrix=False`` to enable the new behavior.\n\n Examples\n ========\n\n >>> from sympy import DiracDelta, exp, laplace_transform\n >>> from sympy.abc import t, s, a\n >>> laplace_transform(t**4, t, s)\n (24/s**5, 0, True)\n >>> laplace_transform(t**a, t, s)\n (gamma(a + 1)/(s*s**a), 0, re(a) > -1)\n >>> laplace_transform(DiracDelta(t)-a*exp(-a*t),t,s)\n (s/(a + s), Max(0, -a), True)\n\n See Also\n ========\n\n inverse_laplace_transform, mellin_transform, fourier_transform\n hankel_transform, inverse_hankel_transform\n\n ",
"language": "en",
"n_whitespaces": 463,
"n_words": 300,
"vocab_size": 192
} | def laplace_transform(f, t, s, legacy_matrix=True, **hints):
r
debug('\n***** laplace_transform(%s, %s, %s)'%(f, t, s))
if isinstance(f, MatrixBase) and hasattr(f, 'applyfunc'):
conds = not hints.get('noconds', False)
if conds and legacy_matrix:
SymPyDeprecationWarning(
feature="laplace_transform of a Matrix with noconds=False (default)",
useinstead="the option legacy_matrix=False to get the new behaviour",
issue=21504,
deprecated_since_version="1.9"
).warn()
return f.applyfunc(lambda fij: laplace_transform(fij, t, s, **hints))
else:
elements_trans = [laplace_transform(fij, t, s, **hints) for fij in f]
if conds:
elements, avals, conditions = zip(*elements_trans)
f_laplace = type(f)(*f.shape, elements)
return f_laplace, Max(*avals), And(*conditions)
else:
return type(f)(*f.shape, elements_trans)
return LaplaceTransform(f, t, s).doit(**hints)
@_noconds_(True) |
74,867 | 256,315 | 171 | test/benchmarks/nq_to_squad.py | 71 | 14 | def reduce_annotations(anno_types, answers):
for at in set(anno_types):
assert at in ("no_answer", "short_answer")
if anno_types.count("short_answer") >= anno_types.count("no_answer"):
majority = "short_answer"
is_impossible = False
else:
majority = "no_answer"
is_impossible = True
answers = [a for at, a in zip(anno_types, answers) if at == majority]
reduction = len(anno_types) - | 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> | reduce_annotations | a59bca366174d9c692fa19750c24d65f47660ef7 | haystack | nq_to_squad.py | 10 | 20 | https://github.com/deepset-ai/haystack.git | 6 | 112 | 0 | 45 | 191 | Python | {
"docstring": "\n In cases where there is annotator disagreement, this fn picks either only the short_answers or only the no_answers,\n depending on which is more numerous, with a bias towards picking short_answers.\n\n Note: By this stage, all long_answer annotations and all samples with yes/no answer have been removed.\n This leaves just no_answer and short_answers",
"language": "en",
"n_whitespaces": 64,
"n_words": 52,
"vocab_size": 44
} | def reduce_annotations(anno_types, answers):
for at in set(anno_types):
assert at in ("no_answer", "short_answer")
if anno_types.count("short_answer") >= anno_types.count("no_answer"):
majority = "short_answer"
is_impossible = False
else:
majority = "no_answer"
is_impossible = True
answers = [a for at, a in zip(anno_types, answers) if at == majority]
reduction = len(anno_types) - len(answers)
assert reduction < 3
if not is_impossible:
global n_no_ans
n_no_ans += reduction
else:
global n_short
n_short += reduction
answers = []
return answers, is_impossible
|
|
107,162 | 308,405 | 222 | homeassistant/components/mqtt/cover.py | 35 | 22 | async def async_open_cover(self, **kwargs):
await mqtt.async_publish(
self.ha | Add mqtt encoding support for publishing (#62739)
* encoding support for mqtt publishing - todo tests
* signature allows None values for qos and retain
* common test for mqtt publishing encoding
* better test with command templates
* more tests
* fix tests alarm control panel+tests light basic
* tests light json and template
* add tests vacuum and fix tests light_template | async_open_cover | d0c4f0fec4216e4193da716001b5e13e1e3f2106 | core | cover.py | 14 | 16 | https://github.com/home-assistant/core.git | 3 | 98 | 0 | 32 | 150 | Python | {
"docstring": "Move the cover up.\n\n This method is a coroutine.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 9,
"vocab_size": 9
} | async def async_open_cover(self, **kwargs):
await mqtt.async_publish(
self.hass,
self._config.get(CONF_COMMAND_TOPIC),
self._config[CONF_PAYLOAD_OPEN],
self._config[CONF_QOS],
self._config[CONF_RETAIN],
self._config[CONF_ENCODING],
)
if self._optimistic:
# Optimistically assume that cover has changed state.
self._state = STATE_OPEN
if self._config.get(CONF_GET_POSITION_TOPIC):
self._position = self.find_percentage_in_range(
self._config[CONF_POSITION_OPEN], COVER_PAYLOAD
)
self.async_write_ha_state()
|
|
3,191 | 20,042 | 176 | pipenv/patched/notpip/_vendor/distro.py | 43 | 10 | def _parse_distro_release_content(line):
# type: (str) -> Dict[str, str]
matches = _DISTRO_RELEASE_CONTENT_REVERSED_PATTERN.match(line.strip()[::-1])
distro_info = {}
if matches:
# regexp ensures non-None
distro_info["name"] = matches.group(3 | 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 | _parse_distro_release_content | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | distro.py | 13 | 12 | https://github.com/pypa/pipenv.git | 5 | 109 | 0 | 32 | 201 | Python | {
"docstring": "\n Parse a line from a distro release file.\n\n Parameters:\n * line: Line from the distro release file. Must be a unicode string\n or a UTF-8 encoded byte string.\n\n Returns:\n A dictionary containing all information items.\n ",
"language": "en",
"n_whitespaces": 97,
"n_words": 35,
"vocab_size": 28
} | def _parse_distro_release_content(line):
# type: (str) -> Dict[str, str]
matches = _DISTRO_RELEASE_CONTENT_REVERSED_PATTERN.match(line.strip()[::-1])
distro_info = {}
if matches:
# regexp ensures non-None
distro_info["name"] = matches.group(3)[::-1]
if matches.group(2):
distro_info["version_id"] = matches.group(2)[::-1]
if matches.group(1):
distro_info["codename"] = matches.group(1)[::-1]
elif line:
distro_info["name"] = line.strip()
return distro_info
_distro = LinuxDistribution()
|
|
6,866 | 37,755 | 333 | src/transformers/modeling_utils.py | 167 | 45 | def load_sharded_checkpoint(model, folder, strict=True):
# Load the index
index_file = os.path.join(folder, WEIGHTS_INDEX_NAME)
if not os.path.isfile(index_file):
raise ValueError(f"Can't find a checkpoint index ({WEIGHTS_INDEX_NAME}) in {folder}.")
with open(index_file, "r", encoding="utf-8") as f:
index = json.load(f)
shard_files = list(set(index["weight_map"].values()))
# If strict=True, error before loading any of the state dicts.
loaded_keys = index["weight_map"].keys()
model_keys = model.state_dict().keys()
missing_keys = [key for key in model_keys if key not in loaded_keys]
unexpected_keys = [key for key in loaded_keys if key not in model_keys]
if strict and (len(missing_keys) > 0 or len(unexpected_keys) > 0):
error_message = f"Error(s) in loading state_dict for {model.__class__.__name__}"
if len(missing_keys) > 0:
str_missing_keys = ", | Make Trainer compatible with sharded checkpoints (#17053)
* Make Trainer compatible with sharded checkpoints
* Add doc | load_sharded_checkpoint | a8fa2f91f409a0657937016b983b74f58a07ae72 | transformers | modeling_utils.py | 15 | 26 | https://github.com/huggingface/transformers.git | 14 | 264 | 0 | 104 | 468 | Python | {
"docstring": "\n This is the same as\n [`torch.nn.Module.load_state_dict`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=load_state_dict#torch.nn.Module.load_state_dict)\n but for a sharded checkpoint.\n\n This load is performed efficiently: each checkpoint shard is loaded one by one in RAM and deleted after being\n loaded in the model.\n\n Args:\n model (`torch.nn.Module`): The model in which to load the checkpoint.\n folder (`str` or `os.PathLike`): A path to a folder containing the sharded checkpoint.\n strict (`bool`, *optional`, defaults to `True`):\n Whether to strictly enforce that the keys in the model state dict match the keys in the sharded checkpoint.\n\n Returns:\n `NamedTuple`: A named tuple with `missing_keys` and `unexpected_keys` fields\n - `missing_keys` is a list of str containing the missing keys\n - `unexpected_keys` is a list of str containing the unexpected keys\n ",
"language": "en",
"n_whitespaces": 201,
"n_words": 115,
"vocab_size": 67
} | def load_sharded_checkpoint(model, folder, strict=True):
# Load the index
index_file = os.path.join(folder, WEIGHTS_INDEX_NAME)
if not os.path.isfile(index_file):
raise ValueError(f"Can't find a checkpoint index ({WEIGHTS_INDEX_NAME}) in {folder}.")
with open(index_file, "r", encoding="utf-8") as f:
index = json.load(f)
shard_files = list(set(index["weight_map"].values()))
# If strict=True, error before loading any of the state dicts.
loaded_keys = index["weight_map"].keys()
model_keys = model.state_dict().keys()
missing_keys = [key for key in model_keys if key not in loaded_keys]
unexpected_keys = [key for key in loaded_keys if key not in model_keys]
if strict and (len(missing_keys) > 0 or len(unexpected_keys) > 0):
error_message = f"Error(s) in loading state_dict for {model.__class__.__name__}"
if len(missing_keys) > 0:
str_missing_keys = ",".join([f'"{k}"' for k in missing_keys])
error_message += f"\nMissing key(s): {str_missing_keys}."
if len(unexpected_keys) > 0:
str_unexpected_keys = ",".join([f'"{k}"' for k in unexpected_keys])
error_message += f"\nMissing key(s): {str_unexpected_keys}."
raise RuntimeError(error_message)
for shard_file in shard_files:
state_dict = torch.load(os.path.join(folder, shard_file))
model.load_state_dict(state_dict, strict=False)
# Make sure memory is fred before we load the next state dict.
del state_dict
gc.collect()
# Return the same thing as PyTorch load_state_dict function.
return torch.nn.modules.module._IncompatibleKeys(missing_keys, unexpected_keys)
|
|
@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs | 13,980 | 65,661 | 62 | erpnext/controllers/queries.py | 86 | 27 | def customer_query(doctype, txt, searchfield, start, page_len, filters):
conditions = []
cust_master_name = frappe.defaults.get_user_default("cust_master_name")
if cust_master_name == "Customer Name":
fields = ["name", "customer_group", "territory"]
else:
fields = ["name", "customer_name", "customer_group", "territory"]
fields = get_fields("Customer", fields)
searchfields = frappe.get_meta("Customer").get_search_fields()
searchfields = " or ".join(field + " like %(txt)s" for field in searchfields)
return frappe.db.sql(
.format(
**{
"fields": ", ".join(fields),
"scond": searchfields,
"mcond": get_match_cond(doctype),
"fc | style: format code with black | customer_query | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | queries.py | 16 | 30 | https://github.com/frappe/erpnext.git | 3 | 172 | 1 | 69 | 322 | Python | {
"docstring": "select {fields} from `tabCustomer`\n\t\twhere docstatus < 2\n\t\t\tand ({scond}) and disabled=0\n\t\t\t{fcond} {mcond}\n\t\torder by\n\t\t\tif(locate(%(_txt)s, name), locate(%(_txt)s, name), 99999),\n\t\t\tif(locate(%(_txt)s, customer_name), locate(%(_txt)s, customer_name), 99999),\n\t\t\tidx desc,\n\t\t\tname, customer_name\n\t\tlimit %(start)s, %(page_len)s",
"language": "en",
"n_whitespaces": 23,
"n_words": 33,
"vocab_size": 27
} | def customer_query(doctype, txt, searchfield, start, page_len, filters):
conditions = []
cust_master_name = frappe.defaults.get_user_default("cust_master_name")
if cust_master_name == "Customer Name":
fields = ["name", "customer_group", "territory"]
else:
fields = ["name", "customer_name", "customer_group", "territory"]
fields = get_fields("Customer", fields)
searchfields = frappe.get_meta("Customer").get_search_fields()
searchfields = " or ".join(field + " like %(txt)s" for field in searchfields)
return frappe.db.sql(
.format(
**{
"fields": ", ".join(fields),
"scond": searchfields,
"mcond": get_match_cond(doctype),
"fcond": get_filters_cond(doctype, filters, conditions).replace("%", "%%"),
}
),
{"txt": "%%%s%%" % txt, "_txt": txt.replace("%", ""), "start": start, "page_len": page_len},
)
# searches for supplier
@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs |
18,042 | 85,773 | 21 | src/sentry/tagstore/base.py | 7 | 6 | def get_group_tag_value_count(self, group, environment_id, key):
raise No | feat(perf_issues): Fix `GroupTagKeyDetailsEndpoint` to work for performance issues (#38860)
This allows this endpoint to return results for performance issues. | get_group_tag_value_count | 72e351082168f68cbaa5700a51e8ed577222e887 | sentry | base.py | 6 | 2 | https://github.com/getsentry/sentry.git | 1 | 14 | 0 | 7 | 22 | Python | {
"docstring": "\n >>> get_group_tag_value_count(group, 3, 'key1')\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 4,
"vocab_size": 4
} | def get_group_tag_value_count(self, group, environment_id, key):
raise NotImplementedError
|
|
9,144 | 47,522 | 174 | tests/jobs/test_scheduler_job.py | 47 | 35 | def test_enqueue_task_instances_sets_ti_state_to_None_if_dagrun_in_finish_state(self, state, dag_maker):
dag_i | Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator` | test_enqueue_task_instances_sets_ti_state_to_None_if_dagrun_in_finish_state | 49e336ae0302b386a2f47269a6d13988382d975f | airflow | test_scheduler_job.py | 11 | 17 | https://github.com/apache/airflow.git | 1 | 139 | 0 | 38 | 233 | Python | {
"docstring": "This tests that task instances whose dagrun is in finished state are not queued",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 14
} | def test_enqueue_task_instances_sets_ti_state_to_None_if_dagrun_in_finish_state(self, state, dag_maker):
dag_id = 'SchedulerJobTest.test_enqueue_task_instances_with_queued_state'
task_id_1 = 'dummy'
session = settings.Session()
with dag_maker(dag_id=dag_id, start_date=DEFAULT_DATE, session=session):
task1 = EmptyOperator(task_id=task_id_1)
self.scheduler_job = SchedulerJob(subdir=os.devnull)
dr1 = dag_maker.create_dagrun(state=state)
ti = dr1.get_task_instance(task1.task_id, session)
ti.state = State.SCHEDULED
session.merge(ti)
session.commit()
with patch.object(BaseExecutor, 'queue_command') as mock_queue_command:
self.scheduler_job._enqueue_task_instances_with_queued_state([ti])
ti.refresh_from_db()
assert ti.state == State.NONE
mock_queue_command.assert_not_called()
|
|
12,746 | 61,907 | 633 | .venv/lib/python3.8/site-packages/pip/_vendor/distlib/compat.py | 155 | 21 | def match_hostname(cert, hostname):
if not cert:
raise ValueError("empty or no certificate, match_hostname needs a "
"SSL socket or SSL context with either "
"CERT_OPTIONAL or CERT_REQUIRED")
dnsnames = []
san = cert.get('subjectAltName', ())
for key, value in san:
if key == 'DNS':
if _dnsname_match(value, hostname):
return
dnsnames.append(value)
if not dnsnames:
# The subject is only checked when there is no dNSName entry
# in subjectAltName
for sub in cert.get('subject', ()):
for key, value in sub:
# XXX according to RFC 2818, the most specific Common Name
# must be used.
if key == 'commonName':
| upd; format | match_hostname | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | compat.py | 15 | 30 | https://github.com/jindongwang/transferlearning.git | 12 | 166 | 0 | 106 | 314 | Python | {
"docstring": "Verify that *cert* (in decoded format as returned by\n SSLSocket.getpeercert()) matches the *hostname*. RFC 2818 and RFC 6125\n rules are followed, but IP addresses are not accepted for *hostname*.\n\n CertificateError is raised on failure. On success, the function\n returns nothing.\n ",
"language": "en",
"n_whitespaces": 76,
"n_words": 40,
"vocab_size": 36
} | def match_hostname(cert, hostname):
if not cert:
raise ValueError("empty or no certificate, match_hostname needs a "
"SSL socket or SSL context with either "
"CERT_OPTIONAL or CERT_REQUIRED")
dnsnames = []
san = cert.get('subjectAltName', ())
for key, value in san:
if key == 'DNS':
if _dnsname_match(value, hostname):
return
dnsnames.append(value)
if not dnsnames:
# The subject is only checked when there is no dNSName entry
# in subjectAltName
for sub in cert.get('subject', ()):
for key, value in sub:
# XXX according to RFC 2818, the most specific Common Name
# must be used.
if key == 'commonName':
if _dnsname_match(value, hostname):
return
dnsnames.append(value)
if len(dnsnames) > 1:
raise CertificateError("hostname %r "
"doesn't match either of %s"
% (hostname, ', '.join(map(repr, dnsnames))))
elif len(dnsnames) == 1:
raise CertificateError("hostname %r "
"doesn't match %r"
% (hostname, dnsnames[0]))
else:
raise CertificateError("no appropriate commonName or "
"subjectAltName fields were found")
try:
from types import SimpleNamespace as Container
except ImportError: # pragma: no cover |
|
22,008 | 104,860 | 112 | src/datasets/iterable_dataset.py | 18 | 19 | def take(self, n) -> "IterableDataset":
ex_iterable = TakeExamplesIterable(self._ex_iterable, n)
return iterable_dataset(
ex_iterable=ex_iterable,
info=self._info.copy(),
split=self._split,
format_type=self._format_type,
shuffling=copy.deepcopy(self._shuffling),
token_per_repo_id=self._token_per_repo_id, | Stream private zipped images (#4173)
* keep track of repo_id and token to decode remote images
* add test
* fix
* docstrings + comments
* fix string_to_dict
* fix tests | take | f51b6994db27ea69261ef919fb7775928f9ec10b | datasets | iterable_dataset.py | 11 | 29 | https://github.com/huggingface/datasets.git | 1 | 67 | 0 | 17 | 106 | Python | {
"docstring": "\n Create a new IterableDataset with only the first ``n`` elements.\n\n Args:\n n (:obj:`int`): number of elements to take.\n\n Example:\n\n ```py\n >>> from datasets import load_dataset\n >>> ds = load_dataset(\"rotten_tomatoes\", split=\"train\", streaming=True)\n >>> small_ds = ds.take(2)\n >>> list(small_ds)\n [{'label': 1,\n 'text': 'the rock is destined to be the 21st century\\'s new \" conan \" and that he\\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'},\n {'label': 1,\n 'text': 'the gorgeously elaborate continuation of \" the lord of the rings \" trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\\'s expanded vision of j . r . r . tolkien\\'s middle-earth .'}]\n ```\n ",
"language": "en",
"n_whitespaces": 230,
"n_words": 117,
"vocab_size": 90
} | def take(self, n) -> "IterableDataset":
ex_iterable = TakeExamplesIterable(self._ex_iterable, n)
return iterable_dataset(
ex_iterable=ex_iterable,
info=self._info.copy(),
split=self._split,
format_type=self._format_type,
shuffling=copy.deepcopy(self._shuffling),
token_per_repo_id=self._token_per_repo_id,
)
|
|
19,068 | 94,333 | 615 | tests/sentry/event_manager/test_event_manager.py | 66 | 29 | def test_category_match_group(self):
from sentry.grouping.enhancer import Enhancements
enhancement = Enhancements.from_config_string(
,
)
event = make_event(
platform="native",
exception={
"values": [
{
"type": "Hello",
"stacktrace": {
"frames": [
{
"function": "foo",
},
{
"function": "bar",
}, | test(event_manager): Fix incorrect invocations of manager.save (#36615) | test_category_match_group | 39cfdcb446e74732c67ce07d7dd8d8d5ace471b1 | sentry | test_event_manager.py | 20 | 38 | https://github.com/getsentry/sentry.git | 1 | 154 | 0 | 47 | 265 | Python | {
"docstring": "\n Regression test to ensure categories are applied consistently and don't\n produce hash mismatches.\n \n function:foo category=foo_like\n category:foo_like -group\n ",
"language": "en",
"n_whitespaces": 73,
"n_words": 17,
"vocab_size": 17
} | def test_category_match_group(self):
from sentry.grouping.enhancer import Enhancements
enhancement = Enhancements.from_config_string(
,
)
event = make_event(
platform="native",
exception={
"values": [
{
"type": "Hello",
"stacktrace": {
"frames": [
{
"function": "foo",
},
{
"function": "bar",
},
]
},
}
]
},
)
manager = EventManager(event)
manager.normalize()
grouping_config = {
"enhancements": enhancement.dumps(),
"id": "mobile:2021-02-12",
}
manager.get_data()["grouping_config"] = grouping_config
event1 = manager.save(self.project.id)
event2 = Event(event1.project_id, event1.event_id, data=event1.data)
assert event1.get_hashes().hashes == event2.get_hashes(grouping_config).hashes
|
|
19,326 | 96,559 | 366 | src/sentry/plugins/bases/notify.py | 48 | 26 | def notify(self, notification, raise_exception=False):
event = notification.event
try:
return self.notify_users(
event.group, event, triggering_rules=[r.label for r in notification.rules]
)
except (
ApiError,
HTTPError,
InvalidIdentity,
PluginError,
SSLError,
UrllibHTTPError,
) as err:
self.logger.info(
"notification-plugin.notify-failed",
extra={
"error": str(err),
"plugin": self.slug,
"project_id": event.group.project_id,
"organization_id": event.group.project.organization_id,
},
)
if raise_exception:
| fix(plugins): Silence error (#32042) | notify | 542484c0cd71625e62e086f3f7c5aaf85360f724 | sentry | notify.py | 16 | 26 | https://github.com/getsentry/sentry.git | 4 | 114 | 0 | 45 | 175 | Python | {
"docstring": "\n This calls the notify_users method of the plugin.\n Normally this method eats the error and logs it but if we\n set raise_exception=True like we do for the test plugin button,\n the exception is raised\n ",
"language": "en",
"n_whitespaces": 70,
"n_words": 34,
"vocab_size": 28
} | def notify(self, notification, raise_exception=False):
event = notification.event
try:
return self.notify_users(
event.group, event, triggering_rules=[r.label for r in notification.rules]
)
except (
ApiError,
HTTPError,
InvalidIdentity,
PluginError,
SSLError,
UrllibHTTPError,
) as err:
self.logger.info(
"notification-plugin.notify-failed",
extra={
"error": str(err),
"plugin": self.slug,
"project_id": event.group.project_id,
"organization_id": event.group.project.organization_id,
},
)
if raise_exception:
raise err
return False
|
|
17,345 | 82,298 | 51 | cms/tests/test_rendering.py | 16 | 14 | def test_processors(self):
from djangocms_text_ckeditor.cms_plugins import TextPlugin
from cms.plugin_pool import plugin_pool
instance = CMSPlugin.objects.all()[0].get_plugin_instance()[0]
load_from_string = self.load_template_from_string
| Enabled isort workflow (#7200)
* Ran isort
* Enabled isort workflow
Co-authored-by: Vinit Kumar <mail@vinitkumar.me> | test_processors | a3110e1ff24085373898c7d2a85f628abeb8518d | django-cms | test_rendering.py | 13 | 27 | https://github.com/django-cms/django-cms.git | 1 | 169 | 0 | 13 | 69 | Python | {
"docstring": "\n Tests that plugin processors and plugin context processors can be defined\n in settings and are working and that extra plugin context processors can be\n passed to PluginContext.\n ",
"language": "en",
"n_whitespaces": 56,
"n_words": 27,
"vocab_size": 17
} | def test_processors(self):
from djangocms_text_ckeditor.cms_plugins import TextPlugin
from cms.plugin_pool import plugin_pool
instance = CMSPlugin.objects.all()[0].get_plugin_instance()[0]
load_from_string = self.load_template_from_string
|
|
115,025 | 316,447 | 25 | tests/test_config_entries.py | 13 | 9 | async def test_unique_id_ignore(hass, manager):
async_setup_entry = AsyncMock(return_value=False)
mock_integration(hass, MockModule("comp", async_setup_entry=async_setup_entry))
mock_entity_platform(hass, "config_flow.comp", None)
| Search/replace RESULT_TYPE_* by FlowResultType enum (#74642) | test_unique_id_ignore | 7cd68381f1d4f58930ffd631dfbfc7159d459832 | core | test_config_entries.py | 10 | 24 | https://github.com/home-assistant/core.git | 1 | 185 | 0 | 13 | 63 | Python | {
"docstring": "Test that we can ignore flows that are in progress and have a unique ID.",
"language": "en",
"n_whitespaces": 14,
"n_words": 15,
"vocab_size": 14
} | async def test_unique_id_ignore(hass, manager):
async_setup_entry = AsyncMock(return_value=False)
mock_integration(hass, MockModule("comp", async_setup_entry=async_setup_entry))
mock_entity_platform(hass, "config_flow.comp", None)
|
|
53,448 | 212,840 | 58 | PySimpleGUI.py | 19 | 10 | def bind(self, bind_string, key, propagate=True):
if not self._is_window_created('tried Window.bind'):
return
self.TKroot.bind(bind_string, lambda evt: self._user_bind_callback(bind_string, evt, propagate))
self.user_bind_d | Added propagate parameter to the Element.bind and Window.bind methods. Indicates whether tkinter should propagate the event to the corresponding element/window or stop with the user callback | bind | b3680477c755277192715b343e9cd4254de7c45e | PySimpleGUI | PySimpleGUI.py | 10 | 5 | https://github.com/PySimpleGUI/PySimpleGUI.git | 2 | 54 | 0 | 19 | 85 | Python | {
"docstring": "\n Used to add tkinter events to a Window.\n The tkinter specific data is in the Window's member variable user_bind_event\n :param bind_string: The string tkinter expected in its bind function\n :type bind_string: (str)\n :param key: The event that will be generated when the tkinter event occurs\n :type key: str | int | tuple | object\n :param propagate: If True then tkinter will be told to propagate the event\n :type propagate: (bool)\n ",
"language": "en",
"n_whitespaces": 157,
"n_words": 70,
"vocab_size": 46
} | def bind(self, bind_string, key, propagate=True):
if not self._is_window_created('tried Window.bind'):
return
self.TKroot.bind(bind_string, lambda evt: self._user_bind_callback(bind_string, evt, propagate))
self.user_bind_dict[bind_string] = key
|
|
52,997 | 211,000 | 759 | ppdet/modeling/heads/cascade_head.py | 167 | 52 | def forward(self, body_feats=None, rois=None, rois_num=None, inputs=None):
targets = []
if self.training:
rois, rois_num, targets = self.bbox_assigner(rois, rois_num, inputs)
targets_list = [targets]
self.assigned_rois = (rois, rois_num)
self.assigned_targets = targets
pred_bbox = None
head_out_list = []
for i in range(self.num_cascade_stages):
if i > 0:
rois, rois_num = self._get_rois_from_boxes(pred_bbox,
inputs['im_shape'])
if self.training:
rois, rois_num, targets = self.bbox_assigner(
rois, rois_num, inputs, i, is_cascade=True)
targets_list.append(targets)
rois_feat = self.roi_extractor(body_feats, rois, rois_num)
bbox_feat = self.head(rois_feat, i)
scores = self.bbox_score_list[i](bbox_feat)
deltas = self.bbox_delta_list[i](bbox_feat)
| upgrade cascade model (#6346)
* add reg_class_agnostic
* add loss_rpn_bbox | forward | d409ec06779e9de0cdbd76af4dc2c00b4b58ccb0 | PaddleDetection | cascade_head.py | 17 | 41 | https://github.com/PaddlePaddle/PaddleDetection.git | 10 | 390 | 0 | 107 | 585 | Python | {
"docstring": "\n body_feats (list[Tensor]): Feature maps from backbone\n rois (Tensor): RoIs generated from RPN module\n rois_num (Tensor): The number of RoIs in each image\n inputs (dict{Tensor}): The ground-truth of image\n ",
"language": "en",
"n_whitespaces": 64,
"n_words": 28,
"vocab_size": 22
} | def forward(self, body_feats=None, rois=None, rois_num=None, inputs=None):
targets = []
if self.training:
rois, rois_num, targets = self.bbox_assigner(rois, rois_num, inputs)
targets_list = [targets]
self.assigned_rois = (rois, rois_num)
self.assigned_targets = targets
pred_bbox = None
head_out_list = []
for i in range(self.num_cascade_stages):
if i > 0:
rois, rois_num = self._get_rois_from_boxes(pred_bbox,
inputs['im_shape'])
if self.training:
rois, rois_num, targets = self.bbox_assigner(
rois, rois_num, inputs, i, is_cascade=True)
targets_list.append(targets)
rois_feat = self.roi_extractor(body_feats, rois, rois_num)
bbox_feat = self.head(rois_feat, i)
scores = self.bbox_score_list[i](bbox_feat)
deltas = self.bbox_delta_list[i](bbox_feat)
# TODO (lyuwenyu) Is it correct for only one class ?
if not self.reg_class_agnostic and i < self.num_cascade_stages - 1:
deltas = deltas.reshape([-1, self.num_classes, 4])
labels = scores[:, :-1].argmax(axis=-1)
deltas = deltas[paddle.arange(deltas.shape[0]), labels]
head_out_list.append([scores, deltas, rois])
pred_bbox = self._get_pred_bbox(deltas, rois, self.bbox_weight[i])
if self.training:
loss = {}
for stage, value in enumerate(zip(head_out_list, targets_list)):
(scores, deltas, rois), targets = value
loss_stage = self.get_loss(scores, deltas, targets, rois,
self.bbox_weight[stage])
for k, v in loss_stage.items():
loss[k + "_stage{}".format(
stage)] = v / self.num_cascade_stages
return loss, bbox_feat
else:
scores, deltas, self.refined_rois = self.get_prediction(
head_out_list)
return (deltas, scores), self.head
|
|
56,997 | 223,601 | 24 | python3.10.4/Lib/email/_header_value_parser.py | 12 | 7 | def get_fws(value):
newvalue = value.lstrip()
fws = WhiteSpaceTerminal(value[:len(value)-len(newvalue)], 'fws')
return fws, newvalue
| add python 3.10.4 for windows | get_fws | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | _header_value_parser.py | 13 | 4 | https://github.com/XX-net/XX-Net.git | 1 | 37 | 0 | 10 | 64 | Python | {
"docstring": "FWS = 1*WSP\n\n This isn't the RFC definition. We're using fws to represent tokens where\n folding can be done, but when we are parsing the *un*folding has already\n been done so we don't need to watch out for CRLF.\n\n ",
"language": "en",
"n_whitespaces": 52,
"n_words": 39,
"vocab_size": 36
} | def get_fws(value):
newvalue = value.lstrip()
fws = WhiteSpaceTerminal(value[:len(value)-len(newvalue)], 'fws')
return fws, newvalue
|
|
37,386 | 158,218 | 147 | d2l/mxnet.py | 42 | 21 | def load_data_snli(batch_size, num_steps=50):
num_workers = d2l.get_dataloader_workers()
data_dir = d2l.download_extract('SNLI')
train_data = read_snli(data_dir, True)
test_data = read_snli(data_dir, False)
train_set = SNLIDataset(train_data, | [PaddlePaddle] Merge master into Paddle branch (#1186)
* change 15.2 title in chinese version (#1109)
change title ’15.2. 情感分析：使用递归神经网络‘ to ’15.2. 情感分析：使用循环神经网络‘
* 修改部分语义表述 (#1105)
* Update r0.17.5 (#1120)
* Bump versions in installation
* 94行typo: （“bert.mall”）->（“bert.small”） (#1129)
* line 313: "bert.mall" -> "bert.small" (#1130)
* fix: update language as native reader (#1114)
* Fix the translation of "stride" (#1115)
* Update index.md (#1118)
修改部分语义表述
* Update self-attention-and-positional-encoding.md (#1133)
依照本书的翻译习惯，将pooling翻译成汇聚
* maybe a comment false (#1149)
* maybe a little false
* maybe a little false
* A minor bug in the rcnn section (Chinese edition) (#1148)
* Update bert.md (#1137)
一个笔误
# 假设batch_size=2，num_pred_positions=3
# 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1]
* Update calculus.md (#1135)
* fix typo in git documentation (#1106)
* fix: Update the Chinese translation in lr-scheduler.md (#1136)
* Update lr-scheduler.md
* Update chapter_optimization/lr-scheduler.md
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
* fix translation for kaggle-house-price.md (#1107)
* fix translation for kaggle-house-price.md
* fix translation for kaggle-house-price.md
Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com>
* Update weight-decay.md (#1150)
* Update weight-decay.md
关于“k多选d”这一部分，中文读者使用排列组合的方式可能更容易理解
关于“给定k个变量，阶数的个数为...”这句话是有歧义的，不是很像中国话，应该是说“阶数为d的项的个数为...”。
并增加了一句对“因此即使是阶数上的微小变化，比如从$2$到$3$，也会显著增加我们模型的复杂性。”的解释
解释为何会增加复杂性以及为何需要细粒度工具。
* Update chapter_multilayer-perceptrons/weight-decay.md
yep
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
* Update chapter_multilayer-perceptrons/weight-decay.md
yep
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
* Fix a spelling error (#1161)
* Update gru.md (#1152)
The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state.
翻译错误
* Unify the function naming (#1113)
Unify naming of the function 'init_xavier()'.
* Update mlp-concise.md (#1166)
* Update mlp-concise.md
语句不通顺
* Update environment.md
语序异常
* Update config.ini
* fix the imprecise description (#1168)
Co-authored-by: yuande <yuande>
* fix typo in chapter_natural-language-processing-pretraining/glove.md (#1175)
* Fix some typos. (#1163)
* Update batch-norm.md (#1170)
fixing typos u->x in article
* Update linear-regression.md (#1090)
We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that
原译文把who也直接翻译出来了。
* Update mlp.md (#1117)
* Update mlp.md
修改部分语义表述
* Update chapter_multilayer-perceptrons/mlp.md
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
* Update chapter_multilayer-perceptrons/mlp.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
* Correct a translation error. (#1091)
* Correct a translation error.
* Update chapter_computer-vision/image-augmentation.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Update aws.md (#1121)
* Update aws.md
* Update chapter_appendix-tools-for-deep-learning/aws.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Update image-augmentation.md (#1093)
* Update anchor.md (#1088)
fix a minor issue in code
* Update anchor.md
* Update image-augmentation.md
* fix typo and improve translation in chapter_linear-networks\softmax-regression.md (#1087)
* Avoid `torch.meshgrid` user warning (#1174)
Avoids the following user warning:
```python
~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
```
* bump to 2.0.0-beta1
* Update sequence.md
* bump beta1 on readme
* Add latex code block background to config
* BLD: Bump python support version 3.9 (#1183)
* BLD: Bump python support version 3.9
* Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4
* BLD: Bump torch and tensorflow
* Update Jenkinsfile
* Update chapter_installation/index.md
* Update chapter_installation/index.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Update config.ini
* Update INFO.md
* Update INFO.md
* Drop mint to show code in pdf, use Inconsolata font, apply code cell color (#1187)
* resolve the conflicts
* revise from publisher (#1089)
* revise from publisher
* d2l api
* post_latex
* revise from publisher
* revise ch11
* Delete d2l-Copy1.bib
* clear cache
* rm d2lbook clear
* debug anchor
* keep original d2l doc
Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal>
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
Co-authored-by: Aston Zhang <asv325@gmail.com>
* 重复语句 (#1188)
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Improve expression for chapter_preliminaries/pandas.md (#1184)
* Update pandas.md
* Improve expression
* Improve expression
* Update chapter_preliminaries/pandas.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Improce expression for chapter_preliminaries/linear-algebra.md (#1185)
* Improce expression
* Improve code comments
* Update chapter_preliminaries/linear-algebra.md
* Update chapter_preliminaries/linear-algebra.md
* Update chapter_preliminaries/linear-algebra.md
* Update chapter_preliminaries/linear-algebra.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Fix multibox_detection bugs
* Update d2l to 0.17.5 version
* restore older version
* Upgrade pandas
* change to python3.8
* Test warning log
* relocate warning log
* test logs filtering
* Update gru.md
* Add DeprecationWarning filter
* Test warning log
* Update attention mechanisms & computational performance
* Update multilayer perceptron& linear & convolution networks & computer vision
* Update recurrent&optimition&nlp pretraining & nlp applications
* ignore warnings
* Update index.md
* Update linear networks
* Update multilayer perceptrons&deep learning computation
* Update preliminaries
* Check and Add warning filter
* Update kaggle-cifar10.md
* Update object-detection-dataset.md
* Update ssd.md fcn.md
* Update hybridize.md
* Update hybridize.md
Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com>
Co-authored-by: zhou201505013 <39976863+zhou201505013@users.noreply.github.com>
Co-authored-by: Xinwei Liu <xinzone@outlook.com>
Co-authored-by: Anirudh Dagar <anirudhdagar6@gmail.com>
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
Co-authored-by: hugo_han <57249629+HugoHann@users.noreply.github.com>
Co-authored-by: gyro永不抽风 <1247006353@qq.com>
Co-authored-by: CanChengZheng <zcc550169544@163.com>
Co-authored-by: linlin <jajupmochi@gmail.com>
Co-authored-by: iuk <liukun0104@gmail.com>
Co-authored-by: yoos <49556860+liyunlongaaa@users.noreply.github.com>
Co-authored-by: Mr. Justice Lawrence John Wargrave <65226618+RUCWargrave@users.noreply.github.com>
Co-authored-by: Chiyuan Fu <fuchiyuan2019@outlook.com>
Co-authored-by: Sunhuashan <48636870+Sunhuashan@users.noreply.github.com>
Co-authored-by: Haiker Sun <haizhou.uestc2011@gmail.com>
Co-authored-by: Ming Liu <akira.liu@njnu.edu.cn>
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
Co-authored-by: silenceZheng66 <13754430639@163.com>
Co-authored-by: Wenchao Yan <56541797+YWonchall@users.noreply.github.com>
Co-authored-by: Kiki2049 <55939997+Kiki2049@users.noreply.github.com>
Co-authored-by: Krahets <krahets@163.com>
Co-authored-by: friedmainfunction <73703265+friedmainfunction@users.noreply.github.com>
Co-authored-by: Jameson <miraclecome@gmail.com>
Co-authored-by: P. Yao <12227516+YaoPengCN@users.noreply.github.com>
Co-authored-by: Yulv-git <34329208+Yulv-git@users.noreply.github.com>
Co-authored-by: Liu,Xiao <45966993+liuxiao916@users.noreply.github.com>
Co-authored-by: YIN, Gang <1246410+yingang@users.noreply.github.com>
Co-authored-by: Joe-HZ <58297431+Joe-HZ@users.noreply.github.com>
Co-authored-by: lybloveyou <102609904+lybloveyou@users.noreply.github.com>
Co-authored-by: VigourJiang <jiangfuqiang154@163.com>
Co-authored-by: zxhd863943427 <74853597+zxhd863943427@users.noreply.github.com>
Co-authored-by: LYF <27893441+liyufan@users.noreply.github.com>
Co-authored-by: Aston Zhang <asv325@gmail.com>
Co-authored-by: xiaotinghe <xiaotih@amazon.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal>
Co-authored-by: Holly-Max <60691735+Holly-Max@users.noreply.github.com>
Co-authored-by: HinGwenWoong <peterhuang0323@qq.com>
Co-authored-by: Shuai Zhang <cheungdaven@gmail.com> | load_data_snli | b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2 | d2l-zh | mxnet.py | 9 | 12 | https://github.com/d2l-ai/d2l-zh.git | 1 | 109 | 0 | 32 | 165 | Python | {
"docstring": "Download the SNLI dataset and return data iterators and vocabulary.\n\n Defined in :numref:`sec_natural-language-inference-and-dataset`",
"language": "en",
"n_whitespaces": 15,
"n_words": 13,
"vocab_size": 12
} | def load_data_snli(batch_size, num_steps=50):
num_workers = d2l.get_dataloader_workers()
data_dir = d2l.download_extract('SNLI')
train_data = read_snli(data_dir, True)
test_data = read_snli(data_dir, False)
train_set = SNLIDataset(train_data, num_steps)
test_set = SNLIDataset(test_data, num_steps, train_set.vocab)
train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True,
num_workers=num_workers)
test_iter = gluon.data.DataLoader(test_set, batch_size, shuffle=False,
num_workers=num_workers)
return train_iter, test_iter, train_set.vocab
|
|
34,972 | 151,197 | 189 | freqtrade/freqai/utils.py | 84 | 34 | def plot_feature_importance(model, feature_names, pair, train_dir, count_max=50) -> None:
try:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
except ImportError:
logger.exception("Module plotly not found \n Please install using `pip3 install plotly`")
exit(1)
from freqtrade.plot.plotting import store_plot_file
# Gather feature importance from model
if "c | plot features as html instead of png | plot_feature_importance | 86aa875bc9d5edeba04f908fe45b011e52045c83 | freqtrade | utils.py | 13 | 37 | https://github.com/freqtrade/freqtrade.git | 4 | 229 | 0 | 67 | 261 | Python | {
"docstring": "\n Plot Best and Worst Features by importance for CatBoost model.\n Called once per sub-train.\n Usage: plot_feature_importance(\n model=model,\n feature_names=dk.training_features_list,\n pair=pair,\n train_dir=dk.data_path)\n ",
"language": "en",
"n_whitespaces": 89,
"n_words": 20,
"vocab_size": 20
} | def plot_feature_importance(model, feature_names, pair, train_dir, count_max=50) -> None:
try:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
except ImportError:
logger.exception("Module plotly not found \n Please install using `pip3 install plotly`")
exit(1)
from freqtrade.plot.plotting import store_plot_file
# Gather feature importance from model
if "catboost.core" in str(model.__class__):
feature_importance = model.get_feature_importance()
elif "lightgbm.sklearn" in str(model.__class__):
feature_importance = model.feature_importances_
else:
raise NotImplementedError(f"Cannot extract feature importance for {model.__class__}")
# Data preparation
fi_df = pd.DataFrame({
"feature_names": np.array(feature_names),
"feature_importance": np.array(feature_importance)
})
fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
# Plotting |
|
51,034 | 205,205 | 95 | django/db/backends/sqlite3/introspection.py | 23 | 12 | def get_primary_key_column(self, cursor, table_name):
cursor.execute(
"PRAGMA table_info(%s)" % self.connection.ops.quote_name(table_name)
)
for _, name, *_, pk in cursor.fetchall():
if pk:
return name
return | Refs #33476 -- Reformatted code with Black. | get_primary_key_column | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | introspection.py | 12 | 8 | https://github.com/django/django.git | 3 | 50 | 0 | 22 | 80 | Python | {
"docstring": "Return the column name of the primary key for the given table.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 10
} | def get_primary_key_column(self, cursor, table_name):
cursor.execute(
"PRAGMA table_info(%s)" % self.connection.ops.quote_name(table_name)
)
for _, name, *_, pk in cursor.fetchall():
if pk:
return name
return None
|
|
17,455 | 82,603 | 301 | cms/utils/setup.py | 95 | 14 | def validate_settings():
try:
django_backend = [x for x in settings.TEMPLATES
if x['BACKEND'] == 'django.template.backends.django.DjangoTemplates'][0]
except IndexError:
raise ImproperlyConfigured(
"django CMS requires django.template.context_processors.request in "
"'django.template.backends.django.DjangoTemplates' context processors."
| fix: Adds a deprecation warning for SEND_BROKEN_LINK_EMAILS (#7420)
* Fix: toolbar bug 3.10.rc1
* Feat: Dark mode support, including input from @marksweb, bugfix for tooltips
* Upstream change to be able to merge
* Feat: Dark mode support, including input from @marksweb, bugfix for tooltips
* Revert "Fix: toolbar bug 3.10.rc1"
This reverts commit 592a2b604e8f72b8e9c948e83163394cc6e8fe3d.
* Fix: Recommit toolbar fix (??)
* Fix: After lint failure: Remove spaces added by PyCharm
* Fix: Wizzard button color
* Fix: Correct toolbar according to cms_path
Fix: Avoid unnecessary toolbar loading
* TASK: use isort to sort imports
* Fix: Move CMS.API.Toolbar.get_color_scheme to CMS.API.Helpers.getColorScheme and CMS.API.Toolbar.set_color_scheme to CMS.API.Helpers.setColorScheme
* Fix: Typo in comment
* Fix: Typos in comments
* Fix: Typos in comments
* Add: Changelog entry
* Fix: base unit test for js frontend
* Add: Basic set/get color scheme test
* fix: deprecate SEND_BROKEN_LINK_EMAILS setting
* fix: flake8 w504
Co-authored-by: Vinit Kumar <mail@vinitkumar.me>
Co-authored-by: Simon Krull <krull@punkt.de>
Co-authored-by: Mark Walker <theshow@gmail.com> | validate_settings | d38f4a1cc7fc6b9e06a01622dd584329b73b410d | django-cms | setup.py | 14 | 21 | https://github.com/django-cms/django-cms.git | 8 | 108 | 0 | 68 | 201 | Python | {
"docstring": "\n Check project settings file for required options\n ",
"language": "en",
"n_whitespaces": 14,
"n_words": 7,
"vocab_size": 7
} | def validate_settings():
try:
django_backend = [x for x in settings.TEMPLATES
if x['BACKEND'] == 'django.template.backends.django.DjangoTemplates'][0]
except IndexError:
raise ImproperlyConfigured(
"django CMS requires django.template.context_processors.request in "
"'django.template.backends.django.DjangoTemplates' context processors."
)
context_processors = django_backend.get('OPTIONS', {}).get('context_processors', [])
if ('django.core.context_processors.request' not in context_processors and # noqa: W504
'django.template.context_processors.request' not in context_processors):
raise ImproperlyConfigured("django CMS requires django.template.context_processors.request in "
"'django.template.backends.django.DjangoTemplates' context processors.")
if (
hasattr(settings, "SEND_BROKEN_LINK_EMAILS") and # noqa: W504
"django.middleware.common.BrokenLinkEmailsMiddleware" not in getattr(settings, "MIDDLEWARE", [])
):
warnings.warn('The setting "SEND_BROKEN_LINK_EMAILS" will not be honored by django CMS as of version 4.1. '
'Add "django.middleware.common.BrokenLinkEmailsMiddleware" to your MIDDLEWARE settings '
'instead.', DeprecationWarning)
|
|
35,209 | 152,965 | 44 | modin/config/envvars.py | 16 | 4 | def get(cls):
min_partition_size = super().get()
assert min_partition_size > 0, "`min_partition_size` should be > 0"
return min_partition_size
| REFACTOR-#3768: change 'compute_chunksize' signature (#3769)
Co-authored-by: Yaroslav Igoshev <Poolliver868@mail.ru>
Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com> | get | 0bdc482d6f1682e103b4c4d7ee7c4d505d2d3b1c | modin | envvars.py | 10 | 4 | https://github.com/modin-project/modin.git | 1 | 23 | 0 | 13 | 42 | Python | {
"docstring": "\n Get ``MinPartitionSize`` with extra checks.\n\n Returns\n -------\n int\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 8,
"vocab_size": 8
} | def get(cls):
min_partition_size = super().get()
assert min_partition_size > 0, "`min_partition_size` should be > 0"
return min_partition_size
|
|
27,510 | 124,089 | 531 | python/ray/tune/examples/pbt_function.py | 207 | 25 | def pbt_function(config):
lr = config["lr"]
accuracy = 0.0 # end = 1000
start = 0
if session.get_checkpoint():
state = session.get_checkpoint().to_dict()
accuracy = state["acc"]
start = state["step"]
midpoint = 100 # lr starts decreasing after acc > midpoint
q_tolerance = 3 # penalize exceeding lr by more than this multiple
noise_level = 2 # add gaussian noise to the acc increase
# triangle wave:
# - start at 0.001 @ t=0,
# - peak at 0.01 @ t=midpoint,
# - end at 0.001 @ t=midpoint * 2,
for step in range(start, 100):
if accuracy < midpoint:
optimal_lr = 0.01 * accuracy / midpoint
else:
optimal_lr = 0.01 - 0.01 * (accuracy - midpoint) / midpoint
optimal_lr = min(0.01, max(0.001, optimal_lr))
# compute accuracy increase
q_err = max(lr, optimal_lr) / min(lr, optimal_lr)
if q_err < q_tolerance:
accuracy | [air] update documentation to use `session.report` (#26051)
Update documentation to use `session.report`.
Next steps:
1. Update our internal caller to use `session.report`. Most importantly, CheckpointManager and DataParallelTrainer.
2. Update `get_trial_resources` to use PGF notions to incorporate the requirement of ResourceChangingScheduler. @Yard1
3. After 2 is done, change all `tune.get_trial_resources` to `session.get_trial_resources`
4. [internal implementation] remove special checkpoint handling logic from huggingface trainer. Optimize the flow for checkpoint conversion with `session.report`.
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com> | pbt_function | ac831fded416381ad3c7fe2ba135eaa4aaab5879 | ray | pbt_function.py | 16 | 37 | https://github.com/ray-project/ray.git | 7 | 253 | 0 | 114 | 407 | Python | {
"docstring": "Toy PBT problem for benchmarking adaptive learning rate.\n\n The goal is to optimize this trainable's accuracy. The accuracy increases\n fastest at the optimal lr, which is a function of the current accuracy.\n\n The optimal lr schedule for this problem is the triangle wave as follows.\n Note that many lr schedules for real models also follow this shape:\n\n best lr\n ^\n | /\\\n | / \\\n | / \\\n | / \\\n ------------> accuracy\n\n In this problem, using PBT with a population of 2-4 is sufficient to\n roughly approximate this lr schedule. Higher population sizes will yield\n faster convergence. Training will not converge without PBT.\n ",
"language": "en",
"n_whitespaces": 177,
"n_words": 104,
"vocab_size": 71
} | def pbt_function(config):
lr = config["lr"]
accuracy = 0.0 # end = 1000
start = 0
if session.get_checkpoint():
state = session.get_checkpoint().to_dict()
accuracy = state["acc"]
start = state["step"]
midpoint = 100 # lr starts decreasing after acc > midpoint
q_tolerance = 3 # penalize exceeding lr by more than this multiple
noise_level = 2 # add gaussian noise to the acc increase
# triangle wave:
# - start at 0.001 @ t=0,
# - peak at 0.01 @ t=midpoint,
# - end at 0.001 @ t=midpoint * 2,
for step in range(start, 100):
if accuracy < midpoint:
optimal_lr = 0.01 * accuracy / midpoint
else:
optimal_lr = 0.01 - 0.01 * (accuracy - midpoint) / midpoint
optimal_lr = min(0.01, max(0.001, optimal_lr))
# compute accuracy increase
q_err = max(lr, optimal_lr) / min(lr, optimal_lr)
if q_err < q_tolerance:
accuracy += (1.0 / q_err) * random.random()
elif lr > optimal_lr:
accuracy -= (q_err - q_tolerance) * random.random()
accuracy += noise_level * np.random.normal()
accuracy = max(0, accuracy)
checkpoint = None
if step % 3 == 0:
checkpoint = Checkpoint.from_dict({"acc": accuracy, "step": start})
session.report(
{
"mean_accuracy": accuracy,
"cur_lr": lr,
"optimal_lr": optimal_lr, # for debugging
"q_err": q_err, # for debugging
"done": accuracy > midpoint * 2, # this stops the training process
},
checkpoint=checkpoint,
)
|
|
84,764 | 284,498 | 77 | openbb_terminal/stocks/options/hedge/hedge_model.py | 53 | 12 | def add_hedge_option(price, implied_volatility, strike, days, side):
# Determine delta position given the option
delta = calc_delta(price, implied_volatility, strike, days, 0, side)
# Determine gamma position given the option
gamma = calc_gamma(price, implied_volatility, strike, days, 0)
# Determine vega position given the option
vega = calc_vega(price, implied_volatility, strike, days, 0)
return delta, gamma, vega
| Feature/hedge (#1768)
* [Bug] Incorrect log for reddit keys. #1733 fix
* Create new feature-hedge
* Significantly improve code of hedge menu
* More robust
* Robustness
* Fix tests
* Fix can't multiply sequence by non-int of type 'numpy.float64' error
* Temporary fix of singular matrix error. Return first feasible solution
* Update Hugo Documentation
* Combining menus and cleaning up code
* Tidy up call_exp
* Update tests Round 1
* Update tests Round 2
* Fix linting error
* Fix linting?
* Fixed glitch
Co-authored-by: JerBouma <jer.bouma@gmail.com>
Co-authored-by: James Maslek <jmaslek11@gmail.com>
Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com>
Co-authored-by: colin99d <colin99delahunty@gmail.com>
Co-authored-by: didierlopes.eth <dro.lopes@campus.fct.unl.pt> | add_hedge_option | 54a1b6f545a0016c576e9e00eef5c003d229dacf | OpenBBTerminal | hedge_model.py | 8 | 5 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 1 | 64 | 0 | 25 | 88 | Python | {
"docstring": "Determine the delta, gamma and vega value of the portfolio and/or options.\n\n Parameters\n ----------\n price: int\n The price.\n implied_volatility: float\n The implied volatility.\n strike: float\n The strike price.\n days: float\n The amount of days until expiration. Use annual notation thus a month would be 30 / 360.\n sign: int\n Whether you have a long (1) or short (-1) position\n\n Returns\n -------\n delta: float\n gamma: float\n portfolio: float\n ",
"language": "en",
"n_whitespaces": 141,
"n_words": 67,
"vocab_size": 54
} | def add_hedge_option(price, implied_volatility, strike, days, side):
# Determine delta position given the option
delta = calc_delta(price, implied_volatility, strike, days, 0, side)
# Determine gamma position given the option
gamma = calc_gamma(price, implied_volatility, strike, days, 0)
# Determine vega position given the option
vega = calc_vega(price, implied_volatility, strike, days, 0)
return delta, gamma, vega
|
|
57,028 | 223,645 | 93 | python3.10.4/Lib/email/charset.py | 33 | 9 | def header_encode(self, string):
codec = self.output_codec or 'us-ascii'
header_bytes = _encode(string, codec)
# 7bit/8bit encodings return the string unchanged (modulo conversions)
encoder_module = self._get_encoder(header_bytes)
if encoder_module is None:
return string
return encoder | add python 3.10.4 for windows | header_encode | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | charset.py | 8 | 7 | https://github.com/XX-net/XX-Net.git | 3 | 47 | 0 | 26 | 78 | Python | {
"docstring": "Header-encode a string by converting it first to bytes.\n\n The type of encoding (base64 or quoted-printable) will be based on\n this charset's `header_encoding`.\n\n :param string: A unicode string for the header. It must be possible\n to encode this string to bytes using the character set's\n output codec.\n :return: The encoded string, with RFC 2047 chrome.\n ",
"language": "en",
"n_whitespaces": 113,
"n_words": 55,
"vocab_size": 47
} | def header_encode(self, string):
codec = self.output_codec or 'us-ascii'
header_bytes = _encode(string, codec)
# 7bit/8bit encodings return the string unchanged (modulo conversions)
encoder_module = self._get_encoder(header_bytes)
if encoder_module is None:
return string
return encoder_module.header_encode(header_bytes, codec)
|
|
7,639 | 42,583 | 720 | nltk/corpus/reader/bcp47.py | 137 | 20 | def data_dict(self, records):
self.version = records[0].replace("File-Date:", "").strip()
dic = {}
dic["deprecated"] = {}
for label in [
"language",
"extlang",
"script",
"region",
"variant",
"redundant",
"grandfathered",
| 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 <Cubiegamedev@gmail.com> | data_dict | f019fbedb3d2b6a2e6b58ec1b38db612b106568b | nltk | bcp47.py | 17 | 44 | https://github.com/nltk/nltk.git | 14 | 294 | 0 | 75 | 484 | Python | {
"docstring": "Convert the BCP-47 language subtag registry to a dictionary",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | def data_dict(self, records):
self.version = records[0].replace("File-Date:", "").strip()
dic = {}
dic["deprecated"] = {}
for label in [
"language",
"extlang",
"script",
"region",
"variant",
"redundant",
"grandfathered",
]:
dic["deprecated"][label] = {}
for record in records[1:]:
fields = [field.split(": ") for field in record.strip().split("\n")]
typ = fields[0][1]
tag = fields[1][1]
if typ not in dic:
dic[typ] = {}
subfields = {}
for field in fields[2:]:
if len(field) == 2:
[key, val] = field
if key not in subfields:
subfields[key] = [val]
else: # multiple value
subfields[key].append(val)
else: # multiline field
subfields[key][-1] += " " + field[0].strip()
if (
"Deprecated" not in record
and typ == "language"
and key == "Description"
):
self.langcode[subfields[key][-1]] = tag
for key in subfields:
if len(subfields[key]) == 1: # single value
subfields[key] = subfields[key][0]
if "Deprecated" in record:
dic["deprecated"][typ][tag] = subfields
else:
dic[typ][tag] = subfields
return dic
|
|
90,844 | 291,740 | 11 | tests/test_core.py | 5 | 6 | def test_async_add_hass_job_schedule_partial_coroutinefunction(event_loop):
| Upgrade pytest-aiohttp (#82475)
* Upgrade pytest-aiohttp
* Make sure executors, tasks and timers are closed
Some test will trigger warnings on garbage collect, these warnings
spills over into next test.
Some test trigger tasks that raise errors on shutdown, these spill
over into next test.
This is to mimic older pytest-aiohttp and it's behaviour on test
cleanup.
Discussions on similar changes for pytest-aiohttp are here:
https://github.com/pytest-dev/pytest-asyncio/pull/309
* Replace loop with event_loop
* Make sure time is frozen for tests
* Make sure the ConditionType is not async
/home-assistant/homeassistant/helpers/template.py:2082: RuntimeWarning: coroutine 'AsyncMockMixin._execute_mock_call' was never awaited
def wrapper(*args, **kwargs):
Enable tracemalloc to get traceback where the object was allocated.
See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
* Increase litejet press tests with a factor 10
The times are simulated anyway, and we can't stop the normal
event from occuring.
* Use async handlers for aiohttp
tests/components/motioneye/test_camera.py::test_get_still_image_from_camera
tests/components/motioneye/test_camera.py::test_get_still_image_from_camera
tests/components/motioneye/test_camera.py::test_get_stream_from_camera
tests/components/motioneye/test_camera.py::test_get_stream_from_camera
tests/components/motioneye/test_camera.py::test_camera_option_stream_url_template
tests/components/motioneye/test_camera.py::test_camera_option_stream_url_template
/Users/joakim/src/hass/home-assistant/venv/lib/python3.9/site-packages/aiohttp/web_urldispatcher.py:189: DeprecationWarning: Bare functions are deprecated, use async ones
warnings.warn(
* Switch to freezegun in modbus tests
The tests allowed clock to tick in between steps
* Make sure skybell object are fully mocked
Old tests would trigger attempts to post to could services:
```
DEBUG:aioskybell:HTTP post https://cloud.myskybell.com/api/v3/login/ Request with headers: {'content-type': 'application/json', 'accept': '*/*', 'x-skybell-app-id': 'd2b542c7-a7e4-4e1e-b77d-2b76911c7c46', 'x-skybell-client-id': '1f36a3c0-6dee-4997-a6db-4e1c67338e57'}
```
* Fix sorting that broke after rebase | test_async_add_hass_job_schedule_partial_coroutinefunction | c576a68d336bc91fd82c299d9b3e5dfdc1c14960 | core | test_core.py | 12 | 8 | https://github.com/home-assistant/core.git | 1 | 82 | 0 | 5 | 34 | Python | {
"docstring": "Test that we schedule partial coros and add jobs to the job pool.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | def test_async_add_hass_job_schedule_partial_coroutinefunction(event_loop):
hass = MagicMock(loop=MagicMock(wraps=event_loop))
|
|
75,208 | 258,256 | 216 | haystack/utils/squad_data.py | 36 | 22 | def to_label_objs(self, answer_type="generative"):
df_labels = self.df[["id", "question", "answer_text", "answer_start", "context", "document_id"]]
record_dicts = df_labels.to_dict("records")
labels = [
Label(
query=record["question"],
answer=Answer(answer=record["answer_text"], answer_type=answer_type),
| refactor: update Squad data (#3513)
* refractor the to_squad data class
* fix the validation label
* refractor the to_squad data class
* fix the validation label
* add the test for the to_label object function
* fix the tests for to_label_objects
* move all the test related to squad data to one file
* remove unused imports
* revert tiny_augmented.json
Co-authored-by: ZanSara <sarazanzo94@gmail.com> | to_label_objs | d114a994f1af71d3721cecd14da6f6b4592043b8 | haystack | squad_data.py | 17 | 16 | https://github.com/deepset-ai/haystack.git | 2 | 124 | 0 | 32 | 206 | Python | {
"docstring": "Export all labels stored in this object to haystack.Label objects",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def to_label_objs(self, answer_type="generative"):
df_labels = self.df[["id", "question", "answer_text", "answer_start", "context", "document_id"]]
record_dicts = df_labels.to_dict("records")
labels = [
Label(
query=record["question"],
answer=Answer(answer=record["answer_text"], answer_type=answer_type),
is_correct_answer=True,
is_correct_document=True,
id=record["id"],
origin=record.get("origin", "gold-label"),
document=Document(content=record.get("context"), id=str(record["document_id"])),
)
for record in record_dicts
]
return labels
|
|
116,994 | 319,781 | 75 | src/documents/tests/test_api.py | 19 | 13 | def test_api_get_storage_path(self):
response = self.client.get("/api/storage_paths/", format="json")
self.assertEqual(response.status_code, 200)
self.assertEqual(response.status_code, 200)
self.assertEqual(response.data["count"], 1)
resp_storage_path = response.data["results"][0]
self.assertEqual(resp_storage_path["id"], self.sp1.i | Increases test coverage of storage paths | test_api_get_storage_path | 53baed03895f28f24113d376b089e3ef281b34ed | paperless-ngx | test_api.py | 10 | 8 | https://github.com/paperless-ngx/paperless-ngx.git | 1 | 94 | 0 | 16 | 155 | Python | {
"docstring": "\n GIVEN:\n - API request to get all storage paths\n WHEN:\n - API is called\n THEN:\n - Existing storage paths are returned\n ",
"language": "en",
"n_whitespaces": 83,
"n_words": 21,
"vocab_size": 16
} | def test_api_get_storage_path(self):
response = self.client.get("/api/storage_paths/", format="json")
self.assertEqual(response.status_code, 200)
self.assertEqual(response.status_code, 200)
self.assertEqual(response.data["count"], 1)
resp_storage_path = response.data["results"][0]
self.assertEqual(resp_storage_path["id"], self.sp1.id)
self.assertEqual(resp_storage_path["path"], self.sp1.path)
|
|
76,249 | 260,439 | 52 | sklearn/manifold/tests/test_mds.py | 33 | 13 | def test_normalize_metric_warning():
msg = "Normalized stress is not supported"
sim = np.array([[0, 5, 3, 4], | ENH Calculate normed stress (Stress-1) in `manifold.MDS` (#22562)
Co-authored-by: Chiara Marmo <cmarmo@users.noreply.github.com>
Co-authored-by: Roth E Conrad <rotheconrad@gatech.edu>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> | test_normalize_metric_warning | ae51c13af76af206e6815d0ca0d0052f73167caa | scikit-learn | test_mds.py | 10 | 5 | https://github.com/scikit-learn/scikit-learn.git | 1 | 82 | 0 | 29 | 117 | Python | {
"docstring": "\n Test that a UserWarning is emitted when using normalized stress with\n metric-MDS.\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 12,
"vocab_size": 12
} | def test_normalize_metric_warning():
msg = "Normalized stress is not supported"
sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
with pytest.raises(ValueError, match=msg):
mds.smacof(sim, metric=True, normalized_stress=True)
|
|
13,576 | 64,188 | 32 | erpnext/patches/v13_0/add_bin_unique_constraint.py | 54 | 25 | def delete_and_patch_duplicate_bins():
duplicate_bins = frappe.db.sql(, as_dict=1)
for duplicate_bin in duplicate_bins:
existing_bins = frappe.get_list("Bin",
filters={
"item_code": duplicate_bin.item_code,
"warehouse": duplicate_bin.warehouse
},
fields=["name"],
order_by="creation",)
# keep last one
existing_bins.pop()
for broken_bin in existing_bins:
frappe.delete_doc("Bin", broken_bin.name)
qty_dict = {
"reserved_qty": get_reserved_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
"indented_qty": get_indented_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
"ordered_qty": get_ordered_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
"planned_qty": get_planned_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
"actual_qty": get_bal | refactor: patch for fixing broken bins
fix(patch): delete fully broken bins
if bin doesn't have item_code or warehouse then it's not recoverable. | delete_and_patch_duplicate_bins | c2ecc7a2d1da839423fd768821b1f77ddcf7f53d | erpnext | add_bin_unique_constraint.py | 14 | 30 | https://github.com/frappe/erpnext.git | 3 | 158 | 0 | 47 | 254 | Python | {
"docstring": "\n\t\tSELECT\n\t\t\titem_code, warehouse, count(*) as bin_count\n\t\tFROM\n\t\t\ttabBin\n\t\tGROUP BY\n\t\t\titem_code, warehouse\n\t\tHAVING\n\t\t\tbin_count > 1\n\t",
"language": "en",
"n_whitespaces": 8,
"n_words": 16,
"vocab_size": 14
} | def delete_and_patch_duplicate_bins():
duplicate_bins = frappe.db.sql(, as_dict=1)
for duplicate_bin in duplicate_bins:
existing_bins = frappe.get_list("Bin",
filters={
"item_code": duplicate_bin.item_code,
"warehouse": duplicate_bin.warehouse
},
fields=["name"],
order_by="creation",)
# keep last one
existing_bins.pop()
for broken_bin in existing_bins:
frappe.delete_doc("Bin", broken_bin.name)
qty_dict = {
"reserved_qty": get_reserved_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
"indented_qty": get_indented_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
"ordered_qty": get_ordered_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
"planned_qty": get_planned_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
"actual_qty": get_balance_qty_from_sle(duplicate_bin.item_code, duplicate_bin.warehouse)
}
update_bin_qty(duplicate_bin.item_code, duplicate_bin.warehouse, qty_dict)
|
|
@pytest.mark.asyncio | 28,504 | 127,689 | 269 | dashboard/modules/job/tests/test_job_agent.py | 74 | 40 | async def test_stop_long_running_job(job_sdk_client):
agent_client, head_client = job_sdk_client
with tempfile.TemporaryDirectory() as tmp_dir:
path = Path(tmp_dir)
driver_script =
test_script_file = path / "test_script.py"
with open(test_script_file, "w+") as file:
file.write(driver_script)
runtime_env = {"working_dir": tmp_dir}
runtime_env = upload_working_dir_if_needed(runtime_env, tmp_dir, logg | [Job Submission][refactor 4/N] Complete the remaining interfaces on JobAgent (#28533)
Signed-off-by: Catch-Bull <burglarralgrub@gmail.com>
just need to implement stop_job, and I remove get_job_info because we can access JobInfoStorage without call `ray.init`. | test_stop_long_running_job | 8840be1942a69b2595a05c5c5556b0daec7abbcd | ray | test_job_agent.py | 13 | 30 | https://github.com/ray-project/ray.git | 1 | 152 | 1 | 57 | 269 | Python | {
"docstring": "\n Submit a job that runs for a while and stop it in the middle.\n \nprint('Hello !')\nimport time\ntime.sleep(300) # This should never finish\nraise RuntimeError('Intentionally failed.')\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 27,
"vocab_size": 26
} | async def test_stop_long_running_job(job_sdk_client):
agent_client, head_client = job_sdk_client
with tempfile.TemporaryDirectory() as tmp_dir:
path = Path(tmp_dir)
driver_script =
test_script_file = path / "test_script.py"
with open(test_script_file, "w+") as file:
file.write(driver_script)
runtime_env = {"working_dir": tmp_dir}
runtime_env = upload_working_dir_if_needed(runtime_env, tmp_dir, logger=logger)
runtime_env = RuntimeEnv(**runtime_env).to_dict()
request = validate_request_type(
{"runtime_env": runtime_env, "entrypoint": "python test_script.py"},
JobSubmitRequest,
)
submit_result = await agent_client.submit_job_internal(request)
job_id = submit_result.submission_id
resp = await agent_client.stop_job_internal(job_id)
assert resp.stopped is True
wait_for_condition(
partial(
_check_job, client=head_client, job_id=job_id, status=JobStatus.STOPPED
),
timeout=10,
)
@pytest.mark.asyncio |
83,776 | 281,459 | 48 | gamestonk_terminal/cryptocurrency/due_diligence/dd_controller.py | 20 | 12 | def print_help(self):
source_txt = CRYPTO_SOURCES.get(self.source, "?") if self.source != "" else ""
help_text = f
console.print(text=help_text, menu="Stocks - Due Dil | Terminal Wide Rich (#1161)
* My idea for how we handle Rich moving forward
* remove independent consoles
* FIxed pylint issues
* add a few vars
* Switched print to console
* More transitions
* Changed more prints
* Replaced all prints
* Fixing tabulate
* Finished replace tabulate
* Finished removing rich from Tabulate
* add Panel around menu
* add GST watermark under feature flag
* Fixed 46 tests
* Delete test_screener[False].yaml
* Delete test_screener[True].yaml
* Fixed the rest of the tests
* add help and source color vars and use rgb
* rich on stocks/options
* update rich on disc, dps, sia
* rich in gov, ins and scr menus
* ba and ca menus with rich
* Fixed import issue
* Fixed some tests
* removed termcolor
* Removed prettytable
* add rich to remaining stocks menus
* FIxed linting issue
* Added James' changes
* Updated dependencies
* Add rich to cryptocurrency menu
* refactor economy and forex
* refactor etf with rich
* refactor mfunds
* refactor rich rest
* not specify style so default color works well on any background
* Fixing mypy issues
* Updated tests
* More test fixes
* James' test fixes
* Updating tests : stocks/screener - fix cassettes using BR
* Updating tests : crypto
* Updating tests : disable DEBUG_MODE
* Updating tests : stocks/fa/yfinance
* minor fixes that escape
* Improve the rich table function (that replaces tabulate :D )
* Fixed bad code
* delete rogue file + dcf fix + NoConsole
* sia mypy
* fuck you linter
* fuck you linter pt 2
* skip hehe
* i hate the black linter
* ubuntu mypy attempt
* Update : rich_config + gtff
* Updating tests : conftest
* Updating tests : stocks
* Update : rich_config
* Updating : rich_config
* make panel configurable for Theodore :b
* colors update
* Merged
* Updating : rich_config + feature_flags
* Updating : rich_config
* Updating tests : stocks
* Updating : feature_flags
Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt>
Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
Co-authored-by: james <jmaslek11@gmail.com>
Co-authored-by: jose-donato <zmcdonato@gmail.com> | print_help | 82747072c511beb1b2672846ae2ee4aec53eb562 | OpenBBTerminal | dd_controller.py | 10 | 41 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 2 | 42 | 0 | 18 | 86 | Python | {
"docstring": "Print help[cmds]\n load load a specific cryptocurrency for analysis\n\n[param]Coin: [/param]{self.current_coin}\n[param]Source: [/param]{source_txt}\n\n[src]Glassnode[/src]\n active active addresses\n nonzero addresses with non-zero balances\n change 30d change of supply held on exchange wallets\n eb total balance held on exchanges (in percentage and units)\n[src]Coinglass[/src]\n oi open interest per exchange\n[src]CoinPaprika[/src]\n basic basic information about loaded coin\n ps price and supply related metrics for loaded coin\n mkt all markets for loaded coin\n ex all exchanges where loaded coin is listed\n twitter tweets for loaded coin\n events events related to loaded coin\n[src]CoinGecko[/src]\n info basic information about loaded coin\n market market stats about loaded coin\n ath all time high related stats for loaded coin\n atl all time low related stats for loaded coin\n web found websites for loaded coin e.g forum, homepage\n social social portals urls for loaded coin, e.g reddit, twitter\n score different kind of scores for loaded coin, e.g developer score, sentiment score\n dev github, bitbucket coin development statistics\n bc links to blockchain explorers for loaded coin\n[src]Binance[/src]\n binbook show order book\n balance show coin balance\n[src]Coinbase[/src]\n cbbook show order book\n trades show last trades\n stats show coin stats[/cmds]\n",
"language": "en",
"n_whitespaces": 499,
"n_words": 187,
"vocab_size": 107
} | def print_help(self):
source_txt = CRYPTO_SOURCES.get(self.source, "?") if self.source != "" else ""
help_text = f
console.print(text=help_text, menu="Stocks - Due Diligence")
|
|
18,522 | 89,255 | 225 | tests/sentry/integrations/github/test_client.py | 38 | 20 | def test_get_cached_repo_files_with_all_files(self):
responses.add(
method=responses.GET, | feat(derive-code-mappings): Add caching support for fetching files (#41777)
This improves the readability of the code and separates caching logic to their respective functions.
This allows getting files for a repo with caching support without having to call `get_trees_for_org`.
There will be a follow up PR to improve the caching logic.
Co-authored-by: Mark Story <mark@mark-story.com> | test_get_cached_repo_files_with_all_files | 07558e31bd672fab58cff55cf4e9cf0e02b36654 | sentry | test_client.py | 14 | 17 | https://github.com/getsentry/sentry.git | 1 | 103 | 0 | 32 | 201 | Python | {
"docstring": "Fetch files for repo. All files rather than just source code files",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 10
} | def test_get_cached_repo_files_with_all_files(self):
responses.add(
method=responses.GET,
url=f"https://api.github.com/repos/{self.repo.name}/git/trees/master?recursive=1",
status=200,
json={
"tree": [
{"type": "blob", "path": "src/foo.py"},
{"type": "blob", "path": "README"},
]
},
)
repo_key = f"github:repo:{self.repo.name}:all"
assert cache.get(repo_key) is None
with mock.patch("sentry.integrations.github.client.get_jwt", return_value=b"jwt_token_1"):
files = self.client.get_cached_repo_files(self.repo.name, "master")
assert files == ["src/foo.py"]
|
|
50,810 | 204,604 | 421 | django/core/management/base.py | 79 | 26 | def check_migrations(self):
from django.db.migrations.executor import MigrationExecutor
try:
executor = MigrationExecutor(connections[DEFAULT_DB_ALIAS])
except ImproperlyConfigured:
# No databases are configured (or the dummy one)
return
plan = executor.migration_plan(executor.loader.graph.leaf_nodes())
if plan:
apps_waiting_migration = sorted(
{migration.app_label for migration, backwards in plan}
)
self.stdout.write(
self.style.NOTICE(
"\nYou have %(unapplied_migration_count)s unapplied migration(s). "
"Your project may not work properly until you apply the "
"migrations for app(s): %(apps_waiting_migration)s."
% {
"unapplied_migration_count": len(plan),
"apps_waiting_migration": ", ".join(app | Refs #33476 -- Reformatted code with Black. | check_migrations | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | base.py | 17 | 25 | https://github.com/django/django.git | 4 | 117 | 0 | 69 | 201 | Python | {
"docstring": "\n Print a warning if the set of migrations on disk don't match the\n migrations in the database.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 17,
"vocab_size": 14
} | def check_migrations(self):
from django.db.migrations.executor import MigrationExecutor
try:
executor = MigrationExecutor(connections[DEFAULT_DB_ALIAS])
except ImproperlyConfigured:
# No databases are configured (or the dummy one)
return
plan = executor.migration_plan(executor.loader.graph.leaf_nodes())
if plan:
apps_waiting_migration = sorted(
{migration.app_label for migration, backwards in plan}
)
self.stdout.write(
self.style.NOTICE(
"\nYou have %(unapplied_migration_count)s unapplied migration(s). "
"Your project may not work properly until you apply the "
"migrations for app(s): %(apps_waiting_migration)s."
% {
"unapplied_migration_count": len(plan),
"apps_waiting_migration": ", ".join(apps_waiting_migration),
}
)
)
self.stdout.write(
self.style.NOTICE("Run 'python manage.py migrate' to apply them.")
)
|
|
41,910 | 176,449 | 232 | networkx/algorithms/chordal.py | 82 | 24 | def find_induced_nodes(G, s, t, treewidth_bound=sys.maxsize):
if not is_chordal(G):
raise nx.NetworkXError("Input graph is not chordal.")
H = nx.Graph(G)
H.add_edge(s, t)
induced_nodes = set()
triplet = _find_chordality_breaker(H, s, treewidth_bound)
while triplet:
(u, v, w) = triplet
induced_nodes.update(triplet)
for n in triplet:
if n != s:
H.add_edge(s, n)
triplet = _find_chordality_breaker(H, s, tre | Minor improvements from general code readthrough (#5414)
* Add deprecated directive to reversed docstring.
* Add missing dep directives to shpfiles.
* Remove defn of INF sentinel.
* typo.
* str -> comment in forloop.
* STY: appropriate casing for var name. | find_induced_nodes | cc1db275efc709cb964ce88abbfa877798d58c10 | networkx | chordal.py | 16 | 21 | https://github.com/networkx/networkx.git | 8 | 149 | 0 | 60 | 233 | Python | {
"docstring": "Returns the set of induced nodes in the path from s to t.\n\n Parameters\n ----------\n G : graph\n A chordal NetworkX graph\n s : node\n Source node to look for induced nodes\n t : node\n Destination node to look for induced nodes\n treewidth_bound: float\n Maximum treewidth acceptable for the graph H. The search\n for induced nodes will end as soon as the treewidth_bound is exceeded.\n\n Returns\n -------\n induced_nodes : Set of nodes\n The set of induced nodes in the path from s to t in G\n\n Raises\n ------\n NetworkXError\n The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.\n If the input graph is an instance of one of these classes, a\n :exc:`NetworkXError` is raised.\n The algorithm can only be applied to chordal graphs. If the input\n graph is found to be non-chordal, a :exc:`NetworkXError` is raised.\n\n Examples\n --------\n >>> G = nx.Graph()\n >>> G = nx.generators.classic.path_graph(10)\n >>> induced_nodes = nx.find_induced_nodes(G, 1, 9, 2)\n >>> sorted(induced_nodes)\n [1, 2, 3, 4, 5, 6, 7, 8, 9]\n\n Notes\n -----\n G must be a chordal graph and (s,t) an edge that is not in G.\n\n If a treewidth_bound is provided, the search for induced nodes will end\n as soon as the treewidth_bound is exceeded.\n\n The algorithm is inspired by Algorithm 4 in [1]_.\n A formal definition of induced node can also be found on that reference.\n\n References\n ----------\n .. [1] Learning Bounded Treewidth Bayesian Networks.\n Gal Elidan, Stephen Gould; JMLR, 9(Dec):2699--2731, 2008.\n http://jmlr.csail.mit.edu/papers/volume9/elidan08a/elidan08a.pdf\n ",
"language": "en",
"n_whitespaces": 416,
"n_words": 239,
"vocab_size": 126
} | def find_induced_nodes(G, s, t, treewidth_bound=sys.maxsize):
if not is_chordal(G):
raise nx.NetworkXError("Input graph is not chordal.")
H = nx.Graph(G)
H.add_edge(s, t)
induced_nodes = set()
triplet = _find_chordality_breaker(H, s, treewidth_bound)
while triplet:
(u, v, w) = triplet
induced_nodes.update(triplet)
for n in triplet:
if n != s:
H.add_edge(s, n)
triplet = _find_chordality_breaker(H, s, treewidth_bound)
if induced_nodes:
# Add t and the second node in the induced path from s to t.
induced_nodes.add(t)
for u in G[s]:
if len(induced_nodes & set(G[u])) == 2:
induced_nodes.add(u)
break
return induced_nodes
|
|
@pytest.fixture | 40,573 | 170,548 | 45 | pandas/conftest.py | 33 | 11 | def any_skipna_inferred_dtype(request):
inferred_dtype, values = request.param
values = np.array(values, dtype=object) # | STYLE fix: pylint "consider-using-from" (#49335)
* use from import
* delete empty file
Co-authored-by: carlotta <c.fabian@turbit.de>
Co-authored-by: cfabian <cfabian@student.42wolfsburg.de> | any_skipna_inferred_dtype | f9ff3796329e4bedb4a5477739f5eb8d2e40761d | pandas | conftest.py | 9 | 4 | https://github.com/pandas-dev/pandas.git | 1 | 29 | 1 | 24 | 60 | Python | {
"docstring": "\n Fixture for all inferred dtypes from _libs.lib.infer_dtype\n\n The covered (inferred) types are:\n * 'string'\n * 'empty'\n * 'bytes'\n * 'mixed'\n * 'mixed-integer'\n * 'mixed-integer-float'\n * 'floating'\n * 'integer'\n * 'decimal'\n * 'boolean'\n * 'datetime64'\n * 'datetime'\n * 'date'\n * 'timedelta'\n * 'time'\n * 'period'\n * 'interval'\n\n Returns\n -------\n inferred_dtype : str\n The string for the inferred dtype from _libs.lib.infer_dtype\n values : np.ndarray\n An array of object dtype that will be inferred to have\n `inferred_dtype`\n\n Examples\n --------\n >>> from pandas._libs import lib\n >>>\n >>> def test_something(any_skipna_inferred_dtype):\n ... inferred_dtype, values = any_skipna_inferred_dtype\n ... # will pass\n ... assert lib.infer_dtype(values, skipna=True) == inferred_dtype\n ",
"language": "en",
"n_whitespaces": 230,
"n_words": 100,
"vocab_size": 68
} | def any_skipna_inferred_dtype(request):
inferred_dtype, values = request.param
values = np.array(values, dtype=object) # object dtype to avoid casting
# correctness of inference tested in tests/dtypes/test_inference.py
return inferred_dtype, values
# ----------------------------------------------------------------
# Misc
# ----------------------------------------------------------------
@pytest.fixture |
1,609 | 9,409 | 66 | reconstruction/ostec/external/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py | 43 | 16 | def downsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
r
assert isinstance(factor, int) | initialize ostec | downsample_2d | 7375ee364e0df2a417f92593e09557f1b2a3575a | insightface | upfirdn_2d.py | 11 | 28 | https://github.com/deepinsight/insightface.git | 3 | 87 | 0 | 36 | 153 | Python | {
"docstring": "Downsample a batch of 2D images with the given filter.\n\n Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`\n and downsamples each image with the given filter. The filter is normalized so that\n if the input pixels are constant, they will be scaled by the specified `gain`.\n Pixels outside the image are assumed to be zero, and the filter is padded with\n zeros so that its shape is a multiple of the downsampling factor.\n\n Args:\n x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.\n k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable).\n The default is `[1] * factor`, which corresponds to average pooling.\n factor: Integer downsampling factor (default: 2).\n gain: Scaling factor for signal magnitude (default: 1.0).\n data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`).\n impl: Name of the implementation to use. Can be `\"ref\"` or `\"cuda\"` (default).\n\n Returns:\n Tensor of the shape `[N, C, H // factor, W // factor]` or\n `[N, H // factor, W // factor, C]`, and same datatype as `x`.\n ",
"language": "en",
"n_whitespaces": 327,
"n_words": 181,
"vocab_size": 106
} | def downsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
r
assert isinstance(factor, int) and factor >= 1
if k is None:
k = [1] * factor
k = _setup_kernel(k) * gain
p = k.shape[0] - factor
return _simple_upfirdn_2d(x, k, down=factor, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)
#----------------------------------------------------------------------------
|
|
40,239 | 168,224 | 373 | pandas/core/groupby/grouper.py | 136 | 11 | def _check_deprecated_resample_kwargs(kwargs, origin):
# Deprecation warning of `base` and `loffset` since v1.1.0:
# we are raising the warning here to be able to set the `stacklevel`
# properly since we need to raise the `base` and `loffset` deprecation
# warning from three different cases:
# core/generic.py::NDFrame.resample
# core/groupby/groupby.py::GroupBy.resample
# core/groupby/grouper.py::Grouper
# raising these warnings from TimeGrouper directly would fail the test:
# tests/resample/test_deprecated.py::test_deprecating_on_loffset_and_base
if kwargs.get("base", None) is not None:
warnings.warn(
"'base' in .resample() and in Grouper() is deprecated.\n"
"The new arguments that you should use are 'offset' or 'origin'.\n"
'\n>>> df.resample(freq="3s", base=2)\n'
"\nbecomes:\n"
'\n>>> df.resample(freq="3s", offset="2s")\n',
FutureWarning,
stacklevel=find_stack_level(inspect.currentframe()),
| PERF cache find_stack_level (#48023)
cache stacklevel | _check_deprecated_resample_kwargs | 2f8d0a36703e81e4dca52ca9fe4f58c910c1b304 | pandas | grouper.py | 14 | 22 | https://github.com/pandas-dev/pandas.git | 3 | 83 | 0 | 85 | 176 | Python | {
"docstring": "\n Check for use of deprecated parameters in ``resample`` and related functions.\n\n Raises the appropriate warnings if these parameters are detected.\n Only sets an approximate ``stacklevel`` for the warnings (see #37603, #36629).\n\n Parameters\n ----------\n kwargs : dict\n Dictionary of keyword arguments to check for deprecated parameters.\n origin : object\n From where this function is being called; either Grouper or TimeGrouper. Used\n to determine an approximate stacklevel.\n ",
"language": "en",
"n_whitespaces": 111,
"n_words": 65,
"vocab_size": 54
} | def _check_deprecated_resample_kwargs(kwargs, origin):
# Deprecation warning of `base` and `loffset` since v1.1.0:
# we are raising the warning here to be able to set the `stacklevel`
# properly since we need to raise the `base` and `loffset` deprecation
# warning from three different cases:
# core/generic.py::NDFrame.resample
# core/groupby/groupby.py::GroupBy.resample
# core/groupby/grouper.py::Grouper
# raising these warnings from TimeGrouper directly would fail the test:
# tests/resample/test_deprecated.py::test_deprecating_on_loffset_and_base
if kwargs.get("base", None) is not None:
warnings.warn(
"'base' in .resample() and in Grouper() is deprecated.\n"
"The new arguments that you should use are 'offset' or 'origin'.\n"
'\n>>> df.resample(freq="3s", base=2)\n'
"\nbecomes:\n"
'\n>>> df.resample(freq="3s", offset="2s")\n',
FutureWarning,
stacklevel=find_stack_level(inspect.currentframe()),
)
if kwargs.get("loffset", None) is not None:
warnings.warn(
"'loffset' in .resample() and in Grouper() is deprecated.\n"
'\n>>> df.resample(freq="3s", loffset="8H")\n'
"\nbecomes:\n"
"\n>>> from pandas.tseries.frequencies import to_offset"
'\n>>> df = df.resample(freq="3s").mean()'
'\n>>> df.index = df.index.to_timestamp() + to_offset("8H")\n',
FutureWarning,
stacklevel=find_stack_level(inspect.currentframe()),
)
|
|
8,400 | 44,887 | 45 | airflow/providers/google/cloud/hooks/datacatalog.py | 13 | 8 | def get_conn(self) -> DataCatalogClient:
| Extract ClientInfo to module level (#21554) | get_conn | 1b568d73e1dfb838a3a0446e3a6063b9f27f04b8 | airflow | datacatalog.py | 13 | 5 | https://github.com/apache/airflow.git | 2 | 36 | 0 | 12 | 60 | Python | {
"docstring": "Retrieves client library object that allow access to Cloud Data Catalog service.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | def get_conn(self) -> DataCatalogClient:
if not self._client:
self._client = DataCatalogClient(credentials=self._get_credentials(), client_info=CLIENT_INFO)
return self._client
|
|
11,591 | 56,932 | 82 | src/prefect/blocks/kubernetes.py | 13 | 8 | def get_api_client(self) -> ApiClient:
try:
return new_client_from_config_dict(
config_dict=self.config, context=self.context
)
| organizational changes for the KubernetesClusterConfig and add from_environment classmethod | get_api_client | 574d10ff7612661b37801c811862f18998521d58 | prefect | kubernetes.py | 11 | 10 | https://github.com/PrefectHQ/prefect.git | 2 | 29 | 0 | 13 | 49 | Python | {
"docstring": "\n Returns an instance of the kubernetes api client with a specific context\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 12,
"vocab_size": 12
} | def get_api_client(self) -> ApiClient:
try:
return new_client_from_config_dict(
config_dict=self.config, context=self.context
)
except ConfigException:
raise
|
|
43,822 | 182,433 | 258 | src/textual/_arrangement.py | 75 | 25 | def cuts(self) -> list[list[int]]:
if self._cuts is not None:
return self._cuts
width = self.width
height = self.height
screen_region = Region(0, 0, width, height)
cuts_sets = [{0, width} for | ws | cuts | 57a05c7bbd14728f0dbde8b8e55d6f086362c35e | textual | _arrangement.py | 16 | 23 | https://github.com/Textualize/textual.git | 9 | 143 | 0 | 51 | 218 | Python | {
"docstring": "Get vertical cuts.\n\n A cut is every point on a line where a widget starts or ends.\n\n Returns:\n list[list[int]]: A list of cuts for every line.\n ",
"language": "en",
"n_whitespaces": 58,
"n_words": 26,
"vocab_size": 23
} | def cuts(self) -> list[list[int]]:
if self._cuts is not None:
return self._cuts
width = self.width
height = self.height
screen_region = Region(0, 0, width, height)
cuts_sets = [{0, width} for _ in range(height)]
if self.map is not None:
for region, order, clip in self.map.values():
region = region.intersection(clip)
if region and (region in screen_region):
region_cuts = region.x_extents
for y in region.y_range:
cuts_sets[y].update(region_cuts)
# Sort the cuts for each line
self._cuts = [sorted(cut_set) for cut_set in cuts_sets]
return self._cuts
|
|
80,576 | 270,861 | 35 | keras/engine/base_layer_utils.py | 12 | 4 | def is_subclassed(layer):
return (
la | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | is_subclassed | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | base_layer_utils.py | 11 | 5 | https://github.com/keras-team/keras.git | 2 | 32 | 0 | 10 | 58 | Python | {
"docstring": "Returns True if the object is a subclassed layer or subclassed model.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 11
} | def is_subclassed(layer):
return (
layer.__module__.find("keras.engine") == -1
and layer.__module__.find("keras.layers") == -1
)
|
|
48,249 | 196,925 | 62 | sympy/matrices/dense.py | 9 | 6 | def _mat(self):
sympy_deprecation_warning(
,
deprecated_since_version="1.9",
active_deprecations_target="deprecated-private-matrix-attributes"
)
return | Update the deprecation of the _mat and _smat Matrix properties | _mat | 0b4d5fa57d64b1102e51e03ed80013e16053bf96 | sympy | dense.py | 9 | 10 | https://github.com/sympy/sympy.git | 1 | 23 | 0 | 9 | 42 | Python | {
"docstring": "\n The private _mat attribute of Matrix is deprecated. Use the\n .flat() method instead.\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 13,
"vocab_size": 13
} | def _mat(self):
sympy_deprecation_warning(
,
deprecated_since_version="1.9",
active_deprecations_target="deprecated-private-matrix-attributes"
)
return self.flat()
|
|
78,274 | 266,037 | 115 | netbox/extras/tests/test_customfields.py | 34 | 22 | def test_missing_required_field(self):
cf3 = CustomField(type=CustomFieldTypeChoices.TYPE_TEXT, name='baz', required=True)
cf3.save()
cf3.conte | Closes #10052: The cf attribute now returns deserialized custom field data | test_missing_required_field | ea6d86e6c4bb6037465410db6205a7471bc81a6c | netbox | test_customfields.py | 11 | 10 | https://github.com/netbox-community/netbox.git | 1 | 92 | 0 | 28 | 165 | Python | {
"docstring": "\n Check that a ValidationError is raised if any required custom fields are not present.\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 14,
"vocab_size": 14
} | def test_missing_required_field(self):
cf3 = CustomField(type=CustomFieldTypeChoices.TYPE_TEXT, name='baz', required=True)
cf3.save()
cf3.content_types.set([ContentType.objects.get_for_model(Site)])
site = Site(name='Test Site', slug='test-site')
# Set custom field data with a required field omitted
site.custom_field_data['foo'] = 'abc'
with self.assertRaises(ValidationError):
site.clean()
site.custom_field_data['baz'] = 'def'
site.clean()
|
|
16,736 | 78,230 | 40 | wagtail/admin/tests/test_templatetags.py | 11 | 9 | def test_basic(self):
context = Context({})
template =
expected =
self.assertHTMLEqual(expected, Template( | Introduce new template fragment composition tags | test_basic | 524cab82e33b43463b746c3df1a80657b3ae874a | wagtail | test_templatetags.py | 11 | 15 | https://github.com/wagtail/wagtail.git | 1 | 34 | 0 | 9 | 60 | Python | {
"docstring": "\n {% load wagtailadmin_tags %}\n {% fragment as my_fragment %}\n <p>Hello, World</p>\n {% endfragment %}\n Text coming after:\n {{ my_fragment }}\n \n Text coming after:\n <p>Hello, World</p>\n ",
"language": "en",
"n_whitespaces": 129,
"n_words": 25,
"vocab_size": 15
} | def test_basic(self):
context = Context({})
template =
expected =
self.assertHTMLEqual(expected, Template(template).render(context))
|
|
40,094 | 167,732 | 75 | pandas/core/arrays/sparse/accessor.py | 14 | 8 | def to_dense(self) -> Series:
from pandas import Series
return Series(
self._parent.array.to_dense | TYP: more return annotations in core/ (#47618)
* TYP: more return annotations in core/
* from __future__ import annotations
* more __future__ | to_dense | f65417656ba8c59438d832b6e2a431f78d40c21c | pandas | accessor.py | 11 | 32 | https://github.com/pandas-dev/pandas.git | 1 | 42 | 0 | 14 | 67 | Python | {
"docstring": "\n Convert a Series from sparse values to dense.\n\n .. versionadded:: 0.25.0\n\n Returns\n -------\n Series:\n A Series with the same values, stored as a dense array.\n\n Examples\n --------\n >>> series = pd.Series(pd.arrays.SparseArray([0, 1, 0]))\n >>> series\n 0 0\n 1 1\n 2 0\n dtype: Sparse[int64, 0]\n\n >>> series.sparse.to_dense()\n 0 0\n 1 1\n 2 0\n dtype: int64\n ",
"language": "en",
"n_whitespaces": 217,
"n_words": 54,
"vocab_size": 39
} | def to_dense(self) -> Series:
from pandas import Series
return Series(
self._parent.array.to_dense(),
index=self._parent.index,
name=self._parent.name,
)
|
Subsets and Splits