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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/hypertreelist.py | python | HyperTreeList.GetAGWWindowStyleFlag | (self) | return agwStyle | Returns the :class:`HyperTreeList` window style flag.
:see: :meth:`~HyperTreeList.SetAGWWindowStyleFlag` for a list of valid window styles. | Returns the :class:`HyperTreeList` window style flag. | [
"Returns",
"the",
":",
"class",
":",
"HyperTreeList",
"window",
"style",
"flag",
"."
] | def GetAGWWindowStyleFlag(self):
"""
Returns the :class:`HyperTreeList` window style flag.
:see: :meth:`~HyperTreeList.SetAGWWindowStyleFlag` for a list of valid window styles.
"""
agwStyle = self._agwStyle
if self._main_win:
agwStyle |= self._main_win.GetAGWWindowStyleFlag()
return agwStyle | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/debug/cli/profile_analyzer_cli.py | python | ProfileAnalyzer._get_list_profile_lines | (
self, device_name, device_index, device_count,
profile_datum_list, sort_by, sort_reverse, time_unit,
device_name_filter=None, node_name_filter=None, op_type_filter=None,
screen_cols=80) | return debugger_cli_common.rich_text_lines_from_rich_line_list(output) | Get `RichTextLines` object for list_profile command for a given device.
Args:
device_name: (string) Device name.
device_index: (int) Device index.
device_count: (int) Number of devices.
profile_datum_list: List of `ProfileDatum` objects.
sort_by: (string) Identifier of column to sort. Sort identifier
must match value of SORT_OPS_BY_OP_NAME, SORT_OPS_BY_OP_TYPE,
SORT_OPS_BY_EXEC_TIME, SORT_OPS_BY_MEMORY or SORT_OPS_BY_LINE.
sort_reverse: (bool) Whether to sort in descending instead of default
(ascending) order.
time_unit: time unit, must be in cli_shared.TIME_UNITS.
device_name_filter: Regular expression to filter by device name.
node_name_filter: Regular expression to filter by node name.
op_type_filter: Regular expression to filter by op type.
screen_cols: (int) Number of columns available on the screen (i.e.,
available screen width).
Returns:
`RichTextLines` object containing a table that displays profiling
information for each op. | Get `RichTextLines` object for list_profile command for a given device. | [
"Get",
"RichTextLines",
"object",
"for",
"list_profile",
"command",
"for",
"a",
"given",
"device",
"."
] | def _get_list_profile_lines(
self, device_name, device_index, device_count,
profile_datum_list, sort_by, sort_reverse, time_unit,
device_name_filter=None, node_name_filter=None, op_type_filter=None,
screen_cols=80):
"""Get `RichTextLines` object for list_profile command for a given device.
Args:
device_name: (string) Device name.
device_index: (int) Device index.
device_count: (int) Number of devices.
profile_datum_list: List of `ProfileDatum` objects.
sort_by: (string) Identifier of column to sort. Sort identifier
must match value of SORT_OPS_BY_OP_NAME, SORT_OPS_BY_OP_TYPE,
SORT_OPS_BY_EXEC_TIME, SORT_OPS_BY_MEMORY or SORT_OPS_BY_LINE.
sort_reverse: (bool) Whether to sort in descending instead of default
(ascending) order.
time_unit: time unit, must be in cli_shared.TIME_UNITS.
device_name_filter: Regular expression to filter by device name.
node_name_filter: Regular expression to filter by node name.
op_type_filter: Regular expression to filter by op type.
screen_cols: (int) Number of columns available on the screen (i.e.,
available screen width).
Returns:
`RichTextLines` object containing a table that displays profiling
information for each op.
"""
profile_data = ProfileDataTableView(profile_datum_list, time_unit=time_unit)
# Calculate total time early to calculate column widths.
total_op_time = sum(datum.op_time for datum in profile_datum_list)
total_exec_time = sum(datum.node_exec_stats.all_end_rel_micros
for datum in profile_datum_list)
device_total_row = [
"Device Total", "",
cli_shared.time_to_readable_str(total_op_time,
force_time_unit=time_unit),
cli_shared.time_to_readable_str(total_exec_time,
force_time_unit=time_unit)]
# Calculate column widths.
column_widths = [
len(column_name) for column_name in profile_data.column_names()]
for col in range(len(device_total_row)):
column_widths[col] = max(column_widths[col], len(device_total_row[col]))
for col in range(len(column_widths)):
for row in range(profile_data.row_count()):
column_widths[col] = max(
column_widths[col], len(profile_data.value(
row,
col,
device_name_filter=device_name_filter,
node_name_filter=node_name_filter,
op_type_filter=op_type_filter)))
column_widths[col] += 2 # add margin between columns
# Add device name.
output = [RL("-" * screen_cols)]
device_row = "Device %d of %d: %s" % (
device_index + 1, device_count, device_name)
output.append(RL(device_row))
output.append(RL())
# Add headers.
base_command = "list_profile"
row = RL()
for col in range(profile_data.column_count()):
column_name = profile_data.column_names()[col]
sort_id = profile_data.column_sort_id(col)
command = "%s -s %s" % (base_command, sort_id)
if sort_by == sort_id and not sort_reverse:
command += " -r"
head_menu_item = debugger_cli_common.MenuItem(None, command)
row += RL(column_name, font_attr=[head_menu_item, "bold"])
row += RL(" " * (column_widths[col] - len(column_name)))
output.append(row)
# Add data rows.
for row in range(profile_data.row_count()):
new_row = RL()
for col in range(profile_data.column_count()):
new_cell = profile_data.value(
row,
col,
device_name_filter=device_name_filter,
node_name_filter=node_name_filter,
op_type_filter=op_type_filter)
new_row += new_cell
new_row += RL(" " * (column_widths[col] - len(new_cell)))
output.append(new_row)
# Add stat totals.
row_str = ""
for width, row in zip(column_widths, device_total_row):
row_str += ("{:<%d}" % width).format(row)
output.append(RL())
output.append(RL(row_str))
return debugger_cli_common.rich_text_lines_from_rich_line_list(output) | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/ipaddress.py | python | _BaseNetwork.address_exclude | (self, other) | Remove an address from a larger block.
For example:
addr1 = ip_network('192.0.2.0/28')
addr2 = ip_network('192.0.2.1/32')
list(addr1.address_exclude(addr2)) =
[IPv4Network('192.0.2.0/32'), IPv4Network('192.0.2.2/31'),
IPv4Network('192.0.2.4/30'), IPv4Network('192.0.2.8/29')]
or IPv6:
addr1 = ip_network('2001:db8::1/32')
addr2 = ip_network('2001:db8::1/128')
list(addr1.address_exclude(addr2)) =
[ip_network('2001:db8::1/128'),
ip_network('2001:db8::2/127'),
ip_network('2001:db8::4/126'),
ip_network('2001:db8::8/125'),
...
ip_network('2001:db8:8000::/33')]
Args:
other: An IPv4Network or IPv6Network object of the same type.
Returns:
An iterator of the IPv(4|6)Network objects which is self
minus other.
Raises:
TypeError: If self and other are of differing address
versions, or if other is not a network object.
ValueError: If other is not completely contained by self. | Remove an address from a larger block. | [
"Remove",
"an",
"address",
"from",
"a",
"larger",
"block",
"."
] | def address_exclude(self, other):
"""Remove an address from a larger block.
For example:
addr1 = ip_network('192.0.2.0/28')
addr2 = ip_network('192.0.2.1/32')
list(addr1.address_exclude(addr2)) =
[IPv4Network('192.0.2.0/32'), IPv4Network('192.0.2.2/31'),
IPv4Network('192.0.2.4/30'), IPv4Network('192.0.2.8/29')]
or IPv6:
addr1 = ip_network('2001:db8::1/32')
addr2 = ip_network('2001:db8::1/128')
list(addr1.address_exclude(addr2)) =
[ip_network('2001:db8::1/128'),
ip_network('2001:db8::2/127'),
ip_network('2001:db8::4/126'),
ip_network('2001:db8::8/125'),
...
ip_network('2001:db8:8000::/33')]
Args:
other: An IPv4Network or IPv6Network object of the same type.
Returns:
An iterator of the IPv(4|6)Network objects which is self
minus other.
Raises:
TypeError: If self and other are of differing address
versions, or if other is not a network object.
ValueError: If other is not completely contained by self.
"""
if not self._version == other._version:
raise TypeError("%s and %s are not of the same version" % (
self, other))
if not isinstance(other, _BaseNetwork):
raise TypeError("%s is not a network object" % other)
if not other.subnet_of(self):
raise ValueError('%s not contained in %s' % (other, self))
if other == self:
return
# Make sure we're comparing the network of other.
other = other.__class__('%s/%s' % (other.network_address,
other.prefixlen))
s1, s2 = self.subnets()
while s1 != other and s2 != other:
if other.subnet_of(s1):
yield s2
s1, s2 = s1.subnets()
elif other.subnet_of(s2):
yield s1
s1, s2 = s2.subnets()
else:
# If we got here, there's a bug somewhere.
raise AssertionError('Error performing exclusion: '
's1: %s s2: %s other: %s' %
(s1, s2, other))
if s1 == other:
yield s2
elif s2 == other:
yield s1
else:
# If we got here, there's a bug somewhere.
raise AssertionError('Error performing exclusion: '
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(s1, s2, other)) | [
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apache/impala | 8ddac48f3428c86f2cbd037ced89cfb903298b12 | shell/impala_shell.py | python | ImpalaShell.do_version | (self, args) | Prints the Impala build version | Prints the Impala build version | [
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"Impala",
"build",
"version"
] | def do_version(self, args):
"""Prints the Impala build version"""
print("Shell version: %s" % VERSION_STRING, file=sys.stderr)
print("Server version: %s" % self.server_version, file=sys.stderr) | [
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stan-dev/math | 5fd79f89933269a4ca4d8dd1fde2a36d53d4768c | lib/boost_1.75.0/tools/build/src/util/__init__.py | python | value_to_jam | (value, methods=False) | return exported_name | Makes a token to refer to a Python value inside Jam language code.
The token is merely a string that can be passed around in Jam code and
eventually passed back. For example, we might want to pass PropertySet
instance to a tag function and it might eventually call back
to virtual_target.add_suffix_and_prefix, passing the same instance.
For values that are classes, we'll also make class methods callable
from Jam.
Note that this is necessary to make a bit more of existing Jamfiles work.
This trick should not be used to much, or else the performance benefits of
Python port will be eaten. | Makes a token to refer to a Python value inside Jam language code. | [
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] | def value_to_jam(value, methods=False):
"""Makes a token to refer to a Python value inside Jam language code.
The token is merely a string that can be passed around in Jam code and
eventually passed back. For example, we might want to pass PropertySet
instance to a tag function and it might eventually call back
to virtual_target.add_suffix_and_prefix, passing the same instance.
For values that are classes, we'll also make class methods callable
from Jam.
Note that this is necessary to make a bit more of existing Jamfiles work.
This trick should not be used to much, or else the performance benefits of
Python port will be eaten.
"""
global __value_id
r = __python_to_jam.get(value, None)
if r:
return r
exported_name = '###_' + str(__value_id)
__value_id = __value_id + 1
__python_to_jam[value] = exported_name
__jam_to_python[exported_name] = value
if methods and type(value) == types.InstanceType:
for field_name in dir(value):
field = getattr(value, field_name)
if callable(field) and not field_name.startswith("__"):
bjam.import_rule("", exported_name + "." + field_name, field)
return exported_name | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/io/formats/style.py | python | Styler.format | (self, formatter, subset=None, na_rep: Optional[str] = None) | return self | Format the text display value of cells.
Parameters
----------
formatter : str, callable, dict or None
If ``formatter`` is None, the default formatter is used
subset : IndexSlice
An argument to ``DataFrame.loc`` that restricts which elements
``formatter`` is applied to.
na_rep : str, optional
Representation for missing values.
If ``na_rep`` is None, no special formatting is applied
.. versionadded:: 1.0.0
Returns
-------
self : Styler
Notes
-----
``formatter`` is either an ``a`` or a dict ``{column name: a}`` where
``a`` is one of
- str: this will be wrapped in: ``a.format(x)``
- callable: called with the value of an individual cell
The default display value for numeric values is the "general" (``g``)
format with ``pd.options.display.precision`` precision.
Examples
--------
>>> df = pd.DataFrame(np.random.randn(4, 2), columns=['a', 'b'])
>>> df.style.format("{:.2%}")
>>> df['c'] = ['a', 'b', 'c', 'd']
>>> df.style.format({'c': str.upper}) | Format the text display value of cells. | [
"Format",
"the",
"text",
"display",
"value",
"of",
"cells",
"."
] | def format(self, formatter, subset=None, na_rep: Optional[str] = None):
"""
Format the text display value of cells.
Parameters
----------
formatter : str, callable, dict or None
If ``formatter`` is None, the default formatter is used
subset : IndexSlice
An argument to ``DataFrame.loc`` that restricts which elements
``formatter`` is applied to.
na_rep : str, optional
Representation for missing values.
If ``na_rep`` is None, no special formatting is applied
.. versionadded:: 1.0.0
Returns
-------
self : Styler
Notes
-----
``formatter`` is either an ``a`` or a dict ``{column name: a}`` where
``a`` is one of
- str: this will be wrapped in: ``a.format(x)``
- callable: called with the value of an individual cell
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Examples
--------
>>> df = pd.DataFrame(np.random.randn(4, 2), columns=['a', 'b'])
>>> df.style.format("{:.2%}")
>>> df['c'] = ['a', 'b', 'c', 'd']
>>> df.style.format({'c': str.upper})
"""
if formatter is None:
assert self._display_funcs.default_factory is not None
formatter = self._display_funcs.default_factory()
if subset is None:
row_locs = range(len(self.data))
col_locs = range(len(self.data.columns))
else:
subset = _non_reducing_slice(subset)
if len(subset) == 1:
subset = subset, self.data.columns
sub_df = self.data.loc[subset]
row_locs = self.data.index.get_indexer_for(sub_df.index)
col_locs = self.data.columns.get_indexer_for(sub_df.columns)
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for col, col_formatter in formatter.items():
# formatter must be callable, so '{}' are converted to lambdas
col_formatter = _maybe_wrap_formatter(col_formatter, na_rep)
col_num = self.data.columns.get_indexer_for([col])[0]
for row_num in row_locs:
self._display_funcs[(row_num, col_num)] = col_formatter
else:
# single scalar to format all cells with
formatter = _maybe_wrap_formatter(formatter, na_rep)
locs = product(*(row_locs, col_locs))
for i, j in locs:
self._display_funcs[(i, j)] = formatter
return self | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/programs/scripts/metadata/Music/musicbrainzngs/musicbrainz.py | python | get_recording_by_id | (id, includes=[], release_status=[], release_type=[]) | return _do_mb_query("recording", id, includes, params) | Get the recording with the MusicBrainz `id` as a dict
with a 'recording' key.
*Available includes*: {includes} | Get the recording with the MusicBrainz `id` as a dict
with a 'recording' key. | [
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] | def get_recording_by_id(id, includes=[], release_status=[], release_type=[]):
"""Get the recording with the MusicBrainz `id` as a dict
with a 'recording' key.
*Available includes*: {includes}"""
params = _check_filter_and_make_params("recording", includes,
release_status, release_type)
return _do_mb_query("recording", id, includes, params) | [
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gemrb/gemrb | 730206eed8d1dd358ca5e69a62f9e099aa22ffc6 | gemrb/GUIScripts/LUSpellSelection.py | python | RowIndex | () | Determines which factor to use in scrolling of spells
It depends on if it is character generation where you have
4 rows of 6 spells (24), or it is sorcs level up window where there
is 4 rows of 5 spells and 5th row of 4 spell, but you may also use 25th slot there
and it is 5 rows of 5 with 25 spells seen at once. | Determines which factor to use in scrolling of spells | [
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] | def RowIndex ():
"""Determines which factor to use in scrolling of spells
It depends on if it is character generation where you have
4 rows of 6 spells (24), or it is sorcs level up window where there
is 4 rows of 5 spells and 5th row of 4 spell, but you may also use 25th slot there
and it is 5 rows of 5 with 25 spells seen at once. """
SpellTopIndex = GemRB.GetVar ("SpellTopIndex")
if chargen:
return ( SpellTopIndex + 1 ) * 6 - 6
elif IWD2: # 30 during level-up
return ( SpellTopIndex + 1 ) * 6 - 6
elif EnhanceGUI:
return ( SpellTopIndex + 1 ) * 5 - 5
else:
return SpellTopIndex | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | ToolBarToolBase.CanBeToggled | (*args, **kwargs) | return _controls_.ToolBarToolBase_CanBeToggled(*args, **kwargs) | CanBeToggled(self) -> bool | CanBeToggled(self) -> bool | [
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"""CanBeToggled(self) -> bool"""
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/sparsemax/python/ops/sparsemax_loss.py | python | sparsemax_loss | (logits, sparsemax, labels, name=None) | Computes sparsemax loss function [1].
[1]: https://arxiv.org/abs/1602.02068
Args:
logits: A `Tensor`. Must be one of the following types: `half`, `float32`,
`float64`.
sparsemax: A `Tensor`. Must have the same type as `logits`.
labels: A `Tensor`. Must have the same type as `logits`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `logits`. | Computes sparsemax loss function [1]. | [
"Computes",
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"[",
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"]",
"."
] | def sparsemax_loss(logits, sparsemax, labels, name=None):
"""Computes sparsemax loss function [1].
[1]: https://arxiv.org/abs/1602.02068
Args:
logits: A `Tensor`. Must be one of the following types: `half`, `float32`,
`float64`.
sparsemax: A `Tensor`. Must have the same type as `logits`.
labels: A `Tensor`. Must have the same type as `logits`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `logits`.
"""
with ops.name_scope(name, "sparsemax_loss",
[logits, sparsemax, labels]) as name:
logits = ops.convert_to_tensor(logits, name="logits")
sparsemax = ops.convert_to_tensor(sparsemax, name="sparsemax")
labels = ops.convert_to_tensor(labels, name="labels")
# In the paper, they call the logits z.
# A constant can be substracted from logits to make the algorithm
# more numerically stable in theory. However, there are really no major
# source numerical instability in this algorithm.
z = logits
# sum over support
# Use a conditional where instead of a multiplication to support z = -inf.
# If z = -inf, and there is no support (sparsemax = 0), a multiplication
# would cause 0 * -inf = nan, which is not correct in this case.
sum_s = array_ops.where(
math_ops.logical_or(sparsemax > 0, math_ops.is_nan(sparsemax)),
sparsemax * (z - 0.5 * sparsemax), array_ops.zeros_like(sparsemax))
# - z_k + ||q||^2
q_part = labels * (0.5 * labels - z)
# Fix the case where labels = 0 and z = -inf, where q_part would
# otherwise be 0 * -inf = nan. But since the lables = 0, no cost for
# z = -inf should be consideredself.
# The code below also coveres the case where z = inf. Howeverm in this
# caose the sparsemax will be nan, which means the sum_s will also be nan,
# therefor this case doesn't need addtional special treatment.
q_part_safe = array_ops.where(
math_ops.logical_and(math_ops.equal(labels, 0), math_ops.is_inf(z)),
array_ops.zeros_like(z), q_part)
return math_ops.reduce_sum(sum_s + q_part_safe, axis=1) | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/summary/event_multiplexer.py | python | EventMultiplexer.AddRun | (self, path, name=None) | return self | Add a run to the multiplexer.
If the name is not specified, it is the same as the path.
If a run by that name exists, and we are already watching the right path,
do nothing. If we are watching a different path, replace the event
accumulator.
If `Reload` has been called, it will `Reload` the newly created
accumulators.
Args:
path: Path to the event files (or event directory) for given run.
name: Name of the run to add. If not provided, is set to path.
Returns:
The `EventMultiplexer`. | Add a run to the multiplexer. | [
"Add",
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] | def AddRun(self, path, name=None):
"""Add a run to the multiplexer.
If the name is not specified, it is the same as the path.
If a run by that name exists, and we are already watching the right path,
do nothing. If we are watching a different path, replace the event
accumulator.
If `Reload` has been called, it will `Reload` the newly created
accumulators.
Args:
path: Path to the event files (or event directory) for given run.
name: Name of the run to add. If not provided, is set to path.
Returns:
The `EventMultiplexer`.
"""
if name is None or name is '':
name = path
accumulator = None
with self._accumulators_mutex:
if name not in self._accumulators or self._paths[name] != path:
if name in self._paths and self._paths[name] != path:
# TODO(danmane) - Make it impossible to overwrite an old path with
# a new path (just give the new path a distinct name)
logging.warning('Conflict for name %s: old path %s, new path %s',
name, self._paths[name], path)
logging.info('Constructing EventAccumulator for %s', path)
accumulator = event_accumulator.EventAccumulator(
path,
size_guidance=self._size_guidance,
purge_orphaned_data=self.purge_orphaned_data)
self._accumulators[name] = accumulator
self._paths[name] = path
if accumulator:
if self._reload_called:
accumulator.Reload()
return self | [
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LisaAnne/lisa-caffe-public | 49b8643ddef23a4f6120017968de30c45e693f59 | python/caffe/io.py | python | arraylist_to_blobprotovecor_str | (arraylist) | return vec.SerializeToString() | Converts a list of arrays to a serialized blobprotovec, which could be
then passed to a network for processing. | Converts a list of arrays to a serialized blobprotovec, which could be
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"""Converts a list of arrays to a serialized blobprotovec, which could be
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"""
vec = caffe_pb2.BlobProtoVector()
vec.blobs.extend([array_to_blobproto(arr) for arr in arraylist])
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBUnixSignals.GetNumSignals | (self) | return _lldb.SBUnixSignals_GetNumSignals(self) | GetNumSignals(SBUnixSignals self) -> int32_t | GetNumSignals(SBUnixSignals self) -> int32_t | [
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return _lldb.SBUnixSignals_GetNumSignals(self) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/computation/eval.py | python | _convert_expression | (expr) | return s | Convert an object to an expression.
This function converts an object to an expression (a unicode string) and
checks to make sure it isn't empty after conversion. This is used to
convert operators to their string representation for recursive calls to
:func:`~pandas.eval`.
Parameters
----------
expr : object
The object to be converted to a string.
Returns
-------
str
The string representation of an object.
Raises
------
ValueError
* If the expression is empty. | Convert an object to an expression. | [
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] | def _convert_expression(expr) -> str:
"""
Convert an object to an expression.
This function converts an object to an expression (a unicode string) and
checks to make sure it isn't empty after conversion. This is used to
convert operators to their string representation for recursive calls to
:func:`~pandas.eval`.
Parameters
----------
expr : object
The object to be converted to a string.
Returns
-------
str
The string representation of an object.
Raises
------
ValueError
* If the expression is empty.
"""
s = pprint_thing(expr)
_check_expression(s)
return s | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/compiler/pycodegen.py | python | compile | (source, filename, mode, flags=None, dont_inherit=None) | return gen.code | Replacement for builtin compile() function | Replacement for builtin compile() function | [
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] | def compile(source, filename, mode, flags=None, dont_inherit=None):
"""Replacement for builtin compile() function"""
if flags is not None or dont_inherit is not None:
raise RuntimeError, "not implemented yet"
if mode == "single":
gen = Interactive(source, filename)
elif mode == "exec":
gen = Module(source, filename)
elif mode == "eval":
gen = Expression(source, filename)
else:
raise ValueError("compile() 3rd arg must be 'exec' or "
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gen.compile()
return gen.code | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Tools/bgen/bgen/bgenType.py | python | Type.mkvalueArgs | (self, name) | return name | Return an argument for use with Py_BuildValue().
Example: int.mkvalueArgs("spam") returns the string "spam". | Return an argument for use with Py_BuildValue(). | [
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] | def mkvalueArgs(self, name):
"""Return an argument for use with Py_BuildValue().
Example: int.mkvalueArgs("spam") returns the string "spam".
"""
return name | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py2/IPython/core/ultratb.py | python | with_patch_inspect | (f) | return wrapped | decorator for monkeypatching inspect.findsource | decorator for monkeypatching inspect.findsource | [
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"""decorator for monkeypatching inspect.findsource"""
def wrapped(*args, **kwargs):
save_findsource = inspect.findsource
save_getargs = inspect.getargs
inspect.findsource = findsource
inspect.getargs = getargs
try:
return f(*args, **kwargs)
finally:
inspect.findsource = save_findsource
inspect.getargs = save_getargs
return wrapped | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | StaticText_GetClassDefaultAttributes | (*args, **kwargs) | return _controls_.StaticText_GetClassDefaultAttributes(*args, **kwargs) | StaticText_GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
control -- which is a much better idea than hard coding specific
colours or fonts which might look completely out of place on the
user's system, especially if it uses themes.
The variant parameter is only relevant under Mac currently and is
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"""
StaticText_GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
control -- which is a much better idea than hard coding specific
colours or fonts which might look completely out of place on the
user's system, especially if it uses themes.
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return _controls_.StaticText_GetClassDefaultAttributes(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/dataview.py | python | DataViewModelNotifier.ItemChanged | (*args, **kwargs) | return _dataview.DataViewModelNotifier_ItemChanged(*args, **kwargs) | ItemChanged(self, DataViewItem item) -> bool
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/stats/morestats.py | python | mood | (x, y, axis=0) | return z, pval | Perform Mood's test for equal scale parameters.
Mood's two-sample test for scale parameters is a non-parametric
test for the null hypothesis that two samples are drawn from the
same distribution with the same scale parameter.
Parameters
----------
x, y : array_like
Arrays of sample data.
axis : int, optional
The axis along which the samples are tested. `x` and `y` can be of
different length along `axis`.
If `axis` is None, `x` and `y` are flattened and the test is done on
all values in the flattened arrays.
Returns
-------
z : scalar or ndarray
The z-score for the hypothesis test. For 1-D inputs a scalar is
returned.
p-value : scalar ndarray
The p-value for the hypothesis test.
See Also
--------
fligner : A non-parametric test for the equality of k variances
ansari : A non-parametric test for the equality of 2 variances
bartlett : A parametric test for equality of k variances in normal samples
levene : A parametric test for equality of k variances
Notes
-----
The data are assumed to be drawn from probability distributions ``f(x)``
and ``f(x/s) / s`` respectively, for some probability density function f.
The null hypothesis is that ``s == 1``.
For multi-dimensional arrays, if the inputs are of shapes
``(n0, n1, n2, n3)`` and ``(n0, m1, n2, n3)``, then if ``axis=1``, the
resulting z and p values will have shape ``(n0, n2, n3)``. Note that
``n1`` and ``m1`` don't have to be equal, but the other dimensions do.
Examples
--------
>>> from scipy import stats
>>> np.random.seed(1234)
>>> x2 = np.random.randn(2, 45, 6, 7)
>>> x1 = np.random.randn(2, 30, 6, 7)
>>> z, p = stats.mood(x1, x2, axis=1)
>>> p.shape
(2, 6, 7)
Find the number of points where the difference in scale is not significant:
>>> (p > 0.1).sum()
74
Perform the test with different scales:
>>> x1 = np.random.randn(2, 30)
>>> x2 = np.random.randn(2, 35) * 10.0
>>> stats.mood(x1, x2, axis=1)
(array([-5.7178125 , -5.25342163]), array([ 1.07904114e-08, 1.49299218e-07])) | Perform Mood's test for equal scale parameters. | [
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"""
Perform Mood's test for equal scale parameters.
Mood's two-sample test for scale parameters is a non-parametric
test for the null hypothesis that two samples are drawn from the
same distribution with the same scale parameter.
Parameters
----------
x, y : array_like
Arrays of sample data.
axis : int, optional
The axis along which the samples are tested. `x` and `y` can be of
different length along `axis`.
If `axis` is None, `x` and `y` are flattened and the test is done on
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Returns
-------
z : scalar or ndarray
The z-score for the hypothesis test. For 1-D inputs a scalar is
returned.
p-value : scalar ndarray
The p-value for the hypothesis test.
See Also
--------
fligner : A non-parametric test for the equality of k variances
ansari : A non-parametric test for the equality of 2 variances
bartlett : A parametric test for equality of k variances in normal samples
levene : A parametric test for equality of k variances
Notes
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The data are assumed to be drawn from probability distributions ``f(x)``
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The null hypothesis is that ``s == 1``.
For multi-dimensional arrays, if the inputs are of shapes
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Examples
--------
>>> from scipy import stats
>>> np.random.seed(1234)
>>> x2 = np.random.randn(2, 45, 6, 7)
>>> x1 = np.random.randn(2, 30, 6, 7)
>>> z, p = stats.mood(x1, x2, axis=1)
>>> p.shape
(2, 6, 7)
Find the number of points where the difference in scale is not significant:
>>> (p > 0.1).sum()
74
Perform the test with different scales:
>>> x1 = np.random.randn(2, 30)
>>> x2 = np.random.randn(2, 35) * 10.0
>>> stats.mood(x1, x2, axis=1)
(array([-5.7178125 , -5.25342163]), array([ 1.07904114e-08, 1.49299218e-07]))
"""
x = np.asarray(x, dtype=float)
y = np.asarray(y, dtype=float)
if axis is None:
x = x.flatten()
y = y.flatten()
axis = 0
# Determine shape of the result arrays
res_shape = tuple([x.shape[ax] for ax in range(len(x.shape)) if ax != axis])
if not (res_shape == tuple([y.shape[ax] for ax in range(len(y.shape)) if
ax != axis])):
raise ValueError("Dimensions of x and y on all axes except `axis` "
"should match")
n = x.shape[axis]
m = y.shape[axis]
N = m + n
if N < 3:
raise ValueError("Not enough observations.")
xy = np.concatenate((x, y), axis=axis)
if axis != 0:
xy = np.rollaxis(xy, axis)
xy = xy.reshape(xy.shape[0], -1)
# Generalized to the n-dimensional case by adding the axis argument, and
# using for loops, since rankdata is not vectorized. For improving
# performance consider vectorizing rankdata function.
all_ranks = np.zeros_like(xy)
for j in range(xy.shape[1]):
all_ranks[:, j] = stats.rankdata(xy[:, j])
Ri = all_ranks[:n]
M = np.sum((Ri - (N + 1.0) / 2)**2, axis=0)
# Approx stat.
mnM = n * (N * N - 1.0) / 12
varM = m * n * (N + 1.0) * (N + 2) * (N - 2) / 180
z = (M - mnM) / sqrt(varM)
# sf for right tail, cdf for left tail. Factor 2 for two-sidedness
z_pos = z > 0
pval = np.zeros_like(z)
pval[z_pos] = 2 * distributions.norm.sf(z[z_pos])
pval[~z_pos] = 2 * distributions.norm.cdf(z[~z_pos])
if res_shape == ():
# Return scalars, not 0-D arrays
z = z[0]
pval = pval[0]
else:
z.shape = res_shape
pval.shape = res_shape
return z, pval | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/core/fromnumeric.py | python | shape | (a) | return result | Return the shape of an array.
Parameters
----------
a : array_like
Input array.
Returns
-------
shape : tuple of ints
The elements of the shape tuple give the lengths of the
corresponding array dimensions.
See Also
--------
alen
ndarray.shape : Equivalent array method.
Examples
--------
>>> np.shape(np.eye(3))
(3, 3)
>>> np.shape([[1, 2]])
(1, 2)
>>> np.shape([0])
(1,)
>>> np.shape(0)
()
>>> a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
>>> np.shape(a)
(2,)
>>> a.shape
(2,) | Return the shape of an array. | [
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"""
Return the shape of an array.
Parameters
----------
a : array_like
Input array.
Returns
-------
shape : tuple of ints
The elements of the shape tuple give the lengths of the
corresponding array dimensions.
See Also
--------
alen
ndarray.shape : Equivalent array method.
Examples
--------
>>> np.shape(np.eye(3))
(3, 3)
>>> np.shape([[1, 2]])
(1, 2)
>>> np.shape([0])
(1,)
>>> np.shape(0)
()
>>> a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
>>> np.shape(a)
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>>> a.shape
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try:
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except AttributeError:
result = asarray(a).shape
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WeitaoVan/L-GM-loss | 598582f0631bac876b3eeb8d6c4cd1d780269e03 | examples/pycaffe/layers/pascal_multilabel_datalayers.py | python | PascalMultilabelDataLayerSync.reshape | (self, bottom, top) | There is no need to reshape the data, since the input is of fixed size
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facebook/folly | 744a0a698074d1b013813065fe60f545aa2c9b94 | build/fbcode_builder/getdeps/subcmd.py | python | SubCmd.run | (self, args) | return 0 | perform the command | perform the command | [
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NERSC/timemory | 431912b360ff50d1a160d7826e2eea04fbd1037f | timemory/trace/tracer.py | python | Tracer.__enter__ | (self, *args, **kwargs) | Context manager start function | Context manager start function | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | PostEvent | (*args, **kwargs) | return _core_.PostEvent(*args, **kwargs) | PostEvent(EvtHandler dest, Event event)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/distutils/dir_util.py | python | _build_cmdtuple | (path, cmdtuples) | Helper for remove_tree(). | Helper for remove_tree(). | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/glcanvas.py | python | GLCanvas.GetPalette | (*args, **kwargs) | return _glcanvas.GLCanvas_GetPalette(*args, **kwargs) | GetPalette(self) -> Palette | GetPalette(self) -> Palette | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Validation/RecoTrack/python/plotting/ntupleDataFormat.py | python | TrackingVertex.nSourceTrackingParticles | (self) | return self._tree.simvtx_sourceSimIdx[self._index].size() | Returns the number of source TrackingParticles. | Returns the number of source TrackingParticles. | [
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h2oai/datatable | 753197c3f76041dd6468e0f6a9708af92d80f6aa | docs/_ext/xcontributors.py | python | UserRepository._compute_version_strings | (self, maxsize, newname, oldname) | self._data.sorted_versions = [<version>] # list of strings
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vstr = v2_to_vstr[version_tuple[:2]]
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self._data.doc_versions = doc_versions | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/linesearch.py | python | _nonmonotone_line_search_cruz | (f, x_k, d, prev_fs, eta,
gamma=1e-4, tau_min=0.1, tau_max=0.5) | return alpha, xp, fp, Fp | Nonmonotone backtracking line search as described in [1]_
Parameters
----------
f : callable
Function returning a tuple ``(f, F)`` where ``f`` is the value
of a merit function and ``F`` the residual.
x_k : ndarray
Initial position
d : ndarray
Search direction
prev_fs : float
List of previous merit function values. Should have ``len(prev_fs) <= M``
where ``M`` is the nonmonotonicity window parameter.
eta : float
Allowed merit function increase, see [1]_
gamma, tau_min, tau_max : float, optional
Search parameters, see [1]_
Returns
-------
alpha : float
Step length
xp : ndarray
Next position
fp : float
Merit function value at next position
Fp : ndarray
Residual at next position
References
----------
[1] "Spectral residual method without gradient information for solving
large-scale nonlinear systems of equations." W. La Cruz,
J.M. Martinez, M. Raydan. Math. Comp. **75**, 1429 (2006). | Nonmonotone backtracking line search as described in [1]_ | [
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gamma=1e-4, tau_min=0.1, tau_max=0.5):
"""
Nonmonotone backtracking line search as described in [1]_
Parameters
----------
f : callable
Function returning a tuple ``(f, F)`` where ``f`` is the value
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x_k : ndarray
Initial position
d : ndarray
Search direction
prev_fs : float
List of previous merit function values. Should have ``len(prev_fs) <= M``
where ``M`` is the nonmonotonicity window parameter.
eta : float
Allowed merit function increase, see [1]_
gamma, tau_min, tau_max : float, optional
Search parameters, see [1]_
Returns
-------
alpha : float
Step length
xp : ndarray
Next position
fp : float
Merit function value at next position
Fp : ndarray
Residual at next position
References
----------
[1] "Spectral residual method without gradient information for solving
large-scale nonlinear systems of equations." W. La Cruz,
J.M. Martinez, M. Raydan. Math. Comp. **75**, 1429 (2006).
"""
f_k = prev_fs[-1]
f_bar = max(prev_fs)
alpha_p = 1
alpha_m = 1
alpha = 1
while True:
xp = x_k + alpha_p * d
fp, Fp = f(xp)
if fp <= f_bar + eta - gamma * alpha_p**2 * f_k:
alpha = alpha_p
break
alpha_tp = alpha_p**2 * f_k / (fp + (2*alpha_p - 1)*f_k)
xp = x_k - alpha_m * d
fp, Fp = f(xp)
if fp <= f_bar + eta - gamma * alpha_m**2 * f_k:
alpha = -alpha_m
break
alpha_tm = alpha_m**2 * f_k / (fp + (2*alpha_m - 1)*f_k)
alpha_p = np.clip(alpha_tp, tau_min * alpha_p, tau_max * alpha_p)
alpha_m = np.clip(alpha_tm, tau_min * alpha_m, tau_max * alpha_m)
return alpha, xp, fp, Fp | [
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rodeofx/OpenWalter | 6116fbe3f04f1146c854afbfbdbe944feaee647e | walter/maya/scripts/walterPanel/walterMayaTraverser.py | python | WalterMayaImplementation.getLayersAssignationPlug | (self, nodeName) | return dependNode.findPlug("layersAssignation", False) | Return MPlug node.layersAssignation. | Return MPlug node.layersAssignation. | [
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] | def getLayersAssignationPlug(self, nodeName):
"""Return MPlug node.layersAssignation."""
# Walter Standin
dependNode = self.getDependNode(nodeName)
if not dependNode:
return
# Get walterStandin.layersAssignation
return dependNode.findPlug("layersAssignation", False) | [
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tensorflow/deepmath | b5b721f54de1d5d6a02d78f5da5995237f9995f9 | deepmath/premises/model_definition_cnn.py | python | Model.axiom_embedding | (self, axioms) | return self.make_embedding(axioms) | Compute the embedding for each of the axioms. | Compute the embedding for each of the axioms. | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/mozbuild/mozbuild/makeutil.py | python | Makefile.create_rule | (self, targets=[]) | return rule | Create a new rule in the makefile for the given targets.
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Create a new rule in the makefile for the given targets.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/isotonic.py | python | IsotonicRegression._build_y | (self, X, y, sample_weight, trim_duplicates=True) | Build the y_ IsotonicRegression. | Build the y_ IsotonicRegression. | [
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] | def _build_y(self, X, y, sample_weight, trim_duplicates=True):
"""Build the y_ IsotonicRegression."""
check_consistent_length(X, y, sample_weight)
X, y = [check_array(x, ensure_2d=False) for x in [X, y]]
y = as_float_array(y)
self._check_fit_data(X, y, sample_weight)
# Determine increasing if auto-determination requested
if self.increasing == 'auto':
self.increasing_ = check_increasing(X, y)
else:
self.increasing_ = self.increasing
# If sample_weights is passed, removed zero-weight values and clean
# order
if sample_weight is not None:
sample_weight = check_array(sample_weight, ensure_2d=False)
mask = sample_weight > 0
X, y, sample_weight = X[mask], y[mask], sample_weight[mask]
else:
sample_weight = np.ones(len(y))
order = np.lexsort((y, X))
X, y, sample_weight = [astype(array[order], np.float64, copy=False)
for array in [X, y, sample_weight]]
unique_X, unique_y, unique_sample_weight = _make_unique(
X, y, sample_weight)
# Store _X_ and _y_ to maintain backward compat during the deprecation
# period of X_ and y_
self._X_ = X = unique_X
self._y_ = y = isotonic_regression(unique_y, unique_sample_weight,
self.y_min, self.y_max,
increasing=self.increasing_)
# Handle the left and right bounds on X
self.X_min_, self.X_max_ = np.min(X), np.max(X)
if trim_duplicates:
# Remove unnecessary points for faster prediction
keep_data = np.ones((len(y),), dtype=bool)
# Aside from the 1st and last point, remove points whose y values
# are equal to both the point before and the point after it.
keep_data[1:-1] = np.logical_or(
np.not_equal(y[1:-1], y[:-2]),
np.not_equal(y[1:-1], y[2:])
)
return X[keep_data], y[keep_data]
else:
# The ability to turn off trim_duplicates is only used to it make
# easier to unit test that removing duplicates in y does not have
# any impact the resulting interpolation function (besides
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return X, y | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_grad/grad_implementations.py | python | bprop_tuple_getitem | (data, idx, out, dout) | return F.tuple_setitem(C.zeros_like(data), idx, dout), C.zeros_like(idx) | Backpropagator for primitive `tuple_getitem`. | Backpropagator for primitive `tuple_getitem`. | [
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] | def bprop_tuple_getitem(data, idx, out, dout):
"""Backpropagator for primitive `tuple_getitem`."""
return F.tuple_setitem(C.zeros_like(data), idx, dout), C.zeros_like(idx) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tarfile.py | python | TarFile.utime | (self, tarinfo, targetpath) | Set modification time of targetpath according to tarinfo. | Set modification time of targetpath according to tarinfo. | [
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] | def utime(self, tarinfo, targetpath):
"""Set modification time of targetpath according to tarinfo.
"""
if not hasattr(os, 'utime'):
return
try:
os.utime(targetpath, (tarinfo.mtime, tarinfo.mtime))
except OSError:
raise ExtractError("could not change modification time") | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/util.py | python | get_logger | () | return _logger | Returns logger used by multiprocessing | Returns logger used by multiprocessing | [
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] | def get_logger():
'''
Returns logger used by multiprocessing
'''
global _logger
import logging
logging._acquireLock()
try:
if not _logger:
_logger = logging.getLogger(LOGGER_NAME)
_logger.propagate = 0
# XXX multiprocessing should cleanup before logging
if hasattr(atexit, 'unregister'):
atexit.unregister(_exit_function)
atexit.register(_exit_function)
else:
atexit._exithandlers.remove((_exit_function, (), {}))
atexit._exithandlers.append((_exit_function, (), {}))
finally:
logging._releaseLock()
return _logger | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/operator_pd_cholesky.py | python | OperatorPDCholesky.get_shape | (self) | return self._chol.get_shape() | `TensorShape` giving static shape. | `TensorShape` giving static shape. | [
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"shape",
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] | def get_shape(self):
"""`TensorShape` giving static shape."""
return self._chol.get_shape() | [
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Slicer/Slicer | ba9fadf332cb0303515b68d8d06a344c82e3e3e5 | Utilities/Templates/Modules/Scripted/TemplateKey.py | python | TemplateKeyWidget.exit | (self) | Called each time the user opens a different module. | Called each time the user opens a different module. | [
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"""
Called each time the user opens a different module.
"""
# Do not react to parameter node changes (GUI wlil be updated when the user enters into the module)
self.removeObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode) | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/python/turicreate/util/_cloudpickle/_cloudpickle_py27.py | python | CloudPickler.save_function_tuple | (self, func) | Pickles an actual func object.
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extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later. | Pickles an actual func object. | [
"Pickles",
"an",
"actual",
"func",
"object",
"."
] | def save_function_tuple(self, func):
""" Pickles an actual func object.
A func comprises: code, globals, defaults, closure, and dict. We
extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later.
"""
if is_tornado_coroutine(func):
self.save_reduce(_rebuild_tornado_coroutine, (func.__wrapped__,), obj=func)
return
save = self.save
write = self.write
(
code,
f_globals,
defaults,
closure_values,
dct,
base_globals,
) = self.extract_func_data(func)
save(_fill_function) # skeleton function updater
write(pickle.MARK) # beginning of tuple that _fill_function expects
self._save_subimports(
code, itertools.chain(f_globals.values(), closure_values or ()),
)
# create a skeleton function object and memoize it
save(_make_skel_func)
save(
(
code,
len(closure_values) if closure_values is not None else -1,
base_globals,
)
)
write(pickle.REDUCE)
self.memoize(func)
# save the rest of the func data needed by _fill_function
save(f_globals)
save(defaults)
save(dct)
save(func.__module__)
save(closure_values)
write(pickle.TUPLE)
write(pickle.REDUCE) | [
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qt/qt | 0a2f2382541424726168804be2c90b91381608c6 | src/3rdparty/webkit/Source/ThirdParty/gyp/pylib/gyp/generator/msvs.py | python | _GetLibraries | (config, spec) | return [re.sub('^(\-l)', '', lib) for lib in libraries] | Returns the list of libraries for this configuration.
Arguments:
config: The dictionnary that defines the special processing to be done
for this configuration.
spec: The target dictionary containing the properties of the target.
Returns:
The list of directory paths. | Returns the list of libraries for this configuration. | [
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"the",
"list",
"of",
"libraries",
"for",
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"configuration",
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] | def _GetLibraries(config, spec):
"""Returns the list of libraries for this configuration.
Arguments:
config: The dictionnary that defines the special processing to be done
for this configuration.
spec: The target dictionary containing the properties of the target.
Returns:
The list of directory paths.
"""
libraries = spec.get('libraries', [])
# Strip out -l, as it is not used on windows (but is needed so we can pass
# in libraries that are assumed to be in the default library path).
return [re.sub('^(\-l)', '', lib) for lib in libraries] | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/src/motionplanning.py | python | get_plan_json_string | () | return _motionplanning.get_plan_json_string() | r"""
get_plan_json_string() -> std::string
Saves planner values to a JSON string. | r"""
get_plan_json_string() -> std::string | [
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] | def get_plan_json_string() -> "std::string":
r"""
get_plan_json_string() -> std::string
Saves planner values to a JSON string.
"""
return _motionplanning.get_plan_json_string() | [
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psmoveservice/PSMoveService | 22bbe20e9de53f3f3581137bce7b88e2587a27e7 | misc/python/pypsmove/transformations.py | python | orthogonalization_matrix | (lengths, angles) | return numpy.array([
[ a*sinb*math.sqrt(1.0-co*co), 0.0, 0.0, 0.0],
[-a*sinb*co, b*sina, 0.0, 0.0],
[ a*cosb, b*cosa, c, 0.0],
[ 0.0, 0.0, 0.0, 1.0]]) | Return orthogonalization matrix for crystallographic cell coordinates.
Angles are expected in degrees.
The de-orthogonalization matrix is the inverse.
>>> O = orthogonalization_matrix([10, 10, 10], [90, 90, 90])
>>> numpy.allclose(O[:3, :3], numpy.identity(3, float) * 10)
True
>>> O = orthogonalization_matrix([9.8, 12.0, 15.5], [87.2, 80.7, 69.7])
>>> numpy.allclose(numpy.sum(O), 43.063229)
True | Return orthogonalization matrix for crystallographic cell coordinates. | [
"Return",
"orthogonalization",
"matrix",
"for",
"crystallographic",
"cell",
"coordinates",
"."
] | def orthogonalization_matrix(lengths, angles):
"""Return orthogonalization matrix for crystallographic cell coordinates.
Angles are expected in degrees.
The de-orthogonalization matrix is the inverse.
>>> O = orthogonalization_matrix([10, 10, 10], [90, 90, 90])
>>> numpy.allclose(O[:3, :3], numpy.identity(3, float) * 10)
True
>>> O = orthogonalization_matrix([9.8, 12.0, 15.5], [87.2, 80.7, 69.7])
>>> numpy.allclose(numpy.sum(O), 43.063229)
True
"""
a, b, c = lengths
angles = numpy.radians(angles)
sina, sinb, _ = numpy.sin(angles)
cosa, cosb, cosg = numpy.cos(angles)
co = (cosa * cosb - cosg) / (sina * sinb)
return numpy.array([
[ a*sinb*math.sqrt(1.0-co*co), 0.0, 0.0, 0.0],
[-a*sinb*co, b*sina, 0.0, 0.0],
[ a*cosb, b*cosa, c, 0.0],
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dmlc/nnvm | dab5ce8ab6adbf4edd8bd2fa89f1a99f343b6e38 | python/nnvm/symbol.py | python | Symbol.list_input_names | (self, option='all') | return [_base.py_str(sarr[i]) for i in range(size.value)] | List all the inputs in the symbol.
Parameters
----------
option : {'all', 'read_only', 'aux_state'}, optional
The listing option
- 'all' will list all the arguments.
- 'read_only' lists arguments that are readed by the graph.
- 'aux_state' lists arguments that are mutated by the graph as state.
Returns
-------
args : list of string
List of all the arguments. | List all the inputs in the symbol. | [
"List",
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"the",
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"in",
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"."
] | def list_input_names(self, option='all'):
"""List all the inputs in the symbol.
Parameters
----------
option : {'all', 'read_only', 'aux_state'}, optional
The listing option
- 'all' will list all the arguments.
- 'read_only' lists arguments that are readed by the graph.
- 'aux_state' lists arguments that are mutated by the graph as state.
Returns
-------
args : list of string
List of all the arguments.
"""
size = _ctypes.c_uint()
sarr = _ctypes.POINTER(_ctypes.c_char_p)()
_check_call(_LIB.NNSymbolListInputNames(
self.handle, self._get_list_copt(option),
_ctypes.byref(size), _ctypes.byref(sarr)))
return [_base.py_str(sarr[i]) for i in range(size.value)] | [
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plumonito/dtslam | 5994bb9cf7a11981b830370db206bceb654c085d | 3rdparty/eigen-3.2.2/debug/gdb/printers.py | python | EigenMatrixPrinter.__init__ | (self, variety, val) | Extract all the necessary information | Extract all the necessary information | [
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"the",
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"information"
] | def __init__(self, variety, val):
"Extract all the necessary information"
# Save the variety (presumably "Matrix" or "Array") for later usage
self.variety = variety
# The gdb extension does not support value template arguments - need to extract them by hand
type = val.type
if type.code == gdb.TYPE_CODE_REF:
type = type.target()
self.type = type.unqualified().strip_typedefs()
tag = self.type.tag
regex = re.compile('\<.*\>')
m = regex.findall(tag)[0][1:-1]
template_params = m.split(',')
template_params = map(lambda x:x.replace(" ", ""), template_params)
if template_params[1] == '-0x00000000000000001' or template_params[1] == '-0x000000001' or template_params[1] == '-1':
self.rows = val['m_storage']['m_rows']
else:
self.rows = int(template_params[1])
if template_params[2] == '-0x00000000000000001' or template_params[2] == '-0x000000001' or template_params[2] == '-1':
self.cols = val['m_storage']['m_cols']
else:
self.cols = int(template_params[2])
self.options = 0 # default value
if len(template_params) > 3:
self.options = template_params[3];
self.rowMajor = (int(self.options) & 0x1)
self.innerType = self.type.template_argument(0)
self.val = val
# Fixed size matrices have a struct as their storage, so we need to walk through this
self.data = self.val['m_storage']['m_data']
if self.data.type.code == gdb.TYPE_CODE_STRUCT:
self.data = self.data['array']
self.data = self.data.cast(self.innerType.pointer()) | [
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PaddlePaddle/Anakin | 5fd68a6cc4c4620cd1a30794c1bf06eebd3f4730 | tools/external_converter_v2/parser/graph_io.py | python | NodeProtoIO.add_attr | (self, value_name, data, data_type_str) | set tensor data:
value_name : var name
data : real data
data_type_str : data type desc ("string"
"int"
"float"
"bool"
"tensor"
"shape"
"list_value") | set tensor data:
value_name : var name
data : real data
data_type_str : data type desc ("string"
"int"
"float"
"bool"
"tensor"
"shape"
"list_value") | [
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"""
set tensor data:
value_name : var name
data : real data
data_type_str : data type desc ("string"
"int"
"float"
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self.node_proto.attr[value_name].CopyFrom(self.attr_warpper(data, data_type_str)) | [
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google/mozc | 7329757e1ad30e327c1ae823a8302c79482d6b9c | src/build_tools/versioning_files.py | python | _GetSha1Digest | (file_path) | return sha.digest() | Returns the sha1 hash of the file. | Returns the sha1 hash of the file. | [
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] | def _GetSha1Digest(file_path):
"""Returns the sha1 hash of the file."""
sha = hashlib.sha1()
with open(file_path, 'rb') as f:
data = f.read()
sha.update(data)
return sha.digest() | [
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ros-planning/moveit2 | dd240ef6fd8b9932a7a53964140f2952786187a9 | moveit_commander/src/moveit_commander/move_group.py | python | MoveGroupCommander.get_active_joints | (self) | return self._g.get_active_joints() | Get the active joints of this group | Get the active joints of this group | [
"Get",
"the",
"active",
"joints",
"of",
"this",
"group"
] | def get_active_joints(self):
""" Get the active joints of this group """
return self._g.get_active_joints() | [
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SoarGroup/Soar | a1c5e249499137a27da60533c72969eef3b8ab6b | scons/scons-local-4.1.0/SCons/Tool/mwcc.py | python | set_vars | (env) | return 1 | Set MWCW_VERSION, MWCW_VERSIONS, and some codewarrior environment vars
MWCW_VERSIONS is set to a list of objects representing installed versions
MWCW_VERSION is set to the version object that will be used for building.
MWCW_VERSION can be set to a string during Environment
construction to influence which version is chosen, otherwise
the latest one from MWCW_VERSIONS is used.
Returns true if at least one version is found, false otherwise | Set MWCW_VERSION, MWCW_VERSIONS, and some codewarrior environment vars | [
"Set",
"MWCW_VERSION",
"MWCW_VERSIONS",
"and",
"some",
"codewarrior",
"environment",
"vars"
] | def set_vars(env):
"""Set MWCW_VERSION, MWCW_VERSIONS, and some codewarrior environment vars
MWCW_VERSIONS is set to a list of objects representing installed versions
MWCW_VERSION is set to the version object that will be used for building.
MWCW_VERSION can be set to a string during Environment
construction to influence which version is chosen, otherwise
the latest one from MWCW_VERSIONS is used.
Returns true if at least one version is found, false otherwise
"""
desired = env.get('MWCW_VERSION', '')
# return right away if the variables are already set
if isinstance(desired, MWVersion):
return 1
elif desired is None:
return 0
versions = find_versions()
version = None
if desired:
for v in versions:
if str(v) == desired:
version = v
elif versions:
version = versions[-1]
env['MWCW_VERSIONS'] = versions
env['MWCW_VERSION'] = version
if version is None:
return 0
env.PrependENVPath('PATH', version.clpath)
env.PrependENVPath('PATH', version.dllpath)
ENV = env['ENV']
ENV['CWFolder'] = version.path
ENV['LM_LICENSE_FILE'] = version.license
plus = lambda x: '+%s' % x
ENV['MWCIncludes'] = os.pathsep.join(map(plus, version.includes))
ENV['MWLibraries'] = os.pathsep.join(map(plus, version.libs))
return 1 | [
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francinexue/xuefu | b6ff79747a42e020588c0c0a921048e08fe4680c | cnx/strategy/tickPosition.py | python | Position.entryActive | (self) | return self.__entryOrder is not None and self.__entryOrder.isActive() | Returns True if the entry order is active. | Returns True if the entry order is active. | [
"Returns",
"True",
"if",
"the",
"entry",
"order",
"is",
"active",
"."
] | def entryActive(self):
"""Returns True if the entry order is active."""
return self.__entryOrder is not None and self.__entryOrder.isActive() | [
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] | https://github.com/francinexue/xuefu/blob/b6ff79747a42e020588c0c0a921048e08fe4680c/cnx/strategy/tickPosition.py#L219-L221 | |
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/history.py | python | HistoryManager.writeout_cache | (self, conn=None) | Write any entries in the cache to the database. | Write any entries in the cache to the database. | [
"Write",
"any",
"entries",
"in",
"the",
"cache",
"to",
"the",
"database",
"."
] | def writeout_cache(self, conn=None):
"""Write any entries in the cache to the database."""
if conn is None:
conn = self.db
with self.db_input_cache_lock:
try:
self._writeout_input_cache(conn)
except sqlite3.IntegrityError:
self.new_session(conn)
print("ERROR! Session/line number was not unique in",
"database. History logging moved to new session",
self.session_number)
try:
# Try writing to the new session. If this fails, don't
# recurse
self._writeout_input_cache(conn)
except sqlite3.IntegrityError:
pass
finally:
self.db_input_cache = []
with self.db_output_cache_lock:
try:
self._writeout_output_cache(conn)
except sqlite3.IntegrityError:
print("!! Session/line number for output was not unique",
"in database. Output will not be stored.")
finally:
self.db_output_cache = [] | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/framework/python/ops/variables.py | python | get_or_create_global_step | (graph=None) | return globalstep | Returns and create (if necessary) the global step variable.
Args:
graph: The graph in which to create the global step. If missing, use default
graph.
Returns:
the tensor representing the global step variable. | Returns and create (if necessary) the global step variable. | [
"Returns",
"and",
"create",
"(",
"if",
"necessary",
")",
"the",
"global",
"step",
"variable",
"."
] | def get_or_create_global_step(graph=None):
"""Returns and create (if necessary) the global step variable.
Args:
graph: The graph in which to create the global step. If missing, use default
graph.
Returns:
the tensor representing the global step variable.
"""
graph = ops.get_default_graph() if graph is None else graph
globalstep = get_global_step(graph)
if globalstep is None:
globalstep = create_global_step(graph)
return globalstep | [
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Samsung/veles | 95ed733c2e49bc011ad98ccf2416ecec23fbf352 | veles/external/daemon/daemon.py | python | DaemonContext.__init__ | (
self,
chroot_directory=None,
working_directory='/',
umask=0,
uid=None,
gid=None,
prevent_core=True,
detach_process=None,
files_preserve=None,
pidfile=None,
stdin=None,
stdout=None,
stderr=None,
signal_map=None,
) | Set up a new instance. | Set up a new instance. | [
"Set",
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"instance",
"."
] | def __init__(
self,
chroot_directory=None,
working_directory='/',
umask=0,
uid=None,
gid=None,
prevent_core=True,
detach_process=None,
files_preserve=None,
pidfile=None,
stdin=None,
stdout=None,
stderr=None,
signal_map=None,
):
""" Set up a new instance. """
self.chroot_directory = chroot_directory
self.working_directory = working_directory
self.umask = umask
self.prevent_core = prevent_core
self.files_preserve = files_preserve
self.pidfile = pidfile
self.stdin = stdin
self.stdout = stdout
self.stderr = stderr
if uid is None:
uid = os.getuid()
elif isinstance(uid, str):
try:
uid = int(uid)
except ValueError:
uid = getpwnam(uid).pw_uid
self.uid = uid
if gid is None:
gid = os.getgid()
elif isinstance(gid, str):
try:
gid = int(gid)
except ValueError:
gid = getgrnam(gid).gr_gid
self.gid = gid
if detach_process is None:
detach_process = is_detach_process_context_required()
self.detach_process = detach_process
if signal_map is None:
signal_map = make_default_signal_map()
self.signal_map = signal_map
self._is_open = False | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/mailbox.py | python | MHMessage.add_sequence | (self, sequence) | Add sequence to list of sequences including the message. | Add sequence to list of sequences including the message. | [
"Add",
"sequence",
"to",
"list",
"of",
"sequences",
"including",
"the",
"message",
"."
] | def add_sequence(self, sequence):
"""Add sequence to list of sequences including the message."""
if isinstance(sequence, str):
if not sequence in self._sequences:
self._sequences.append(sequence)
else:
raise TypeError('sequence type must be str: %s' % type(sequence)) | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/logging/handlers.py | python | HTTPHandler.emit | (self, record) | Emit a record.
Send the record to the Web server as an URL-encoded dictionary | Emit a record. | [
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"a",
"record",
"."
] | def emit(self, record):
"""
Emit a record.
Send the record to the Web server as an URL-encoded dictionary
"""
try:
import httplib, urllib
host = self.host
h = httplib.HTTP(host)
url = self.url
data = urllib.urlencode(self.mapLogRecord(record))
if self.method == "GET":
if (string.find(url, '?') >= 0):
sep = '&'
else:
sep = '?'
url = url + "%c%s" % (sep, data)
h.putrequest(self.method, url)
# support multiple hosts on one IP address...
# need to strip optional :port from host, if present
i = string.find(host, ":")
if i >= 0:
host = host[:i]
h.putheader("Host", host)
if self.method == "POST":
h.putheader("Content-type",
"application/x-www-form-urlencoded")
h.putheader("Content-length", str(len(data)))
h.endheaders()
if self.method == "POST":
h.send(data)
h.getreply() #can't do anything with the result
except (KeyboardInterrupt, SystemExit):
raise
except:
self.handleError(record) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/internals/array_manager.py | python | BaseArrayManager.make_empty | (self: T, axes=None) | return type(self)(arrays, axes) | Return an empty ArrayManager with the items axis of len 0 (no columns) | Return an empty ArrayManager with the items axis of len 0 (no columns) | [
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"""Return an empty ArrayManager with the items axis of len 0 (no columns)"""
if axes is None:
axes = [self.axes[1:], Index([])]
arrays: list[np.ndarray | ExtensionArray] = []
return type(self)(arrays, axes) | [
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pristineio/webrtc-mirror | 7a5bcdffaab90a05bc1146b2b1ea71c004e54d71 | tools_webrtc/valgrind/gdb_helper.py | python | AddressTable.ResolveAll | (self) | Carry out all lookup requests. | Carry out all lookup requests. | [
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load_address = self._load_addresses[binary]
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self._translation[binary] = addr
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/applications/workbench/workbench/plotting/plotscriptgenerator/legend.py | python | generate_title_font_commands | (legend, legend_object_var) | return title_commands | Generate commands for setting properties for the legend title font. | Generate commands for setting properties for the legend title font. | [
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"""
Generate commands for setting properties for the legend title font.
"""
title_commands = []
kwargs = LegendProperties.from_legend(legend)
_remove_kwargs_if_default(kwargs)
if 'title_font' in kwargs:
title_commands.append(legend_object_var + ".get_title().set_fontname('" + kwargs['title_font'] + "')")
if 'title_color' in kwargs:
title_commands.append(legend_object_var + ".get_title().set_color('" + kwargs['title_color'] + "')")
if 'title_size' in kwargs:
title_commands.append(legend_object_var + ".get_title().set_fontsize('" + str(kwargs['title_size']) + "')")
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | python/artm/score_tracker.py | python | SparsityPhiScoreTracker.__init__ | (self, score) | :Properties:
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* total_tokens - number of all rows in Phi.
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* Note: every field is a list of info about score on all synchronizations.
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* zero_tokens - number of zero rows in Phi.
* total_tokens - number of all rows in Phi.
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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | scripts/cpp_lint.py | python | FilesBelongToSameModule | (filename_cc, filename_h) | return files_belong_to_same_module, common_path | Check if these two filenames belong to the same module.
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foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
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filename_cc: is the path for the .cc file
filename_h: is the path for the header path
Returns:
Tuple with a bool and a string:
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string: the additional prefix needed to open the header file. | Check if these two filenames belong to the same module. | [
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"""Check if these two filenames belong to the same module.
The concept of a 'module' here is a as follows:
foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
same 'module' if they are in the same directory.
some/path/public/xyzzy and some/path/internal/xyzzy are also considered
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"""
if not filename_cc.endswith('.cc'):
return (False, '')
filename_cc = filename_cc[:-len('.cc')]
if filename_cc.endswith('_unittest'):
filename_cc = filename_cc[:-len('_unittest')]
elif filename_cc.endswith('_test'):
filename_cc = filename_cc[:-len('_test')]
filename_cc = filename_cc.replace('/public/', '/')
filename_cc = filename_cc.replace('/internal/', '/')
if not filename_h.endswith('.h'):
return (False, '')
filename_h = filename_h[:-len('.h')]
if filename_h.endswith('-inl'):
filename_h = filename_h[:-len('-inl')]
filename_h = filename_h.replace('/public/', '/')
filename_h = filename_h.replace('/internal/', '/')
files_belong_to_same_module = filename_cc.endswith(filename_h)
common_path = ''
if files_belong_to_same_module:
common_path = filename_cc[:-len(filename_h)]
return files_belong_to_same_module, common_path | [
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trailofbits/sienna-locomotive | 09bc1a0bea7d7a33089422c62e0d3c715ecb7ce0 | sl2/harness/instrument.py | python | kill | () | Ends a sequence of fuzzing runs. | Ends a sequence of fuzzing runs. | [
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"""
Ends a sequence of fuzzing runs.
"""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/aui.py | python | AuiManager.DetachPane | (*args, **kwargs) | return _aui.AuiManager_DetachPane(*args, **kwargs) | DetachPane(self, Window window) -> bool | DetachPane(self, Window window) -> bool | [
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"""DetachPane(self, Window window) -> bool"""
return _aui.AuiManager_DetachPane(*args, **kwargs) | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | grc/core/utils/extract_docs.py | python | docstring_from_make | (key, imports, make) | return doc_strings | Extract the documentation from the python __doc__ strings
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Args:
key: the block key
imports: a list of import statements (string) to execute
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"""
Extract the documentation from the python __doc__ strings
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Args:
key: the block key
imports: a list of import statements (string) to execute
make: block constructor template
Returns:
a list of tuples (block_name, doc string)
"""
try:
blk_cls = make.partition('(')[0].strip()
if '$' in blk_cls:
raise ValueError('Not an identifier')
ns = dict()
exec(imports.strip(), ns)
blk = eval(blk_cls, ns)
doc_strings = {key: blk.__doc__}
except (ImportError, AttributeError, SyntaxError, ValueError):
doc_strings = docstring_guess_from_key(key)
return doc_strings | [
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/android/gyp/copy_ex.py | python | DoRenaming | (options, deps) | Copy and rename files given in options.renaming_sources and update deps. | Copy and rename files given in options.renaming_sources and update deps. | [
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"""Copy and rename files given in options.renaming_sources and update deps."""
src_files = list(itertools.chain.from_iterable(
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for f in options.renaming_sources))
dest_files = list(itertools.chain.from_iterable(
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if (len(src_files) != len(dest_files)):
print('Renaming source and destination files not match.')
sys.exit(-1)
for src, dest in zip(src_files, dest_files):
if os.path.isdir(src):
print('renaming diretory is not supported.')
sys.exit(-1)
else:
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/protorpc/protorpc/remote.py | python | RequestState.__init__ | (self,
remote_host=None,
remote_address=None,
server_host=None,
server_port=None) | Constructor.
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remote_host: Assigned to property.
remote_address: Assigned to property.
server_host: Assigned to property.
server_port: Assigned to property. | Constructor. | [
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remote_host=None,
remote_address=None,
server_host=None,
server_port=None):
"""Constructor.
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remote_host: Assigned to property.
remote_address: Assigned to property.
server_host: Assigned to property.
server_port: Assigned to property.
"""
self.__remote_host = remote_host
self.__remote_address = remote_address
self.__server_host = server_host
self.__server_port = server_port | [
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Tarsnap/tarsnap-gui | 60a1d7816747ac71a4573673df8ee1b81ef1cb99 | util/generate_loading_gif.py | python | draw_frame | (framenum) | return image | Draw a frame of the animation. | Draw a frame of the animation. | [
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""" Draw a frame of the animation. """
# Create new image and drawing surface.
image = PIL.Image.new('LA', (SIZE, SIZE), (1, 255))
draw = PIL.ImageDraw.Draw(image)
# Draw the dots.
for i in range(VISUAL_DOTS):
pos = ((framenum - i) % TOTAL_DOTS)
# The Qt background is (239,239,239) so this fades from 0
# to 240 (but stops at 180).
gray = round(240/4*i)
draw_dot(draw, pos, (gray, 0))
return image | [
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Slicer/Slicer | ba9fadf332cb0303515b68d8d06a344c82e3e3e5 | Modules/Scripted/DICOMPlugins/DICOMImageSequencePlugin.py | python | DICOMImageSequencePluginClass.examineFiles | (self,files) | return loadables | Returns a list of DICOMLoadable instances
corresponding to ways of interpreting the
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""" Returns a list of DICOMLoadable instances
corresponding to ways of interpreting the
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"""
self.detailedLogging = slicer.util.settingsValue('DICOM/detailedLogging', False, converter=slicer.util.toBool)
supportedSOPClassUIDs = [
'1.2.840.10008.5.1.4.1.1.12.1', # X-Ray Angiographic Image Storage
'1.2.840.10008.5.1.4.1.1.12.2', # X-Ray Fluoroscopy Image Storage
'1.2.840.10008.5.1.4.1.1.3.1', # Ultrasound Multiframe Image Storage
'1.2.840.10008.5.1.4.1.1.6.1', # Ultrasound Image Storage
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'1.2.840.10008.5.1.4.1.1.4', # MR Image Storage (will be only accepted if cine-MRI)
]
# Modalities that typically acquire 2D image sequences:
suppportedSecondaryCaptureModalities = ['US', 'XA', 'RF', 'ES']
# Each instance will be a loadable, that will result in one sequence browser node
# and usually one sequence (except simultaneous biplane acquisition, which will
# result in two sequences).
# Each pedal press on the XA/RF acquisition device creates a new instance number,
# but if the device has two imaging planes (biplane) then two sequences
# will be acquired, which have the same instance number. These two sequences
# are synchronized in time, therefore they have to be assigned to the same
# browser node.
instanceNumberToLoadableIndex = {}
loadables = []
canBeCineMri = True
cineMriTriggerTimes = set()
cineMriImageOrientations = set()
cineMriInstanceNumberToFilenameIndex = {}
for filePath in files:
# Quick check of SOP class UID without parsing the file...
try:
sopClassUID = slicer.dicomDatabase.fileValue(filePath, self.tags['sopClassUID'])
if not (sopClassUID in supportedSOPClassUIDs):
# Unsupported class
continue
# Only accept MRI if it looks like cine-MRI
if sopClassUID != '1.2.840.10008.5.1.4.1.1.4': # MR Image Storage (will be only accepted if cine-MRI)
canBeCineMri = False
if not canBeCineMri and sopClassUID == '1.2.840.10008.5.1.4.1.1.4': # MR Image Storage
continue
except Exception as e:
# Quick check could not be completed (probably Slicer DICOM database is not initialized).
# No problem, we'll try to parse the file and check the SOP class UID then.
pass
instanceNumber = slicer.dicomDatabase.fileValue(filePath, self.tags['instanceNumber'])
if canBeCineMri and sopClassUID == '1.2.840.10008.5.1.4.1.1.4': # MR Image Storage
if not instanceNumber:
# no instance number, probably not cine-MRI
canBeCineMri = False
if self.detailedLogging:
logging.debug("No instance number attribute found, the series will not be considered as a cine MRI")
continue
cineMriInstanceNumberToFilenameIndex[int(instanceNumber)] = filePath
cineMriTriggerTimes.add(slicer.dicomDatabase.fileValue(filePath, self.tags['triggerTime']))
cineMriImageOrientations.add(slicer.dicomDatabase.fileValue(filePath, self.tags['orientation']))
else:
modality = slicer.dicomDatabase.fileValue(filePath, self.tags['modality'])
if sopClassUID == '1.2.840.10008.5.1.4.1.1.7': # Secondary Capture Image Storage
if modality not in suppportedSecondaryCaptureModalities:
# practice of dumping secondary capture images into the same series
# is only prevalent in US and XA/RF modalities
continue
if not (instanceNumber in instanceNumberToLoadableIndex.keys()):
# new instance number
seriesNumber = slicer.dicomDatabase.fileValue(filePath, self.tags['seriesNumber'])
seriesDescription = slicer.dicomDatabase.fileValue(filePath, self.tags['seriesDescription'])
photometricInterpretation = slicer.dicomDatabase.fileValue(filePath, self.tags['photometricInterpretation'])
name = ''
if seriesNumber:
name = f'{seriesNumber}:'
if modality:
name = f'{name} {modality}'
if seriesDescription:
name = f'{name} {seriesDescription}'
if instanceNumber:
name = f'{name} [{instanceNumber}]'
loadable = DICOMLoadable()
loadable.singleSequence = False # put each instance in a separate sequence
loadable.files = [filePath]
loadable.name = name.strip() # remove leading and trailing spaces, if any
loadable.warning = "Image spacing may need to be calibrated for accurate size measurements."
loadable.tooltip = f"{modality} image sequence"
loadable.selected = True
# Confidence is slightly larger than default scalar volume plugin's (0.5)
# but still leaving room for more specialized plugins.
loadable.confidence = 0.7
loadable.grayscale = ('MONOCHROME' in photometricInterpretation)
# Add to loadables list
loadables.append(loadable)
instanceNumberToLoadableIndex[instanceNumber] = len(loadables)-1
else:
# existing instance number, add this file
loadableIndex = instanceNumberToLoadableIndex[instanceNumber]
loadables[loadableIndex].files.append(filePath)
loadable.tooltip = f"{modality} image sequence ({len(loadables[loadableIndex].files)} planes)"
if canBeCineMri and len(cineMriInstanceNumberToFilenameIndex) > 1:
# Get description from first
ds = dicom.read_file(cineMriInstanceNumberToFilenameIndex[next(iter(cineMriInstanceNumberToFilenameIndex))], stop_before_pixels=True)
name = ''
if hasattr(ds, 'SeriesNumber') and ds.SeriesNumber:
name = f'{ds.SeriesNumber}:'
if hasattr(ds, 'Modality') and ds.Modality:
name = f'{name} {ds.Modality}'
if hasattr(ds, 'SeriesDescription') and ds.SeriesDescription:
name = f'{name} {ds.SeriesDescription}'
loadable = DICOMLoadable()
loadable.singleSequence = True # put all instances in a single sequence
loadable.instanceNumbers = sorted(cineMriInstanceNumberToFilenameIndex)
loadable.files = [cineMriInstanceNumberToFilenameIndex[instanceNumber] for instanceNumber in loadable.instanceNumbers]
loadable.name = name.strip() # remove leading and trailing spaces, if any
loadable.tooltip = f"{ds.Modality} image sequence"
loadable.selected = True
if len(cineMriTriggerTimes)>3:
if self.detailedLogging:
logging.debug("Several different trigger times found ("+repr(cineMriTriggerTimes)+") - assuming this series is a cine MRI")
# This is likely a cardiac cine acquisition.
if len(cineMriImageOrientations) > 1:
if self.detailedLogging:
logging.debug("Several different image orientations found ("+repr(cineMriImageOrientations)+") - assuming this series is a rotational cine MRI")
# Multivolume importer sets confidence=0.9-1.0, so we need to set a bit higher confidence to be selected by default
loadable.confidence = 1.05
else:
if self.detailedLogging:
logging.debug("All image orientations are the same ("+repr(cineMriImageOrientations)+") - probably the MultiVolume plugin should load this")
# Multivolume importer sets confidence=0.9-1.0, so we need to set a bit lower confidence to allow multivolume selected by default
loadable.confidence = 0.85
else:
# This may be a 3D acquisition,so set lower confidence than scalar volume's default (0.5)
if self.detailedLogging:
logging.debug("Only one or few different trigger times found ("+repr(cineMriTriggerTimes)+") - assuming this series is not a cine MRI")
loadable.confidence = 0.4
loadable.grayscale = ('MONOCHROME' in ds.PhotometricInterpretation)
# Add to loadables list
loadables.append(loadable)
return loadables | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/generic.py | python | NDFrame._check_label_or_level_ambiguity | (self, key, axis: int = 0) | Check whether `key` is ambiguous.
By ambiguous, we mean that it matches both a level of the input
`axis` and a label of the other axis.
Parameters
----------
key : str or object
Label or level name.
axis : int, default 0
Axis that levels are associated with (0 for index, 1 for columns).
Raises
------
ValueError: `key` is ambiguous | Check whether `key` is ambiguous. | [
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] | def _check_label_or_level_ambiguity(self, key, axis: int = 0) -> None:
"""
Check whether `key` is ambiguous.
By ambiguous, we mean that it matches both a level of the input
`axis` and a label of the other axis.
Parameters
----------
key : str or object
Label or level name.
axis : int, default 0
Axis that levels are associated with (0 for index, 1 for columns).
Raises
------
ValueError: `key` is ambiguous
"""
axis = self._get_axis_number(axis)
other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis)
if (
key is not None
and is_hashable(key)
and key in self.axes[axis].names
and any(key in self.axes[ax] for ax in other_axes)
):
# Build an informative and grammatical warning
level_article, level_type = (
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label_article, label_type = (
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msg = (
f"'{key}' is both {level_article} {level_type} level and "
f"{label_article} {label_type} label, which is ambiguous."
)
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/distributions/distribution.py | python | Distribution.cdf | (self, value) | Returns the cumulative density/mass function evaluated at
`value`.
Args:
value (Tensor): | Returns the cumulative density/mass function evaluated at
`value`. | [
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"""
Returns the cumulative density/mass function evaluated at
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Args:
value (Tensor):
"""
raise NotImplementedError | [
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albarji/proxTV | 69062fe54335976e83fe9269a09d795ae37470ba | prox_tv/__init__.py | python | tvp_2d | (x, w_col, w_row, p_col, p_row, n_threads=1, max_iters=0) | return y | r"""2D proximal operator for any :math:`\ell_p` norm.
Specifically, this optimizes the following program:
.. math::
\mathrm{min}_y \frac{1}{2}\|x-y\|^2 + w^r \|D_\mathrm{row}(y)\|_{p_1} +
w^c \|D_\mathrm{col}(y) \|_{p_2},
where :math:`\mathrm D_{row}` and :math:`\mathrm D_{col}` take the
differences accross rows and columns respectively.
Parameters
----------
y : numpy array
The matrix signal we are approximating.
p_col : float
Column norm.
p_row : float
Row norm.
w_col : float
Column penalty.
w_row : float
Row penalty.
Returns
-------
numpy array
The solution of the optimization problem. | r"""2D proximal operator for any :math:`\ell_p` norm. | [
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] | def tvp_2d(x, w_col, w_row, p_col, p_row, n_threads=1, max_iters=0):
r"""2D proximal operator for any :math:`\ell_p` norm.
Specifically, this optimizes the following program:
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Column norm.
p_row : float
Row norm.
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Column penalty.
w_row : float
Row penalty.
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"""
assert w_col >= 0
assert w_row >= 0
assert p_col >= 1
assert p_row >= 1
info = np.zeros(_N_INFO)
x = np.asfortranarray(x, dtype='float64')
w_col = force_float_scalar(w_col)
w_row = force_float_scalar(w_row)
p_col = force_float_scalar(p_col)
p_row = force_float_scalar(p_row)
y = np.zeros(np.shape(x), order='F')
_call(lib.DR2_TV,
x.shape[0], x.shape[1], x, w_col, w_row, p_col, p_row, y,
n_threads, max_iters, info)
return y | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/v8/third_party/jinja2/environment.py | python | _environment_sanity_check | (environment) | return environment | Perform a sanity check on the environment. | Perform a sanity check on the environment. | [
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] | def _environment_sanity_check(environment):
"""Perform a sanity check on the environment."""
assert issubclass(environment.undefined, Undefined), 'undefined must ' \
'be a subclass of undefined because filters depend on it.'
assert environment.block_start_string != \
environment.variable_start_string != \
environment.comment_start_string, 'block, variable and comment ' \
'start strings must be different'
assert environment.newline_sequence in ('\r', '\r\n', '\n'), \
'newline_sequence set to unknown line ending string.'
return environment | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/cygprofile/cygprofile_utils.py | python | WarningCollector.WriteEnd | (self, message) | Once all warnings have been printed, use this to print the number of
elided warnings. | Once all warnings have been printed, use this to print the number of
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if self._warnings > self._max_warnings:
logging.log(self._level, '%d more warnings for: %s' % (
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rootm0s/Protectors | 5b3f4d11687a5955caf9c3af30666c4bfc2c19ab | OWASP-ZSC/module/readline_windows/pyreadline/rlmain.py | python | BaseReadline.get_begidx | (self) | return self.mode.begidx | Get the beginning index of the readline tab-completion scope. | Get the beginning index of the readline tab-completion scope. | [
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'''Get the beginning index of the readline tab-completion scope.'''
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generalized-intelligence/GAAS | 29ab17d3e8a4ba18edef3a57c36d8db6329fac73 | algorithms/src/LocalizationAndMapping/registration_localization/fast_gicp/thirdparty/Eigen/debug/gdb/printers.py | python | EigenMatrixPrinter.__init__ | (self, variety, val) | Extract all the necessary information | Extract all the necessary information | [
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# Save the variety (presumably "Matrix" or "Array") for later usage
self.variety = variety
# The gdb extension does not support value template arguments - need to extract them by hand
type = val.type
if type.code == gdb.TYPE_CODE_REF:
type = type.target()
self.type = type.unqualified().strip_typedefs()
tag = self.type.tag
regex = re.compile('\<.*\>')
m = regex.findall(tag)[0][1:-1]
template_params = m.split(',')
template_params = [x.replace(" ", "") for x in template_params]
if template_params[1] == '-0x00000000000000001' or template_params[1] == '-0x000000001' or template_params[1] == '-1':
self.rows = val['m_storage']['m_rows']
else:
self.rows = int(template_params[1])
if template_params[2] == '-0x00000000000000001' or template_params[2] == '-0x000000001' or template_params[2] == '-1':
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self.val = val
# Fixed size matrices have a struct as their storage, so we need to walk through this
self.data = self.val['m_storage']['m_data']
if self.data.type.code == gdb.TYPE_CODE_STRUCT:
self.data = self.data['array']
self.data = self.data.cast(self.innerType.pointer()) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/_pydecimal.py | python | _sqrt_nearest | (n, a) | return a | Closest integer to the square root of the positive integer n. a is
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will do for a, but the closer a is to the square root of n the
faster convergence will be. | Closest integer to the square root of the positive integer n. a is
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"""Closest integer to the square root of the positive integer n. a is
an initial approximation to the square root. Any positive integer
will do for a, but the closer a is to the square root of n the
faster convergence will be.
"""
if n <= 0 or a <= 0:
raise ValueError("Both arguments to _sqrt_nearest should be positive.")
b=0
while a != b:
b, a = a, a--n//a>>1
return a | [
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/ConfigSet.py | python | ConfigSet.get_merged_dict | (self) | return merged_table | Computes the merged dictionary from the fusion of self and all its parent
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"""
Computes the merged dictionary from the fusion of self and all its parent
:rtype: a ConfigSet object
"""
table_list = []
env = self
while 1:
table_list.insert(0, env.table)
try:
env = env.parent
except AttributeError:
break
merged_table = {}
for table in table_list:
merged_table.update(table)
return merged_table | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/pubsub/core/kwargs/topicmgrimpl.py | python | getRootTopicSpec | () | return argsDocs, reqdArgs | If using kwargs protocol, then root topic takes no args. | If using kwargs protocol, then root topic takes no args. | [
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] | def getRootTopicSpec():
"""If using kwargs protocol, then root topic takes no args."""
argsDocs = {}
reqdArgs = ()
return argsDocs, reqdArgs | [
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/tools/gyp/pylib/gyp/generator/msvs.py | python | _FixPaths | (paths) | return [_FixPath(i) for i in paths] | Fix each of the paths of the list. | Fix each of the paths of the list. | [
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"""Fix each of the paths of the list."""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/stc.py | python | StyledTextCtrl.TextWidth | (*args, **kwargs) | return _stc.StyledTextCtrl_TextWidth(*args, **kwargs) | TextWidth(self, int style, String text) -> int
Measure the pixel width of some text in a particular style.
NUL terminated text argument.
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"""
TextWidth(self, int style, String text) -> int
Measure the pixel width of some text in a particular style.
NUL terminated text argument.
Does not handle tab or control characters.
"""
return _stc.StyledTextCtrl_TextWidth(*args, **kwargs) | [
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LLNL/lbann | 26083e6c86050302ce33148aea70f62e61cacb92 | python/lbann/launcher/batch_script.py | python | BatchScript.write | (self, overwrite=False) | Write script to file.
The working directory is created if needed.
Args:
overwrite (bool): Whether to overwrite script file if it
already exists (default: false). | Write script to file. | [
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"""Write script to file.
The working directory is created if needed.
Args:
overwrite (bool): Whether to overwrite script file if it
already exists (default: false).
"""
# Create directories if needed
os.makedirs(self.work_dir, exist_ok=True)
os.makedirs(os.path.dirname(self.script_file), exist_ok=True)
# Check if script file already exists
if not overwrite and os.path.isfile(self.script_file):
raise RuntimeError('Attempted to write batch script to {}, '
'but it already exists'
.format(self.script_file))
# Write script to file
with open(self.script_file, 'w') as f:
for line in self.header:
f.write('{}\n'.format(line))
f.write('\n')
for line in self.body:
f.write('{}\n'.format(line))
# Make script file executable
os.chmod(self.script_file, 0o755) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/sets.py | python | Set.discard | (self, element) | Remove an element from a set if it is a member.
If the element is not a member, do nothing. | Remove an element from a set if it is a member. | [
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] | def discard(self, element):
"""Remove an element from a set if it is a member.
If the element is not a member, do nothing.
"""
try:
self.remove(element)
except KeyError:
pass | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/polynomial/polynomial.py | python | polydiv | (c1, c2) | Divide one polynomial by another.
Returns the quotient-with-remainder of two polynomials `c1` / `c2`.
The arguments are sequences of coefficients, from lowest order term
to highest, e.g., [1,2,3] represents ``1 + 2*x + 3*x**2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of polynomial coefficients ordered from low to high.
Returns
-------
[quo, rem] : ndarrays
Of coefficient series representing the quotient and remainder.
See Also
--------
polyadd, polysub, polymul, polypow
Examples
--------
>>> import numpy.polynomial as P
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> P.polydiv(c1,c2)
(array([ 3.]), array([-8., -4.]))
>>> P.polydiv(c2,c1)
(array([ 0.33333333]), array([ 2.66666667, 1.33333333])) | Divide one polynomial by another. | [
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] | def polydiv(c1, c2):
"""
Divide one polynomial by another.
Returns the quotient-with-remainder of two polynomials `c1` / `c2`.
The arguments are sequences of coefficients, from lowest order term
to highest, e.g., [1,2,3] represents ``1 + 2*x + 3*x**2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of polynomial coefficients ordered from low to high.
Returns
-------
[quo, rem] : ndarrays
Of coefficient series representing the quotient and remainder.
See Also
--------
polyadd, polysub, polymul, polypow
Examples
--------
>>> import numpy.polynomial as P
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> P.polydiv(c1,c2)
(array([ 3.]), array([-8., -4.]))
>>> P.polydiv(c2,c1)
(array([ 0.33333333]), array([ 2.66666667, 1.33333333]))
"""
# c1, c2 are trimmed copies
[c1, c2] = pu.as_series([c1, c2])
if c2[-1] == 0 :
raise ZeroDivisionError()
len1 = len(c1)
len2 = len(c2)
if len2 == 1 :
return c1/c2[-1], c1[:1]*0
elif len1 < len2 :
return c1[:1]*0, c1
else :
dlen = len1 - len2
scl = c2[-1]
c2 = c2[:-1]/scl
i = dlen
j = len1 - 1
while i >= 0 :
c1[i:j] -= c2*c1[j]
i -= 1
j -= 1
return c1[j+1:]/scl, pu.trimseq(c1[:j+1]) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/msgpack/__init__.py | python | pack | (o, stream, **kwargs) | Pack object `o` and write it to `stream`
See :class:`Packer` for options. | Pack object `o` and write it to `stream` | [
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"and",
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"to",
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] | def pack(o, stream, **kwargs):
"""
Pack object `o` and write it to `stream`
See :class:`Packer` for options.
"""
packer = Packer(**kwargs)
stream.write(packer.pack(o)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/osx_cocoa/gizmos.py | python | DynamicSashWindow.GetVScrollBar | (*args, **kwargs) | return _gizmos.DynamicSashWindow_GetVScrollBar(*args, **kwargs) | GetVScrollBar(self, Window child) -> ScrollBar | GetVScrollBar(self, Window child) -> ScrollBar | [
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"""GetVScrollBar(self, Window child) -> ScrollBar"""
return _gizmos.DynamicSashWindow_GetVScrollBar(*args, **kwargs) | [
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ArduPilot/ardupilot | 6e684b3496122b8158ac412b609d00004b7ac306 | Tools/scripts/apj_tool.py | python | embedded_defaults.find | (self) | find defaults in firmware | find defaults in firmware | [
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] | def find(self):
'''find defaults in firmware'''
# these are the magic headers from AP_Param.cpp
magic_str = "PARMDEF".encode('ascii')
param_magic = [ 0x55, 0x37, 0xf4, 0xa0, 0x38, 0x5d, 0x48, 0x5b ]
def u_ord(c):
return ord(c) if sys.version_info.major < 3 else c
while True:
i = self.firmware[self.offset:].find(magic_str)
if i == -1:
print("No param area found")
return None
matched = True
for j in range(len(param_magic)):
if u_ord(self.firmware[self.offset+i+j+8]) != param_magic[j]:
matched = False
break
if not matched:
self.offset += i+8
continue
self.offset += i
self.max_len, self.length = struct.unpack("<HH", self.firmware[self.offset+16:self.offset+20])
return True | [
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bakwc/JamSpell | ab5ade201df3e52d99c3a1d38ec422cf4ded1795 | evaluate/context_spell_prototype.py | python | edits2 | (word) | return (e2 for e1 in edits1(word) for e2 in edits1(e1)) | All edits that are two edits away from `word`. | All edits that are two edits away from `word`. | [
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"word",
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] | def edits2(word):
"All edits that are two edits away from `word`."
return (e2 for e1 in edits1(word) for e2 in edits1(e1)) | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/programs/scripts/internetcontent/nv_python_libs/common/common_api.py | python | Common.initializeMythDB | (self) | Import the MythTV database bindings
return nothing | Import the MythTV database bindings
return nothing | [
"Import",
"the",
"MythTV",
"database",
"bindings",
"return",
"nothing"
] | def initializeMythDB(self):
''' Import the MythTV database bindings
return nothing
'''
try:
from MythTV import MythDB, MythLog, MythError
try:
'''Create an instance of each: MythDB
'''
MythLog._setlevel('none') # Some non option -M cannot have any logging on stdout
self.mythdb = MythDB()
except MythError as e:
sys.stderr.write('\n! Error - %s\n' % e.args[0])
filename = os.path.expanduser("~")+'/.mythtv/config.xml'
if not os.path.isfile(filename):
sys.stderr.write('\n! Error - A correctly configured (%s) file must exist\n' % filename)
else:
sys.stderr.write('\n! Error - Check that (%s) is correctly configured\n' % filename)
sys.exit(1)
except Exception as e:
sys.stderr.write("\n! Error - Creating an instance caused an error for one of: MythDB. error(%s)\n" % e)
sys.exit(1)
except Exception as e:
sys.stderr.write("\n! Error - MythTV python bindings could not be imported. error(%s)\n" % e)
sys.exit(1) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/setuptools/sandbox.py | python | override_temp | (replacement) | Monkey-patch tempfile.tempdir with replacement, ensuring it exists | Monkey-patch tempfile.tempdir with replacement, ensuring it exists | [
"Monkey",
"-",
"patch",
"tempfile",
".",
"tempdir",
"with",
"replacement",
"ensuring",
"it",
"exists"
] | def override_temp(replacement):
"""
Monkey-patch tempfile.tempdir with replacement, ensuring it exists
"""
os.makedirs(replacement, exist_ok=True)
saved = tempfile.tempdir
tempfile.tempdir = replacement
try:
yield
finally:
tempfile.tempdir = saved | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/osx_cocoa/gizmos.py | python | TreeListCtrl.GetFirstExpandedItem | (*args, **kwargs) | return _gizmos.TreeListCtrl_GetFirstExpandedItem(*args, **kwargs) | GetFirstExpandedItem(self) -> TreeItemId | GetFirstExpandedItem(self) -> TreeItemId | [
"GetFirstExpandedItem",
"(",
"self",
")",
"-",
">",
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] | def GetFirstExpandedItem(*args, **kwargs):
"""GetFirstExpandedItem(self) -> TreeItemId"""
return _gizmos.TreeListCtrl_GetFirstExpandedItem(*args, **kwargs) | [
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wy1iu/LargeMargin_Softmax_Loss | c3e9f20e4f16e2b4daf7d358a614366b9b39a6ec | python/caffe/pycaffe.py | python | _Net_params | (self) | return self._params_dict | An OrderedDict (bottom to top, i.e., input to output) of network
parameters indexed by name; each is a list of multiple blobs (e.g.,
weights and biases) | An OrderedDict (bottom to top, i.e., input to output) of network
parameters indexed by name; each is a list of multiple blobs (e.g.,
weights and biases) | [
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"""
An OrderedDict (bottom to top, i.e., input to output) of network
parameters indexed by name; each is a list of multiple blobs (e.g.,
weights and biases)
"""
if not hasattr(self, '_params_dict'):
self._params_dict = OrderedDict([(name, lr.blobs)
for name, lr in zip(
self._layer_names, self.layers)
if len(lr.blobs) > 0])
return self._params_dict | [
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tensorflow/deepmath | b5b721f54de1d5d6a02d78f5da5995237f9995f9 | deepmath/guidance/clause_loom.py | python | weave_fast_clauses | (clauses,
embed,
apply_,
not_,
or_,
and_=None,
shuffle=True,
seed=None) | return tuple(clause_loom.output_tensor(ts) for ts in or_.output_type_shapes) | Weave serialized FastClauses using TensorLoom.
Computes embeddings for a list of FastClause protos, which can either
represent a single negated conjecture (if and_ is specified) or a batch
of clauses (if and_ is None).
In the description of the LoomOps below, vocab_id must be VOCAB_ID.
Args:
clauses: 1-D `string` tensor of serialized `FastClause` protos.
embed: LoomOp to embed vocabulary ids: vocab_id -> embedding.
apply_: LoomOp for curried function application:
embedding -> embedding -> embedding.
not_: LoomOp for negation: embedding -> embedding.
or_: LoomOp for or: embedding -> embedding -> embedding.
and_: LooOp for and (embedding -> embedding -> embedding) or None if
clauses is a batch of individual clauses.
shuffle: Whether to randomly shuffle ands and ors.
seed: Optional seed for random number generation.
Returns:
The final embeddings of each clause, or of the whole conjunction
if and_ is given. | Weave serialized FastClauses using TensorLoom. | [
"Weave",
"serialized",
"FastClauses",
"using",
"TensorLoom",
"."
] | def weave_fast_clauses(clauses,
embed,
apply_,
not_,
or_,
and_=None,
shuffle=True,
seed=None):
"""Weave serialized FastClauses using TensorLoom.
Computes embeddings for a list of FastClause protos, which can either
represent a single negated conjecture (if and_ is specified) or a batch
of clauses (if and_ is None).
In the description of the LoomOps below, vocab_id must be VOCAB_ID.
Args:
clauses: 1-D `string` tensor of serialized `FastClause` protos.
embed: LoomOp to embed vocabulary ids: vocab_id -> embedding.
apply_: LoomOp for curried function application:
embedding -> embedding -> embedding.
not_: LoomOp for negation: embedding -> embedding.
or_: LoomOp for or: embedding -> embedding -> embedding.
and_: LooOp for and (embedding -> embedding -> embedding) or None if
clauses is a batch of individual clauses.
shuffle: Whether to randomly shuffle ands and ors.
seed: Optional seed for random number generation.
Returns:
The final embeddings of each clause, or of the whole conjunction
if and_ is given.
"""
def weaver_op(**kwds):
seed1, seed2 = tf.get_seed(seed)
return gen_clause_ops.fast_clause_weaver(
clauses=clauses,
shuffle=shuffle,
seed=seed1,
seed2=seed2,
conjunction=and_ is not None,
**kwds)
ops = {'embed': embed, 'apply': apply_, 'not': not_, 'or': or_}
if and_ is not None:
ops['and'] = and_
clause_loom = loom.Loom(named_ops=ops, weaver_op=weaver_op)
return tuple(clause_loom.output_tensor(ts) for ts in or_.output_type_shapes) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/sparse/linalg/_norm.py | python | norm | (x, ord=None, axis=None) | Norm of a sparse matrix
This function is able to return one of seven different matrix norms,
depending on the value of the ``ord`` parameter.
Parameters
----------
x : a sparse matrix
Input sparse matrix.
ord : {non-zero int, inf, -inf, 'fro'}, optional
Order of the norm (see table under ``Notes``). inf means numpy's
`inf` object.
axis : {int, 2-tuple of ints, None}, optional
If `axis` is an integer, it specifies the axis of `x` along which to
compute the vector norms. If `axis` is a 2-tuple, it specifies the
axes that hold 2-D matrices, and the matrix norms of these matrices
are computed. If `axis` is None then either a vector norm (when `x`
is 1-D) or a matrix norm (when `x` is 2-D) is returned.
Returns
-------
n : float or ndarray
Notes
-----
Some of the ord are not implemented because some associated functions like,
_multi_svd_norm, are not yet available for sparse matrix.
This docstring is modified based on numpy.linalg.norm.
https://github.com/numpy/numpy/blob/master/numpy/linalg/linalg.py
The following norms can be calculated:
===== ============================
ord norm for sparse matrices
===== ============================
None Frobenius norm
'fro' Frobenius norm
inf max(sum(abs(x), axis=1))
-inf min(sum(abs(x), axis=1))
0 abs(x).sum(axis=axis)
1 max(sum(abs(x), axis=0))
-1 min(sum(abs(x), axis=0))
2 Not implemented
-2 Not implemented
other Not implemented
===== ============================
The Frobenius norm is given by [1]_:
:math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
References
----------
.. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
Examples
--------
>>> from scipy.sparse import *
>>> import numpy as np
>>> from scipy.sparse.linalg import norm
>>> a = np.arange(9) - 4
>>> a
array([-4, -3, -2, -1, 0, 1, 2, 3, 4])
>>> b = a.reshape((3, 3))
>>> b
array([[-4, -3, -2],
[-1, 0, 1],
[ 2, 3, 4]])
>>> b = csr_matrix(b)
>>> norm(b)
7.745966692414834
>>> norm(b, 'fro')
7.745966692414834
>>> norm(b, np.inf)
9
>>> norm(b, -np.inf)
2
>>> norm(b, 1)
7
>>> norm(b, -1)
6 | Norm of a sparse matrix | [
"Norm",
"of",
"a",
"sparse",
"matrix"
] | def norm(x, ord=None, axis=None):
"""
Norm of a sparse matrix
This function is able to return one of seven different matrix norms,
depending on the value of the ``ord`` parameter.
Parameters
----------
x : a sparse matrix
Input sparse matrix.
ord : {non-zero int, inf, -inf, 'fro'}, optional
Order of the norm (see table under ``Notes``). inf means numpy's
`inf` object.
axis : {int, 2-tuple of ints, None}, optional
If `axis` is an integer, it specifies the axis of `x` along which to
compute the vector norms. If `axis` is a 2-tuple, it specifies the
axes that hold 2-D matrices, and the matrix norms of these matrices
are computed. If `axis` is None then either a vector norm (when `x`
is 1-D) or a matrix norm (when `x` is 2-D) is returned.
Returns
-------
n : float or ndarray
Notes
-----
Some of the ord are not implemented because some associated functions like,
_multi_svd_norm, are not yet available for sparse matrix.
This docstring is modified based on numpy.linalg.norm.
https://github.com/numpy/numpy/blob/master/numpy/linalg/linalg.py
The following norms can be calculated:
===== ============================
ord norm for sparse matrices
===== ============================
None Frobenius norm
'fro' Frobenius norm
inf max(sum(abs(x), axis=1))
-inf min(sum(abs(x), axis=1))
0 abs(x).sum(axis=axis)
1 max(sum(abs(x), axis=0))
-1 min(sum(abs(x), axis=0))
2 Not implemented
-2 Not implemented
other Not implemented
===== ============================
The Frobenius norm is given by [1]_:
:math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
References
----------
.. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
Examples
--------
>>> from scipy.sparse import *
>>> import numpy as np
>>> from scipy.sparse.linalg import norm
>>> a = np.arange(9) - 4
>>> a
array([-4, -3, -2, -1, 0, 1, 2, 3, 4])
>>> b = a.reshape((3, 3))
>>> b
array([[-4, -3, -2],
[-1, 0, 1],
[ 2, 3, 4]])
>>> b = csr_matrix(b)
>>> norm(b)
7.745966692414834
>>> norm(b, 'fro')
7.745966692414834
>>> norm(b, np.inf)
9
>>> norm(b, -np.inf)
2
>>> norm(b, 1)
7
>>> norm(b, -1)
6
"""
if not issparse(x):
raise TypeError("input is not sparse. use numpy.linalg.norm")
# Check the default case first and handle it immediately.
if axis is None and ord in (None, 'fro', 'f'):
return _sparse_frobenius_norm(x)
# Some norms require functions that are not implemented for all types.
x = x.tocsr()
if axis is None:
axis = (0, 1)
elif not isinstance(axis, tuple):
msg = "'axis' must be None, an integer or a tuple of integers"
try:
int_axis = int(axis)
except TypeError:
raise TypeError(msg)
if axis != int_axis:
raise TypeError(msg)
axis = (int_axis,)
nd = 2
if len(axis) == 2:
row_axis, col_axis = axis
if not (-nd <= row_axis < nd and -nd <= col_axis < nd):
raise ValueError('Invalid axis %r for an array with shape %r' %
(axis, x.shape))
if row_axis % nd == col_axis % nd:
raise ValueError('Duplicate axes given.')
if ord == 2:
raise NotImplementedError
#return _multi_svd_norm(x, row_axis, col_axis, amax)
elif ord == -2:
raise NotImplementedError
#return _multi_svd_norm(x, row_axis, col_axis, amin)
elif ord == 1:
return abs(x).sum(axis=row_axis).max(axis=col_axis)[0,0]
elif ord == Inf:
return abs(x).sum(axis=col_axis).max(axis=row_axis)[0,0]
elif ord == -1:
return abs(x).sum(axis=row_axis).min(axis=col_axis)[0,0]
elif ord == -Inf:
return abs(x).sum(axis=col_axis).min(axis=row_axis)[0,0]
elif ord in (None, 'f', 'fro'):
# The axis order does not matter for this norm.
return _sparse_frobenius_norm(x)
else:
raise ValueError("Invalid norm order for matrices.")
elif len(axis) == 1:
a, = axis
if not (-nd <= a < nd):
raise ValueError('Invalid axis %r for an array with shape %r' %
(axis, x.shape))
if ord == Inf:
M = abs(x).max(axis=a)
elif ord == -Inf:
M = abs(x).min(axis=a)
elif ord == 0:
# Zero norm
M = (x != 0).sum(axis=a)
elif ord == 1:
# special case for speedup
M = abs(x).sum(axis=a)
elif ord in (2, None):
M = sqrt(abs(x).power(2).sum(axis=a))
else:
try:
ord + 1
except TypeError:
raise ValueError('Invalid norm order for vectors.')
M = np.power(abs(x).power(ord).sum(axis=a), 1 / ord)
return M.A.ravel()
else:
raise ValueError("Improper number of dimensions to norm.") | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | FontData.SetAllowSymbols | (*args, **kwargs) | return _windows_.FontData_SetAllowSymbols(*args, **kwargs) | SetAllowSymbols(self, bool allowSymbols)
Under MS Windows, determines whether symbol fonts can be selected. Has
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"""
SetAllowSymbols(self, bool allowSymbols)
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/idl/idl/syntax.py | python | Import.__init__ | (self, file_name, line, column) | Construct an Imports section. | Construct an Imports section. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/distutils/command/config.py | python | config.try_run | (self, body, headers=None, include_dirs=None, libraries=None,
library_dirs=None, lang="c") | return ok | Try to compile, link to an executable, and run a program
built from 'body' and 'headers'. Return true on success, false
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self._check_compiler()
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/ftplib.py | python | FTP.pwd | (self) | return parse257(resp) | Return current working directory. | Return current working directory. | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/server/wsgi/serve/snippets/util/sparse_tree.py | python | _GeneralRemoveAbstractPath | (parts, store) | Remove path parts from store, when store's top-layer is not repeated.
I believe that removing a whole subtree is just an artifact of
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user-accessible 'template' stuff. Does no 'required'ness checking;
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Returns true if deletion deleted /something/ (doesn't check the
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subtree has no other children (ie is {}), and so on, up. If there is
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This expects the tree to be 'newstyle', as all sparse trees
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parts: the path elements to remove.
store: store to remove path from.
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"""Remove path parts from store, when store's top-layer is not repeated.
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key_should_live = _RemoveAbstractPathFromRepeatedPartOfSparse(
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key_should_live = _GeneralRemoveAbstractPath(rest, substore)
if not key_should_live:
del store[key]
return bool(store)
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google/skia | 82d65d0487bd72f5f7332d002429ec2dc61d2463 | tools/copyright/fileparser.py | python | CParser.CreateCopyrightBlock | (self, year, holder) | return self.COPYRIGHT_BLOCK_FORMAT % (year, holder) | Returns a copyright block suitable for this language, with the
given attributes.
@param year year in which to hold copyright (defaults to DEFAULT_YEAR)
@param holder holder of copyright (defaults to DEFAULT_HOLDER) | Returns a copyright block suitable for this language, with the
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"""Returns a copyright block suitable for this language, with the
given attributes.
@param year year in which to hold copyright (defaults to DEFAULT_YEAR)
@param holder holder of copyright (defaults to DEFAULT_HOLDER)
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if not year:
year = self.DEFAULT_YEAR
if not holder:
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/sparse/spfuncs.py | python | count_blocks | (A,blocksize) | For a given blocksize=(r,c) count the number of occupied
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r,c = blocksize
if r < 1 or c < 1:
raise ValueError('r and c must be positive')
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/mailbox.py | python | Mailbox.popitem | (self) | Delete an arbitrary (key, message) pair and return it. | Delete an arbitrary (key, message) pair and return it. | [
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"return",
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",",
"self",
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"pop",
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"'No messages in mailbox'",
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] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/mailbox.py#L156-L161 |
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