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@sympify_method_args | 48,788 | 198,089 | 34 | sympy/core/expr.py | 12 | 7 | def _imaginary_unit_as_coefficient(arg):
if getattr(arg, 'is_real', True):
return None
else:
return arg.as_coefficient( | move _imaginary_unit_as_coefficient to sympy.core.expr | _imaginary_unit_as_coefficient | bad8e3c1d614a05a0b1c6a05c21720f8751f0f2b | sympy | expr.py | 11 | 5 | https://github.com/sympy/sympy.git | 2 | 29 | 1 | 11 | 54 | Python | {
"docstring": " Helper to extract symbolic coefficient for imaginary unit ",
"language": "en",
"n_whitespaces": 9,
"n_words": 8,
"vocab_size": 8
} | def _imaginary_unit_as_coefficient(arg):
if getattr(arg, 'is_real', True):
return None
else:
return arg.as_coefficient(S.ImaginaryUnit)
@sympify_method_args |
83,198 | 279,959 | 100 | keras/optimizers/optimizer_experimental/optimizer.py | 19 | 8 | def from_config(cls, config, custom_objects=None):
if "learning_rate" in config:
if isinstance(config["learning_rate"], dict):
config["learning_rate"] = learning_rate_schedule.deserialize(
config["learning_rate"], custom_objects=custom_objects
| Some changes on the new optimizer:
1. Include `custom_objects` in `from_config` for deserializing custom learning rate.
2. Handle the error of seeing unrecognized variable with a better error message.
PiperOrigin-RevId: 476505974 | from_config | 51a6050b936ec87cd684fc1a052f79785ec9aaec | keras | optimizer.py | 14 | 7 | https://github.com/keras-team/keras.git | 3 | 52 | 0 | 18 | 88 | Python | {
"docstring": "Creates an optimizer from its config.\n\n This method is the reverse of `get_config`, capable of instantiating the\n same optimizer from the config dictionary.\n\n Args:\n config: A Python dictionary, typically the output of get_config.\n custom_objects: A Python dictionary mapping names to additional\n user-defined Python objects needed to recreate this optimizer.\n\n Returns:\n An optimizer instance.\n ",
"language": "en",
"n_whitespaces": 134,
"n_words": 53,
"vocab_size": 41
} | def from_config(cls, config, custom_objects=None):
if "learning_rate" in config:
if isinstance(config["learning_rate"], dict):
config["learning_rate"] = learning_rate_schedule.deserialize(
config["learning_rate"], custom_objects=custom_objects
)
return cls(**config)
|
|
117,113 | 320,283 | 363 | src/paperless_mail/tests/test_parsers_live.py | 81 | 32 | def test_generate_pdf_from_mail(self):
mail = self.parser.get_parsed(os.path.join(self.SAMPLE_FILES, "html.eml"))
pdf_path = os.path.join(self.parser.tempdir, "html.eml.pdf")
with open(pdf_path, "wb") as file:
file.write(self.parser.generate_pdf_from_mail(mail))
converted = os.path.join(
self.parser.tempdir,
"html.eml.pdf.webp",
)
run_convert(
density=300,
scale="500x5000>",
alpha="remove", | add test comments | test_generate_pdf_from_mail | 4aa318598fd0dc6c5d4e08dd2a13e7bf614511ec | paperless-ngx | test_parsers_live.py | 12 | 30 | https://github.com/paperless-ngx/paperless-ngx.git | 1 | 176 | 0 | 70 | 291 | Python | {
"docstring": "\n GIVEN:\n - Fresh start\n WHEN:\n - pdf generation from simple eml file is requested\n THEN:\n - gotenberg is called and the resulting file is returned and look as expected.\n ",
"language": "en",
"n_whitespaces": 91,
"n_words": 29,
"vocab_size": 23
} | def test_generate_pdf_from_mail(self):
mail = self.parser.get_parsed(os.path.join(self.SAMPLE_FILES, "html.eml"))
pdf_path = os.path.join(self.parser.tempdir, "html.eml.pdf")
with open(pdf_path, "wb") as file:
file.write(self.parser.generate_pdf_from_mail(mail))
converted = os.path.join(
self.parser.tempdir,
"html.eml.pdf.webp",
)
run_convert(
density=300,
scale="500x5000>",
alpha="remove",
strip=True,
trim=False,
auto_orient=True,
input_file=f"{pdf_path}", # Do net define an index to convert all pages.
output_file=converted,
logging_group=None,
)
self.assertTrue(os.path.isfile(converted))
thumb_hash = self.imagehash(converted)
# The created pdf is not reproducible. But the converted image should always look the same.
expected_hash = self.imagehash(
os.path.join(self.SAMPLE_FILES, "html.eml.pdf.webp"),
)
self.assertEqual(
thumb_hash,
expected_hash,
f"PDF looks different. Check if {converted} looks weird.",
)
|
|
53,380 | 212,750 | 460 | DemoPrograms/Demo_Desktop_Widget_Drive_Usage_Gauges.py | 100 | 33 | def new(self, degree=0, color=None):
(center_x, center_y, angle, inner_radius, outer_radius,
outer_color, pointer_color, origin_color, line_width) = self.all
pointer_color = color or pointer_color
if self.figure != []:
for figure in self.figure:
self.graph_elem.DeleteFigure(figure)
self.figure = []
d = degree - 90
self.all[2] = degree
dx1 = int(2 * inner_radius * math.sin(d / 180 * math.pi))
dy1 = int(2 * inner_radius * math.cos(d / 180 * math.pi))
dx2 = int(outer_radius * math.sin(d / 180 * math.pi))
dy2 = int(outer_radius * math.cos(d / 180 * math.pi))
self.figure.app | More demo programs updates 🤦♂️ wow.....I thought for sure these were checked in.... | new | 430d1bc77fcdc0969a66ff370ec5e7e590423c83 | PySimpleGUI | Demo_Desktop_Widget_Drive_Usage_Gauges.py | 13 | 19 | https://github.com/PySimpleGUI/PySimpleGUI.git | 4 | 238 | 0 | 61 | 354 | Python | {
"docstring": "\n Draw new pointer by angle, erase old pointer if exist\n degree defined as clockwise from negative x-axis.\n ",
"language": "en",
"n_whitespaces": 51,
"n_words": 17,
"vocab_size": 16
} | def new(self, degree=0, color=None):
(center_x, center_y, angle, inner_radius, outer_radius,
outer_color, pointer_color, origin_color, line_width) = self.all
pointer_color = color or pointer_color
if self.figure != []:
for figure in self.figure:
self.graph_elem.DeleteFigure(figure)
self.figure = []
d = degree - 90
self.all[2] = degree
dx1 = int(2 * inner_radius * math.sin(d / 180 * math.pi))
dy1 = int(2 * inner_radius * math.cos(d / 180 * math.pi))
dx2 = int(outer_radius * math.sin(d / 180 * math.pi))
dy2 = int(outer_radius * math.cos(d / 180 * math.pi))
self.figure.append(self.graph_elem.DrawLine((center_x - dx1, center_y - dy1),
(center_x + dx2, center_y + dy2),
color=pointer_color, width=line_width))
self.figure.append(self.graph_elem.DrawCircle((center_x, center_y), inner_radius,
fill_color=origin_color, line_color=outer_color, line_width=line_width))
|
|
10,241 | 50,918 | 610 | modules/image/object_detection/yolov3_darknet53_vehicles/processor.py | 172 | 48 | def postprocess(paths, images, data_out, score_thresh, label_names, output_dir, handle_id, visualization=True):
results = data_out.copy_to_cpu()
lod = data_out.lod()[0]
check_dir(output_dir)
if paths:
assert type(paths) is list, "type(paths) is not list."
if handle_id < len(paths):
unhandled_paths = paths[handle_id:]
unhandled_paths_num = len(unhandled_paths)
else:
unhandled_paths_num = 0
if images is not None:
if handle_id < len(images):
unhandled_paths = None
unhandled_paths_num = len(images) - handle_id
else:
unhandled_paths_num = 0
output = list()
for index in range(len(lod) - 1):
output_i = {'data': []}
if unhandled_paths and index < unhandled_paths_num:
org_img_path = unhandled_paths[index]
org_img = Image.open(org_img_path)
else:
org_img = images[index - unhandled_paths_num]
org_img = org_img.astype(np.uint8)
org_img = Image.fromarray(org_img[:, :, ::-1])
if visualization:
org_img_path = get_save_image_name(org_img, output_dir, 'image_numpy_{}'.format((handle_id + index)))
org_img.save(org_img_path)
org_img_height = org_img.height
org_img_width = org_img.width
result_i = results[lod[index]:lod[index + 1]]
for row in result_i:
if len(row) != 6:
continue
if row[1] < score_thresh:
continue
category_id = int(row[0])
confidence = row[1]
bbox = row[2:]
dt = {}
dt['label'] = label_names[category_id]
dt['confidence'] = float(confidence)
dt['left'], dt['top'], dt['right'], dt['bottom'] = clip_bbox(bbox, org_img_width, org_img_height)
output_i['data'].append(dt)
output.append(output_i)
if visualization:
output_i['save_path'] = draw | update yolov3_darknet53_vehicles (#1957)
* update yolov3_darknet53_vehicles
* update gpu config
* update
* add clean func
* update save inference model | postprocess | 7a847a39b1da6e6867031f52f713d92391b9729d | PaddleHub | processor.py | 19 | 50 | https://github.com/PaddlePaddle/PaddleHub.git | 13 | 380 | 0 | 107 | 611 | Python | {
"docstring": "\n postprocess the lod_tensor produced by Executor.run\n\n Args:\n paths (list[str]): The paths of images.\n images (list(numpy.ndarray)): images data, shape of each is [H, W, C]\n data_out (lod_tensor): data output of predictor.\n output_dir (str): The path to store output images.\n visualization (bool): Whether to save image or not.\n score_thresh (float): the low limit of bounding box.\n label_names (list[str]): label names.\n handle_id (int): The number of images that have been handled.\n\n Returns:\n res (list[dict]): The result of vehicles detecion. keys include 'data', 'save_path', the corresponding value is:\n data (dict): the result of object detection, keys include 'left', 'top', 'right', 'bottom', 'label', 'confidence', the corresponding value is:\n left (float): The X coordinate of the upper left corner of the bounding box;\n top (float): The Y coordinate of the upper left corner of the bounding box;\n right (float): The X coordinate of the lower right corner of the bounding box;\n bottom (float): The Y coordinate of the lower right corner of the bounding box;\n label (str): The label of detection result;\n confidence (float): The confidence of detection result.\n save_path (str): The path to save output images.\n ",
"language": "en",
"n_whitespaces": 369,
"n_words": 181,
"vocab_size": 92
} | def postprocess(paths, images, data_out, score_thresh, label_names, output_dir, handle_id, visualization=True):
results = data_out.copy_to_cpu()
lod = data_out.lod()[0]
check_dir(output_dir)
if paths:
assert type(paths) is list, "type(paths) is not list."
if handle_id < len(paths):
unhandled_paths = paths[handle_id:]
unhandled_paths_num = len(unhandled_paths)
else:
unhandled_paths_num = 0
if images is not None:
if handle_id < len(images):
unhandled_paths = None
unhandled_paths_num = len(images) - handle_id
else:
unhandled_paths_num = 0
output = list()
for index in range(len(lod) - 1):
output_i = {'data': []}
if unhandled_paths and index < unhandled_paths_num:
org_img_path = unhandled_paths[index]
org_img = Image.open(org_img_path)
else:
org_img = images[index - unhandled_paths_num]
org_img = org_img.astype(np.uint8)
org_img = Image.fromarray(org_img[:, :, ::-1])
if visualization:
org_img_path = get_save_image_name(org_img, output_dir, 'image_numpy_{}'.format((handle_id + index)))
org_img.save(org_img_path)
org_img_height = org_img.height
org_img_width = org_img.width
result_i = results[lod[index]:lod[index + 1]]
for row in result_i:
if len(row) != 6:
continue
if row[1] < score_thresh:
continue
category_id = int(row[0])
confidence = row[1]
bbox = row[2:]
dt = {}
dt['label'] = label_names[category_id]
dt['confidence'] = float(confidence)
dt['left'], dt['top'], dt['right'], dt['bottom'] = clip_bbox(bbox, org_img_width, org_img_height)
output_i['data'].append(dt)
output.append(output_i)
if visualization:
output_i['save_path'] = draw_bounding_box_on_image(org_img_path, output_i['data'], output_dir)
return output
|
|
@pytest.fixture(params=_index_or_series_objs.keys()) | 40,068 | 167,616 | 102 | pandas/conftest.py | 59 | 28 | def series_with_multilevel_index() -> Series:
arrays = [
["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"],
["one", "two", "one", "two", "one", "two", "one", "two"],
]
tuples = zip(*arrays)
index = MultiIndex.from_tuples(tuples)
da | TYP: misc return type annotations (#47558) | series_with_multilevel_index | f538568afc2c76c2d738d32e3544cf9fe6742960 | pandas | conftest.py | 10 | 14 | https://github.com/pandas-dev/pandas.git | 1 | 92 | 1 | 45 | 249 | Python | {
"docstring": "\n Fixture with a Series with a 2-level MultiIndex.\n ",
"language": "en",
"n_whitespaces": 15,
"n_words": 8,
"vocab_size": 6
} | def series_with_multilevel_index() -> Series:
arrays = [
["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"],
["one", "two", "one", "two", "one", "two", "one", "two"],
]
tuples = zip(*arrays)
index = MultiIndex.from_tuples(tuples)
data = np.random.randn(8)
ser = Series(data, index=index)
ser[3] = np.NaN
return ser
_narrow_series = {
f"{dtype.__name__}-series": tm.make_rand_series(name="a", dtype=dtype)
for dtype in tm.NARROW_NP_DTYPES
}
_index_or_series_objs = {**indices_dict, **_series, **_narrow_series}
@pytest.fixture(params=_index_or_series_objs.keys()) |
106,436 | 307,668 | 46 | homeassistant/components/group/__init__.py | 10 | 3 | def _async_stop(self) -> None:
if self._async_unsub_state_changed:
self._async_unsub_state_changed()
self._async_unsub_state_ch | Improve type hints in group (#78350) | _async_stop | 5cccb248307d138a33c353544c57dc997b4fe917 | core | __init__.py | 9 | 8 | https://github.com/home-assistant/core.git | 2 | 23 | 0 | 10 | 41 | Python | {
"docstring": "Unregister the group from Home Assistant.\n\n This method must be run in the event loop.\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 15,
"vocab_size": 14
} | def _async_stop(self) -> None:
if self._async_unsub_state_changed:
self._async_unsub_state_changed()
self._async_unsub_state_changed = None
|
|
@pytest.fixture(scope="session") | 11,567 | 56,814 | 129 | tests/conftest.py | 51 | 12 | def test_database_connection_url(generate_test_database_connection_url):
url = generate_test_database_connection_url
if url is None:
yield None
else:
# TODO: https://github.com/PrefectHQ/orion/issues/2045
# Also temporarily override the environment variable, so that child
# subprocesses that we spin off are correctly configured as well
original_envvar = os.environ.get("PREFECT_ORION_DATABASE_CONNECTION_URL")
os.environ["PREFECT_ORION_DATABASE_CONNECTION_URL"] = url
with temporary_settings({PREFECT_ORION_DATABASE_CONNECTION_URL: url}):
yield url
os.environ["PREFECT_ORION_DATABASE_CONNECTION_URL"] = original_envvar
@pytest. | :facepalm: I got bitten by the async fixture context issue. Fixed and added comments to help future developers. | test_database_connection_url | ef032ee4a8f5d357a6e8dadf4021177ccc71f767 | prefect | conftest.py | 14 | 10 | https://github.com/PrefectHQ/prefect.git | 2 | 56 | 1 | 39 | 122 | Python | {
"docstring": "\n Update the setting for the database connection url to the generated value from\n `generate_test_database_connection_url`\n\n This _must_ be separate from the generation of the test url because async fixtures\n are run in a separate context from the test suite.\n ",
"language": "en",
"n_whitespaces": 54,
"n_words": 38,
"vocab_size": 28
} | def test_database_connection_url(generate_test_database_connection_url):
url = generate_test_database_connection_url
if url is None:
yield None
else:
# TODO: https://github.com/PrefectHQ/orion/issues/2045
# Also temporarily override the environment variable, so that child
# subprocesses that we spin off are correctly configured as well
original_envvar = os.environ.get("PREFECT_ORION_DATABASE_CONNECTION_URL")
os.environ["PREFECT_ORION_DATABASE_CONNECTION_URL"] = url
with temporary_settings({PREFECT_ORION_DATABASE_CONNECTION_URL: url}):
yield url
os.environ["PREFECT_ORION_DATABASE_CONNECTION_URL"] = original_envvar
@pytest.fixture(scope="session") |
27,941 | 125,665 | 84 | python/ray/tune/examples/optuna_define_by_run_example.py | 50 | 9 | def define_by_run_func(trial) -> Optional[Dict[str, Any]]:
# This param is not used in the objective function.
activation = trial.suggest_categorical("activation", ["relu", "tanh"])
trial.suggest_float("width", 0, 20)
trial.suggest_float("height", -100, 100)
# Define-by-run allows for conditional search spaces.
if activation == "relu":
trial.suggest_float(" | [air/tuner/docs] Update docs for Tuner() API 2a: Tune examples (non-docs) (#26931)
Splitting up #26884: This PR includes changes to use Tuner() instead of tune.run() for all examples included in python/ray/tune/examples
Signed-off-by: xwjiang2010 <[email protected]>
Signed-off-by: Kai Fricke <[email protected]>
Co-authored-by: xwjiang2010 <[email protected]>
Co-authored-by: Richard Liaw <[email protected]> | define_by_run_func | 8d7b865614f3635def12c42b653f8acd8b4ae56a | ray | optuna_define_by_run_example.py | 10 | 18 | https://github.com/ray-project/ray.git | 2 | 72 | 0 | 46 | 127 | Python | {
"docstring": "Define-by-run function to create the search space.\n\n Ensure no actual computation takes place here. That should go into\n the trainable passed to ``Tuner`` (in this example, that's\n ``easy_objective``).\n\n For more information, see https://optuna.readthedocs.io/en/stable\\\n/tutorial/10_key_features/002_configurations.html\n\n This function should either return None or a dict with constant values.\n ",
"language": "en",
"n_whitespaces": 63,
"n_words": 46,
"vocab_size": 42
} | def define_by_run_func(trial) -> Optional[Dict[str, Any]]:
# This param is not used in the objective function.
activation = trial.suggest_categorical("activation", ["relu", "tanh"])
trial.suggest_float("width", 0, 20)
trial.suggest_float("height", -100, 100)
# Define-by-run allows for conditional search spaces.
if activation == "relu":
trial.suggest_float("mult", 1, 2)
# Return all constants in a dictionary.
return {"steps": 100}
|
|
43,616 | 181,843 | 142 | tpot/base.py | 34 | 16 | def _compile_to_sklearn(self, expr):
sklearn_pipeline_str = generate_pipeline_code(
expr_to_tree(expr, self._pset), self.operators
)
sklearn_pipeline = eval(sklearn_pipeline_str, self.operators_context)
sklearn_pipeline.memory = self._memory
if self.random_state:
# Fix random sta | Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068. | _compile_to_sklearn | 388616b6247ca4ea8de4e2f340d6206aee523541 | tpot | base.py | 11 | 11 | https://github.com/EpistasisLab/tpot.git | 2 | 61 | 0 | 30 | 97 | Python | {
"docstring": "Compile a DEAP pipeline into a sklearn pipeline.\n\n Parameters\n ----------\n expr: DEAP individual\n The DEAP pipeline to be compiled\n\n Returns\n -------\n sklearn_pipeline: sklearn.pipeline.Pipeline\n ",
"language": "en",
"n_whitespaces": 83,
"n_words": 23,
"vocab_size": 19
} | def _compile_to_sklearn(self, expr):
sklearn_pipeline_str = generate_pipeline_code(
expr_to_tree(expr, self._pset), self.operators
)
sklearn_pipeline = eval(sklearn_pipeline_str, self.operators_context)
sklearn_pipeline.memory = self._memory
if self.random_state:
# Fix random state when the operator allows
set_param_recursive(
sklearn_pipeline.steps, "random_state", self.random_state
)
return sklearn_pipeline
|
|
47,902 | 196,402 | 189 | sympy/matrices/repmatrix.py | 49 | 13 | def equals(self, other, failing_expression=False):
if self.shape != getattr(other, 'shape', None):
retu | Moved imports to higher level | equals | 59d22b6bb7287613d598611027f640d068ca5748 | sympy | repmatrix.py | 13 | 12 | https://github.com/sympy/sympy.git | 7 | 93 | 0 | 32 | 141 | Python | {
"docstring": "Applies ``equals`` to corresponding elements of the matrices,\n trying to prove that the elements are equivalent, returning True\n if they are, False if any pair is not, and None (or the first\n failing expression if failing_expression is True) if it cannot\n be decided if the expressions are equivalent or not. This is, in\n general, an expensive operation.\n\n Examples\n ========\n\n >>> from sympy import Matrix\n >>> from sympy.abc import x\n >>> A = Matrix([x*(x - 1), 0])\n >>> B = Matrix([x**2 - x, 0])\n >>> A == B\n False\n >>> A.simplify() == B.simplify()\n True\n >>> A.equals(B)\n True\n >>> A.equals(2)\n False\n\n See Also\n ========\n sympy.core.expr.Expr.equals\n ",
"language": "en",
"n_whitespaces": 264,
"n_words": 103,
"vocab_size": 72
} | def equals(self, other, failing_expression=False):
if self.shape != getattr(other, 'shape', None):
return False
rv = True
for i in range(self.rows):
for j in range(self.cols):
ans = self[i, j].equals(other[i, j], failing_expression)
if ans is False:
return False
elif ans is not True and rv is True:
rv = ans
return rv
|
|
73,623 | 251,177 | 44 | mitmproxy/addons/blocklist.py | 8 | 6 | def load(self, loader):
loader.add_option(
"b | use Python 3.9+ typing | load | fdde9ba3b3caaa2654048cec0af07bfcc3a6a3f8 | mitmproxy | blocklist.py | 8 | 11 | https://github.com/mitmproxy/mitmproxy.git | 1 | 23 | 0 | 8 | 37 | Python | {
"docstring": "\n Block matching requests and return an empty response with the specified HTTP status.\n Option syntax is \"/flow-filter/status-code\", where flow-filter describes\n which requests this rule should be applied to and status-code is the HTTP status code to return for\n blocked requests. The separator (\"/\" in the example) can be any character.\n Setting a non-standard status code of 444 will close the connection without sending a response.\n ",
"language": "en",
"n_whitespaces": 132,
"n_words": 65,
"vocab_size": 52
} | def load(self, loader):
loader.add_option(
"block_list", Sequence[str], [],
)
|
|
1,691 | 9,778 | 59 | gensim/models/translation_matrix.py | 24 | 15 | def train(self, tagged_docs):
m1 = [self.source_lang_vec.dv[item.tags].flatten() for item in tagged_docs]
m2 = [self.ta | Replace np.multiply with np.square and copyedit in translation_matrix.py (#3374)
* Replace np.multiply with np.square and copyedit
* Copyedit translation_matrix.py
Co-authored-by: Michael Penkov <[email protected]> | train | 77c3a7ff5254346146d0e9eedf8e84fb6d577878 | gensim | translation_matrix.py | 12 | 5 | https://github.com/RaRe-Technologies/gensim.git | 3 | 76 | 0 | 17 | 116 | Python | {
"docstring": "Build the translation matrix to map from the source model's vectors to target model's vectors\n\n Parameters\n ----------\n tagged_docs : list of :class:`~gensim.models.doc2vec.TaggedDocument`, Documents\n that will be used for training, both the source language document vector and\n target language document vector trained on those tagged documents.\n\n Returns\n -------\n numpy.ndarray\n Translation matrix that maps from the source model's vectors to target model's vectors.\n\n ",
"language": "en",
"n_whitespaces": 143,
"n_words": 61,
"vocab_size": 41
} | def train(self, tagged_docs):
m1 = [self.source_lang_vec.dv[item.tags].flatten() for item in tagged_docs]
m2 = [self.target_lang_vec.dv[item.tags].flatten() for item in tagged_docs]
self.translation_matrix = np.linalg.lstsq(m2, m1, -1)[0]
return self.translation_matrix
|
|
12,993 | 62,568 | 35 | .venv/lib/python3.8/site-packages/pip/_vendor/html5lib/serializer.py | 20 | 11 | def serialize(input, tree="etree", encoding=None, **serializer_opts):
# XXX: Should we cache this?
walker = treewalkers.getTreeWalker(tree)
s = HTMLSerializer(**serializer_opts)
return s.render(walker(input), encoding)
| upd; format | serialize | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | serializer.py | 9 | 4 | https://github.com/jindongwang/transferlearning.git | 1 | 44 | 0 | 19 | 73 | Python | {
"docstring": "Serializes the input token stream using the specified treewalker\n\n :arg input: the token stream to serialize\n\n :arg tree: the treewalker to use\n\n :arg encoding: the encoding to use\n\n :arg serializer_opts: any options to pass to the\n :py:class:`html5lib.serializer.HTMLSerializer` that gets created\n\n :returns: the tree serialized as a string\n\n Example:\n\n >>> from html5lib.html5parser import parse\n >>> from html5lib.serializer import serialize\n >>> token_stream = parse('<html><body><p>Hi!</p></body></html>')\n >>> serialize(token_stream, omit_optional_tags=False)\n '<html><head></head><body><p>Hi!</p></body></html>'\n\n ",
"language": "en",
"n_whitespaces": 109,
"n_words": 66,
"vocab_size": 43
} | def serialize(input, tree="etree", encoding=None, **serializer_opts):
# XXX: Should we cache this?
walker = treewalkers.getTreeWalker(tree)
s = HTMLSerializer(**serializer_opts)
return s.render(walker(input), encoding)
|
|
1,853 | 10,563 | 83 | jina/parsers/__init__.py | 28 | 13 | def set_client_cli_parser(parser=None):
if not parser:
from jina.parsers.base import set_base_parser
parser = set_base_parser()
from jina.parsers.peapods.runtimes.remote import mixin_client_gateway_parser
from jina.parsers.client import (
mixin_client_features_parser,
mixin_comm_protocol_parser,
)
mixin_client_gateway_parser(parser)
mixin_client_features_parser(parser)
mixin_comm_protocol_parser(parser)
| refactor: use absolute imports (#4167) | set_client_cli_parser | cea300655ed8be70d74c390ca12e8b09fb741665 | jina | __init__.py | 10 | 13 | https://github.com/jina-ai/jina.git | 2 | 64 | 0 | 23 | 99 | Python | {
"docstring": "Set the parser for the cli client\n\n :param parser: an optional existing parser to build upon\n :return: the parser\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 19,
"vocab_size": 15
} | def set_client_cli_parser(parser=None):
if not parser:
from jina.parsers.base import set_base_parser
parser = set_base_parser()
from jina.parsers.peapods.runtimes.remote import mixin_client_gateway_parser
from jina.parsers.client import (
mixin_client_features_parser,
mixin_comm_protocol_parser,
)
mixin_client_gateway_parser(parser)
mixin_client_features_parser(parser)
mixin_comm_protocol_parser(parser)
return parser
|
|
5,141 | 27,924 | 111 | saleor/graphql/discount/mutations/sale_create.py | 36 | 17 | def send_sale_toggle_notification(info, instance, catalogue):
manager = info.context.plugins
now = datetime.now(pytz.utc)
start_date = instance.start_date
end_date = instance.end_date
if (start_date and start_date <= now) and (not end_date or not end_date <= now):
manager.sale_toggle(instance, catalogue)
| New event for starting and ending sales (#10110)
* Add sale started and sale ended webhooks
* Add started_notification_sent and ended_notification_sent flags to Sale model
* Add sale_webhook_schedule
* Add send_sale_started_and_sale_ended_notifications discount task
* Add tests for discount tasks
* Move sale task celery beat schedule to settings
* Add tests for sale_webhook_schedule
* Add sale_started and sale_ended methods to PluginSample
* Update send_sale_started_and_sale_ended_notifications logging
* Update SaleUpdate mutation - ensure the notification is sent and the flag is changed if needed
* Update SaleCreate mutation - send sale_creatd and sale_ended notifications
* Optimize fetch_catalogue_info
* Clean up
* Apply code review suggestions
* Add SALE_TOGGLE webhook
* Use sale_toggle webhook instead of sale_started and sale_ended
* Delete sale_started and sale_eded wbhooks
* Drop notification flags from Sale model
* Add missing docstrings and comments
* Fix failing tests
* Update changelog
* Add description for SaleToggle event type
* Update discount task and webhook schedule
* Set notification_sent_datetime to current date by default
* Fix typo in comment | send_sale_toggle_notification | 67492396aa41d068cac82e8fa328f218b5951d13 | saleor | sale_create.py | 12 | 9 | https://github.com/saleor/saleor.git | 5 | 79 | 0 | 26 | 128 | Python | {
"docstring": "Send a notification about starting or ending sale if it hasn't been sent yet.\n\n Send the notification when the start date is before the current date and the\n sale is not already finished.\n ",
"language": "en",
"n_whitespaces": 54,
"n_words": 33,
"vocab_size": 25
} | def send_sale_toggle_notification(info, instance, catalogue):
manager = info.context.plugins
now = datetime.now(pytz.utc)
start_date = instance.start_date
end_date = instance.end_date
if (start_date and start_date <= now) and (not end_date or not end_date <= now):
manager.sale_toggle(instance, catalogue)
instance.notification_sent_datetime = now
instance.save(update_fields=["notification_sent_datetime"])
|
|
@bcoo_todense_p.def_impl | 26,536 | 119,028 | 11 | jax/experimental/sparse/bcoo.py | 10 | 9 | def bcoo_todense(data, indices, *, spinfo):
return bcoo_todense_p.bind(jnp.asarray(data), jnp.asarray(indices), spinfo=spin | [sparse] generalize metadata argument in BCOO primitives | bcoo_todense | 2c20d82776fea482aaf52e18ebad4f7fce5c3a81 | jax | bcoo.py | 9 | 2 | https://github.com/google/jax.git | 1 | 35 | 1 | 10 | 61 | Python | {
"docstring": "Convert batched sparse matrix to a dense matrix.\n\n Args:\n data : array of shape ``batch_dims + (nse,) + block_dims``.\n indices : array of shape ``batch_dims + (n_sparse, nse)``\n spinfo : BCOOInfo. In particular, this includes the shape\n of the matrix, which is equal to ``batch_dims + sparse_dims + block_dims``\n where ``len(sparse_dims) == n_sparse``\n\n Returns:\n mat : array with specified shape and dtype matching ``data``\n ",
"language": "en",
"n_whitespaces": 89,
"n_words": 64,
"vocab_size": 46
} | def bcoo_todense(data, indices, *, spinfo):
return bcoo_todense_p.bind(jnp.asarray(data), jnp.asarray(indices), spinfo=spinfo)
@bcoo_todense_p.def_impl |
30,685 | 135,648 | 48 | rllib/utils/actor_manager.py | 16 | 10 | def ignore_ray_errors(self) -> Iterator[ResultOrError]:
return self._Iterator(
[r for r in self.result_or_errors if not isinstance(r.ge | Refactor ActorManager to store underlying remote actors in dict. (#29953)
Signed-off-by: Jun Gong <[email protected]> | ignore_ray_errors | b84dac2609bd587c43ed17bb6fa18fb7241a41de | ray | actor_manager.py | 14 | 10 | https://github.com/ray-project/ray.git | 3 | 38 | 0 | 16 | 61 | Python | {
"docstring": "Return an iterator over the results, skipping only Ray errors.\n\n Similar to ignore_errors, but only skips Errors raised from the\n Ray framework. This is useful for application that wants to handle\n errors from user code differently.\n ",
"language": "en",
"n_whitespaces": 64,
"n_words": 36,
"vocab_size": 31
} | def ignore_ray_errors(self) -> Iterator[ResultOrError]:
return self._Iterator(
[r for r in self.result_or_errors if not isinstance(r.get(), RayError)]
)
|
|
@dataclasses.dataclass | 117,906 | 321,782 | 70 | qutebrowser/utils/usertypes.py | 21 | 7 | def certificate_was_accepted(self) -> None:
if not self.is_overridable():
return False
if self._certificate_accepted is None:
raise ValueError("No decision taken yet")
return self._certificate_accepted
| lint: Fix remaining pylint issues | certificate_was_accepted | ec8eebf99640d5a73072d05e73c6ea9b2ebea556 | qutebrowser | usertypes.py | 10 | 7 | https://github.com/qutebrowser/qutebrowser.git | 3 | 34 | 1 | 17 | 67 | Python | {
"docstring": "Check whether the certificate was accepted by the user.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 8
} | def certificate_was_accepted(self) -> None:
if not self.is_overridable():
return False
if self._certificate_accepted is None:
raise ValueError("No decision taken yet")
return self._certificate_accepted
@dataclasses.dataclass |
35,292 | 153,191 | 65 | modin/core/execution/ray/implementations/pandas_on_ray/partitioning/virtual_partition.py | 11 | 6 | def mask(self, row_indices, col_indices):
| FIX-#3675: Expand virtual partitioning utility (#3886)
Co-authored-by: mvashishtha <[email protected]>
Co-authored-by: jeffreykennethli <[email protected]>
Co-authored-by: Anatoly Myachev <[email protected]>
Co-authored-by: Vasily Litvinov <[email protected]>
Co-authored-by: Alexey Prutskov <[email protected]>
Co-authored-by: Mahesh Vashishtha <[email protected]>
Co-authored-by: Naren Krishna <[email protected]>
Co-authored-by: Yaroslav Igoshev <[email protected]>
Co-authored-by: Dmitry Chigarev <[email protected]>
Co-authored-by: Yaroslav Igoshev <[email protected]>
Co-authored-by: Doris Lee <[email protected]>
Co-authored-by: Aditya Parameswaran <[email protected]>
Co-authored-by: Rehan Sohail Durrani <[email protected]>
Co-authored-by: Susmit Vengurlekar <[email protected]>
Signed-off-by: Devin Petersohn <[email protected]> | mask | 8d1004fdbdaa05700613c8e6287641a732acf606 | modin | virtual_partition.py | 12 | 6 | https://github.com/modin-project/modin.git | 1 | 30 | 0 | 11 | 47 | Python | {
"docstring": "\n Create (synchronously) a mask that extracts the indices provided.\n\n Parameters\n ----------\n row_indices : list-like, slice or label\n The row labels for the rows to extract.\n col_indices : list-like, slice or label\n The column labels for the columns to extract.\n\n Returns\n -------\n PandasOnRayDataframeVirtualPartition\n A new ``PandasOnRayDataframeVirtualPartition`` object,\n materialized.\n ",
"language": "en",
"n_whitespaces": 155,
"n_words": 47,
"vocab_size": 35
} | def mask(self, row_indices, col_indices):
return (
self.force_materialization()
.list_of_partitions_to_combine[0]
.mask(row_indices, col_indices)
)
|
|
48,898 | 198,386 | 92 | sympy/integrals/intpoly.py | 46 | 20 | def left_integral3D(facets, index, expr, vertices, hp_param, degree):
value = S.Zero
facet = facets[index]
x0 = vertices[fac | Cleanup loops and ranges | left_integral3D | 7d773eb18daaef3c54f34d1ac6cbc5b83a5bb16c | sympy | intpoly.py | 14 | 10 | https://github.com/sympy/sympy.git | 2 | 103 | 0 | 37 | 149 | Python | {
"docstring": "Computes the left integral of Eq 10 in Chin et al.\n\n Explanation\n ===========\n\n For the 3D case, this is the sum of the integral values over constituting\n line segments of the face (which is accessed by facets[index]) multiplied\n by the distance between the first point of facet and that line segment.\n\n Parameters\n ==========\n\n facets :\n List of faces of the 3-Polytope.\n index :\n Index of face over which integral is to be calculated.\n expr :\n Input polynomial.\n vertices :\n List of vertices that constitute the 3-Polytope.\n hp_param :\n The hyperplane parameters of the face.\n degree :\n Degree of the ``expr``.\n\n Examples\n ========\n\n >>> from sympy.integrals.intpoly import left_integral3D\n >>> cube = [[(0, 0, 0), (0, 0, 5), (0, 5, 0), (0, 5, 5), (5, 0, 0),\\\n (5, 0, 5), (5, 5, 0), (5, 5, 5)],\\\n [2, 6, 7, 3], [3, 7, 5, 1], [7, 6, 4, 5], [1, 5, 4, 0],\\\n [3, 1, 0, 2], [0, 4, 6, 2]]\n >>> facets = cube[1:]\n >>> vertices = cube[0]\n >>> left_integral3D(facets, 3, 1, vertices, ([0, -1, 0], -5), 0)\n -50\n ",
"language": "en",
"n_whitespaces": 333,
"n_words": 177,
"vocab_size": 108
} | def left_integral3D(facets, index, expr, vertices, hp_param, degree):
value = S.Zero
facet = facets[index]
x0 = vertices[facet[0]]
facet_len = len(facet)
for i, fac in enumerate(facet):
side = (vertices[fac], vertices[facet[(i + 1) % facet_len]])
value += distance_to_side(x0, side, hp_param[0]) * \
lineseg_integrate(facet, i, side, expr, degree)
return value
|
|
6,017 | 32,887 | 111 | src/transformers/models/deberta/modeling_tf_deberta.py | 32 | 21 | def xdropout(self, inputs):
mask = tf.cast(
1
- tf.compat.v1.distributions.Bernoulli(probs=1.0 - self.drop_prob).sample(sample_shape=shape_list(inputs)),
tf.bool,
| TF: XLA-trainable DeBERTa v2 (#18546)
* fix deberta issues
* add different code paths for gpu and tpu
* shorter gpu take along axis
* Stable Dropout without tf cond
* variable must be float | xdropout | 34aad0dac000508015d09ed7cf7c88adb5a0e308 | transformers | modeling_tf_deberta.py | 16 | 11 | https://github.com/huggingface/transformers.git | 2 | 105 | 0 | 27 | 143 | Python | {
"docstring": "\n Applies dropout to the inputs, as vanilla dropout, but also scales the remaining elements up by 1/drop_prob.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 17,
"vocab_size": 16
} | def xdropout(self, inputs):
mask = tf.cast(
1
- tf.compat.v1.distributions.Bernoulli(probs=1.0 - self.drop_prob).sample(sample_shape=shape_list(inputs)),
tf.bool,
)
scale = tf.convert_to_tensor(1.0 / (1 - self.drop_prob), dtype=tf.float32)
if self.drop_prob > 0:
inputs = tf.where(mask, 0.0, inputs) * scale
|
|
26,500 | 118,955 | 122 | lib/tests/streamlit/legacy_add_rows_test.py | 35 | 17 | def test_deltas_that_melt_dataframes(self):
deltas = self._get_deltas_that_melt_dataframe | Remove legacy "`add_rows` coalescing" from ForwardMsgQueue (#4485)
Removes the `add_rows` legacy DataFrame concatenation implementation _from Python_. (To be clear: `add_rows` still works for legacy DataFrames, but the logic is frontend-only now. This is already how Arrow DataFrame concatenation is implemented.)
### Background
- An app's script sends `Delta` messages to the frontend by sticking them in the `ForwardMsgQueue`.
- Generally, `ForwardMsgQueue` has either 0 or 1 messages in it, because the main thread is constantly emptying the queue and sending its contents to the frontend, so messages don't have time to back up. However, sometimes 2+ messages will accumulate before the queue is emptied.
- `ForwardMsgQueue` has a couple optimizations where it tries to avoid sending out redundant messages to the frontend
- One of those optimizations relates to `add_rows`: for legacy DataFrame serialization *only*, if the queue has a DataFrame message *and* an add_rows message that appends to that same DataFrame, it will perform the add_rows *operation* on the original message and skip sending the add_rows message. (Again, this only applies to legacy DataFrame serialization - by default, apps use Arrow for DataFrame serialization, and this add_rows optimization doesn't exist for that code path: add_rows is *always* applied on the frontend for Arrow.)
- `add_rows` will raise an error if the two DataFrames being concatenated are incompatible (for example, if they have different shapes).
- Because `ForwardMsgQueue` currently does its enqueuing on the script thread, this isn't an issue: the script will catch the `add_rows` error and show an exception message on the frontend.
### The issue
- We're moving to a world where `ScriptRunner` and the main thread are kept as separate as possible.
- This means that `ScriptRunner` will no longer enqueue directly into the `ForwardMsgQueue`. Instead, the queue (which is owned by `AppSession`) will be filled only by `AppSession`, and only on the main thread.
- This means that **`add_rows` errors will no longer be caught by the script thread**, and so the script won't be able to generate an exception message to send to the frontend.
- As things currently stands, this would mean that add_rows errors will just be silently swallowed by the main thread, which we don't want.
### The solution + PR notes
- We're just ripping out `add_rows` server-side handling for legacy DataFrames. This brings the `legacy` code path a bit closer to the `Arrow` code path and eliminates a lot of code.
- The bulk of this PR is concerned with updating legacy DataFrame tests, many of which relied on `add_rows` happening on the server.
- Notably, our e2e tests (with one exception) do *not* change, because there's no observable behavior changes on the frontend.
- The one exception is that we did not (and still don't) have e2e tests for handling `add_rows` errors that happen on the frontend. And in fact, we do a terrible job with these errors - we just show an exception modal overlay with a bad error message and no indication that it's related to an add_rows operation. I'll create a ticket to address this (it'll require some product input.) | test_deltas_that_melt_dataframes | 0f76064dbb9b9405173effe7f872aa8a8dba69cc | streamlit | legacy_add_rows_test.py | 13 | 8 | https://github.com/streamlit/streamlit.git | 2 | 74 | 0 | 30 | 116 | Python | {
"docstring": "Some element types require that their dataframes are\n 'melted' (https://pandas.pydata.org/docs/reference/api/pandas.melt.html)\n before being sent to the frontend. Test that the melting occurs.\n ",
"language": "en",
"n_whitespaces": 43,
"n_words": 21,
"vocab_size": 19
} | def test_deltas_that_melt_dataframes(self):
deltas = self._get_deltas_that_melt_dataframes()
for delta in deltas:
el = delta(DATAFRAME)
el._legacy_add_rows(NEW_ROWS)
df_proto = _get_data_frame(self.get_delta_from_queue())
# Test that the add_rows delta is properly melted
rows = df_proto.data.cols[0].int64s.data
self.assertEqual([2, 3, 4, 2, 3, 4], rows)
|
|
45,948 | 188,973 | 1,294 | psutil/_pslinux.py | 326 | 47 | def sensors_temperatures():
ret = collections.defaultdict(list)
basenames = glob.glob('/sys/class/hwmon/hwmon*/temp*_*')
# CentOS has an intermediate /device directory:
# https://github.com/giampaolo/psutil/issues/971
# https://github.com/nicolargo/glances/issues/1060
basenames.extend(glob.glob('/sys/class/hwmon/hwmon*/device/temp*_*'))
basenames = sorted(set([x.split('_')[0] for x in basenames]))
# Only add the coretemp hwmon entries if they're not already in
# /sys/class/hwmon/
# https://github.com/giampaolo/psutil/issues/1708
# https://github.com/giampaolo/psutil/pull/1648
basenames2 = glob.glob(
'/sys/devices/platform/coretemp.*/hwmon/hwmon*/temp*_*')
repl = re.compile('/sys/devices/platform/coretemp.*/hwmon/')
for name in basenames2:
altname = repl.sub('/sys/class/hwmon/', name)
if altname not in basenames:
basenames.append(name)
for base in basenames:
try:
path = base + '_input'
current = float(bcat(path)) / 1000.0
path = os.path.join(os.path.dirname(base), 'name')
unit_name = cat(path).strip()
except (IOError, OSError, ValueError):
# A lot of things can go wrong here, so let's just skip the
# whole entry. Sure thing is Linux's /sys/class/hwmon really
# is a stinky broken mess.
# https://github.com/giampaolo/psutil/issues/1009
# https://github.com/giampaolo/psutil/issues/1101
# https://github.com/giampaolo/psutil/issues/1129
# https://github.com/giampaolo/psutil/issues/1245
# https://github.com/giampaolo/psutil/issues/1323
continue
high = bcat(base + '_max', fallback=None)
critical = bcat(base + '_crit', fallback=None)
label = cat(base + '_label', fallback='').strip()
if high is not None:
try:
high = float(high) / 1000.0
except ValueError:
high = None
if critical is not None:
try:
critical = float(critical) / 1000.0
except ValueError:
critical = None
ret[unit_name].append((label, current, high, critical))
# Indication that no sensors were detected in /sys/class/hwmon/
if not basenames:
basenames = glob.glob('/sys/class/thermal/thermal_zone*')
basenames = sorted(set(basenames))
for base in basenames:
try:
path = os.path.join(base, 'temp')
current = float(bcat(path)) / 1000.0
path = os.path.join(base, 'type')
unit_name = cat(path).strip()
except (IOError, OSError, ValueError) as err:
debug(err)
continue
trip_paths = glob.glob(base + '/trip_point*')
trip_points = set(['_'.join(
os.path.basename(p).split('_')[0:3]) for p in trip_paths])
critical = None
hi | [Linux] cat/bcat utils refactoring (#2053) | sensors_temperatures | 46cb6c212a870b36bd0af17c48dd29f53468734b | psutil | _pslinux.py | 21 | 72 | https://github.com/giampaolo/psutil.git | 21 | 563 | 0 | 150 | 928 | Python | {
"docstring": "Return hardware (CPU and others) temperatures as a dict\n including hardware name, label, current, max and critical\n temperatures.\n\n Implementation notes:\n - /sys/class/hwmon looks like the most recent interface to\n retrieve this info, and this implementation relies on it\n only (old distros will probably use something else)\n - lm-sensors on Ubuntu 16.04 relies on /sys/class/hwmon\n - /sys/class/thermal/thermal_zone* is another one but it's more\n difficult to parse\n ",
"language": "en",
"n_whitespaces": 101,
"n_words": 65,
"vocab_size": 54
} | def sensors_temperatures():
ret = collections.defaultdict(list)
basenames = glob.glob('/sys/class/hwmon/hwmon*/temp*_*')
# CentOS has an intermediate /device directory:
# https://github.com/giampaolo/psutil/issues/971
# https://github.com/nicolargo/glances/issues/1060
basenames.extend(glob.glob('/sys/class/hwmon/hwmon*/device/temp*_*'))
basenames = sorted(set([x.split('_')[0] for x in basenames]))
# Only add the coretemp hwmon entries if they're not already in
# /sys/class/hwmon/
# https://github.com/giampaolo/psutil/issues/1708
# https://github.com/giampaolo/psutil/pull/1648
basenames2 = glob.glob(
'/sys/devices/platform/coretemp.*/hwmon/hwmon*/temp*_*')
repl = re.compile('/sys/devices/platform/coretemp.*/hwmon/')
for name in basenames2:
altname = repl.sub('/sys/class/hwmon/', name)
if altname not in basenames:
basenames.append(name)
for base in basenames:
try:
path = base + '_input'
current = float(bcat(path)) / 1000.0
path = os.path.join(os.path.dirname(base), 'name')
unit_name = cat(path).strip()
except (IOError, OSError, ValueError):
# A lot of things can go wrong here, so let's just skip the
# whole entry. Sure thing is Linux's /sys/class/hwmon really
# is a stinky broken mess.
# https://github.com/giampaolo/psutil/issues/1009
# https://github.com/giampaolo/psutil/issues/1101
# https://github.com/giampaolo/psutil/issues/1129
# https://github.com/giampaolo/psutil/issues/1245
# https://github.com/giampaolo/psutil/issues/1323
continue
high = bcat(base + '_max', fallback=None)
critical = bcat(base + '_crit', fallback=None)
label = cat(base + '_label', fallback='').strip()
if high is not None:
try:
high = float(high) / 1000.0
except ValueError:
high = None
if critical is not None:
try:
critical = float(critical) / 1000.0
except ValueError:
critical = None
ret[unit_name].append((label, current, high, critical))
# Indication that no sensors were detected in /sys/class/hwmon/
if not basenames:
basenames = glob.glob('/sys/class/thermal/thermal_zone*')
basenames = sorted(set(basenames))
for base in basenames:
try:
path = os.path.join(base, 'temp')
current = float(bcat(path)) / 1000.0
path = os.path.join(base, 'type')
unit_name = cat(path).strip()
except (IOError, OSError, ValueError) as err:
debug(err)
continue
trip_paths = glob.glob(base + '/trip_point*')
trip_points = set(['_'.join(
os.path.basename(p).split('_')[0:3]) for p in trip_paths])
critical = None
high = None
for trip_point in trip_points:
path = os.path.join(base, trip_point + "_type")
trip_type = cat(path, fallback='').strip()
if trip_type == 'critical':
critical = bcat(os.path.join(base, trip_point + "_temp"),
fallback=None)
elif trip_type == 'high':
high = bcat(os.path.join(base, trip_point + "_temp"),
fallback=None)
if high is not None:
try:
high = float(high) / 1000.0
except ValueError:
high = None
if critical is not None:
try:
critical = float(critical) / 1000.0
except ValueError:
critical = None
ret[unit_name].append(('', current, high, critical))
return dict(ret)
|
|
15,901 | 72,541 | 223 | wagtail/admin/views/pages/workflow.py | 68 | 36 | def preview_revision_for_task(request, page_id, task_id):
| Reformat with black | preview_revision_for_task | d10f15e55806c6944827d801cd9c2d53f5da4186 | wagtail | workflow.py | 15 | 24 | https://github.com/wagtail/wagtail.git | 3 | 140 | 0 | 57 | 221 | Python | {
"docstring": "Preview the revision linked to the in-progress TaskState of a specified Task. This enables pages in moderation\n to be edited and new TaskStates linked to the new revisions created, with preview links remaining valid",
"language": "en",
"n_whitespaces": 36,
"n_words": 34,
"vocab_size": 28
} | def preview_revision_for_task(request, page_id, task_id):
page = get_object_or_404(Page, id=page_id)
task = get_object_or_404(Task, id=task_id).specific
try:
task_state = TaskState.objects.get(
page_revision__page=page, task=task, status=TaskState.STATUS_IN_PROGRESS
)
except TaskState.DoesNotExist:
messages.error(
request,
_(
"The page '{0}' is not currently awaiting moderation in task '{1}'."
).format(page.get_admin_display_title(), task.name),
)
return redirect("wagtailadmin_home")
revision = task_state.page_revision
if not task.get_actions(page, request.user):
raise PermissionDenied
page_to_view = revision.as_page_object()
# TODO: provide workflow actions within this view
return page_to_view.make_preview_request(
request,
page.default_preview_mode,
extra_request_attrs={"revision_id": revision.id},
)
|
|
56,492 | 221,726 | 94 | python3.10.4/Lib/contextlib.py | 44 | 9 | def push_async_callback(self, callback, /, *args, **kwds):
_exit_wrapper = self._create_async_cb_wrapper(callback, *args, **kwds)
# We changed the signature, so using @wraps is not appropriate, but
# setti | add python 3.10.4 for windows | push_async_callback | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | contextlib.py | 9 | 5 | https://github.com/XX-net/XX-Net.git | 1 | 45 | 0 | 39 | 73 | Python | {
"docstring": "Registers an arbitrary coroutine function and arguments.\n\n Cannot suppress exceptions.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 10,
"vocab_size": 10
} | def push_async_callback(self, callback, /, *args, **kwds):
_exit_wrapper = self._create_async_cb_wrapper(callback, *args, **kwds)
# We changed the signature, so using @wraps is not appropriate, but
# setting __wrapped__ may still help with introspection.
_exit_wrapper.__wrapped__ = callback
self._push_exit_callback(_exit_wrapper, False)
return callback # Allow use as a decorator
|
|
13,285 | 63,394 | 789 | .venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py | 135 | 35 | def scanString(self, instring, maxMatches=_MAX_INT, overlap=False):
if not self.streamlined:
self.streamline()
for e in self.ignoreExprs:
e.streamline()
if not self.keepTabs:
instring = _ustr(instring).expandtabs()
instrlen = len(instring)
loc = 0
preparseFn = self.preParse
parseFn = self._parse
ParserEleme | upd; format | scanString | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | pyparsing.py | 20 | 41 | https://github.com/jindongwang/transferlearning.git | 13 | 217 | 0 | 80 | 354 | Python | {
"docstring": "\n Scan the input string for expression matches. Each match will return the\n matching tokens, start location, and end location. May be called with optional\n ``maxMatches`` argument, to clip scanning after 'n' matches are found. If\n ``overlap`` is specified, then overlapping matches will be reported.\n\n Note that the start and end locations are reported relative to the string\n being parsed. See :class:`parseString` for more information on parsing\n strings with embedded tabs.\n\n Example::\n\n source = \"sldjf123lsdjjkf345sldkjf879lkjsfd987\"\n print(source)\n for tokens, start, end in Word(alphas).scanString(source):\n print(' '*start + '^'*(end-start))\n print(' '*start + tokens[0])\n\n prints::\n\n sldjf123lsdjjkf345sldkjf879lkjsfd987\n ^^^^^\n sldjf\n ^^^^^^^\n lsdjjkf\n ^^^^^^\n sldkjf\n ^^^^^^\n lkjsfd\n ",
"language": "en",
"n_whitespaces": 442,
"n_words": 99,
"vocab_size": 78
} | def scanString(self, instring, maxMatches=_MAX_INT, overlap=False):
if not self.streamlined:
self.streamline()
for e in self.ignoreExprs:
e.streamline()
if not self.keepTabs:
instring = _ustr(instring).expandtabs()
instrlen = len(instring)
loc = 0
preparseFn = self.preParse
parseFn = self._parse
ParserElement.resetCache()
matches = 0
try:
while loc <= instrlen and matches < maxMatches:
try:
preloc = preparseFn(instring, loc)
nextLoc, tokens = parseFn(instring, preloc, callPreParse=False)
except ParseException:
loc = preloc + 1
else:
if nextLoc > loc:
matches += 1
yield tokens, preloc, nextLoc
if overlap:
nextloc = preparseFn(instring, loc)
if nextloc > loc:
loc = nextLoc
else:
loc += 1
else:
loc = nextLoc
else:
loc = preloc + 1
except ParseBaseException as exc:
if ParserElement.verbose_stacktrace:
raise
else:
# catch and re-raise exception from here, clearing out pyparsing internal stack trace
if getattr(exc, '__traceback__', None) is not None:
exc.__traceback__ = self._trim_traceback(exc.__traceback__)
raise exc
|
|
35,932 | 154,339 | 83 | modin/core/execution/dask/implementations/pandas_on_dask/partitioning/partition.py | 18 | 10 | def add_to_apply_calls(self, func, *args, length=None, width=None, **kwargs):
return PandasOnDaskDataframePartition(
self._data,
call_queue=self.call_queue + [[func, args, kwargs]],
length=length,
width=width,
)
| PERF-#4794: Compute caches in `_propagate_index_objs` (#4888)
Co-authored-by: Mahesh Vashishtha <[email protected]>
Signed-off-by: Myachev <[email protected]> | add_to_apply_calls | 39b36eb2a2e3bf3d612933e1c78545a8bb28cde4 | modin | partition.py | 11 | 7 | https://github.com/modin-project/modin.git | 1 | 54 | 0 | 18 | 76 | Python | {
"docstring": "\n Add a function to the call queue.\n\n Parameters\n ----------\n func : callable\n Function to be added to the call queue.\n *args : iterable\n Additional positional arguments to be passed in `func`.\n length : distributed.Future or int, optional\n Length, or reference to length, of wrapped ``pandas.DataFrame``.\n width : distributed.Future or int, optional\n Width, or reference to width, of wrapped ``pandas.DataFrame``.\n **kwargs : dict\n Additional keyword arguments to be passed in `func`.\n\n Returns\n -------\n PandasOnDaskDataframePartition\n A new ``PandasOnDaskDataframePartition`` object.\n\n Notes\n -----\n The keyword arguments are sent as a dictionary.\n ",
"language": "en",
"n_whitespaces": 259,
"n_words": 87,
"vocab_size": 54
} | def add_to_apply_calls(self, func, *args, length=None, width=None, **kwargs):
return PandasOnDaskDataframePartition(
self._data,
call_queue=self.call_queue + [[func, args, kwargs]],
length=length,
width=width,
)
|
|
6,208 | 34,178 | 141 | src/transformers/feature_extraction_utils.py | 48 | 18 | def to_json_string(self) -> str:
dictionary = self.to_dict()
for key, value in dictionary.items():
if isinstance(value, np.ndarray):
dictionary[key] = value.tolist()
# make sure private name "_processor_class" is correctly
# saved as "processor_class"
_processor_class = dictionary.pop("_processor_class", None)
if _processor_class is not None:
dictionary["processor_class"] = _processor_class
return json.dumps( | [ASR pipeline] correct with lm pipeline (#15200)
* [ASR pipeline] correct with lm pipeline
* improve error | to_json_string | 497346d07ec39da3a7f38a7e0a67a4906c141ea3 | transformers | feature_extraction_utils.py | 12 | 15 | https://github.com/huggingface/transformers.git | 4 | 85 | 0 | 40 | 142 | Python | {
"docstring": "\n Serializes this instance to a JSON string.\n\n Returns:\n `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.\n ",
"language": "en",
"n_whitespaces": 56,
"n_words": 23,
"vocab_size": 20
} | def to_json_string(self) -> str:
dictionary = self.to_dict()
for key, value in dictionary.items():
if isinstance(value, np.ndarray):
dictionary[key] = value.tolist()
# make sure private name "_processor_class" is correctly
# saved as "processor_class"
_processor_class = dictionary.pop("_processor_class", None)
if _processor_class is not None:
dictionary["processor_class"] = _processor_class
return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"
|
|
5,070 | 26,810 | 357 | saleor/core/permissions/__init__.py | 125 | 28 | def one_of_permissions_or_auth_filter_required(context, permissions):
if not permissions:
return True
authorization_filters = [
p for p in permissions if isinstance(p, AuthorizationFilters)
]
permissions = [p for p in permissions if not isinstance(p, AuthorizationFilters)]
granted_by_permissions = False
granted_by_authorization_filters = False
# TODO: move this function from graphql to core
from saleor.graphql.utils import get_user_or_app_from_context
is_app = bool(getattr(context, "app", None))
requestor = get_user_or_app_from_context(context)
if permissions:
perm_checks_results = []
for permission in permissions:
if is_app and permission == AccountPermissions.MANAGE_STAFF:
# `MANAGE_STAFF` permission for apps is not supported, as apps using it
# could create a staff user with full access.
perm_ch | Include permissions in schema descriptions of protected fields (#9428)
* POC Generate permission description for queries
* Use PermissionField for app queries
* Rename functions checking object ownership
* Switch to PermissionsField in more fields
* CR fixes
* Add missing descriptions | one_of_permissions_or_auth_filter_required | f0a988e798dd86befbbf7a0eda1bc1a8cc94dda2 | saleor | __init__.py | 16 | 29 | https://github.com/saleor/saleor.git | 14 | 172 | 0 | 76 | 278 | Python | {
"docstring": "Determine whether user or app has rights to perform an action.\n\n The `context` parameter is the Context instance associated with the request.\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 22,
"vocab_size": 21
} | def one_of_permissions_or_auth_filter_required(context, permissions):
if not permissions:
return True
authorization_filters = [
p for p in permissions if isinstance(p, AuthorizationFilters)
]
permissions = [p for p in permissions if not isinstance(p, AuthorizationFilters)]
granted_by_permissions = False
granted_by_authorization_filters = False
# TODO: move this function from graphql to core
from saleor.graphql.utils import get_user_or_app_from_context
is_app = bool(getattr(context, "app", None))
requestor = get_user_or_app_from_context(context)
if permissions:
perm_checks_results = []
for permission in permissions:
if is_app and permission == AccountPermissions.MANAGE_STAFF:
# `MANAGE_STAFF` permission for apps is not supported, as apps using it
# could create a staff user with full access.
perm_checks_results.append(False)
else:
perm_checks_results.append(requestor.has_perm(permission))
granted_by_permissions = any(perm_checks_results)
if authorization_filters:
auth_filters_results = []
for p in authorization_filters:
perm_fn = resolve_authorization_filter_fn(p)
if perm_fn:
res = perm_fn(context)
auth_filters_results.append(bool(res))
granted_by_authorization_filters = any(auth_filters_results)
return granted_by_permissions or granted_by_authorization_filters
|
|
33,344 | 144,928 | 45 | python/ray/_private/runtime_env/_clonevirtualenv.py | 19 | 8 | def _dirmatch(path, matchwith):
matchlen = len(matchwith)
if (path.startswith(matchwith)
| [runtime env] Support clone `virtualenv` from an existing `virtualenv` (#22309)
Before this PR, we can't run ray in virtualenv, cause `runtime_env` does not support create a new virtualenv from an existing virtualenv.
More details:https://github.com/ray-project/ray/pull/21801#discussion_r796848499
Co-authored-by: 捕牛 <[email protected]> | _dirmatch | 4c73560b313821fbfbb8c943e02c8b298b7c1731 | ray | _clonevirtualenv.py | 11 | 6 | https://github.com/ray-project/ray.git | 3 | 45 | 0 | 18 | 73 | Python | {
"docstring": "Check if path is within matchwith's tree.\n >>> _dirmatch('/home/foo/bar', '/home/foo/bar')\n True\n >>> _dirmatch('/home/foo/bar/', '/home/foo/bar')\n True\n >>> _dirmatch('/home/foo/bar/etc', '/home/foo/bar')\n True\n >>> _dirmatch('/home/foo/bar2', '/home/foo/bar')\n False\n >>> _dirmatch('/home/foo/bar2/etc', '/home/foo/bar')\n False\n ",
"language": "en",
"n_whitespaces": 60,
"n_words": 27,
"vocab_size": 16
} | def _dirmatch(path, matchwith):
matchlen = len(matchwith)
if (path.startswith(matchwith)
and path[matchlen:matchlen + 1] in [os.sep, '']):
return True
return False
|
|
70,270 | 244,173 | 79 | mmdet/models/dense_heads/tood_head.py | 37 | 13 | def deform_sampling(self, feat, offset):
# it is an equivalent implementation of bilinear interpolation
b, c, h, w = feat.shape
weight = feat.new_ones(c, 1, 1, 1)
y = deform_conv2d(feat, offset, weight, 1, 0, 1, c, c)
return y
| [Fix] Avoid invalid bbox after deform_sampling (#7567)
* Avoid invalid bbox after deform_sampling
* replace in-place opertion with torch.where, update docstring
* Update | deform_sampling | b403751bd409795cf63fcc6aa7ee280326358bac | mmdetection | tood_head.py | 8 | 5 | https://github.com/open-mmlab/mmdetection.git | 1 | 57 | 0 | 30 | 79 | Python | {
"docstring": "Sampling the feature x according to offset.\n\n Args:\n feat (Tensor): Feature\n offset (Tensor): Spatial offset for feature sampling\n ",
"language": "en",
"n_whitespaces": 54,
"n_words": 18,
"vocab_size": 15
} | def deform_sampling(self, feat, offset):
# it is an equivalent implementation of bilinear interpolation
b, c, h, w = feat.shape
weight = feat.new_ones(c, 1, 1, 1)
y = deform_conv2d(feat, offset, weight, 1, 0, 1, c, c)
return y
|
|
17,285 | 81,965 | 694 | awxkit/awxkit/api/pages/page.py | 171 | 47 | def page_identity(self, response, request_json=None):
request_path = response.request.path_url
if request_path == '/migrations_notran/':
raise exc.IsMigrating('You have been redirected to the migration-in-progress page.')
request_method = response.request.method.lower()
self.last_elapsed = response.elapsed
if isinstance(request_json, dict) and 'ds' in request_json:
ds = request_json.ds
else:
ds = None
data = self.extract_data(response)
exc_str = "%s (%s) received" % (http.responses[response.status_code], response.status_code)
exception = exception_from_status_code(response.status_code)
if exception:
raise exception(exc_str, data)
if response.status_code in (http.OK, http.CREATED, http.ACCEPTED):
# Not all JSON responses include a URL. Grab it from the request
# object, if needed.
if 'url' in data:
endpoint = data['url']
| Register pages for the Instance peers and install bundle endpoints
This includes exposing a new interface for Page objects, Page.bytes,
to return the full bytestring contents of the response. | page_identity | 68a44529b6b77d2d43d7099b654560bfd8bbf518 | awx | page.py | 13 | 44 | https://github.com/ansible/awx.git | 15 | 337 | 0 | 104 | 536 | Python | {
"docstring": "Takes a `requests.Response` and\n returns a new __item_class__ instance if the request method is not a get, or returns\n a __class__ instance if the request path is different than the caller's `endpoint`.\n ",
"language": "en",
"n_whitespaces": 56,
"n_words": 32,
"vocab_size": 22
} | def page_identity(self, response, request_json=None):
request_path = response.request.path_url
if request_path == '/migrations_notran/':
raise exc.IsMigrating('You have been redirected to the migration-in-progress page.')
request_method = response.request.method.lower()
self.last_elapsed = response.elapsed
if isinstance(request_json, dict) and 'ds' in request_json:
ds = request_json.ds
else:
ds = None
data = self.extract_data(response)
exc_str = "%s (%s) received" % (http.responses[response.status_code], response.status_code)
exception = exception_from_status_code(response.status_code)
if exception:
raise exception(exc_str, data)
if response.status_code in (http.OK, http.CREATED, http.ACCEPTED):
# Not all JSON responses include a URL. Grab it from the request
# object, if needed.
if 'url' in data:
endpoint = data['url']
else:
endpoint = request_path
data = objectify_response_json(response)
if request_method in ('get', 'patch', 'put'):
# Update existing resource and return it
if are_same_endpoint(self.endpoint, request_path):
self.json = data
self.r = response
return self
registered_type = get_registered_page(request_path, request_method)
return registered_type(self.connection, endpoint=endpoint, json=data, last_elapsed=response.elapsed, r=response, ds=ds)
elif response.status_code == http.FORBIDDEN:
if is_license_invalid(response):
raise exc.LicenseInvalid(exc_str, data)
elif is_license_exceeded(response):
raise exc.LicenseExceeded(exc_str, data)
else:
raise exc.Forbidden(exc_str, data)
elif response.status_code == http.BAD_REQUEST:
if is_license_invalid(response):
raise exc.LicenseInvalid(exc_str, data)
if is_duplicate_error(response):
raise exc.Duplicate(exc_str, data)
else:
raise exc.BadRequest(exc_str, data)
else:
raise exc.Unknown(exc_str, data)
|
|
21,442 | 102,077 | 108 | lib/sysinfo.py | 33 | 16 | def _installed_conda(self):
if not self._is_conda:
return None
with Popen("conda list", shell=True, stdout=PIPE, stderr=PIPE) as conda:
stdout, stderr = conda.communicate()
if stderr:
return "Could not get package list"
installed = stdout.decode(self._encoding, errors="replace").splitlines()
return "\n".join(installed)
| Allow decoding errors | _installed_conda | 48c886b3dce3d3117ad16edaf35c8abd28dc51f5 | faceswap | sysinfo.py | 12 | 9 | https://github.com/deepfakes/faceswap.git | 3 | 73 | 0 | 28 | 128 | Python | {
"docstring": " str: The list of installed Conda packages within Faceswap's scope. ",
"language": "en",
"n_whitespaces": 11,
"n_words": 10,
"vocab_size": 10
} | def _installed_conda(self):
if not self._is_conda:
return None
with Popen("conda list", shell=True, stdout=PIPE, stderr=PIPE) as conda:
stdout, stderr = conda.communicate()
if stderr:
return "Could not get package list"
installed = stdout.decode(self._encoding, errors="replace").splitlines()
return "\n".join(installed)
|
|
7,193 | 39,296 | 86 | recommenders/models/sasrec/model.py | 30 | 14 | def embedding(self, input_seq):
seq_embeddings = self.item_embedding_layer(input_seq)
seq_embeddings = seq_embeddings * (self.embedding_dim ** 0.5)
# FIXME
positional_seq = tf.expand_dims(tf.range(tf.shape(input_seq)[1]), 0)
| doc | embedding | d38dffc30c18e9e3280863b32dcc71d01757b181 | recommenders | model.py | 13 | 7 | https://github.com/microsoft/recommenders.git | 1 | 86 | 0 | 22 | 132 | Python | {
"docstring": "Compute the sequence and positional embeddings.\n\n Args:\n input_seq (tf.Tensor): Input sequence\n \n Returns:\n tf.Tensor, tf.Tensor:\n - Sequence embeddings.\n - Positional embeddings.\n \n ",
"language": "en",
"n_whitespaces": 101,
"n_words": 20,
"vocab_size": 16
} | def embedding(self, input_seq):
seq_embeddings = self.item_embedding_layer(input_seq)
seq_embeddings = seq_embeddings * (self.embedding_dim ** 0.5)
# FIXME
positional_seq = tf.expand_dims(tf.range(tf.shape(input_seq)[1]), 0)
positional_seq = tf.tile(positional_seq, [tf.shape(input_seq)[0], 1])
positional_embeddings = self.positional_embedding_layer(positional_seq)
return seq_embeddings, positional_embeddings
|
|
78,648 | 266,902 | 808 | lib/ansible/utils/display.py | 223 | 36 | def display(self, msg, color=None, stderr=False, screen_only=False, log_only=False, newline=True):
nocolor = msg
if not log_only:
has_newline = msg.endswith(u'\n')
if has_newline:
msg2 = msg[:-1]
else:
msg2 = msg
if color:
msg2 = stringc(msg2, color)
if has_newline or newline:
msg2 = msg2 + u'\n'
msg2 = to_bytes(msg2, encoding=self._output_encoding(stderr=stderr))
# Convert back to text string
# We first convert to a byte string so that we get rid of
# characters that are invalid in the user's locale
msg2 = to_text(msg2, self._output_encoding(stderr=stderr), errors='replace')
# Note: After Display() class is refactored need to update the log capture
# code in 'bin/ansible-connection' (and other relevant places).
if not stderr:
fileobj = sys.stdout
else:
fileobj = sys.stderr
fileobj.write(msg2)
try:
fileobj.flush()
except IOError as e:
# Ignore EPIPE in case fileobj has been prematurely closed, eg.
# when piping to "head -n1"
if e.errno != errno.EPIPE:
raise
if logger and not screen_only:
# We first convert to a byte string so that we get rid of
# color and characters that are invalid in the u | Remove obsolete Python 2.x controller code. | display | 6f445ca6e5c9c8b85ccc5062e00508c69ca26fde | ansible | display.py | 16 | 34 | https://github.com/ansible/ansible.git | 13 | 229 | 0 | 128 | 385 | Python | {
"docstring": " Display a message to the user\n\n Note: msg *must* be a unicode string to prevent UnicodeError tracebacks.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 17,
"vocab_size": 15
} | def display(self, msg, color=None, stderr=False, screen_only=False, log_only=False, newline=True):
nocolor = msg
if not log_only:
has_newline = msg.endswith(u'\n')
if has_newline:
msg2 = msg[:-1]
else:
msg2 = msg
if color:
msg2 = stringc(msg2, color)
if has_newline or newline:
msg2 = msg2 + u'\n'
msg2 = to_bytes(msg2, encoding=self._output_encoding(stderr=stderr))
# Convert back to text string
# We first convert to a byte string so that we get rid of
# characters that are invalid in the user's locale
msg2 = to_text(msg2, self._output_encoding(stderr=stderr), errors='replace')
# Note: After Display() class is refactored need to update the log capture
# code in 'bin/ansible-connection' (and other relevant places).
if not stderr:
fileobj = sys.stdout
else:
fileobj = sys.stderr
fileobj.write(msg2)
try:
fileobj.flush()
except IOError as e:
# Ignore EPIPE in case fileobj has been prematurely closed, eg.
# when piping to "head -n1"
if e.errno != errno.EPIPE:
raise
if logger and not screen_only:
# We first convert to a byte string so that we get rid of
# color and characters that are invalid in the user's locale
msg2 = to_bytes(nocolor.lstrip(u'\n'))
# Convert back to text string
msg2 = to_text(msg2, self._output_encoding(stderr=stderr))
lvl = logging.INFO
if color:
# set logger level based on color (not great)
try:
lvl = color_to_log_level[color]
except KeyError:
# this should not happen, but JIC
raise AnsibleAssertionError('Invalid color supplied to display: %s' % color)
# actually log
logger.log(lvl, msg2)
|
|
78,254 | 265,983 | 105 | netbox/extras/views.py | 23 | 13 | def get_queryset(self, request):
queryset = SavedFilter.objects.all()
user = request.user
if user.is_superuser:
return queryset
if user.is_anonymous:
return queryset.filter(shared=True)
| Closes #9623: Implement saved filters (#10801)
* Initial work on saved filters
* Return only enabled/shared filters
* Add tests
* Clean up filtering of usable SavedFilters | get_queryset | 484efdaf75f267a43f9321b938fda1bc967b9e53 | netbox | views.py | 11 | 10 | https://github.com/netbox-community/netbox.git | 3 | 62 | 0 | 18 | 101 | Python | {
"docstring": "\n Return only shared SavedFilters, or those owned by the current user, unless\n this is a superuser.\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 16,
"vocab_size": 16
} | def get_queryset(self, request):
queryset = SavedFilter.objects.all()
user = request.user
if user.is_superuser:
return queryset
if user.is_anonymous:
return queryset.filter(shared=True)
return queryset.filter(
Q(shared=True) | Q(user=user)
)
|
|
77,779 | 264,666 | 264 | netbox/extras/api/views.py | 64 | 36 | def list(self, request):
| Save old JobResults | list | f13a00b2dd33bffc3048c861b494096df457f212 | netbox | views.py | 18 | 18 | https://github.com/netbox-community/netbox.git | 4 | 135 | 0 | 54 | 222 | Python | {
"docstring": "\n Compile all reports and their related results (if any). Result data is deferred in the list view.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 17,
"vocab_size": 17
} | def list(self, request):
report_list = []
report_content_type = ContentType.objects.get(app_label='extras', model='report')
results = {
r.name: r
for r in JobResult.objects.filter(
obj_type=report_content_type,
status__in=JobResultStatusChoices.TERMINAL_STATE_CHOICES
).order_by('name', '-created').distinct('name').defer('data')
}
# Iterate through all available Reports.
for module_name, reports in get_reports():
for report in reports:
# Attach the relevant JobResult (if any) to each Report.
report.result = results.get(report.full_name, None)
report_list.append(report)
serializer = serializers.ReportSerializer(report_list, many=True, context={
'request': request,
})
return Response(serializer.data)
|
|
78,253 | 265,967 | 121 | netbox/extras/filtersets.py | 38 | 12 | def _usable(self, queryset, name, value):
user = self.request.user if self.request else None
if not user or user.is_anonymous:
if value:
return queryset.filter(enabled=True, shared=True)
return queryset.filter(Q(enabled=False) | Q(shared=False))
| Closes #9623: Implement saved filters (#10801)
* Initial work on saved filters
* Return only enabled/shared filters
* Add tests
* Clean up filtering of usable SavedFilters | _usable | 484efdaf75f267a43f9321b938fda1bc967b9e53 | netbox | filtersets.py | 15 | 9 | https://github.com/netbox-community/netbox.git | 6 | 127 | 0 | 27 | 199 | Python | {
"docstring": "\n Return only SavedFilters that are both enabled and are shared (or belong to the current user).\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 16,
"vocab_size": 15
} | def _usable(self, queryset, name, value):
user = self.request.user if self.request else None
if not user or user.is_anonymous:
if value:
return queryset.filter(enabled=True, shared=True)
return queryset.filter(Q(enabled=False) | Q(shared=False))
if value:
return queryset.filter(enabled=True).filter(Q(shared=True) | Q(user=user))
return queryset.filter(Q(enabled=False) | Q(Q(shared=False) & ~Q(user=user)))
|
|
14,040 | 65,853 | 12 | erpnext/education/api.py | 23 | 11 | def get_current_enrollment(student, academic_year=None):
current_academic_year = academic_year or frappe.defaul | style: format code with black | get_current_enrollment | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | api.py | 11 | 19 | https://github.com/frappe/erpnext.git | 3 | 55 | 0 | 21 | 85 | Python | {
"docstring": "\n\t\tselect\n\t\t\tname as program_enrollment, student_name, program, student_batch_name as student_batch,\n\t\t\tstudent_category, academic_term, academic_year\n\t\tfrom\n\t\t\t`tabProgram Enrollment`\n\t\twhere\n\t\t\tstudent = %s and academic_year = %s\n\t\torder by creation",
"language": "en",
"n_whitespaces": 18,
"n_words": 26,
"vocab_size": 22
} | def get_current_enrollment(student, academic_year=None):
current_academic_year = academic_year or frappe.defaults.get_defaults().academic_year
program_enrollment_list = frappe.db.sql(
,
(student, current_academic_year),
as_dict=1,
)
if program_enrollment_list:
return program_enrollment_list[0]
else:
return None
|
|
@keras_export("keras.applications.inception_resnet_v2.preprocess_input") | 83,430 | 280,749 | 443 | keras/applications/inception_resnet_v2.py | 180 | 28 | def inception_resnet_block(x, scale, block_type, block_idx, activation="relu"):
if block_type == "block35":
branch_0 = conv2d_bn(x, 32, 1)
branch_1 = conv2d_bn(x, 32, 1)
branch_1 = conv2d_bn(branch_1, 32, 3)
branch_2 = conv2d_bn(x, 32, 1)
branch_2 = conv2d_bn(branch_2, 48, 3)
branch_2 = conv2d_bn(branch_2, 64, 3)
branches = [branch_0, branch_1, branch_2]
elif block_type == "block17":
branch_0 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(x, 128, 1)
branch_1 = conv2d_bn(branch_1, 160, [1, 7])
branch_1 = conv2d_bn(branch_1, 192, [7, 1])
branches = [branch_0, branch_1]
elif block_type == "block8":
branch_0 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(branch_1, 224, [1, 3])
branch_1 = conv2d_bn(branch_1, 256, [3, 1])
branches = [branch_0, branch_1]
else:
raise ValueError(
"Unknown Inception-ResNet block type. "
'Expects "block35", "block17" or "block8", '
"but got: " + str(block_type)
)
block_name = block_type + "_" + str(block_idx)
channel_axis = 1 if backend.image_data_format() == "channels_first" else 3
mixed = layers.Concatenate(axis=channel_axis, name=block_name + "_mixed")(
branch | Removes the serialization of lambdas Keras tests where necessary and adds SafeModeScope all other lambda-based serialization tests.
PiperOrigin-RevId: 495432774 | inception_resnet_block | e52c89c7d1bd52d1f0db0da86a72322ba72c1dc1 | keras | inception_resnet_v2.py | 14 | 44 | https://github.com/keras-team/keras.git | 6 | 336 | 1 | 93 | 520 | Python | {
"docstring": "Adds an Inception-ResNet block.\n\n This function builds 3 types of Inception-ResNet blocks mentioned\n in the paper, controlled by the `block_type` argument (which is the\n block name used in the official TF-slim implementation):\n - Inception-ResNet-A: `block_type='block35'`\n - Inception-ResNet-B: `block_type='block17'`\n - Inception-ResNet-C: `block_type='block8'`\n\n Args:\n x: input tensor.\n scale: scaling factor to scale the residuals (i.e., the output of passing\n `x` through an inception module) before adding them to the shortcut\n branch. Let `r` be the output from the residual branch, the output of\n this block will be `x + scale * r`.\n block_type: `'block35'`, `'block17'` or `'block8'`, determines the network\n structure in the residual branch.\n block_idx: an `int` used for generating layer names. The Inception-ResNet\n blocks are repeated many times in this network. We use `block_idx` to\n identify each of the repetitions. For example, the first\n Inception-ResNet-A block will have `block_type='block35', block_idx=0`,\n and the layer names will have a common prefix `'block35_0'`.\n activation: activation function to use at the end of the block (see\n [activations](../activations.md)). When `activation=None`, no activation\n is applied\n (i.e., \"linear\" activation: `a(x) = x`).\n\n Returns:\n Output tensor for the block.\n\n Raises:\n ValueError: if `block_type` is not one of `'block35'`,\n `'block17'` or `'block8'`.\n ",
"language": "en",
"n_whitespaces": 344,
"n_words": 193,
"vocab_size": 130
} | def inception_resnet_block(x, scale, block_type, block_idx, activation="relu"):
if block_type == "block35":
branch_0 = conv2d_bn(x, 32, 1)
branch_1 = conv2d_bn(x, 32, 1)
branch_1 = conv2d_bn(branch_1, 32, 3)
branch_2 = conv2d_bn(x, 32, 1)
branch_2 = conv2d_bn(branch_2, 48, 3)
branch_2 = conv2d_bn(branch_2, 64, 3)
branches = [branch_0, branch_1, branch_2]
elif block_type == "block17":
branch_0 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(x, 128, 1)
branch_1 = conv2d_bn(branch_1, 160, [1, 7])
branch_1 = conv2d_bn(branch_1, 192, [7, 1])
branches = [branch_0, branch_1]
elif block_type == "block8":
branch_0 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(branch_1, 224, [1, 3])
branch_1 = conv2d_bn(branch_1, 256, [3, 1])
branches = [branch_0, branch_1]
else:
raise ValueError(
"Unknown Inception-ResNet block type. "
'Expects "block35", "block17" or "block8", '
"but got: " + str(block_type)
)
block_name = block_type + "_" + str(block_idx)
channel_axis = 1 if backend.image_data_format() == "channels_first" else 3
mixed = layers.Concatenate(axis=channel_axis, name=block_name + "_mixed")(
branches
)
up = conv2d_bn(
mixed,
backend.int_shape(x)[channel_axis],
1,
activation=None,
use_bias=True,
name=block_name + "_conv",
)
x = CustomScaleLayer()(x, up, scale)
if activation is not None:
x = layers.Activation(activation, name=block_name + "_ac")(x)
return x
@keras_export("keras.applications.inception_resnet_v2.preprocess_input") |
19,814 | 100,317 | 152 | lib/gui/analysis/stats.py | 38 | 14 | def _get_calculations(self):
for selection in self._selections:
if selection == "raw":
continue
logger.debug("Calculating: %s", selection)
method = getattr(self, f"_calc_{selection}")
raw_keys = [key for key in self._stats if key.startswith("raw_")]
for key in raw_keys:
selected_key = f"{selection}_{key.replace('raw_', '')}"
self._stats[selected_key] = method(self._sta | Update code to support Tensorflow versions up to 2.8 (#1213)
* Update maximum tf version in setup + requirements
* - bump max version of tf version in launcher
- standardise tf version check
* update keras get_custom_objects for tf>2.6
* bugfix: force black text in GUI file dialogs (linux)
* dssim loss - Move to stock tf.ssim function
* Update optimizer imports for compatibility
* fix logging for tf2.8
* Fix GUI graphing for TF2.8
* update tests
* bump requirements.txt versions
* Remove limit on nvidia-ml-py
* Graphing bugfixes
- Prevent live graph from displaying if data not yet available
* bugfix: Live graph. Collect loss labels correctly
* fix: live graph - swallow inconsistent loss errors
* Bugfix: Prevent live graph from clearing during training
* Fix graphing for AMD | _get_calculations | c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf | faceswap | stats.py | 15 | 10 | https://github.com/deepfakes/faceswap.git | 6 | 79 | 0 | 28 | 156 | Python | {
"docstring": " Perform the required calculations and populate :attr:`stats`. ",
"language": "en",
"n_whitespaces": 8,
"n_words": 7,
"vocab_size": 7
} | def _get_calculations(self):
for selection in self._selections:
if selection == "raw":
continue
logger.debug("Calculating: %s", selection)
method = getattr(self, f"_calc_{selection}")
raw_keys = [key for key in self._stats if key.startswith("raw_")]
for key in raw_keys:
selected_key = f"{selection}_{key.replace('raw_', '')}"
self._stats[selected_key] = method(self._stats[key])
|
|
36,917 | 157,377 | 809 | ldm/models/diffusion/dpm_solver/dpm_solver.py | 228 | 37 | def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
if solver_type not in ['dpm_solver', 'taylor']:
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
ns = self.noise_schedule
dims = x.dim()
model_prev_1, model_prev_0 = model_prev_list
t_prev_1, t_prev_0 = t_prev_list
lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
t_prev_0), ns.marginal_lambda(t)
log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
alpha_t = torch.exp(log_alpha_t)
h_0 = lambda_prev_0 - lambda_prev_1
h = lambda_t - lambda_prev_0
r0 = h_0 / h
D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
if self.predict_x0:
if solver_type == 'dpm_solver':
x_t = (
expand_dims(sigma_t / sigma_prev_0, dims) * x
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
- 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
)
elif solver_type == 'taylor':
x_t = (
expand_dims(sigma_t / sigma_prev_0, dims) * x
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
)
else:
if solver_type == 'dpm_solver':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * mo | release more models | multistep_dpm_solver_second_update | ca86da3a30c4e080d4db8c25fca73de843663cb4 | stablediffusion | dpm_solver.py | 25 | 43 | https://github.com/Stability-AI/stablediffusion.git | 7 | 449 | 0 | 88 | 681 | Python | {
"docstring": "\n Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.\n Args:\n x: A pytorch tensor. The initial value at time `s`.\n model_prev_list: A list of pytorch tensor. The previous computed model values.\n t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)\n t: A pytorch tensor. The ending time, with the shape (x.shape[0],).\n solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.\n The type slightly impacts the performance. We recommend to use 'dpm_solver' type.\n Returns:\n x_t: A pytorch tensor. The approximated solution at time `t`.\n ",
"language": "en",
"n_whitespaces": 201,
"n_words": 91,
"vocab_size": 57
} | def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
if solver_type not in ['dpm_solver', 'taylor']:
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
ns = self.noise_schedule
dims = x.dim()
model_prev_1, model_prev_0 = model_prev_list
t_prev_1, t_prev_0 = t_prev_list
lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
t_prev_0), ns.marginal_lambda(t)
log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
alpha_t = torch.exp(log_alpha_t)
h_0 = lambda_prev_0 - lambda_prev_1
h = lambda_t - lambda_prev_0
r0 = h_0 / h
D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
if self.predict_x0:
if solver_type == 'dpm_solver':
x_t = (
expand_dims(sigma_t / sigma_prev_0, dims) * x
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
- 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
)
elif solver_type == 'taylor':
x_t = (
expand_dims(sigma_t / sigma_prev_0, dims) * x
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
)
else:
if solver_type == 'dpm_solver':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
- 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
)
elif solver_type == 'taylor':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
- expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
)
return x_t
|
|
41,599 | 175,317 | 64 | Lib/enum.py | 25 | 17 | def global_enum(cls, update_str=False):
if issubclass(cls, Flag):
cls.__repr__ = global_flag_repr
else:
cls.__repr__ = global_enum_repr
if not issubclass(cls, ReprEnum) or update_str:
cls.__str__ = global_str
sys.modules[cls.__module__].__dict__.updat | bpo-40066: [Enum] update str() and format() output (GH-30582)
Undo rejected PEP-663 changes:
- restore `repr()` to its 3.10 status
- restore `str()` to its 3.10 status
New changes:
- `IntEnum` and `IntFlag` now leave `__str__` as the original `int.__str__` so that str() and format() return the same result
- zero-valued flags without a name have a slightly changed repr(), e.g. `repr(Color(0)) == '<Color: 0>'`
- update `dir()` for mixed-in types to return all the methods and attributes of the mixed-in type
- added `_numeric_repr_` to `Flag` to control display of unnamed values
- enums without doc strings have a more comprehensive doc string added
- `ReprEnum` added -- inheriting from this makes it so only `__repr__` is replaced, not `__str__` nor `__format__`; `IntEnum`, `IntFlag`, and `StrEnum` all inherit from `ReprEnum` | global_enum | acf7403f9baea3ae1119fc6b4a3298522188bf96 | cpython | enum.py | 10 | 9 | https://github.com/python/cpython.git | 4 | 65 | 0 | 20 | 104 | Python | {
"docstring": "\n decorator that makes the repr() of an enum member reference its module\n instead of its class; also exports all members to the enum's module's\n global namespace\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 26,
"vocab_size": 23
} | def global_enum(cls, update_str=False):
if issubclass(cls, Flag):
cls.__repr__ = global_flag_repr
else:
cls.__repr__ = global_enum_repr
if not issubclass(cls, ReprEnum) or update_str:
cls.__str__ = global_str
sys.modules[cls.__module__].__dict__.update(cls.__members__)
return cls
|
|
80,939 | 272,022 | 584 | keras/engine/training_v1.py | 101 | 28 | def create_training_target(self, target, run_eagerly=False):
if self.has_training_target():
raise ValueError(
"The training_target field for the _TrainingEndpoint "
"instance has already been populated"
)
if run_eagerly:
# When run_eagerly, the target tensor is ignored, and the None placeholder
# is created instead.
self.training_target = | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | create_training_target | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | training_v1.py | 17 | 35 | https://github.com/keras-team/keras.git | 7 | 172 | 0 | 64 | 276 | Python | {
"docstring": "Create training_target instance and update the self.training_target.\n\n Note that the input target should just be a tensor or None, and\n corresponding training target will be created based on the output and\n loss_fn.\n\n Args:\n target: the target tensor for the current output. Could be None.\n run_eagerly: boolean, whether the model is in run_eagerly mode.\n\n Raises:\n ValueError if the training_target field for the current instance has\n already been populated.\n ",
"language": "en",
"n_whitespaces": 145,
"n_words": 67,
"vocab_size": 49
} | def create_training_target(self, target, run_eagerly=False):
if self.has_training_target():
raise ValueError(
"The training_target field for the _TrainingEndpoint "
"instance has already been populated"
)
if run_eagerly:
# When run_eagerly, the target tensor is ignored, and the None placeholder
# is created instead.
self.training_target = _TrainingTarget(
None, feedable=True, skip_target_weights=False
)
return
if self.should_skip_target():
self.training_target = _TrainingTarget(None)
else:
if target is not None and not backend.is_placeholder(target):
feedable = False
skip_target_weights = True
else:
feedable = True
skip_target_weights = False
if target is None:
target_dtype = losses.LABEL_DTYPES_FOR_LOSSES.get(
self.loss_fn, backend.dtype(self.output)
)
target = backend.placeholder(
ndim=len(self.shape),
name=self.output_name + "_target",
sparse=backend.is_sparse(self.output),
dtype=target_dtype,
)
self.training_target = _TrainingTarget(
target,
feedable=feedable,
skip_target_weights=skip_target_weights,
)
|
|
8,117 | 43,999 | 130 | tests/models/test_dag.py | 45 | 23 | def test_set_task_instance_state(run_id, execution_date, session, dag_maker):
start_date = datetime_tz(2020, 1, 1)
with dag_maker("test_set_task_instance_state", start_date=start_date, session=session) as dag:
task_1 = DummyOperator(task_id="task_1")
task_2 = DummyOperator(task_id="ta | Use `DagRun.run_id` instead of `execution_date` when updating state of TIs(UI & REST API) (#18724)
We can now use run_id as well as execution_date to update states
of task instances
Co-authored-by: Tzu-ping Chung <[email protected]>
Co-authored-by: Ash Berlin-Taylor <[email protected]> | test_set_task_instance_state | 2b4bf7fe67fc656ceb7bdaad36453b7a5b83ef04 | airflow | test_dag.py | 12 | 39 | https://github.com/apache/airflow.git | 2 | 321 | 0 | 38 | 188 | Python | {
"docstring": "Test that set_task_instance_state updates the TaskInstance state and clear downstream failed",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def test_set_task_instance_state(run_id, execution_date, session, dag_maker):
start_date = datetime_tz(2020, 1, 1)
with dag_maker("test_set_task_instance_state", start_date=start_date, session=session) as dag:
task_1 = DummyOperator(task_id="task_1")
task_2 = DummyOperator(task_id="task_2")
task_3 = DummyOperator(task_id="task_3")
task_4 = DummyOperator(task_id="task_4")
task_5 = DummyOperator(task_id="task_5")
task_1 >> [task_2, task_3, task_4, task_5]
dagrun = dag_maker.create_dagrun(
run_id=run_id,
execution_date=execution_date,
state=State.FAILED,
run_type=DagRunType.SCHEDULED,
)
|
|
95,433 | 296,453 | 260 | homeassistant/components/roon/config_flow.py | 56 | 22 | async def async_step_link(self, user_input=None):
errors = {}
if user_input is not None:
# Do not authenticate if the host is already configured
self._async_abort_entries_match({CONF_HOST: self._host})
try:
info = await authenticate(
self.hass, self._host, self._port, self._servers
)
except InvalidAuth:
errors["base"] = "invalid_auth"
except Exception: # pylint: dis | Improve roon integraton (#66000)
* Update to new library, revise discovery to work with new library, specify port to work with new library.
* Move user gui to fallback.
* Revise tests.
* Handle old config.
* Improve debugging, refresh faster on load.
* Remove duplicate.
* Bump library version.
* Fix docstring per review.
* Review suggestion
Co-authored-by: Martin Hjelmare <[email protected]>
* Review suggestion
Co-authored-by: Martin Hjelmare <[email protected]>
* Add check for duplicate host.
* Add error message to strings.
* Tidy.
* Review changes.
* Remove default.
Co-authored-by: Martin Hjelmare <[email protected]> | async_step_link | 23264c8fd4a3f8bcff5961ed11cab6388d3c67a4 | core | config_flow.py | 14 | 16 | https://github.com/home-assistant/core.git | 4 | 107 | 0 | 46 | 182 | Python | {
"docstring": "Handle linking and authenticting with the roon server.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | async def async_step_link(self, user_input=None):
errors = {}
if user_input is not None:
# Do not authenticate if the host is already configured
self._async_abort_entries_match({CONF_HOST: self._host})
try:
info = await authenticate(
self.hass, self._host, self._port, self._servers
)
except InvalidAuth:
errors["base"] = "invalid_auth"
except Exception: # pylint: disable=broad-except
_LOGGER.exception("Unexpected exception")
errors["base"] = "unknown"
else:
return self.async_create_entry(title=DEFAULT_NAME, data=info)
return self.async_show_form(step_id="link", errors=errors)
|
|
54,138 | 215,744 | 24 | tests/pytests/unit/utils/win_dacl/test_get_sid_string.py | 12 | 10 | def test_get_sid_string_none():
sid_obj = sa | Add tests, migrate some tests to pytest | test_get_sid_string_none | 3bb43882e727b1d36abe2e501759c9c5e9048ecf | salt | test_get_sid_string.py | 10 | 4 | https://github.com/saltstack/salt.git | 1 | 39 | 0 | 11 | 66 | Python | {
"docstring": "\n Validate getting a null sid (S-1-0-0) when a null sid is passed\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 12,
"vocab_size": 9
} | def test_get_sid_string_none():
sid_obj = salt.utils.win_dacl.get_sid(None)
assert isinstance(sid_obj, pywintypes.SIDType)
assert salt.utils.win_dacl.get_sid_string(sid_obj) == "S-1-0-0"
|
|
118,175 | 322,461 | 31 | paddlenlp/datasets/dataset.py | 10 | 8 | def read(self, filename, split='train'):
label_list = self.get_labels()
vocab_info = self.get_vocab()
| [cblue] support converting labels of multi-tasks | read | ba3ea1cffa14d8fddb4d61239d691eba1d711a1d | PaddleNLP | dataset.py | 8 | 40 | https://github.com/PaddlePaddle/PaddleNLP.git | 12 | 260 | 0 | 9 | 46 | Python | {
"docstring": "\n Returns a dataset containing all the examples that can be read from the file path.\n\n If `self.lazy` is False, this eagerly reads all instances from `self._read()`\n and returns a `MapDataset`.\n\n If `self.lazy` is True, this returns an `IterDataset`, which internally\n relies on the generator created from `self._read()` to lazily produce examples.\n In this case your implementation of `_read()` must also be lazy\n (that is, not load all examples into memory at once).\n\n Args:\n filename (str): Path of data file to read, usually provided by `_get_data` \n function.\n split (str, optional): The split name of selected dataset. This only makes\n a different when data files of different splits have different structures.\n \n Returns:\n A `MapDataset|IterDataset`.\n ",
"language": "en",
"n_whitespaces": 255,
"n_words": 112,
"vocab_size": 86
} | def read(self, filename, split='train'):
label_list = self.get_labels()
vocab_info = self.get_vocab()
|
|
16,279 | 74,629 | 47 | wagtail/core/tests/test_whitelist.py | 12 | 9 | def test_no_rule_for_attr(self):
tag = self.soup.b
fn = attribute_rule({"snowman": "barbec | Reformat with black | test_no_rule_for_attr | d10f15e55806c6944827d801cd9c2d53f5da4186 | wagtail | test_whitelist.py | 11 | 5 | https://github.com/wagtail/wagtail.git | 1 | 38 | 0 | 11 | 70 | Python | {
"docstring": "\n Test that attribute_rule() drops attributes for\n which no rule has been defined.\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 12,
"vocab_size": 12
} | def test_no_rule_for_attr(self):
tag = self.soup.b
fn = attribute_rule({"snowman": "barbecue"})
fn(tag)
self.assertEqual(str(tag), "<b>baz</b>")
|
|
@DeveloperAPI | 29,861 | 132,902 | 477 | python/ray/util/check_serialize.py | 103 | 26 | def _inspect_generic_serialization(base_obj, depth, parent, failure_set):
assert not inspect.isfunction(base_obj)
functions = inspect.getmembers(base_obj, predicate=inspect.isfunction)
found = False
with _printer.indent():
for name, obj in functions:
serializable, _ = inspect_serializability(
obj,
name=name,
depth=depth - 1,
_parent=parent,
_failure_set=failure_set,
)
found = found or not serializable
| [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | _inspect_generic_serialization | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | check_serialize.py | 14 | 37 | https://github.com/ray-project/ray.git | 11 | 184 | 1 | 60 | 302 | Python | {
"docstring": "Adds the first-found non-serializable element to the failure_set.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 7
} | def _inspect_generic_serialization(base_obj, depth, parent, failure_set):
assert not inspect.isfunction(base_obj)
functions = inspect.getmembers(base_obj, predicate=inspect.isfunction)
found = False
with _printer.indent():
for name, obj in functions:
serializable, _ = inspect_serializability(
obj,
name=name,
depth=depth - 1,
_parent=parent,
_failure_set=failure_set,
)
found = found or not serializable
if found:
break
with _printer.indent():
members = inspect.getmembers(base_obj)
for name, obj in members:
if name.startswith("__") and name.endswith("__") or inspect.isbuiltin(obj):
continue
serializable, _ = inspect_serializability(
obj,
name=name,
depth=depth - 1,
_parent=parent,
_failure_set=failure_set,
)
found = found or not serializable
if found:
break
if not found:
_printer.print(
f"WARNING: Did not find non-serializable object in {base_obj}. "
"This may be an oversight."
)
return found
@DeveloperAPI |
49,752 | 200,643 | 863 | sympy/combinatorics/perm_groups.py | 287 | 20 | def is_dihedral(self):
r
if self._is_dihedral is not None:
return self._is_dihedral
order = self.order()
if order % 2 == 1:
self._is_dihedral = False
return False
if or | Add a `PermutationGroup.is_dihedral` property | is_dihedral | 624e6f073d5d20e78484f5a0b477469f83678b88 | sympy | perm_groups.py | 12 | 75 | https://github.com/sympy/sympy.git | 18 | 314 | 0 | 131 | 508 | Python | {
"docstring": "\n Return ``True`` if the group is dihedral.\n\n Examples\n ========\n\n >>> from sympy.combinatorics.perm_groups import PermutationGroup\n >>> from sympy.combinatorics.permutations import Permutation\n >>> from sympy.combinatorics.named_groups import SymmetricGroup, CyclicGroup\n >>> G = PermutationGroup(Permutation(1, 6)(2, 5)(3, 4), Permutation(0, 1, 2, 3, 4, 5, 6))\n >>> G.is_dihedral\n True\n >>> G = SymmetricGroup(3)\n >>> G.is_dihedral\n True\n >>> G = CyclicGroup(6)\n >>> G.is_dihedral\n False\n\n References\n ==========\n\n .. [1] https://math.stackexchange.com/a/827273\n .. [2] https://kconrad.math.uconn.edu/blurbs/grouptheory/dihedral.pdf\n .. [3] https://kconrad.math.uconn.edu/blurbs/grouptheory/dihedral2.pdf\n .. [4] https://en.wikipedia.org/wiki/Dihedral_group\n ",
"language": "en",
"n_whitespaces": 225,
"n_words": 70,
"vocab_size": 48
} | def is_dihedral(self):
r
if self._is_dihedral is not None:
return self._is_dihedral
order = self.order()
if order % 2 == 1:
self._is_dihedral = False
return False
if order == 2:
self._is_dihedral = True
return True
if order == 4:
# The dihedral group of order 4 is the Klein 4-group.
self._is_dihedral = not self.is_cyclic
return self._is_dihedral
if self.is_abelian:
# The only abelian dihedral groups are the ones of orders 2 and 4.
self._is_dihedral = False
return False
# Now we know the group is of even order >= 6, and nonabelian.
n = order // 2
# Handle special cases where there are exactly two generators.
gens = self.generators
if len(gens) == 2:
x, y = gens
a, b = x.order(), y.order()
# Make a >= b
if a < b:
x, y, a, b = y, x, b, a
# Using Theorem 2.1 of [3]:
if {a, b} == {2}:
self._is_dihedral = True
return True
# Using Theorem 1.1 of [3]:
if (a, b) == (n, 2) and y*x*y == ~x:
self._is_dihedral = True
return True
# Procede with algorithm of [1]
# Find elements of orders 2 and n
order_2, order_n = [], []
for p in self.elements:
k = p.order()
if k == 2:
order_2.append(p)
elif k == n:
order_n.append(p)
if len(order_2) != n + 1 - (n % 2):
self._is_dihedral = False
return False
if not order_n:
self._is_dihedral = False
return False
x = order_n[0]
# Want an element y of order 2 that is not a power of x
# (i.e. that is not the 180-deg rotation, when n is even).
y = order_2[0]
if n % 2 == 0 and y == x**(n//2):
y = order_2[1]
self._is_dihedral = (y*x*y == ~x)
return self._is_dihedral
|
|
78,343 | 266,230 | 45 | netbox/dcim/signals.py | 14 | 12 | def extend_rearport_cable_paths(instance, created, **kwargs):
if created:
rearport = instance.rear_port
for cablepath in CablePath.objects.filter(_nodes__contains=rearport):
| Fixes #10969: Update cable paths ending at associated rear port when creating new front ports | extend_rearport_cable_paths | 4e27e8d3dd2cbfe3279bda3631ca92a7facdd334 | netbox | signals.py | 11 | 5 | https://github.com/netbox-community/netbox.git | 3 | 38 | 0 | 14 | 62 | Python | {
"docstring": "\n When a new FrontPort is created, add it to any CablePaths which end at its corresponding RearPort.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 17,
"vocab_size": 17
} | def extend_rearport_cable_paths(instance, created, **kwargs):
if created:
rearport = instance.rear_port
for cablepath in CablePath.objects.filter(_nodes__contains=rearport):
cablepath.retrace()
|
|
19,840 | 100,345 | 1,104 | lib/gui/utils.py | 281 | 50 | def _load_images_to_cache(self, image_files, frame_dims, thumbnail_size):
logger.debug("Number image_files: %s, frame_dims: %s, thumbnail_size: %s",
len(image_files), frame_dims, thumbnail_size)
num_images = (frame_dims[0] // thumbnail_size) * (frame_dims[1] // thumbnail_size)
logger.debug("num_images: %s", num_images)
if num_images == 0:
return False
samples = []
start_idx = len(image_files) - num_images if len(image_files) > num_images else 0
show_files = sorted(image_files, key=os.path.getctime)[start_idx:]
dropped_files = []
for fname in show_files:
try:
img = Image.open(fname)
except PermissionError as err:
logger.debug("Permission error opening preview file: '%s'. Original error: %s",
fname, str(err))
dropped_files.append(fname)
continue
except Exception as err: # pylint:disable=broad-except
| Update code to support Tensorflow versions up to 2.8 (#1213)
* Update maximum tf version in setup + requirements
* - bump max version of tf version in launcher
- standardise tf version check
* update keras get_custom_objects for tf>2.6
* bugfix: force black text in GUI file dialogs (linux)
* dssim loss - Move to stock tf.ssim function
* Update optimizer imports for compatibility
* fix logging for tf2.8
* Fix GUI graphing for TF2.8
* update tests
* bump requirements.txt versions
* Remove limit on nvidia-ml-py
* Graphing bugfixes
- Prevent live graph from displaying if data not yet available
* bugfix: Live graph. Collect loss labels correctly
* fix: live graph - swallow inconsistent loss errors
* Bugfix: Prevent live graph from clearing during training
* Fix graphing for AMD | _load_images_to_cache | c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf | faceswap | utils.py | 17 | 61 | https://github.com/deepfakes/faceswap.git | 13 | 488 | 0 | 176 | 811 | Python | {
"docstring": " Load preview images to the image cache.\n\n Load new images and append to cache, filtering the cache the number of thumbnails that will\n fit inside the display panel.\n\n Parameters\n ----------\n image_files: list\n A list of new image files that have been modified since the last check\n frame_dims: tuple\n The (width (`int`), height (`int`)) of the display panel that will display the preview\n thumbnail_size: int\n The size of each thumbnail that should be created\n\n Returns\n -------\n bool\n ``True`` if images were successfully loaded to cache otherwise ``False``\n ",
"language": "en",
"n_whitespaces": 209,
"n_words": 86,
"vocab_size": 60
} | def _load_images_to_cache(self, image_files, frame_dims, thumbnail_size):
logger.debug("Number image_files: %s, frame_dims: %s, thumbnail_size: %s",
len(image_files), frame_dims, thumbnail_size)
num_images = (frame_dims[0] // thumbnail_size) * (frame_dims[1] // thumbnail_size)
logger.debug("num_images: %s", num_images)
if num_images == 0:
return False
samples = []
start_idx = len(image_files) - num_images if len(image_files) > num_images else 0
show_files = sorted(image_files, key=os.path.getctime)[start_idx:]
dropped_files = []
for fname in show_files:
try:
img = Image.open(fname)
except PermissionError as err:
logger.debug("Permission error opening preview file: '%s'. Original error: %s",
fname, str(err))
dropped_files.append(fname)
continue
except Exception as err: # pylint:disable=broad-except
# Swallow any issues with opening an image rather than spamming console
# Can happen when trying to read partially saved images
logger.debug("Error opening preview file: '%s'. Original error: %s",
fname, str(err))
dropped_files.append(fname)
continue
width, height = img.size
scaling = thumbnail_size / max(width, height)
logger.debug("image width: %s, height: %s, scaling: %s", width, height, scaling)
try:
img = img.resize((int(width * scaling), int(height * scaling)))
except OSError as err:
# Image only gets loaded when we call a method, so may error on partial loads
logger.debug("OS Error resizing preview image: '%s'. Original error: %s",
fname, err)
dropped_files.append(fname)
continue
if img.size[0] != img.size[1]:
# Pad to square
new_img = Image.new("RGB", (thumbnail_size, thumbnail_size))
new_img.paste(img, ((thumbnail_size - img.size[0])//2,
(thumbnail_size - img.size[1])//2))
img = new_img
draw = ImageDraw.Draw(img)
draw.rectangle(((0, 0), (thumbnail_size, thumbnail_size)), outline="#E5E5E5", width=1)
samples.append(np.array(img))
samples = np.array(samples)
if not np.any(samples):
logger.debug("No preview images collected.")
return False
if dropped_files:
logger.debug("Removing dropped files: %s", dropped_files)
show_files = [fname for fname in show_files if fname not in dropped_files]
self._previewcache["filenames"] = (self._previewcache["filenames"] +
show_files)[-num_images:]
cache = self._previewcache["images"]
if cache is None:
logger.debug("Creating new cache")
cache = samples[-num_images:]
else:
logger.debug("Appending to existing cache")
cache = np.concatenate((cache, samples))[-num_images:]
self._previewcache["images"] = cache
logger.debug("Cache shape: %s", self._previewcache["images"].shape)
return True
|
|
79,173 | 267,897 | 25 | test/lib/ansible_test/_internal/commands/integration/coverage.py | 11 | 7 | def target_profile(self) -> t.Optional[PosixProfile]:
retur | ansible-test - Use more native type hints. (#78435)
* ansible-test - Use more native type hints.
Simple search and replace to switch from comments to native type hints for return types of functions with no arguments.
* ansible-test - Use more native type hints.
Conversion of simple single-line function annotation type comments to native type hints.
* ansible-test - Use more native type hints.
Conversion of single-line function annotation type comments with default values to native type hints.
* ansible-test - Use more native type hints.
Manual conversion of type annotation comments for functions which have pylint directives. | target_profile | 3eb0485dd92c88cc92152d3656d94492db44b183 | ansible | coverage.py | 10 | 3 | https://github.com/ansible/ansible.git | 2 | 33 | 0 | 11 | 51 | Python | {
"docstring": "The POSIX target profile, if it uses a different Python interpreter than the controller, otherwise None.",
"language": "en",
"n_whitespaces": 15,
"n_words": 16,
"vocab_size": 16
} | def target_profile(self) -> t.Optional[PosixProfile]:
return t.cast(PosixProfile, self.profiles[0]) if self.profiles else None
|
|
85,817 | 286,444 | 2,754 | openbb_terminal/portfolio/portfolio_model.py | 512 | 71 | def preprocess_transactions(self):
p_bar = tqdm(range(14), desc="Preprocessing transactions")
try:
# 0. If optional fields not in the transactions add missing
optional_fields = [
"Sector",
"Industry",
"Country",
"Region",
"Fees",
"Premium",
"ISIN",
]
if not set(optional_fields).issubset(set(self.__transactions.columns)):
for field in optional_fields:
if field not in self.__transactions.columns:
self.__transactions[field] = np.nan
p_bar.n += 1
p_bar.refresh()
# 1. Convert Date to datetime
self.__transactions["Date"] = pd.to_datetime(self.__transactions["Date"])
p_bar.n += 1
p_bar.refresh()
# 2. Sort transactions by date
self.__transactions = self.__transactions.sort_values(by="Date")
p_bar.n += 1
p_bar.refre | Portfolio menu bug fixes (#3204)
* normalized way moving average is treated and prevent huge exception prompt
* changed descriptions on docs
* change to check_positive_float
* add integration tests
* fix linting
* add more integration tests
* add more integration tests
* fix linting
* add some po integration tests
* fix flag without prompt
* change orderbook to transactions
* limit warning to portfolio
* change print help
* format portfolio var
* reformat controller
* reformat es
* remove autocompletion
* change print help
* add percentage symbol to summary
* fix holp holv
* fix scripts
* update website doc
* add tqdm progress bars
* fix table spacing
* identify mret tables
* remove positive float from rfr
* flake8
* pylint
* fix reports
* revert to old menu help
* revert to old menu help
* Update test_portfolio.openbb
* quick change on empty lines
Co-authored-by: hjoaquim <[email protected]>
Co-authored-by: James Maslek <[email protected]> | preprocess_transactions | f9086d6f38cf5de4bf3e44be0b4ccd332dbaca46 | OpenBBTerminal | portfolio_model.py | 23 | 137 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 14 | 838 | 0 | 267 | 1,446 | Python | {
"docstring": "Method to preprocess, format and compute auxiliary fields.\n\n Preprocessing steps:\n 0. If optional fields not in the transactions add missing\n 1. Convert Date to datetime\n 2. Sort transactions by date\n 3. Capitalize Ticker and Type [of instrument...]\n 4. Translate side: [\"deposit\", \"buy\"] -> 1 and [\"withdrawal\", \"sell\"] -> -1\n 5. Convert quantity to signed integer\n 6. Determining the investment/divestment value\n 7. Reformat crypto tickers to yfinance format (e.g. BTC -> BTC-USD)\n 8. Reformat STOCK/ETF tickers to yfinance format if ISIN provided\n 9. Remove unsupported ISINs that came out empty\n 10. Create tickers dictionary with structure {'Type': [Ticker]}\n 11. Create list with tickers except cash\n 12. Save transactions inception date\n 13. Populate fields Sector, Industry and Country\n ",
"language": "en",
"n_whitespaces": 284,
"n_words": 116,
"vocab_size": 92
} | def preprocess_transactions(self):
p_bar = tqdm(range(14), desc="Preprocessing transactions")
try:
# 0. If optional fields not in the transactions add missing
optional_fields = [
"Sector",
"Industry",
"Country",
"Region",
"Fees",
"Premium",
"ISIN",
]
if not set(optional_fields).issubset(set(self.__transactions.columns)):
for field in optional_fields:
if field not in self.__transactions.columns:
self.__transactions[field] = np.nan
p_bar.n += 1
p_bar.refresh()
# 1. Convert Date to datetime
self.__transactions["Date"] = pd.to_datetime(self.__transactions["Date"])
p_bar.n += 1
p_bar.refresh()
# 2. Sort transactions by date
self.__transactions = self.__transactions.sort_values(by="Date")
p_bar.n += 1
p_bar.refresh()
# 3. Capitalize Ticker and Type [of instrument...]
self.__transactions["Ticker"] = self.__transactions["Ticker"].map(
lambda x: x.upper()
)
self.__transactions["Type"] = self.__transactions["Type"].map(
lambda x: x.upper()
)
p_bar.n += 1
p_bar.refresh()
# 4. Translate side: ["deposit", "buy"] -> 1 and ["withdrawal", "sell"] -> -1
self.__transactions["Signal"] = self.__transactions["Side"].map(
lambda x: 1
if x.lower() in ["deposit", "buy"]
else (-1 if x.lower() in ["withdrawal", "sell"] else 0)
)
p_bar.n += 1
p_bar.refresh()
# 5. Convert quantity to signed integer
self.__transactions["Quantity"] = (
abs(self.__transactions["Quantity"]) * self.__transactions["Signal"]
)
p_bar.n += 1
p_bar.refresh()
# 6. Determining the investment/divestment value
self.__transactions["Investment"] = (
self.__transactions["Quantity"] * self.__transactions["Price"]
+ self.__transactions["Fees"]
)
p_bar.n += 1
p_bar.refresh()
# 7. Reformat crypto tickers to yfinance format (e.g. BTC -> BTC-USD)
crypto_trades = self.__transactions[self.__transactions.Type == "CRYPTO"]
self.__transactions.loc[
(self.__transactions.Type == "CRYPTO"), "Ticker"
] = [
f"{crypto}-{currency}"
for crypto, currency in zip(
crypto_trades.Ticker, crypto_trades.Currency
)
]
p_bar.n += 1
p_bar.refresh()
# 8. Reformat STOCK/ETF tickers to yfinance format if ISIN provided.
# If isin not valid ticker is empty
self.__transactions["yf_Ticker"] = self.__transactions["ISIN"].apply(
lambda x: yf.utils.get_ticker_by_isin(x) if not pd.isna(x) else np.nan
)
empty_tickers = list(
self.__transactions[
(self.__transactions["yf_Ticker"] == "")
| (self.__transactions["yf_Ticker"].isna())
]["Ticker"].unique()
)
# If ticker from isin is empty it is not valid in yfinance, so check if user provided ticker is supported
removed_tickers = []
for item in empty_tickers:
with contextlib.redirect_stdout(None):
# Suppress yfinance failed download message if occurs
valid_ticker = not (
yf.download(
item,
start=datetime.datetime.now() + datetime.timedelta(days=-5),
progress=False,
).empty
)
if valid_ticker:
# Invalid ISIN but valid ticker
self.__transactions.loc[
self.__transactions["Ticker"] == item, "yf_Ticker"
] = np.nan
else:
self.__transactions.loc[
self.__transactions["Ticker"] == item, "yf_Ticker"
] = ""
removed_tickers.append(item)
# Merge reformated tickers into Ticker
self.__transactions["Ticker"] = self.__transactions["yf_Ticker"].fillna(
self.__transactions["Ticker"]
)
p_bar.n += 1
p_bar.refresh()
# 9. Remove unsupported ISINs that came out empty
self.__transactions.drop(
self.__transactions[self.__transactions["Ticker"] == ""].index,
inplace=True,
)
p_bar.n += 1
p_bar.refresh()
# 10. Create tickers dictionary with structure {'Type': [Ticker]}
for ticker_type in set(self.__transactions["Type"]):
self.tickers[ticker_type] = list(
set(
self.__transactions[
self.__transactions["Type"].isin([ticker_type])
]["Ticker"]
)
)
p_bar.n += 1
p_bar.refresh()
# 11. Create list with tickers except cash
self.tickers_list = list(set(self.__transactions["Ticker"]))
p_bar.n += 1
p_bar.refresh()
# 12. Save transactions inception date
self.inception_date = self.__transactions["Date"][0]
p_bar.n += 1
p_bar.refresh()
# 13. Populate fields Sector, Industry and Country
if (
self.__transactions.loc[
self.__transactions["Type"] == "STOCK",
optional_fields,
]
.isnull()
.values.any()
):
# If any fields is empty for stocks (overwrites any info there)
self.load_company_data()
p_bar.n += 1
p_bar.refresh()
# Warn user of removed ISINs
if removed_tickers:
console.print(
f"\n\n[red]The following tickers are not supported and were removed: {removed_tickers}."
f"\nManually edit the 'Ticker' field with the proper Yahoo Finance suffix or provide a valid ISIN."
f"\nSuffix info on 'Yahoo Finance market coverage':"
" https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html"
f"\nE.g. IWDA -> IWDA.AS[/red]"
)
except Exception:
console.print("\nCould not preprocess transactions.")
|
|
79,831 | 269,013 | 39 | keras/optimizers/optimizer_v2/optimizer_v2.py | 27 | 7 | def _var_key(var):
# pylint: disable=protected-access
# Get the distributed variable if it exists.
if hasattr(var, "_distributed_container"):
var = var._distributed_container()
if getattr(var, "_in_g | Support checkpointing ShardedVariables in optimizer slot variables.
PiperOrigin-RevId: 429577423 | _var_key | 75d70a610dffe927d89ceb400d79bb7f9027b26e | keras | optimizer_v2.py | 10 | 6 | https://github.com/keras-team/keras.git | 3 | 39 | 0 | 23 | 69 | Python | {
"docstring": "Key for representing a primary variable, for looking up slots.\n\n In graph mode the name is derived from the var shared name.\n In eager mode the name is derived from the var unique id.\n If distribution strategy exists, get the primary variable first.\n\n Args:\n var: the variable.\n\n Returns:\n the unique name of the variable.\n ",
"language": "en",
"n_whitespaces": 66,
"n_words": 54,
"vocab_size": 35
} | def _var_key(var):
# pylint: disable=protected-access
# Get the distributed variable if it exists.
if hasattr(var, "_distributed_container"):
var = var._distributed_container()
if getattr(var, "_in_graph_mode", False):
return var._shared_name
return var._unique_id
|
|
76,836 | 261,492 | 57 | sklearn/ensemble/tests/test_stacking.py | 26 | 19 | def test_stacking_classifier_base_regressor():
X_train, X_test, y_train, y_test = train_test_split(
scale(X_iris), y_iris, stratify=y_iris, random_state=42
)
clf = StackingClassifier(estimators=[("ridge", Ridge())])
clf.fit(X_train, y_train)
clf.predict(X_test)
clf.predict_proba(X_test)
asser | ENH StackingClassifier allows regressors in its first layer (#24538)
Co-authored-by: Tom Dupré la Tour <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]> | test_stacking_classifier_base_regressor | b1807ff8ead319a08294beeaae90c3f03b2bb8ac | scikit-learn | test_stacking.py | 13 | 9 | https://github.com/scikit-learn/scikit-learn.git | 1 | 79 | 0 | 25 | 121 | Python | {
"docstring": "Check that a regressor can be used as the first layer in `StackingClassifier`.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | def test_stacking_classifier_base_regressor():
X_train, X_test, y_train, y_test = train_test_split(
scale(X_iris), y_iris, stratify=y_iris, random_state=42
)
clf = StackingClassifier(estimators=[("ridge", Ridge())])
clf.fit(X_train, y_train)
clf.predict(X_test)
clf.predict_proba(X_test)
assert clf.score(X_test, y_test) > 0.8
|
|
23,079 | 108,151 | 580 | lib/matplotlib/backends/backend_svg.py | 145 | 27 | def _get_style_dict(self, gc, rgbFace):
attrib = {}
forced_alpha = gc.get_forced_alpha()
if gc.get_hatch() is not None:
attrib['fill'] = "url(#%s)" % self._get_hatch(gc, rgbFace)
if (rgbFace is not None and len(rgbFace) == 4 and rgbFace[3] != 1.0
and not forced_alpha):
attrib['fill-opacity'] = _short_float_fmt(rgbFace[3])
else:
if rgbFace is None:
attrib['fill'] = 'none'
else:
if tuple(rgbFace[:3]) != (0, 0, 0):
attrib['fill'] = rgb2hex(rgbFace)
if (len(rgbFace) == 4 and rgbFace[3] != 1.0
and not forced_alpha):
attrib['fill-opacity'] = _short_float_fmt(rgbFace[3])
if forced_alpha and gc.get_alpha() != 1.0:
attrib['opacity'] = _short_float_fmt(gc.get_alpha())
offset, seq = gc.get_dashes()
if seq is not None:
attrib['stroke-dasharray'] = ','.join(
_short_float_fmt(val) for val in seq)
attrib['stroke-dashoffset'] = _short_float_fmt(float(offset))
linewidth = gc.get_linewidth()
if linewidth:
| Deprecate functions in backends | _get_style_dict | ec410abbb3a721e31f3aaa61e9e4f941467e35e1 | matplotlib | backend_svg.py | 17 | 37 | https://github.com/matplotlib/matplotlib.git | 21 | 342 | 0 | 76 | 558 | Python | {
"docstring": "Generate a style string from the GraphicsContext and rgbFace.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | def _get_style_dict(self, gc, rgbFace):
attrib = {}
forced_alpha = gc.get_forced_alpha()
if gc.get_hatch() is not None:
attrib['fill'] = "url(#%s)" % self._get_hatch(gc, rgbFace)
if (rgbFace is not None and len(rgbFace) == 4 and rgbFace[3] != 1.0
and not forced_alpha):
attrib['fill-opacity'] = _short_float_fmt(rgbFace[3])
else:
if rgbFace is None:
attrib['fill'] = 'none'
else:
if tuple(rgbFace[:3]) != (0, 0, 0):
attrib['fill'] = rgb2hex(rgbFace)
if (len(rgbFace) == 4 and rgbFace[3] != 1.0
and not forced_alpha):
attrib['fill-opacity'] = _short_float_fmt(rgbFace[3])
if forced_alpha and gc.get_alpha() != 1.0:
attrib['opacity'] = _short_float_fmt(gc.get_alpha())
offset, seq = gc.get_dashes()
if seq is not None:
attrib['stroke-dasharray'] = ','.join(
_short_float_fmt(val) for val in seq)
attrib['stroke-dashoffset'] = _short_float_fmt(float(offset))
linewidth = gc.get_linewidth()
if linewidth:
rgb = gc.get_rgb()
attrib['stroke'] = rgb2hex(rgb)
if not forced_alpha and rgb[3] != 1.0:
attrib['stroke-opacity'] = _short_float_fmt(rgb[3])
if linewidth != 1.0:
attrib['stroke-width'] = _short_float_fmt(linewidth)
if gc.get_joinstyle() != 'round':
attrib['stroke-linejoin'] = gc.get_joinstyle()
if gc.get_capstyle() != 'butt':
attrib['stroke-linecap'] = _capstyle_d[gc.get_capstyle()]
return attrib
|
|
25,734 | 116,362 | 348 | tests/unit/test_executor.py | 85 | 28 | def test_union(self, mock_handler):
self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})
# --- use predictor ---
predictor = {
'name': 'task_model',
'predict': 'p',
'dtypes': {
'p': dtype.float,
'a': dtype.integer,
'b': dtype.categorical,
'c': dtype.datetime
},
'predicted_value': 'ccc'
}
self.set_predictor(predictor)
sql =
# union all
ret = self.command_executor.execute_command(
parse_sql(sql.format(union='ALL'), dialect='mindsdb'))
assert ret.error_code is None
ret_df = self.ret_to_df(ret)
assert list(ret_df.columns) = | union command
#2852 | test_union | 61f6f6c3c8154fa0629df8a016d449ceded99879 | mindsdb | test_executor.py | 14 | 35 | https://github.com/mindsdb/mindsdb.git | 1 | 201 | 0 | 53 | 346 | Python | {
"docstring": "\n SELECT a as a1, b as target\n FROM pg.tasks\n UNION {union}\n SELECT model.a as a2, model.p as target2\n FROM pg.tasks as t\n JOIN mindsdb.task_model as model\n WHERE t.a=1 \n ",
"language": "en",
"n_whitespaces": 131,
"n_words": 28,
"vocab_size": 20
} | def test_union(self, mock_handler):
self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})
# --- use predictor ---
predictor = {
'name': 'task_model',
'predict': 'p',
'dtypes': {
'p': dtype.float,
'a': dtype.integer,
'b': dtype.categorical,
'c': dtype.datetime
},
'predicted_value': 'ccc'
}
self.set_predictor(predictor)
sql =
# union all
ret = self.command_executor.execute_command(
parse_sql(sql.format(union='ALL'), dialect='mindsdb'))
assert ret.error_code is None
ret_df = self.ret_to_df(ret)
assert list(ret_df.columns) == ['a1', 'target']
assert ret_df.shape[0] == 3 + 2
# union
ret = self.command_executor.execute_command(
parse_sql(sql.format(union=''), dialect='mindsdb'))
assert ret.error_code is None
ret_df = self.ret_to_df(ret)
assert list(ret_df.columns) == ['a1', 'target']
assert ret_df.shape[0] == 3
|
|
8,522 | 45,252 | 89 | tests/utils/test_db_cleanup.py | 21 | 13 | def test_run_cleanup_tables(self, clean_table_mock, table_names):
base_kwargs = dict(
| Add `db clean` CLI command for purging old data (#20838)
CLI command to delete old rows from airflow metadata database.
Notes:
* Must supply "purge before date".
* Can optionally provide table list.
* Dry run will only print the number of rows meeting criteria.
* If not dry run, will require the user to confirm before deleting. | test_run_cleanup_tables | c75774d3a31efe749f55ba16e782737df9f53af4 | airflow | test_db_cleanup.py | 9 | 8 | https://github.com/apache/airflow.git | 2 | 52 | 0 | 21 | 79 | Python | {
"docstring": "\n ``_cleanup_table`` should be called for each table in subset if one\n is provided else should be called for all tables.\n ",
"language": "en",
"n_whitespaces": 42,
"n_words": 20,
"vocab_size": 16
} | def test_run_cleanup_tables(self, clean_table_mock, table_names):
base_kwargs = dict(
clean_before_timestamp=None,
dry_run=None,
verbose=None,
)
run_cleanup(**base_kwargs, table_names=table_names)
assert clean_table_mock.call_count == len(table_names) if table_names else len(config_dict)
|
|
8,455 | 45,040 | 21 | tests/models/test_xcom.py | 7 | 6 | def test_set_serialize_call_old_signature(self, get_import, session):
serialize_watcher = | Add params dag_id, task_id etc to XCom.serialize_value (#19505)
When implementing a custom XCom backend, in order to store XCom objects organized by dag_id, run_id etc, we need to pass those params to `serialize_value`. | test_set_serialize_call_old_signature | 56285eee04285d8b6fac90911248d7e9dd5504d8 | airflow | test_xcom.py | 8 | 16 | https://github.com/apache/airflow.git | 1 | 82 | 0 | 7 | 26 | Python | {
"docstring": "\n When XCom.serialize_value takes only param ``value``, other kwargs should be ignored.\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 11
} | def test_set_serialize_call_old_signature(self, get_import, session):
serialize_watcher = MagicMock()
|
|
69,715 | 241,856 | 152 | scipy/stats/_stats_py.py | 63 | 17 | def gmean(a, axis=0, dtype=None, weights=None):
if not isinstance(a, np.ndarray):
# if not an ndarray object attempt to convert it
log_a = np.log(np.array(a, dtype=dtype))
elif dtype:
# Must change the default dtype allowing array type
if isinstance(a, np.ma.MaskedArray):
| ENH: stats: add `axis` tuple and `nan_policy` to `gmean` (#14657)
* ENH: stats: add `axis` tuple and `nan_policy` to `gmean`
Co-authored-by: Pamphile ROY <[email protected]> | gmean | 465da5496a8dda099646e9d5947f24dfc0ec44e9 | scipy | _stats_py.py | 17 | 13 | https://github.com/scipy/scipy.git | 5 | 147 | 0 | 45 | 228 | Python | {
"docstring": "Compute the geometric mean along the specified axis.\n\n Return the geometric average of the array elements.\n That is: n-th root of (x1 * x2 * ... * xn)\n\n Parameters\n ----------\n a : array_like\n Input array or object that can be converted to an array.\n axis : int or None, optional\n Axis along which the geometric mean is computed. Default is 0.\n If None, compute over the whole array `a`.\n dtype : dtype, optional\n Type of the returned array and of the accumulator in which the\n elements are summed. If dtype is not specified, it defaults to the\n dtype of a, unless a has an integer dtype with a precision less than\n that of the default platform integer. In that case, the default\n platform integer is used.\n weights : array_like, optional\n The `weights` array must be broadcastable to the same shape as `a`.\n Default is None, which gives each value a weight of 1.0.\n\n Returns\n -------\n gmean : ndarray\n See `dtype` parameter above.\n\n See Also\n --------\n numpy.mean : Arithmetic average\n numpy.average : Weighted average\n hmean : Harmonic mean\n\n Notes\n -----\n The geometric average is computed over a single dimension of the input\n array, axis=0 by default, or all values in the array if axis=None.\n float64 intermediate and return values are used for integer inputs.\n Beginning in SciPy 1.9, ``np.matrix`` inputs are converted to\n ``np.ndarray``s before the calculation is performed. In this case, the\n output will be a scalar or ``np.ndarray`` of appropriate shape rather than\n a 2D ``np.matrix``. Similarly, while masked elements of masked arrays\n are still ignored, the output will be a scalar or ``np.ndarray`` rather\n than a masked array with ``mask=False``.\n\n References\n ----------\n .. [1] \"Weighted Geometric Mean\", *Wikipedia*, https://en.wikipedia.org/wiki/Weighted_geometric_mean.\n\n Examples\n --------\n >>> from scipy.stats import gmean\n >>> gmean([1, 4])\n 2.0\n >>> gmean([1, 2, 3, 4, 5, 6, 7])\n 3.3800151591412964\n\n ",
"language": "en",
"n_whitespaces": 493,
"n_words": 301,
"vocab_size": 177
} | def gmean(a, axis=0, dtype=None, weights=None):
if not isinstance(a, np.ndarray):
# if not an ndarray object attempt to convert it
log_a = np.log(np.array(a, dtype=dtype))
elif dtype:
# Must change the default dtype allowing array type
if isinstance(a, np.ma.MaskedArray):
log_a = np.log(np.ma.asarray(a, dtype=dtype))
else:
log_a = np.log(np.asarray(a, dtype=dtype))
else:
log_a = np.log(a)
if weights is not None:
weights = np.asanyarray(weights, dtype=dtype)
return np.exp(np.average(log_a, axis=axis, weights=weights))
|
|
50,868 | 204,741 | 47 | django/core/serializers/base.py | 15 | 5 | def handle_m2m_field(self, obj, field):
raise NotImplementedError(
"subclasses of Serializer must provide a handle_m2m_field() method"
)
| Refs #33476 -- Reformatted code with Black. | handle_m2m_field | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | base.py | 8 | 4 | https://github.com/django/django.git | 1 | 15 | 0 | 15 | 27 | Python | {
"docstring": "\n Called to handle a ManyToManyField.\n ",
"language": "en",
"n_whitespaces": 20,
"n_words": 5,
"vocab_size": 5
} | def handle_m2m_field(self, obj, field):
raise NotImplementedError(
"subclasses of Serializer must provide a handle_m2m_field() method"
)
|
|
25,818 | 116,724 | 209 | mindsdb/integrations/handlers/hana_handler/hana_handler.py | 61 | 20 | def check_connection(self) -> StatusResponse:
response = StatusResponse(False)
need_to_close = self.is_connected is False
try:
connection = self.connect()
with connection.cursor() as cur:
cur.execute('SELECT * FROM SYS.M_DATABASE')
response.success = True
except dbapi.Error as e:
log.error(f'Error connecting to SAP HANA {self.address}, {e}!')
response.error_message = e
if response.su | feat: add sap hana integration | check_connection | db6291bc6a2cbea0154bd41c3abff3f6cfb7bc8a | mindsdb | hana_handler.py | 13 | 20 | https://github.com/mindsdb/mindsdb.git | 6 | 103 | 0 | 42 | 188 | Python | {
"docstring": "\n Check the connection of the SAP HANA database\n :return: success status and error message if error occurs\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 17,
"vocab_size": 15
} | def check_connection(self) -> StatusResponse:
response = StatusResponse(False)
need_to_close = self.is_connected is False
try:
connection = self.connect()
with connection.cursor() as cur:
cur.execute('SELECT * FROM SYS.M_DATABASE')
response.success = True
except dbapi.Error as e:
log.error(f'Error connecting to SAP HANA {self.address}, {e}!')
response.error_message = e
if response.success is True and need_to_close:
self.disconnect()
if response.success is False and self.is_connected is True:
self.is_connected = False
return response
|
|
47,454 | 195,867 | 185 | sympy/matrices/common.py | 90 | 18 | def extract(self, rowsList, colsList):
r
if not is_sequence(rowsList) or not is_sequence(colsList):
rais | Improved documentation formatting | extract | cda8dfe6f45dc5ed394c2f5cda706cd6c729f713 | sympy | common.py | 12 | 56 | https://github.com/sympy/sympy.git | 15 | 136 | 0 | 49 | 208 | Python | {
"docstring": "Return a submatrix by specifying a list of rows and columns.\n Negative indices can be given. All indices must be in the range\n $-n \\le i < n$ where $n$ is the number of rows or columns.\n\n Examples\n ========\n\n >>> from sympy import Matrix\n >>> m = Matrix(4, 3, range(12))\n >>> m\n Matrix([\n [0, 1, 2],\n [3, 4, 5],\n [6, 7, 8],\n [9, 10, 11]])\n >>> m.extract([0, 1, 3], [0, 1])\n Matrix([\n [0, 1],\n [3, 4],\n [9, 10]])\n\n Rows or columns can be repeated:\n\n >>> m.extract([0, 0, 1], [-1])\n Matrix([\n [2],\n [2],\n [5]])\n\n Every other row can be taken by using range to provide the indices:\n\n >>> m.extract(range(0, m.rows, 2), [-1])\n Matrix([\n [2],\n [8]])\n\n RowsList or colsList can also be a list of booleans, in which case\n the rows or columns corresponding to the True values will be selected:\n\n >>> m.extract([0, 1, 2, 3], [True, False, True])\n Matrix([\n [0, 2],\n [3, 5],\n [6, 8],\n [9, 11]])\n ",
"language": "en",
"n_whitespaces": 426,
"n_words": 156,
"vocab_size": 95
} | def extract(self, rowsList, colsList):
r
if not is_sequence(rowsList) or not is_sequence(colsList):
raise TypeError("rowsList and colsList must be iterable")
# ensure rowsList and colsList are lists of integers
if rowsList and all(isinstance(i, bool) for i in rowsList):
rowsList = [index for index, item in enumerate(rowsList) if item]
if colsList and all(isinstance(i, bool) for i in colsList):
colsList = [index for index, item in enumerate(colsList) if item]
# ensure everything is in range
rowsList = [a2idx(k, self.rows) for k in rowsList]
colsList = [a2idx(k, self.cols) for k in colsList]
return self._eval_extract(rowsList, colsList)
|
|
48,217 | 196,850 | 144 | sympy/integrals/integrals.py | 48 | 16 | def integrate(*args, meijerg=None, conds='piecewise', risch=None, heurisch=None, manual=None, **kwargs):
doit_flags = {
'deep': False,
'meijerg': meijerg,
'conds': conds,
'risch': risch,
'heurisch': heurisch,
'manual': manual
}
integral = Integral(*args, **kwargs)
if isinstance(integral, Integral):
return integral.doit(**doit_flags)
else:
new_args = [a.doit(**doit_flags) if isinstance(a, Integral) else a
for a in integral.args]
return integral.func(*new_args)
| Fix a few docstring formatting issues | integrate | 1eeb01e15f06c6692a5bfd6fd2d2a3002d864a07 | sympy | integrals.py | 14 | 16 | https://github.com/sympy/sympy.git | 4 | 119 | 0 | 43 | 190 | Python | {
"docstring": "integrate(f, var, ...)\n\n Explanation\n ===========\n\n Compute definite or indefinite integral of one or more variables\n using Risch-Norman algorithm and table lookup. This procedure is\n able to handle elementary algebraic and transcendental functions\n and also a huge class of special functions, including Airy,\n Bessel, Whittaker and Lambert.\n\n var can be:\n\n - a symbol -- indefinite integration\n - a tuple (symbol, a) -- indefinite integration with result\n given with ``a`` replacing ``symbol``\n - a tuple (symbol, a, b) -- definite integration\n\n Several variables can be specified, in which case the result is\n multiple integration. (If var is omitted and the integrand is\n univariate, the indefinite integral in that variable will be performed.)\n\n Indefinite integrals are returned without terms that are independent\n of the integration variables. (see examples)\n\n Definite improper integrals often entail delicate convergence\n conditions. Pass conds='piecewise', 'separate' or 'none' to have\n these returned, respectively, as a Piecewise function, as a separate\n result (i.e. result will be a tuple), or not at all (default is\n 'piecewise').\n\n **Strategy**\n\n SymPy uses various approaches to definite integration. One method is to\n find an antiderivative for the integrand, and then use the fundamental\n theorem of calculus. Various functions are implemented to integrate\n polynomial, rational and trigonometric functions, and integrands\n containing DiracDelta terms.\n\n SymPy also implements the part of the Risch algorithm, which is a decision\n procedure for integrating elementary functions, i.e., the algorithm can\n either find an elementary antiderivative, or prove that one does not\n exist. There is also a (very successful, albeit somewhat slow) general\n implementation of the heuristic Risch algorithm. This algorithm will\n eventually be phased out as more of the full Risch algorithm is\n implemented. See the docstring of Integral._eval_integral() for more\n details on computing the antiderivative using algebraic methods.\n\n The option risch=True can be used to use only the (full) Risch algorithm.\n This is useful if you want to know if an elementary function has an\n elementary antiderivative. If the indefinite Integral returned by this\n function is an instance of NonElementaryIntegral, that means that the\n Risch algorithm has proven that integral to be non-elementary. Note that\n by default, additional methods (such as the Meijer G method outlined\n below) are tried on these integrals, as they may be expressible in terms\n of special functions, so if you only care about elementary answers, use\n risch=True. Also note that an unevaluated Integral returned by this\n function is not necessarily a NonElementaryIntegral, even with risch=True,\n as it may just be an indication that the particular part of the Risch\n algorithm needed to integrate that function is not yet implemented.\n\n Another family of strategies comes from re-writing the integrand in\n terms of so-called Meijer G-functions. Indefinite integrals of a\n single G-function can always be computed, and the definite integral\n of a product of two G-functions can be computed from zero to\n infinity. Various strategies are implemented to rewrite integrands\n as G-functions, and use this information to compute integrals (see\n the ``meijerint`` module).\n\n The option manual=True can be used to use only an algorithm that tries\n to mimic integration by hand. This algorithm does not handle as many\n integrands as the other algorithms implemented but may return results in\n a more familiar form. The ``manualintegrate`` module has functions that\n return the steps used (see the module docstring for more information).\n\n In general, the algebraic methods work best for computing\n antiderivatives of (possibly complicated) combinations of elementary\n functions. The G-function methods work best for computing definite\n integrals from zero to infinity of moderately complicated\n combinations of special functions, or indefinite integrals of very\n simple combinations of special functions.\n\n The strategy employed by the integration code is as follows:\n\n - If computing a definite integral, and both limits are real,\n and at least one limit is +- oo, try the G-function method of\n definite integration first.\n\n - Try to find an antiderivative, using all available methods, ordered\n by performance (that is try fastest method first, slowest last; in\n particular polynomial integration is tried first, Meijer\n G-functions second to last, and heuristic Risch last).\n\n - If still not successful, try G-functions irrespective of the\n limits.\n\n The option meijerg=True, False, None can be used to, respectively:\n always use G-function methods and no others, never use G-function\n methods, or use all available methods (in order as described above).\n It defaults to None.\n\n Examples\n ========\n\n >>> from sympy import integrate, log, exp, oo\n >>> from sympy.abc import a, x, y\n\n >>> integrate(x*y, x)\n x**2*y/2\n\n >>> integrate(log(x), x)\n x*log(x) - x\n\n >>> integrate(log(x), (x, 1, a))\n a*log(a) - a + 1\n\n >>> integrate(x)\n x**2/2\n\n Terms that are independent of x are dropped by indefinite integration:\n\n >>> from sympy import sqrt\n >>> integrate(sqrt(1 + x), (x, 0, x))\n 2*(x + 1)**(3/2)/3 - 2/3\n >>> integrate(sqrt(1 + x), x)\n 2*(x + 1)**(3/2)/3\n\n >>> integrate(x*y)\n Traceback (most recent call last):\n ...\n ValueError: specify integration variables to integrate x*y\n\n Note that ``integrate(x)`` syntax is meant only for convenience\n in interactive sessions and should be avoided in library code.\n\n >>> integrate(x**a*exp(-x), (x, 0, oo)) # same as conds='piecewise'\n Piecewise((gamma(a + 1), re(a) > -1),\n (Integral(x**a*exp(-x), (x, 0, oo)), True))\n\n >>> integrate(x**a*exp(-x), (x, 0, oo), conds='none')\n gamma(a + 1)\n\n >>> integrate(x**a*exp(-x), (x, 0, oo), conds='separate')\n (gamma(a + 1), re(a) > -1)\n\n See Also\n ========\n\n Integral, Integral.doit\n\n ",
"language": "en",
"n_whitespaces": 1292,
"n_words": 865,
"vocab_size": 406
} | def integrate(*args, meijerg=None, conds='piecewise', risch=None, heurisch=None, manual=None, **kwargs):
doit_flags = {
'deep': False,
'meijerg': meijerg,
'conds': conds,
'risch': risch,
'heurisch': heurisch,
'manual': manual
}
integral = Integral(*args, **kwargs)
if isinstance(integral, Integral):
return integral.doit(**doit_flags)
else:
new_args = [a.doit(**doit_flags) if isinstance(a, Integral) else a
for a in integral.args]
return integral.func(*new_args)
|
|
45,535 | 186,624 | 84 | certbot-apache/certbot_apache/_internal/augeasparser.py | 23 | 14 | def parsed_paths(self) -> List[str]:
res_paths: List[str] = []
paths = self.parser.existing_paths
for directory | Add typing to certbot.apache (#9071)
* Add typing to certbot.apache
Co-authored-by: Adrien Ferrand <[email protected]> | parsed_paths | 7d9e9a49005de7961e84d2a7c608db57dbab3046 | certbot | augeasparser.py | 14 | 16 | https://github.com/certbot/certbot.git | 3 | 57 | 0 | 20 | 89 | Python | {
"docstring": "\n Returns a list of file paths that have currently been parsed into the parser\n tree. The returned list may include paths with wildcard characters, for\n example: ['/etc/apache2/conf.d/*.load']\n\n This is typically called on the root node of the ParserNode tree.\n\n :returns: list of file paths of files that have been parsed\n ",
"language": "en",
"n_whitespaces": 93,
"n_words": 50,
"vocab_size": 35
} | def parsed_paths(self) -> List[str]:
res_paths: List[str] = []
paths = self.parser.existing_paths
for directory in paths:
for filename in paths[directory]:
res_paths.append(os.path.join(directory, filename))
return res_paths
|
|
77,592 | 264,082 | 142 | PyInstaller/building/utils.py | 45 | 14 | def _check_guts_toc_mtime(attr_name, old_toc, new_toc, last_build, pyc=False):
for dest_name, src_name, typecode in old_toc:
if misc.mtime(src_name) > last_build:
| building: clean up the _check_guts_* helpers | _check_guts_toc_mtime | 21655572a6af55cefb05d0b0afbeb0b0db39ea19 | pyinstaller | utils.py | 15 | 11 | https://github.com/pyinstaller/pyinstaller.git | 6 | 82 | 0 | 34 | 131 | Python | {
"docstring": "\n Rebuild is required if mtimes of files listed in old TOC are newer than last_build.\n\n If pyc=True, check for .py files as well.\n\n Use this for calculated/analysed values read from cache.\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 31,
"vocab_size": 29
} | def _check_guts_toc_mtime(attr_name, old_toc, new_toc, last_build, pyc=False):
for dest_name, src_name, typecode in old_toc:
if misc.mtime(src_name) > last_build:
logger.info("Building because %s changed", src_name)
return True
elif pyc and typecode == 'PYMODULE':
py_filename = src_name[:-1]
if misc.mtime(py_filename) > last_build:
logger.info("Building because %s changed", py_filename)
return True
return False
|
|
25,826 | 116,753 | 213 | mindsdb/integrations/handlers/teradata_handler/teradata_handler.py | 65 | 20 | def check_connection(self) -> StatusResponse:
response = StatusResponse(False)
need_to_close = self.is_co | feat: add teradata integration | check_connection | 47c5e0ac2d89807f8ff7239d423a3d346bd39a1e | mindsdb | teradata_handler.py | 13 | 20 | https://github.com/mindsdb/mindsdb.git | 6 | 103 | 0 | 44 | 188 | Python | {
"docstring": "\n Check the connection of the Teradata database\n :return: success status and error message if error occurs\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 16,
"vocab_size": 14
} | def check_connection(self) -> StatusResponse:
response = StatusResponse(False)
need_to_close = self.is_connected is False
try:
connection = self.connect()
with connection.cursor() as cur:
cur.execute('SELECT 1 FROM (SELECT 1 AS "dual") AS "dual"')
response.success = True
except teradatasql.Error as e:
log.error(f'Error connecting to Teradata {self.host}, {e}!')
response.error_message = e
if response.success is True and need_to_close:
self.disconnect()
if response.success is False and self.is_connected is True:
self.is_connected = False
return response
|
|
5,969 | 32,666 | 148 | utils/prepare_for_doc_test.py | 79 | 22 | def process_doc_file(code_file, add_new_line=True):
with open(code_file, "r", encoding="utf-8", newline="\n") as f:
code = f.read()
# fmt: off
splits = code.split("```")
if len(splits) % 2 != 1:
raise ValueError("The number of occurrences of ``` should be an even number.")
splits = [s if i % 2 == 0 else process_code_block(s, add_new_line=add_new_line) for i, s in enumerate(splits)]
clean_code = "```".join(splits)
# fmt: | Add a check regarding the number of occurrences of ``` (#18389)
Co-authored-by: ydshieh <[email protected]> | process_doc_file | bd6d1b430080aaf7d9a15f908b95631242da3fb0 | transformers | prepare_for_doc_test.py | 14 | 13 | https://github.com/huggingface/transformers.git | 5 | 132 | 0 | 57 | 236 | Python | {
"docstring": "\n Process given file.\n\n Args:\n code_file (`str` or `os.PathLike`): The file in which we want to style the docstring.\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 18,
"vocab_size": 18
} | def process_doc_file(code_file, add_new_line=True):
with open(code_file, "r", encoding="utf-8", newline="\n") as f:
code = f.read()
# fmt: off
splits = code.split("```")
if len(splits) % 2 != 1:
raise ValueError("The number of occurrences of ``` should be an even number.")
splits = [s if i % 2 == 0 else process_code_block(s, add_new_line=add_new_line) for i, s in enumerate(splits)]
clean_code = "```".join(splits)
# fmt: on
diff = clean_code != code
if diff:
print(f"Overwriting content of {code_file}.")
with open(code_file, "w", encoding="utf-8", newline="\n") as f:
f.write(clean_code)
|
|
20,661 | 101,241 | 330 | plugins/extract/align/_base.py | 82 | 28 | def finalize(self, batch):
for face, landmarks in zip(batch["detected_faces"], batch["landmarks"]):
if not isinstance(landmarks, np.ndarray):
landmarks = np.array(landmarks)
face._landmarks_xy = landmarks
logger.trace("Item out: %s", {key: val.shape if isinstance(val, np.ndarray) else val
for key, val in batch.items()})
for filename, face in zip(batch["filename"], batch["detected_faces"]):
self._output_faces.append(face) | lib.align updates:
- alignments.py
- Add typed dicts for imported alignments
- Explicitly check for presence of thumb value in alignments dict
- linting
- detected_face.py
- Typing
- Linting
- Legacy support for pre-aligned face
- Update dependencies to new property names | finalize | 5e73437be47f2410439a3c6716de96354e6a0c94 | faceswap | _base.py | 13 | 18 | https://github.com/deepfakes/faceswap.git | 7 | 174 | 0 | 66 | 280 | Python | {
"docstring": " Finalize the output from Aligner\n\n This should be called as the final task of each `plugin`.\n\n Pairs the detected faces back up with their original frame before yielding each frame.\n\n Parameters\n ----------\n batch : dict\n The final ``dict`` from the `plugin` process. It must contain the `keys`:\n ``detected_faces``, ``landmarks``, ``filename``\n\n Yields\n ------\n :class:`~plugins.extract.pipeline.ExtractMedia`\n The :attr:`DetectedFaces` list will be populated for this class with the bounding boxes\n and landmarks for the detected faces found in the frame.\n ",
"language": "en",
"n_whitespaces": 184,
"n_words": 76,
"vocab_size": 59
} | def finalize(self, batch):
for face, landmarks in zip(batch["detected_faces"], batch["landmarks"]):
if not isinstance(landmarks, np.ndarray):
landmarks = np.array(landmarks)
face._landmarks_xy = landmarks
logger.trace("Item out: %s", {key: val.shape if isinstance(val, np.ndarray) else val
for key, val in batch.items()})
for filename, face in zip(batch["filename"], batch["detected_faces"]):
self._output_faces.append(face)
if len(self._output_faces) != self._faces_per_filename[filename]:
continue
output = self._extract_media.pop(filename)
output.add_detected_faces(self._output_faces)
self._output_faces = []
logger.trace("Final Output: (filename: '%s', image shape: %s, detected_faces: %s, "
"item: %s)",
output.filename, output.image_shape, output.detected_faces, output)
yield output
# <<< PROTECTED METHODS >>> #
# << PROCESS_INPUT WRAPPER >> |
|
52,763 | 209,670 | 49 | scapy/layers/dcerpc.py | 27 | 8 | def find_dcerpc_interface(name):
try:
return next(x for x in DCE_RPC_INTERFACES.values() if x.name == name)
except StopItera | [MS-RPCE] and [MS-SMB] major update (#3683)
* Various fixes regarding DCE/RPC build
* DCE/RPC sessions
* Cleanup unused code
* Add missing GSS_WRAP algo names
* Add find_dcerpc_interface
* Split SMB client and server
* Missing StrFixedLenFieldUtf16
* Remove unfinished smbserver feature
* Friendlier getter for SMB2
* DceRpcNak
* Improve NDR parsing (a lot)
* Minor SMB2 improvements
* BIG NDR refactor + Dissect pointer deferal
* Build with pointer deferral
* Small build bugs
* SMB2 logoff, fix rawToken in SMB standalone
* Add security providers from MS-RPCE to DCERPC
* Cleanup ptr_pack of NDRPacketListField
* Clearer exception in find_dcerpc_interface
* Add minor_version attribute
* Fix computation of auth_pad in sec_trailer
* Fix a WTF bug
* Compute length for NDR arrays
* Pass enum to EnumField
* Match union attributes from response with request
* Improve SMB server
* Small bug in pointer deferal dissection
* Add user-friendly utils
* Add a few NDR tests
* More user-friendly improvements
* Bug: parent not copied in clone_with
* Build: propagate NDR64 and bug fix
* Default close response parameters
* Fix Python 2.7
* Fix SMB2_Create_Context offset
* Fix SMB2 create context
* SMB2: support chain, improvements
* Fix ioctl error
* SMB: check computeNTProofStr
* Fix UTCField default
* Improve FileId capabilities
* SMB2: contexts
* Typos
* Minor NDRUnion fixes
* Py2 fixes | find_dcerpc_interface | ca10c5cf00425d0178998ec0b006cbb65ddbfb54 | scapy | dcerpc.py | 12 | 5 | https://github.com/secdev/scapy.git | 4 | 35 | 0 | 27 | 62 | Python | {
"docstring": "\n Find an interface object through the name in the IDL\n ",
"language": "en",
"n_whitespaces": 17,
"n_words": 10,
"vocab_size": 9
} | def find_dcerpc_interface(name):
try:
return next(x for x in DCE_RPC_INTERFACES.values() if x.name == name)
except StopIteration:
raise AttributeError("Unknown interface !")
# --- NDR fields - [C706] chap 14
|
|
43,806 | 182,367 | 194 | tests/test_animator.py | 86 | 16 | def test_animatable():
animatable = AnimateTest()
# Fake wall-clock time
time = 100.0
# Object that does the animation
animation = SimpleAnimation(
animatable,
"bar",
time,
3.0,
start_value=Animatable(20.0),
end_value=Animatable(50.0),
final_value=Animatable(50.0),
easing=lambda x: x,
)
assert | fix and test for animator | test_animatable | 8be6ea91f6e8a8d24d385975f1a5a7714cf27894 | textual | test_animator.py | 11 | 23 | https://github.com/Textualize/textual.git | 1 | 170 | 0 | 49 | 222 | Python | {
"docstring": "Test SimpleAnimation works with the Animatable protocol",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 7
} | def test_animatable():
animatable = AnimateTest()
# Fake wall-clock time
time = 100.0
# Object that does the animation
animation = SimpleAnimation(
animatable,
"bar",
time,
3.0,
start_value=Animatable(20.0),
end_value=Animatable(50.0),
final_value=Animatable(50.0),
easing=lambda x: x,
)
assert animation(time) is False
assert animatable.bar.value == 20.0
assert animation(time + 1.0) is False
assert animatable.bar.value == 30.0
assert animation(time + 2.0) is False
assert animatable.bar.value == 40.0
assert animation(time + 2.9) is False
assert pytest.approx(animatable.bar.value, 49.0)
assert animation(time + 3.0) is True # True to indicate animation is complete
assert animatable.bar.value == 50.0
|
|
53,068 | 211,340 | 650 | ppdet/metrics/map_utils.py | 125 | 38 | def update(self, bbox, score, label, gt_box, gt_label, difficult=None):
if difficult is None:
difficult = np.zeros_like(gt_label)
# record class gt count
for gtl, diff in zip(gt_label, difficult):
if self.evaluate_difficult or int(diff) == 0:
self.class_gt_counts[int(np.array(gtl))] += 1
# record class score positive
visited = [False] * len(gt_label)
for b, s, l in zip(bbox, score, label):
pred = b.tolist() if isinstance(b, np.ndarray) else b
max_idx = -1
max_overlap = -1.0
for i, gl in enumerate(gt_label):
if int(gl) == int(l):
if len(gt_box[i]) == 8:
overlap = calc_rbox_iou(pred, gt_box[i])
else:
| Refactor rbox (#6704)
* refactor rbox
* modify the code of save results
* fix some problem
* add .gitignore in dataset/dota
* fix test anno path | update | e55e41945d42db787a0f7c557d53d06a6b24536b | PaddleDetection | map_utils.py | 19 | 31 | https://github.com/PaddlePaddle/PaddleDetection.git | 15 | 303 | 0 | 81 | 450 | Python | {
"docstring": "\n Update metric statics from given prediction and ground\n truth infomations.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 10,
"vocab_size": 10
} | def update(self, bbox, score, label, gt_box, gt_label, difficult=None):
if difficult is None:
difficult = np.zeros_like(gt_label)
# record class gt count
for gtl, diff in zip(gt_label, difficult):
if self.evaluate_difficult or int(diff) == 0:
self.class_gt_counts[int(np.array(gtl))] += 1
# record class score positive
visited = [False] * len(gt_label)
for b, s, l in zip(bbox, score, label):
pred = b.tolist() if isinstance(b, np.ndarray) else b
max_idx = -1
max_overlap = -1.0
for i, gl in enumerate(gt_label):
if int(gl) == int(l):
if len(gt_box[i]) == 8:
overlap = calc_rbox_iou(pred, gt_box[i])
else:
overlap = jaccard_overlap(pred, gt_box[i],
self.is_bbox_normalized)
if overlap > max_overlap:
max_overlap = overlap
max_idx = i
if max_overlap > self.overlap_thresh:
if self.evaluate_difficult or \
int(np.array(difficult[max_idx])) == 0:
if not visited[max_idx]:
self.class_score_poss[int(l)].append([s, 1.0])
visited[max_idx] = True
else:
self.class_score_poss[int(l)].append([s, 0.0])
else:
self.class_score_poss[int(l)].append([s, 0.0])
|
|
16,182 | 73,936 | 77 | wagtail/core/permission_policies/collections.py | 20 | 12 | def _get_permission_objects_for_actions(self, actions):
permission_codenames = [
"%s_%s" % (action, self.model_name) for action in actions
]
return Permission.objects.filter(
content_ty | Reformat with black | _get_permission_objects_for_actions | d10f15e55806c6944827d801cd9c2d53f5da4186 | wagtail | collections.py | 10 | 7 | https://github.com/wagtail/wagtail.git | 2 | 42 | 0 | 20 | 66 | Python | {
"docstring": "\n Get a queryset of the Permission objects for the given actions\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 10
} | def _get_permission_objects_for_actions(self, actions):
permission_codenames = [
"%s_%s" % (action, self.model_name) for action in actions
]
return Permission.objects.filter(
content_type=self._content_type, codename__in=permission_codenames
)
|
|
117,569 | 321,163 | 411 | qutebrowser/browser/webengine/webview.py | 99 | 31 | def createWindow(self, wintype):
debug_type = debug.qenum_key(QWebEnginePage, wintype)
background = config.val.tabs.background
log.webview.debug("createWindow with type {}, background {}".format(
debug_type, background))
if wintype == QWebEnginePage.WebWindowType.WebBrowserWindow:
# Shift-Alt-Click
target = usertypes.ClickTarget.window
elif wintype == QWebEnginePage.WebWindowType.WebDialog:
log.webview.warning("{} requested, but we don't support "
"that!".format(debug_type))
target = usertypes.ClickTarget.tab
elif wintype == QWebEnginePage.WebWindowType.WebBrowserTab:
# Middle-click / Ctrl-Click with Shift
# FIXME:qtwebengine this also affects target=_blank links...
if background:
target = usertypes.Click | Run scripts/dev/rewrite_enums.py | createWindow | 0877fb0d78635692e481c8bde224fac5ad0dd430 | qutebrowser | webview.py | 14 | 25 | https://github.com/qutebrowser/qutebrowser.git | 7 | 172 | 0 | 60 | 287 | Python | {
"docstring": "Called by Qt when a page wants to create a new window.\n\n This function is called from the createWindow() method of the\n associated QWebEnginePage, each time the page wants to create a new\n window of the given type. This might be the result, for example, of a\n JavaScript request to open a document in a new window.\n\n Args:\n wintype: This enum describes the types of window that can be\n created by the createWindow() function.\n\n QWebEnginePage::WebBrowserWindow:\n A complete web browser window.\n QWebEnginePage::WebBrowserTab:\n A web browser tab.\n QWebEnginePage::WebDialog:\n A window without decoration.\n QWebEnginePage::WebBrowserBackgroundTab:\n A web browser tab without hiding the current visible\n WebEngineView.\n\n Return:\n The new QWebEngineView object.\n ",
"language": "en",
"n_whitespaces": 397,
"n_words": 106,
"vocab_size": 66
} | def createWindow(self, wintype):
debug_type = debug.qenum_key(QWebEnginePage, wintype)
background = config.val.tabs.background
log.webview.debug("createWindow with type {}, background {}".format(
debug_type, background))
if wintype == QWebEnginePage.WebWindowType.WebBrowserWindow:
# Shift-Alt-Click
target = usertypes.ClickTarget.window
elif wintype == QWebEnginePage.WebWindowType.WebDialog:
log.webview.warning("{} requested, but we don't support "
"that!".format(debug_type))
target = usertypes.ClickTarget.tab
elif wintype == QWebEnginePage.WebWindowType.WebBrowserTab:
# Middle-click / Ctrl-Click with Shift
# FIXME:qtwebengine this also affects target=_blank links...
if background:
target = usertypes.ClickTarget.tab
else:
target = usertypes.ClickTarget.tab_bg
elif wintype == QWebEnginePage.WebWindowType.WebBrowserBackgroundTab:
# Middle-click / Ctrl-Click
if background:
target = usertypes.ClickTarget.tab_bg
else:
target = usertypes.ClickTarget.tab
else:
raise ValueError("Invalid wintype {}".format(debug_type))
tab = shared.get_tab(self._win_id, target)
return tab._widget # pylint: disable=protected-access
|
|
75,856 | 259,662 | 40 | sklearn/ensemble/_gb.py | 12 | 11 | def predict(self, X):
raw_predictions = self.decision_function(X)
encode | DEP loss_ attribute in gradient boosting (#23079) | predict | 0d669dc419524eff7f45032f4c18253e627a055b | scikit-learn | _gb.py | 9 | 4 | https://github.com/scikit-learn/scikit-learn.git | 1 | 39 | 0 | 11 | 63 | Python | {
"docstring": "Predict class for X.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input samples. Internally, it will be converted to\n ``dtype=np.float32`` and if a sparse matrix is provided\n to a sparse ``csr_matrix``.\n\n Returns\n -------\n y : ndarray of shape (n_samples,)\n The predicted values.\n ",
"language": "en",
"n_whitespaces": 140,
"n_words": 47,
"vocab_size": 39
} | def predict(self, X):
raw_predictions = self.decision_function(X)
encoded_labels = self._loss._raw_prediction_to_decision(raw_predictions)
return self.classes_.take(encoded_labels, axis=0)
|
|
42,319 | 177,255 | 29 | networkx/algorithms/operators/all.py | 17 | 6 | def union_all(graphs, rename=()):
R = None
| Make all.py generator friendly (#5984)
* Make compose_all generator friendly
* Make disjoint_union_all and intersection_all generator friendly
* Refactor disjoint_union_all to yield relabeled graphs
* Make union_all generator friendly
* Fix intersection_all
* Fix union_all signature
* Allow passing an infinite rename generator to union_all
* Copy over generalizations to binary.py
* Clean up rename
* Simplify first_label in disjoint_union_all
* Simplify disjoint_union_all
* Add missing R.graph.update in intersection_all | union_all | 50ff08de69c6e9541cd6c029bede5dabf56cfe73 | networkx | all.py | 8 | 66 | https://github.com/networkx/networkx.git | 8 | 194 | 0 | 16 | 35 | Python | {
"docstring": "Returns the union of all graphs.\n\n The graphs must be disjoint, otherwise an exception is raised.\n\n Parameters\n ----------\n graphs : iterable\n Iterable of NetworkX graphs\n\n rename : iterable , optional\n Node names of graphs can be changed by specifying the tuple\n rename=('G-','H-') (for example). Node \"u\" in G is then renamed\n \"G-u\" and \"v\" in H is renamed \"H-v\". Infinite generators (like itertools.count)\n are also supported.\n\n Returns\n -------\n U : a graph with the same type as the first graph in list\n\n Raises\n ------\n ValueError\n If `graphs` is an empty list.\n\n Notes\n -----\n To force a disjoint union with node relabeling, use\n disjoint_union_all(G,H) or convert_node_labels_to integers().\n\n Graph, edge, and node attributes are propagated to the union graph.\n If a graph attribute is present in multiple graphs, then the value\n from the last graph in the list with that attribute is used.\n\n See Also\n --------\n union\n disjoint_union_all\n ",
"language": "en",
"n_whitespaces": 252,
"n_words": 146,
"vocab_size": 100
} | def union_all(graphs, rename=()):
R = None
seen_nodes = set()
# rename graph to obtain disjoint node labels |
|
76,685 | 261,192 | 123 | sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py | 63 | 26 | def test_unknown_category_that_are_negative():
rng = np.random.RandomState(42)
| FIX Treat gradient boosting categoricals outside the bounds as unknown during predict (#24283) | test_unknown_category_that_are_negative | 072b481600c48662fd4893fdce461113becd207a | scikit-learn | test_gradient_boosting.py | 12 | 14 | https://github.com/scikit-learn/scikit-learn.git | 1 | 157 | 0 | 53 | 238 | Python | {
"docstring": "Check that unknown categories that are negative does not error.\n\n Non-regression test for #24274.\n ",
"language": "en",
"n_whitespaces": 20,
"n_words": 14,
"vocab_size": 13
} | def test_unknown_category_that_are_negative():
rng = np.random.RandomState(42)
n_samples = 1000
X = np.c_[rng.rand(n_samples), rng.randint(4, size=n_samples)]
y = np.zeros(shape=n_samples)
y[X[:, 1] % 2 == 0] = 1
hist = HistGradientBoostingRegressor(
random_state=0,
categorical_features=[False, True],
max_iter=10,
).fit(X, y)
# Check that negative values from the second column are treated like a
# missing category
X_test_neg = np.asarray([[1, -2], [3, -4]])
X_test_nan = np.asarray([[1, np.nan], [3, np.nan]])
assert_allclose(hist.predict(X_test_neg), hist.predict(X_test_nan))
|
|
38,909 | 161,098 | 764 | ppg_extractor/encoder/encoder_layer.py | 225 | 32 | def forward(self, x_input, mask, cache=None):
if isinstance(x_input, tuple):
x, pos_emb = x_input[0], x_input[1]
else:
x, pos_emb = x_input, None
# whether to use macaron style
if self.feed_forward_macaron is not None:
| Init ppg extractor and ppg2mel (#375)
* Init ppg extractor and ppg2mel
* add preprocess and training
* FIx known issues
* Update __init__.py
Allow to gen audio
* Fix length issue
* Fix bug of preparing fid
* Fix sample issues
* Add UI usage of PPG-vc | forward | b617a87ee40ab384767a27335313c2c65ee094ec | MockingBird | encoder_layer.py | 14 | 53 | https://github.com/babysor/MockingBird.git | 19 | 449 | 0 | 80 | 699 | Python | {
"docstring": "Compute encoded features.\n\n :param torch.Tensor x_input: encoded source features, w/o pos_emb\n tuple((batch, max_time_in, size), (1, max_time_in, size))\n or (batch, max_time_in, size)\n :param torch.Tensor mask: mask for x (batch, max_time_in)\n :param torch.Tensor cache: cache for x (batch, max_time_in - 1, size)\n :rtype: Tuple[torch.Tensor, torch.Tensor]\n ",
"language": "en",
"n_whitespaces": 92,
"n_words": 43,
"vocab_size": 31
} | def forward(self, x_input, mask, cache=None):
if isinstance(x_input, tuple):
x, pos_emb = x_input[0], x_input[1]
else:
x, pos_emb = x_input, None
# whether to use macaron style
if self.feed_forward_macaron is not None:
residual = x
if self.normalize_before:
x = self.norm_ff_macaron(x)
x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
if not self.normalize_before:
x = self.norm_ff_macaron(x)
# multi-headed self-attention module
residual = x
if self.normalize_before:
x = self.norm_mha(x)
if cache is None:
x_q = x
else:
assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
x_q = x[:, -1:, :]
residual = residual[:, -1:, :]
mask = None if mask is None else mask[:, -1:, :]
if pos_emb is not None:
x_att = self.self_attn(x_q, x, x, pos_emb, mask)
else:
x_att = self.self_attn(x_q, x, x, mask)
if self.concat_after:
x_concat = torch.cat((x, x_att), dim=-1)
x = residual + self.concat_linear(x_concat)
else:
x = residual + self.dropout(x_att)
if not self.normalize_before:
x = self.norm_mha(x)
# convolution module
if self.conv_module is not None:
residual = x
if self.normalize_before:
x = self.norm_conv(x)
x = residual + self.dropout(self.conv_module(x))
if not self.normalize_before:
x = self.norm_conv(x)
# feed forward module
residual = x
if self.normalize_before:
x = self.norm_ff(x)
x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm_ff(x)
if self.conv_module is not None:
x = self.norm_final(x)
if cache is not None:
x = torch.cat([cache, x], dim=1)
if pos_emb is not None:
return (x, pos_emb), mask
return x, mask
|
|
46,502 | 191,364 | 58 | tests/unit_tests/test_prompt.py | 21 | 8 | def test_prompt_invalid_template_format() -> None:
template = "This is a {foo} test."
input_variables = ["foo"]
with pytest.raises(ValueError):
| initial commit | test_prompt_invalid_template_format | 18aeb720126a68201c7e3b5a617139c27c779496 | langchain | test_prompt.py | 11 | 8 | https://github.com/hwchase17/langchain.git | 1 | 37 | 0 | 20 | 69 | Python | {
"docstring": "Test initializing a prompt with invalid template format.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | def test_prompt_invalid_template_format() -> None:
template = "This is a {foo} test."
input_variables = ["foo"]
with pytest.raises(ValueError):
Prompt(
input_variables=input_variables, template=template, template_format="bar"
)
|
|
73,140 | 249,805 | 205 | tests/rest/admin/test_user.py | 48 | 13 | def test_medium_does_not_exist(self) -> None:
# test for unknown medium
url = "/_synapse/admin/v1/threepid/publickey/users/unknown-key"
channel = self.make_request(
"GET",
url,
access_token=self.admin_user_tok,
)
self.assertEqual(404, channel.code, msg=channel.json_body)
self.assertEqual(Codes.NOT_FOUND, channel.json_body["errcode"])
# test for unknown user with a known medium
url = "/_synapse/admin/v1/threepid/email/users/unknown"
channel = self.make_request(
"GET",
url,
acce | Add an Admin API endpoint for looking up users based on 3PID (#14405) | test_medium_does_not_exist | a3623af74e0af0d2f6cbd37b47dc54a1acd314d5 | synapse | test_user.py | 10 | 19 | https://github.com/matrix-org/synapse.git | 1 | 110 | 0 | 28 | 178 | Python | {
"docstring": "Tests that both a lookup for a medium that does not exist and a user that\n doesn't exist with that third party ID returns a 404",
"language": "en",
"n_whitespaces": 32,
"n_words": 26,
"vocab_size": 19
} | def test_medium_does_not_exist(self) -> None:
# test for unknown medium
url = "/_synapse/admin/v1/threepid/publickey/users/unknown-key"
channel = self.make_request(
"GET",
url,
access_token=self.admin_user_tok,
)
self.assertEqual(404, channel.code, msg=channel.json_body)
self.assertEqual(Codes.NOT_FOUND, channel.json_body["errcode"])
# test for unknown user with a known medium
url = "/_synapse/admin/v1/threepid/email/users/unknown"
channel = self.make_request(
"GET",
url,
access_token=self.admin_user_tok,
)
self.assertEqual(404, channel.code, msg=channel.json_body)
self.assertEqual(Codes.NOT_FOUND, channel.json_body["errcode"])
|
|
@dataclass | 121,103 | 337,646 | 282 | src/accelerate/utils/dataclasses.py | 88 | 19 | def deepspeed_config_process(self, prefix="", mismatches=None, config=None, must_match=True, **kwargs):
mismatches = [] if mismatches is None else mismatches
if config is None:
config = self.deepspeed_config
for key, value in config.items():
if isinstance(value, dict):
self.deepspeed_config_process(
prefix=prefix + key + ".", mismatches=mismatches, config=value, must_match=must | DeepSpeed Revamp (#405)
* deepspeed revamp
* Update dataclasses.py
* Update deepspeed.py
* quality
* fixing code
* quality
* FIx imports
* saving 16bit model in zero stage 3
1. Saving 16bit model in zero stage 3
2. zero init in stage 3 support using HFDeepSpeedConfig
* quality
* adding test and fixing bugs
* update makefile for deepspeed tests
* Update test.yml
* adding `deepspeed` as requirement for tests
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <[email protected]>
* quality
* addressing comments
* add example and minor updates
1. Add example to show the usage of config file with revamped deepspeed support.
2. update required deepspeed version to 0.6.5
2. reverting `reinit` change as it is not required,
3. raising Exception when using `clip_grad_value` with DeepSpeed/FSDP.
* Documentation and Zero-3 Inference Support
1. Changes to support ZeRo Stage-3 Inference support.
2. minor bug fixes.
3. Documentation.
* doc fix
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <[email protected]>
* addressing comments
* update doc to address comments and bug fixes
1. update tests and add new one testing autofill functionality of `prepare` method.
2. fix bug related to zero-3 init related to HFDeepSpeedConfig
3. Update documentation addressing comments.
* removing image and hosting it on `documentation-images` dataset
* check for hidden_size for zero_opt heurisitics
Co-authored-by: Sylvain Gugger <[email protected]> | deepspeed_config_process | 1703b79a797dab765996764707186def7533d8fd | accelerate | dataclasses.py | 14 | 17 | https://github.com/huggingface/accelerate.git | 7 | 137 | 1 | 68 | 228 | Python | {
"docstring": "Process the DeepSpeed config with the values from the kwargs.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 8
} | def deepspeed_config_process(self, prefix="", mismatches=None, config=None, must_match=True, **kwargs):
mismatches = [] if mismatches is None else mismatches
if config is None:
config = self.deepspeed_config
for key, value in config.items():
if isinstance(value, dict):
self.deepspeed_config_process(
prefix=prefix + key + ".", mismatches=mismatches, config=value, must_match=must_match, **kwargs
)
else:
self.fill_match(prefix + key, mismatches, must_match=must_match, **kwargs)
if len(mismatches) > 0 and prefix == "":
mismatches_msg = "\n".join(mismatches)
raise ValueError(
"Please correct the following DeepSpeed config values that mismatch kwargs "
f" values:\n{mismatches_msg}\nThe easiest method is to set these DeepSpeed config values to 'auto'."
)
@dataclass |
83,169 | 279,889 | 125 | keras/engine/training.py | 27 | 10 | def get_metrics_result(self):
# Collect metrics to return
return_metrics = {}
for metric in self.metrics:
result = metric.r | Expose Model get_metrics_result on Keras Model as a public API
PiperOrigin-RevId: 475681912 | get_metrics_result | 8cf91871ce167d63069c99120f8580a4976a59d0 | keras | training.py | 13 | 9 | https://github.com/keras-team/keras.git | 3 | 50 | 0 | 22 | 84 | Python | {
"docstring": "Returns the model's metrics values as a dict.\n\n If any of the metric result is a dict (containing multiple metrics),\n each of them gets added to the top level returned dict of this method.\n\n Returns:\n A `dict` containing values of the metrics listed in `self.metrics`.\n Example:\n `{'loss': 0.2, 'accuracy': 0.7}`.\n ",
"language": "en",
"n_whitespaces": 105,
"n_words": 50,
"vocab_size": 40
} | def get_metrics_result(self):
# Collect metrics to return
return_metrics = {}
for metric in self.metrics:
result = metric.result()
if isinstance(result, dict):
return_metrics.update(result)
else:
return_metrics[metric.name] = result
return return_metrics
|
|
55,539 | 218,899 | 593 | python3.10.4/Lib/lib2to3/refactor.py | 95 | 23 | def refactor_docstring(self, input, filename):
result = []
block = None
block_lineno = None
indent = None
lineno = 0
for line in input.splitlines(keepends=True):
lineno += 1
if line.lstrip().startswith(self.PS1):
if block is not None:
result.extend(self.refactor_doctest(block, block_lineno,
indent, filename))
block_lineno = lineno
block = [line | add python 3.10.4 for windows | refactor_docstring | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | refactor.py | 18 | 31 | https://github.com/XX-net/XX-Net.git | 9 | 211 | 0 | 47 | 333 | Python | {
"docstring": "Refactors a docstring, looking for doctests.\n\n This returns a modified version of the input string. It looks\n for doctests, which start with a \">>>\" prompt, and may be\n continued with \"...\" prompts, as long as the \"...\" is indented\n the same as the \">>>\".\n\n (Unfortunately we can't use the doctest module's parser,\n since, like most parsers, it is not geared towards preserving\n the original source.)\n ",
"language": "en",
"n_whitespaces": 122,
"n_words": 65,
"vocab_size": 52
} | def refactor_docstring(self, input, filename):
result = []
block = None
block_lineno = None
indent = None
lineno = 0
for line in input.splitlines(keepends=True):
lineno += 1
if line.lstrip().startswith(self.PS1):
if block is not None:
result.extend(self.refactor_doctest(block, block_lineno,
indent, filename))
block_lineno = lineno
block = [line]
i = line.find(self.PS1)
indent = line[:i]
elif (indent is not None and
(line.startswith(indent + self.PS2) or
line == indent + self.PS2.rstrip() + "\n")):
block.append(line)
else:
if block is not None:
result.extend(self.refactor_doctest(block, block_lineno,
indent, filename))
block = None
indent = None
result.append(line)
if block is not None:
result.extend(self.refactor_doctest(block, block_lineno,
indent, filename))
return "".join(result)
|
|
8,643 | 45,557 | 419 | tests/utils/test_edgemodifier.py | 89 | 23 | def test_complex_reversed_dag(self, test_complex_taskgroup_dag, complex_dag_expected_edges):
(
dag,
group,
(
group_dm1,
group_dm2,
group_dm3,
dm_in1,
dm_in2,
dm_in3,
dm_in4,
dm_out1,
dm_out2,
dm_out3,
dm_out4,
op_in1,
op_out1,
),
) = test_complex_taskgroup_dag
group_dm1 << [group_dm2, group_dm3]
group << dm_in1
group << Label('label dm_in2 <=> group') << dm_in2
group << Label('label dm_in3/dm_in4 <=> group') << [dm_in3, dm_in4]
group << Label('label op_in1 <=> group') << XComArg(op_in1, 'test_key')
dm_out1 << group
dm_out2 << Label('label group <=> dm_out2') << group
[dm_out3, dm_out4] << Label('label group <=> dm_out3/dm_out4') << group
XComArg(op_out1, 'test_key') << Label('label group <=> op_out1') << group
compare_dag_edges(dag_edges(dag), complex_dag_expected_edges)
| EdgeModifier refactoring (#21404) | test_complex_reversed_dag | ace8c6e942ff5554639801468b971915b7c0e9b9 | airflow | test_edgemodifier.py | 10 | 30 | https://github.com/apache/airflow.git | 1 | 150 | 0 | 50 | 210 | Python | {
"docstring": "Tests the complex reversed dag with a TaskGroup and a Label",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 10
} | def test_complex_reversed_dag(self, test_complex_taskgroup_dag, complex_dag_expected_edges):
(
dag,
group,
(
group_dm1,
group_dm2,
group_dm3,
dm_in1,
dm_in2,
dm_in3,
dm_in4,
dm_out1,
dm_out2,
dm_out3,
dm_out4,
op_in1,
op_out1,
),
) = test_complex_taskgroup_dag
group_dm1 << [group_dm2, group_dm3]
group << dm_in1
group << Label('label dm_in2 <=> group') << dm_in2
group << Label('label dm_in3/dm_in4 <=> group') << [dm_in3, dm_in4]
group << Label('label op_in1 <=> group') << XComArg(op_in1, 'test_key')
dm_out1 << group
dm_out2 << Label('label group <=> dm_out2') << group
[dm_out3, dm_out4] << Label('label group <=> dm_out3/dm_out4') << group
XComArg(op_out1, 'test_key') << Label('label group <=> op_out1') << group
compare_dag_edges(dag_edges(dag), complex_dag_expected_edges)
|
|
118,121 | 322,323 | 80 | paddlenlp/ops/faster_transformer/sample/plato_inference.py | 30 | 12 | def postprocess_response(token_ids, tokenizer):
eos_pos = len(token_ids)
for i, tok_id in enumerate(token_ids):
if tok_id == tokenizer.sep_token_id:
eos_pos = i
break
token_ids = token_ids[:eos_pos]
tokens = tokenizer.convert_ids_to_tokens(token_ids)
tokens = tokenizer.merge_subword(tokens)
return tokens
| FasterUnifiedTransformer/PLATO support dy2sta (#1717)
* support ut dy2sta
* use jit load | postprocess_response | 4c36ef9e41ea6b0e43935bdf6b2f1b4a1f8de809 | PaddleNLP | plato_inference.py | 10 | 10 | https://github.com/PaddlePaddle/PaddleNLP.git | 3 | 60 | 0 | 22 | 98 | Python | {
"docstring": "Post-process the decoded sequence. Truncate from the first <eos>.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 8
} | def postprocess_response(token_ids, tokenizer):
eos_pos = len(token_ids)
for i, tok_id in enumerate(token_ids):
if tok_id == tokenizer.sep_token_id:
eos_pos = i
break
token_ids = token_ids[:eos_pos]
tokens = tokenizer.convert_ids_to_tokens(token_ids)
tokens = tokenizer.merge_subword(tokens)
return tokens
|
|
121,193 | 338,219 | 201 | src/accelerate/accelerator.py | 66 | 16 | def clip_grad_norm_(self, parameters, max_norm, norm_type=2):
if self.distributed_type == DistributedType.FSDP:
self.unscale_gradients()
parameters = [p for p in parameters]
for model in self._models:
if parameters == [p for p in model.parameters()]:
return mod | Return unclipped gradient from grad_clip_norm_ (#756) | clip_grad_norm_ | 693d46826e32507376d44f99967df4710886c984 | accelerate | accelerator.py | 14 | 11 | https://github.com/huggingface/accelerate.git | 7 | 101 | 0 | 48 | 156 | Python | {
"docstring": "\n Should be used in place of `torch.nn.utils.clip_grad_norm_`.\n\n Returns:\n `torch.Tensor`: Total norm of the parameter gradients (viewed as a single vector).\n\n Example:\n\n ```python\n >>> from accelerate import Accelerator\n\n >>> accelerator = Accelerator(gradient_accumulation_steps=2)\n >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)\n\n >>> for (input, target) in dataloader:\n ... optimizer.zero_grad()\n ... output = model(input)\n ... loss = loss_func(output, target)\n ... accelerator.backward(loss)\n ... if accelerator.sync_gradients:\n ... accelerator.clip_grad_norm_(model.parameters(), max_grad_norm)\n ... optimizer.step()\n ```\n ",
"language": "en",
"n_whitespaces": 232,
"n_words": 69,
"vocab_size": 52
} | def clip_grad_norm_(self, parameters, max_norm, norm_type=2):
if self.distributed_type == DistributedType.FSDP:
self.unscale_gradients()
parameters = [p for p in parameters]
for model in self._models:
if parameters == [p for p in model.parameters()]:
return model.clip_grad_norm_(max_norm, norm_type)
elif self.distributed_type == DistributedType.DEEPSPEED:
# `accelerator.backward(loss)` is doing that automatically. Therefore, it's implementation is not needed
# We cannot return the gradient norm because DeepSpeed does it.
return None
self.unscale_gradients()
return torch.nn.utils.clip_grad_norm_(parameters, max_norm, norm_type=norm_type)
|
|
56,689 | 222,663 | 399 | python3.10.4/Lib/distutils/command/build_clib.py | 108 | 15 | def check_library_list(self, libraries):
if not isinstance(libraries, list):
raise DistutilsSetupError(
"'libraries' option must be a list of tuples")
for lib in libraries:
if not isinstance(lib, tuple) and len(lib) != 2:
raise DistutilsSetupError(
"each element of 'libraries' must a 2-tuple")
name, build_info = lib
if not isinstance(name, str):
raise DistutilsSetupError(
"first element of each tuple in 'libraries' "
"must be a string (the library name)")
if '/' in name or (os.sep != '/' and os.sep in name):
raise DistutilsSetupError("bad library name '%s': "
"may not contain directory separators" % lib[0])
if not isinstance(build_info, dict):
raise DistutilsSetupError(
"second element of each tuple in 'libraries' "
"mus | add python 3.10.4 for windows | check_library_list | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | build_clib.py | 14 | 20 | https://github.com/XX-net/XX-Net.git | 10 | 113 | 0 | 65 | 199 | Python | {
"docstring": "Ensure that the list of libraries is valid.\n\n `library` is presumably provided as a command option 'libraries'.\n This method checks that it is a list of 2-tuples, where the tuples\n are (library_name, build_info_dict).\n\n Raise DistutilsSetupError if the structure is invalid anywhere;\n just returns otherwise.\n ",
"language": "en",
"n_whitespaces": 86,
"n_words": 44,
"vocab_size": 35
} | def check_library_list(self, libraries):
if not isinstance(libraries, list):
raise DistutilsSetupError(
"'libraries' option must be a list of tuples")
for lib in libraries:
if not isinstance(lib, tuple) and len(lib) != 2:
raise DistutilsSetupError(
"each element of 'libraries' must a 2-tuple")
name, build_info = lib
if not isinstance(name, str):
raise DistutilsSetupError(
"first element of each tuple in 'libraries' "
"must be a string (the library name)")
if '/' in name or (os.sep != '/' and os.sep in name):
raise DistutilsSetupError("bad library name '%s': "
"may not contain directory separators" % lib[0])
if not isinstance(build_info, dict):
raise DistutilsSetupError(
"second element of each tuple in 'libraries' "
"must be a dictionary (build info)")
|
|
3,366 | 20,431 | 563 | pipenv/patched/notpip/_vendor/pygments/lexer.py | 130 | 16 | def _process_new_state(cls, new_state, unprocessed, processed):
if isinstance(new_state, str):
# an existing state
if new_state == '#pop':
return -1
elif new_state in unprocessed:
return (new_state,)
elif new_state == '#push':
return new_state
elif new_state[:5] == '#pop:':
return -int(new_state[5:])
else:
assert False, 'unknown new state %r' % new_state
elif isinstance(new_state, combined):
# combine a new state from existing ones
tmp_state = '_tmp_%d' % cls._tmpname
cls._tmpname += 1
itokens = []
for istate in new_state:
assert istate != new_state, 'circular state ref %r' % istate
itokens.extend(cls | 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 | _process_new_state | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | lexer.py | 15 | 30 | https://github.com/pypa/pipenv.git | 11 | 177 | 0 | 69 | 288 | Python | {
"docstring": "Preprocess the state transition action of a token definition.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | def _process_new_state(cls, new_state, unprocessed, processed):
if isinstance(new_state, str):
# an existing state
if new_state == '#pop':
return -1
elif new_state in unprocessed:
return (new_state,)
elif new_state == '#push':
return new_state
elif new_state[:5] == '#pop:':
return -int(new_state[5:])
else:
assert False, 'unknown new state %r' % new_state
elif isinstance(new_state, combined):
# combine a new state from existing ones
tmp_state = '_tmp_%d' % cls._tmpname
cls._tmpname += 1
itokens = []
for istate in new_state:
assert istate != new_state, 'circular state ref %r' % istate
itokens.extend(cls._process_state(unprocessed,
processed, istate))
processed[tmp_state] = itokens
return (tmp_state,)
elif isinstance(new_state, tuple):
# push more than one state
for istate in new_state:
assert (istate in unprocessed or
istate in ('#pop', '#push')), \
'unknown new state ' + istate
return new_state
else:
assert False, 'unknown new state def %r' % new_state
|
|
75,661 | 259,226 | 169 | sklearn/preprocessing/tests/test_encoders.py | 89 | 25 | def test_ohe_infrequent_two_levels_user_cats():
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtyp | ENH Adds infrequent categories to OneHotEncoder (#16018)
* ENH Completely adds infrequent categories
* STY Linting
* STY Linting
* DOC Improves wording
* DOC Lint
* BUG Fixes
* CLN Address comments
* CLN Address comments
* DOC Uses math to description float min_frequency
* DOC Adds comment regarding drop
* BUG Fixes method name
* DOC Clearer docstring
* TST Adds more tests
* FIX Fixes mege
* CLN More pythonic
* CLN Address comments
* STY Flake8
* CLN Address comments
* DOC Fix
* MRG
* WIP
* ENH Address comments
* STY Fix
* ENH Use functiion call instead of property
* ENH Adds counts feature
* CLN Rename variables
* DOC More details
* CLN Remove unneeded line
* CLN Less lines is less complicated
* CLN Less diffs
* CLN Improves readiabilty
* BUG Fix
* CLN Address comments
* TST Fix
* CLN Address comments
* CLN Address comments
* CLN Move docstring to userguide
* DOC Better wrapping
* TST Adds test to handle_unknown='error'
* ENH Spelling error in docstring
* BUG Fixes counter with nan values
* BUG Removes unneeded test
* BUG Fixes issue
* ENH Sync with main
* DOC Correct settings
* DOC Adds docstring
* DOC Immprove user guide
* DOC Move to 1.0
* DOC Update docs
* TST Remove test
* DOC Update docstring
* STY Linting
* DOC Address comments
* ENH Neater code
* DOC Update explaination for auto
* Update sklearn/preprocessing/_encoders.py
Co-authored-by: Roman Yurchak <[email protected]>
* TST Uses docstring instead of comments
* TST Remove call to fit
* TST Spelling error
* ENH Adds support for drop + infrequent categories
* ENH Adds infrequent_if_exist option
* DOC Address comments for user guide
* DOC Address comments for whats_new
* DOC Update docstring based on comments
* CLN Update test with suggestions
* ENH Adds computed property infrequent_categories_
* DOC Adds where the infrequent column is located
* TST Adds more test for infrequent_categories_
* DOC Adds docstring for _compute_drop_idx
* CLN Moves _convert_to_infrequent_idx into its own method
* TST Increases test coverage
* TST Adds failing test
* CLN Careful consideration of dropped and inverse_transform
* STY Linting
* DOC Adds docstrinb about dropping infrequent
* DOC Uses only
* DOC Numpydoc
* TST Includes test for get_feature_names_out
* DOC Move whats new
* DOC Address docstring comments
* DOC Docstring changes
* TST Better comments
* TST Adds check for handle_unknown='ignore' for infrequent
* CLN Make _infrequent_indices private
* CLN Change min_frequency default to None
* DOC Adds comments
* ENH adds support for max_categories=1
* ENH Describe lexicon ordering for ties
* DOC Better docstring
* STY Fix
* CLN Error when explicity dropping an infrequent category
* STY Grammar
Co-authored-by: Joel Nothman <[email protected]>
Co-authored-by: Roman Yurchak <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]> | test_ohe_infrequent_two_levels_user_cats | 7f0006c8aad1a09621ad19c3db19c3ff0555a183 | scikit-learn | test_encoders.py | 16 | 18 | https://github.com/scikit-learn/scikit-learn.git | 2 | 203 | 0 | 67 | 332 | Python | {
"docstring": "Test that the order of the categories provided by a user is respected.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 12
} | def test_ohe_infrequent_two_levels_user_cats():
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object
).T
ohe = OneHotEncoder(
categories=[["c", "d", "a", "b"]],
sparse=False,
handle_unknown="infrequent_if_exist",
max_categories=2,
).fit(X_train)
assert_array_equal(ohe.infrequent_categories_, [["c", "d", "a"]])
X_test = [["b"], ["a"], ["c"], ["d"], ["e"]]
expected = np.array([[1, 0], [0, 1], [0, 1], [0, 1], [0, 1]])
X_trans = ohe.transform(X_test)
assert_allclose(expected, X_trans)
# 'infrequent' is used to denote the infrequent categories for
# `inverse_transform`
expected_inv = [[col] for col in ["b"] + ["infrequent_sklearn"] * 4]
X_inv = ohe.inverse_transform(X_trans)
assert_array_equal(expected_inv, X_inv)
|
|
76,200 | 260,354 | 119 | sklearn/decomposition/_sparse_pca.py | 34 | 14 | def fit(self, X, y=None):
self._v | MAINT Use _validate_params in SparsePCA and MiniBatchSparsePCA (#23710)
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: jeremiedbb <[email protected]> | fit | db6123fe40400828918037f3fae949bfcc4d9d05 | scikit-learn | _sparse_pca.py | 10 | 11 | https://github.com/scikit-learn/scikit-learn.git | 2 | 85 | 0 | 24 | 135 | Python | {
"docstring": "Fit the model from data in X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training vector, where `n_samples` is the number of samples\n and `n_features` is the number of features.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n ",
"language": "en",
"n_whitespaces": 153,
"n_words": 53,
"vocab_size": 43
} | def fit(self, X, y=None):
self._validate_params()
random_state = check_random_state(self.random_state)
X = self._validate_data(X)
self.mean_ = X.mean(axis=0)
X = X - self.mean_
if self.n_components is None:
n_components = X.shape[1]
else:
n_components = self.n_components
return self._fit(X, n_components, random_state)
|
|
11,200 | 55,077 | 74 | tests/cli/test_profile.py | 21 | 11 | def test_create_profile():
invoke_and_assert(
["profile", "create", "foo"],
expected_output=(
f
),
)
profiles = load_profiles()
assert profiles["foo"] == Profile(
name="foo", settings={}, source=PREFECT_PROFI | Add tests for profile CLI | test_create_profile | 808660dd04465fc796a34e835467e8ae1f2449b3 | prefect | test_profile.py | 11 | 21 | https://github.com/PrefectHQ/prefect.git | 1 | 52 | 0 | 20 | 89 | Python | {
"docstring": "\n Created profile 'foo'.\n\n Switch to your new profile with:\n\n prefect profile use 'foo'\n\n Or, to use it for a single command, include the `-p` option:\n\n prefect -p 'foo' config view\n ",
"language": "en",
"n_whitespaces": 105,
"n_words": 30,
"vocab_size": 24
} | def test_create_profile():
invoke_and_assert(
["profile", "create", "foo"],
expected_output=(
f
),
)
profiles = load_profiles()
assert profiles["foo"] == Profile(
name="foo", settings={}, source=PREFECT_PROFILES_PATH.value()
)
|
|
23,693 | 109,648 | 160 | lib/matplotlib/tests/test_axes.py | 97 | 21 | def test_mixed_errorbar_polar_caps():
fig = plt.figure()
ax = plt.subplot(111, projection='polar')
# symmetric errorbars
th_sym = [1, 2, 3]
r_sym = [0.9]*3
ax.errorbar(th_sym, r_sym, xerr=0.35, yerr=0.2, fmt="o")
# long errorbars
th_long = [np.pi/2 + .1, np.pi + .1]
r_long = [1.8, 2.2]
ax.errorbar(th_long, r_long, xerr=0.8 * np.pi, yerr=0.15, fmt="o")
# asymmetric errorbars
th_asym = [4*np.pi/3 + .1, 5*np.pi/3 + .1, 2*np.pi-0.1]
r_asym = [1.1]*3
xerr = [[.3, .3, .2], [.2, .3, .3]]
| Curved polar errorbars
- uses _interpolation_steps
- prefers transform MarkerStyle in init over _transform property
- adjusted what's new
- added more tests for overlapping, asymmetric and long errorbars
- combine all tests to a single figure
- remove overlappnig since it does not work same on all platforms
- rework test figure, add overlapping, might work by avoiding grid
- update what's new with image and link to example | test_mixed_errorbar_polar_caps | 907f78dbf959c0609ab484c59e840eea3eafee31 | matplotlib | test_axes.py | 11 | 17 | https://github.com/matplotlib/matplotlib.git | 1 | 273 | 0 | 72 | 348 | Python | {
"docstring": "\n Mix several polar errorbar use cases in a single test figure.\n\n It is advisable to position individual points off the grid. If there are\n problems with reproducibility of this test, consider removing grid.\n ",
"language": "en",
"n_whitespaces": 46,
"n_words": 33,
"vocab_size": 32
} | def test_mixed_errorbar_polar_caps():
fig = plt.figure()
ax = plt.subplot(111, projection='polar')
# symmetric errorbars
th_sym = [1, 2, 3]
r_sym = [0.9]*3
ax.errorbar(th_sym, r_sym, xerr=0.35, yerr=0.2, fmt="o")
# long errorbars
th_long = [np.pi/2 + .1, np.pi + .1]
r_long = [1.8, 2.2]
ax.errorbar(th_long, r_long, xerr=0.8 * np.pi, yerr=0.15, fmt="o")
# asymmetric errorbars
th_asym = [4*np.pi/3 + .1, 5*np.pi/3 + .1, 2*np.pi-0.1]
r_asym = [1.1]*3
xerr = [[.3, .3, .2], [.2, .3, .3]]
yerr = [[.35, .5, .5], [.5, .35, .5]]
ax.errorbar(th_asym, r_asym, xerr=xerr, yerr=yerr, fmt="o")
# overlapping errorbar
th_over = [2.1]
r_over = [3.1]
ax.errorbar(th_over, r_over, xerr=10, yerr=.2, fmt="o")
|
|
41,701 | 176,114 | 46 | tests/test_eval_model.py | 11 | 3 | def test_edgeql_for_01(self):
self.assert_test_query(
r,
{(1, 1), (2, 2), (3, 3)},
)
| Pull assert_data_shape out of testbase.server and use it for model tests (#3315) | test_edgeql_for_01 | 20ca6e2fa7bab2adc8c37d8c42049076c692782e | edgedb | test_eval_model.py | 9 | 7 | https://github.com/edgedb/edgedb.git | 1 | 33 | 0 | 11 | 46 | Python | {
"docstring": "\n FOR X IN {1,2,3} UNION ((SELECT X), (SELECT X));\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 9,
"vocab_size": 9
} | def test_edgeql_for_01(self):
self.assert_test_query(
r,
{(1, 1), (2, 2), (3, 3)},
)
|
|
117,320 | 320,737 | 78 | qutebrowser/browser/downloadview.py | 17 | 13 | def on_clicked(self, index):
if not index.isValid():
return
item = self._model().data(index, downloads.ModelRole.item)
if item.done and item.successful:
item.open_file()
item.remove()
| mypy: Upgrade to PyQt5-stubs 5.15.6.0
For some unknown reason, those new stubs cause a *lot* of things now to be
checked by mypy which formerly probably got skipped due to Any being implied
somewhere.
The stubs themselves mainly improved, with a couple of regressions too.
In total, there were some 337 (!) new mypy errors. This commit fixes almost all
of them, and the next commit improves a fix to get things down to 0 errors
again.
Overview of the changes:
==== qutebrowser/app.py
- Drop type ignore due to improved stubs.
==== qutebrowser/browser/browsertab.py
- Specify the type of _widget members more closely than just QWidget.
This is debatable: I suppose the abstract stuff shouldn't need to know
anything about the concrete backends at all. But it seems like we cut some
corners when initially implementing things, and put some code in browsertab.py
just because the APIs of both backends happened to be compatible. Perhaps
something to reconsider once we drop QtWebKit and hopefully implement a dummy
backend.
- Add an additional assertion in AbstractAction.run_string. This is already
covered by the isinstance(member, self.action_base) above it, but that's too
dynamic for mypy to understand.
- Fix the return type of AbstractScroller.pos_px, which is a QPoint (with x
and y components), not a single int.
- Fix the return type of AbstractScroller.pos_perc, which is a Tuple (with x
and y components), not a single int.
- Fix the argument types of AbstractScroller.to_perc, as it's possible to pass
fractional percentages too.
- Specify the type for AbstractHistoryPrivate._history. See above (_widget) re
this being debatable.
- Fix the return type of AbstractTabPrivate.event_target(), which can be None
(see #3888).
- Fix the return type of AbstractTabPrivate.run_js_sync, which is Any (the JS
return value), not None.
- Fix the argument type for AbstractTabPrivate.toggle_inspector: position can
be None to use the last used position.
- Declare the type of sub-objects of AbstractTab.
- Fix the return value of AbstractTab.icon(), which is the QIcon, not None.
==== qutebrowser/browser/commands.py
- Make sure the active window is a MainWindow (with a .win_id attribute).
==== qutebrowser/browser/downloadview.py
- Add _model() which makes sure that self.model() is a DownloadModel, not None
or any other model. This is needed because other methods access a variety of
custom attributes on it, e.g. last_index().
==== qutebrowser/browser/greasemonkey.py
- Add an ignore for AbstractDownload.requested_url which we patch onto the
downloads. Probably would be nicer to add it as a proper attribute which always
gets set by the DownloadManager.
==== qutebrowser/browser/hints.py
- Remove type ignores for QUrl.toString().
- Add a new type ignore for combining different URL flags (which works, but is
not exactly type safe... still probably a regression in the stubs).
- Make sure the things we get back from self._get_keyparser are what we actually
expect. Probably should introduce a TypedDict (and/or overloads for
_get_keyparser with typing.Literal) to teach mypy about the exact return value.
See #7098.
This is needed because we access Hint/NormalKeyParser-specific attributes such
as .set_inhibited_timout() or .update_bindings().
==== qutebrowser/browser/inspector.py
- Similar changes than in browsertab.py to make some types where we share API
(e.g. .setPage()) more concrete. Didn't work out unfortunately, see next
commit.
==== qutebrowser/browser/network/pac.py
- Remove now unneeded type ignore for signal.
==== qutebrowser/browser/qtnetworkdownloads.py
- Make sure that downloads is a qtnetworkdownloads.DownloadItem (rather than an
AbstractDownload), so that we can call ._uses_nam() on it.
==== qutebrowser/browser/qutescheme.py
- Remove now unneeded type ignore for QUrl flags.
==== qutebrowser/browser/urlmarks.py
- Specify the type of UrlMarkManager._lineparser, as those only get initialized
in _init_lineparser of subclasses, so mypy doesn't know it's supposed to exist.
==== qutebrowser/browser/webelem.py
- New casts to turn single KeyboardModifier (enum) entries into
KeyboardModifiers (flags). Might not be needed anymore with Qt 6.
- With that, casting the final value is now unneeded.
==== qutebrowser/browser/webengine/notification.py
- Remove now unneeded type ignore for signal.
- Make sure the self.sender() we get in HerbeNotificationAdapter._on_finished()
is a QProcess, not just any QObject.
==== qutebrowser/browser/webengine/webenginedownloads.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/browser/webengine/webengineelem.py
- Specify the type of WebEngineElement._tab.
- Remove now unneeded type ignore for mixed flags.
==== qutebrowser/browser/webengine/webengineinspector.py
- See changes to inspector.py and next commit.
- Remove now unneeded type ignore for signal.
==== qutebrowser/browser/webengine/webenginequtescheme.py
- Remove now unneeded type ignore for mixed flags.
==== qutebrowser/browser/webengine/webenginesettings.py
- Ignore access of .setter attribute which we patch onto QWebEngineProfile.
Would be nice to have a subclass or wrapper-class instead.
==== qutebrowser/browser/webengine/webenginetab.py
- Specified the type of _widget members more closely than just QWidget.
See browsertab.py changes for details.
- Remove some now-unneeded type ignores for creating FindFlags.
- Specify more concrete types for WebEngineTab members where we actually need to
access WebEngine-specific attributes.
- Make sure the page we get is our custom WebEnginePage subclass, not just any
QWebEnginePage. This is needed because we access custom attributes on it.
==== qutebrowser/browser/webengine/webview.py
- Make sure the page we get is our custom WebEnginePage subclass, not just any
QWebEnginePage. This is needed because we access custom attributes on it.
==== qutebrowser/browser/webkit/network/networkreply.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/browser/webkit/webkitinspector.py
- See changes to inspector.py and next commit.
==== qutebrowser/browser/webkit/webkittab.py
- Specify the type of _widget members more closely than just QWidget.
See browsertab.py changes for details.
- Add a type ignore for WebKitAction because our workaround needs to
treat them as ints (which is allowed by PyQt, even if not type-safe).
- Add new ignores for findText calls: The text is a QString and can be None; the
flags are valid despite mypy thinking they aren't (stubs regression?).
- Specify the type for WebKitHistoryPrivate._history, because we access
WebKit-specific attributes. See above (_widget) re this being debatable.
- Make mypy aware that .currentFrame() and .frameAt() can return None (stubs
regression?).
- Make sure the .page() and .page().networkAccessManager() are our subclasses
rather than the more generic QtWebKit objects, as we use custom attributes.
- Add new type ignores for signals (stubs regression!)
==== qutebrowser/browser/webkit/webpage.py
- Make sure the .networkAccessManager() is our subclass rather than the more
generic QtWebKit object, as we use custom attributes.
- Replace a cast by a type ignore. The cast didn't work anymore.
==== qutebrowser/browser/webkit/webview.py
- Make sure the .page() is our subclass rather than the more generic QtWebKit
object, as we use custom attributes.
==== qutebrowser/commands/userscripts.py
- Remove now unneeded type ignore for signal.
==== qutebrowser/completion/completer.py
- Add a new _completion() getter (which ensures it actually gets the completion
view) rather than accessing the .parent() directly (which could be any QObject).
==== qutebrowser/completion/completiondelegate.py
- Make sure self.parent() is a CompletionView (no helper method as there is only
one instance).
- Remove a now-unneeded type ignore for adding QSizes.
==== qutebrowser/completion/completionwidget.py
- Add a ._model() getter which ensures that we get a CompletionModel (with
custom attributes) rather than Qt's .model() which can be any QAbstractItemModel
(or None).
- Removed a now-unneeded type ignore for OR-ing flags.
==== qutebrowser/completion/models/completionmodel.py
- Remove now unneeded type ignores for signals.
- Ignore a complaint about .set_pattern() not being defined. Completion
categories don't share any common parent class, so it would be good to introduce
a typing.Protocol for this. See #7098.
==== qutebrowser/components/misccommands.py
- Removed a now-unneeded type ignore for OR-ing flags.
==== qutebrowser/components/readlinecommands.py
- Make sure QApplication.instance() is a QApplication (and not just a
QCoreApplication). This includes the former "not None" check.
==== qutebrowser/components/scrollcommands.py
- Add basic annotation for "funcs" dict. Could have a callable protocol to
specify it needs a count kwarg, see #7098.
==== qutebrowser/config/stylesheet.py
- Correctly specify that stylesheet apply to QWidgets, not any QObject.
- Ignore an attr-defined for obj.STYLESHEET. Perhaps could somehow teach mypy
about this with overloads and protocols (stylesheet for set_register being None
=> STYLESHEET needs to be defined, otherwise anything goes), but perhaps not
worth the troble. See #7098.
==== qutebrowser/keyinput/keyutils.py
- Remove some now-unneeded type ignores and add a cast for using a single enum
value as flags. Might need to look at this again with Qt 6 support.
==== qutebrowser/keyinput/modeman.py
- Add a FIXME for using a TypedDict, see comments for hints.py above.
==== qutebrowser/mainwindow/mainwindow.py
- Remove now-unneeded type ignores for calling with OR-ed flags.
- Improve where we cast from WindowType to WindowFlags, no int needed
- Use new .tab_bar() getter, see below.
==== qutebrowser/mainwindow/prompt.py
- Remove now-unneeded type ignores for calling with OR-ed flags.
==== qutebrowser/mainwindow/statusbar/bar.py
- Adjust type ignores around @pyqtProperty. The fact one is still needed seems
like a stub regression.
==== qutebrowser/mainwindow/statusbar/command.py
- Fix type for setText() override (from QLineEdit): text can be None
(QString in C++).
==== qutebrowser/mainwindow/statusbar/url.py
- Adjust type ignores around @pyqtProperty. The fact one is still needed seems
like a stub regression.
==== qutebrowser/mainwindow/tabbedbrowser.py
- Specify that TabDeque manages browser tabs, not any QWidgets. It accesses
AbstractTab-specific attributes.
- Make sure that the .tabBar() we get is a tabwidget.TabBar, as we access
.maybe_hide.
- Fix the annotations for stored marks: Scroll positions are a QPoint, not int.
- Add _current_tab() and _tab_by_idx() wrappers for .currentWidget() and
.widget(), which ensures that the return values are valid AbstractTabs (or None
for _tab_by_idx). This is needed because we access AbstractTab-specific
attributes.
- For some places, where the tab can be None, continue using .currentTab() but
add asserts.
- Remove some now-unneeded [unreachable] ignores, as mypy knows about the None
possibility now.
==== qutebrowser/mainwindow/tabwidget.py
- Add new tab_bar() and _tab_by_idx() helpers which check that the .tabBar() and
.widget() are of type TabBar and AbstractTab, respectively.
- Add additional assertions where we expect ._tab_by_idx() to never be None.
- Remove dead code in get_tab_fields for handling a None y scroll position. I
was unable to find any place in the code where this could be set to None.
- Remove some now-unneeded type ignores and casts, as mypy now knows that
_type_by_idx() could be None.
- Work around a strange instance where mypy complains about not being able to
find the type of TabBar.drag_in_progress from TabWidget._toggle_visibility,
despite it clearly being shown as a bool *inside* that class without any
annotation.
- Add a ._tab_widget() getter in TabBar which ensures that the .parent() is in
fact a TabWidget.
==== qutebrowser/misc/crashsignal.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/misc/editor.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/misc/ipc.py
- Remove now unneeded type ignores for signals.
- Add new type ignores for .error() which is both a signal and a getter
(stub regression?). Won't be relevant for Qt 6 anymore, as the signal was
renamed to errorOccurred in 5.15.
==== qutebrowser/misc/objects.py
- Make sure mypy knows that objects.app is our custom Application (with custom
attributes) rather than any QApplication.
==== qutebrowser/utils/objreg.py
- Ignore attr-defined for .win_id attributes. Maybe could add a typing.Protocol,
but ideally, the whole objreg stuff should die one day anyways.
==== tests/unit/completion/test_completer.py
- Make CompletionWidgetStub inherit from CompletionView so that it passes the
new isinstance() asserts in completer.py (see above). | on_clicked | a20bb67a878b2e68abf8268c1b0a27f018d01352 | qutebrowser | downloadview.py | 10 | 7 | https://github.com/qutebrowser/qutebrowser.git | 4 | 54 | 0 | 16 | 91 | Python | {
"docstring": "Handle clicking of an item.\n\n Args:\n index: The QModelIndex of the clicked item.\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 13,
"vocab_size": 11
} | def on_clicked(self, index):
if not index.isValid():
return
item = self._model().data(index, downloads.ModelRole.item)
if item.done and item.successful:
item.open_file()
item.remove()
|
|
@add_start_docstrings(
"""
XLM-RoBERTa-xlarge Model transformer with a sequence classification/regression head on top (a linear layer on top
of the pooled output) e.g. for GLUE tasks.
""",
XLM_ROBERTA_XL_START_DOCSTRING,
) | 6,311 | 34,690 | 44 | src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | 27 | 6 | def _tie_weights(self):
# To tie those two weights if they get disconnec | Add support for XLM-R XL and XXL models by modeling_xlm_roberta_xl.py (#13727)
* add xlm roberta xl
* add convert xlm xl fairseq checkpoint to pytorch
* fix init and documents for xlm-roberta-xl
* fix indention
* add test for XLM-R xl,xxl
* fix model hub name
* fix some stuff
* up
* correct init
* fix more
* fix as suggestions
* add torch_device
* fix default values of doc strings
* fix leftovers
* merge to master
* up
* correct hub names
* fix docs
* fix model
* up
* finalize
* last fix
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <[email protected]>
* add copied from
* make style
Co-authored-by: Patrick von Platen <[email protected]>
Co-authored-by: Sylvain Gugger <[email protected]> | _tie_weights | e09473a817c5e5871e11cc81004355ef30250502 | transformers | modeling_xlm_roberta_xl.py | 8 | 2 | https://github.com/huggingface/transformers.git | 1 | 14 | 1 | 27 | 38 | Python | {
"docstring": "\n XLM-RoBERTa-xlarge Model transformer with a sequence classification/regression head on top (a linear layer on top\n of the pooled output) e.g. for GLUE tasks.\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 23,
"vocab_size": 21
} | def _tie_weights(self):
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
self.bias = self.decoder.bias
@add_start_docstrings(
,
XLM_ROBERTA_XL_START_DOCSTRING,
) |
3,071 | 19,706 | 23 | pipenv/installers.py | 9 | 5 | def matches_minor(self, other):
re | Issue 4993 Add standard pre commit hooks and apply linting. (#4994)
* Add .pre-commit-config.yaml to the project and exclude tests (for now). This does not include the MyPy linting that pip does but does include everything else. | matches_minor | 9a3b3ce70621af6f9adaa9eeac9cf83fa149319c | pipenv | installers.py | 8 | 2 | https://github.com/pypa/pipenv.git | 1 | 28 | 0 | 9 | 43 | Python | {
"docstring": "Check whether this version matches the other in (major, minor).",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def matches_minor(self, other):
return (self.major, self.minor) == (other.major, other.minor)
|
|
25,781 | 116,582 | 73 | mindsdb/integrations/handlers/lightwood_handler/tests/test_lightwood_handler.py | 24 | 15 | def test_04_query_predictor_single_where_condition(self):
time.sleep(120) # TODO
query = f
response = self.handler.native_query(query)
self.assertTrue(response.type == RESPONSE_TYPE.TABLE)
self.assertTrue(len(response.data_frame) == 1)
self.assertTrue(response.data_frame['sqft'][0] == 100)
self.assertTrue(response.data_frame['rental_price'][0] is not None)
| test fix | test_04_query_predictor_single_where_condition | b999051fd8153a1d3624471cac5483867116f985 | mindsdb | test_lightwood_handler.py | 11 | 12 | https://github.com/mindsdb/mindsdb.git | 1 | 83 | 0 | 21 | 143 | Python | {
"docstring": "\n SELECT target\n from {self.test_model_1}\n WHERE sqft=100\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 6,
"vocab_size": 6
} | def test_04_query_predictor_single_where_condition(self):
time.sleep(120) # TODO
query = f
response = self.handler.native_query(query)
self.assertTrue(response.type == RESPONSE_TYPE.TABLE)
self.assertTrue(len(response.data_frame) == 1)
self.assertTrue(response.data_frame['sqft'][0] == 100)
self.assertTrue(response.data_frame['rental_price'][0] is not None)
|
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