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@router.get("/version") | 11,931 | 59,709 | 13 | src/prefect/orion/api/admin.py | 8 | 8 | async def read_settings() -> prefect.settings.Settings:
r | Add secret flag to settings and obfuscate by default when displayed (#7465) | read_settings | 902dfa4bd3b6e330e4374eb1e04de064148a2f32 | prefect | admin.py | 10 | 7 | https://github.com/PrefectHQ/prefect.git | 1 | 23 | 1 | 8 | 56 | Python | {
"docstring": "\n Get the current Orion settings.\n\n Secret setting values will be obfuscated.\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 11,
"vocab_size": 11
} | async def read_settings() -> prefect.settings.Settings:
return prefect.settings.get_current_settings().with_obfuscated_secrets()
@router.get("/version") |
36,505 | 156,012 | 59 | dask/array/core.py | 20 | 10 | def map_overlap(self, func, depth, boundary=None, trim=True, **kwargs):
from dask.array.overlap import map_overlap
return map_overlap(
| absolufy-imports - No relative - PEP8 (#8796)
Conversation in https://github.com/dask/distributed/issues/5889 | map_overlap | cccb9d8d8e33a891396b1275c2448c352ef40c27 | dask | core.py | 8 | 5 | https://github.com/dask/dask.git | 1 | 51 | 0 | 19 | 71 | Python | {
"docstring": "Map a function over blocks of the array with some overlap\n\n We share neighboring zones between blocks of the array, then map a\n function, then trim away the neighboring strips.\n\n Note that this function will attempt to automatically determine the output\n array type before computing it, please refer to the ``meta`` keyword argument\n in :func:`map_blocks <dask.array.core.map_blocks>` if you expect that the function will not succeed when\n operating on 0-d arrays.\n\n Parameters\n ----------\n func: function\n The function to apply to each extended block\n depth: int, tuple, or dict\n The number of elements that each block should share with its neighbors\n If a tuple or dict then this can be different per axis\n boundary: str, tuple, dict\n How to handle the boundaries.\n Values include 'reflect', 'periodic', 'nearest', 'none',\n or any constant value like 0 or np.nan\n trim: bool\n Whether or not to trim ``depth`` elements from each block after\n calling the map function.\n Set this to False if your mapping function already does this for you\n **kwargs:\n Other keyword arguments valid in :func:`map_blocks <dask.array.core.map_blocks>`.\n\n Examples\n --------\n >>> import dask.array as da\n >>> x = np.array([1, 1, 2, 3, 3, 3, 2, 1, 1])\n >>> x = da.from_array(x, chunks=5)\n >>> def derivative(x):\n ... return x - np.roll(x, 1)\n\n >>> y = x.map_overlap(derivative, depth=1, boundary=0)\n >>> y.compute()\n array([ 1, 0, 1, 1, 0, 0, -1, -1, 0])\n\n >>> import dask.array as da\n >>> x = np.arange(16).reshape((4, 4))\n >>> d = da.from_array(x, chunks=(2, 2))\n >>> y = d.map_overlap(lambda x: x + x.size, depth=1, boundary='reflect')\n >>> y.compute()\n array([[16, 17, 18, 19],\n [20, 21, 22, 23],\n [24, 25, 26, 27],\n [28, 29, 30, 31]])\n\n >>> func = lambda x: x + x.size\n >>> depth = {0: 1, 1: 1}\n >>> boundary = {0: 'reflect', 1: 'none'}\n >>> d.map_overlap(func, depth, boundary).compute() # doctest: +NORMALIZE_WHITESPACE\n array([[12, 13, 14, 15],\n [16, 17, 18, 19],\n [20, 21, 22, 23],\n [24, 25, 26, 27]])\n\n >>> x = np.arange(16).reshape((4, 4))\n >>> d = da.from_array(x, chunks=(2, 2))\n >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=np.array(()))\n >>> y\n dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=numpy.ndarray>\n >>> y.compute()\n array([[ 4, 6, 8, 10],\n [ 8, 10, 12, 14],\n [20, 22, 24, 26],\n [24, 26, 28, 30]])\n\n >>> import cupy # doctest: +SKIP\n >>> x = cupy.arange(16).reshape((4, 4)) # doctest: +SKIP\n >>> d = da.from_array(x, chunks=(2, 2)) # doctest: +SKIP\n >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=cupy.array(())) # doctest: +SKIP\n >>> y # doctest: +SKIP\n dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=cupy.ndarray>\n >>> y.compute() # doctest: +SKIP\n array([[ 4, 6, 8, 10],\n [ 8, 10, 12, 14],\n [20, 22, 24, 26],\n [24, 26, 28, 30]])\n ",
"language": "en",
"n_whitespaces": 1096,
"n_words": 435,
"vocab_size": 223
} | def map_overlap(self, func, depth, boundary=None, trim=True, **kwargs):
from dask.array.overlap import map_overlap
return map_overlap(
func, self, depth=depth, boundary=boundary, trim=trim, **kwargs
)
|
|
50,614 | 204,011 | 98 | django/contrib/gis/gdal/raster/source.py | 33 | 7 | def _flush(self):
# Raise an Exception if the value is being changed in read mode.
if not self._write:
raise GDALException(
"Raster needs to be opened in write mode to change values."
)
capi | Refs #33476 -- Reformatted code with Black. | _flush | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | source.py | 10 | 6 | https://github.com/django/django.git | 2 | 25 | 0 | 30 | 47 | Python | {
"docstring": "\n Flush all data from memory into the source file if it exists.\n The data that needs flushing are geotransforms, coordinate systems,\n nodata_values and pixel values. This function will be called\n automatically wherever it is needed.\n ",
"language": "en",
"n_whitespaces": 71,
"n_words": 35,
"vocab_size": 33
} | def _flush(self):
# Raise an Exception if the value is being changed in read mode.
if not self._write:
raise GDALException(
"Raster needs to be opened in write mode to change values."
)
capi.flush_ds(self._ptr)
|
|
55,798 | 219,783 | 185 | python3.10.4/Lib/_pydecimal.py | 82 | 10 | def _round(self, places, rounding):
if places <= 0:
raise ValueError("argument should be at least 1 in _round")
if self._is_special or not self:
return Decimal(self)
ans = self._rescale(self.adjusted()+1-places, rounding)
# it ca | add python 3.10.4 for windows | _round | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | _pydecimal.py | 14 | 9 | https://github.com/XX-net/XX-Net.git | 5 | 84 | 0 | 66 | 141 | Python | {
"docstring": "Round a nonzero, nonspecial Decimal to a fixed number of\n significant figures, using the given rounding mode.\n\n Infinities, NaNs and zeros are returned unaltered.\n\n This operation is quiet: it raises no flags, and uses no\n information from the context.\n\n ",
"language": "en",
"n_whitespaces": 74,
"n_words": 39,
"vocab_size": 35
} | def _round(self, places, rounding):
if places <= 0:
raise ValueError("argument should be at least 1 in _round")
if self._is_special or not self:
return Decimal(self)
ans = self._rescale(self.adjusted()+1-places, rounding)
# it can happen that the rescale alters the adjusted exponent;
# for example when rounding 99.97 to 3 significant figures.
# When this happens we end up with an extra 0 at the end of
# the number; a second rescale fixes this.
if ans.adjusted() != self.adjusted():
ans = ans._rescale(ans.adjusted()+1-places, rounding)
return ans
|
|
4,181 | 22,104 | 24 | pipenv/patched/pip/_vendor/requests/models.py | 10 | 5 | def is_redirect(self):
r | Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir. | is_redirect | cd5a9683be69c86c8f3adcd13385a9bc5db198ec | pipenv | models.py | 8 | 2 | https://github.com/pypa/pipenv.git | 2 | 18 | 0 | 9 | 33 | Python | {
"docstring": "True if this Response is a well-formed HTTP redirect that could have\n been processed automatically (by :meth:`Session.resolve_redirects`).\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 17,
"vocab_size": 17
} | def is_redirect(self):
return "location" in self.headers and self.status_code in REDIRECT_STATI
|
|
39,293 | 162,760 | 65 | research/neo_peq/legacy_frequency_response.py | 19 | 13 | def write_eqapo_graphic_eq(self, file_path, normalize=True):
file_path = os.path.abspath(file_path)
s = self.eqapo_graphic_eq(normalize=normalize)
with open(file_path, 'w', encoding='utf-8') as f:
f.write(s)
return s
| Added PEQ configs to CLI and function interfaces. Improved default value handling for PEQ parameters and added more predefined configs. Removed legacy PEQ optimization. Fixed readme write. Improved shelf filter initialization. Added plot method to PEQ. Notebook for comparing old and new optimizers. Bug fixes. | write_eqapo_graphic_eq | 9120cdffe618c6c2ff16fe6a311b6a1367efdbc8 | AutoEq | legacy_frequency_response.py | 12 | 6 | https://github.com/jaakkopasanen/AutoEq.git | 1 | 54 | 0 | 17 | 92 | Python | {
"docstring": "Writes equalization graph to a file as Equalizer APO config.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def write_eqapo_graphic_eq(self, file_path, normalize=True):
file_path = os.path.abspath(file_path)
s = self.eqapo_graphic_eq(normalize=normalize)
with open(file_path, 'w', encoding='utf-8') as f:
f.write(s)
return s
|
|
43,393 | 181,605 | 142 | tests/export_tests.py | 29 | 14 | def test_export_pipeline_6():
pipeline_string = (
'DecisionTreeClassifier(SelectPercentile(input_matrix, SelectPercentile__percentile=20),'
'DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8,'
'DecisionTreeClassifier__min_samples_leaf=5, DecisionTreeClassifier__min_samples_split=5)'
)
pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
expected_code =
exported_code = export_pipeline(pipeline, tpot_obj.operators,
tpot_obj._pset, random_state=42,
data_file_path='test_path')
assert expected_code | Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068. | test_export_pipeline_6 | 388616b6247ca4ea8de4e2f340d6206aee523541 | tpot | export_tests.py | 10 | 35 | https://github.com/EpistasisLab/tpot.git | 1 | 55 | 0 | 24 | 96 | Python | {
"docstring": "Assert that exported_pipeline() generated a compile source file with random_state and data_file_path.import numpy as np\nimport pandas as pd\nfrom sklearn.feature_selection import SelectPercentile, f_classif\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.tree import DecisionTreeClassifier\nfrom tpot.export_utils import set_param_recursive\n\n# NOTE: Make sure that the outcome column is labeled 'target' in the data file\ntpot_data = pd.read_csv('test_path', sep='COLUMN_SEPARATOR', dtype=np.float64)\nfeatures = tpot_data.drop('target', axis=1)\ntraining_features, testing_features, training_target, testing_target = \\\\\n train_test_split(features, tpot_data['target'], random_state=42)\n\nexported_pipeline = make_pipeline(\n SelectPercentile(score_func=f_classif, percentile=20),\n DecisionTreeClassifier(criterion=\"gini\", max_depth=8, min_samples_leaf=5, min_samples_split=5)\n)\n# Fix random state for all the steps in exported pipeline\nset_param_recursive(exported_pipeline.steps, 'random_state', 42)\n\nexported_pipeline.fit(training_features, training_target)\nresults = exported_pipeline.predict(testing_features)\n",
"language": "en",
"n_whitespaces": 102,
"n_words": 102,
"vocab_size": 82
} | def test_export_pipeline_6():
pipeline_string = (
'DecisionTreeClassifier(SelectPercentile(input_matrix, SelectPercentile__percentile=20),'
'DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8,'
'DecisionTreeClassifier__min_samples_leaf=5, DecisionTreeClassifier__min_samples_split=5)'
)
pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
expected_code =
exported_code = export_pipeline(pipeline, tpot_obj.operators,
tpot_obj._pset, random_state=42,
data_file_path='test_path')
assert expected_code == exported_code
|
|
42,027 | 176,659 | 669 | networkx/algorithms/connectivity/tests/test_edge_kcomponents.py | 275 | 32 | def _check_edge_connectivity(G):
# Construct the auxiliary graph that can be used to make each k-cc or k-sub
aux_graph = EdgeComponentAuxGraph.construct(G)
# memoize the local connectivity in this graph
memo = {}
for k in it.count(1):
# Test "local" k-edge-components and k-edge-subgraphs
ccs_local = fset(aux_graph.k_edge_components(k))
ccs_subgraph = fset(aux_graph.k_edge_subgraphs(k))
# Check connectivity properties that should be guaranteed by the
# algorithms.
_assert_local_cc_edge_connectivity(G, ccs_local, k, memo)
_assert_subgraph_edge_connectivity(G, ccs_subgraph, k)
if k == 1 or k == 2 and not G.is_directed():
assert (
ccs_local == ccs_subgraph
), "Subgraphs and components should be the same when k == 1 or (k == 2 and not G.directed())"
if G.is_directed():
# Test special case methods are the same as the aux graph
if k == 1:
alt_sccs = fset(nx.strongly_connected_components(G))
assert alt_sccs == ccs_local, "k=1 failed alt"
assert alt_sccs == ccs_subgraph, "k=1 failed alt"
else:
# Test special case methods are the same as the aux gra | doc: fix typos in docstring and comment (#5647) | _check_edge_connectivity | 26b7de005ac562786f72b24a73af5a59bbab6953 | networkx | test_edge_kcomponents.py | 18 | 32 | https://github.com/networkx/networkx.git | 13 | 235 | 0 | 128 | 393 | Python | {
"docstring": "\n Helper - generates all k-edge-components using the aux graph. Checks the\n both local and subgraph edge connectivity of each cc. Also checks that\n alternate methods of computing the k-edge-ccs generate the same result.\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 33,
"vocab_size": 29
} | def _check_edge_connectivity(G):
# Construct the auxiliary graph that can be used to make each k-cc or k-sub
aux_graph = EdgeComponentAuxGraph.construct(G)
# memoize the local connectivity in this graph
memo = {}
for k in it.count(1):
# Test "local" k-edge-components and k-edge-subgraphs
ccs_local = fset(aux_graph.k_edge_components(k))
ccs_subgraph = fset(aux_graph.k_edge_subgraphs(k))
# Check connectivity properties that should be guaranteed by the
# algorithms.
_assert_local_cc_edge_connectivity(G, ccs_local, k, memo)
_assert_subgraph_edge_connectivity(G, ccs_subgraph, k)
if k == 1 or k == 2 and not G.is_directed():
assert (
ccs_local == ccs_subgraph
), "Subgraphs and components should be the same when k == 1 or (k == 2 and not G.directed())"
if G.is_directed():
# Test special case methods are the same as the aux graph
if k == 1:
alt_sccs = fset(nx.strongly_connected_components(G))
assert alt_sccs == ccs_local, "k=1 failed alt"
assert alt_sccs == ccs_subgraph, "k=1 failed alt"
else:
# Test special case methods are the same as the aux graph
if k == 1:
alt_ccs = fset(nx.connected_components(G))
assert alt_ccs == ccs_local, "k=1 failed alt"
assert alt_ccs == ccs_subgraph, "k=1 failed alt"
elif k == 2:
alt_bridge_ccs = fset(bridge_components(G))
assert alt_bridge_ccs == ccs_local, "k=2 failed alt"
assert alt_bridge_ccs == ccs_subgraph, "k=2 failed alt"
# if new methods for k == 3 or k == 4 are implemented add them here
# Check the general subgraph method works by itself
alt_subgraph_ccs = fset(
[set(C.nodes()) for C in general_k_edge_subgraphs(G, k=k)]
)
assert alt_subgraph_ccs == ccs_subgraph, "alt subgraph method failed"
# Stop once k is larger than all special case methods
# and we cannot break down ccs any further.
if k > 2 and all(len(cc) == 1 for cc in ccs_local):
break
# ----------------
# Misc tests
# ----------------
|
|
50,000 | 201,816 | 163 | tests/backends/tests.py | 53 | 17 | def test_sequence_name_length_limits_flush(self):
# A full flush is expensive to the full test, so we dig into the
# internals to generate the likely offending SQL and run it manually
# Some convenience aliases
VLM = VeryLongModelNameZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
VLM_m2m = (
VLM.m2m_ | Refs #33476 -- Reformatted code with Black. | test_sequence_name_length_limits_flush | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | tests.py | 10 | 11 | https://github.com/django/django.git | 1 | 60 | 0 | 44 | 98 | Python | {
"docstring": "\n Sequence resetting as part of a flush with model with long name and\n long pk name doesn't error (#8901).\n ",
"language": "en",
"n_whitespaces": 41,
"n_words": 19,
"vocab_size": 16
} | def test_sequence_name_length_limits_flush(self):
# A full flush is expensive to the full test, so we dig into the
# internals to generate the likely offending SQL and run it manually
# Some convenience aliases
VLM = VeryLongModelNameZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
VLM_m2m = (
VLM.m2m_also_quite_long_zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz.through
)
tables = [
VLM._meta.db_table,
VLM_m2m._meta.db_table,
]
sql_list = connection.ops.sql_flush(no_style(), tables, reset_sequences=True)
connection.ops.execute_sql_flush(sql_list)
|
|
72,616 | 249,109 | 762 | tests/rest/admin/test_media.py | 159 | 38 | def test_delete_media(self) -> None:
download_resource = self.media_repo.children[b"download"]
upload_resource = self.media_repo.children[b"upload"]
# Upload some media into the room
response = self.helper.upload_media(
upload_resource,
SMALL_PNG,
tok=self.admin_user_tok,
expect_code=200,
)
# Extract media ID from the response
server_and_media_id = r | Use literals in place of `HTTPStatus` constants in tests (#13469) | test_delete_media | c97042f7eef3748e17c90e48a4122389a89c4735 | synapse | test_media.py | 11 | 61 | https://github.com/matrix-org/synapse.git | 1 | 297 | 0 | 91 | 464 | Python | {
"docstring": "\n Tests that delete a media is successfully\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 7
} | def test_delete_media(self) -> None:
download_resource = self.media_repo.children[b"download"]
upload_resource = self.media_repo.children[b"upload"]
# Upload some media into the room
response = self.helper.upload_media(
upload_resource,
SMALL_PNG,
tok=self.admin_user_tok,
expect_code=200,
)
# Extract media ID from the response
server_and_media_id = response["content_uri"][6:] # Cut off 'mxc://'
server_name, media_id = server_and_media_id.split("/")
self.assertEqual(server_name, self.server_name)
# Attempt to access media
channel = make_request(
self.reactor,
FakeSite(download_resource, self.reactor),
"GET",
server_and_media_id,
shorthand=False,
access_token=self.admin_user_tok,
)
# Should be successful
self.assertEqual(
200,
channel.code,
msg=(
"Expected to receive a 200 on accessing media: %s" % server_and_media_id
),
)
# Test if the file exists
local_path = self.filepaths.local_media_filepath(media_id)
self.assertTrue(os.path.exists(local_path))
url = "/_synapse/admin/v1/media/%s/%s" % (self.server_name, media_id)
# Delete media
channel = self.make_request(
"DELETE",
url,
access_token=self.admin_user_tok,
)
self.assertEqual(200, channel.code, msg=channel.json_body)
self.assertEqual(1, channel.json_body["total"])
self.assertEqual(
media_id,
channel.json_body["deleted_media"][0],
)
# Attempt to access media
channel = make_request(
self.reactor,
FakeSite(download_resource, self.reactor),
"GET",
server_and_media_id,
shorthand=False,
access_token=self.admin_user_tok,
)
self.assertEqual(
HTTPStatus.NOT_FOUND,
channel.code,
msg=(
"Expected to receive a HTTPStatus.NOT_FOUND on accessing deleted media: %s"
% server_and_media_id
),
)
# Test if the file is deleted
self.assertFalse(os.path.exists(local_path))
|
|
1,233 | 7,623 | 114 | ludwig/visualize.py | 40 | 20 | def load_data_for_viz(load_type, model_file_statistics, **kwargs):
supported_load_types = dict(
load_json=load_json,
load_from_file=partial(
load_from_file, dtype=kwargs.get("dtype", int), ground_truth_split=kwar | Encoder refactor V2 (#2370)
* Added base files and some initial code
* More files created, fleshing out binary feature and corresponding encoders
* Added more schema infra
* Registered all feature encoders
* Separated feature utils infra
* Added all preprocessing classes
* Filled out rest of schema configs
* Fixed preproc dataclass
* Fixed small errors blocking import
* Tests should be passing
* Deleted unnecesssary files and removed commented out code
* fixed flake8
* Fixed most tests
* fixed pattern validation
* Fixed missing val strategies and solved custom encoder update issue
* Removed preprocessing from features due to schema SSOT
* fix flake 8
* Started encoder schema work
* Parallel CNN Encoder
* StackedCNN Encoder
* Added image encoders
* Finished sequence encoders
* Partway through text encoders
* Added text encoders
* Bag Encoders
* Binary and Date Encoders
* category, date, h3, and set encoders
* Wired up encoder schemas
* Switched input feature encoder schema definitions
* Fixed handful of issues
* Fix schema issues
* Refactored a bunch of test configs
* Small changes
* Removed default param from register_encoder
* Schema working now, working on refactoring
* Finished decoder schemas
* Removed default param from register_decoder
* Added some default params to output features and more decoder work
* Refactored all input feature encoder/decoder referencing
* Refactored pretty much all the tests
* Added back constants
* Solved gbm issue
* Fixed save_load test
* various fixes
* Fixed import issue
* Flake 8 and various fixes
* Solved more failed tests
* Refactored missed tests
* Removed commented lines
* Added init file for decoders schema
* Fixed failing tests
* Fixed hyperopt shared params test
* Added backwards compatability logic and test
* Flake 8
* removed comment
* Added base files and some initial code
* More files created, fleshing out binary feature and corresponding encoders
* Added more schema infra
* Registered all feature encoders
* Separated feature utils infra
* Added all preprocessing classes
* Filled out rest of schema configs
* Fixed preproc dataclass
* Fixed small errors blocking import
* Tests should be passing
* Deleted unnecesssary files and removed commented out code
* fixed flake8
* Fixed most tests
* fixed pattern validation
* Fixed missing val strategies and solved custom encoder update issue
* Removed preprocessing from features due to schema SSOT
* fix flake 8
* Started encoder schema work
* Parallel CNN Encoder
* StackedCNN Encoder
* Added image encoders
* Finished sequence encoders
* Partway through text encoders
* Added text encoders
* Bag Encoders
* Binary and Date Encoders
* category, date, h3, and set encoders
* Wired up encoder schemas
* Switched input feature encoder schema definitions
* Fixed handful of issues
* Fix schema issues
* Refactored a bunch of test configs
* Small changes
* Removed default param from register_encoder
* Schema working now, working on refactoring
* Finished decoder schemas
* Removed default param from register_decoder
* Added some default params to output features and more decoder work
* Refactored all input feature encoder/decoder referencing
* Refactored pretty much all the tests
* Added back constants
* Solved gbm issue
* Fixed save_load test
* various fixes
* Fixed import issue
* Flake 8 and various fixes
* Solved more failed tests
* Refactored missed tests
* Removed commented lines
* Added init file for decoders schema
* Fixed failing tests
* Fixed hyperopt shared params test
* Added backwards compatability logic and test
* Flake 8
* removed comment
* Skipping CTRL Encoder test since it's blasting memory
* Fixed audio_feature test
* Addressed failing tests
* Fixed backwards compatability
* Fixed more failing tests
* Flake 8
* Fixed more tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Refactored default logic for all features
* Fixed H3 weighted_sum encoder wrong type
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix import issue
* Mark slow HF tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fixed defaults tests
* Pin Ray nightly version
* fix link
* pin torch to 07/26
* cleanup
* upgrade ray pinned version to enable parquet partition filtering
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* downgrade Ray to ensure TensorDtypes are not inferred during Ray Dataset <=> Dask conversions
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Removed custom encoder decoder helper method
* unpin torch
* Flake 8
* Daniel feedback
* Small fixes
* Fixed default weights init
* Added test with encoder dependencies for global defaults
* Fixed Arnav's test
* Addressed Arnav's feedback
* Address nit
* Addressed feedback
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address nit
* Fix test
* Initial feedback refactor
* More refactoring
* Added vocab field to all text_encoder configs
* More refactoring
* Fixed more tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix audio feature test, also s/logging/logger.
* param names should start with lowercase s/N/n
* Re-added schema utils used in encoder refactor.
* Removes unused overwrite_defaults()
* Oops, name is passed to feature as a kwarg not a member of the feature config. Why? Probably should change that.
* Change lowercase default back to True. Fixes test_strings_utils
* Set feature validation error with output size 1.
* MLP mixer encoder needs num_channels.
* Use schema.dump instead of .__dict__ to convert marshmallow dataclass to dict
* (x,) in python is a tuple with a single element x. Watch out for this when defining schemas.
* Construct features by using build_single_input/output to share code for deserializing feature configs. Also changes ECD to BaseModel, IMO its confusing to import ECD to use a class method from BaseModel.
* Fix test_trainer_utils, adds convenience method BaseFeature.load_from_dictionary
* Use feature load_from_dictionary instead of BaseModel in feature tests.
* Populate encoder and decoder types in shared test fixtures, fixes error expectations in test_validate_config_combiner.py
* Fixes test_validate_config_misc.py by ensuring only one option of OneOf allows None, because OneOf fails validation if more than one condition match.
* Updates test_defaults.py
* Adds type, column, proc_column to feature schemas. Revert feature tests by passing in config dict again.
* decorate feature base classes with @dataclass, fixes failure building input features in trainer.
* Implement _serialize for PreprocessingDataclassField.
* use type(feature) to get schema class.
* Fix test_trainer_utils.py
* audio_feature requires embedding_size, but passthrough encoder does not have this property. Technically, passthrough encoder is not supported for audio features.
* Wow, apparently the order of elements in the oneOf affects which error message we get from jsonschema.
* Get default encoders from feature schema.
* Get encoder defaults from schema in config_utils.py
* Make number feature allow decoders without clip property
* s/list/List
* Adds reduce_output to h3 encoder.
* Moves decoder params into nested decoder.
* Update processing parameters with computed_fill_value.
* Removes test code.
* Adds input_size to decoder base because some features assume decoders have an input_size
* dense encoder not supported for bag features, changed to embed.
* Adds input_size param to dense encoder schema, since its a required parameter of dense encoder.
* Fixes vector feature input_size in encoder metadata.
* Fixes test reducers, set sequence reduce mode in output feature base.
* Don't nest encoder parameters in decoder
* Fixes test_torchscript, get num_classes from encoder config.
* Audio feature padding is float, not int.
* Adds temp check for threshold to fix GBM tests.
* Adds missing value strategy drop_row for vector feature in test.
* Drop row should work even if computed_fill_value is an empty string
* Removes duplicated TOP_K constant.
* Consolidated set_default_values
* Removes commented-out defaults.
* Remove load_config from OutputFeature, it isn't doing anything here.
* Removes comment.
* Fix type annotations for input/output feature constructors.
* Fixes output feature dependencies being ignored.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Adds test for construction of output features with dependencies.
* Encoder/Decoder config now lives on encoder/decoder object
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fixes decoder params to match their respective classes. Moves fc_stack params and threshold back to output feature.
* Make clip property of number output feature again.
* Adds threshold property to set feature schema, use this property instead of storing it in the decoder.
* input_size in output_feature instead of decoder.
* Made vector_size property of vector_feature.
* Fixed gbm tests
* Fixed flake 8
* Re-adds num_classes as member of category output feature.
* Makes vocab_size match vocab used in preprocessing.
* num_classes in CategoryOutputFeature.
* Moves num_classes from decoder to category output feature.
* Fixes test_model_training_options. Copies fc_layer keys into decoder if they are present on output features.
* Adds field descriptors for fc_layers params in BaseOutputFeatureConfig.
Co-authored-by: connor-mccorm <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: connor-mccorm <[email protected]>
Co-authored-by: Geoffrey Angus <[email protected]>
Co-authored-by: Arnav Garg <[email protected]>
Co-authored-by: Daniel Treiman <[email protected]> | load_data_for_viz | 03b4ab273abd7e22a56bb550b56f3d667200abf9 | ludwig | visualize.py | 15 | 14 | https://github.com/ludwig-ai/ludwig.git | 3 | 86 | 0 | 37 | 139 | Python | {
"docstring": "Load model file data in to list of .\n\n :param load_type: type of the data loader to be used.\n :param model_file_statistics: JSON file or list of json files containing any\n model experiment stats.\n :return List of training statistics loaded as json objects.\n ",
"language": "en",
"n_whitespaces": 64,
"n_words": 42,
"vocab_size": 32
} | def load_data_for_viz(load_type, model_file_statistics, **kwargs):
supported_load_types = dict(
load_json=load_json,
load_from_file=partial(
load_from_file, dtype=kwargs.get("dtype", int), ground_truth_split=kwargs.get("ground_truth_split", 2)
),
)
loader = supported_load_types[load_type]
try:
stats_per_model = [loader(stats_f) for stats_f in model_file_statistics]
except (TypeError, AttributeError):
logger.exception(f"Unable to open model statistics file {model_file_statistics}!")
raise
return stats_per_model
|
|
1,663 | 9,733 | 134 | gensim/models/doc2vec.py | 52 | 15 | def scan_vocab(self, corpus_iterable=None, corpus_file=None, progress_per=100000, trim_rule=None):
logger.info("collecting all words and their counts")
if corpus_file | re #2809: update the doc2vec notebook | scan_vocab | 490676cc34d909b8a361fa1ae1e835263a13673b | gensim | doc2vec.py | 10 | 10 | https://github.com/RaRe-Technologies/gensim.git | 2 | 83 | 0 | 43 | 127 | Python | {
"docstring": "Create the model's vocabulary: a mapping from unique words in the corpus to their frequency count.\n\n Parameters\n ----------\n documents : iterable of :class:`~gensim.models.doc2vec.TaggedDocument`, optional\n The tagged documents used to create the vocabulary. Their tags can be either str tokens or ints (faster).\n corpus_file : str, optional\n Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format.\n You may use this argument instead of `documents` to get performance boost. Only one of `documents` or\n `corpus_file` arguments need to be passed (not both of them).\n progress_per : int\n Progress will be logged every `progress_per` documents.\n trim_rule : function, optional\n Vocabulary trimming rule, specifies whether certain words should remain in the vocabulary,\n be trimmed away, or handled using the default (discard if word count < min_count).\n Can be None (min_count will be used, look to :func:`~gensim.utils.keep_vocab_item`),\n or a callable that accepts parameters (word, count, min_count) and returns either\n :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`.\n The rule, if given, is only used to prune vocabulary during\n :meth:`~gensim.models.doc2vec.Doc2Vec.build_vocab` and is not stored as part of the model.\n\n The input parameters are of the following types:\n * `word` (str) - the word we are examining\n * `count` (int) - the word's frequency count in the corpus\n * `min_count` (int) - the minimum count threshold.\n\n Returns\n -------\n (int, int)\n Tuple of `(total words in the corpus, number of documents)`.\n\n ",
"language": "en",
"n_whitespaces": 487,
"n_words": 218,
"vocab_size": 148
} | def scan_vocab(self, corpus_iterable=None, corpus_file=None, progress_per=100000, trim_rule=None):
logger.info("collecting all words and their counts")
if corpus_file is not None:
corpus_iterable = TaggedLineDocument(corpus_file)
total_words, corpus_count = self._scan_vocab(corpus_iterable, progress_per, trim_rule)
logger.info(
"collected %i word types and %i unique tags from a corpus of %i examples and %i words",
len(self.raw_vocab), len(self.dv), corpus_count, total_words,
)
return total_words, corpus_count
|
|
56,800 | 222,893 | 1,131 | python3.10.4/Lib/distutils/dist.py | 337 | 50 | def _parse_command_opts(self, parser, args):
# late import because of mutual dependence between these modules
from distutils.cmd import Command
# Pull the current command from the head of the command line
command = args[0]
if not command_re.match(command):
raise SystemExit("invalid command name '%s'" % command)
self.commands.append(command)
# Dig up the command class that implements this command, so we
# 1) know that it's a valid command, and 2) know which options
# it takes.
try:
cmd_class = self.get_command_class(command)
except DistutilsModuleError as msg:
raise DistutilsArgError(msg)
# Require that the command class be derived from Command -- want
# to be sure that the basic "command" interface is implemented.
if not issubclass(cmd_class, Command):
raise Distut | add python 3.10.4 for windows | _parse_command_opts | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | dist.py | 19 | 54 | https://github.com/XX-net/XX-Net.git | 18 | 357 | 0 | 203 | 597 | Python | {
"docstring": "Parse the command-line options for a single command.\n 'parser' must be a FancyGetopt instance; 'args' must be the list\n of arguments, starting with the current command (whose options\n we are about to parse). Returns a new version of 'args' with\n the next command at the front of the list; will be the empty\n list if there are no more commands on the command line. Returns\n None if the user asked for help on this command.\n ",
"language": "en",
"n_whitespaces": 126,
"n_words": 75,
"vocab_size": 48
} | def _parse_command_opts(self, parser, args):
# late import because of mutual dependence between these modules
from distutils.cmd import Command
# Pull the current command from the head of the command line
command = args[0]
if not command_re.match(command):
raise SystemExit("invalid command name '%s'" % command)
self.commands.append(command)
# Dig up the command class that implements this command, so we
# 1) know that it's a valid command, and 2) know which options
# it takes.
try:
cmd_class = self.get_command_class(command)
except DistutilsModuleError as msg:
raise DistutilsArgError(msg)
# Require that the command class be derived from Command -- want
# to be sure that the basic "command" interface is implemented.
if not issubclass(cmd_class, Command):
raise DistutilsClassError(
"command class %s must subclass Command" % cmd_class)
# Also make sure that the command object provides a list of its
# known options.
if not (hasattr(cmd_class, 'user_options') and
isinstance(cmd_class.user_options, list)):
msg = ("command class %s must provide "
"'user_options' attribute (a list of tuples)")
raise DistutilsClassError(msg % cmd_class)
# If the command class has a list of negative alias options,
# merge it in with the global negative aliases.
negative_opt = self.negative_opt
if hasattr(cmd_class, 'negative_opt'):
negative_opt = negative_opt.copy()
negative_opt.update(cmd_class.negative_opt)
# Check for help_options in command class. They have a different
# format (tuple of four) so we need to preprocess them here.
if (hasattr(cmd_class, 'help_options') and
isinstance(cmd_class.help_options, list)):
help_options = fix_help_options(cmd_class.help_options)
else:
help_options = []
# All commands support the global options too, just by adding
# in 'global_options'.
parser.set_option_table(self.global_options +
cmd_class.user_options +
help_options)
parser.set_negative_aliases(negative_opt)
(args, opts) = parser.getopt(args[1:])
if hasattr(opts, 'help') and opts.help:
self._show_help(parser, display_options=0, commands=[cmd_class])
return
if (hasattr(cmd_class, 'help_options') and
isinstance(cmd_class.help_options, list)):
help_option_found=0
for (help_option, short, desc, func) in cmd_class.help_options:
if hasattr(opts, parser.get_attr_name(help_option)):
help_option_found=1
if callable(func):
func()
else:
raise DistutilsClassError(
"invalid help function %r for help option '%s': "
"must be a callable object (function, etc.)"
% (func, help_option))
if help_option_found:
return
# Put the options from the command-line into their official
# holding pen, the 'command_options' dictionary.
opt_dict = self.get_option_dict(command)
for (name, value) in vars(opts).items():
opt_dict[name] = ("command line", value)
return args
|
|
69,784 | 242,085 | 658 | scipy/stats/_distn_infrastructure.py | 213 | 28 | def interval(self, confidence=None, *args, **kwds):
# This function was originally written with parameter `alpha`, but
# `alpha` is also the name of a shape parameter of two distributions.
# This block allows the functi | MAINT: stats: update deprecation warning version information | interval | 547d1bb522562a1ba38961d13932fffc2bb92edf | scipy | _distn_infrastructure.py | 14 | 30 | https://github.com/scipy/scipy.git | 10 | 219 | 0 | 134 | 366 | Python | {
"docstring": "Confidence interval with equal areas around the median.\n\n .. deprecated:: 1.9.0\n Parameter `alpha` is replaced by parameter `confidence` to avoid\n name collisions with the shape parameter `alpha` of some\n distributions. Parameter `alpha` will be removed in the second\n release after 1.9.0.\n\n Parameters\n ----------\n confidence : array_like of float\n Probability that an rv will be drawn from the returned range.\n Each value should be in the range [0, 1].\n arg1, arg2, ... : array_like\n The shape parameter(s) for the distribution (see docstring of the\n instance object for more information).\n loc : array_like, optional\n location parameter, Default is 0.\n scale : array_like, optional\n scale parameter, Default is 1.\n\n Returns\n -------\n a, b : ndarray of float\n end-points of range that contain ``100 * alpha %`` of the rv's\n possible values.\n\n ",
"language": "en",
"n_whitespaces": 333,
"n_words": 128,
"vocab_size": 90
} | def interval(self, confidence=None, *args, **kwds):
# This function was originally written with parameter `alpha`, but
# `alpha` is also the name of a shape parameter of two distributions.
# This block allows the function to accept both `alpha` and its
# replacement `confidence` during a deprecation period; it can be
# removed in the second release after 1.9.0.
# See description of logic in `moment` method.
has_shape_alpha = (self.shapes is not None
and "alpha" in (self.shapes.split(", ")))
got_confidence = confidence is not None
got_keyword_alpha = kwds.get("alpha", None) is not None
if not got_confidence and ((not got_keyword_alpha)
or (got_keyword_alpha and has_shape_alpha)):
message = ("interval() missing 1 required positional argument: "
"`confidence`")
raise TypeError(message)
if got_keyword_alpha and not has_shape_alpha:
if got_confidence:
# this will change to "interval got unexpected argument alpha"
message = "interval() got multiple values for first argument"
raise TypeError(message)
else:
message = ("Use of keyword argument `alpha` for method "
"`interval` is deprecated. Use first positional "
"argument or keyword argument `confidence` "
"instead.")
confidence = kwds.pop("alpha")
warnings.warn(message, DeprecationWarning, stacklevel=2)
alpha = confidence
alpha = asarray(alpha)
if np.any((alpha > 1) | (alpha < 0)):
raise ValueError("alpha must be between 0 and 1 inclusive")
q1 = (1.0-alpha)/2
q2 = (1.0+alpha)/2
a = self.ppf(q1, *args, **kwds)
b = self.ppf(q2, *args, **kwds)
return a, b
|
|
21,158 | 101,754 | 336 | tools/alignments/jobs_faces.py | 80 | 25 | def __call__(self) -> bool:
for meta in tqdm(self._face_alignments,
desc="Updating Alignments File from PNG Header",
leave=False):
src = meta["source"]
alignment = meta["alignments"]
if not any(alignment.get(key, {}) for key in self._updatable_keys):
continue
faces = self._alignments.get_faces_in_frame(src["source_filename"])
if len(faces) < src["face_index"] + 1: # list index out of range
logger.debug("Skipped face '%s'. Index does not exist in alignments file",
src["original_filename"])
continue
face = faces[src["face_index"]]
self._check_and_update(alignment, face)
retval = False
if self._counts:
retval = True
logger.info("Updated alignments file from PNG Data: %s", self._counts)
return retval
| Alignments Tool updates
- Copy info back to alignments file from faces | __call__ | c79175cbde5600bebd65785f3821fc74b3a80cbe | faceswap | jobs_faces.py | 13 | 27 | https://github.com/deepfakes/faceswap.git | 6 | 138 | 0 | 62 | 231 | Python | {
"docstring": " Parse through the face data updating any entries in the alignments file.\n\n Returns\n -------\n bool\n ``True`` if any alignment information was updated otherwise ``False``\n ",
"language": "en",
"n_whitespaces": 64,
"n_words": 24,
"vocab_size": 22
} | def __call__(self) -> bool:
for meta in tqdm(self._face_alignments,
desc="Updating Alignments File from PNG Header",
leave=False):
src = meta["source"]
alignment = meta["alignments"]
if not any(alignment.get(key, {}) for key in self._updatable_keys):
continue
faces = self._alignments.get_faces_in_frame(src["source_filename"])
if len(faces) < src["face_index"] + 1: # list index out of range
logger.debug("Skipped face '%s'. Index does not exist in alignments file",
src["original_filename"])
continue
face = faces[src["face_index"]]
self._check_and_update(alignment, face)
retval = False
if self._counts:
retval = True
logger.info("Updated alignments file from PNG Data: %s", self._counts)
return retval
|
|
116,979 | 319,622 | 116 | src/documents/tests/test_file_handling.py | 22 | 19 | def test_dynamic_path(self):
doc = Document.objects.create(
title="does not matter",
created=timezone.make_aware(datetime.datetime(2020, 6, 25, 7, 36, 51, 153)),
| Feature: Dynamic document storage pathes (#916)
* Added devcontainer
* Add feature storage pathes
* Exclude tests and add versioning
* Check escaping
* Check escaping
* Check quoting
* Echo
* Escape
* Escape :
* Double escape \
* Escaping
* Remove if
* Escape colon
* Missing \
* Esacpe :
* Escape all
* test
* Remove sed
* Fix exclude
* Remove SED command
* Add LD_LIBRARY_PATH
* Adjusted to v1.7
* Updated test-cases
* Remove devcontainer
* Removed internal build-file
* Run pre-commit
* Corrected flak8 error
* Adjusted to v1.7
* Updated test-cases
* Corrected flak8 error
* Adjusted to new plural translations
* Small adjustments due to code-review backend
* Adjusted line-break
* Removed PAPERLESS prefix from settings variables
* Corrected style change due to search+replace
* First documentation draft
* Revert changes to Pipfile
* Add sphinx-autobuild with keep-outdated
* Revert merge error that results in wrong storage path is evaluated
* Adjust styles of generated files ...
* Adds additional testing to cover dynamic storage path functionality
* Remove unnecessary condition
* Add hint to edit storage path dialog
* Correct spelling of pathes to paths
* Minor documentation tweaks
* Minor typo
* improving wrapping of filter editor buttons with new storage path button
* Update .gitignore
* Fix select border radius in non input-groups
* Better storage path edit hint
* Add note to edit storage path dialog re document_renamer
* Add note to bulk edit storage path re document_renamer
* Rename FILTER_STORAGE_DIRECTORY to PATH
* Fix broken filter rule parsing
* Show default storage if unspecified
* Remove note re storage path on bulk edit
* Add basic validation of filename variables
Co-authored-by: Markus Kling <[email protected]>
Co-authored-by: Trenton Holmes <[email protected]>
Co-authored-by: Michael Shamoon <[email protected]>
Co-authored-by: Quinn Casey <[email protected]> | test_dynamic_path | 69ef26dab04d51e7e102dcb33cd98ddc6ad975fd | paperless-ngx | test_file_handling.py | 13 | 10 | https://github.com/paperless-ngx/paperless-ngx.git | 1 | 81 | 0 | 22 | 127 | Python | {
"docstring": "\n GIVEN:\n - A document with a defined storage path\n WHEN:\n - the filename is generated for the document\n THEN:\n - the generated filename uses the defined storage path for the document\n ",
"language": "en",
"n_whitespaces": 93,
"n_words": 31,
"vocab_size": 17
} | def test_dynamic_path(self):
doc = Document.objects.create(
title="does not matter",
created=timezone.make_aware(datetime.datetime(2020, 6, 25, 7, 36, 51, 153)),
mime_type="application/pdf",
pk=2,
checksum="2",
storage_path=StoragePath.objects.create(path="TestFolder/{created}"),
)
self.assertEqual(generate_filename(doc), "TestFolder/2020-06-25.pdf")
|
|
34,736 | 150,412 | 71 | freqtrade/rpc/replicate/__init__.py | 17 | 8 | async def follower_loop(self):
try:
await self._connect_to_leaders()
except Exception as e:
logger.error("Exception occurred in follower loop: ")
logger.exception(e)
| initial concept for replicate, basic leader and follower logic | follower_loop | 9f6bba40af1a407f190a89f5c0c8b4e3f528ba46 | freqtrade | __init__.py | 11 | 6 | https://github.com/freqtrade/freqtrade.git | 2 | 31 | 0 | 17 | 60 | Python | {
"docstring": "\n Main follower coroutine\n\n This starts all of the leader connection coros\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 11,
"vocab_size": 11
} | async def follower_loop(self):
try:
await self._connect_to_leaders()
except Exception as e:
logger.error("Exception occurred in follower loop: ")
logger.exception(e)
|
|
21,711 | 103,727 | 52 | kitty_tests/check_build.py | 18 | 11 | def test_launcher_ensures_stdio(self):
from kitty.constants import kitty_exe
import subprocess
exe = kitty_exe()
cp = subprocess.run([exe, '+runpy', ])
self.assertEqual(cp.returncode, 0)
| Fix regression in 0.26.0 that caused launching kitty without working STDIO handles to result in high CPU usage and prewarming failing
Fixes #5444 | test_launcher_ensures_stdio | 6604e0d015fbd7a3e5602a6f3831d786b4ed659d | kitty | check_build.py | 10 | 15 | https://github.com/kovidgoyal/kitty.git | 1 | 42 | 0 | 16 | 71 | Python | {
"docstring": "\\\nimport os, sys\nif sys.stdin:\n os.close(sys.stdin.fileno())\nif sys.stdout:\n os.close(sys.stdout.fileno())\nif sys.stderr:\n os.close(sys.stderr.fileno())\nos.execlp('kitty', 'kitty', '+runpy', 'import sys; raise SystemExit(1 if sys.stdout is None or sys.stdin is None or sys.stderr is None else 0)')\n",
"language": "en",
"n_whitespaces": 37,
"n_words": 34,
"vocab_size": 26
} | def test_launcher_ensures_stdio(self):
from kitty.constants import kitty_exe
import subprocess
exe = kitty_exe()
cp = subprocess.run([exe, '+runpy', ])
self.assertEqual(cp.returncode, 0)
|
|
78,160 | 265,647 | 106 | netbox/dcim/tests/test_forms.py | 20 | 13 | def test_interface_label_count_mismatch(self):
| Fixes #10247: Allow changing selected device/VM when creating a new component (#10312)
* Initial work on #10247
* Continued work on #10247
* Clean up component creation tests
* Move valdiation of replicated field to form
* Clean up ordering of fields in component creation forms
* Omit fieldset header if none
* Clean up ordering of fields in component template creation forms
* View tests should not move component templates to new device type
* Define replication_fields on VMInterfaceCreateForm
* Clean up expandable field help texts
* Update comments
* Update component bulk update forms & views to support new replication fields
* Fix ModularDeviceComponentForm parent class
* Fix bulk creation of VM interfaces (thanks @kkthxbye-code!) | test_interface_label_count_mismatch | c4b7ab067a914349abd88398dd9bfef9f6c2f806 | netbox | test_forms.py | 10 | 10 | https://github.com/netbox-community/netbox.git | 1 | 58 | 0 | 19 | 105 | Python | {
"docstring": "\n Check that attempting to generate a differing number of names and labels results in a validation error.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 17,
"vocab_size": 16
} | def test_interface_label_count_mismatch(self):
bad_interface_data = {
'device': self.device.pk,
'name': 'eth[0-9]',
'label': 'Interface[0-1]',
'type': InterfaceTypeChoices.TYPE_1GE_GBIC,
}
form = InterfaceCreateForm(bad_interface_data)
self.assertFalse(form.is_valid())
self.assertIn('label', form.errors)
|
|
75,716 | 259,334 | 22 | sklearn/preprocessing/_data.py | 13 | 8 | def power_transform(X, method="yeo-johnson", *, standardize=True, copy=True):
pt = PowerTransformer(method=method, stand | DOC Ensures that preprocessing._data.power_transform passes numpydoc validation (#22802)
Co-authored-by: Jérémie du Boisberranger <[email protected]> | power_transform | 5cccdef4378fcdb863467414ee638c6f5e51a19a | scikit-learn | _data.py | 9 | 3 | https://github.com/scikit-learn/scikit-learn.git | 1 | 43 | 0 | 13 | 67 | Python | {
"docstring": "Parametric, monotonic transformation to make data more Gaussian-like.\n\n Power transforms are a family of parametric, monotonic transformations\n that are applied to make data more Gaussian-like. This is useful for\n modeling issues related to heteroscedasticity (non-constant variance),\n or other situations where normality is desired.\n\n Currently, power_transform supports the Box-Cox transform and the\n Yeo-Johnson transform. The optimal parameter for stabilizing variance and\n minimizing skewness is estimated through maximum likelihood.\n\n Box-Cox requires input data to be strictly positive, while Yeo-Johnson\n supports both positive or negative data.\n\n By default, zero-mean, unit-variance normalization is applied to the\n transformed data.\n\n Read more in the :ref:`User Guide <preprocessing_transformer>`.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The data to be transformed using a power transformation.\n\n method : {'yeo-johnson', 'box-cox'}, default='yeo-johnson'\n The power transform method. Available methods are:\n\n - 'yeo-johnson' [1]_, works with positive and negative values\n - 'box-cox' [2]_, only works with strictly positive values\n\n .. versionchanged:: 0.23\n The default value of the `method` parameter changed from\n 'box-cox' to 'yeo-johnson' in 0.23.\n\n standardize : bool, default=True\n Set to True to apply zero-mean, unit-variance normalization to the\n transformed output.\n\n copy : bool, default=True\n Set to False to perform inplace computation during transformation.\n\n Returns\n -------\n X_trans : ndarray of shape (n_samples, n_features)\n The transformed data.\n\n See Also\n --------\n PowerTransformer : Equivalent transformation with the\n Transformer API (e.g. as part of a preprocessing\n :class:`~sklearn.pipeline.Pipeline`).\n\n quantile_transform : Maps data to a standard normal distribution with\n the parameter `output_distribution='normal'`.\n\n Notes\n -----\n NaNs are treated as missing values: disregarded in ``fit``, and maintained\n in ``transform``.\n\n For a comparison of the different scalers, transformers, and normalizers,\n see :ref:`examples/preprocessing/plot_all_scaling.py\n <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.\n\n References\n ----------\n\n .. [1] I.K. Yeo and R.A. Johnson, \"A new family of power transformations to\n improve normality or symmetry.\" Biometrika, 87(4), pp.954-959,\n (2000).\n\n .. [2] G.E.P. Box and D.R. Cox, \"An Analysis of Transformations\", Journal\n of the Royal Statistical Society B, 26, 211-252 (1964).\n\n Examples\n --------\n >>> import numpy as np\n >>> from sklearn.preprocessing import power_transform\n >>> data = [[1, 2], [3, 2], [4, 5]]\n >>> print(power_transform(data, method='box-cox'))\n [[-1.332... -0.707...]\n [ 0.256... -0.707...]\n [ 1.076... 1.414...]]\n\n .. warning:: Risk of data leak.\n Do not use :func:`~sklearn.preprocessing.power_transform` unless you\n know what you are doing. A common mistake is to apply it to the entire\n data *before* splitting into training and test sets. This will bias the\n model evaluation because information would have leaked from the test\n set to the training set.\n In general, we recommend using\n :class:`~sklearn.preprocessing.PowerTransformer` within a\n :ref:`Pipeline <pipeline>` in order to prevent most risks of data\n leaking, e.g.: `pipe = make_pipeline(PowerTransformer(),\n LogisticRegression())`.\n ",
"language": "en",
"n_whitespaces": 771,
"n_words": 421,
"vocab_size": 267
} | def power_transform(X, method="yeo-johnson", *, standardize=True, copy=True):
pt = PowerTransformer(method=method, standardize=standardize, copy=copy)
return pt.fit_transform(X)
|
|
41,742 | 176,172 | 29 | networkx/generators/small.py | 17 | 5 | def desargues_graph(create_using=None):
G = LCF_graph(20, [5, -5, 9, -9], 5, create_using)
G.name = "Desargues Graph"
return G
| Docstrings for the small.py module (#5240)
* added description for the first 5 small graphs
* modified descriptions based on comment and added description for two more functions
* added doctrings to all the functions
* Minor touchups.
Co-authored-by: Ross Barnowski <[email protected]> | desargues_graph | dec723f072eb997a497a159dbe8674cd39999ee9 | networkx | small.py | 10 | 4 | https://github.com/networkx/networkx.git | 1 | 37 | 0 | 15 | 58 | Python | {
"docstring": "\n Returns the Desargues Graph\n\n The Desargues Graph is a non-planar, distance-transitive cubic graph\n with 20 nodes and 30 edges [1]_.\n It is a symmetric graph. It can be represented in LCF notation\n as [5,-5,9,-9]^5 [2]_.\n\n Parameters\n ----------\n create_using : NetworkX graph constructor, optional (default=nx.Graph)\n Graph type to create. If graph instance, then cleared before populated.\n\n Returns\n -------\n G : networkx Graph\n Desargues Graph with 20 nodes and 30 edges\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Desargues_graph\n .. [2] https://mathworld.wolfram.com/DesarguesGraph.html\n ",
"language": "en",
"n_whitespaces": 139,
"n_words": 77,
"vocab_size": 56
} | def desargues_graph(create_using=None):
G = LCF_graph(20, [5, -5, 9, -9], 5, create_using)
G.name = "Desargues Graph"
return G
|
|
1,175 | 7,282 | 180 | ludwig/schema/features/utils.py | 36 | 7 | def get_output_feature_jsonschema():
output_feature_types = sorted(list(output_type_registry.keys()))
return {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {"type": "string"},
"type": {"type": "string", "enum": output_feature_types},
"column": {"type": "string"},
},
"additionalProperties": True,
"allOf": get_outpu | Input/Output Feature Schema Refactor (#2147)
* Added base files and some initial code
* More files created, fleshing out binary feature and corresponding encoders
* Added more schema infra
* Registered all feature encoders
* Separated feature utils infra
* Added all preprocessing classes
* Filled out rest of schema configs
* Fixed preproc dataclass
* Fixed small errors blocking import
* Tests should be passing
* Deleted unnecesssary files and removed commented out code
* fixed flake8
* Fixed most tests
* fixed pattern validation
* Fixed missing val strategies and solved custom encoder update issue
* Removed preprocessing from features due to schema SSOT
* fix flake 8
* fix flake 8
* fix flake 8
* Using encoder/decoder registries
* Address NIT
* Address feedback
* Adding constants, remove computed_fill_value, swapped in registries
* Addressed Feedback
* Flake8
* Making tied a constant
* Added base feature classes
* Added parameter metadata for computed fill value
* Small fix
* Add pattern back into string
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> | get_output_feature_jsonschema | 6909ae16047d422b94ed4cbd1a753e6b34540ff9 | ludwig | utils.py | 14 | 16 | https://github.com/ludwig-ai/ludwig.git | 1 | 85 | 0 | 28 | 167 | Python | {
"docstring": "This function returns a JSON schema structured to only requires a `type` key and then conditionally applies\n a corresponding output feature's field constraints.\n\n Returns: JSON Schema\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 26,
"vocab_size": 23
} | def get_output_feature_jsonschema():
output_feature_types = sorted(list(output_type_registry.keys()))
return {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {"type": "string"},
"type": {"type": "string", "enum": output_feature_types},
"column": {"type": "string"},
},
"additionalProperties": True,
"allOf": get_output_feature_conds(),
"required": ["name", "type"],
},
}
|
|
43,114 | 180,242 | 82 | demo/blocks_component_shortcut/run.py | 29 | 14 | def greet(str):
return str
with gr.Blocks() as demo:
with gr.Row():
text1 = gr.component("textarea")
text2 = gr.TextArea()
text3 = gr.templates.TextArea()
text1.change(greet, text1, text2)
text2.change(greet, text2, text3)
text3.change(greet, text3, text1)
demo | update-shortcut-syntax (#1234)
* update-shortcut-syntax
- fix&update gr.component
- create a demo introducing shortcuts within Blocks
* update-shortcut-syntax
- tweaks
* update-shortcut-syntax
- tweaks
* update-shortcut-syntax
- fix formatting
* update-shortcut-syntax
- tweaks
- fix tests
* update-shortcut-syntax
- tweaks
- fix tests
* update-shortcut-syntax
- tweaks
- fix tests | greet | 2de9ee8bfb43dc1f6d71e16ed1fe18ea164edd4c | gradio | run.py | 11 | 2 | https://github.com/gradio-app/gradio.git | 1 | 7 | 0 | 26 | 141 | Python | {
"docstring": "\n You can make use of str shortcuts you use in Interface within Blocks as well.\n \n Interface shortcut example:\n Interface(greet, \"textarea\", \"textarea\")\n \n You can use \n 1. gr.component()\n 2. gr.templates.Template()\n 3. gr.Template()\n All the templates are listed in gradio/templates.py\n ",
"language": "en",
"n_whitespaces": 74,
"n_words": 37,
"vocab_size": 31
} | def greet(str):
return str
with gr.Blocks() as demo:
with gr.Row():
text1 = gr.component("textarea")
text2 = gr.TextArea()
text3 = gr.templates.TextArea()
text1.change(greet, text1, text2)
text2.change(greet, text2, text3)
text3.change(greet, text3, text1)
demo.launch()
|
|
43,601 | 181,817 | 84 | tpot/base.py | 23 | 10 | def _impute_values(self, features):
if self.verbosity > 1:
| Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068. | _impute_values | 388616b6247ca4ea8de4e2f340d6206aee523541 | tpot | base.py | 12 | 7 | https://github.com/EpistasisLab/tpot.git | 3 | 53 | 0 | 21 | 91 | Python | {
"docstring": "Impute missing values in a feature set.\n\n Parameters\n ----------\n features: array-like {n_samples, n_features}\n A feature matrix\n\n Returns\n -------\n array-like {n_samples, n_features}\n ",
"language": "en",
"n_whitespaces": 81,
"n_words": 21,
"vocab_size": 17
} | def _impute_values(self, features):
if self.verbosity > 1:
print("Imputing missing values in feature set")
if self._fitted_imputer is None:
self._fitted_imputer = SimpleImputer(strategy="median")
self._fitted_imputer.fit(features)
return self._fitted_imputer.transform(features)
|
|
50,700 | 204,336 | 70 | django/contrib/sites/shortcuts.py | 34 | 9 | def get_current_site(request):
# Import is inside the function because its point is to avoid importing the
# Site models when django.contrib.sites isn't installed.
if apps.is_installed("django.contrib.sites"):
from | Refs #33476 -- Reformatted code with Black. | get_current_site | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | shortcuts.py | 10 | 6 | https://github.com/django/django.git | 2 | 35 | 0 | 29 | 66 | Python | {
"docstring": "\n Check if contrib.sites is installed and return either the current\n ``Site`` object or a ``RequestSite`` object based on the request.\n ",
"language": "en",
"n_whitespaces": 30,
"n_words": 20,
"vocab_size": 18
} | def get_current_site(request):
# Import is inside the function because its point is to avoid importing the
# Site models when django.contrib.sites isn't installed.
if apps.is_installed("django.contrib.sites"):
from .models import Site
return Site.objects.get_current(request)
else:
return RequestSite(request)
|
|
77,782 | 264,670 | 235 | netbox/extras/scripts.py | 103 | 28 | def get_scripts(use_names=False):
scripts = OrderedDict()
# Iterate through all modules within the scripts path. These are the user-created files in which reports are
# defined.
for importer, module_name, _ in pkgutil.iter_modules([settings.SCRIPTS_ROOT]):
# Remove cached module to ensu | Save old JobResults | get_scripts | f13a00b2dd33bffc3048c861b494096df457f212 | netbox | scripts.py | 12 | 17 | https://github.com/netbox-community/netbox.git | 11 | 156 | 0 | 66 | 247 | Python | {
"docstring": "\n Return a dict of dicts mapping all scripts to their modules. Set use_names to True to use each module's human-\n defined name in place of the actual module name.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 29,
"vocab_size": 26
} | def get_scripts(use_names=False):
scripts = OrderedDict()
# Iterate through all modules within the scripts path. These are the user-created files in which reports are
# defined.
for importer, module_name, _ in pkgutil.iter_modules([settings.SCRIPTS_ROOT]):
# Remove cached module to ensure consistency with filesystem
if module_name in sys.modules:
del sys.modules[module_name]
module = importer.find_module(module_name).load_module(module_name)
if use_names and hasattr(module, 'name'):
module_name = module.name
module_scripts = OrderedDict()
script_order = getattr(module, "script_order", ())
ordered_scripts = [cls for cls in script_order if is_script(cls)]
unordered_scripts = [cls for _, cls in inspect.getmembers(module, is_script) if cls not in script_order]
for cls in [*ordered_scripts, *unordered_scripts]:
module_scripts[cls.__name__] = cls
if module_scripts:
scripts[module_name] = module_scripts
return scripts
|
|
78,268 | 266,015 | 74 | netbox/extras/plugins/templates.py | 24 | 9 | def render(self, template_name, extra_context=None):
if extra_context is None:
extra_context = {}
elif not isinstance(extra_context, dict):
raise TypeError("extra_context must be a dictionary")
return get_template(template_name).render({**self.context, * | Reorganize plugin resources | render | e7f54c5867cf49126bbf95e28633e4283c2bbcb2 | netbox | templates.py | 11 | 6 | https://github.com/netbox-community/netbox.git | 3 | 53 | 0 | 23 | 87 | Python | {
"docstring": "\n Convenience method for rendering the specified Django template using the default context data. An additional\n context dictionary may be passed as `extra_context`.\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 22,
"vocab_size": 20
} | def render(self, template_name, extra_context=None):
if extra_context is None:
extra_context = {}
elif not isinstance(extra_context, dict):
raise TypeError("extra_context must be a dictionary")
return get_template(template_name).render({**self.context, **extra_context})
|
|
@keras_export("keras.applications.mobilenet_v3.decode_predictions") | 82,630 | 278,617 | 11 | keras/applications/mobilenet_v3.py | 6 | 4 | def preprocess_input(x, data_format=None):
return x
@keras_export("keras.applications. | Remove pylint comments.
PiperOrigin-RevId: 452353044 | preprocess_input | 3613c3defc39c236fb1592c4f7ba1a9cc887343a | keras | mobilenet_v3.py | 7 | 2 | https://github.com/keras-team/keras.git | 1 | 12 | 1 | 6 | 32 | Python | {
"docstring": "A placeholder method for backward compatibility.\n\n The preprocessing logic has been included in the mobilenet_v3 model\n implementation. Users are no longer required to call this method to\n normalize the input data. This method does nothing and only kept as a\n placeholder to align the API surface between old and new version of model.\n\n Args:\n x: A floating point `numpy.array` or a `tf.Tensor`.\n data_format: Optional data format of the image tensor/array. Defaults to\n None, in which case the global setting\n `tf.keras.backend.image_data_format()` is used (unless you changed it,\n it defaults to \"channels_last\").{mode}\n\n Returns:\n Unchanged `numpy.array` or `tf.Tensor`.\n ",
"language": "en",
"n_whitespaces": 152,
"n_words": 95,
"vocab_size": 76
} | def preprocess_input(x, data_format=None):
return x
@keras_export("keras.applications.mobilenet_v3.decode_predictions") |
@register.simple_tag | 16,502 | 76,338 | 10 | wagtail/admin/templatetags/wagtailadmin_tags.py | 5 | 7 | def message_level_tag(message):
return MESSAGE_TAGS.get(message.level)
| Prevent custom MESSAGE_TAGS settings from leaking into admin styles
Fixes a test failure against Django main.
In #2552, a fix was applied to ensure that the project-level MESSAGE_TAGS setting was ignored, allowing end-users to customise that setting for their own projects without it leaking into Wagtail admin styles.
Unfortunately, the test was flawed (or was broken in a Django regression at some point): in Django <=4.0, MESSAGE_TAGS was not affected by override_settings after the first request, which meant that unless the test was run in isolation, the custom classname that was supposed to flag up the problem never got applied, and the test always succeeded.
The change to SVG icons broke the intent of #2552, since it used message.level_tag for the icon's classname (and this picks up MESSAGE_TAGS customisations), but due to the broken test this went unnoticed.
https://github.com/django/django/commit/24b316536a7ee4c54a54f632de1852aecb4038c0 fixed the override_settings behaviour, making the test fail as it should have done long ago.
Here we adjust the test to not rely on override_settings (so that it does what it's supposed to do on all Django versions), fix a test that gets broken as a side effect (because it's unnecessarily checking message.level_tag), and fixes our SVG-icon-powered message include to bypass the MESSAGE_TAGS setting like the old implementation did.
Confusing? Yes. | message_level_tag | 1838fbfb1a720e0a286c989dbdea03dfde6af4a5 | wagtail | wagtailadmin_tags.py | 8 | 2 | https://github.com/wagtail/wagtail.git | 1 | 15 | 1 | 5 | 34 | Python | {
"docstring": "\n Return the tag for this message's level as defined in\n django.contrib.messages.constants.DEFAULT_TAGS, ignoring the project-level\n MESSAGE_TAGS setting (which end-users might customise).\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 20,
"vocab_size": 19
} | def message_level_tag(message):
return MESSAGE_TAGS.get(message.level)
@register.simple_tag |
75,848 | 259,648 | 422 | sklearn/metrics/_regression.py | 141 | 19 | def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric"):
check_cons | ENH add D2 pinbal score and D2 absolute error score (#22118) | _check_reg_targets | aeeac1c1d634dc80abc93fb30b3fe48e1d709b64 | scikit-learn | _regression.py | 17 | 35 | https://github.com/scikit-learn/scikit-learn.git | 10 | 234 | 0 | 93 | 371 | Python | {
"docstring": "Check that y_true and y_pred belong to the same regression task.\n\n Parameters\n ----------\n y_true : array-like\n\n y_pred : array-like\n\n multioutput : array-like or string in ['raw_values', uniform_average',\n 'variance_weighted'] or None\n None is accepted due to backward compatibility of r2_score().\n\n dtype : str or list, default=\"numeric\"\n the dtype argument passed to check_array.\n\n Returns\n -------\n type_true : one of {'continuous', continuous-multioutput'}\n The type of the true target data, as output by\n 'utils.multiclass.type_of_target'.\n\n y_true : array-like of shape (n_samples, n_outputs)\n Ground truth (correct) target values.\n\n y_pred : array-like of shape (n_samples, n_outputs)\n Estimated target values.\n\n multioutput : array-like of shape (n_outputs) or string in ['raw_values',\n uniform_average', 'variance_weighted'] or None\n Custom output weights if ``multioutput`` is array-like or\n just the corresponding argument if ``multioutput`` is a\n correct keyword.\n ",
"language": "en",
"n_whitespaces": 240,
"n_words": 124,
"vocab_size": 70
} | def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric"):
check_consistent_length(y_true, y_pred)
y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)
if y_true.ndim == 1:
y_true = y_true.reshape((-1, 1))
if y_pred.ndim == 1:
y_pred = y_pred.reshape((-1, 1))
if y_true.shape[1] != y_pred.shape[1]:
raise ValueError(
"y_true and y_pred have different number of output ({0}!={1})".format(
y_true.shape[1], y_pred.shape[1]
)
)
n_outputs = y_true.shape[1]
allowed_multioutput_str = ("raw_values", "uniform_average", "variance_weighted")
if isinstance(multioutput, str):
if multioutput not in allowed_multioutput_str:
raise ValueError(
"Allowed 'multioutput' string values are {}. "
"You provided multioutput={!r}".format(
allowed_multioutput_str, multioutput
)
)
elif multioutput is not None:
multioutput = check_array(multioutput, ensure_2d=False)
if n_outputs == 1:
raise ValueError("Custom weights are useful only in multi-output cases.")
elif n_outputs != len(multioutput):
raise ValueError(
"There must be equally many custom weights (%d) as outputs (%d)."
% (len(multioutput), n_outputs)
)
y_type = "continuous" if n_outputs == 1 else "continuous-multioutput"
return y_type, y_true, y_pred, multioutput
|
|
31,945 | 140,391 | 417 | python/ray/serve/deployment.py | 73 | 18 | def bind(self, *args, **kwargs) -> Union[ClassNode, FunctionNode]:
| [Deployment Graph] Simplify our use of DeploymentSchema (#25202) | bind | 820cf4fdcae6b274588e23b312d5255d1b418e10 | ray | deployment.py | 14 | 33 | https://github.com/ray-project/ray.git | 4 | 128 | 0 | 46 | 183 | Python | {
"docstring": "Bind the provided arguments and return a class or function node.\n\n The returned bound deployment can be deployed or bound to other\n deployments to create a deployment graph.\n ",
"language": "en",
"n_whitespaces": 49,
"n_words": 28,
"vocab_size": 23
} | def bind(self, *args, **kwargs) -> Union[ClassNode, FunctionNode]:
copied_self = copy(self)
copied_self._func_or_class = "dummpy.module"
schema_shell = deployment_to_schema(copied_self)
if inspect.isfunction(self._func_or_class):
return FunctionNode(
self._func_or_class,
args, # Used to bind and resolve DAG only, can take user input
kwargs, # Used to bind and resolve DAG only, can take user input
self._ray_actor_options or dict(),
other_args_to_resolve={
"deployment_schema": schema_shell,
"is_from_serve_deployment": True,
},
)
else:
return ClassNode(
self._func_or_class,
args,
kwargs,
cls_options=self._ray_actor_options or dict(),
other_args_to_resolve={
"deployment_schema": schema_shell,
"is_from_serve_deployment": True,
},
)
|
|
20,605 | 101,184 | 150 | tools/manual/faceviewer/viewport.py | 24 | 15 | def _obtain_mask(cls, detected_face, mask_type):
mas | lib.detected_face.Mask
- Add source + target offset and coverage to set_sub_crop method | _obtain_mask | 32950897376b48e0f08b46385602e4df902cf49e | faceswap | viewport.py | 12 | 10 | https://github.com/deepfakes/faceswap.git | 3 | 77 | 0 | 21 | 126 | Python | {
"docstring": " Obtain the mask for the correct \"face\" centering that is used in the thumbnail display.\n\n Parameters\n -----------\n detected_face: :class:`lib.align.DetectedFace`\n The Detected Face object to obtain the mask for\n mask_type: str\n The type of mask to obtain\n\n Returns\n -------\n :class:`numpy.ndarray` or ``None``\n The single channel mask of requested mask type, if it exists, otherwise ``None``\n ",
"language": "en",
"n_whitespaces": 144,
"n_words": 54,
"vocab_size": 40
} | def _obtain_mask(cls, detected_face, mask_type):
mask = detected_face.mask.get(mask_type)
if not mask:
return None
if mask.stored_centering != "face":
face = AlignedFace(detected_face.landmarks_xy)
mask.set_sub_crop(face.pose.offset[mask.stored_centering],
face.pose.offset["face"],
centering="face")
return mask.mask.squeeze()
|
|
118,284 | 322,908 | 337 | examples/model_interpretation/task/senti/rnn/model.py | 104 | 33 | def forward(self, input, mask=None):
forward_input, backward_input = paddle.chunk(input, chunks=2, axis=2)
# elementwise-sum forward_x and backward_x
# Shape: (batch_size, max_seq_len, hidden_size)
h = paddle.add_n([forward_input, backward_input])
# Shape: (batch_size, hidden_size, 1)
att_weight = self.att_weight.tile(
repeat_times=(paddle.shape(h)[0], 1, 1))
# Shape: (batch_size, max_seq_len, 1)
att_score = paddle.bmm(paddle.tanh(h), att_weight)
if mask is not None:
# mask, remove the effect of 'PAD'
mask | Add NLP model interpretation (#1752)
* upload NLP interpretation
* fix problems and relocate project
* remove abandoned picture
* remove abandoned picture
* fix dead link in README
* fix dead link in README
* fix code style problems
* fix CR round 1
* remove .gitkeep files
* fix code style
* fix file encoding problem
* fix code style
* delete duplicated files due to directory rebuild
* fix CR round 2
* fix code style
* fix ernie tokenizer
* fix code style
* fix problem from CR round 1
* fix bugs
* fix README
* remove duplicated files
* deal with diff of old and new tokenizer results
* fix CR round 4
* fix code style
* add missing dependence
* fix broken import path
* move some data file to cloud
* MRC upper case to lower case
Co-authored-by: Zeyu Chen <[email protected]>
Co-authored-by: binlinquge <xxx>
Co-authored-by: Guo Sheng <[email protected]> | forward | 93cae49c0c572b5c1ac972759140fbe924b0374d | PaddleNLP | model.py | 14 | 18 | https://github.com/PaddlePaddle/PaddleNLP.git | 2 | 211 | 0 | 70 | 329 | Python | {
"docstring": "\n Args:\n input (paddle.Tensor) of shape (batch, seq_len, input_size): Tensor containing the features of the input sequence.\n mask (paddle.Tensor) of shape (batch, seq_len) :\n Tensor is a bool tensor, whose each element identifies whether the input word id is pad token or not. \n Defaults to `None`.\n ",
"language": "en",
"n_whitespaces": 113,
"n_words": 45,
"vocab_size": 34
} | def forward(self, input, mask=None):
forward_input, backward_input = paddle.chunk(input, chunks=2, axis=2)
# elementwise-sum forward_x and backward_x
# Shape: (batch_size, max_seq_len, hidden_size)
h = paddle.add_n([forward_input, backward_input])
# Shape: (batch_size, hidden_size, 1)
att_weight = self.att_weight.tile(
repeat_times=(paddle.shape(h)[0], 1, 1))
# Shape: (batch_size, max_seq_len, 1)
att_score = paddle.bmm(paddle.tanh(h), att_weight)
if mask is not None:
# mask, remove the effect of 'PAD'
mask = paddle.cast(mask, dtype='float32')
mask = mask.unsqueeze(axis=-1)
inf_tensor = paddle.full(
shape=mask.shape, dtype='float32', fill_value=-INF)
att_score = paddle.multiply(att_score, mask) + paddle.multiply(
inf_tensor, (1 - mask))
# Shape: (batch_size, max_seq_len, 1)
att_weight = F.softmax(att_score, axis=1)
# Shape: (batch_size, lstm_hidden_size)
reps = paddle.bmm(h.transpose(perm=(0, 2, 1)),
att_weight).squeeze(axis=-1)
reps = paddle.tanh(reps)
return reps, att_weight
|
|
70,242 | 244,108 | 133 | mmdet/models/dense_heads/maskformer_head.py | 33 | 15 | def simple_test(self, feats, img_metas, **kwargs):
all_cls_scores, all_mask_preds = self(feats, img_metas)
mask_cls_results = all_cls_scores[-1]
mask_pred_results = all_mask_preds[-1]
# upsample masks
img_shape = img_metas[0]['batch_input_shape']
mask_pred_results = F.interpolate(
mask_pred_results,
size=(img_shape[0], img_shape[1]),
mode='bilinear',
align_corners=False)
return mask_cls_results, mask_pred_results
| [Enhance] MaskFormer refactor (#7471)
* maskformer refactor
update docstring
update docstring
update unit test
update unit test
update unit test
* remove redundant code
* update unit test | simple_test | 4bb184bae070f37febb10f82bee3a217dc1ad7c5 | mmdetection | maskformer_head.py | 11 | 11 | https://github.com/open-mmlab/mmdetection.git | 1 | 80 | 0 | 27 | 125 | Python | {
"docstring": "Test without augmentaton.\n\n Args:\n feats (list[Tensor]): Multi-level features from the\n upstream network, each is a 4D-tensor.\n img_metas (list[dict]): List of image information.\n\n Returns:\n tuple: A tuple contains two tensors.\n\n - mask_cls_results (Tensor): Mask classification logits,\\\n shape (batch_size, num_queries, cls_out_channels).\n Note `cls_out_channels` should includes background.\n - mask_pred_results (Tensor): Mask logits, shape \\\n (batch_size, num_queries, h, w).\n ",
"language": "en",
"n_whitespaces": 191,
"n_words": 55,
"vocab_size": 49
} | def simple_test(self, feats, img_metas, **kwargs):
all_cls_scores, all_mask_preds = self(feats, img_metas)
mask_cls_results = all_cls_scores[-1]
mask_pred_results = all_mask_preds[-1]
# upsample masks
img_shape = img_metas[0]['batch_input_shape']
mask_pred_results = F.interpolate(
mask_pred_results,
size=(img_shape[0], img_shape[1]),
mode='bilinear',
align_corners=False)
return mask_cls_results, mask_pred_results
|
|
52,490 | 208,731 | 308 | IPython/core/ultratb.py | 76 | 25 | def _format_list(self, extracted_list):
Colors = self.Colors
list = []
for ind, (filename, lineno, name, line) in enumerate(extracted_list):
normalCol, nameCol, fileCol, lineCol = (
# Emphasize the last entry
(Colors.normalEm, Colors.nameEm, Colors.filenameEm, Colors.line)
if ind == len(extracted_list) - 1
else (Colors.Normal, Colors.name, Colors.filename, "")
)
fns = _format_filename(filename, fileCol, normalCol, lineno=lineno)
item = f"{normalCol} {fns}"
if name != "<module>":
item += f" in {nameCol}{name}{normalCol}\n"
| Restore lineno's for Input mapped files (#13560)
* Implement lineno's for Input mapped files
* Adopt In [123], line 123 format
* Revert "Set co_name for cells run line by line. Fixes https://github.com/ipython/ipykernel/issues/841"
(This reverts commit d11e987f174a15f1640f8006c86f58d884c3faa4.)
* Omit mention of ", in <module>" for input tracebacks
* Input cell -> Cell
* Remove <module> from traceback doctests
* Use f-string for `in ...' format
* Simplify _format_list logic, converting to f-strings | _format_list | a72418e2dcdfc3c91f70d724d16d2691a41c9c24 | ipython | ultratb.py | 14 | 19 | https://github.com/ipython/ipython.git | 5 | 134 | 0 | 61 | 248 | Python | {
"docstring": "Format a list of traceback entry tuples for printing.\n\n Given a list of tuples as returned by extract_tb() or\n extract_stack(), return a list of strings ready for printing.\n Each string in the resulting list corresponds to the item with the\n same index in the argument list. Each string ends in a newline;\n the strings may contain internal newlines as well, for those items\n whose source text line is not None.\n\n Lifted almost verbatim from traceback.py\n ",
"language": "en",
"n_whitespaces": 132,
"n_words": 75,
"vocab_size": 53
} | def _format_list(self, extracted_list):
Colors = self.Colors
list = []
for ind, (filename, lineno, name, line) in enumerate(extracted_list):
normalCol, nameCol, fileCol, lineCol = (
# Emphasize the last entry
(Colors.normalEm, Colors.nameEm, Colors.filenameEm, Colors.line)
if ind == len(extracted_list) - 1
else (Colors.Normal, Colors.name, Colors.filename, "")
)
fns = _format_filename(filename, fileCol, normalCol, lineno=lineno)
item = f"{normalCol} {fns}"
if name != "<module>":
item += f" in {nameCol}{name}{normalCol}\n"
else:
item += "\n"
if line:
item += f"{lineCol} {line.strip()}{normalCol}\n"
list.append(item)
return list
|
|
47,530 | 196,030 | 351 | sympy/calculus/euler.py | 146 | 37 | def euler_equations(L, funcs=(), vars=()):
r
funcs = tuple(funcs) if iterable(funcs) else (funcs,)
if not funcs:
funcs = tuple(L.atoms(Function))
else:
for f in funcs:
if not isinstance(f, Function):
raise TypeError('Function expected, got: %s' % f)
vars = tuple(vars) if iterable(vars) else (vars,)
if not vars:
vars = funcs[0].args
else:
vars = tuple(sympify(var) for var in vars)
if not all(isinstance(v, Symbol) for v in vars):
raise TypeError('Variables are not symbols, got %s' % vars)
for f in funcs:
| Updated import locations | euler_equations | 498015021131af4dbb07eb110e5badaba8250c7b | sympy | euler.py | 19 | 86 | https://github.com/sympy/sympy.git | 18 | 281 | 0 | 84 | 436 | Python | {
"docstring": "\n Find the Euler-Lagrange equations [1]_ for a given Lagrangian.\n\n Parameters\n ==========\n\n L : Expr\n The Lagrangian that should be a function of the functions listed\n in the second argument and their derivatives.\n\n For example, in the case of two functions `f(x,y)`, `g(x,y)` and\n two independent variables `x`, `y` the Lagrangian would have the form:\n\n .. math:: L\\left(f(x,y),g(x,y),\\frac{\\partial f(x,y)}{\\partial x},\n \\frac{\\partial f(x,y)}{\\partial y},\n \\frac{\\partial g(x,y)}{\\partial x},\n \\frac{\\partial g(x,y)}{\\partial y},x,y\\right)\n\n In many cases it is not necessary to provide anything, except the\n Lagrangian, it will be auto-detected (and an error raised if this\n couldn't be done).\n\n funcs : Function or an iterable of Functions\n The functions that the Lagrangian depends on. The Euler equations\n are differential equations for each of these functions.\n\n vars : Symbol or an iterable of Symbols\n The Symbols that are the independent variables of the functions.\n\n Returns\n =======\n\n eqns : list of Eq\n The list of differential equations, one for each function.\n\n Examples\n ========\n\n >>> from sympy import euler_equations, Symbol, Function\n >>> x = Function('x')\n >>> t = Symbol('t')\n >>> L = (x(t).diff(t))**2/2 - x(t)**2/2\n >>> euler_equations(L, x(t), t)\n [Eq(-x(t) - Derivative(x(t), (t, 2)), 0)]\n >>> u = Function('u')\n >>> x = Symbol('x')\n >>> L = (u(t, x).diff(t))**2/2 - (u(t, x).diff(x))**2/2\n >>> euler_equations(L, u(t, x), [t, x])\n [Eq(-Derivative(u(t, x), (t, 2)) + Derivative(u(t, x), (x, 2)), 0)]\n\n References\n ==========\n\n .. [1] https://en.wikipedia.org/wiki/Euler%E2%80%93Lagrange_equation\n\n ",
"language": "en",
"n_whitespaces": 454,
"n_words": 224,
"vocab_size": 139
} | def euler_equations(L, funcs=(), vars=()):
r
funcs = tuple(funcs) if iterable(funcs) else (funcs,)
if not funcs:
funcs = tuple(L.atoms(Function))
else:
for f in funcs:
if not isinstance(f, Function):
raise TypeError('Function expected, got: %s' % f)
vars = tuple(vars) if iterable(vars) else (vars,)
if not vars:
vars = funcs[0].args
else:
vars = tuple(sympify(var) for var in vars)
if not all(isinstance(v, Symbol) for v in vars):
raise TypeError('Variables are not symbols, got %s' % vars)
for f in funcs:
if not vars == f.args:
raise ValueError("Variables %s do not match args: %s" % (vars, f))
order = max([len(d.variables) for d in L.atoms(Derivative)
if d.expr in funcs] + [0])
eqns = []
for f in funcs:
eq = diff(L, f)
for i in range(1, order + 1):
for p in combinations_with_replacement(vars, i):
eq = eq + S.NegativeOne**i*diff(L, diff(f, *p), *p)
new_eq = Eq(eq, 0)
if isinstance(new_eq, Eq):
eqns.append(new_eq)
return eqns
|
|
17,162 | 81,161 | 207 | awx/main/tasks/callback.py | 65 | 11 | def delay_update(self, skip_if_already_set=False, **kwargs):
for key, value in kwargs.items():
if key in self.extra_update_fields and skip_if_already_set:
continue
elif key in self.extra_update_fields and key in ('job_explanation', 'result_traceback'):
if str(value) in self.extra_update_fields.get(key, ''):
continue # if already set, avoid duplicating messages
# In the case of these fields, we do not want to lose any prior information, so combine valu | Delay update of artifacts and error fields until final job save (#11832)
* Delay update of artifacts until final job save
Save tracebacks from receptor module to callback object
Move receptor traceback check up to be more logical
Use new mock_me fixture to avoid DB call with me method
Update the special runner message to the delay_update pattern
* Move special runner message into post-processing of callback fields | delay_update | 452744b67e02823879e722fe574984a2d760ed60 | awx | callback.py | 18 | 10 | https://github.com/ansible/awx.git | 7 | 105 | 0 | 50 | 174 | Python | {
"docstring": "Stash fields that should be saved along with the job status change",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | def delay_update(self, skip_if_already_set=False, **kwargs):
for key, value in kwargs.items():
if key in self.extra_update_fields and skip_if_already_set:
continue
elif key in self.extra_update_fields and key in ('job_explanation', 'result_traceback'):
if str(value) in self.extra_update_fields.get(key, ''):
continue # if already set, avoid duplicating messages
# In the case of these fields, we do not want to lose any prior information, so combine values
self.extra_update_fields[key] = '\n'.join([str(self.extra_update_fields[key]), str(value)])
else:
self.extra_update_fields[key] = value
|
|
12,428 | 61,179 | 164 | .venv/lib/python3.8/site-packages/pip/_internal/utils/filesystem.py | 68 | 26 | def adjacent_tmp_file(path, **kwargs):
# type: (str, **Any) -> Iterator[BinaryIO]
with NamedTemporaryFile(
delete=False,
dir=os.path.dirname(path),
prefix=os.path.basename(path),
suffix=".tmp",
**kwargs,
) as f:
result = cast(BinaryIO, f)
try:
yield result
finally:
result.flush()
os.fsync(result.fileno())
# Tenacity raises RetryError by default, explictly raise the original exception
_replace_retry = retry(reraise=True, | upd; format | adjacent_tmp_file | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | filesystem.py | 14 | 14 | https://github.com/jindongwang/transferlearning.git | 2 | 78 | 0 | 60 | 180 | Python | {
"docstring": "Return a file-like object pointing to a tmp file next to path.\n\n The file is created securely and is ensured to be written to disk\n after the context reaches its end.\n\n kwargs will be passed to tempfile.NamedTemporaryFile to control\n the way the temporary file will be opened.\n ",
"language": "en",
"n_whitespaces": 62,
"n_words": 47,
"vocab_size": 33
} | def adjacent_tmp_file(path, **kwargs):
# type: (str, **Any) -> Iterator[BinaryIO]
with NamedTemporaryFile(
delete=False,
dir=os.path.dirname(path),
prefix=os.path.basename(path),
suffix=".tmp",
**kwargs,
) as f:
result = cast(BinaryIO, f)
try:
yield result
finally:
result.flush()
os.fsync(result.fileno())
# Tenacity raises RetryError by default, explictly raise the original exception
_replace_retry = retry(reraise=True, stop=stop_after_delay(1), wait=wait_fixed(0.25))
replace = _replace_retry(os.replace)
# test_writable_dir and _test_writable_dir_win are copied from Flit,
# with the author's agreement to also place them under pip's license. |
|
3,195 | 20,046 | 176 | pipenv/patched/notpip/_vendor/distro.py | 45 | 17 | def _lsb_release_info(self):
# type: () -> Dict[str, str]
if not self.include_lsb:
return {}
with open(os.devnull, "wb") as devnull:
try:
cmd = ("lsb_release", "-a")
stdout = subprocess.check_output(cmd, stderr=devnull)
# Command not found or lsb_release returned error
except (OSError, subprocess.CalledProcessError):
return {}
content = self._to_str(stdout).splitlines()
return self._parse_lsb_release_content( | 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 | _lsb_release_info | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | distro.py | 13 | 11 | https://github.com/pypa/pipenv.git | 3 | 79 | 0 | 38 | 138 | Python | {
"docstring": "\n Get the information items from the lsb_release command output.\n\n Returns:\n A dictionary containing all information items.\n ",
"language": "en",
"n_whitespaces": 49,
"n_words": 16,
"vocab_size": 14
} | def _lsb_release_info(self):
# type: () -> Dict[str, str]
if not self.include_lsb:
return {}
with open(os.devnull, "wb") as devnull:
try:
cmd = ("lsb_release", "-a")
stdout = subprocess.check_output(cmd, stderr=devnull)
# Command not found or lsb_release returned error
except (OSError, subprocess.CalledProcessError):
return {}
content = self._to_str(stdout).splitlines()
return self._parse_lsb_release_content(content)
|
|
38,299 | 159,507 | 107 | rasa/engine/graph.py | 35 | 16 | def as_dict(self) -> Dict[Text, Any]:
serializable_graph_schema: Dict[Text, Dict[Text, Any]] = {"nodes": {}}
for node | fix type annotation in rasa.engine | as_dict | 9fc462da870f69f9976be3bc081675844b9f64c2 | rasa | graph.py | 12 | 12 | https://github.com/RasaHQ/rasa.git | 2 | 72 | 0 | 28 | 137 | Python | {
"docstring": "Returns graph schema in a serializable format.\n\n Returns:\n The graph schema in a format which can be dumped as JSON or other formats.\n ",
"language": "en",
"n_whitespaces": 48,
"n_words": 23,
"vocab_size": 19
} | def as_dict(self) -> Dict[Text, Any]:
serializable_graph_schema: Dict[Text, Dict[Text, Any]] = {"nodes": {}}
for node_name, node in self.nodes.items():
serializable = dataclasses.asdict(node)
# Classes are not JSON serializable (surprise)
serializable["uses"] = f"{node.uses.__module__}.{node.uses.__name__}"
serializable_graph_schema["nodes"][node_name] = serializable
return serializable_graph_schema
|
|
51,065 | 205,284 | 240 | django/db/migrations/executor.py | 52 | 20 | def _create_project_state(self, with_applied_migrations=False):
state = ProjectState(real_apps=self.loader.unmigrated_apps)
if with_applied_migrations:
# Create the forwards plan Django would follow on an empty database
full_plan = self.migration_plan(
self.loader.graph.leaf_nodes(), clean_start=True
| Refs #33476 -- Reformatted code with Black. | _create_project_state | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | executor.py | 14 | 15 | https://github.com/django/django.git | 6 | 101 | 0 | 42 | 157 | Python | {
"docstring": "\n Create a project state including all the applications without\n migrations and applied migrations if with_applied_migrations=True.\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 15,
"vocab_size": 14
} | def _create_project_state(self, with_applied_migrations=False):
state = ProjectState(real_apps=self.loader.unmigrated_apps)
if with_applied_migrations:
# Create the forwards plan Django would follow on an empty database
full_plan = self.migration_plan(
self.loader.graph.leaf_nodes(), clean_start=True
)
applied_migrations = {
self.loader.graph.nodes[key]
for key in self.loader.applied_migrations
if key in self.loader.graph.nodes
}
for migration, _ in full_plan:
if migration in applied_migrations:
migration.mutate_state(state, preserve=False)
return state
|
|
@control_command(
variadic='headers',
signature='[key1=value1 [key2=value2 [... [keyN=valueN]]]]',
) | 52,249 | 208,224 | 90 | celery/worker/control.py | 58 | 14 | def revoke(state, task_id, terminate=False, signal=None, **kwargs):
# pylint: disable=redefined-outer-name
# XXX Note that this redefines `terminate`:
# Outside of this scope that is a function.
# supports list argument since 3.1
task_ids, task_ | New control command `revoke_by_stamped_headers` (#7838)
* Added pytest-order==1.0.1
* Added a new control command `revoke_by_stamped_headers` to revoke tasks by their
stamped header instead of task id (terminate only works on running tasks in memory) | revoke | 5092598fb88c1f18e3fe709861cdb31df90a7264 | celery | control.py | 12 | 4 | https://github.com/celery/celery.git | 2 | 56 | 1 | 51 | 115 | Python | {
"docstring": "Revoke task by task id (or list of ids).\n\n Keyword Arguments:\n terminate (bool): Also terminate the process if the task is active.\n signal (str): Name of signal to use for terminate (e.g., ``KILL``).\n ",
"language": "en",
"n_whitespaces": 53,
"n_words": 33,
"vocab_size": 26
} | def revoke(state, task_id, terminate=False, signal=None, **kwargs):
# pylint: disable=redefined-outer-name
# XXX Note that this redefines `terminate`:
# Outside of this scope that is a function.
# supports list argument since 3.1
task_ids, task_id = set(maybe_list(task_id) or []), None
task_ids = _revoke(state, task_ids, terminate, signal, **kwargs)
return ok(f'tasks {task_ids} flagged as revoked')
@control_command(
variadic='headers',
signature='[key1=value1 [key2=value2 [... [keyN=valueN]]]]',
) |
76,660 | 261,119 | 100 | sklearn/utils/sparsefuncs.py | 47 | 12 | def inplace_swap_row_csc(X, m, n):
for t in [m, n]:
if isinstance(t, np.ndarray):
raise TypeError("m and n should be valid integers")
if m < 0:
m += X.shape[0]
if n < 0:
n += X.shape[0]
m_mask = X.indices == m
X.indices[X.i | DOC Ensures that inplace_swap_row_csc passes numpydoc validation (#24513) | inplace_swap_row_csc | affb0cb49412eb5992d2fad0d765b50a2db1344c | scikit-learn | sparsefuncs.py | 12 | 11 | https://github.com/scikit-learn/scikit-learn.git | 5 | 87 | 0 | 32 | 135 | Python | {
"docstring": "Swap two rows of a CSC matrix in-place.\n\n Parameters\n ----------\n X : sparse matrix of shape (n_samples, n_features)\n Matrix whose two rows are to be swapped. It should be of\n CSC format.\n\n m : int\n Index of the row of X to be swapped.\n\n n : int\n Index of the row of X to be swapped.\n ",
"language": "en",
"n_whitespaces": 102,
"n_words": 56,
"vocab_size": 31
} | def inplace_swap_row_csc(X, m, n):
for t in [m, n]:
if isinstance(t, np.ndarray):
raise TypeError("m and n should be valid integers")
if m < 0:
m += X.shape[0]
if n < 0:
n += X.shape[0]
m_mask = X.indices == m
X.indices[X.indices == n] = m
X.indices[m_mask] = n
|
|
16,058 | 73,591 | 139 | wagtail/contrib/table_block/tests.py | 29 | 8 | def test_render_empty_table(self):
| Reformat with black | test_render_empty_table | d10f15e55806c6944827d801cd9c2d53f5da4186 | wagtail | tests.py | 12 | 19 | https://github.com/wagtail/wagtail.git | 1 | 67 | 0 | 22 | 105 | Python | {
"docstring": "\n An empty table should render okay.\n \n <table>\n <tbody>\n <tr><td></td><td></td><td></td></tr>\n <tr><td></td><td></td><td></td></tr>\n <tr><td></td><td></td><td></td></tr>\n </tbody>\n </table>\n ",
"language": "en",
"n_whitespaces": 145,
"n_words": 13,
"vocab_size": 11
} | def test_render_empty_table(self):
block = TableBlock()
result = block.render(
{
"first_row_is_table_header": False,
"first_col_is_header": False,
"data": [[None, None, None], [None, None, None], [None, None, None]],
}
)
expected =
self.assertHTMLEqual(result, expected)
|
|
47,876 | 196,376 | 56 | sympy/matrices/dense.py | 21 | 8 | def rot_axis3(theta):
ct = cos(theta) | Moved imports to higher level | rot_axis3 | 59d22b6bb7287613d598611027f640d068ca5748 | sympy | dense.py | 9 | 7 | https://github.com/sympy/sympy.git | 1 | 51 | 0 | 18 | 76 | Python | {
"docstring": "Returns a rotation matrix for a rotation of theta (in radians) about\n the 3-axis.\n\n Examples\n ========\n\n >>> from sympy import pi, rot_axis3\n\n A rotation of pi/3 (60 degrees):\n\n >>> theta = pi/3\n >>> rot_axis3(theta)\n Matrix([\n [ 1/2, sqrt(3)/2, 0],\n [-sqrt(3)/2, 1/2, 0],\n [ 0, 0, 1]])\n\n If we rotate by pi/2 (90 degrees):\n\n >>> rot_axis3(pi/2)\n Matrix([\n [ 0, 1, 0],\n [-1, 0, 0],\n [ 0, 0, 1]])\n\n See Also\n ========\n\n rot_axis1: Returns a rotation matrix for a rotation of theta (in radians)\n about the 1-axis\n rot_axis2: Returns a rotation matrix for a rotation of theta (in radians)\n about the 2-axis\n ",
"language": "en",
"n_whitespaces": 208,
"n_words": 100,
"vocab_size": 49
} | def rot_axis3(theta):
ct = cos(theta)
st = sin(theta)
lil = ((ct, st, 0),
(-st, ct, 0),
(0, 0, 1))
return Matrix(lil)
|
|
47,592 | 196,092 | 56 | sympy/combinatorics/graycode.py | 17 | 9 | def current(self):
rv = self._current or '0'
if not isinstance(rv, str):
rv = bin(rv)[2:]
return rv.rjust(self.n, '0') | Updated import locations | current | 498015021131af4dbb07eb110e5badaba8250c7b | sympy | graycode.py | 11 | 5 | https://github.com/sympy/sympy.git | 3 | 43 | 0 | 15 | 74 | Python | {
"docstring": "\n Returns the currently referenced Gray code as a bit string.\n\n Examples\n ========\n\n >>> from sympy.combinatorics import GrayCode\n >>> GrayCode(3, start='100').current\n '100'\n ",
"language": "en",
"n_whitespaces": 71,
"n_words": 21,
"vocab_size": 20
} | def current(self):
rv = self._current or '0'
if not isinstance(rv, str):
rv = bin(rv)[2:]
return rv.rjust(self.n, '0')
|
|
120,738 | 335,307 | 360 | src/diffusers/models/unet_sde_score_estimation.py | 210 | 30 | def upsample_conv_2d(x, w, k=None, factor=2, gain=1):
assert isinstance(factor, int) and factor >= 1
# Check weight shape.
assert len(w.shape) == 4
convH = w.shape[2]
convW = w.shape[3]
inC = w.shape[1]
assert convW == convH
# Setup filter kernel.
if k is None:
k = [1] * factor
k = _setup_kernel(k) * (gain * (factor**2))
p = (k.shape[0] - factor) - (convW - 1)
stride = (factor, factor)
# Determine data dimensions.
stride = [1, 1, factor, factor]
output_shape = ((_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW)
output_padding = (
output_shape[0] - (_shape(x, 2) - 1) * stride[0] - convH,
output_shape[1] - (_shape(x, 3) - 1) * stride[1] - convW,
)
assert output_padding[0] >= 0 and output_padding[1] >= 0
num_groups = _shape(x, 1) // inC
# Transpose weights.
w = torch.reshape(w, (num_groups, -1, inC, convH, convW))
w = w[..., ::-1, ::-1].permute(0, 2, 1, 3, 4)
w = torch.reshape(w, (num_groups * inC, -1, convH, convW))
x = F.conv_transpose2d(x, w, stride=stride, output_padding=output_padding, padding=0)
# Original TF code.
# x | add score estimation model | upsample_conv_2d | ac796924dff7241d9b516ea27faaa7b2f12434fd | diffusers | unet_sde_score_estimation.py | 14 | 25 | https://github.com/huggingface/diffusers.git | 4 | 356 | 0 | 114 | 547 | Python | {
"docstring": "Fused `upsample_2d()` followed by `tf.nn.conv2d()`.\n\n Padding is performed only once at the beginning, not between the\n operations.\n The fused op is considerably more efficient than performing the same\n calculation\n using standard TensorFlow ops. It supports gradients of arbitrary order.\n Args:\n x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,\n C]`.\n w: Weight tensor of the shape `[filterH, filterW, inChannels,\n outChannels]`. Grouped convolution can be performed by `inChannels =\n x.shape[0] // numGroups`.\n k: FIR filter of the shape `[firH, firW]` or `[firN]`\n (separable). The default is `[1] * factor`, which corresponds to\n nearest-neighbor upsampling.\n factor: Integer upsampling factor (default: 2).\n gain: Scaling factor for signal magnitude (default: 1.0).\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": 280,
"n_words": 139,
"vocab_size": 102
} | def upsample_conv_2d(x, w, k=None, factor=2, gain=1):
assert isinstance(factor, int) and factor >= 1
# Check weight shape.
assert len(w.shape) == 4
convH = w.shape[2]
convW = w.shape[3]
inC = w.shape[1]
assert convW == convH
# Setup filter kernel.
if k is None:
k = [1] * factor
k = _setup_kernel(k) * (gain * (factor**2))
p = (k.shape[0] - factor) - (convW - 1)
stride = (factor, factor)
# Determine data dimensions.
stride = [1, 1, factor, factor]
output_shape = ((_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW)
output_padding = (
output_shape[0] - (_shape(x, 2) - 1) * stride[0] - convH,
output_shape[1] - (_shape(x, 3) - 1) * stride[1] - convW,
)
assert output_padding[0] >= 0 and output_padding[1] >= 0
num_groups = _shape(x, 1) // inC
# Transpose weights.
w = torch.reshape(w, (num_groups, -1, inC, convH, convW))
w = w[..., ::-1, ::-1].permute(0, 2, 1, 3, 4)
w = torch.reshape(w, (num_groups * inC, -1, convH, convW))
x = F.conv_transpose2d(x, w, stride=stride, output_padding=output_padding, padding=0)
# Original TF code.
# x = tf.nn.conv2d_transpose(
# x,
# w,
# output_shape=output_shape,
# strides=stride,
# padding='VALID',
# data_format=data_format)
# JAX equivalent
return upfirdn2d(x, torch.tensor(k, device=x.device), pad=((p + 1) // 2 + factor - 1, p // 2 + 1))
|
|
3,837 | 21,441 | 175 | pipenv/patched/notpip/_vendor/distlib/_backport/tarfile.py | 32 | 11 | def read(self, size=None):
if size is None:
t = []
while True:
buf = self._read(self.bufsize | Vendor in pip 22.1.2 | read | c69d55f7c82d5ae2cce542bcfb98d043ca4836a0 | pipenv | tarfile.py | 13 | 13 | https://github.com/pypa/pipenv.git | 4 | 71 | 0 | 25 | 121 | Python | {
"docstring": "Return the next size number of bytes from the stream.\n If size is not defined, return all bytes of the stream\n up to EOF.\n ",
"language": "en",
"n_whitespaces": 51,
"n_words": 24,
"vocab_size": 19
} | def read(self, size=None):
if size is None:
t = []
while True:
buf = self._read(self.bufsize)
if not buf:
break
t.append(buf)
buf = "".join(t)
else:
buf = self._read(size)
self.pos += len(buf)
return buf
|
|
553 | 3,767 | 89 | airbyte-integrations/connectors/source-facebook-marketing/source_facebook_marketing/streams/base_streams.py | 16 | 8 | def state(self) -> Mapping[str, Any]:
if self._cursor_value:
return {
self.cursor_field: self._cursor_value,
| 🎉 🎉 Source FB Marketing: performance and reliability fixes (#9805)
* Facebook Marketing performance improvement
* add comments and little refactoring
* fix integration tests with the new config
* improve job status handling, limit concurrency to 10
* fix campaign jobs, refactor manager
* big refactoring of async jobs, support random order of slices
* update source _read_incremental to hook new state logic
* fix issues with timeout
* remove debugging and clean up, improve retry logic
* merge changes from #8234
* fix call super _read_increment
* generalize batch execution, add use_batch flag
* improve coverage, do some refactoring of spec
* update test, remove overrides of source
* add split by AdSet
* add smaller insights
* fix end_date < start_date case
* add account_id to PK
* add notes
* fix new streams
* fix reversed incremental stream
* update spec.json for SAT
* upgrade CDK and bump version
Co-authored-by: Dmytro Rezchykov <[email protected]>
Co-authored-by: Eugene Kulak <[email protected]> | state | a3aae8017a0a40ff2006e2567f71dccb04c997a5 | airbyte | base_streams.py | 10 | 8 | https://github.com/airbytehq/airbyte.git | 2 | 38 | 0 | 15 | 61 | Python | {
"docstring": "State getter, get current state and serialize it to emmit Airbyte STATE message",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | def state(self) -> Mapping[str, Any]:
if self._cursor_value:
return {
self.cursor_field: self._cursor_value,
"include_deleted": self._include_deleted,
}
return {}
|
|
55,965 | 220,322 | 55 | python3.10.4/Lib/asyncio/base_events.py | 23 | 6 | def set_task_factory(self, factory):
if factory is not None and not callable(factory):
raise TypeError('task factory must b | add python 3.10.4 for windows | set_task_factory | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | base_events.py | 10 | 4 | https://github.com/XX-net/XX-Net.git | 3 | 30 | 0 | 20 | 52 | Python | {
"docstring": "Set a task factory that will be used by loop.create_task().\n\n If factory is None the default task factory will be set.\n\n If factory is a callable, it should have a signature matching\n '(loop, coro)', where 'loop' will be a reference to the active\n event loop, 'coro' will be a coroutine object. The callable\n must return a Future.\n ",
"language": "en",
"n_whitespaces": 100,
"n_words": 57,
"vocab_size": 39
} | def set_task_factory(self, factory):
if factory is not None and not callable(factory):
raise TypeError('task factory must be a callable or None')
self._task_factory = factory
|
|
39,394 | 163,186 | 79 | pandas/core/arrays/datetimes.py | 37 | 10 | def date(self) -> npt.NDArray[np.object_]:
# If the Timestamps have a timezone that is not UTC,
# | DOC: Improve doc summaries in series.rst (#45237) | date | 521259299f7829da667ba39302ec77acedde9e5e | pandas | datetimes.py | 9 | 9 | https://github.com/pandas-dev/pandas.git | 1 | 31 | 0 | 32 | 56 | Python | {
"docstring": "\n Returns numpy array of python :class:`datetime.date` objects.\n\n Namely, the date part of Timestamps without time and\n timezone information.\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 18,
"vocab_size": 17
} | def date(self) -> npt.NDArray[np.object_]:
# If the Timestamps have a timezone that is not UTC,
# convert them into their i8 representation while
# keeping their timezone and not using UTC
timestamps = self._local_timestamps()
return ints_to_pydatetime(timestamps, box="date")
|
|
69,952 | 243,004 | 172 | src/PIL/Image.py | 54 | 14 | def apply_transparency(self):
if self.mode != "P" or "transparency" not in self.info:
return
from . import ImagePalette
palette = self.getpalette("RGBA")
transparency = self.info["transparency"]
if isinstance(tra | Added apply_transparency() | apply_transparency | 11be1631433f252b816802aef1a3cd109bd308c7 | Pillow | Image.py | 13 | 14 | https://github.com/python-pillow/Pillow.git | 5 | 110 | 0 | 41 | 186 | Python | {
"docstring": "\n If a P mode image has a \"transparency\" key in the info dictionary,\n remove the key and apply the transparency to the palette instead.\n ",
"language": "en",
"n_whitespaces": 46,
"n_words": 24,
"vocab_size": 19
} | def apply_transparency(self):
if self.mode != "P" or "transparency" not in self.info:
return
from . import ImagePalette
palette = self.getpalette("RGBA")
transparency = self.info["transparency"]
if isinstance(transparency, bytes):
for i, alpha in enumerate(transparency):
palette[i * 4 + 3] = alpha
else:
palette[transparency * 4 + 3] = 0
self.palette = ImagePalette.ImagePalette("RGBA", bytes(palette))
self.palette.dirty = 1
del self.info["transparency"]
|
|
19,002 | 93,655 | 19 | src/sentry/utils/assets.py | 10 | 9 | def get_frontend_app_asset_url(module, key):
args = (settings.STATIC_FRONTEND_APP_URL.rstrip("/"), module, key.lstrip("/"))
return "{}/{}/{}".format(* | ref(js): Remove broken frontend asset cache busting (#36953) | get_frontend_app_asset_url | 2992f33c2d084f2542af647c6b76b54c351cc5a5 | sentry | assets.py | 10 | 3 | https://github.com/getsentry/sentry.git | 1 | 37 | 0 | 10 | 65 | Python | {
"docstring": "\n Returns an asset URL that is unversioned. These assets should have a\n `Cache-Control: max-age=0, must-revalidate` so that clients must validate with the origin\n server before using their locally cached asset.\n\n Example:\n {% frontend_app_asset_url 'sentry' 'sentry.css' %}\n => \"/_static/dist/sentry/sentry.css\"\n ",
"language": "en",
"n_whitespaces": 65,
"n_words": 38,
"vocab_size": 37
} | def get_frontend_app_asset_url(module, key):
args = (settings.STATIC_FRONTEND_APP_URL.rstrip("/"), module, key.lstrip("/"))
return "{}/{}/{}".format(*args)
|
|
26,690 | 119,812 | 197 | jax/_src/lax/linalg.py | 139 | 19 | def tridiagonal_solve(dl, d, du, b):
r
if dl.ndim != 1 or d.ndim != 1 or du.ndim != 1:
raise ValueError('dl, d and du must be vectors')
if dl.shape != d.shape or d.shape != du.shape:
raise ValueError(
f'dl={dl.shape}, d={d.shape} and du={du.shape} must all be `[m]`')
if b.ndim != 2:
raise ValueError(f'b={b.shape} must be a matrix')
m, = dl.shape
if m < 3:
raise ValueError(f'm ({m}) must be >= 3')
ldb, n = b.shape
if ldb < max(1, m):
raise ValueError(f'Leading dimension of b={ldb} must be ≥ max(1, {m})')
if dl.dtype != d.dtype or d.dtype != du.dtype or du.dtype != | DOC: add missing linalg functionality to docs | tridiagonal_solve | c66f5dda60aa5df7b6aa2f09d3ce88c4249b6f34 | jax | linalg.py | 13 | 39 | https://github.com/google/jax.git | 13 | 200 | 0 | 90 | 379 | Python | {
"docstring": "Computes the solution of a tridiagonal linear system.\n\n This function computes the solution of a tridiagonal linear system:\n\n .. math::\n A . X = B\n\n Args:\n dl: The lower diagonal of A: ``dl[i] := A[i, i-1]`` for i in ``[0,m)``.\n Note that ``dl[0] = 0``.\n d: The middle diagnoal of A: ``d[i] := A[i, i]`` for i in ``[0,m)``.\n du: The upper diagonal of A: ``du[i] := A[i, i+1]`` for i in ``[0,m)``.\n Note that ``dl[m - 1] = 0``.\n b: Right hand side matrix.\n\n Returns:\n Solution ``X`` of tridiagonal system.\n ",
"language": "en",
"n_whitespaces": 125,
"n_words": 91,
"vocab_size": 57
} | def tridiagonal_solve(dl, d, du, b):
r
if dl.ndim != 1 or d.ndim != 1 or du.ndim != 1:
raise ValueError('dl, d and du must be vectors')
if dl.shape != d.shape or d.shape != du.shape:
raise ValueError(
f'dl={dl.shape}, d={d.shape} and du={du.shape} must all be `[m]`')
if b.ndim != 2:
raise ValueError(f'b={b.shape} must be a matrix')
m, = dl.shape
if m < 3:
raise ValueError(f'm ({m}) must be >= 3')
ldb, n = b.shape
if ldb < max(1, m):
raise ValueError(f'Leading dimension of b={ldb} must be ≥ max(1, {m})')
if dl.dtype != d.dtype or d.dtype != du.dtype or du.dtype != b.dtype:
raise ValueError(f'dl={dl.dtype}, d={d.dtype}, du={du.dtype} and '
f'b={b.dtype} must be the same dtype,')
t = dl.dtype
if t not in (np.float32, np.float64):
raise ValueError(f'Only f32/f64 are supported, got {t}')
return tridiagonal_solve_p.bind(dl, d, du, b, m=m, n=n, ldb=ldb, t=t)
# Schur Decomposition
|
|
39,828 | 166,568 | 289 | pandas/util/_print_versions.py | 72 | 14 | def _get_dependency_info() -> dict[str, JSONSerializable]:
deps = [
"pandas",
# required
"numpy",
"pytz",
"dateutil",
# install / build,
"setuptools",
"pip",
"Cython",
# test
"pytest",
"hypothesis",
# docs
"sphinx",
# Other, need a min version
"blosc",
"feather",
"xlsxwriter",
"lxml.etree",
"html5lib",
"pymysql",
"psycopg2",
"jinja2",
| fix pandas.show_versions() and remove pin for setuptools (#47096) | _get_dependency_info | 44b660dc4a07f4fb507c31795ae63dca2e6e9440 | pandas | _print_versions.py | 12 | 32 | https://github.com/pandas-dev/pandas.git | 3 | 106 | 0 | 61 | 191 | Python | {
"docstring": "\n Returns dependency information as a JSON serializable dictionary.\n ",
"language": "en",
"n_whitespaces": 15,
"n_words": 8,
"vocab_size": 8
} | def _get_dependency_info() -> dict[str, JSONSerializable]:
deps = [
"pandas",
# required
"numpy",
"pytz",
"dateutil",
# install / build,
"setuptools",
"pip",
"Cython",
# test
"pytest",
"hypothesis",
# docs
"sphinx",
# Other, need a min version
"blosc",
"feather",
"xlsxwriter",
"lxml.etree",
"html5lib",
"pymysql",
"psycopg2",
"jinja2",
# Other, not imported.
"IPython",
"pandas_datareader",
]
deps.extend(list(VERSIONS))
result: dict[str, JSONSerializable] = {}
for modname in deps:
mod = import_optional_dependency(modname, errors="ignore")
result[modname] = get_version(mod) if mod else None
return result
|
|
14,318 | 66,758 | 41 | erpnext/patches/v13_0/germany_fill_debtor_creditor_number.py | 60 | 16 | def execute():
company_list = frappe.get_all("Company", filters={"country": "Germany"})
for company in company_list:
party_account_list = frappe.get_all(
"Party Account",
filters={"company": company.name},
fields=["name", "account", "debtor_creditor_number"],
)
for party_account in party_account_list:
if (not party_account.account) or party_account.debtor_creditor_number:
# account empty or debtor_creditor_number already filled
continue
account_number = frappe.db.get_value("Account", party_account.account, "account_number")
if not account_number:
continue
frappe.db.set_value(
"Party Account", party_account.name | style: format code with black | execute | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | germany_fill_debtor_creditor_number.py | 14 | 18 | https://github.com/frappe/erpnext.git | 6 | 126 | 0 | 46 | 218 | Python | {
"docstring": "Move account number into the new custom field debtor_creditor_number.\n\n\tGerman companies used to use a dedicated payable/receivable account for\n\tevery party to mimick party accounts in the external accounting software\n\t\"DATEV\". This is no longer necessary. The reference ID for DATEV will be\n\tstored in a new custom field \"debtor_creditor_number\".\n\t",
"language": "en",
"n_whitespaces": 45,
"n_words": 50,
"vocab_size": 40
} | def execute():
company_list = frappe.get_all("Company", filters={"country": "Germany"})
for company in company_list:
party_account_list = frappe.get_all(
"Party Account",
filters={"company": company.name},
fields=["name", "account", "debtor_creditor_number"],
)
for party_account in party_account_list:
if (not party_account.account) or party_account.debtor_creditor_number:
# account empty or debtor_creditor_number already filled
continue
account_number = frappe.db.get_value("Account", party_account.account, "account_number")
if not account_number:
continue
frappe.db.set_value(
"Party Account", party_account.name, "debtor_creditor_number", account_number
)
frappe.db.set_value("Party Account", party_account.name, "account", "")
|
|
77,108 | 262,049 | 78 | TTS/tts/datasets/dataset.py | 21 | 16 | def compute_or_load(self, wav_file):
pitch_file = self.create_pitch_file_path(wav_file, self.cache_path)
if not os.path.exists(pitch_file):
pitch | Refactor TTSDataset ⚡️ | compute_or_load | 176b712c1a40cf630da9a77f1826836723c40fde | TTS | dataset.py | 11 | 7 | https://github.com/coqui-ai/TTS.git | 2 | 64 | 0 | 18 | 102 | Python | {
"docstring": "\n compute pitch and return a numpy array of pitch values\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 10,
"vocab_size": 9
} | def compute_or_load(self, wav_file):
pitch_file = self.create_pitch_file_path(wav_file, self.cache_path)
if not os.path.exists(pitch_file):
pitch = self._compute_and_save_pitch(self.ap, wav_file, pitch_file)
else:
pitch = np.load(pitch_file)
return pitch.astype(np.float32)
|
|
49,581 | 200,282 | 816 | sympy/testing/runtests.py | 272 | 33 | def check_output(self, want, got, optionflags):
# Handle the common case first, for efficiency:
# if they're string-identical, always return true.
if got == want:
return True
# TODO parse integers as well ?
# Parse floats and compare them. If some of the parsed floats contain
# ellipses, skip the comparison.
matches = self.num_got_rgx.finditer(got)
numbers_got = [match.group(1) for match in matches] # list of strs
matches = self.num_want_rgx.finditer(want)
numbers_want = [match.group(1) for match in matches] # list of strs
if len(numbers_got) != len(numbers_want):
return False
if len(numbers_got) > 0:
nw_ = []
for ng, nw in zip(numbers_got, numbers_want):
if '...' in nw:
nw_.append(ng)
continue
else:
nw_.append(nw)
if abs(float(ng)-float(nw)) > 1e-5:
return False
got = self.num_got_rgx.sub(r'%s', got)
got = got % tuple(nw_)
# <BLANKLINE> can be used as a special sequence to signify a
# blank line, unless the DONT_ACCEPT_BLANKLINE flag is used.
if not (optionflags & pdoctest.DONT_ACCEPT_BLANKLINE):
# Replace <BLANKLINE> in want with a blank line.
want = re.sub(r'(?m)^%s\s*?$' % re.escape(pdoctest.BLANKLINE_MARKER),
'', want)
# If a line in got contains only spaces, then remove the
# spaces.
got = re.sub(r'(?m)^\s*?$', '', got)
if got == want:
return True
# This flag causes doctest to ignore any differences in the
# contents of whitespace strings. Note that this can be used
# in conjunction with the ELLIPSIS flag.
if optionflags & pdoctest.NORMALIZE_WHITESPACE:
got = ' '.join(got.split())
want = ' '.join(want.split())
if got == want:
return True
# The ELLIPSIS flag | runtests.py: Undo auto-formatting, re-add changes to blacklist for scipy, numpy | check_output | 6d2bbf80752549276a968fd4af78231c569d55c5 | sympy | runtests.py | 15 | 36 | https://github.com/sympy/sympy.git | 15 | 276 | 0 | 149 | 459 | Python | {
"docstring": "\n Return True iff the actual output from an example (`got`)\n matches the expected output (`want`). These strings are\n always considered to match if they are identical; but\n depending on what option flags the test runner is using,\n several non-exact match types are also possible. See the\n documentation for `TestRunner` for more information about\n option flags.\n ",
"language": "en",
"n_whitespaces": 114,
"n_words": 55,
"vocab_size": 46
} | def check_output(self, want, got, optionflags):
# Handle the common case first, for efficiency:
# if they're string-identical, always return true.
if got == want:
return True
# TODO parse integers as well ?
# Parse floats and compare them. If some of the parsed floats contain
# ellipses, skip the comparison.
matches = self.num_got_rgx.finditer(got)
numbers_got = [match.group(1) for match in matches] # list of strs
matches = self.num_want_rgx.finditer(want)
numbers_want = [match.group(1) for match in matches] # list of strs
if len(numbers_got) != len(numbers_want):
return False
if len(numbers_got) > 0:
nw_ = []
for ng, nw in zip(numbers_got, numbers_want):
if '...' in nw:
nw_.append(ng)
continue
else:
nw_.append(nw)
if abs(float(ng)-float(nw)) > 1e-5:
return False
got = self.num_got_rgx.sub(r'%s', got)
got = got % tuple(nw_)
# <BLANKLINE> can be used as a special sequence to signify a
# blank line, unless the DONT_ACCEPT_BLANKLINE flag is used.
if not (optionflags & pdoctest.DONT_ACCEPT_BLANKLINE):
# Replace <BLANKLINE> in want with a blank line.
want = re.sub(r'(?m)^%s\s*?$' % re.escape(pdoctest.BLANKLINE_MARKER),
'', want)
# If a line in got contains only spaces, then remove the
# spaces.
got = re.sub(r'(?m)^\s*?$', '', got)
if got == want:
return True
# This flag causes doctest to ignore any differences in the
# contents of whitespace strings. Note that this can be used
# in conjunction with the ELLIPSIS flag.
if optionflags & pdoctest.NORMALIZE_WHITESPACE:
got = ' '.join(got.split())
want = ' '.join(want.split())
if got == want:
return True
# The ELLIPSIS flag says to let the sequence "..." in `want`
# match any substring in `got`.
if optionflags & pdoctest.ELLIPSIS:
if pdoctest._ellipsis_match(want, got):
return True
# We didn't find any match; return false.
return False
|
|
16,102 | 73,775 | 120 | wagtail/core/models/__init__.py | 24 | 20 | def start(self, workflow_state, user=None):
task_state = self.get_task_state_class()(workflow_state=workflow_state)
task_state.status = TaskState.STATUS_IN_PROGRESS
task_state.page_revision = workflow_state.page.get_latest_revision()
task_state.task = self
task_state.save()
task_submitted.send(
sender=task_state.specific.__class__,
instance=task_state.specific,
user=user,
)
return task_state
| Reformat with black | start | d10f15e55806c6944827d801cd9c2d53f5da4186 | wagtail | __init__.py | 10 | 12 | https://github.com/wagtail/wagtail.git | 1 | 77 | 0 | 20 | 122 | Python | {
"docstring": "Start this task on the provided workflow state by creating an instance of TaskState",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 14
} | def start(self, workflow_state, user=None):
task_state = self.get_task_state_class()(workflow_state=workflow_state)
task_state.status = TaskState.STATUS_IN_PROGRESS
task_state.page_revision = workflow_state.page.get_latest_revision()
task_state.task = self
task_state.save()
task_submitted.send(
sender=task_state.specific.__class__,
instance=task_state.specific,
user=user,
)
return task_state
|
|
14,424 | 67,084 | 117 | erpnext/regional/germany/utils/datev/datev_csv.py | 155 | 40 | def get_datev_csv(data, filters, csv_class):
empty_df = pd.DataFrame(columns=csv_class.COLUMNS)
data_df = pd.DataFrame.from_records(data)
result = empty_df.append(data_df, sort=True)
if csv_class.DATA_CATEGORY == DataCategory.TRANSACTIONS:
result["Belegdatum"] = pd.to_datetime(result[" | style: format code with black | get_datev_csv | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | datev_csv.py | 13 | 27 | https://github.com/frappe/erpnext.git | 3 | 247 | 0 | 107 | 435 | Python | {
"docstring": "\n\tFill in missing columns and return a CSV in DATEV Format.\n\n\tFor automatic processing, DATEV requires the first line of the CSV file to\n\thold meta data such as the length of account numbers oder the category of\n\tthe data.\n\n\tArguments:\n\tdata -- array of dictionaries\n\tfilters -- dict\n\tcsv_class -- defines DATA_CATEGORY, FORMAT_NAME and COLUMNS\n\t",
"language": "en",
"n_whitespaces": 48,
"n_words": 56,
"vocab_size": 42
} | def get_datev_csv(data, filters, csv_class):
empty_df = pd.DataFrame(columns=csv_class.COLUMNS)
data_df = pd.DataFrame.from_records(data)
result = empty_df.append(data_df, sort=True)
if csv_class.DATA_CATEGORY == DataCategory.TRANSACTIONS:
result["Belegdatum"] = pd.to_datetime(result["Belegdatum"])
result["Beleginfo - Inhalt 6"] = pd.to_datetime(result["Beleginfo - Inhalt 6"])
result["Beleginfo - Inhalt 6"] = result["Beleginfo - Inhalt 6"].dt.strftime("%d%m%Y")
result["Fälligkeit"] = pd.to_datetime(result["Fälligkeit"])
result["Fälligkeit"] = result["Fälligkeit"].dt.strftime("%d%m%y")
result.sort_values(by="Belegdatum", inplace=True, kind="stable", ignore_index=True)
if csv_class.DATA_CATEGORY == DataCategory.ACCOUNT_NAMES:
result["Sprach-ID"] = "de-DE"
data = result.to_csv(
# Reason for str(';'): https://github.com/pandas-dev/pandas/issues/6035
sep=";",
# European decimal seperator
decimal=",",
# Windows "ANSI" encoding
encoding="latin_1",
# format date as DDMM
date_format="%d%m",
# Windows line terminator
line_terminator="\r\n",
# Do not number rows
index=False,
# Use all columns defined above
columns=csv_class.COLUMNS,
# Quote most fields, even currency values with "," separator
quoting=QUOTE_NONNUMERIC,
)
data = data.encode("latin_1", errors="replace")
header = get_header(filters, csv_class)
header = ";".join(header).encode("latin_1", errors="replace")
# 1st Row: Header with meta data
# 2nd Row: Data heading (Überschrift der Nutzdaten), included in `data` here.
# 3rd - nth Row: Data (Nutzdaten)
return header + b"\r\n" + data
|
|
49,659 | 200,453 | 36 | sympy/stats/random_matrix_models.py | 21 | 11 | def CircularSymplecticEnsemble(sym, dim):
sym, dim = _symbol_converter(sym), _sympify(dim)
model = CircularSymplecticEnsembleModel(sym, dim)
rmp = RandomMatrixPSpace(sym, model=model)
return RandomMatrixSymbol(sym, dim, di | Fix various typos
Found via `codespell -q 3 -L aboves,aline,ans,aother,arithmetics,assum,atleast,braket,clen,declar,declars,dorder,dum,enew,fo,fro,inout,iself,ist,ket,lamda,lightyear,lightyears,nd,numer,numers,orderd,ot,pring,rcall,rever,ro,ser,siz,splitted,sring,supercedes,te,tht,unequality,upto,vas,versin,whet` | CircularSymplecticEnsemble | 24f1e7730119fe958cc8e28411f790c9a5ec04eb | sympy | random_matrix_models.py | 9 | 5 | https://github.com/sympy/sympy.git | 1 | 52 | 0 | 18 | 80 | Python | {
"docstring": "\n Represents Circular Symplectic Ensembles.\n\n Examples\n ========\n\n >>> from sympy.stats import CircularSymplecticEnsemble as CSE\n >>> from sympy.stats import joint_eigen_distribution\n >>> C = CSE('S', 1)\n >>> joint_eigen_distribution(C)\n Lambda(t[1], Product(Abs(exp(I*t[_j]) - exp(I*t[_k]))**4, (_j, _k + 1, 1), (_k, 1, 0))/(2*pi))\n\n Note\n ====\n\n As can be seen above in the example, density of CiruclarSymplecticEnsemble\n is not evaluated because the exact definition is based on haar measure of\n unitary group which is not unique.\n ",
"language": "en",
"n_whitespaces": 112,
"n_words": 69,
"vocab_size": 57
} | def CircularSymplecticEnsemble(sym, dim):
sym, dim = _symbol_converter(sym), _sympify(dim)
model = CircularSymplecticEnsembleModel(sym, dim)
rmp = RandomMatrixPSpace(sym, model=model)
return RandomMatrixSymbol(sym, dim, dim, pspace=rmp)
|
|
7,845 | 43,154 | 35 | airflow/models/taskinstance.py | 12 | 4 | def _executor_config_comparator(x, y):
try:
return x == y
except AttributeError:
return False
| Don't crash scheduler if exec config has old k8s objects (#24117)
From time to time k8s library objects change their attrs. If executor config is stored with old version, and unpickled with new version, we can get attribute errors that can crash the scheduler (see https://github.com/apache/airflow/issues/23727).
Here we update handling so that we fail the task but don't crash the scheduler. | _executor_config_comparator | 0c41f437674f135fe7232a368bf9c198b0ecd2f0 | airflow | taskinstance.py | 8 | 5 | https://github.com/apache/airflow.git | 2 | 19 | 0 | 11 | 33 | Python | {
"docstring": "\n The TaskInstance.executor_config attribute is a pickled object that may contain\n kubernetes objects. If the installed library version has changed since the\n object was originally pickled, due to the underlying ``__eq__`` method on these\n objects (which converts them to JSON), we may encounter attribute errors. In this\n case we should replace the stored object.\n ",
"language": "en",
"n_whitespaces": 73,
"n_words": 53,
"vocab_size": 45
} | def _executor_config_comparator(x, y):
try:
return x == y
except AttributeError:
return False
|
|
87,761 | 288,605 | 370 | tests/util/test_color.py | 112 | 3 | def test_color_temperature_to_rgbww():
# Coldest color temperature -> only cold channel enabled
assert color_util.color_temperature_to_rgbww(6535, 255, 2000, 6535) == (
0,
0,
0,
255,
0,
)
assert color_util.color_temperatu | Use Kelvin as the preferred color temperature unit (#79591)
* Use Kelvin as the preferred white temperature unit
* Update homekit
* Adjust tests | test_color_temperature_to_rgbww | 47d0598e75487f63901931875f69f802a477df13 | core | test_color.py | 8 | 43 | https://github.com/home-assistant/core.git | 1 | 161 | 0 | 34 | 207 | Python | {
"docstring": "Test color temp to warm, cold conversion.\n\n Temperature values must be in mireds\n Home Assistant uses rgbcw for rgbww\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 19,
"vocab_size": 19
} | def test_color_temperature_to_rgbww():
# Coldest color temperature -> only cold channel enabled
assert color_util.color_temperature_to_rgbww(6535, 255, 2000, 6535) == (
0,
0,
0,
255,
0,
)
assert color_util.color_temperature_to_rgbww(6535, 128, 2000, 6535) == (
0,
0,
0,
128,
0,
)
# Warmest color temperature -> only cold channel enabled
assert color_util.color_temperature_to_rgbww(2000, 255, 2000, 6535) == (
0,
0,
0,
0,
255,
)
assert color_util.color_temperature_to_rgbww(2000, 128, 2000, 6535) == (
0,
0,
0,
0,
128,
)
# Warmer than mid point color temperature -> More warm than cold channel enabled
assert color_util.color_temperature_to_rgbww(2881, 255, 2000, 6535) == (
0,
0,
0,
112,
143,
)
assert color_util.color_temperature_to_rgbww(2881, 128, 2000, 6535) == (
0,
0,
0,
56,
72,
)
|
|
26,330 | 118,626 | 176 | lib/tests/streamlit/report_context_test.py | 33 | 20 | def test_set_page_config_first(self):
fake_enqueue = lambda msg: None
| Rename and refactor `Report` machinery (#4141)
This refactor renames (almost) everything related to the outdated "report" concept with more precise concepts that we use throughout our code, primarily "script run", "session", and "app". | test_set_page_config_first | 704eab3478cf69847825b23dabf15813a8ac9fa2 | streamlit | report_context_test.py | 10 | 17 | https://github.com/streamlit/streamlit.git | 1 | 84 | 0 | 26 | 148 | Python | {
"docstring": "st.set_page_config must be called before other st commands\n when the script has been marked as started",
"language": "en",
"n_whitespaces": 22,
"n_words": 16,
"vocab_size": 16
} | def test_set_page_config_first(self):
fake_enqueue = lambda msg: None
ctx = ScriptRunContext(
"TestSessionID",
fake_enqueue,
"",
SessionState(),
UploadedFileManager(),
)
ctx.on_script_start()
markdown_msg = ForwardMsg()
markdown_msg.delta.new_element.markdown.body = "foo"
msg = ForwardMsg()
msg.page_config_changed.title = "foo"
ctx.enqueue(markdown_msg)
with self.assertRaises(StreamlitAPIException):
ctx.enqueue(msg)
|
|
21,290 | 101,909 | 150 | lib/gui/display_command.py | 38 | 15 | def set_vars(self) -> None:
tk_vars = super().set_vars()
smoothgraph = tk.DoubleVar()
smoothgraph.set(0.900)
tk_vars["smoothg | Typing - lib.gui.display_command | set_vars | dab823a3eb7a5257cb1e0818ee10ed234d3de97f | faceswap | display_command.py | 10 | 25 | https://github.com/deepfakes/faceswap.git | 1 | 103 | 0 | 24 | 177 | Python | {
"docstring": " Add graphing specific variables to the default variables.\n\n Overrides original method.\n\n Returns\n -------\n dict\n The variable names with their corresponding tkinter variable\n ",
"language": "en",
"n_whitespaces": 69,
"n_words": 22,
"vocab_size": 21
} | def set_vars(self) -> None:
tk_vars = super().set_vars()
smoothgraph = tk.DoubleVar()
smoothgraph.set(0.900)
tk_vars["smoothgraph"] = smoothgraph
raw_var = tk.BooleanVar()
raw_var.set(True)
tk_vars["raw_data"] = raw_var
smooth_var = tk.BooleanVar()
smooth_var.set(True)
tk_vars["smooth_data"] = smooth_var
iterations_var = tk.IntVar()
iterations_var.set(10000)
tk_vars["display_iterations"] = iterations_var
logger.debug(tk_vars)
return tk_vars
|
|
11,975 | 60,026 | 226 | src/prefect/infrastructure/kubernetes.py | 82 | 9 | def _configure_kubernetes_library_client(self) -> None:
# TODO: Investigate returning a configured client so calls on other threads
# will not invalidate the config needed here
# if a k8s cluster block is provided to the flow runner, use that
if self.cluster_config:
self.cluster_config.configure_client()
else:
# If no block specified, try to load Kubernetes configuration within a cluster. If that doesn't
# work, try to load the configuration from the local environment, a | Use cluster uid and namespace instead of cluster "name" for Kubernetes job identifiers (#7747)
Co-authored-by: peytonrunyan <[email protected]>
Co-authored-by: Peyton <[email protected]> | _configure_kubernetes_library_client | 0c9ee0876133bde14ce070a89557fc31cd905bac | prefect | kubernetes.py | 14 | 14 | https://github.com/PrefectHQ/prefect.git | 3 | 45 | 0 | 62 | 85 | Python | {
"docstring": "\n Set the correct kubernetes client configuration.\n\n WARNING: This action is not threadsafe and may override the configuration\n specified by another `KubernetesJob` instance.\n ",
"language": "en",
"n_whitespaces": 61,
"n_words": 22,
"vocab_size": 21
} | def _configure_kubernetes_library_client(self) -> None:
# TODO: Investigate returning a configured client so calls on other threads
# will not invalidate the config needed here
# if a k8s cluster block is provided to the flow runner, use that
if self.cluster_config:
self.cluster_config.configure_client()
else:
# If no block specified, try to load Kubernetes configuration within a cluster. If that doesn't
# work, try to load the configuration from the local environment, allowing
# any further ConfigExceptions to bubble up.
try:
kubernetes.config.load_incluster_config()
except kubernetes.config.ConfigException:
kubernetes.config.load_kube_config()
|
|
19,261 | 95,966 | 657 | tests/sentry/api/endpoints/test_project_rules.py | 116 | 34 | def test_runs_alert_rule_action_creator(self, mock_alert_rule_action_creator):
self.login_as(user=self.user)
project = self.create_project()
self.create_sentry_app(
name="Pied Piper",
organization=project.organization,
schema={"elements": [self.create_alert_rule_action_schema()]},
)
install = self.create_sentry_app_installation(
slug="pied-piper", organization=project.organization
)
actions = [
{
"id": "sentry.rules.actions.notify_event_sentry_app.NotifyEventSentryAppAction",
"settings": [
{"name": "title", "value": "Team Rocket"},
{"name": "summary", "value": "We're blasting off again."},
],
"sentryAppInstallationUuid": install.uuid,
"hasSchemaFormConfig": True,
},
]
url = reverse(
"sentry-api-0-proj | feat(alert-rule-action): New data structure for alert-rule-action settings (#31444)
Objective:
Originally the issue was with serializing the settings field for alert webhooks and fighting with the serializers. Instead we decided to convert the dictionary to an array of dictionaries with keys name and value. | test_runs_alert_rule_action_creator | 3c8b4477340a7fe276c57c9b598c161b309c4fbd | sentry | test_project_rules.py | 15 | 51 | https://github.com/getsentry/sentry.git | 1 | 291 | 0 | 91 | 513 | Python | {
"docstring": "\n Ensures that Sentry Apps with schema forms (UI components)\n receive a payload when an alert rule is created with them.\n ",
"language": "en",
"n_whitespaces": 42,
"n_words": 20,
"vocab_size": 19
} | def test_runs_alert_rule_action_creator(self, mock_alert_rule_action_creator):
self.login_as(user=self.user)
project = self.create_project()
self.create_sentry_app(
name="Pied Piper",
organization=project.organization,
schema={"elements": [self.create_alert_rule_action_schema()]},
)
install = self.create_sentry_app_installation(
slug="pied-piper", organization=project.organization
)
actions = [
{
"id": "sentry.rules.actions.notify_event_sentry_app.NotifyEventSentryAppAction",
"settings": [
{"name": "title", "value": "Team Rocket"},
{"name": "summary", "value": "We're blasting off again."},
],
"sentryAppInstallationUuid": install.uuid,
"hasSchemaFormConfig": True,
},
]
url = reverse(
"sentry-api-0-project-rules",
kwargs={"organization_slug": project.organization.slug, "project_slug": project.slug},
)
response = self.client.post(
url,
data={
"name": "my super cool rule",
"owner": f"user:{self.user.id}",
"conditions": [],
"filters": [],
"actions": actions,
"filterMatch": "any",
"actionMatch": "any",
"frequency": 30,
},
format="json",
)
assert response.status_code == 200, response.content
assert response.data["id"]
rule = Rule.objects.get(id=response.data["id"])
assert rule.data["actions"] == actions
kwargs = {
"install": install,
"fields": actions[0].get("settings"),
}
call_kwargs = mock_alert_rule_action_creator.call_args[1]
assert call_kwargs["install"].id == kwargs["install"].id
assert call_kwargs["fields"] == kwargs["fields"]
|
|
115,587 | 317,011 | 987 | homeassistant/components/icloud/account.py | 199 | 43 | def _determine_interval(self) -> int:
intervals = {"default": self._max_interval}
for device in self._devices.values():
# Max interval if no location
if device.location is None:
continue
current_zone = run_callback_threadsafe(
self.hass.loop,
async_active_zone,
self.hass,
device.location[DEVICE_LOCATION_LATITUDE],
device.location[DEVICE_LOCATION_LONGITUDE],
device.location[DEVICE_LOCATION_HORIZONTAL_ACCURACY],
).result()
# Max interval if in zone
if current_zone is not None:
continue
zones = (
self.hass.states.get(entity_id)
for entity_id in sorted(self.hass.states.entity_ids("zone"))
)
distances = []
for zone_state in zones:
if zone_state is None:
continue
zone_state_lat = zone_state.attributes[DEVICE_LOCATION_LATITUDE]
zone_state_long = zone_state.attributes[DEVICE_LOCATION_LONGITUDE]
zone_distance = distance(
| Remove icloud from mypy ignore list (#75007) | _determine_interval | 6ac05784a63f7490f875959139ef903034bc45b0 | core | account.py | 16 | 52 | https://github.com/home-assistant/core.git | 13 | 290 | 0 | 120 | 454 | Python | {
"docstring": "Calculate new interval between two API fetch (in minutes).",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | def _determine_interval(self) -> int:
intervals = {"default": self._max_interval}
for device in self._devices.values():
# Max interval if no location
if device.location is None:
continue
current_zone = run_callback_threadsafe(
self.hass.loop,
async_active_zone,
self.hass,
device.location[DEVICE_LOCATION_LATITUDE],
device.location[DEVICE_LOCATION_LONGITUDE],
device.location[DEVICE_LOCATION_HORIZONTAL_ACCURACY],
).result()
# Max interval if in zone
if current_zone is not None:
continue
zones = (
self.hass.states.get(entity_id)
for entity_id in sorted(self.hass.states.entity_ids("zone"))
)
distances = []
for zone_state in zones:
if zone_state is None:
continue
zone_state_lat = zone_state.attributes[DEVICE_LOCATION_LATITUDE]
zone_state_long = zone_state.attributes[DEVICE_LOCATION_LONGITUDE]
zone_distance = distance(
device.location[DEVICE_LOCATION_LATITUDE],
device.location[DEVICE_LOCATION_LONGITUDE],
zone_state_lat,
zone_state_long,
)
if zone_distance is not None:
distances.append(round(zone_distance / 1000, 1))
# Max interval if no zone
if not distances:
continue
mindistance = min(distances)
# Calculate out how long it would take for the device to drive
# to the nearest zone at 120 km/h:
interval = round(mindistance / 2)
# Never poll more than once per minute
interval = max(interval, 1)
if interval > 180:
# Three hour drive?
# This is far enough that they might be flying
interval = self._max_interval
if (
device.battery_level is not None
and device.battery_level <= 33
and mindistance > 3
):
# Low battery - let's check half as often
interval = interval * 2
intervals[device.name] = interval
return max(
int(min(intervals.items(), key=operator.itemgetter(1))[1]),
self._max_interval,
)
|
|
4,164 | 22,084 | 93 | pipenv/patched/pip/_vendor/requests/models.py | 25 | 11 | def prepare_cookies(self, cookies):
if isinstance(cookies, cookielib.CookieJar | Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir. | prepare_cookies | cd5a9683be69c86c8f3adcd13385a9bc5db198ec | pipenv | models.py | 11 | 8 | https://github.com/pypa/pipenv.git | 3 | 57 | 0 | 18 | 93 | Python | {
"docstring": "Prepares the given HTTP cookie data.\n\n This function eventually generates a ``Cookie`` header from the\n given cookies using cookielib. Due to cookielib's design, the header\n will not be regenerated if it already exists, meaning this function\n can only be called once for the life of the\n :class:`PreparedRequest <PreparedRequest>` object. Any subsequent calls\n to ``prepare_cookies`` will have no actual effect, unless the \"Cookie\"\n header is removed beforehand.\n ",
"language": "en",
"n_whitespaces": 122,
"n_words": 66,
"vocab_size": 54
} | def prepare_cookies(self, cookies):
if isinstance(cookies, cookielib.CookieJar):
self._cookies = cookies
else:
self._cookies = cookiejar_from_dict(cookies)
cookie_header = get_cookie_header(self._cookies, self)
if cookie_header is not None:
self.headers["Cookie"] = cookie_header
|
|
7,355 | 40,216 | 88 | dash/testing/browser.py | 23 | 11 | def wait_for_contains_text(self, selector, text, timeout=None):
return self._wait_for(
method=contains_text,
args=(selector, text),
timeout=timeout,
msg=f"text -> {text} not found inside element within {timeout or self._wait_timeout}s",
)
| f-strings everywhere! fffff | wait_for_contains_text | c3c84b9ecf16bcc61ed80ec39d511af92fe07f2c | dash | browser.py | 12 | 7 | https://github.com/plotly/dash.git | 1 | 41 | 0 | 23 | 73 | Python | {
"docstring": "Explicit wait until the element's text contains the expected `text`.\n\n timeout if not set, equals to the fixture's `wait_timeout`\n shortcut to `WebDriverWait` with customized `contains_text`\n condition.\n ",
"language": "en",
"n_whitespaces": 54,
"n_words": 26,
"vocab_size": 23
} | def wait_for_contains_text(self, selector, text, timeout=None):
return self._wait_for(
method=contains_text,
args=(selector, text),
timeout=timeout,
msg=f"text -> {text} not found inside element within {timeout or self._wait_timeout}s",
)
|
|
49,035 | 198,681 | 99 | sympy/physics/continuum_mechanics/truss.py | 23 | 10 | def remove_member(self, label):
if label not in list(self._members):
raise ValueError("No such member exists in the Truss")
else:
| default values for supports and loads removed along with other changes | remove_member | 73b2975a89b45ef437f11b697d39796f755a856b | sympy | truss.py | 16 | 8 | https://github.com/sympy/sympy.git | 2 | 104 | 0 | 22 | 162 | Python | {
"docstring": "\n This method removes a member from the given truss.\n\n Parameters\n ==========\n label: String or Symbol\n The label for the member to be removed.\n\n Examples\n ========\n\n >>> from sympy.physics.continuum_mechanics.truss import Truss\n >>> t = Truss()\n >>> t.add_node('A', 0, 0)\n >>> t.add_node('B', 3, 0)\n >>> t.add_node('C', 2, 2)\n >>> t.add_member('AB', 'A', 'B')\n >>> t.add_member('AC', 'A', 'C')\n >>> t.add_member('BC', 'B', 'C')\n >>> t.members\n {'AB': ['A', 'B'], 'AC': ['A', 'C'], 'BC': ['B', 'C']}\n >>> t.remove_member('AC')\n >>> t.members\n {'AB': ['A', 'B'], 'BC': ['B', 'C']}\n ",
"language": "en",
"n_whitespaces": 231,
"n_words": 79,
"vocab_size": 55
} | def remove_member(self, label):
if label not in list(self._members):
raise ValueError("No such member exists in the Truss")
else:
self._nodes_occupied.pop(tuple([self._members[label][0], self._members[label][1]]))
self._nodes_occupied.pop(tuple([self._members[label][1], self._members[label][0]]))
self._members.pop(label)
self._internal_forces.pop(label)
|
|
26,306 | 118,583 | 84 | lib/tests/server_test_case.py | 24 | 15 | def _create_mock_app_session(*args, **kwargs):
mock_id = mock.PropertyMock(
return_value="mock_id:%s" % ServerTestCase._next_session_id
)
ServerTestCase._next_session_id += 1
mock_session = mock.MagicMock(AppSession, autospec=True, *args, **kwargs)
type(mock_sessi | Rename and refactor `Report` machinery (#4141)
This refactor renames (almost) everything related to the outdated "report" concept with more precise concepts that we use throughout our code, primarily "script run", "session", and "app". | _create_mock_app_session | 704eab3478cf69847825b23dabf15813a8ac9fa2 | streamlit | server_test_case.py | 11 | 8 | https://github.com/streamlit/streamlit.git | 1 | 57 | 0 | 19 | 93 | Python | {
"docstring": "Create a mock AppSession. Each mocked instance will have\n its own unique ID.",
"language": "en",
"n_whitespaces": 19,
"n_words": 13,
"vocab_size": 13
} | def _create_mock_app_session(*args, **kwargs):
mock_id = mock.PropertyMock(
return_value="mock_id:%s" % ServerTestCase._next_session_id
)
ServerTestCase._next_session_id += 1
mock_session = mock.MagicMock(AppSession, autospec=True, *args, **kwargs)
type(mock_session).id = mock_id
return mock_session
|
|
7,605 | 42,543 | 34 | nltk/parse/util.py | 17 | 4 | def taggedsents_to_conll(sentences):
for sentence in sentences:
yield from taggedsent_to_conll(sentence)
yield "\n\n"
############################################################# | Docstring tests (#3050)
* fixed pytests
* fixed more pytests
* fixed more pytest and changed multiline pytest issues fixes for snowball.py and causal.py
* fixed pytests (mainly multiline or rounding issues)
* fixed treebank pytests, removed test for return_string=True (deprecated)
* fixed destructive.py pytests, removed test for return_string=True (deprecated)
* fixed pytest (rounding issues)
* fixed pytest (initialised missing object)
* fixed pytest (formatting issues)
* fixed pytest (formatting issues)
* fixed pytest (formatting issues)
* added pytest +SKIP for deprecated module stanford
* updated AUTHORS.md
* changed docstring corrections by usage of ELLIPSIS and different roundings
* fixed AUTHORS.md to be consistent
* Fix framenet doctest formatting with pprint
* Change docstring on MultiListBox.__init__
I believe the original typo was misinterpreted and changed to something that was not originally intended.
Co-authored-by: Jan Lennartz <[email protected]>
Co-authored-by: Tom Aarsen <[email protected]>
Co-authored-by: Tom Aarsen <[email protected]> | taggedsents_to_conll | 8a4cf5d94eb94b6427c5d1d7907ba07b119932c5 | nltk | util.py | 10 | 4 | https://github.com/nltk/nltk.git | 2 | 19 | 0 | 15 | 41 | Python | {
"docstring": "\n A module to convert the a POS tagged document stream\n (i.e. list of list of tuples, a list of sentences) and yield lines\n in CONLL format. This module yields one line per word and two newlines\n for end of sentence.\n\n >>> from nltk import word_tokenize, sent_tokenize, pos_tag\n >>> text = \"This is a foobar sentence. Is that right?\"\n >>> sentences = [pos_tag(word_tokenize(sent)) for sent in sent_tokenize(text)]\n >>> for line in taggedsents_to_conll(sentences): # doctest: +NORMALIZE_WHITESPACE\n ... if line:\n ... print(line, end=\"\")\n 1\tThis\t_\tDT\tDT\t_\t0\ta\t_\t_\n 2\tis\t_\tVBZ\tVBZ\t_\t0\ta\t_\t_\n 3\ta\t_\tDT\tDT\t_\t0\ta\t_\t_\n 4\tfoobar\t_\tJJ\tJJ\t_\t0\ta\t_\t_\n 5\tsentence\t_\tNN\tNN\t_\t0\ta\t_\t_\n 6\t.\t\t_\t.\t.\t_\t0\ta\t_\t_\n <BLANKLINE>\n <BLANKLINE>\n 1\tIs\t_\tVBZ\tVBZ\t_\t0\ta\t_\t_\n 2\tthat\t_\tIN\tIN\t_\t0\ta\t_\t_\n 3\tright\t_\tNN\tNN\t_\t0\ta\t_\t_\n 4\t?\t_\t.\t.\t_\t0\ta\t_\t_\n <BLANKLINE>\n <BLANKLINE>\n\n :param sentences: Input sentences to parse\n :type sentence: list(list(tuple(str, str)))\n :rtype: iter(str)\n :return: a generator yielding sentences in CONLL format.\n ",
"language": "en",
"n_whitespaces": 214,
"n_words": 204,
"vocab_size": 91
} | def taggedsents_to_conll(sentences):
for sentence in sentences:
yield from taggedsent_to_conll(sentence)
yield "\n\n"
######################################################################
# { Test Suites
######################################################################
|
|
19,953 | 100,480 | 244 | plugins/train/model/phaze_a.py | 82 | 18 | def _get_input_shape(self):
arch = self.config["enc_architecture"]
enforce_size = _MODEL_MAPPING[arch].get("enforce_for_weig | Phaze-A: Add MobileNetV3 encoder | _get_input_shape | 0189029dbaad486e623353ee4a8451af8c85f4e4 | faceswap | phaze_a.py | 17 | 16 | https://github.com/deepfakes/faceswap.git | 4 | 139 | 0 | 60 | 232 | Python | {
"docstring": " Obtain the input shape for the model.\n\n Input shape is calculated from the selected Encoder's input size, scaled to the user\n selected Input Scaling, rounded down to the nearest 16 pixels.\n\n Notes\n -----\n Some models (NasNet) require the input size to be of a certain dimension if loading\n imagenet weights. In these instances resize inputs and raise warning message\n\n Returns\n -------\n tuple\n The shape tuple for the input size to the Phaze-A model\n ",
"language": "en",
"n_whitespaces": 155,
"n_words": 73,
"vocab_size": 53
} | def _get_input_shape(self):
arch = self.config["enc_architecture"]
enforce_size = _MODEL_MAPPING[arch].get("enforce_for_weights", False)
default_size = _MODEL_MAPPING[arch]["default_size"]
scaling = self.config["enc_scaling"] / 100
min_size = _MODEL_MAPPING[arch].get("min_size", 32)
size = int(max(min_size, min(default_size, ((default_size * scaling) // 16) * 16)))
if self.config["enc_load_weights"] and enforce_size and scaling != 1.0:
logger.warning("%s requires input size to be %spx when loading imagenet weights. "
"Adjusting input size from %spx to %spx",
arch, default_size, size, default_size)
retval = (default_size, default_size, 3)
else:
retval = (size, size, 3)
logger.debug("Encoder input set to: %s", retval)
return retval
|
|
16,543 | 76,578 | 572 | wagtail/admin/panels.py | 148 | 12 | def get_form_options(self):
options = {}
if not getattr(self.widget_overrides, "is_original_method", False):
warn(
"The `widget_overrides` method (on %r) is deprecated; | Introduce a get_form_options method to combine widget_overrides / required_fields / required_formsets / field_permissions | get_form_options | ae79eb4cb29b84bb8379fcf0957e6837164c5933 | wagtail | panels.py | 12 | 35 | https://github.com/wagtail/wagtail.git | 5 | 168 | 0 | 60 | 300 | Python | {
"docstring": "\n Return a dictionary of attributes such as 'fields', 'formsets' and 'widgets'\n which should be incorporated into the form class definition to generate a form\n that this EditHandler can use.\n This will only be called after binding to a model (i.e. self.model is available).\n ",
"language": "en",
"n_whitespaces": 79,
"n_words": 43,
"vocab_size": 38
} | def get_form_options(self):
options = {}
if not getattr(self.widget_overrides, "is_original_method", False):
warn(
"The `widget_overrides` method (on %r) is deprecated; "
"these should be returned from `get_form_options` as a "
"`widgets` item instead." % type(self),
category=RemovedInWagtail219Warning,
)
options["widgets"] = self.widget_overrides()
if not getattr(self.required_fields, "is_original_method", False):
warn(
"The `required_fields` method (on %r) is deprecated; "
"these should be returned from `get_form_options` as a "
"`fields` item instead." % type(self),
category=RemovedInWagtail219Warning,
)
options["fields"] = self.required_fields()
if not getattr(self.required_formsets, "is_original_method", False):
warn(
"The `required_formsets` method (on %r) is deprecated; "
"these should be returned from `get_form_options` as a "
"`formsets` item instead." % type(self),
category=RemovedInWagtail219Warning,
)
options["formsets"] = self.required_formsets()
if not getattr(self.field_permissions, "is_original_method", False):
warn(
"The `field_permissions` method (on %r) is deprecated; "
"these should be returned from `get_form_options` as a "
"`field_permissions` item instead." % type(self),
category=RemovedInWagtail219Warning,
)
options["field_permissions"] = self.field_permissions()
return options
# RemovedInWagtail219Warning - edit handlers should override get_form_options instead |
|
12,267 | 60,730 | 101 | .venv/lib/python3.8/site-packages/pip/_internal/index/package_finder.py | 29 | 10 | def evaluate_links(self, link_evaluator, links):
# type: (LinkEvaluator, Iterable[Link]) -> List[InstallationCandidate]
candidates = []
for link in self._sort_links(links):
candidate = self.get_install_candidate(link_evaluator, | upd; format | evaluate_links | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | package_finder.py | 11 | 7 | https://github.com/jindongwang/transferlearning.git | 3 | 48 | 0 | 26 | 77 | Python | {
"docstring": "\n Convert links that are candidates to InstallationCandidate objects.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 8,
"vocab_size": 8
} | def evaluate_links(self, link_evaluator, links):
# type: (LinkEvaluator, Iterable[Link]) -> List[InstallationCandidate]
candidates = []
for link in self._sort_links(links):
candidate = self.get_install_candidate(link_evaluator, link)
if candidate is not None:
candidates.append(candidate)
return candidates
|
|
35,855 | 154,199 | 70 | modin/core/storage_formats/base/query_compiler.py | 20 | 7 | def columnarize(self):
| REFACTOR-#4796: Introduce constant for __reduced__ column name (#4799)
Co-authored-by: Mahesh Vashishtha <[email protected]>
Co-authored-by: Alexey Prutskov <[email protected]>
Co-authored-by: Yaroslav Igoshev <[email protected]>
Signed-off-by: Jonathan Shi <[email protected]> | columnarize | 3f985ed6864cc1b5b587094d75ca5b2695e4139f | modin | query_compiler.py | 12 | 6 | https://github.com/modin-project/modin.git | 4 | 44 | 0 | 17 | 72 | Python | {
"docstring": "\n Transpose this QueryCompiler if it has a single row but multiple columns.\n\n This method should be called for QueryCompilers representing a Series object,\n i.e. ``self.is_series_like()`` should be True.\n\n Returns\n -------\n BaseQueryCompiler\n Transposed new QueryCompiler or self.\n ",
"language": "en",
"n_whitespaces": 97,
"n_words": 36,
"vocab_size": 32
} | def columnarize(self):
if len(self.columns) != 1 or (
len(self.index) == 1 and self.index[0] == MODIN_UNNAMED_SERIES_LABEL
):
return self.transpose()
return self
|
|
12,305 | 60,858 | 34 | .venv/lib/python3.8/site-packages/pip/_internal/models/wheel.py | 13 | 6 | def get_formatted_file_tags(self):
# type: () -> List[str]
return sorted(str(tag) for tag in self. | upd; format | get_formatted_file_tags | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | wheel.py | 9 | 2 | https://github.com/jindongwang/transferlearning.git | 2 | 20 | 0 | 13 | 35 | Python | {
"docstring": "Return the wheel's tags as a sorted list of strings.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def get_formatted_file_tags(self):
# type: () -> List[str]
return sorted(str(tag) for tag in self.file_tags)
|
|
39,090 | 161,892 | 555 | tests/test_syntax.py | 85 | 18 | def test_python_render():
syntax = Panel.fit(
Syntax(
CODE,
lexer="python",
line_numbers=True,
line_range=(2, 10),
theme="monokai",
code_width=60,
word_wrap=True,
),
padding=0,
)
rendered_syntax = render(syntax)
print(repr(rendered_syntax))
expected = '╭─────────────────────────────────────────────────────────────────╮\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 2 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;230;219;116;48;2;39;40;34m\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 3 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mvalues\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m)\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 4 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mtry\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 5 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mnext\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m)\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 6 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mexcept\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;166;226;46;48;2;39;40;34mStopIteration\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 7 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39 | fix for syntax measure | test_python_render | ac69488768e9c54cdef26e45b26a1b42ebf2f5d3 | rich | test_syntax.py | 12 | 17 | https://github.com/Textualize/rich.git | 1 | 69 | 0 | 57 | 289 | Python | {
"docstring": "Iterate and generate a tuple with a flag for first \\x1b[0m\\x1b[48;2;39;40;34m \\x1b[0m│\\n│\\x1b[48;2;39;40;34m \\x1b[0m\\x1b[38;2;230;219;116;48;2;39;40;34mand last value.",
"language": "en",
"n_whitespaces": 19,
"n_words": 15,
"vocab_size": 14
} | def test_python_render():
syntax = Panel.fit(
Syntax(
CODE,
lexer="python",
line_numbers=True,
line_range=(2, 10),
theme="monokai",
code_width=60,
word_wrap=True,
),
padding=0,
)
rendered_syntax = render(syntax)
print(repr(rendered_syntax))
expected = '╭─────────────────────────────────────────────────────────────────╮\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 2 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;230;219;116;48;2;39;40;34m\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 3 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mvalues\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m)\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 4 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mtry\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 5 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mnext\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m)\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 6 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mexcept\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;166;226;46;48;2;39;40;34mStopIteration\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 7 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mreturn\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 8 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mfirst\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mTrue\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 9 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mfor\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mvalue\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34min\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m10 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34myield\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mfirst\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mFalse\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\x1b[48;2;39;40;34m \x1b[0m│\n╰─────────────────────────────────────────────────────────────────╯\n'
assert rendered_syntax == expected
|
|
102,133 | 303,311 | 151 | tests/components/recorder/test_history.py | 73 | 22 | def test_state_changes_during_period_multiple_entities_single_test(hass_recorder):
hass = hass_recorder()
start = dt_util.utcnow()
test_entites = {f"sensor.{i}": str(i) for i in range(30)}
for entity_id, value in test_entites.items():
hass.states.set(entity_id, value)
wait_recording_done(hass)
end = dt_util.utcnow()
hist = history.state_changes_during_period(hass, start, end, None)
for entity_id, value in test_entites.items():
hist[entity_id][0].state == value
for entity_id, value in test_entites.items():
hist = history.state_changes_during_period(hass, start, end, entity_id)
assert len(hist) == 1
hist[entity_id][0].state == value
hist = history.state_change | Fix state_changes_during_period history query when no entities are passed (#73139) | test_state_changes_during_period_multiple_entities_single_test | de2e9b6d77adb7f86c6ec4aa0a50428ec8606dc3 | core | test_history.py | 11 | 18 | https://github.com/home-assistant/core.git | 6 | 183 | 0 | 32 | 284 | Python | {
"docstring": "Test state change during period with multiple entities in the same test.\n\n This test ensures the sqlalchemy query cache does not\n generate incorrect results.\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 24,
"vocab_size": 23
} | def test_state_changes_during_period_multiple_entities_single_test(hass_recorder):
hass = hass_recorder()
start = dt_util.utcnow()
test_entites = {f"sensor.{i}": str(i) for i in range(30)}
for entity_id, value in test_entites.items():
hass.states.set(entity_id, value)
wait_recording_done(hass)
end = dt_util.utcnow()
hist = history.state_changes_during_period(hass, start, end, None)
for entity_id, value in test_entites.items():
hist[entity_id][0].state == value
for entity_id, value in test_entites.items():
hist = history.state_changes_during_period(hass, start, end, entity_id)
assert len(hist) == 1
hist[entity_id][0].state == value
hist = history.state_changes_during_period(hass, start, end, None)
for entity_id, value in test_entites.items():
hist[entity_id][0].state == value
|
|
51,586 | 206,612 | 91 | django/utils/dateformat.py | 37 | 7 | def O(self): # NOQA: E743, E741
if self._no_timezone_or_datetime_is_ambiguous_or_imaginary:
return ""
seconds = sel | Refs #33476 -- Reformatted code with Black. | O | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | dateformat.py | 10 | 7 | https://github.com/django/django.git | 3 | 43 | 0 | 27 | 93 | Python | {
"docstring": "\n Difference to Greenwich time in hours; e.g. '+0200', '-0430'.\n\n If timezone information is not available, return an empty string.\n ",
"language": "en",
"n_whitespaces": 41,
"n_words": 19,
"vocab_size": 19
} | def O(self): # NOQA: E743, E741
if self._no_timezone_or_datetime_is_ambiguous_or_imaginary:
return ""
seconds = self.Z()
sign = "-" if seconds < 0 else "+"
seconds = abs(seconds)
return "%s%02d%02d" % (sign, seconds // 3600, (seconds // 60) % 60)
|
|
17,087 | 80,656 | 337 | awx/main/utils/common.py | 155 | 16 | def convert_mem_str_to_bytes(mem_str):
# If there is no suffix, the memory sourced from the request is in bytes
if mem_str.isdigit():
return int(mem_str)
conversions = {
'Ei': lambda x: x * 2**60,
'E': lambda x: x * 1 | Fixup conversion of memory and cpu settings to support k8s resource request format (#11725)
fix memory and cpu settings to suport k8s resource request format
* fix conversion of memory setting to bytes
This setting has not been getting set by default, and needed some fixing
up to be compatible with setting the memory in the same way as we set it
in the operator, as well as with other changes from last year which
assume that ansible runner is returning memory in bytes.
This way we can start setting this setting in the operator, and get a
more accurate reflection of how much memory is available to the control
pod in k8s.
On platforms where services are all sharing memory, we deduct a
penalty from the memory available. On k8s we don't need to do this
because the web, redis, and task containers each have memory
allocated to them.
* Support CPU setting expressed in units used by k8s
This setting has not been getting set by default, and needed some fixing
up to be compatible with setting the CPU resource request/limits in the
same way as we set it in the resource requests/limits.
This way we can start setting this setting in the
operator, and get a more accurate reflection of how much cpu is
available to the control pod in k8s.
Because cpu on k8s can be partial cores, migrate cpu field to decimal.
k8s does not allow granularity of less than 100m (equivalent to 0.1 cores), so only
store up to 1 decimal place.
fix analytics to deal with decimal cpu
need to use DjangoJSONEncoder when Decimal fields in data passed to
json.dumps | convert_mem_str_to_bytes | 799968460d4794bcd9959f57a2b97846b9a00bb7 | awx | common.py | 14 | 29 | https://github.com/ansible/awx.git | 6 | 234 | 0 | 86 | 400 | Python | {
"docstring": "Convert string with suffix indicating units to memory in bytes (base 2)\n\n Useful for dealing with memory setting that may be expressed in units compatible with\n kubernetes.\n\n See https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory\n ",
"language": "en",
"n_whitespaces": 41,
"n_words": 29,
"vocab_size": 24
} | def convert_mem_str_to_bytes(mem_str):
# If there is no suffix, the memory sourced from the request is in bytes
if mem_str.isdigit():
return int(mem_str)
conversions = {
'Ei': lambda x: x * 2**60,
'E': lambda x: x * 10**18,
'Pi': lambda x: x * 2**50,
'P': lambda x: x * 10**15,
'Ti': lambda x: x * 2**40,
'T': lambda x: x * 10**12,
'Gi': lambda x: x * 2**30,
'G': lambda x: x * 10**9,
'Mi': lambda x: x * 2**20,
'M': lambda x: x * 10**6,
'Ki': lambda x: x * 2**10,
'K': lambda x: x * 10**3,
}
mem = 0
mem_unit = None
for i, char in enumerate(mem_str):
if not char.isdigit():
mem_unit = mem_str[i:]
mem = int(mem_str[:i])
break
if not mem_unit or mem_unit not in conversions.keys():
error = f"Unsupported value for SYSTEM_TASK_ABS_MEM: {mem_str}, memory must be expressed in bytes or with known suffix: {conversions.keys()}. Falling back to 1 byte"
logger.warning(error)
return 1
return max(1, conversions[mem_unit](mem))
|
|
19,015 | 93,732 | 525 | src/sentry/integrations/jira_server/integration.py | 103 | 37 | def sync_status_outbound(self, external_issue, is_resolved, project_id, **kwargs):
client = self.get_client()
jira_issue = client.get_issue(external_issue.key)
jira_project = jira_issue["fields"]["project"]
try:
external_project = IntegrationExternalProject.objects.get(
external_id=jira_project["id"],
organization_integration_id__in=OrganizationIntegration.objects.filter(
organization_id=external_issue.organization_id,
integration_id=external_issue.integration_id,
),
)
except IntegrationExternalProject.DoesNotExist:
return
| ref(Jira): Split Jira Cloud and Jira Server (#37034)
* Split Jira Cloud and Jira Server | sync_status_outbound | 2fbf550ec05c8501cbc9eca62e73526e717dcbdf | sentry | integration.py | 17 | 36 | https://github.com/getsentry/sentry.git | 8 | 213 | 0 | 81 | 352 | Python | {
"docstring": "\n Propagate a sentry issue's status to a linked issue's status.\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 10,
"vocab_size": 8
} | def sync_status_outbound(self, external_issue, is_resolved, project_id, **kwargs):
client = self.get_client()
jira_issue = client.get_issue(external_issue.key)
jira_project = jira_issue["fields"]["project"]
try:
external_project = IntegrationExternalProject.objects.get(
external_id=jira_project["id"],
organization_integration_id__in=OrganizationIntegration.objects.filter(
organization_id=external_issue.organization_id,
integration_id=external_issue.integration_id,
),
)
except IntegrationExternalProject.DoesNotExist:
return
jira_status = (
external_project.resolved_status if is_resolved else external_project.unresolved_status
)
# don't bother updating if it's already the status we'd change it to
if jira_issue["fields"]["status"]["id"] == jira_status:
return
try:
transitions = client.get_transitions(external_issue.key)
except ApiHostError:
raise IntegrationError("Could not reach host to get transitions.")
try:
transition = [t for t in transitions if t.get("to", {}).get("id") == jira_status][0]
except IndexError:
# TODO(jess): Email for failure
logger.warning(
"jira.status-sync-fail",
extra={
"organization_id": external_issue.organization_id,
"integration_id": external_issue.integration_id,
"issue_key": external_issue.key,
},
)
return
client.transition_issue(external_issue.key, transition["id"])
|
|
23,225 | 108,514 | 166 | lib/matplotlib/axes/_base.py | 44 | 17 | def _sci(self, im):
_api.check_isinstance(
(mpl.contour.ContourSet, mcoll.Collection, mimage.AxesImage),
im=im)
if isinstance(im, mpl.contour.ContourSet):
if im.collections[0] not in self._children:
raise ValueError("ContourSet must be in current Axes")
elif im | Cleanup documentation generation for pyplot
- remove the awkward `pyplot.plotting()` function, which only served
as a namespace to take up the docs for pyplot and output them via
`.. autofunction`
- Instead generate the same information using `.. autosummary::`. We
have to list the desired methods here explicitly. I've added a test
that these are the same as previously auto-generated in the
`plotting()` docstring. If we change anything in pyplot, we'll be
notified through the test failure that we have to adapt the
autosummary list.
- Removed the docstring generation logic
`_setup_pyplot_info_docstrings()`. Apart from generating the
`plotting()` docstring, this added docstrings to the pyplot colormap
setters. Instead, we now add these docstrings directly via
boilerplate.py
Co-authored-by: Elliott Sales de Andrade <[email protected]> | _sci | 032316bc6c7798fca6c82de24167c975f237687f | matplotlib | _base.py | 12 | 11 | https://github.com/matplotlib/matplotlib.git | 4 | 81 | 0 | 33 | 130 | Python | {
"docstring": "\n Set the current image.\n\n This image will be the target of colormap functions like\n ``pyplot.viridis``, and other functions such as `~.pyplot.clim`. The\n current image is an attribute of the current Axes.\n ",
"language": "en",
"n_whitespaces": 68,
"n_words": 31,
"vocab_size": 24
} | def _sci(self, im):
_api.check_isinstance(
(mpl.contour.ContourSet, mcoll.Collection, mimage.AxesImage),
im=im)
if isinstance(im, mpl.contour.ContourSet):
if im.collections[0] not in self._children:
raise ValueError("ContourSet must be in current Axes")
elif im not in self._children:
raise ValueError("Argument must be an image, collection, or "
"ContourSet in this Axes")
self._current_image = im
|
|
36,665 | 156,512 | 26 | dask/typing.py | 12 | 5 | def __dask_postpersist__(self) -> tuple[PostPersistCallable, tuple]:
raise NotImplementedError("Inheriting c | Collection Protocol (#8674)
[PEP 544](https://www.python.org/dev/peps/pep-0544/) introduces the `Protocol` class to the `typing` module in Python 3.8 (the soon be the minimum supported version, https://github.com/dask/community/issues/213). Writing new Dask collections for [dask-awkward](https://github.com/ContinuumIO/dask-awkward/) has had me thinking about working on a `DaskCollection` protocol. I imagine the benefits to be:
- usage with static type checkers
- other activity in this area at
- #8295
- #8706
- #8854
- Python supporting IDEs take advantage of typing
- self-documenting; some improvements to [the custom collections page](https://docs.dask.org/en/latest/custom-collections.html) of the docs. The protocol docs can be autogenerated and added to that page.
- purely opt-in feature
The `typing.runtime_checkable` decorator allows use of `isinstance(x, DaskCollection)` in any code base
that uses Dask collections; for example:
```python
>>> from dask.typing import DaskCollection
>>> import dask.array as da
>>> x = da.zeros((10, 3))
>>> isinstance(x, DaskCollection)
True
```
(though this is an order of magnitude slower than `dask.base.is_dask_collection` which only checks for `x.__dask_graph__() is not None`; static typing checking & built-in interface documentation are the core benefits IMO)
Something else that came up in the brief discussion on a call last week was having `{Scheduler,Worker,Nanny}Plugin` protocols in `distributed`; and perhaps those are better places to start introducing protocols to Dask since on the user side typically more folks would write plugins than new collections. | __dask_postpersist__ | 1e783d9a714160e968936cb22d54d085959ab09e | dask | typing.py | 8 | 21 | https://github.com/dask/dask.git | 1 | 18 | 0 | 12 | 32 | Python | {
"docstring": "Rebuilder function and optional arguments to contruct a persisted collection.\n\n Returns\n -------\n PostPersistCallable\n Callable that rebuilds the collection. The signature\n should be\n ``rebuild(dsk: Mapping, *args: Any, rename: Mapping[str, str] | None)``.\n The callable should return an equivalent Dask collection\n with the same keys as `self`, but with results that are\n computed through a different graph. In the case of\n :py:func:`dask.persist`, the new graph will have just the\n output keys and the values already computed.\n tuple[Any, ...]\n Optional arugments passed to the rebuild callable. If no\n additional arguments are to be passed then this must be an\n empty tuple.\n\n ",
"language": "en",
"n_whitespaces": 254,
"n_words": 98,
"vocab_size": 76
} | def __dask_postpersist__(self) -> tuple[PostPersistCallable, tuple]:
raise NotImplementedError("Inheriting class must implement this method.")
|
|
13,627 | 64,407 | 21 | erpnext/patches/v4_2/repost_reserved_qty.py | 32 | 11 | def execute():
for doctype in ("Sales Order Item", "Bin | fix: avoid creating bins without item-wh
Co-Authored-By: Shadrak Gurupnor <[email protected]>
Co-Authored-By: Saurabh <[email protected]> | execute | c36bd7e1a6fe48c5fff4765e843571a0d6560dd1 | erpnext | repost_reserved_qty.py | 13 | 30 | https://github.com/frappe/erpnext.git | 5 | 70 | 0 | 29 | 118 | Python | {
"docstring": "\n\t\tselect\n\t\t\tdistinct item_code, warehouse\n\t\tfrom\n\t\t\t(\n\t\t\t\t(\n\t\t\t\t\tselect distinct item_code, warehouse\n\t\t\t\t\t\t\t\tfrom `tabSales Order Item` where docstatus=1\n\t\t\t\t) UNION (\n\t\t\t\t\tselect distinct item_code, warehouse\n\t\t\t\t\tfrom `tabPacked Item` where docstatus=1 and parenttype='Sales Order'\n\t\t\t\t)\n\t\t\t) so_item\n\t\twhere\n\t\t\texists(select name from tabItem where name=so_item.item_code and ifnull(is_stock_item, 0)=1)\n\tdelete from tabBin\n\t\twhere exists(\n\t\t\tselect name from tabItem where name=tabBin.item_code and ifnull(is_stock_item, 0) = 0\n\t\t)\n\t",
"language": "en",
"n_whitespaces": 44,
"n_words": 62,
"vocab_size": 31
} | def execute():
for doctype in ("Sales Order Item", "Bin"):
frappe.reload_doctype(doctype)
repost_for = frappe.db.sql()
for item_code, warehouse in repost_for:
if not (item_code and warehouse):
continue
update_bin_qty(item_code, warehouse, {
"reserved_qty": get_reserved_qty(item_code, warehouse)
})
frappe.db.sql()
|
|
5,291 | 30,043 | 85 | saleor/account/migrations/0071_group.py | 26 | 12 | def rename_group_tables_reverse(apps, schema_editor):
Group = apps.get_model("auth", "Group")
schema_editor.alter_db_table(
Group,
"account_group",
"auth_group",
)
PermissionGroup = Group.permissions.through
schema_editor.alter_db_table(
PermissionGroup,
"account_group_permissions" | Drop Djanog Auth | rename_group_tables_reverse | 72c120ae8eeb34e5a3f9840fb1ab1de1fca52fb5 | saleor | 0071_group.py | 9 | 13 | https://github.com/saleor/saleor.git | 1 | 46 | 0 | 20 | 100 | Python | {
"docstring": "\nALTER TABLE account_group RENAME CONSTRAINT account_group_pkey\n TO auth_group_pkey;\n\nALTER TABLE account_group RENAME CONSTRAINT account_group_name_key\n TO auth_group_name_key;\n\nALTER INDEX IF EXISTS account_group_name_034e9f3f_like\n RENAME TO auth_group_name_a6ea08ec_like;\n\nALTER TABLE auth_group_permissions\n ADD CONSTRAINT auth_group_permissions_group_id_permission_id_0cd325b0_uniq\n UNIQUE (group_id, permission_id);\n\nALTER TABLE auth_group_permissions\n ADD CONSTRAINT auth_group_permissions_group_id_b120cbf9_fk_auth_group_id\n FOREIGN KEY (group_id) REFERENCES auth_group (id) DEFERRABLE INITIALLY DEFERRED;\n\nALTER TABLE auth_group_permissions\n ADD CONSTRAINT auth_group_permissio_permission_id_84c5c92e_fk_auth_perm\n FOREIGN KEY (permission_id) REFERENCES auth_permission (id)\n DEFERRABLE INITIALLY DEFERRED;\n\nALTER TABLE account_user_groups\n ADD CONSTRAINT userprofile_user_groups_group_id_c7eec74e_fk_auth_group_id\n FOREIGN KEY (group_id) REFERENCES auth_group (id) DEFERRABLE INITIALLY DEFERRED;\n\nALTER TABLE account_user_user_permissions\n ADD CONSTRAINT userprofile_user_use_permission_id_1caa8a71_fk_auth_perm\n FOREIGN KEY (permission_id) REFERENCES auth_permission (id)\n DEFERRABLE INITIALLY DEFERRED;\n\nALTER TABLE app_app_permissions\n ADD CONSTRAINT account_serviceaccou_permission_id_449791f0_fk_auth_perm\n FOREIGN KEY (permission_id) REFERENCES auth_permission (id)\n DEFERRABLE INITIALLY DEFERRED;\n\nALTER TABLE app_appextension_permissions\n ADD CONSTRAINT app_appextension_per_permission_id_cb6c3ce0_fk_auth_perm\n FOREIGN KEY (permission_id) REFERENCES auth_permission (id)\n DEFERRABLE INITIALLY DEFERRED;\n\nALTER TABLE app_appinstallation_permissions\n ADD CONSTRAINT app_appinstallation__permission_id_4ee9f6c8_fk_auth_perm\n FOREIGN KEY (permission_id) REFERENCES auth_permission (id)\n DEFERRABLE INITIALLY DEFERRED;\n",
"language": "en",
"n_whitespaces": 199,
"n_words": 138,
"vocab_size": 44
} | def rename_group_tables_reverse(apps, schema_editor):
Group = apps.get_model("auth", "Group")
schema_editor.alter_db_table(
Group,
"account_group",
"auth_group",
)
PermissionGroup = Group.permissions.through
schema_editor.alter_db_table(
PermissionGroup,
"account_group_permissions",
"auth_group_permissions",
)
RENAME_CONSTRAINTS_AND_INDEX_REVERSE =
DROP_OLD_CONSTRAINTS_REVERSE_FROM_0072 =
DROP_OLD_CONSTRAINTS_REVERSE_FROM_APP_0018 =
|
|
49,897 | 201,197 | 29 | tests/auth_tests/test_context_processors.py | 8 | 6 | def test_session_is_accessed(self):
| Refs #33476 -- Reformatted code with Black. | test_session_is_accessed | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | test_context_processors.py | 9 | 3 | https://github.com/django/django.git | 1 | 24 | 0 | 8 | 45 | Python | {
"docstring": "\n The session is accessed if the auth context processor\n is used and relevant attributes accessed.\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 15,
"vocab_size": 14
} | def test_session_is_accessed(self):
response = self.client.get("/auth_processor_attr_access/")
self.assertContains(response, "Session accessed")
|
|
56,628 | 222,539 | 206 | python3.10.4/Lib/distutils/_msvccompiler.py | 71 | 17 | def _find_vc2017():
root = os.environ.get("ProgramFiles(x86)") or os.environ.get("ProgramFiles")
if not root:
return None, None
try:
path = subprocess.check_output([
os.path.join(root, "Micro | add python 3.10.4 for windows | _find_vc2017 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | _msvccompiler.py | 16 | 19 | https://github.com/XX-net/XX-Net.git | 5 | 135 | 0 | 55 | 275 | Python | {
"docstring": "Returns \"15, path\" based on the result of invoking vswhere.exe\n If no install is found, returns \"None, None\"\n\n The version is returned to avoid unnecessarily changing the function\n result. It may be ignored when the path is not None.\n\n If vswhere.exe is not available, by definition, VS 2017 is not\n installed.\n ",
"language": "en",
"n_whitespaces": 69,
"n_words": 51,
"vocab_size": 41
} | def _find_vc2017():
root = os.environ.get("ProgramFiles(x86)") or os.environ.get("ProgramFiles")
if not root:
return None, None
try:
path = subprocess.check_output([
os.path.join(root, "Microsoft Visual Studio", "Installer", "vswhere.exe"),
"-latest",
"-prerelease",
"-requires", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64",
"-property", "installationPath",
"-products", "*",
], encoding="mbcs", errors="strict").strip()
except (subprocess.CalledProcessError, OSError, UnicodeDecodeError):
return None, None
path = os.path.join(path, "VC", "Auxiliary", "Build")
if os.path.isdir(path):
return 15, path
return None, None
PLAT_SPEC_TO_RUNTIME = {
'x86' : 'x86',
'x86_amd64' : 'x64',
'x86_arm' : 'arm',
'x86_arm64' : 'arm64'
}
|
|
81,493 | 275,868 | 244 | keras/saving/hdf5_format.py | 127 | 21 | def save_attributes_to_hdf5_group(group, name, data):
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
bad_attributes = [x for x in data if len(x) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"The following attributes cannot be saved to HDF5 file because they "
f"are larger than {HDF5_OBJECT_HEADER_LIMIT} bytes: {bad_attributes}"
)
data_npy = np.asarray(data)
num_chunks = 1
chunked_data = np.array_split(data_npy, num_chunks)
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data):
num_chunks += 1
chunked_data = np.array_split(data_npy, num_chunks)
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(chunked_data):
group.attrs["%s%d" % (name, chunk_id)] = chunk_data
else:
group.attrs[name] = data
| Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | save_attributes_to_hdf5_group | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | hdf5_format.py | 13 | 18 | https://github.com/keras-team/keras.git | 8 | 123 | 0 | 88 | 208 | Python | {
"docstring": "Saves attributes (data) of the specified name into the HDF5 group.\n\n This method deals with an inherent problem of HDF5 file which is not\n able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes.\n\n Args:\n group: A pointer to a HDF5 group.\n name: A name of the attributes to save.\n data: Attributes data to store.\n\n Raises:\n RuntimeError: If any single attribute is too large to be saved.\n ",
"language": "en",
"n_whitespaces": 106,
"n_words": 65,
"vocab_size": 49
} | def save_attributes_to_hdf5_group(group, name, data):
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
bad_attributes = [x for x in data if len(x) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"The following attributes cannot be saved to HDF5 file because they "
f"are larger than {HDF5_OBJECT_HEADER_LIMIT} bytes: {bad_attributes}"
)
data_npy = np.asarray(data)
num_chunks = 1
chunked_data = np.array_split(data_npy, num_chunks)
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data):
num_chunks += 1
chunked_data = np.array_split(data_npy, num_chunks)
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(chunked_data):
group.attrs["%s%d" % (name, chunk_id)] = chunk_data
else:
group.attrs[name] = data
|
|
52,639 | 209,197 | 742 | scapy/layers/tls/record.py | 165 | 34 | def dispatch_hook(cls, _pkt=None, *args, **kargs):
if _pkt is not None:
plen = len(_pkt)
if plen >= 2:
byte0, byte1 = struct.unpack("BB", _pkt[:2])
s = kargs.get("tls_ | Update the TLS13 notebook to spec | dispatch_hook | c96fbb8487051e209dfee788eff857e9ca1fed72 | scapy | record.py | 19 | 25 | https://github.com/secdev/scapy.git | 18 | 192 | 0 | 107 | 302 | Python | {
"docstring": "\n If the TLS class was called on raw SSLv2 data, we want to return an\n SSLv2 record instance. We acknowledge the risk of SSLv2 packets with a\n msglen of 0x1403, 0x1503, 0x1603 or 0x1703 which will never be casted\n as SSLv2 records but TLS ones instead, but hey, we can't be held\n responsible for low-minded extensibility choices.\n ",
"language": "en",
"n_whitespaces": 100,
"n_words": 57,
"vocab_size": 48
} | def dispatch_hook(cls, _pkt=None, *args, **kargs):
if _pkt is not None:
plen = len(_pkt)
if plen >= 2:
byte0, byte1 = struct.unpack("BB", _pkt[:2])
s = kargs.get("tls_session", None)
if byte0 not in _tls_type or byte1 != 3: # Unknown type
# Check SSLv2: either the session is already SSLv2,
# either the packet looks like one. As said above, this
# isn't 100% reliable, but Wireshark does the same
if s and (s.tls_version == 0x0002 or
s.advertised_tls_version == 0x0002) or \
(_ssl_looks_like_sslv2(_pkt) and (not s or
s.tls_version is None)):
from scapy.layers.tls.record_sslv2 import SSLv2
return SSLv2
# Not SSLv2: continuation
return _TLSEncryptedContent
# Check TLS 1.3
if s and _tls_version_check(s.tls_version, 0x0304):
_has_cipher = lambda x: (
x and not isinstance(x.cipher, Cipher_NULL)
)
if (_has_cipher(s.rcs) or _has_cipher(s.prcs)) and \
byte0 == 0x17:
from scapy.layers.tls.record_tls13 import TLS13
return TLS13
if plen < 5:
# Layer detected as TLS but too small to be a
# parsed. Scapy should not try to decode them
return _TLSEncryptedContent
return TLS
# Parsing methods
|
|
14,164 | 66,292 | 12 | erpnext/hr/utils.py | 20 | 9 | def get_leave_period(from_date, to_date, company):
leave_period = frappe.db.sql(
,
{"from_date": from_date, "to_date": to_date, "company": company},
as_dict=1,
) | style: format code with black | get_leave_period | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | utils.py | 11 | 15 | https://github.com/frappe/erpnext.git | 2 | 43 | 0 | 18 | 69 | Python | {
"docstring": "\n\t\tselect name, from_date, to_date\n\t\tfrom `tabLeave Period`\n\t\twhere company=%(company)s and is_active=1\n\t\t\tand (from_date between %(from_date)s and %(to_date)s\n\t\t\t\tor to_date between %(from_date)s and %(to_date)s\n\t\t\t\tor (from_date < %(from_date)s and to_date > %(to_date)s))\n\t",
"language": "en",
"n_whitespaces": 25,
"n_words": 31,
"vocab_size": 19
} | def get_leave_period(from_date, to_date, company):
leave_period = frappe.db.sql(
,
{"from_date": from_date, "to_date": to_date, "company": company},
as_dict=1,
)
if leave_period:
return leave_period
|
|
33,065 | 143,826 | 136 | rllib/policy/sample_batch.py | 49 | 22 | def rows(self) -> Iterator[Dict[str, TensorType]]:
# Do we add seq_lens=[1] to each row?
seq_lens = None if self.get(SampleBatch.SEQ_LENS) is None else np.array([1])
self_as_dict = {k: v for k, v in self.items()}
for i in range(self.count):
yield tree.map_structure_with_path(
lambda p, v: v[i] if p[0] != self.SEQ_LENS else seq_lens,
self_as_dict,
)
| [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | rows | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | sample_batch.py | 14 | 27 | https://github.com/ray-project/ray.git | 5 | 95 | 0 | 42 | 144 | Python | {
"docstring": "Returns an iterator over data rows, i.e. dicts with column values.\n\n Note that if `seq_lens` is set in self, we set it to [1] in the rows.\n\n Yields:\n The column values of the row in this iteration.\n\n Examples:\n >>> batch = SampleBatch({\n ... \"a\": [1, 2, 3],\n ... \"b\": [4, 5, 6],\n ... \"seq_lens\": [1, 2]\n ... })\n >>> for row in batch.rows():\n print(row)\n {\"a\": 1, \"b\": 4, \"seq_lens\": [1]}\n {\"a\": 2, \"b\": 5, \"seq_lens\": [1]}\n {\"a\": 3, \"b\": 6, \"seq_lens\": [1]}\n ",
"language": "en",
"n_whitespaces": 247,
"n_words": 82,
"vocab_size": 58
} | def rows(self) -> Iterator[Dict[str, TensorType]]:
# Do we add seq_lens=[1] to each row?
seq_lens = None if self.get(SampleBatch.SEQ_LENS) is None else np.array([1])
self_as_dict = {k: v for k, v in self.items()}
for i in range(self.count):
yield tree.map_structure_with_path(
lambda p, v: v[i] if p[0] != self.SEQ_LENS else seq_lens,
self_as_dict,
)
|
|
80,539 | 270,715 | 125 | keras/engine/base_layer.py | 38 | 10 | def _dedup_weights(self, weights):
output, seen_ids = [], set()
for w in weights:
if id(w) not in seen_ids:
output.append(w)
# Track the Variable's identity to avoid __eq__ issues.
seen_ids.add(id(w))
return output
# SavedModel properties. Please see keras/saving/saved_model for | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | _dedup_weights | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | base_layer.py | 13 | 7 | https://github.com/keras-team/keras.git | 3 | 49 | 0 | 35 | 83 | Python | {
"docstring": "Dedupe weights while maintaining order as much as possible.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 8
} | def _dedup_weights(self, weights):
output, seen_ids = [], set()
for w in weights:
if id(w) not in seen_ids:
output.append(w)
# Track the Variable's identity to avoid __eq__ issues.
seen_ids.add(id(w))
return output
# SavedModel properties. Please see keras/saving/saved_model for details.
|
|
45,126 | 185,717 | 95 | src/textual/dom.py | 31 | 11 | def ancestors_with_self(self) -> list[DOMNode]:
nodes: list[MessagePump | None] = []
add_node = nodes.append
node: MessagePump | None = self
while node is not None:
| Don't include self in DOMNode.ancestors any more
As well as dropping `self` from the list that DOMNode.ancestors provides,
this commit also adds DOMNode.ancestors_with_self, which maintains the
previous behaviour of DOMNode.ancestors. | ancestors_with_self | e3130f95c69648916f121e779a325b6f6f87e6ba | textual | dom.py | 9 | 12 | https://github.com/Textualize/textual.git | 2 | 56 | 0 | 26 | 92 | Python | {
"docstring": "list[DOMNode]: A list of Nodes by tracing a path all the way back to App.\n\n Note: This is inclusive of ``self``.\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 21,
"vocab_size": 20
} | def ancestors_with_self(self) -> list[DOMNode]:
nodes: list[MessagePump | None] = []
add_node = nodes.append
node: MessagePump | None = self
while node is not None:
add_node(node)
node = node._parent
return cast("list[DOMNode]", nodes)
|
|
18,067 | 85,981 | 14,028 | src/sentry/search/events/datasets/metrics.py | 747 | 53 | def function_converter(self) -> Mapping[str, fields.MetricsFunction]:
resolve_metric_id = {
"name": "metric_id",
"fn": lambda args: self.resolve_metric(args["column"]),
}
function_converter = {
function.name: function
for function in [
# Note while the discover version of apdex, count_miserable, user_misery
# accepts arguments, because this is precomputed with tags no parameters
# are available
fields.MetricsFunction(
"apdex",
optional_args=[fields.NullableNumberRange("satisfaction", 0, None)],
snql_distribution=self._resolve_apdex_function,
default_result_type="number",
),
fields.MetricsFunction(
"avg",
required_args=[
fields.MetricArg(
"column",
allowed_columns=constants.METRIC_DURATION_COLUMNS,
)
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: Function(
"avgIf",
[
Column("value"),
Function(
"equals",
[
Column("metric_id"),
args["metric_id"],
],
),
],
alias,
),
result_type_fn=self.reflective_result_type(),
default_result_type="integer",
),
fields.MetricsFunction(
"count_miserable",
required_args=[
fields.MetricArg(
"column", allowed_columns=["user"], allow_custom_measurements=False
)
],
optional_args=[fields.NullableNumberRange("satisfaction", 0, None)],
calculated_args=[resolve_metric_id],
snql_set=self._resolve_count_miserable_function,
default_result_type="integer",
),
fields.MetricsFunction(
"count_unparameterized_transactions",
snql_distribution=lambda args, alias: Function(
"countIf",
[
Column("value"),
Function(
"and",
[
Function(
"equals",
[
Column("metric_id"),
self.resolve_metric("transaction.duration"),
],
),
Function(
"equals",
[
self.builder.column("transaction"),
self.builder.resolve_tag_value("<< unparameterized >>"),
],
),
],
),
],
alias,
),
# Not yet exposed, need to add far more validation around tag&value
private=True,
default_result_type="integer",
),
fields.MetricsFunction(
"count_null_transactions",
snql_distribution=lambda args, alias: Function(
"countIf",
[
Column("value"),
Function(
"and",
[
Function(
"equals",
[
Column("metric_id"),
self.resolve_metric("transaction.duration"),
],
),
Function(
"equals",
[
self.builder.column("transaction"),
"" if self.builder.tag_values_are_strings else 0,
],
),
],
),
],
alias,
),
private=True,
),
fields.MetricsFunction(
"count_has_transaction_name",
snql_distribution=lambda args, alias: Function(
"countIf",
[
Column("value"),
Function(
"and",
[
Function(
"equals",
[
Column("metric_id"),
self.resolve_metric("transaction.duration"),
],
),
Function(
"and",
[
Function(
"notEquals",
[
self.builder.column("transaction"),
""
if self.builder.tag_values_are_strings
else 0,
],
),
Function(
"notEquals",
[
self.builder.column("transaction"),
self.builder.resolve_tag_value(
"<< unparameterized >>"
),
],
),
],
),
],
),
],
alias,
),
private=True,
default_result_type="integer",
),
fields.MetricsFunction(
"user_misery",
optional_args=[
fields.NullableNumberRange("satisfaction", 0, None),
fields.with_default(
constants.MISERY_ALPHA, fields.NumberRange("alpha", 0, None)
),
fields.with_default(
constants.MISERY_BETA, fields.NumberRange("beta", 0, None)
),
],
calculated_args=[],
snql_set=self._resolve_user_misery_function,
default_result_type="number",
),
fields.MetricsFunction(
"p50",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(
args, alias, 0.5
),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"p75",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(
args, alias, 0.75
),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"p90",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(
args, alias, 0.90
),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"p95",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(
args, alias, 0.95
),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"p99",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(
args, alias, 0.99
),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"p100",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(args, alias, 1),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"max",
required_args=[
fields.MetricArg("column"),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: Function(
"maxIf",
[
Column("value"),
Function("equals", [Column("metric_id"), args["metric_id"]]),
],
alias,
),
result_type_fn=self.reflective_result_type(),
),
fields.MetricsFunction(
"min",
required_args=[
fields.MetricArg("column"),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: Function(
"minIf",
[
Column("value"),
Function("equals", [Column("metric_id"), args["metric_id"]]),
],
alias,
),
result_type_fn=self.reflective_result_type(),
),
fields.MetricsFunction(
"sum",
required_args=[
fields.MetricArg("column"),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: Function(
"sumIf",
[
Column("value"),
Function("equals", [Column("metric_id"), args["metric_id"]]),
],
alias,
| fix(mep): Include the column so its countmerge (#39005)
- This was causing these results to overcount since we werent merging
rows correctly. For the purposes of the endpoint we just needed >0 so it
wasn't as noticeable | function_converter | 0099fe517a2044e70567e969f19bcf3fa3b26122 | sentry | metrics.py | 28 | 548 | https://github.com/getsentry/sentry.git | 6 | 2,133 | 0 | 202 | 3,312 | Python | {
"docstring": "While the final functions in clickhouse must have their -Merge combinators in order to function, we don't\n need to add them here since snuba has a FunctionMapper that will add it for us. Basically it turns expressions\n like quantiles(0.9)(value) into quantilesMerge(0.9)(percentiles)\n Make sure to update METRIC_FUNCTION_LIST_BY_TYPE when adding functions here, can't be a dynamic list since the\n Metric Layer will actually handle which dataset each function goes to\n ",
"language": "en",
"n_whitespaces": 103,
"n_words": 68,
"vocab_size": 57
} | def function_converter(self) -> Mapping[str, fields.MetricsFunction]:
resolve_metric_id = {
"name": "metric_id",
"fn": lambda args: self.resolve_metric(args["column"]),
}
function_converter = {
function.name: function
for function in [
# Note while the discover version of apdex, count_miserable, user_misery
# accepts arguments, because this is precomputed with tags no parameters
# are available
fields.MetricsFunction(
"apdex",
optional_args=[fields.NullableNumberRange("satisfaction", 0, None)],
snql_distribution=self._resolve_apdex_function,
default_result_type="number",
),
fields.MetricsFunction(
"avg",
required_args=[
fields.MetricArg(
"column",
allowed_columns=constants.METRIC_DURATION_COLUMNS,
)
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: Function(
"avgIf",
[
Column("value"),
Function(
"equals",
[
Column("metric_id"),
args["metric_id"],
],
),
],
alias,
),
result_type_fn=self.reflective_result_type(),
default_result_type="integer",
),
fields.MetricsFunction(
"count_miserable",
required_args=[
fields.MetricArg(
"column", allowed_columns=["user"], allow_custom_measurements=False
)
],
optional_args=[fields.NullableNumberRange("satisfaction", 0, None)],
calculated_args=[resolve_metric_id],
snql_set=self._resolve_count_miserable_function,
default_result_type="integer",
),
fields.MetricsFunction(
"count_unparameterized_transactions",
snql_distribution=lambda args, alias: Function(
"countIf",
[
Column("value"),
Function(
"and",
[
Function(
"equals",
[
Column("metric_id"),
self.resolve_metric("transaction.duration"),
],
),
Function(
"equals",
[
self.builder.column("transaction"),
self.builder.resolve_tag_value("<< unparameterized >>"),
],
),
],
),
],
alias,
),
# Not yet exposed, need to add far more validation around tag&value
private=True,
default_result_type="integer",
),
fields.MetricsFunction(
"count_null_transactions",
snql_distribution=lambda args, alias: Function(
"countIf",
[
Column("value"),
Function(
"and",
[
Function(
"equals",
[
Column("metric_id"),
self.resolve_metric("transaction.duration"),
],
),
Function(
"equals",
[
self.builder.column("transaction"),
"" if self.builder.tag_values_are_strings else 0,
],
),
],
),
],
alias,
),
private=True,
),
fields.MetricsFunction(
"count_has_transaction_name",
snql_distribution=lambda args, alias: Function(
"countIf",
[
Column("value"),
Function(
"and",
[
Function(
"equals",
[
Column("metric_id"),
self.resolve_metric("transaction.duration"),
],
),
Function(
"and",
[
Function(
"notEquals",
[
self.builder.column("transaction"),
""
if self.builder.tag_values_are_strings
else 0,
],
),
Function(
"notEquals",
[
self.builder.column("transaction"),
self.builder.resolve_tag_value(
"<< unparameterized >>"
),
],
),
],
),
],
),
],
alias,
),
private=True,
default_result_type="integer",
),
fields.MetricsFunction(
"user_misery",
optional_args=[
fields.NullableNumberRange("satisfaction", 0, None),
fields.with_default(
constants.MISERY_ALPHA, fields.NumberRange("alpha", 0, None)
),
fields.with_default(
constants.MISERY_BETA, fields.NumberRange("beta", 0, None)
),
],
calculated_args=[],
snql_set=self._resolve_user_misery_function,
default_result_type="number",
),
fields.MetricsFunction(
"p50",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(
args, alias, 0.5
),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"p75",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(
args, alias, 0.75
),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"p90",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(
args, alias, 0.90
),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"p95",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(
args, alias, 0.95
),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"p99",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(
args, alias, 0.99
),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"p100",
optional_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: self._resolve_percentile(args, alias, 1),
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"max",
required_args=[
fields.MetricArg("column"),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: Function(
"maxIf",
[
Column("value"),
Function("equals", [Column("metric_id"), args["metric_id"]]),
],
alias,
),
result_type_fn=self.reflective_result_type(),
),
fields.MetricsFunction(
"min",
required_args=[
fields.MetricArg("column"),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: Function(
"minIf",
[
Column("value"),
Function("equals", [Column("metric_id"), args["metric_id"]]),
],
alias,
),
result_type_fn=self.reflective_result_type(),
),
fields.MetricsFunction(
"sum",
required_args=[
fields.MetricArg("column"),
],
calculated_args=[resolve_metric_id],
snql_distribution=lambda args, alias: Function(
"sumIf",
[
Column("value"),
Function("equals", [Column("metric_id"), args["metric_id"]]),
],
alias,
),
result_type_fn=self.reflective_result_type(),
),
fields.MetricsFunction(
"sumIf",
required_args=[
fields.ColumnTagArg("if_col"),
fields.FunctionArg("if_val"),
],
calculated_args=[
{
"name": "resolved_val",
"fn": lambda args: self.builder.resolve_tag_value(args["if_val"]),
}
],
snql_counter=lambda args, alias: Function(
"sumIf",
[
Column("value"),
Function("equals", [args["if_col"], args["resolved_val"]]),
],
alias,
),
default_result_type="integer",
),
fields.MetricsFunction(
"percentile",
required_args=[
fields.with_default(
"transaction.duration",
fields.MetricArg(
"column", allowed_columns=constants.METRIC_DURATION_COLUMNS
),
),
fields.NumberRange("percentile", 0, 1),
],
calculated_args=[resolve_metric_id],
snql_distribution=self._resolve_percentile,
result_type_fn=self.reflective_result_type(),
default_result_type="duration",
),
fields.MetricsFunction(
"count_unique",
required_args=[
fields.MetricArg(
"column", allowed_columns=["user"], allow_custom_measurements=False
)
],
calculated_args=[resolve_metric_id],
snql_set=lambda args, alias: Function(
"uniqIf",
[
Column("value"),
Function("equals", [Column("metric_id"), args["metric_id"]]),
],
alias,
),
default_result_type="integer",
),
fields.MetricsFunction(
"uniq",
snql_set=lambda args, alias: Function(
"uniq",
[Column("value")],
alias,
),
),
fields.MetricsFunction(
"uniqIf",
required_args=[
fields.ColumnTagArg("if_col"),
fields.FunctionArg("if_val"),
],
calculated_args=[
{
"name": "resolved_val",
"fn": lambda args: self.builder.resolve_tag_value(args["if_val"]),
}
],
snql_set=lambda args, alias: Function(
"uniqIf",
[
Column("value"),
Function("equals", [args["if_col"], args["resolved_val"]]),
],
alias,
),
default_result_type="integer",
),
fields.MetricsFunction(
"count",
snql_distribution=lambda args, alias: Function(
"countIf",
[
Column("value"),
Function(
"equals",
[
Column("metric_id"),
self.resolve_metric("transaction.duration"),
],
),
],
alias,
),
default_result_type="integer",
),
fields.MetricsFunction(
"count_web_vitals",
required_args=[
fields.MetricArg(
"column",
allowed_columns=[
"measurements.fp",
"measurements.fcp",
"measurements.lcp",
"measurements.fid",
"measurements.cls",
],
allow_custom_measurements=False,
),
fields.SnQLStringArg(
"quality", allowed_strings=["good", "meh", "poor", "any"]
),
],
calculated_args=[resolve_metric_id],
snql_distribution=self._resolve_web_vital_function,
default_result_type="integer",
),
fields.MetricsFunction(
"epm",
snql_distribution=lambda args, alias: Function(
"divide",
[
Function(
"countIf",
[
Column("value"),
Function(
"equals",
[
Column("metric_id"),
self.resolve_metric("transaction.duration"),
],
),
],
),
Function("divide", [args["interval"], 60]),
],
alias,
),
optional_args=[fields.IntervalDefault("interval", 1, None)],
default_result_type="number",
),
fields.MetricsFunction(
"eps",
snql_distribution=lambda args, alias: Function(
"divide",
[
Function(
"countIf",
[
Column("value"),
Function(
"equals",
[
Column("metric_id"),
self.resolve_metric("transaction.duration"),
],
),
],
),
args["interval"],
],
alias,
),
optional_args=[fields.IntervalDefault("interval", 1, None)],
default_result_type="number",
),
fields.MetricsFunction(
"failure_count",
snql_distribution=self._resolve_failure_count,
default_result_type="integer",
),
fields.MetricsFunction(
"failure_rate",
snql_distribution=lambda args, alias: Function(
"divide",
[
self._resolve_failure_count(args),
Function(
"countIf",
[
Column("value"),
Function(
"equals",
[
Column("metric_id"),
self.resolve_metric("transaction.duration"),
],
),
],
),
],
alias,
),
default_result_type="percentage",
),
fields.MetricsFunction(
"histogram",
required_args=[fields.MetricArg("column")],
calculated_args=[resolve_metric_id],
snql_distribution=self._resolve_histogram_function,
default_result_type="number",
private=True,
),
]
}
for alias, name in constants.FUNCTION_ALIASES.items():
if name in function_converter:
function_converter[alias] = function_converter[name].alias_as(alias)
return function_converter
# Field Aliases |
|
42,682 | 178,391 | 2,042 | nuitka/freezer/Standalone.py | 477 | 66 | def copyUsedDLLs(source_dir, dist_dir, standalone_entry_points):
# This is terribly complex, because we check the list of used DLLs
# trying to avoid duplicates, and detecting errors with them not
# being binary identical, so we can report them. And then of course
# we also need to handle OS specifics.
# pylint: disable=too-many-branches,too-many-locals,too-many-statements
used_dlls = detectUsedDLLs(
source_dir=source_dir,
standalone_entry_points=standalone_entry_points,
use_cache=not Options.shallNotUseDependsExeCachedResults()
and not Options.getWindowsDependencyTool() == "depends.exe",
update_cache=not Options.shallNotStoreDependsExeCachedResults()
and not Options.getWindowsDependencyTool() == "depends.exe",
)
removed_dlls = set()
warned_about = set()
# Fist make checks and remove some.
for dll_filename1, sources1 in tuple(iterItems(used_dlls)):
if dll_filename1 in removed_dlls:
continue
for dll_filename2, sources2 in tuple(iterItems(used_dlls)):
if dll_fi | UI: In case of PermissionError, allow uses to retry
* Esp. on Windows it happens a lot that running programs cannot be
updated by Nuitka, this avoids the cryptic error somewhere ranomly. | copyUsedDLLs | 2c20b90946a8aa5ad4ee39ad365ff1b83f182770 | Nuitka | Standalone.py | 19 | 125 | https://github.com/Nuitka/Nuitka.git | 34 | 660 | 0 | 262 | 1,077 | Python | {
"docstring": "Colliding DLL names for %s, checking identity of \\\n'%s' <-> '%s'.\\\nIgnoring non-identical DLLs for '%s'.\n%s used by:\n %s\ndifferent from\n%s used by\n %s",
"language": "en",
"n_whitespaces": 25,
"n_words": 27,
"vocab_size": 22
} | def copyUsedDLLs(source_dir, dist_dir, standalone_entry_points):
# This is terribly complex, because we check the list of used DLLs
# trying to avoid duplicates, and detecting errors with them not
# being binary identical, so we can report them. And then of course
# we also need to handle OS specifics.
# pylint: disable=too-many-branches,too-many-locals,too-many-statements
used_dlls = detectUsedDLLs(
source_dir=source_dir,
standalone_entry_points=standalone_entry_points,
use_cache=not Options.shallNotUseDependsExeCachedResults()
and not Options.getWindowsDependencyTool() == "depends.exe",
update_cache=not Options.shallNotStoreDependsExeCachedResults()
and not Options.getWindowsDependencyTool() == "depends.exe",
)
removed_dlls = set()
warned_about = set()
# Fist make checks and remove some.
for dll_filename1, sources1 in tuple(iterItems(used_dlls)):
if dll_filename1 in removed_dlls:
continue
for dll_filename2, sources2 in tuple(iterItems(used_dlls)):
if dll_filename1 == dll_filename2:
continue
if dll_filename2 in removed_dlls:
continue
# Colliding basenames are an issue to us.
if os.path.basename(dll_filename1) != os.path.basename(dll_filename2):
continue
# May already have been removed earlier
if dll_filename1 not in used_dlls:
continue
if dll_filename2 not in used_dlls:
continue
dll_name = os.path.basename(dll_filename1)
if Options.isShowInclusion():
inclusion_logger.info(
% (dll_name, dll_filename1, dll_filename2)
)
# Check that if a DLL has the same name, if it's identical, then it's easy.
if haveSameFileContents(dll_filename1, dll_filename2):
del used_dlls[dll_filename2]
removed_dlls.add(dll_filename2)
continue
# For Win32 we can check out file versions.
if Utils.isWin32Windows():
dll_version1 = getWindowsDLLVersion(dll_filename1)
dll_version2 = getWindowsDLLVersion(dll_filename2)
if dll_version2 < dll_version1:
del used_dlls[dll_filename2]
removed_dlls.add(dll_filename2)
solved = True
elif dll_version1 < dll_version2:
del used_dlls[dll_filename1]
removed_dlls.add(dll_filename1)
solved = True
else:
solved = False
if solved:
if dll_name not in warned_about and dll_name not in ms_runtime_dlls:
warned_about.add(dll_name)
inclusion_logger.warning(
"Conflicting DLLs for '%s' in your installation, newest file version used, hoping for the best."
% dll_name
)
continue
# So we have conflicting DLLs, in which case we do report the fact.
inclusion_logger.warning(
% (
dll_name,
dll_filename1,
"\n ".join(sources1),
dll_filename2,
"\n ".join(sources2),
)
)
del used_dlls[dll_filename2]
removed_dlls.add(dll_filename2)
dll_map = []
for dll_filename, sources in iterItems(used_dlls):
dll_name = os.path.basename(dll_filename)
target_path = os.path.join(dist_dir, dll_name)
# Sometimes DLL dependencies were copied there already.
if not os.path.exists(target_path):
copyFile(dll_filename, target_path)
dll_map.append((dll_filename, dll_name))
if Options.isShowInclusion():
inclusion_logger.info(
"Included used shared library '%s' (used by %s)."
% (dll_filename, ", ".join(sources))
)
if Utils.isMacOS():
# For macOS, the binary and the DLLs needs to be changed to reflect
# the relative DLL location in the ".dist" folder.
for standalone_entry_point in standalone_entry_points:
fixupBinaryDLLPathsMacOS(
binary_filename=standalone_entry_point.dest_path,
dll_map=dll_map,
original_location=standalone_entry_point.source_path,
)
for original_path, dll_filename in dll_map:
fixupBinaryDLLPathsMacOS(
binary_filename=os.path.join(dist_dir, dll_filename),
dll_map=dll_map,
original_location=original_path,
)
# Remove code signature from CPython installed library
candidate = os.path.join(
dist_dir,
"Python",
)
if os.path.exists(candidate):
removeMacOSCodeSignature(candidate)
# Remove or update rpath settings.
if Utils.getOS() in ("Linux", "Darwin"):
# For Linux, the "rpath" of libraries may be an issue and must be
# removed.
if Utils.isMacOS():
start = 0
else:
start = 1
for standalone_entry_point in standalone_entry_points[start:]:
count = relpath(
path=standalone_entry_point.dest_path, start=dist_dir
).count(os.path.sep)
rpath = os.path.join("$ORIGIN", *([".."] * count))
setSharedLibraryRPATH(standalone_entry_point.dest_path, rpath)
for _original_path, dll_filename in dll_map:
setSharedLibraryRPATH(os.path.join(dist_dir, dll_filename), "$ORIGIN")
if Utils.isWin32Windows():
if python_version < 0x300:
# For Win32, we might have to remove SXS paths
for standalone_entry_point in standalone_entry_points[1:]:
removeSxsFromDLL(standalone_entry_point.dest_path)
for _original_path, dll_filename in dll_map:
removeSxsFromDLL(os.path.join(dist_dir, dll_filename))
|
|
48,671 | 197,718 | 275 | sympy/integrals/transforms.py | 81 | 30 | def _laplace_rule_diff(f, t, s, doit=True, **hints):
hints.pop('simplify', True)
a = Wild('a', exclude=[t])
y = Wild('y')
n = Wild('n', exclude=[t])
g = WildFunction('g', nargs=1)
ma1 = f.match(a*Derivative(g, (t, n)))
if ma1 and ma1[g].args[0] == t and ma1[n].is_integer:
debug('_laplace_apply_rules match:')
debug(' f: %s'%(f,))
debug(' rule: time derivative (1.11, 1.12)')
d = []
for k in range(ma1[n]):
if k==0:
y = ma1[g].func(t).subs(t, 0)
else:
y = Derivative(ma1[g].func(t), (t, k)).subs(t, 0)
d.append(s**(ma1[n]-k | include the coefficient in L(A*x') | _laplace_rule_diff | 392c40aceadd4c7cdeed0fceb93a763927dc0ca1 | sympy | transforms.py | 20 | 22 | https://github.com/sympy/sympy.git | 6 | 258 | 0 | 61 | 404 | Python | {
"docstring": "\n This internal helper function tries to transform an expression containing\n a derivative of an undefined function and returns `None` if it cannot\n do it.\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 24,
"vocab_size": 22
} | def _laplace_rule_diff(f, t, s, doit=True, **hints):
hints.pop('simplify', True)
a = Wild('a', exclude=[t])
y = Wild('y')
n = Wild('n', exclude=[t])
g = WildFunction('g', nargs=1)
ma1 = f.match(a*Derivative(g, (t, n)))
if ma1 and ma1[g].args[0] == t and ma1[n].is_integer:
debug('_laplace_apply_rules match:')
debug(' f: %s'%(f,))
debug(' rule: time derivative (1.11, 1.12)')
d = []
for k in range(ma1[n]):
if k==0:
y = ma1[g].func(t).subs(t, 0)
else:
y = Derivative(ma1[g].func(t), (t, k)).subs(t, 0)
d.append(s**(ma1[n]-k-1)*y)
r = s**ma1[n]*_laplace_apply_rules(ma1[g].func(t), t, s, doit=doit,
**hints)
return ma1[a]*(r - Add(*d))
return None
|
|
17,193 | 81,376 | 752 | awx/sso/pipeline.py | 374 | 25 | def _check_flag(user, flag, attributes, user_flags_settings):
new_flag = False
is_role_key = "is_%s_role" % (flag)
is_attr_key = "is_%s_attr" % (flag)
is_value_key = "is_%s_value" % (flag)
remove_setting = "remove_%ss" % (flag)
# Check to see if we are respecting a role and, if so, does our user have that role?
required_roles = user_flags_settings.get(is_role_key, None)
if required_roles:
matching_roles = _get_matches(required_roles, attributes.get('Role', []))
# We do a 2 layer check here so that we don't spit out the else message if there is no role defined
if matching_roles:
logger.debug("User %s has %s role(s) %s" % (user.username, flag, ', '.join(matching_roles)))
new_flag = True
else:
logger.debug("User %s is missing the %s role(s) %s" % (user.username, flag, ', '.join(required_roles)))
# Next, check to see if we are respecting an attribute; this will take priority over the role if its defined
attr_setting = user_flags_settings.get(is_attr_key, None)
if attr_setting and attributes.get(attr_setting, None):
# Do we have a required value for the attribute
required_value = user_flags_settings.get(is_value_key, None)
if required_value:
| Allow multiple values in SOCIAL_AUTH_SAML_USER_FLAGS_BY_ATTR.is_*_[value|role] settings (#12558) | _check_flag | 782667a34ee45bfe825b29db39c67d4465391bdb | awx | pipeline.py | 19 | 40 | https://github.com/ansible/awx.git | 12 | 339 | 0 | 171 | 561 | Python | {
"docstring": "\n Helper function to set the is_superuser is_system_auditor flags for the SAML adapter\n Returns the new flag and whether or not it changed the flag\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 24,
"vocab_size": 20
} | def _check_flag(user, flag, attributes, user_flags_settings):
new_flag = False
is_role_key = "is_%s_role" % (flag)
is_attr_key = "is_%s_attr" % (flag)
is_value_key = "is_%s_value" % (flag)
remove_setting = "remove_%ss" % (flag)
# Check to see if we are respecting a role and, if so, does our user have that role?
required_roles = user_flags_settings.get(is_role_key, None)
if required_roles:
matching_roles = _get_matches(required_roles, attributes.get('Role', []))
# We do a 2 layer check here so that we don't spit out the else message if there is no role defined
if matching_roles:
logger.debug("User %s has %s role(s) %s" % (user.username, flag, ', '.join(matching_roles)))
new_flag = True
else:
logger.debug("User %s is missing the %s role(s) %s" % (user.username, flag, ', '.join(required_roles)))
# Next, check to see if we are respecting an attribute; this will take priority over the role if its defined
attr_setting = user_flags_settings.get(is_attr_key, None)
if attr_setting and attributes.get(attr_setting, None):
# Do we have a required value for the attribute
required_value = user_flags_settings.get(is_value_key, None)
if required_value:
# If so, check and see if the value of the attr matches the required value
saml_user_attribute_value = attributes.get(attr_setting, None)
matching_values = _get_matches(required_value, saml_user_attribute_value)
if matching_values:
logger.debug("Giving %s %s from attribute %s with matching values %s" % (user.username, flag, attr_setting, ', '.join(matching_values)))
new_flag = True
# if they don't match make sure that new_flag is false
else:
logger.debug(
"Refusing %s for %s because attr %s (%s) did not match value(s) %s"
% (flag, user.username, attr_setting, ", ".join(saml_user_attribute_value), ', '.join(required_value))
)
new_flag = False
# If there was no required value then we can just allow them in because of the attribute
else:
logger.debug("Giving %s %s from attribute %s" % (user.username, flag, attr_setting))
new_flag = True
# Get the users old flag
old_value = getattr(user, "is_%s" % (flag))
# If we are not removing the flag and they were a system admin and now we don't want them to be just return
remove_flag = user_flags_settings.get(remove_setting, True)
if not remove_flag and (old_value and not new_flag):
logger.debug("Remove flag %s preventing removal of %s for %s" % (remove_flag, flag, user.username))
return old_value, False
# If the user was flagged and we are going to make them not flagged make sure there is a message
if old_value and not new_flag:
logger.debug("Revoking %s from %s" % (flag, user.username))
return new_flag, old_value != new_flag
|
|
72,983 | 249,543 | 108 | tests/storage/test_event_federation.py | 30 | 12 | def test_get_backfill_points_in_room(self):
setup_info = self._setup_room_for_backfill_tests()
room_id = setup_info.room_id
backfill_points = self.get_success(
self.store.get_backfill_points_in_room(room_id)
)
backfill_event_ids = [backfill_point[0] for backfi | Only try to backfill event if we haven't tried before recently (#13635)
Only try to backfill event if we haven't tried before recently (exponential backoff). No need to keep trying the same backfill point that fails over and over.
Fix https://github.com/matrix-org/synapse/issues/13622
Fix https://github.com/matrix-org/synapse/issues/8451
Follow-up to https://github.com/matrix-org/synapse/pull/13589
Part of https://github.com/matrix-org/synapse/issues/13356 | test_get_backfill_points_in_room | ac1a31740b6d0dfda4d57a25762aaddfde981caf | synapse | test_event_federation.py | 11 | 10 | https://github.com/matrix-org/synapse.git | 2 | 67 | 0 | 26 | 115 | Python | {
"docstring": "\n Test to make sure we get some backfill points\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 9
} | def test_get_backfill_points_in_room(self):
setup_info = self._setup_room_for_backfill_tests()
room_id = setup_info.room_id
backfill_points = self.get_success(
self.store.get_backfill_points_in_room(room_id)
)
backfill_event_ids = [backfill_point[0] for backfill_point in backfill_points]
self.assertListEqual(
backfill_event_ids, ["b6", "b5", "b4", "2", "b3", "b2", "b1"]
)
|
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