project stringclasses 17
values | bug_id int64 1 169 | buggy stringlengths 81 57.2k | fixed stringlengths 85 31.2k | test_command stringlengths 44 133 |
|---|---|---|---|---|
pandas | 64 | class ExcelFormatter:
# Deprecated in GH#17295, enforced in 1.0.0
raise KeyError("Not all names specified in 'columns' are found")
self.df = df
self.columns = self.df.columns
self.float_format = float_format | class ExcelFormatter:
# Deprecated in GH#17295, enforced in 1.0.0
raise KeyError("Not all names specified in 'columns' are found")
self.df = df.reindex(columns=cols)
self.columns = self.df.columns
self.float_format = float_format | pytest pandas/tests/io/excel/test_writers.py::TestExcelWriter::test_write_subset_columns |
cookiecutter | 3 | def read_user_choice(var_name, options):
))
user_choice = click.prompt(
prompt, type=click.Choice(choices), default=default
)
return choice_map[user_choice]
| def read_user_choice(var_name, options):
))
user_choice = click.prompt(
prompt, type=click.Choice(choices), default=default, show_choices=False
)
return choice_map[user_choice]
| tox tests/test_read_user_choice.py::test_click_invocation |
luigi | 11 | class Scheduler(object):
if (best_task and batched_params and task.family == best_task.family and
len(batched_tasks) < max_batch_size and task.is_batchable() and all(
task.params.get(name) == value for name, value in unbatched_params.items())):
for na... | class Scheduler(object):
if (best_task and batched_params and task.family == best_task.family and
len(batched_tasks) < max_batch_size and task.is_batchable() and all(
task.params.get(name) == value for name, value in unbatched_params.items()) and
... | pytest test/scheduler_api_test.py::SchedulerApiTest::test_batch_ignore_items_not_ready |
pandas | 157 | from pandas.core.dtypes.common import (
is_bool,
is_bool_dtype,
is_categorical_dtype,
is_datetime64_dtype,
is_datetime64tz_dtype,
is_datetimelike,
is_dtype_equal, | from pandas.core.dtypes.common import (
is_bool,
is_bool_dtype,
is_categorical_dtype,
is_datetime64tz_dtype,
is_datetimelike,
is_dtype_equal, | pytest pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_timedelta_tolerance_nearest |
pandas | 157 | class _AsOfMerge(_OrderedMerge):
)
)
if is_datetime64_dtype(lt) or is_datetime64tz_dtype(lt):
if not isinstance(self.tolerance, Timedelta):
raise MergeError(msg)
if self.tolerance < Timedelta(0): | class _AsOfMerge(_OrderedMerge):
)
)
if is_datetimelike(lt):
if not isinstance(self.tolerance, Timedelta):
raise MergeError(msg)
if self.tolerance < Timedelta(0): | pytest pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_timedelta_tolerance_nearest |
matplotlib | 22 | optional.
if bin_range is not None:
bin_range = self.convert_xunits(bin_range)
# We need to do to 'weights' what was done to 'x'
if weights is not None:
w = cbook._reshape_2D(weights, 'weights') | optional.
if bin_range is not None:
bin_range = self.convert_xunits(bin_range)
if not cbook.is_scalar_or_string(bins):
bins = self.convert_xunits(bins)
# We need to do to 'weights' what was done to 'x'
if weights is not None:
w = cbook._reshape_2D(we... | pytest lib/matplotlib/tests/test_axes.py::test_hist_datetime_datasets_bins |
keras | 36 | def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
padding = _preprocess_padding(padding)
if tf_data_format == 'NHWC':
spatial_start_dim = 1
strides = (1, 1) + strides + (1,)
else:
spatial_start_dim = 2
strides = (1, 1, 1) + strides
x = tf.expand_dims(... | def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
padding = _preprocess_padding(padding)
if tf_data_format == 'NHWC':
spatial_start_dim = 1
strides = (1,) + strides * 2 + (1,)
else:
spatial_start_dim = 2
strides = (1, 1) + strides * 2
x = tf.expand_di... | pytest tests/keras/layers/convolutional_test.py::test_separable_conv_1d |
pandas | 31 | from pandas.util._decorators import Appender, Substitution, cache_readonly, doc
from pandas.core.dtypes.cast import maybe_cast_result
from pandas.core.dtypes.common import (
ensure_float,
is_datetime64_dtype,
is_integer_dtype,
is_numeric_dtype,
is_object_dtype, | from pandas.util._decorators import Appender, Substitution, cache_readonly, doc
from pandas.core.dtypes.cast import maybe_cast_result
from pandas.core.dtypes.common import (
ensure_float,
is_bool_dtype,
is_datetime64_dtype,
is_extension_array_dtype,
is_integer_dtype,
is_numeric_dtype,
is_obj... | pytest pandas/tests/groupby/test_function.py::test_groupby_quantile_nullable_array |
pandas | 31 | class GroupBy(_GroupBy[FrameOrSeries]):
)
inference = None
if is_integer_dtype(vals):
inference = np.int64
elif is_datetime64_dtype(vals):
inference = "datetime64[ns]"
vals = np.asarray(vals).astype(np.float)
| class GroupBy(_GroupBy[FrameOrSeries]):
)
inference = None
if is_integer_dtype(vals.dtype):
if is_extension_array_dtype(vals.dtype):
vals = vals.to_numpy(dtype=float, na_value=np.nan)
inference = np.int64
elif is_bo... | pytest pandas/tests/groupby/test_function.py::test_groupby_quantile_nullable_array |
youtube-dl | 40 | import base64
import io
import itertools
import os
from struct import unpack, pack
import time
import xml.etree.ElementTree as etree
from .common import FileDownloader
from .http import HttpFD
from ..utils import (
compat_urllib_request,
compat_urlparse,
format_bytes, | import base64
import io
import itertools
import os
import time
import xml.etree.ElementTree as etree
from .common import FileDownloader
from .http import HttpFD
from ..utils import (
struct_pack,
struct_unpack,
compat_urllib_request,
compat_urlparse,
format_bytes, | python -m unittest -q test.test_utils.TestUtil.test_struct_unpack |
youtube-dl | 40 | class FlvReader(io.BytesIO):
# Utility functions for reading numbers and strings
def read_unsigned_long_long(self):
return unpack('!Q', self.read(8))[0]
def read_unsigned_int(self):
return unpack('!I', self.read(4))[0]
def read_unsigned_char(self):
return unpack('!B', self.rea... | class FlvReader(io.BytesIO):
# Utility functions for reading numbers and strings
def read_unsigned_long_long(self):
return struct_unpack('!Q', self.read(8))[0]
def read_unsigned_int(self):
return struct_unpack('!I', self.read(4))[0]
def read_unsigned_char(self):
return struct_... | python -m unittest -q test.test_utils.TestUtil.test_struct_unpack |
youtube-dl | 40 | def write_flv_header(stream, metadata):
# Script data
stream.write(b'\x12')
# Size of the metadata with 3 bytes
stream.write(pack('!L', len(metadata))[1:])
stream.write(b'\x00\x00\x00\x00\x00\x00\x00')
stream.write(metadata)
# Magic numbers extracted from the output files produced by AdobeHD... | def write_flv_header(stream, metadata):
# Script data
stream.write(b'\x12')
# Size of the metadata with 3 bytes
stream.write(struct_pack('!L', len(metadata))[1:])
stream.write(b'\x00\x00\x00\x00\x00\x00\x00')
stream.write(metadata)
# Magic numbers extracted from the output files produced by ... | python -m unittest -q test.test_utils.TestUtil.test_struct_unpack |
youtube-dl | 40 | import platform
import re
import ssl
import socket
import subprocess
import sys
import traceback | import platform
import re
import ssl
import socket
import struct
import subprocess
import sys
import traceback | python -m unittest -q test.test_utils.TestUtil.test_struct_unpack |
youtube-dl | 40 | def uppercase_escape(s):
return re.sub(
r'\\U([0-9a-fA-F]{8})',
lambda m: compat_chr(int(m.group(1), base=16)), s) | def uppercase_escape(s):
return re.sub(
r'\\U([0-9a-fA-F]{8})',
lambda m: compat_chr(int(m.group(1), base=16)), s)
try:
struct.pack(u'!I', 0)
except TypeError:
# In Python 2.6 (and some 2.7 versions), struct requires a bytes argument
def struct_pack(spec, *args):
if isinstance(s... | python -m unittest -q test.test_utils.TestUtil.test_struct_unpack |
matplotlib | 18 | class RadialLocator(mticker.Locator):
:class:`~matplotlib.ticker.Locator` (which may be different
depending on the scale of the *r*-axis.
"""
def __init__(self, base, axes=None):
self.base = base
self._axes = axes | class RadialLocator(mticker.Locator):
:class:`~matplotlib.ticker.Locator` (which may be different
depending on the scale of the *r*-axis.
"""
def __init__(self, base, axes=None):
self.base = base
self._axes = axes | pytest lib/matplotlib/tests/test_axes.py::test_polar_no_data |
matplotlib | 18 | class RadialLocator(mticker.Locator):
# docstring inherited
return self.base.refresh()
def view_limits(self, vmin, vmax):
vmin, vmax = self.base.view_limits(vmin, vmax)
if vmax > vmin: | class RadialLocator(mticker.Locator):
# docstring inherited
return self.base.refresh()
def nonsingular(self, vmin, vmax):
# docstring inherited
return ((0, 1) if (vmin, vmax) == (-np.inf, np.inf) # Init. limits.
else self.base.nonsingular(vmin, vmax))
def view_... | pytest lib/matplotlib/tests/test_axes.py::test_polar_no_data |
pandas | 102 | def init_ndarray(values, index, columns, dtype=None, copy=False):
return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
elif is_extension_array_dtype(values) or is_extension_array_dtype(dtype):
# GH#19157
if columns is None:
columns = [0]
return arrays_to_m... | def init_ndarray(values, index, columns, dtype=None, copy=False):
return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
elif is_extension_array_dtype(values) or is_extension_array_dtype(dtype):
# GH#19157
if isinstance(values, np.ndarray) and values.ndim > 1:
# GH... | pytest pandas/tests/frame/test_constructors.py::TestDataFrameConstructorWithDatetimeTZ::test_from_2d_ndarray_with_dtype |
thefuck | 9 | def get_new_command(command):
pass
if upstream_option_index is not -1:
command.script_parts.pop(upstream_option_index)
command.script_parts.pop(upstream_option_index)
push_upstream = command.stderr.split('\n')[-3].strip().partition('git ')[2]
return replace_argument(" ".join(command... | def get_new_command(command):
pass
if upstream_option_index is not -1:
command.script_parts.pop(upstream_option_index)
try:
command.script_parts.pop(upstream_option_index)
except IndexError:
# This happens for `git push -u`
pass
push_upstream ... | pytest tests/rules/test_git_push.py::test_get_new_command |
scrapy | 24 | class TunnelingTCP4ClientEndpoint(TCP4ClientEndpoint):
for it.
"""
_responseMatcher = re.compile('HTTP/1\.. 200')
def __init__(self, reactor, host, port, proxyConf, contextFactory,
timeout=30, bindAddress=None): | class TunnelingTCP4ClientEndpoint(TCP4ClientEndpoint):
for it.
"""
_responseMatcher = re.compile(b'HTTP/1\.. 200')
def __init__(self, reactor, host, port, proxyConf, contextFactory,
timeout=30, bindAddress=None): | python -m unittest -q tests.test_downloader_handlers.HttpProxyTestCase.test_download_with_proxy_https_timeout |
scrapy | 24 | class TunnelingTCP4ClientEndpoint(TCP4ClientEndpoint):
def requestTunnel(self, protocol):
"""Asks the proxy to open a tunnel."""
tunnelReq = 'CONNECT %s:%s HTTP/1.1\r\n' % (self._tunneledHost,
self._tunneledPort)
if self._proxyAuthHeader:
... | class TunnelingTCP4ClientEndpoint(TCP4ClientEndpoint):
def requestTunnel(self, protocol):
"""Asks the proxy to open a tunnel."""
tunnelReq = (
b'CONNECT ' +
to_bytes(self._tunneledHost, encoding='ascii') + b':' +
to_bytes(str(self._tunneledPort)) +
b'... | python -m unittest -q tests.test_downloader_handlers.HttpProxyTestCase.test_download_with_proxy_https_timeout |
httpie | 1 | from __future__ import division
import os
import re
import sys
import mimetypes
import threading
from time import sleep, time | from __future__ import division
import os
import re
import sys
import errno
import mimetypes
import threading
from time import sleep, time | pytest tests/test_downloads.py::TestDownloadUtils::test_unique_filename |
httpie | 1 | def filename_from_url(url, content_type):
return fn
def get_unique_filename(filename, exists=os.path.exists):
attempt = 0
while True:
suffix = '-' + str(attempt) if attempt > 0 else ''
if not exists(filename + suffix):
return filename + suffix
attempt += 1
| def filename_from_url(url, content_type):
return fn
def trim_filename(filename, max_len):
if len(filename) > max_len:
trim_by = len(filename) - max_len
name, ext = os.path.splitext(filename)
if trim_by >= len(name):
filename = filename[:-trim_by]
else:
f... | pytest tests/test_downloads.py::TestDownloadUtils::test_unique_filename |
keras | 17 | def categorical_accuracy(y_true, y_pred):
def sparse_categorical_accuracy(y_true, y_pred):
return K.cast(K.equal(K.max(y_true, axis=-1),
K.cast(K.argmax(y_pred, axis=-1), K.floatx())),
K.floatx())
| def categorical_accuracy(y_true, y_pred):
def sparse_categorical_accuracy(y_true, y_pred):
# flatten y_true in case it's in shape (num_samples, 1) instead of (num_samples,)
return K.cast(K.equal(K.flatten(y_true),
K.cast(K.argmax(y_pred, axis=-1), K.floatx())),
K.fl... | pytest tests/keras/metrics_test.py::test_sparse_categorical_accuracy_correctness |
pandas | 123 | class Index(IndexOpsMixin, PandasObject):
pass
# Return an actual float index.
return Float64Index(data, copy=copy, dtype=dtype, name=name)
elif inferred == "string":
pass | class Index(IndexOpsMixin, PandasObject):
pass
# Return an actual float index.
return Float64Index(data, copy=copy, name=name)
elif inferred == "string":
pass | pytest pandas/tests/indexes/test_range.py::TestRangeIndex::test_constructor_corner |
pandas | 123 | from pandas.core.dtypes.common import (
is_float_dtype,
is_integer_dtype,
is_scalar,
needs_i8_conversion,
pandas_dtype,
) | from pandas.core.dtypes.common import (
is_float_dtype,
is_integer_dtype,
is_scalar,
is_signed_integer_dtype,
is_unsigned_integer_dtype,
needs_i8_conversion,
pandas_dtype,
) | pytest pandas/tests/indexes/test_range.py::TestRangeIndex::test_constructor_corner |
pandas | 123 | from pandas.core.dtypes.generic import (
)
from pandas.core.dtypes.missing import isna
from pandas.core import algorithms
import pandas.core.common as com
from pandas.core.indexes.base import Index, InvalidIndexError, _index_shared_docs | from pandas.core.dtypes.generic import (
)
from pandas.core.dtypes.missing import isna
from pandas._typing import Dtype
from pandas.core import algorithms
import pandas.core.common as com
from pandas.core.indexes.base import Index, InvalidIndexError, _index_shared_docs | pytest pandas/tests/indexes/test_range.py::TestRangeIndex::test_constructor_corner |
pandas | 123 | class NumericIndex(Index):
_is_numeric_dtype = True
def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=None):
if fastpath is not None:
warnings.warn(
"The 'fastpath' keyword is deprecated, and will be " | class NumericIndex(Index):
_is_numeric_dtype = True
def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=None):
cls._validate_dtype(dtype)
if fastpath is not None:
warnings.warn(
"The 'fastpath' keyword is deprecated, and will be " | pytest pandas/tests/indexes/test_range.py::TestRangeIndex::test_constructor_corner |
pandas | 123 | class NumericIndex(Index):
name = data.name
return cls._simple_new(subarr, name=name)
@Appender(_index_shared_docs["_maybe_cast_slice_bound"])
def _maybe_cast_slice_bound(self, label, side, kind):
assert kind in ["ix", "loc", "getitem", None] | class NumericIndex(Index):
name = data.name
return cls._simple_new(subarr, name=name)
@classmethod
def _validate_dtype(cls, dtype: Dtype) -> None:
if dtype is None:
return
validation_metadata = {
"int64index": (is_signed_integer_dtype, "signed integer... | pytest pandas/tests/indexes/test_range.py::TestRangeIndex::test_constructor_corner |
pandas | 123 | from pandas.util._decorators import Appender, cache_readonly
from pandas.core.dtypes.common import (
ensure_platform_int,
ensure_python_int,
is_int64_dtype,
is_integer,
is_integer_dtype,
is_list_like, | from pandas.util._decorators import Appender, cache_readonly
from pandas.core.dtypes.common import (
ensure_platform_int,
ensure_python_int,
is_integer,
is_integer_dtype,
is_list_like, | pytest pandas/tests/indexes/test_range.py::TestRangeIndex::test_constructor_corner |
pandas | 123 | class RangeIndex(Int64Index):
# --------------------------------------------------------------------
@staticmethod
def _validate_dtype(dtype):
""" require dtype to be None or int64 """
if not (dtype is None or is_int64_dtype(dtype)):
raise TypeError("Invalid to pass a non-int64... | class RangeIndex(Int64Index):
# --------------------------------------------------------------------
@cache_readonly
def _constructor(self):
""" return the class to use for construction """ | pytest pandas/tests/indexes/test_range.py::TestRangeIndex::test_constructor_corner |
pandas | 82 | def _get_empty_dtype_and_na(join_units):
dtype = upcast_classes["datetimetz"]
return dtype[0], tslibs.NaT
elif "datetime" in upcast_classes:
return np.dtype("M8[ns]"), tslibs.iNaT
elif "timedelta" in upcast_classes:
return np.dtype("m8[ns]"), np.timedelta64("NaT", "ns")
else:... | def _get_empty_dtype_and_na(join_units):
dtype = upcast_classes["datetimetz"]
return dtype[0], tslibs.NaT
elif "datetime" in upcast_classes:
return np.dtype("M8[ns]"), np.datetime64("NaT", "ns")
elif "timedelta" in upcast_classes:
return np.dtype("m8[ns]"), np.timedelta64("NaT", ... | pytest pandas/tests/reshape/merge/test_merge.py::test_merge_datetime_upcast_dtype |
pandas | 10 | class ExtensionBlock(Block):
new_values = self.values if inplace else self.values.copy()
if isinstance(new, np.ndarray) and len(new) == len(mask):
new = new[mask]
mask = _safe_reshape(mask, new_values.shape) | class ExtensionBlock(Block):
new_values = self.values if inplace else self.values.copy()
if isinstance(new, (np.ndarray, ExtensionArray)) and len(new) == len(mask):
new = new[mask]
mask = _safe_reshape(mask, new_values.shape) | pytest pandas/tests/series/methods/test_update.py::TestUpdate::test_update_extension_array_series |
thefuck | 19 | def match(command):
@git_support
def get_new_command(command):
return replace_argument(command.script, 'push', 'push --force')
enabled_by_default = False | def match(command):
@git_support
def get_new_command(command):
return replace_argument(command.script, 'push', 'push --force-with-lease')
enabled_by_default = False | pytest tests/rules/test_git_push_force.py::test_get_new_command |
pandas | 73 | class DataFrame(NDFrame):
# ----------------------------------------------------------------------
# Arithmetic / combination related
def _combine_frame(self, other, func, fill_value=None, level=None):
# at this point we have `self._indexed_same(other)`
if fill_value is None: | class DataFrame(NDFrame):
# ----------------------------------------------------------------------
# Arithmetic / combination related
def _combine_frame(self, other: "DataFrame", func, fill_value=None):
# at this point we have `self._indexed_same(other)`
if fill_value is None: | pytest pandas/tests/frame/test_arithmetic.py::TestFrameFlexArithmetic::test_floordiv_axis0 |
pandas | 73 | class DataFrame(NDFrame):
return new_data
def _combine_match_index(self, other, func):
# at this point we have `self.index.equals(other.index)`
if ops.should_series_dispatch(self, other, func): | class DataFrame(NDFrame):
return new_data
def _combine_match_index(self, other: Series, func):
# at this point we have `self.index.equals(other.index)`
if ops.should_series_dispatch(self, other, func): | pytest pandas/tests/frame/test_arithmetic.py::TestFrameFlexArithmetic::test_floordiv_axis0 |
pandas | 73 | class DataFrame(NDFrame):
new_data = ops.dispatch_to_series(self, other, func)
else:
# fastpath --> operate directly on values
with np.errstate(all="ignore"):
new_data = func(self.values.T, other.values).T
return new_data
def _construct_result(sel... | class DataFrame(NDFrame):
new_data = ops.dispatch_to_series(self, other, func)
else:
# fastpath --> operate directly on values
other_vals = other.values.reshape(-1, 1)
with np.errstate(all="ignore"):
new_data = func(self.values, other_vals)
... | pytest pandas/tests/frame/test_arithmetic.py::TestFrameFlexArithmetic::test_floordiv_axis0 |
pandas | 73 | def mask_zero_div_zero(x, y, result):
return result
if zmask.any():
shape = result.shape
# Flip sign if necessary for -0.0
zneg_mask = zmask & np.signbit(y)
zpos_mask = zmask & ~zneg_mask
nan_mask = (zmask & (x == 0)).ravel()
with np.errstate(invalid="ignor... | def mask_zero_div_zero(x, y, result):
return result
if zmask.any():
# Flip sign if necessary for -0.0
zneg_mask = zmask & np.signbit(y)
zpos_mask = zmask & ~zneg_mask
nan_mask = zmask & (x == 0)
with np.errstate(invalid="ignore"):
neginf_mask = (zpos_ma... | pytest pandas/tests/frame/test_arithmetic.py::TestFrameFlexArithmetic::test_floordiv_axis0 |
pandas | 140 | def _recast_datetimelike_result(result: DataFrame) -> DataFrame:
result = result.copy()
obj_cols = [
idx for idx in range(len(result.columns)) if is_object_dtype(result.dtypes[idx])
]
# See GH#26285 | def _recast_datetimelike_result(result: DataFrame) -> DataFrame:
result = result.copy()
obj_cols = [
idx
for idx in range(len(result.columns))
if is_object_dtype(result.dtypes.iloc[idx])
]
# See GH#26285 | pytest pandas/tests/groupby/test_apply.py::test_apply_datetime_issue |
thefuck | 15 | from thefuck.specific.git import git_support
@git_support
def match(command):
return ('did not match any file(s) known to git.' in command.stderr
and "Did you forget to 'git add'?" in command.stderr)
@git_support
def get_new_command(command):
missing_file = re.findall(
r"error: pathsp... | from thefuck.specific.git import git_support
@git_support
def match(command):
return 'did not match any file(s) known to git.' in command.stderr
@git_support
def get_new_command(command):
missing_file = re.findall(
r"error: pathspec '([^']*)' "
r'did not match any file\(s\) known to git.', co... | pytest tests/rules/test_git_add.py::test_match |
scrapy | 28 | class RFPDupeFilter(BaseDupeFilter):
self.logger = logging.getLogger(__name__)
if path:
self.file = open(os.path.join(path, 'requests.seen'), 'a+')
self.fingerprints.update(x.rstrip() for x in self.file)
@classmethod | class RFPDupeFilter(BaseDupeFilter):
self.logger = logging.getLogger(__name__)
if path:
self.file = open(os.path.join(path, 'requests.seen'), 'a+')
self.file.seek(0)
self.fingerprints.update(x.rstrip() for x in self.file)
@classmethod | python -m unittest -q tests.test_dupefilters.RFPDupeFilterTest.test_dupefilter_path |
thefuck | 5 | from thefuck.specific.git import git_support
@git_support
def match(command):
return ('push' in command.script_parts
and 'set-upstream' in command.output)
def _get_upstream_option_index(command_parts): | from thefuck.specific.git import git_support
@git_support
def match(command):
return ('push' in command.script_parts
and 'git push --set-upstream' in command.output)
def _get_upstream_option_index(command_parts): | pytest tests/rules/test_git_push.py::test_match_bitbucket |
sanic | 3 | class Sanic:
"Endpoint with name `{}` was not found".format(view_name)
)
if view_name == "static" or view_name.endswith(".static"):
filename = kwargs.pop("filename", None)
# it's static folder | class Sanic:
"Endpoint with name `{}` was not found".format(view_name)
)
# If the route has host defined, split that off
# TODO: Retain netloc and path separately in Route objects
host = uri.find("/")
if host > 0:
host, uri = uri[:host], uri[host:... | pytest tests/test_url_for.py::test_routes_with_host |
sanic | 3 | class Sanic:
netloc = kwargs.pop("_server", None)
if netloc is None and external:
netloc = self.config.get("SERVER_NAME", "")
if external:
if not scheme: | class Sanic:
netloc = kwargs.pop("_server", None)
if netloc is None and external:
netloc = host or self.config.get("SERVER_NAME", "")
if external:
if not scheme: | pytest tests/test_url_for.py::test_routes_with_host |
luigi | 27 | class Parameter(object):
return [str(v) for v in x]
return str(x)
def parse_from_input(self, param_name, x):
"""
Parses the parameter value from input ``x``, handling defaults and is_list.
| class Parameter(object):
return [str(v) for v in x]
return str(x)
def parse_from_input(self, param_name, x, task_name=None):
"""
Parses the parameter value from input ``x``, handling defaults and is_list.
| pytest test/parameter_test.py::TestParamWithDefaultFromConfig::testCommandLineNoDefault |
luigi | 27 | class Parameter(object):
:raises MissingParameterException: if x is false-y and no default is specified.
"""
if not x:
if self.has_value:
return self.value
elif self.is_bool:
return False
elif self.is_list: | class Parameter(object):
:raises MissingParameterException: if x is false-y and no default is specified.
"""
if not x:
if self.has_task_value(param_name=param_name, task_name=task_name):
return self.task_value(param_name=param_name, task_name=task_name)
el... | pytest test/parameter_test.py::TestParamWithDefaultFromConfig::testCommandLineNoDefault |
luigi | 27 | class Parameter(object):
description.append('for all instances of class %s' % task_name)
elif self.description:
description.append(self.description)
if self.has_value:
description.append(" [default: %s]" % (self.value,))
if self.is_list:
action = ... | class Parameter(object):
description.append('for all instances of class %s' % task_name)
elif self.description:
description.append(self.description)
if self.has_task_value(param_name=param_name, task_name=task_name):
value = self.task_value(param_name=param_name, task... | pytest test/parameter_test.py::TestParamWithDefaultFromConfig::testCommandLineNoDefault |
luigi | 27 | class Parameter(object):
dest = self.parser_dest(param_name, task_name, glob=False)
if dest is not None:
value = getattr(args, dest, None)
params[param_name] = self.parse_from_input(param_name, value)
def set_global_from_args(self, param_name, task_name, args, is_without_sec... | class Parameter(object):
dest = self.parser_dest(param_name, task_name, glob=False)
if dest is not None:
value = getattr(args, dest, None)
params[param_name] = self.parse_from_input(param_name, value, task_name=task_name)
def set_global_from_args(self, param_name, task_name,... | pytest test/parameter_test.py::TestParamWithDefaultFromConfig::testCommandLineNoDefault |
luigi | 27 | class Parameter(object):
if dest is not None:
value = getattr(args, dest, None)
if value:
self.set_global(self.parse_from_input(param_name, value))
else: # either False (bools) or None (everything else)
self.reset_global()
| class Parameter(object):
if dest is not None:
value = getattr(args, dest, None)
if value:
self.set_global(self.parse_from_input(param_name, value, task_name=task_name))
else: # either False (bools) or None (everything else)
self.reset_global()... | pytest test/parameter_test.py::TestParamWithDefaultFromConfig::testCommandLineNoDefault |
matplotlib | 14 | class Text(Artist):
def update(self, kwargs):
# docstring inherited
# Update bbox last, as it depends on font properties.
sentinel = object() # bbox can be None, so use another sentinel.
bbox = kwargs.pop("bbox", sentinel)
super().update(kwargs)
if bbox is not senti... | class Text(Artist):
def update(self, kwargs):
# docstring inherited
sentinel = object() # bbox can be None, so use another sentinel.
# Update fontproperties first, as it has lowest priority.
fontproperties = kwargs.pop("fontproperties", sentinel)
if fontproperties is not se... | pytest lib/matplotlib/tests/test_text.py::test_fontproperties_kwarg_precedence |
pandas | 161 | class Categorical(ExtensionArray, PandasObject):
raise ValueError("fill value must be in categories")
values_codes = _get_codes_for_values(value, self.categories)
indexer = np.where(values_codes != -1)
codes[indexer] = values_codes[values_codes != -1]... | class Categorical(ExtensionArray, PandasObject):
raise ValueError("fill value must be in categories")
values_codes = _get_codes_for_values(value, self.categories)
indexer = np.where(codes == -1)
codes[indexer] = values_codes[indexer]
# If... | pytest pandas/tests/series/test_missing.py::TestSeriesMissingData::test_fillna_categorical_with_new_categories |
youtube-dl | 23 | def js_to_json(code):
v = m.group(0)
if v in ('true', 'false', 'null'):
return v
elif v.startswith('/*') or v == ',':
return ""
if v[0] in ("'", '"'): | def js_to_json(code):
v = m.group(0)
if v in ('true', 'false', 'null'):
return v
elif v.startswith('/*') or v.startswith('//') or v == ',':
return ""
if v[0] in ("'", '"'): | python -m unittest -q test.test_utils.TestUtil.test_js_to_json_edgecases |
youtube-dl | 23 | def js_to_json(code):
return re.sub(r'''(?sx)
"(?:[^"\\]*(?:\\\\|\\['"nurtbfx/\n]))*[^"\\]*"|
'(?:[^'\\]*(?:\\\\|\\['"nurtbfx/\n]))*[^'\\]*'|
/\*.*?\*/|,(?=\s*[\]}])|
[a-zA-Z_][.a-zA-Z_0-9]*|
\b(?:0[xX][0-9a-fA-F]+|0+[0-7]+)(?:\s*:)?|
[0-9]+(?=\s*:) | def js_to_json(code):
return re.sub(r'''(?sx)
"(?:[^"\\]*(?:\\\\|\\['"nurtbfx/\n]))*[^"\\]*"|
'(?:[^'\\]*(?:\\\\|\\['"nurtbfx/\n]))*[^'\\]*'|
/\*.*?\*/|//[^\n]*|,(?=\s*[\]}])|
[a-zA-Z_][.a-zA-Z_0-9]*|
\b(?:0[xX][0-9a-fA-F]+|0+[0-7]+)(?:\s*:)?|
[0-9]+(?=\s*:) | python -m unittest -q test.test_utils.TestUtil.test_js_to_json_edgecases |
pandas | 52 | class SeriesGroupBy(GroupBy):
val = self.obj._internal_get_values()
# GH 27951
# temporary fix while we wait for NumPy bug 12629 to be fixed
val[isna(val)] = np.datetime64("NaT")
try:
sorter = np.lexsort((val, ids))
except TypeError: # catches object dtype... | class SeriesGroupBy(GroupBy):
val = self.obj._internal_get_values()
codes, _ = algorithms.factorize(val, sort=False)
sorter = np.lexsort((codes, ids))
codes = codes[sorter]
ids = ids[sorter]
# group boundaries are where group ids change
# unique observations ar... | pytest pandas/tests/groupby/test_function.py::test_series_groupby_nunique |
matplotlib | 6 | class Axes(_AxesBase):
except ValueError:
pass # Failed to convert to float array; must be color specs.
else:
# If c can be either mapped values or a RGB(A) color, prefer
# the former if shapes match, the latter otherwise.
if c.siz... | class Axes(_AxesBase):
except ValueError:
pass # Failed to convert to float array; must be color specs.
else:
# handle the documented special case of a 2D array with 1
# row which as RGB(A) to broadcast.
if c.shape == (1, 4) or c.s... | pytest lib/matplotlib/tests/test_axes.py::TestScatter::test_scatter_single_color_c |
scrapy | 32 | class CrawlerProcess(CrawlerRunner):
def __init__(self, settings):
super(CrawlerProcess, self).__init__(settings)
install_shutdown_handlers(self._signal_shutdown)
configure_logging(settings)
log_scrapy_info(settings)
def _signal_shutdown(self, signum, _):
install_shutdow... | class CrawlerProcess(CrawlerRunner):
def __init__(self, settings):
super(CrawlerProcess, self).__init__(settings)
install_shutdown_handlers(self._signal_shutdown)
configure_logging(self.settings)
log_scrapy_info(self.settings)
def _signal_shutdown(self, signum, _):
insta... | python -m unittest -q tests.test_crawler.CrawlerProcessTest.test_crawler_process_accepts_dict |
keras | 9 | def count_leading_spaces(s):
def process_list_block(docstring, starting_point, section_end,
leading_spaces, marker):
ending_point = docstring.find('\n\n', starting_point)
block = docstring[starting_point:(None if ending_point == -1 else
ending_point -... | def count_leading_spaces(s):
def process_list_block(docstring, starting_point, section_end,
leading_spaces, marker):
ending_point = docstring.find('\n\n', starting_point)
block = docstring[starting_point:(ending_point - 1 if ending_point > -1 else
sec... | pytest tests/test_doc_auto_generation.py::test_doc_lists[docs_descriptor1] |
tornado | 14 | class IOLoop(Configurable):
if IOLoop.current(instance=False) is None:
self.make_current()
elif make_current:
if IOLoop.current(instance=False) is None:
raise RuntimeError("current IOLoop already exists")
self.make_current()
| class IOLoop(Configurable):
if IOLoop.current(instance=False) is None:
self.make_current()
elif make_current:
if IOLoop.current(instance=False) is not None:
raise RuntimeError("current IOLoop already exists")
self.make_current()
| python -m unittest -q tornado.test.ioloop_test.TestIOLoopCurrent.test_force_current |
pandas | 69 | class _AtIndexer(_ScalarAccessIndexer):
"can only have integer indexers"
)
else:
if is_integer(i) and not ax.holds_integer():
raise ValueError(
"At based indexing on an non-integer "
... | class _AtIndexer(_ScalarAccessIndexer):
"can only have integer indexers"
)
else:
if is_integer(i) and not (ax.holds_integer() or ax.is_floating()):
raise ValueError(
"At based indexing on an non-integer "... | pytest pandas/tests/indexes/test_numeric.py::TestFloat64Index::test_lookups_datetimelike_values |
pandas | 94 | from pandas.core.dtypes.generic import ABCIndex, ABCIndexClass, ABCSeries
from pandas.core import algorithms
from pandas.core.accessor import PandasDelegate
from pandas.core.arrays import ExtensionArray, ExtensionOpsMixin
from pandas.core.arrays.datetimelike import (
DatetimeLikeArrayMixin,
_ensure_datetimelik... | from pandas.core.dtypes.generic import ABCIndex, ABCIndexClass, ABCSeries
from pandas.core import algorithms
from pandas.core.accessor import PandasDelegate
from pandas.core.arrays import (
DatetimeArray,
ExtensionArray,
ExtensionOpsMixin,
TimedeltaArray,
)
from pandas.core.arrays.datetimelike import (... | pytest pandas/tests/indexes/datetimes/test_constructors.py::TestDatetimeIndex::test_shallow_copy_inherits_array_freq |
pandas | 94 | class DatetimeIndexOpsMixin(ExtensionIndex, ExtensionOpsMixin):
if isinstance(maybe_slice, slice):
return self[maybe_slice]
taken = ExtensionIndex.take(
self, indices, axis, allow_fill, fill_value, **kwargs
)
# keep freq in PeriodArray/Index, reset otherwise
... | class DatetimeIndexOpsMixin(ExtensionIndex, ExtensionOpsMixin):
if isinstance(maybe_slice, slice):
return self[maybe_slice]
return ExtensionIndex.take(
self, indices, axis, allow_fill, fill_value, **kwargs
)
_can_hold_na = True
_na_value = NaT | pytest pandas/tests/indexes/datetimes/test_constructors.py::TestDatetimeIndex::test_shallow_copy_inherits_array_freq |
pandas | 94 | class DatetimeIndexOpsMixin(ExtensionIndex, ExtensionOpsMixin):
@Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)
def repeat(self, repeats, axis=None):
nv.validate_repeat(tuple(), dict(axis=axis))
freq = self.freq if is_period_dtype(self) else None
return self._shallow_copy(sel... | class DatetimeIndexOpsMixin(ExtensionIndex, ExtensionOpsMixin):
@Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)
def repeat(self, repeats, axis=None):
nv.validate_repeat(tuple(), dict(axis=axis))
result = type(self._data)(self.asi8.repeat(repeats), dtype=self.dtype)
return sel... | pytest pandas/tests/indexes/datetimes/test_constructors.py::TestDatetimeIndex::test_shallow_copy_inherits_array_freq |
pandas | 94 | class DatetimeTimedeltaMixin(DatetimeIndexOpsMixin, Int64Index):
self._data._freq = freq
# --------------------------------------------------------------------
# Set Operation Methods
| class DatetimeTimedeltaMixin(DatetimeIndexOpsMixin, Int64Index):
self._data._freq = freq
def _shallow_copy(self, values=None, **kwargs):
if values is None:
values = self._data
if isinstance(values, type(self)):
values = values._data
attributes = self._get_a... | pytest pandas/tests/indexes/datetimes/test_constructors.py::TestDatetimeIndex::test_shallow_copy_inherits_array_freq |
pandas | 135 | class BaseGrouper:
pass
else:
raise
return self._aggregate_series_pure_python(obj, func)
def _aggregate_series_fast(self, obj, func):
func = self._is_builtin_func(func) | class BaseGrouper:
pass
else:
raise
except TypeError as err:
if "ndarray" in str(err):
# raised in libreduction if obj's values is no ndarray
pass
else:
raise
return self._aggregate_series... | pytest pandas/tests/extension/decimal/test_decimal.py::test_groupby_agg_ea_method |
youtube-dl | 18 | class YoutubeDL(object):
force_properties = dict(
(k, v) for k, v in ie_result.items() if v is not None)
for f in ('_type', 'url', 'ie_key'):
if f in force_properties:
del force_properties[f]
new_result = info.copy() | class YoutubeDL(object):
force_properties = dict(
(k, v) for k, v in ie_result.items() if v is not None)
for f in ('_type', 'url', 'id', 'extractor', 'extractor_key', 'ie_key'):
if f in force_properties:
del force_properties[f]
new... | python -m unittest -q test.test_YoutubeDL.TestYoutubeDL.test_do_not_override_ie_key_in_url_transparent |
black | 12 | class BracketTracker:
bracket_match: Dict[Tuple[Depth, NodeType], Leaf] = Factory(dict)
delimiters: Dict[LeafID, Priority] = Factory(dict)
previous: Optional[Leaf] = None
_for_loop_variable: int = 0
_lambda_arguments: int = 0
def mark(self, leaf: Leaf) -> None:
"""Mark `leaf` with brack... | class BracketTracker:
bracket_match: Dict[Tuple[Depth, NodeType], Leaf] = Factory(dict)
delimiters: Dict[LeafID, Priority] = Factory(dict)
previous: Optional[Leaf] = None
_for_loop_depths: List[int] = Factory(list)
_lambda_argument_depths: List[int] = Factory(list)
def mark(self, leaf: Leaf) ->... | python -m unittest -q tests.test_black.BlackTestCase.test_bracket_match |
black | 12 | class BracketTracker:
"""
if leaf.type == token.NAME and leaf.value == "for":
self.depth += 1
self._for_loop_variable += 1
return True
return False
def maybe_decrement_after_for_loop_variable(self, leaf: Leaf) -> bool:
"""See `maybe_increment_for... | class BracketTracker:
"""
if leaf.type == token.NAME and leaf.value == "for":
self.depth += 1
self._for_loop_depths.append(self.depth)
return True
return False
def maybe_decrement_after_for_loop_variable(self, leaf: Leaf) -> bool:
"""See `maybe_i... | python -m unittest -q tests.test_black.BlackTestCase.test_bracket_match |
black | 12 | class BracketTracker:
"""
if leaf.type == token.NAME and leaf.value == "lambda":
self.depth += 1
self._lambda_arguments += 1
return True
return False
def maybe_decrement_after_lambda_arguments(self, leaf: Leaf) -> bool:
"""See `maybe_increment_la... | class BracketTracker:
"""
if leaf.type == token.NAME and leaf.value == "lambda":
self.depth += 1
self._lambda_argument_depths.append(self.depth)
return True
return False
def maybe_decrement_after_lambda_arguments(self, leaf: Leaf) -> bool:
"""See... | python -m unittest -q tests.test_black.BlackTestCase.test_bracket_match |
keras | 20 | def in_top_k(predictions, targets, k):
def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
padding='valid', data_format=None):
data_format = normalize_data_format(data_format)
x = _preprocess_conv2d_input(x, data_format) | def in_top_k(predictions, targets, k):
def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
padding='valid', data_format=None, dilation_rate=(1, 1)):
data_format = normalize_data_format(data_format)
x = _preprocess_conv2d_input(x, data_format) | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
False,
padding,
padding],
output_shape=output_shape)
return _postprocess_conv2d_output(x, data_format)
| def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
False,
padding,
padding],
output_shape=output_shape,
dilation=dilation_rate)
return _postprocess_conv2d_output(x, data_format)
| pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def _preprocess_conv1d_input(x, data_format):
return x, tf_data_format
def _preprocess_conv2d_input(x, data_format):
"""Transpose and cast the input before the conv2d.
# Arguments
x: input tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
# Returns
A tenso... | def _preprocess_conv1d_input(x, data_format):
return x, tf_data_format
def _preprocess_conv2d_input(x, data_format, force_transpose=False):
"""Transpose and cast the input before the conv2d.
# Arguments
x: input tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def _preprocess_conv2d_input(x, data_format):
x = tf.cast(x, 'float32')
tf_data_format = 'NHWC'
if data_format == 'channels_first':
if not _has_nchw_support():
x = tf.transpose(x, (0, 2, 3, 1)) # NCHW -> NHWC
else:
tf_data_format = 'NCHW' | def _preprocess_conv2d_input(x, data_format):
x = tf.cast(x, 'float32')
tf_data_format = 'NHWC'
if data_format == 'channels_first':
if not _has_nchw_support() or force_transpose:
x = tf.transpose(x, (0, 2, 3, 1)) # NCHW -> NHWC
else:
tf_data_format = 'NCHW' | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def conv2d(x, kernel, strides=(1, 1), padding='valid',
def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
padding='valid', data_format=None):
"""2D deconvolution (i.e. transposed convolution).
# Arguments | def conv2d(x, kernel, strides=(1, 1), padding='valid',
def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
padding='valid', data_format=None, dilation_rate=(1, 1)):
"""2D deconvolution (i.e. transposed convolution).
# Arguments | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
data_format: string, `"channels_last"` or `"channels_first"`.
Whether to use Theano or TensorFlow/CNTK data format
for inputs/kernels/outputs.
# Returns
A tensor, result of transposed 2D convolution. | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
data_format: string, `"channels_last"` or `"channels_first"`.
Whether to use Theano or TensorFlow/CNTK data format
for inputs/kernels/outputs.
dilation_rate: tuple of 2 integers.
# Returns
A tensor, result... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
if isinstance(output_shape, (tuple, list)):
output_shape = tf.stack(output_shape)
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
output_shape = (outp... | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
if isinstance(output_shape, (tuple, list)):
output_shape = tf.stack(output_shape)
# tf.nn.atrous_conv2d_transpose input only supports NHWC format
if data_format == 'channels_first' and dilation_rate != (1, 1):
force_transpose = T... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
else:
strides = (1, 1) + strides
x = tf.nn.conv2d_transpose(x, kernel, output_shape, strides,
padding=padding,
data_format=tf_data_format)
if data_format == 'channels_first' a... | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
else:
strides = (1, 1) + strides
if dilation_rate == (1, 1):
x = tf.nn.conv2d_transpose(x, kernel, output_shape, strides,
padding=padding,
data_format=tf_data_form... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def conv2d(x, kernel, strides=(1, 1), padding='valid',
def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
padding='valid', data_format=None):
"""2D deconvolution (transposed convolution).
# Arguments | def conv2d(x, kernel, strides=(1, 1), padding='valid',
def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
padding='valid', data_format=None, dilation_rate=(1, 1)):
"""2D deconvolution (transposed convolution).
# Arguments | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
padding: string, "same" or "valid".
data_format: "channels_last" or "channels_first".
Whether to use Theano or TensorFlow data format
in inputs/kernels/outputs.
# Raises
ValueError: if using an even kernel siz... | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
padding: string, "same" or "valid".
data_format: "channels_last" or "channels_first".
Whether to use Theano or TensorFlow data format
in inputs/kernels/outputs.
dilation_rate: tuple of 2 integers.
# Raises... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
kshp=kernel_shape,
subsample=strides,
border_mode=th_padding,
... | def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
kshp=kernel_shape,
subsample=strides,
border_mode=th_padding,
... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | class Conv2DTranspose(Conv2D):
padding='valid',
output_padding=None,
data_format=None,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform', | class Conv2DTranspose(Conv2D):
padding='valid',
output_padding=None,
data_format=None,
dilation_rate=(1, 1),
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform', | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | class Conv2DTranspose(Conv2D):
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer, | class Conv2DTranspose(Conv2D):
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer, | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | class Conv2DTranspose(Conv2D):
out_height = conv_utils.deconv_length(height,
stride_h, kernel_h,
self.padding,
out_pad_h)
out_width = conv_utils.deconv_length(width,
... | class Conv2DTranspose(Conv2D):
out_height = conv_utils.deconv_length(height,
stride_h, kernel_h,
self.padding,
out_pad_h,
self.d... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | class Conv2DTranspose(Conv2D):
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format)
if self.use_bias:
outputs = K.bias_add( | class Conv2DTranspose(Conv2D):
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.use_bias:
outputs = K.bias_add( | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | class Conv2DTranspose(Conv2D):
stride_h,
kernel_h,
self.padding,
out_pad_h)
output_shape... | class Conv2DTranspose(Conv2D):
stride_h,
kernel_h,
self.padding,
out_pad_h,
... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def conv_input_length(output_length, filter_size, padding, stride):
return (output_length - 1) * stride - 2 * pad + filter_size
def deconv_length(dim_size, stride_size, kernel_size, padding, output_padding):
"""Determines output length of a transposed convolution given input length.
# Arguments | def conv_input_length(output_length, filter_size, padding, stride):
return (output_length - 1) * stride - 2 * pad + filter_size
def deconv_length(dim_size, stride_size, kernel_size, padding,
output_padding, dilation=1):
"""Determines output length of a transposed convolution given input leng... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def deconv_length(dim_size, stride_size, kernel_size, padding, output_padding):
padding: One of `"same"`, `"valid"`, `"full"`.
output_padding: Integer, amount of padding along the output dimension,
Can be set to `None` in which case the output length is inferred.
# Returns
The o... | def deconv_length(dim_size, stride_size, kernel_size, padding, output_padding):
padding: One of `"same"`, `"valid"`, `"full"`.
output_padding: Integer, amount of padding along the output dimension,
Can be set to `None` in which case the output length is inferred.
dilation: dilation r... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
keras | 20 | def deconv_length(dim_size, stride_size, kernel_size, padding, output_padding):
if dim_size is None:
return None
# Infer length if output padding is None, else compute the exact length
if output_padding is None:
if padding == 'valid': | def deconv_length(dim_size, stride_size, kernel_size, padding, output_padding):
if dim_size is None:
return None
# Get the dilated kernel size
kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
# Infer length if output padding is None, else compute the exact length
if output_pa... | pytest tests/keras/layers/convolutional_test.py::test_conv2d_transpose_dilation |
luigi | 6 | def _recursively_freeze(value):
"""
if isinstance(value, Mapping):
return _FrozenOrderedDict(((k, _recursively_freeze(v)) for k, v in value.items()))
elif isinstance(value, list):
return tuple(_recursively_freeze(v) for v in value)
return value
class DictParameter(Parameter):
"""
... | def _recursively_freeze(value):
"""
if isinstance(value, Mapping):
return _FrozenOrderedDict(((k, _recursively_freeze(v)) for k, v in value.items()))
elif isinstance(value, list) or isinstance(value, tuple):
return tuple(_recursively_freeze(v) for v in value)
return value
class _DictPa... | pytest test/parameter_test.py::TestParametersHashability::test_tuple_dict |
luigi | 6 | class DictParameter(Parameter):
tags, that are dynamically constructed outside Luigi), or you have a complex parameter containing logically related
values (like a database connection config).
"""
class _DictParamEncoder(JSONEncoder):
"""
JSON encoder for :py:class:`~DictParameter`, whic... | class DictParameter(Parameter):
tags, that are dynamically constructed outside Luigi), or you have a complex parameter containing logically related
values (like a database connection config).
"""
def normalize(self, value):
"""
Ensure that dictionary parameter is converted to a _FrozenOr... | pytest test/parameter_test.py::TestParametersHashability::test_tuple_dict |
luigi | 6 | class DictParameter(Parameter):
return json.loads(s, object_pairs_hook=_FrozenOrderedDict)
def serialize(self, x):
return json.dumps(x, cls=DictParameter._DictParamEncoder)
class ListParameter(Parameter): | class DictParameter(Parameter):
return json.loads(s, object_pairs_hook=_FrozenOrderedDict)
def serialize(self, x):
return json.dumps(x, cls=_DictParamEncoder)
class ListParameter(Parameter): | pytest test/parameter_test.py::TestParametersHashability::test_tuple_dict |
luigi | 6 | class ListParameter(Parameter):
"""
def normalize(self, x):
"""
Ensure that list parameter is converted to a tuple so it can be hashed.
:param str x: the value to parse.
:return: the normalized (hashable/immutable) value. | class ListParameter(Parameter):
"""
def normalize(self, x):
"""
Ensure that struct is recursively converted to a tuple so it can be hashed.
:param str x: the value to parse.
:return: the normalized (hashable/immutable) value. | pytest test/parameter_test.py::TestParametersHashability::test_tuple_dict |
luigi | 6 | class ListParameter(Parameter):
:param str x: the value to parse.
:return: the parsed value.
"""
return list(json.loads(x))
def serialize(self, x):
""" | class ListParameter(Parameter):
:param str x: the value to parse.
:return: the parsed value.
"""
return list(json.loads(x, object_pairs_hook=_FrozenOrderedDict))
def serialize(self, x):
""" | pytest test/parameter_test.py::TestParametersHashability::test_tuple_dict |
luigi | 6 | class ListParameter(Parameter):
:param x: the value to serialize.
"""
return json.dumps(x)
class TupleParameter(Parameter):
"""
Parameter whose value is a ``tuple`` or ``tuple`` of tuples.
| class ListParameter(Parameter):
:param x: the value to serialize.
"""
return json.dumps(x, cls=_DictParamEncoder)
class TupleParameter(ListParameter):
"""
Parameter whose value is a ``tuple`` or ``tuple`` of tuples.
| pytest test/parameter_test.py::TestParametersHashability::test_tuple_dict |
luigi | 6 | class TupleParameter(Parameter):
$ luigi --module my_tasks MyTask --book_locations '((12,3),(4,15),(52,1))'
"""
def parse(self, x):
"""
Parse an individual value from the input. | class TupleParameter(Parameter):
$ luigi --module my_tasks MyTask --book_locations '((12,3),(4,15),(52,1))'
"""
def parse(self, x):
"""
Parse an individual value from the input. | pytest test/parameter_test.py::TestParametersHashability::test_tuple_dict |
luigi | 6 | class TupleParameter(Parameter):
# Therefore, if json.loads(x) returns a ValueError, try ast.literal_eval(x).
# ast.literal_eval(t_str) == t
try:
return tuple(tuple(x) for x in json.loads(x)) # loop required to parse tuple of tuples
except ValueError:
return lite... | class TupleParameter(Parameter):
# Therefore, if json.loads(x) returns a ValueError, try ast.literal_eval(x).
# ast.literal_eval(t_str) == t
try:
# loop required to parse tuple of tuples
return tuple(tuple(x) for x in json.loads(x, object_pairs_hook=_FrozenOrderedDict))
... | pytest test/parameter_test.py::TestParametersHashability::test_tuple_dict |
spacy | 7 | dependency tree to find the noun phrase they are referring to – for example:
$9.4 million --> Net income.
Compatible with: spaCy v2.0.0+
Last tested with: v2.1.0
"""
from __future__ import unicode_literals, print_function
| dependency tree to find the noun phrase they are referring to – for example:
$9.4 million --> Net income.
Compatible with: spaCy v2.0.0+
Last tested with: v2.2.1
"""
from __future__ import unicode_literals, print_function
| py.test spacy/tests/doc/test_span.py::test_filter_spans |
spacy | 7 | def main(model="en_core_web_sm"):
def filter_spans(spans):
# Filter a sequence of spans so they don't contain overlaps
get_sort_key = lambda span: (span.end - span.start, span.start)
sorted_spans = sorted(spans, key=get_sort_key, reverse=True)
result = []
seen_tokens = set()
for span in sorted_... | def main(model="en_core_web_sm"):
def filter_spans(spans):
# Filter a sequence of spans so they don't contain overlaps
# For spaCy 2.1.4+: this function is available as spacy.util.filter_spans()
get_sort_key = lambda span: (span.end - span.start, -span.start)
sorted_spans = sorted(spans, key=get_sort_k... | py.test spacy/tests/doc/test_span.py::test_filter_spans |
spacy | 7 | def filter_spans(spans):
spans (iterable): The spans to filter.
RETURNS (list): The filtered spans.
"""
get_sort_key = lambda span: (span.end - span.start, span.start)
sorted_spans = sorted(spans, key=get_sort_key, reverse=True)
result = []
seen_tokens = set() | def filter_spans(spans):
spans (iterable): The spans to filter.
RETURNS (list): The filtered spans.
"""
get_sort_key = lambda span: (span.end - span.start, -span.start)
sorted_spans = sorted(spans, key=get_sort_key, reverse=True)
result = []
seen_tokens = set() | py.test spacy/tests/doc/test_span.py::test_filter_spans |
ansible | 9 | class Rhsm(RegistrationBase):
for pool_id, quantity in sorted(pool_ids.items()):
if pool_id in available_pool_ids:
args = [SUBMAN_CMD, 'attach', '--pool', pool_id, '--quantity', quantity]
rc, stderr, stdout = self.module.run_command(args, check_rc=True)
e... | class Rhsm(RegistrationBase):
for pool_id, quantity in sorted(pool_ids.items()):
if pool_id in available_pool_ids:
args = [SUBMAN_CMD, 'attach', '--pool', pool_id]
if quantity is not None:
args.extend(['--quantity', to_native(quantity)])
... | pytest test/units/modules/packaging/os/test_redhat_subscription.py::test_redhat_subscribtion |
ansible | 9 | def main():
module.fail_json(msg='Unable to parse pool_ids option.')
pool_id, quantity = list(value.items())[0]
else:
pool_id, quantity = value, 1
pool_ids[pool_id] = str(quantity)
consumer_type = module.params["consumer_type"]
consumer_name = module.param... | def main():
module.fail_json(msg='Unable to parse pool_ids option.')
pool_id, quantity = list(value.items())[0]
else:
pool_id, quantity = value, None
pool_ids[pool_id] = quantity
consumer_type = module.params["consumer_type"]
consumer_name = module.params[... | pytest test/units/modules/packaging/os/test_redhat_subscription.py::test_redhat_subscribtion |
scrapy | 13 | class ImagesPipeline(FilesPipeline):
# ImagesPipeline. They may be overridden by settings.
MIN_WIDTH = 0
MIN_HEIGHT = 0
EXPIRES = 0
THUMBS = {}
DEFAULT_IMAGES_URLS_FIELD = 'image_urls'
DEFAULT_IMAGES_RESULT_FIELD = 'images' | class ImagesPipeline(FilesPipeline):
# ImagesPipeline. They may be overridden by settings.
MIN_WIDTH = 0
MIN_HEIGHT = 0
EXPIRES = 90
THUMBS = {}
DEFAULT_IMAGES_URLS_FIELD = 'image_urls'
DEFAULT_IMAGES_RESULT_FIELD = 'images' | python -m unittest -q tests.test_pipeline_images.ImagesPipelineTestCaseCustomSettings |
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