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tahoe-lafs/tahoe-lafs
|
766a53b5208c03c45ca0a98e97eee76870276aa1
|
src/allmydata/client.py
|
python
|
_make_secret
|
()
|
return base32.b2a(os.urandom(hashutil.CRYPTO_VAL_SIZE)) + b"\n"
|
Returns a base32-encoded random secret of hashutil.CRYPTO_VAL_SIZE
bytes.
|
Returns a base32-encoded random secret of hashutil.CRYPTO_VAL_SIZE
bytes.
|
[
"Returns",
"a",
"base32",
"-",
"encoded",
"random",
"secret",
"of",
"hashutil",
".",
"CRYPTO_VAL_SIZE",
"bytes",
"."
] |
def _make_secret():
"""
Returns a base32-encoded random secret of hashutil.CRYPTO_VAL_SIZE
bytes.
"""
return base32.b2a(os.urandom(hashutil.CRYPTO_VAL_SIZE)) + b"\n"
|
[
"def",
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"(",
")",
":",
"return",
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".",
"b2a",
"(",
"os",
".",
"urandom",
"(",
"hashutil",
".",
"CRYPTO_VAL_SIZE",
")",
")",
"+",
"b\"\\n\""
] |
https://github.com/tahoe-lafs/tahoe-lafs/blob/766a53b5208c03c45ca0a98e97eee76870276aa1/src/allmydata/client.py#L147-L152
|
|
buke/GreenOdoo
|
3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df
|
source/addons/stock/stock.py
|
python
|
stock_package._complete_name
|
(self, cr, uid, ids, name, args, context=None)
|
return res
|
Forms complete name of location from parent location to child location.
@return: Dictionary of values
|
Forms complete name of location from parent location to child location.
|
[
"Forms",
"complete",
"name",
"of",
"location",
"from",
"parent",
"location",
"to",
"child",
"location",
"."
] |
def _complete_name(self, cr, uid, ids, name, args, context=None):
""" Forms complete name of location from parent location to child location.
@return: Dictionary of values
"""
res = {}
for m in self.browse(cr, uid, ids, context=context):
res[m.id] = m.name
parent = m.parent_id
while parent:
res[m.id] = parent.name + ' / ' + res[m.id]
parent = parent.parent_id
return res
|
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"]",
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"parent",
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https://github.com/buke/GreenOdoo/blob/3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df/source/addons/stock/stock.py#L3907-L3918
|
|
openstack/nova
|
b49b7663e1c3073917d5844b81d38db8e86d05c4
|
nova/db/main/api.py
|
python
|
console_auth_token_destroy_all_by_instance
|
(context, instance_uuid)
|
Delete all console authorizations belonging to the instance.
|
Delete all console authorizations belonging to the instance.
|
[
"Delete",
"all",
"console",
"authorizations",
"belonging",
"to",
"the",
"instance",
"."
] |
def console_auth_token_destroy_all_by_instance(context, instance_uuid):
"""Delete all console authorizations belonging to the instance."""
context.session.query(models.ConsoleAuthToken).\
filter_by(instance_uuid=instance_uuid).delete()
|
[
"def",
"console_auth_token_destroy_all_by_instance",
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",",
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":",
"context",
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"filter_by",
"(",
"instance_uuid",
"=",
"instance_uuid",
")",
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"delete",
"(",
")"
] |
https://github.com/openstack/nova/blob/b49b7663e1c3073917d5844b81d38db8e86d05c4/nova/db/main/api.py#L4701-L4704
|
||
plotly/plotly.py
|
cfad7862594b35965c0e000813bd7805e8494a5b
|
packages/python/plotly/plotly/graph_objs/scatterpolargl/selected/_textfont.py
|
python
|
Textfont.color
|
(self)
|
return self["color"]
|
Sets the text font color of selected points.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
|
Sets the text font color of selected points.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
|
[
"Sets",
"the",
"text",
"font",
"color",
"of",
"selected",
"points",
".",
"The",
"color",
"property",
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"color",
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"as",
":",
"-",
"A",
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"-",
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"/",
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"rgb",
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"0",
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"-",
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"named",
"CSS",
"color",
":",
"aliceblue",
"antiquewhite",
"aqua",
"aquamarine",
"azure",
"beige",
"bisque",
"black",
"blanchedalmond",
"blue",
"blueviolet",
"brown",
"burlywood",
"cadetblue",
"chartreuse",
"chocolate",
"coral",
"cornflowerblue",
"cornsilk",
"crimson",
"cyan",
"darkblue",
"darkcyan",
"darkgoldenrod",
"darkgray",
"darkgrey",
"darkgreen",
"darkkhaki",
"darkmagenta",
"darkolivegreen",
"darkorange",
"darkorchid",
"darkred",
"darksalmon",
"darkseagreen",
"darkslateblue",
"darkslategray",
"darkslategrey",
"darkturquoise",
"darkviolet",
"deeppink",
"deepskyblue",
"dimgray",
"dimgrey",
"dodgerblue",
"firebrick",
"floralwhite",
"forestgreen",
"fuchsia",
"gainsboro",
"ghostwhite",
"gold",
"goldenrod",
"gray",
"grey",
"green",
"greenyellow",
"honeydew",
"hotpink",
"indianred",
"indigo",
"ivory",
"khaki",
"lavender",
"lavenderblush",
"lawngreen",
"lemonchiffon",
"lightblue",
"lightcoral",
"lightcyan",
"lightgoldenrodyellow",
"lightgray",
"lightgrey",
"lightgreen",
"lightpink",
"lightsalmon",
"lightseagreen",
"lightskyblue",
"lightslategray",
"lightslategrey",
"lightsteelblue",
"lightyellow",
"lime",
"limegreen",
"linen",
"magenta",
"maroon",
"mediumaquamarine",
"mediumblue",
"mediumorchid",
"mediumpurple",
"mediumseagreen",
"mediumslateblue",
"mediumspringgreen",
"mediumturquoise",
"mediumvioletred",
"midnightblue",
"mintcream",
"mistyrose",
"moccasin",
"navajowhite",
"navy",
"oldlace",
"olive",
"olivedrab",
"orange",
"orangered",
"orchid",
"palegoldenrod",
"palegreen",
"paleturquoise",
"palevioletred",
"papayawhip",
"peachpuff",
"peru",
"pink",
"plum",
"powderblue",
"purple",
"red",
"rosybrown",
"royalblue",
"rebeccapurple",
"saddlebrown",
"salmon",
"sandybrown",
"seagreen",
"seashell",
"sienna",
"silver",
"skyblue",
"slateblue",
"slategray",
"slategrey",
"snow",
"springgreen",
"steelblue",
"tan",
"teal",
"thistle",
"tomato",
"turquoise",
"violet",
"wheat",
"white",
"whitesmoke",
"yellow",
"yellowgreen"
] |
def color(self):
"""
Sets the text font color of selected points.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["color"]
|
[
"def",
"color",
"(",
"self",
")",
":",
"return",
"self",
"[",
"\"color\"",
"]"
] |
https://github.com/plotly/plotly.py/blob/cfad7862594b35965c0e000813bd7805e8494a5b/packages/python/plotly/plotly/graph_objs/scatterpolargl/selected/_textfont.py#L16-L66
|
|
IronLanguages/ironpython3
|
7a7bb2a872eeab0d1009fc8a6e24dca43f65b693
|
Src/StdLib/Lib/datetime.py
|
python
|
timedelta.__reduce__
|
(self)
|
return (self.__class__, self._getstate())
|
[] |
def __reduce__(self):
return (self.__class__, self._getstate())
|
[
"def",
"__reduce__",
"(",
"self",
")",
":",
"return",
"(",
"self",
".",
"__class__",
",",
"self",
".",
"_getstate",
"(",
")",
")"
] |
https://github.com/IronLanguages/ironpython3/blob/7a7bb2a872eeab0d1009fc8a6e24dca43f65b693/Src/StdLib/Lib/datetime.py#L628-L629
|
|||
thomasweng15/E.V.E.
|
e3bea3e45d0c549eccc6824c9cadbcc6980545f6
|
actions/wolfram.py
|
python
|
Wolfram.open
|
(self, wolfram, text, controller)
|
Open webpage of visual WolframAlpha result.
|
Open webpage of visual WolframAlpha result.
|
[
"Open",
"webpage",
"of",
"visual",
"WolframAlpha",
"result",
"."
] |
def open(self, wolfram, text, controller):
"""Open webpage of visual WolframAlpha result."""
wolfram_url = "http://www.wolframalpha.com/input/?i=" + text.replace(" ", "+")
controller.open(wolfram_url)
|
[
"def",
"open",
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"self",
",",
"wolfram",
",",
"text",
",",
"controller",
")",
":",
"wolfram_url",
"=",
"\"http://www.wolframalpha.com/input/?i=\"",
"+",
"text",
".",
"replace",
"(",
"\" \"",
",",
"\"+\"",
")",
"controller",
".",
"open",
"(",
"wolfram_url",
")"
] |
https://github.com/thomasweng15/E.V.E./blob/e3bea3e45d0c549eccc6824c9cadbcc6980545f6/actions/wolfram.py#L71-L74
|
||
jelmer/xandikos
|
3149a633c388a6f1dffbc6686763fca00f72e3bc
|
xandikos/store/git.py
|
python
|
GitStore.set_displayname
|
(self, displayname)
|
Set the display name.
:param displayname: New display name
|
Set the display name.
|
[
"Set",
"the",
"display",
"name",
"."
] |
def set_displayname(self, displayname):
"""Set the display name.
:param displayname: New display name
"""
self.config.set_displayname(displayname)
|
[
"def",
"set_displayname",
"(",
"self",
",",
"displayname",
")",
":",
"self",
".",
"config",
".",
"set_displayname",
"(",
"displayname",
")"
] |
https://github.com/jelmer/xandikos/blob/3149a633c388a6f1dffbc6686763fca00f72e3bc/xandikos/store/git.py#L481-L486
|
||
robinhood/faust
|
01b4c0ad8390221db71751d80001b0fd879291e2
|
faust/types/settings/settings.py
|
python
|
Settings.value_serializer
|
(self)
|
Default value serializer.
Serializer used for values by default when no serializer
is specified, or a model is not being used.
This can be string, the name of a serializer/codec, or an actual
:class:`faust.serializers.codecs.Codec` instance.
.. seealso::
- The :ref:`codecs` section in the model guide -- for
more information about codecs.
|
Default value serializer.
|
[
"Default",
"value",
"serializer",
"."
] |
def value_serializer(self) -> CodecArg:
"""Default value serializer.
Serializer used for values by default when no serializer
is specified, or a model is not being used.
This can be string, the name of a serializer/codec, or an actual
:class:`faust.serializers.codecs.Codec` instance.
.. seealso::
- The :ref:`codecs` section in the model guide -- for
more information about codecs.
"""
|
[
"def",
"value_serializer",
"(",
"self",
")",
"->",
"CodecArg",
":"
] |
https://github.com/robinhood/faust/blob/01b4c0ad8390221db71751d80001b0fd879291e2/faust/types/settings/settings.py#L1143-L1156
|
||
pantsbuild/pex
|
473c6ac732ed4bc338b4b20a9ec930d1d722c9b4
|
pex/vendor/_vendored/setuptools/setuptools/command/develop.py
|
python
|
VersionlessRequirement.__getattr__
|
(self, name)
|
return getattr(self.__dist, name)
|
[] |
def __getattr__(self, name):
return getattr(self.__dist, name)
|
[
"def",
"__getattr__",
"(",
"self",
",",
"name",
")",
":",
"return",
"getattr",
"(",
"self",
".",
"__dist",
",",
"name",
")"
] |
https://github.com/pantsbuild/pex/blob/473c6ac732ed4bc338b4b20a9ec930d1d722c9b4/pex/vendor/_vendored/setuptools/setuptools/command/develop.py#L237-L238
|
|||
freedombox/FreedomBox
|
335a7f92cc08f27981f838a7cddfc67740598e54
|
plinth/modules/tor/__init__.py
|
python
|
TorApp.__init__
|
(self)
|
Create components for the app.
|
Create components for the app.
|
[
"Create",
"components",
"for",
"the",
"app",
"."
] |
def __init__(self):
"""Create components for the app."""
super().__init__()
info = app_module.Info(app_id=self.app_id, version=self._version,
depends=['names'
], name=_('Tor'), icon_filename='tor',
short_description=_('Anonymity Network'),
description=_description, manual_page='Tor',
clients=manifest.clients,
donation_url='https://donate.torproject.org/')
self.add(info)
menu_item = menu.Menu('menu-tor', info.name, info.short_description,
info.icon_filename, 'tor:index',
parent_url_name='apps')
self.add(menu_item)
packages = Packages('packages-tor', [
'tor', 'tor-geoipdb', 'torsocks', 'obfs4proxy', 'apt-transport-tor'
])
self.add(packages)
domain_type = DomainType('domain-type-tor', _('Tor Onion Service'),
'tor:index', can_have_certificate=False)
self.add(domain_type)
firewall = Firewall('firewall-tor-socks', _('Tor Socks Proxy'),
ports=['tor-socks'], is_external=False)
self.add(firewall)
firewall = Firewall('firewall-tor-relay', _('Tor Bridge Relay'),
ports=['tor-orport', 'tor-obfs3',
'tor-obfs4'], is_external=True)
self.add(firewall)
daemon = Daemon(
'daemon-tor', 'tor@plinth', strict_check=True,
listen_ports=[(9050, 'tcp4'), (9050, 'tcp6'), (9040, 'tcp4'),
(9040, 'tcp6'), (9053, 'udp4'), (9053, 'udp6')])
self.add(daemon)
users_and_groups = UsersAndGroups('users-and-groups-tor',
reserved_usernames=['debian-tor'])
self.add(users_and_groups)
backup_restore = BackupRestore('backup-restore-tor', **manifest.backup)
self.add(backup_restore)
|
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",",
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"manifest",
".",
"backup",
")",
"self",
".",
"add",
"(",
"backup_restore",
")"
] |
https://github.com/freedombox/FreedomBox/blob/335a7f92cc08f27981f838a7cddfc67740598e54/plinth/modules/tor/__init__.py#L44-L91
|
||
TencentCloud/tencentcloud-sdk-python
|
3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2
|
tencentcloud/cdn/v20180606/cdn_client.py
|
python
|
CdnClient.CreateEdgePackTask
|
(self, request)
|
动态打包任务提交接口
:param request: Request instance for CreateEdgePackTask.
:type request: :class:`tencentcloud.cdn.v20180606.models.CreateEdgePackTaskRequest`
:rtype: :class:`tencentcloud.cdn.v20180606.models.CreateEdgePackTaskResponse`
|
动态打包任务提交接口
|
[
"动态打包任务提交接口"
] |
def CreateEdgePackTask(self, request):
"""动态打包任务提交接口
:param request: Request instance for CreateEdgePackTask.
:type request: :class:`tencentcloud.cdn.v20180606.models.CreateEdgePackTaskRequest`
:rtype: :class:`tencentcloud.cdn.v20180606.models.CreateEdgePackTaskResponse`
"""
try:
params = request._serialize()
body = self.call("CreateEdgePackTask", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.CreateEdgePackTaskResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise
else:
raise TencentCloudSDKException(e.message, e.message)
|
[
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"TencentCloudSDKException",
"(",
"e",
".",
"message",
",",
"e",
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"message",
")"
] |
https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/cdn/v20180606/cdn_client.py#L113-L138
|
||
nlloyd/SubliminalCollaborator
|
5c619e17ddbe8acb9eea8996ec038169ddcd50a1
|
libs/twisted/internet/pollreactor.py
|
python
|
PollReactor.removeAll
|
(self)
|
return self._removeAll(
[self._selectables[fd] for fd in self._reads],
[self._selectables[fd] for fd in self._writes])
|
Remove all selectables, and return a list of them.
|
Remove all selectables, and return a list of them.
|
[
"Remove",
"all",
"selectables",
"and",
"return",
"a",
"list",
"of",
"them",
"."
] |
def removeAll(self):
"""
Remove all selectables, and return a list of them.
"""
return self._removeAll(
[self._selectables[fd] for fd in self._reads],
[self._selectables[fd] for fd in self._writes])
|
[
"def",
"removeAll",
"(",
"self",
")",
":",
"return",
"self",
".",
"_removeAll",
"(",
"[",
"self",
".",
"_selectables",
"[",
"fd",
"]",
"for",
"fd",
"in",
"self",
".",
"_reads",
"]",
",",
"[",
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".",
"_selectables",
"[",
"fd",
"]",
"for",
"fd",
"in",
"self",
".",
"_writes",
"]",
")"
] |
https://github.com/nlloyd/SubliminalCollaborator/blob/5c619e17ddbe8acb9eea8996ec038169ddcd50a1/libs/twisted/internet/pollreactor.py#L138-L144
|
|
Minyus/causallift
|
c808b15de7e7912c4c08e9ad4152e4bd33f008c9
|
kedro_cli.py
|
python
|
install
|
()
|
Install project dependencies from both requirements.txt and environment.yml (optional).
|
Install project dependencies from both requirements.txt and environment.yml (optional).
|
[
"Install",
"project",
"dependencies",
"from",
"both",
"requirements",
".",
"txt",
"and",
"environment",
".",
"yml",
"(",
"optional",
")",
"."
] |
def install():
"""Install project dependencies from both requirements.txt and environment.yml (optional)."""
if (Path.cwd() / "src" / "environment.yml").is_file():
call(["conda", "install", "--file", "src/environment.yml", "--yes"])
python_call("pip", ["install", "-U", "-r", "src/requirements.txt"])
|
[
"def",
"install",
"(",
")",
":",
"if",
"(",
"Path",
".",
"cwd",
"(",
")",
"/",
"\"src\"",
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"\"environment.yml\"",
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".",
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"[",
"\"conda\"",
",",
"\"install\"",
",",
"\"--file\"",
",",
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"[",
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",",
"\"-U\"",
",",
"\"-r\"",
",",
"\"src/requirements.txt\"",
"]",
")"
] |
https://github.com/Minyus/causallift/blob/c808b15de7e7912c4c08e9ad4152e4bd33f008c9/kedro_cli.py#L163-L169
|
||
1040003585/WebScrapingWithPython
|
a770fa5b03894076c8c9539b1ffff34424ffc016
|
portia_examle/lib/python2.7/site-packages/wheel/signatures/ed25519py.py
|
python
|
crypto_sign_keypair
|
(seed=None)
|
return Keypair(vkbytes, skbytes+vkbytes)
|
Return (verifying, secret) key from a given seed, or os.urandom(32)
|
Return (verifying, secret) key from a given seed, or os.urandom(32)
|
[
"Return",
"(",
"verifying",
"secret",
")",
"key",
"from",
"a",
"given",
"seed",
"or",
"os",
".",
"urandom",
"(",
"32",
")"
] |
def crypto_sign_keypair(seed=None):
"""Return (verifying, secret) key from a given seed, or os.urandom(32)"""
if seed is None:
seed = os.urandom(PUBLICKEYBYTES)
else:
warnings.warn("ed25519ll should choose random seed.",
RuntimeWarning)
if len(seed) != 32:
raise ValueError("seed must be 32 random bytes or None.")
skbytes = seed
vkbytes = djbec.publickey(skbytes)
return Keypair(vkbytes, skbytes+vkbytes)
|
[
"def",
"crypto_sign_keypair",
"(",
"seed",
"=",
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")",
":",
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"seed",
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"None",
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")",
"return",
"Keypair",
"(",
"vkbytes",
",",
"skbytes",
"+",
"vkbytes",
")"
] |
https://github.com/1040003585/WebScrapingWithPython/blob/a770fa5b03894076c8c9539b1ffff34424ffc016/portia_examle/lib/python2.7/site-packages/wheel/signatures/ed25519py.py#L18-L29
|
|
huggingface/transformers
|
623b4f7c63f60cce917677ee704d6c93ee960b4b
|
src/transformers/utils/dummy_tf_objects.py
|
python
|
TFXLNetLMHeadModel.from_pretrained
|
(cls, *args, **kwargs)
|
[] |
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["tf"])
|
[
"def",
"from_pretrained",
"(",
"cls",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"requires_backends",
"(",
"cls",
",",
"[",
"\"tf\"",
"]",
")"
] |
https://github.com/huggingface/transformers/blob/623b4f7c63f60cce917677ee704d6c93ee960b4b/src/transformers/utils/dummy_tf_objects.py#L2965-L2966
|
||||
emesene/emesene
|
4548a4098310e21b16437bb36223a7f632a4f7bc
|
emesene/e3/base/Logger.py
|
python
|
Account.__init__
|
(self, id_, id_account, account, status, nick='', message='',
path='', cid=None)
|
constructor
|
constructor
|
[
"constructor"
] |
def __init__(self, id_, id_account, account, status, nick='', message='',
path='', cid=None):
'''constructor'''
self.id = id_
self.id_account = id_account
self.account = account
self.status = status
self.nick = nick
self.message = message
self.path = path
self.cid = cid
self.groups = []
|
[
"def",
"__init__",
"(",
"self",
",",
"id_",
",",
"id_account",
",",
"account",
",",
"status",
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",",
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"[",
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] |
https://github.com/emesene/emesene/blob/4548a4098310e21b16437bb36223a7f632a4f7bc/emesene/e3/base/Logger.py#L38-L49
|
||
Franck-Dernoncourt/NeuroNER
|
3817feaf290c1f6e03ae23ea964e68c88d0e7a88
|
neuroner/prepare_pretrained_model.py
|
python
|
prepare_pretrained_model_for_restoring
|
(output_folder_name, epoch_number,
model_name, delete_token_mappings=False)
|
Copy the dataset.pickle, parameters.ini, and model checkpoint files after
removing the data used for training.
The dataset and labels are deleted from dataset.pickle by default. The only
information about the dataset that remain in the pretrained model
is the list of tokens that appears in the dataset and the corresponding token
embeddings learned from the dataset.
If delete_token_mappings is set to True, index_to_token and token_to_index
mappings are deleted from dataset.pickle additionally,
and the corresponding token embeddings are deleted from the model checkpoint
files. In this case, the pretrained model would not contain
any information about the dataset used for training the model.
If you wish to share a pretrained model with delete_token_mappings = True,
it is highly recommended to use some external pre-trained token
embeddings and freeze them while training the model to obtain high performance.
This can be done by specifying the token_pretrained_embedding_filepath
and setting freeze_token_embeddings = True in parameters.ini for training.
|
Copy the dataset.pickle, parameters.ini, and model checkpoint files after
removing the data used for training.
The dataset and labels are deleted from dataset.pickle by default. The only
information about the dataset that remain in the pretrained model
is the list of tokens that appears in the dataset and the corresponding token
embeddings learned from the dataset.
If delete_token_mappings is set to True, index_to_token and token_to_index
mappings are deleted from dataset.pickle additionally,
and the corresponding token embeddings are deleted from the model checkpoint
files. In this case, the pretrained model would not contain
any information about the dataset used for training the model.
If you wish to share a pretrained model with delete_token_mappings = True,
it is highly recommended to use some external pre-trained token
embeddings and freeze them while training the model to obtain high performance.
This can be done by specifying the token_pretrained_embedding_filepath
and setting freeze_token_embeddings = True in parameters.ini for training.
|
[
"Copy",
"the",
"dataset",
".",
"pickle",
"parameters",
".",
"ini",
"and",
"model",
"checkpoint",
"files",
"after",
"removing",
"the",
"data",
"used",
"for",
"training",
".",
"The",
"dataset",
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"labels",
"are",
"deleted",
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"pickle",
"by",
"default",
".",
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"learned",
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"pickle",
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"the",
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"pretrained",
"model",
"would",
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"contain",
"any",
"information",
"about",
"the",
"dataset",
"used",
"for",
"training",
"the",
"model",
".",
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"you",
"wish",
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"share",
"a",
"pretrained",
"model",
"with",
"delete_token_mappings",
"=",
"True",
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"is",
"highly",
"recommended",
"to",
"use",
"some",
"external",
"pre",
"-",
"trained",
"token",
"embeddings",
"and",
"freeze",
"them",
"while",
"training",
"the",
"model",
"to",
"obtain",
"high",
"performance",
".",
"This",
"can",
"be",
"done",
"by",
"specifying",
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"token_pretrained_embedding_filepath",
"and",
"setting",
"freeze_token_embeddings",
"=",
"True",
"in",
"parameters",
".",
"ini",
"for",
"training",
"."
] |
def prepare_pretrained_model_for_restoring(output_folder_name, epoch_number,
model_name, delete_token_mappings=False):
'''
Copy the dataset.pickle, parameters.ini, and model checkpoint files after
removing the data used for training.
The dataset and labels are deleted from dataset.pickle by default. The only
information about the dataset that remain in the pretrained model
is the list of tokens that appears in the dataset and the corresponding token
embeddings learned from the dataset.
If delete_token_mappings is set to True, index_to_token and token_to_index
mappings are deleted from dataset.pickle additionally,
and the corresponding token embeddings are deleted from the model checkpoint
files. In this case, the pretrained model would not contain
any information about the dataset used for training the model.
If you wish to share a pretrained model with delete_token_mappings = True,
it is highly recommended to use some external pre-trained token
embeddings and freeze them while training the model to obtain high performance.
This can be done by specifying the token_pretrained_embedding_filepath
and setting freeze_token_embeddings = True in parameters.ini for training.
'''
input_model_folder = os.path.join('.', 'output', output_folder_name, 'model')
output_model_folder = os.path.join('.', 'trained_models', model_name)
utils.create_folder_if_not_exists(output_model_folder)
# trim and copy dataset.pickle
input_dataset_filepath = os.path.join(input_model_folder, 'dataset.pickle')
output_dataset_filepath = os.path.join(output_model_folder, 'dataset.pickle')
trim_dataset_pickle(input_dataset_filepath, output_dataset_filepath,
delete_token_mappings=delete_token_mappings)
# copy parameters.ini
parameters_filepath = os.path.join(input_model_folder, 'parameters.ini')
shutil.copy(parameters_filepath, output_model_folder)
# (trim and) copy checkpoint files
epoch_number_string = str(epoch_number).zfill(5)
if delete_token_mappings:
input_checkpoint_filepath = os.path.join(input_model_folder,
'model_{0}.ckpt'.format(epoch_number_string))
output_checkpoint_filepath = os.path.join(output_model_folder, 'model.ckpt')
trim_model_checkpoint(parameters_filepath, output_dataset_filepath,
input_checkpoint_filepath, output_checkpoint_filepath)
else:
for filepath in glob.glob(os.path.join(input_model_folder,
'model_{0}.ckpt*'.format(epoch_number_string))):
shutil.copyfile(filepath, os.path.join(output_model_folder,
os.path.basename(filepath).replace('_' + epoch_number_string, '')))
|
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"(",
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"+",
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] |
https://github.com/Franck-Dernoncourt/NeuroNER/blob/3817feaf290c1f6e03ae23ea964e68c88d0e7a88/neuroner/prepare_pretrained_model.py#L82-L131
|
||
whyliam/whyliam.workflows.youdao
|
2dfa7f1de56419dab1c2e70c1a27e5e13ba25a5c
|
workflow/util.py
|
python
|
LockFile.locked
|
(self)
|
return self._lock.is_set()
|
``True`` if file is locked by this instance.
|
``True`` if file is locked by this instance.
|
[
"True",
"if",
"file",
"is",
"locked",
"by",
"this",
"instance",
"."
] |
def locked(self):
"""``True`` if file is locked by this instance."""
return self._lock.is_set()
|
[
"def",
"locked",
"(",
"self",
")",
":",
"return",
"self",
".",
"_lock",
".",
"is_set",
"(",
")"
] |
https://github.com/whyliam/whyliam.workflows.youdao/blob/2dfa7f1de56419dab1c2e70c1a27e5e13ba25a5c/workflow/util.py#L415-L417
|
|
fergusq/tampio
|
b0017d2557a21c47fddc20cfceeb92f4e0376401
|
voikko/libvoikko.py
|
python
|
Voikko.setLibrarySearchPath
|
(cls, searchPath)
|
Set the path to a directory that should be used to search for the native library
before trying to load it from the default (OS specific) lookup path.
|
Set the path to a directory that should be used to search for the native library
before trying to load it from the default (OS specific) lookup path.
|
[
"Set",
"the",
"path",
"to",
"a",
"directory",
"that",
"should",
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"used",
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"search",
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"from",
"the",
"default",
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"OS",
"specific",
")",
"lookup",
"path",
"."
] |
def setLibrarySearchPath(cls, searchPath):
"""Set the path to a directory that should be used to search for the native library
before trying to load it from the default (OS specific) lookup path.
"""
cls._sharedLibrarySearchPath = searchPath
|
[
"def",
"setLibrarySearchPath",
"(",
"cls",
",",
"searchPath",
")",
":",
"cls",
".",
"_sharedLibrarySearchPath",
"=",
"searchPath"
] |
https://github.com/fergusq/tampio/blob/b0017d2557a21c47fddc20cfceeb92f4e0376401/voikko/libvoikko.py#L419-L423
|
||
colour-science/colour
|
38782ac059e8ddd91939f3432bf06811c16667f0
|
colour/models/hdr_ipt.py
|
python
|
XYZ_to_hdr_IPT
|
(XYZ, Y_s=0.2, Y_abs=100, method='Fairchild 2011')
|
return from_range_100(IPT_hdr)
|
Converts from *CIE XYZ* tristimulus values to *hdr-IPT* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Y_s : numeric or array_like
Relative luminance :math:`Y_s` of the surround.
Y_abs : numeric or array_like
Absolute luminance :math:`Y_{abs}` of the scene diffuse white in
:math:`cd/m^2`.
method : str, optional
**{'Fairchild 2011', 'Fairchild 2010'}**,
Computation method.
Returns
-------
ndarray
*hdr-IPT* colourspace array.
Notes
-----
+-------------+-------------------------+---------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+=============+=========================+=====================+
| ``XYZ`` | [0, 1] | [0, 1] |
+-------------+-------------------------+---------------------+
| ``Y_s`` | [0, 1] | [0, 1] |
+-------------+-------------------------+---------------------+
+-------------+-------------------------+---------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+=============+=========================+=====================+
| ``IPT_hdr`` | ``I_hdr`` : [0, 100] | ``I_hdr`` : [0, 1] |
| | | |
| | ``P_hdr`` : [-100, 100] | ``P_hdr`` : [-1, 1] |
| | | |
| | ``T_hdr`` : [-100, 100] | ``T_hdr`` : [-1, 1] |
+-------------+-------------------------+---------------------+
- Input *CIE XYZ* tristimulus values must be adapted to
*CIE Standard Illuminant D Series* *D65*.
References
----------
:cite:`Fairchild2010`, :cite:`Fairchild2011`
Examples
--------
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_to_hdr_IPT(XYZ) # doctest: +ELLIPSIS
array([ 48.3937634..., 42.4499020..., 22.0195403...])
>>> XYZ_to_hdr_IPT(XYZ, method='Fairchild 2010') # doctest: +ELLIPSIS
array([ 30.0287314..., 83.9384506..., 34.9028738...])
|
Converts from *CIE XYZ* tristimulus values to *hdr-IPT* colourspace.
|
[
"Converts",
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"CIE",
"XYZ",
"*",
"tristimulus",
"values",
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"hdr",
"-",
"IPT",
"*",
"colourspace",
"."
] |
def XYZ_to_hdr_IPT(XYZ, Y_s=0.2, Y_abs=100, method='Fairchild 2011'):
"""
Converts from *CIE XYZ* tristimulus values to *hdr-IPT* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Y_s : numeric or array_like
Relative luminance :math:`Y_s` of the surround.
Y_abs : numeric or array_like
Absolute luminance :math:`Y_{abs}` of the scene diffuse white in
:math:`cd/m^2`.
method : str, optional
**{'Fairchild 2011', 'Fairchild 2010'}**,
Computation method.
Returns
-------
ndarray
*hdr-IPT* colourspace array.
Notes
-----
+-------------+-------------------------+---------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+=============+=========================+=====================+
| ``XYZ`` | [0, 1] | [0, 1] |
+-------------+-------------------------+---------------------+
| ``Y_s`` | [0, 1] | [0, 1] |
+-------------+-------------------------+---------------------+
+-------------+-------------------------+---------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+=============+=========================+=====================+
| ``IPT_hdr`` | ``I_hdr`` : [0, 100] | ``I_hdr`` : [0, 1] |
| | | |
| | ``P_hdr`` : [-100, 100] | ``P_hdr`` : [-1, 1] |
| | | |
| | ``T_hdr`` : [-100, 100] | ``T_hdr`` : [-1, 1] |
+-------------+-------------------------+---------------------+
- Input *CIE XYZ* tristimulus values must be adapted to
*CIE Standard Illuminant D Series* *D65*.
References
----------
:cite:`Fairchild2010`, :cite:`Fairchild2011`
Examples
--------
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_to_hdr_IPT(XYZ) # doctest: +ELLIPSIS
array([ 48.3937634..., 42.4499020..., 22.0195403...])
>>> XYZ_to_hdr_IPT(XYZ, method='Fairchild 2010') # doctest: +ELLIPSIS
array([ 30.0287314..., 83.9384506..., 34.9028738...])
"""
XYZ = to_domain_1(XYZ)
method = validate_method(method, HDR_IPT_METHODS)
if method == 'fairchild 2010':
lightness_callable = lightness_Fairchild2010
else:
lightness_callable = lightness_Fairchild2011
e = exponent_hdr_IPT(Y_s, Y_abs, method)[..., np.newaxis]
LMS = vector_dot(MATRIX_IPT_XYZ_TO_LMS, XYZ)
# Domain and range scaling has already be handled.
with domain_range_scale('ignore'):
LMS_prime = np.sign(LMS) * np.abs(lightness_callable(LMS, e))
IPT_hdr = vector_dot(MATRIX_IPT_LMS_P_TO_IPT, LMS_prime)
return from_range_100(IPT_hdr)
|
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] |
https://github.com/colour-science/colour/blob/38782ac059e8ddd91939f3432bf06811c16667f0/colour/models/hdr_ipt.py#L141-L218
|
|
pyqteval/evlal_win
|
ce7fc77ac5cdf864cd6aa9b04b5329501f8f4c92
|
pyinstaller-2.0/PyInstaller/lib/altgraph/GraphAlgo.py
|
python
|
_priorityDictionary.smallest
|
(self)
|
return heap[0][1]
|
Find smallest item after removing deleted items from front of heap.
|
Find smallest item after removing deleted items from front of heap.
|
[
"Find",
"smallest",
"item",
"after",
"removing",
"deleted",
"items",
"from",
"front",
"of",
"heap",
"."
] |
def smallest(self):
'''
Find smallest item after removing deleted items from front of heap.
'''
if len(self) == 0:
raise IndexError, "smallest of empty priorityDictionary"
heap = self.__heap
while heap[0][1] not in self or self[heap[0][1]] != heap[0][0]:
lastItem = heap.pop()
insertionPoint = 0
while 1:
smallChild = 2*insertionPoint+1
if smallChild+1 < len(heap) and heap[smallChild] > heap[smallChild+1] :
smallChild += 1
if smallChild >= len(heap) or lastItem <= heap[smallChild]:
heap[insertionPoint] = lastItem
break
heap[insertionPoint] = heap[smallChild]
insertionPoint = smallChild
return heap[0][1]
|
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"[",
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"[",
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https://github.com/pyqteval/evlal_win/blob/ce7fc77ac5cdf864cd6aa9b04b5329501f8f4c92/pyinstaller-2.0/PyInstaller/lib/altgraph/GraphAlgo.py#L90-L109
|
|
openstack/keystone
|
771c943ad2116193e7bb118c74993c829d93bd71
|
keystone/resource/core.py
|
python
|
Manager.assert_domain_not_federated
|
(self, domain_id, domain)
|
Assert the Domain's name and id do not match the reserved keyword.
Note that the reserved keyword is defined in the configuration file,
by default, it is 'Federated', it is also case insensitive.
If config's option is empty the default hardcoded value 'Federated'
will be used.
:raise AssertionError: if domain named match the value in the config.
|
Assert the Domain's name and id do not match the reserved keyword.
|
[
"Assert",
"the",
"Domain",
"s",
"name",
"and",
"id",
"do",
"not",
"match",
"the",
"reserved",
"keyword",
"."
] |
def assert_domain_not_federated(self, domain_id, domain):
"""Assert the Domain's name and id do not match the reserved keyword.
Note that the reserved keyword is defined in the configuration file,
by default, it is 'Federated', it is also case insensitive.
If config's option is empty the default hardcoded value 'Federated'
will be used.
:raise AssertionError: if domain named match the value in the config.
"""
# NOTE(marek-denis): We cannot create this attribute in the __init__ as
# config values are always initialized to default value.
federated_domain = CONF.federation.federated_domain_name.lower()
if (domain.get('name') and domain['name'].lower() == federated_domain):
raise AssertionError(_('Domain cannot be named %s')
% domain['name'])
if (domain_id.lower() == federated_domain):
raise AssertionError(_('Domain cannot have ID %s')
% domain_id)
|
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")",
"%",
"domain_id",
")"
] |
https://github.com/openstack/keystone/blob/771c943ad2116193e7bb118c74993c829d93bd71/keystone/resource/core.py#L245-L264
|
||
ebranca/owasp-pysec
|
163e10a146db04f40648979e8d7c0c10e7737781
|
pysec/alg.py
|
python
|
knp_first
|
(source, pattern, start=0, stop=None)
|
Return the index of the first occurrence of pattern in
source[start:stop]
|
Return the index of the first occurrence of pattern in
source[start:stop]
|
[
"Return",
"the",
"index",
"of",
"the",
"first",
"occurrence",
"of",
"pattern",
"in",
"source",
"[",
"start",
":",
"stop",
"]"
] |
def knp_first(source, pattern, start=0, stop=None):
"""Return the index of the first occurrence of pattern in
source[start:stop]"""
try:
return knp(source, pattern, start, stop).next()
except StopIteration:
return -1
|
[
"def",
"knp_first",
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"source",
",",
"pattern",
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"0",
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"next",
"(",
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"StopIteration",
":",
"return",
"-",
"1"
] |
https://github.com/ebranca/owasp-pysec/blob/163e10a146db04f40648979e8d7c0c10e7737781/pysec/alg.py#L50-L56
|
||
google/grr
|
8ad8a4d2c5a93c92729206b7771af19d92d4f915
|
grr/server/grr_response_server/flows/general/collectors.py
|
python
|
ArtifactCollectorFlow._AreArtifactsKnowledgeBaseArtifacts
|
(self)
|
return True
|
[] |
def _AreArtifactsKnowledgeBaseArtifacts(self):
knowledgebase_list = config.CONFIG["Artifacts.knowledge_base"]
for artifact_name in self.args.artifact_list:
if artifact_name not in knowledgebase_list:
return False
return True
|
[
"def",
"_AreArtifactsKnowledgeBaseArtifacts",
"(",
"self",
")",
":",
"knowledgebase_list",
"=",
"config",
".",
"CONFIG",
"[",
"\"Artifacts.knowledge_base\"",
"]",
"for",
"artifact_name",
"in",
"self",
".",
"args",
".",
"artifact_list",
":",
"if",
"artifact_name",
"not",
"in",
"knowledgebase_list",
":",
"return",
"False",
"return",
"True"
] |
https://github.com/google/grr/blob/8ad8a4d2c5a93c92729206b7771af19d92d4f915/grr/server/grr_response_server/flows/general/collectors.py#L250-L255
|
|||
maas/maas
|
db2f89970c640758a51247c59bf1ec6f60cf4ab5
|
src/maasserver/api/ssl_keys.py
|
python
|
SSLKeyHandler.delete
|
(self, request, id)
|
return rc.DELETED
|
@description-title Delete an SSL key
@description Deletes the SSL key with the given ID.
@param (int) "id" [required=true] An SSH key ID.
@success (http-status-code) "204" 204
@error (http-status-code) "404" 404
@error (content) "not-found" The requested SSH key is not found.
@error-example "not-found"
Not Found
@error (http-status-code) "403" 403
@error (content) "no-perms" The requesting user does not own the key.
@error-example "no-perms"
Can't delete a key you don't own.
|
@description-title Delete an SSL key
@description Deletes the SSL key with the given ID.
|
[
"@description",
"-",
"title",
"Delete",
"an",
"SSL",
"key",
"@description",
"Deletes",
"the",
"SSL",
"key",
"with",
"the",
"given",
"ID",
"."
] |
def delete(self, request, id):
"""@description-title Delete an SSL key
@description Deletes the SSL key with the given ID.
@param (int) "id" [required=true] An SSH key ID.
@success (http-status-code) "204" 204
@error (http-status-code) "404" 404
@error (content) "not-found" The requested SSH key is not found.
@error-example "not-found"
Not Found
@error (http-status-code) "403" 403
@error (content) "no-perms" The requesting user does not own the key.
@error-example "no-perms"
Can't delete a key you don't own.
"""
key = get_object_or_404(SSLKey, id=id)
if key.user != request.user:
return HttpResponseForbidden(
"Can't delete a key you don't own.",
content_type=(
"text/plain; charset=%s" % settings.DEFAULT_CHARSET
),
)
key.delete()
create_audit_event(
EVENT_TYPES.AUTHORISATION,
ENDPOINT.API,
request,
None,
description="Deleted SSL key id='%s'." % id,
)
return rc.DELETED
|
[
"def",
"delete",
"(",
"self",
",",
"request",
",",
"id",
")",
":",
"key",
"=",
"get_object_or_404",
"(",
"SSLKey",
",",
"id",
"=",
"id",
")",
"if",
"key",
".",
"user",
"!=",
"request",
".",
"user",
":",
"return",
"HttpResponseForbidden",
"(",
"\"Can't delete a key you don't own.\"",
",",
"content_type",
"=",
"(",
"\"text/plain; charset=%s\"",
"%",
"settings",
".",
"DEFAULT_CHARSET",
")",
",",
")",
"key",
".",
"delete",
"(",
")",
"create_audit_event",
"(",
"EVENT_TYPES",
".",
"AUTHORISATION",
",",
"ENDPOINT",
".",
"API",
",",
"request",
",",
"None",
",",
"description",
"=",
"\"Deleted SSL key id='%s'.\"",
"%",
"id",
",",
")",
"return",
"rc",
".",
"DELETED"
] |
https://github.com/maas/maas/blob/db2f89970c640758a51247c59bf1ec6f60cf4ab5/src/maasserver/api/ssl_keys.py#L122-L156
|
|
wucng/TensorExpand
|
4ea58f64f5c5082b278229b799c9f679536510b7
|
TensorExpand/Object detection/faster rcnn/CharlesShang-TFFRCNN-master/experiments/profiling/gprof2dot.py
|
python
|
Profile.__init__
|
(self)
|
[] |
def __init__(self):
Object.__init__(self)
self.functions = {}
self.cycles = []
|
[
"def",
"__init__",
"(",
"self",
")",
":",
"Object",
".",
"__init__",
"(",
"self",
")",
"self",
".",
"functions",
"=",
"{",
"}",
"self",
".",
"cycles",
"=",
"[",
"]"
] |
https://github.com/wucng/TensorExpand/blob/4ea58f64f5c5082b278229b799c9f679536510b7/TensorExpand/Object detection/faster rcnn/CharlesShang-TFFRCNN-master/experiments/profiling/gprof2dot.py#L291-L294
|
||||
PythonTurtle/PythonTurtle
|
929f10892e62ece8d5f230d3e995d380d6493cf3
|
pythonturtle/shelltoprocess/forkedpyshell.py
|
python
|
Shell.setLocalShell
|
(self)
|
Add 'shell' to locals as reference to ShellFacade instance.
|
Add 'shell' to locals as reference to ShellFacade instance.
|
[
"Add",
"shell",
"to",
"locals",
"as",
"reference",
"to",
"ShellFacade",
"instance",
"."
] |
def setLocalShell(self):
"""Add 'shell' to locals as reference to ShellFacade instance."""
self.interp.locals['shell'] = ShellFacade(other=self)
|
[
"def",
"setLocalShell",
"(",
"self",
")",
":",
"self",
".",
"interp",
".",
"locals",
"[",
"'shell'",
"]",
"=",
"ShellFacade",
"(",
"other",
"=",
"self",
")"
] |
https://github.com/PythonTurtle/PythonTurtle/blob/929f10892e62ece8d5f230d3e995d380d6493cf3/pythonturtle/shelltoprocess/forkedpyshell.py#L439-L441
|
||
blinktrade/bitex
|
a4896e7faef9c4aa0ca5325f18b77db67003764e
|
libs/websocket.py
|
python
|
_SSLSocketWrapper.recv
|
(self, bufsize)
|
return self.ssl.read(bufsize)
|
[] |
def recv(self, bufsize):
return self.ssl.read(bufsize)
|
[
"def",
"recv",
"(",
"self",
",",
"bufsize",
")",
":",
"return",
"self",
".",
"ssl",
".",
"read",
"(",
"bufsize",
")"
] |
https://github.com/blinktrade/bitex/blob/a4896e7faef9c4aa0ca5325f18b77db67003764e/libs/websocket.py#L202-L203
|
|||
rajarshd/Multi-Step-Reasoning
|
3218d626839f7217554f38d82e00e4f460b508e4
|
msr/reader/model.py
|
python
|
Model.parallelize
|
(self)
|
Use data parallel to copy the model across several gpus.
This will take all gpus visible with CUDA_VISIBLE_DEVICES.
|
Use data parallel to copy the model across several gpus.
This will take all gpus visible with CUDA_VISIBLE_DEVICES.
|
[
"Use",
"data",
"parallel",
"to",
"copy",
"the",
"model",
"across",
"several",
"gpus",
".",
"This",
"will",
"take",
"all",
"gpus",
"visible",
"with",
"CUDA_VISIBLE_DEVICES",
"."
] |
def parallelize(self):
"""Use data parallel to copy the model across several gpus.
This will take all gpus visible with CUDA_VISIBLE_DEVICES.
"""
self.parallel = True
self.network = torch.nn.DataParallel(self.network)
|
[
"def",
"parallelize",
"(",
"self",
")",
":",
"self",
".",
"parallel",
"=",
"True",
"self",
".",
"network",
"=",
"torch",
".",
"nn",
".",
"DataParallel",
"(",
"self",
".",
"network",
")"
] |
https://github.com/rajarshd/Multi-Step-Reasoning/blob/3218d626839f7217554f38d82e00e4f460b508e4/msr/reader/model.py#L965-L970
|
||
ifwe/digsby
|
f5fe00244744aa131e07f09348d10563f3d8fa99
|
digsby/devplugins/irc/irccmds.py
|
python
|
IRCCommander.raw
|
(self, text)
|
Sends raw text to the server.
|
Sends raw text to the server.
|
[
"Sends",
"raw",
"text",
"to",
"the",
"server",
"."
] |
def raw(self, text):
'Sends raw text to the server.'
self._irc.sendraw(text)
|
[
"def",
"raw",
"(",
"self",
",",
"text",
")",
":",
"self",
".",
"_irc",
".",
"sendraw",
"(",
"text",
")"
] |
https://github.com/ifwe/digsby/blob/f5fe00244744aa131e07f09348d10563f3d8fa99/digsby/devplugins/irc/irccmds.py#L64-L67
|
||
annoviko/pyclustering
|
bf4f51a472622292627ec8c294eb205585e50f52
|
pyclustering/nnet/__init__.py
|
python
|
network.set_connection
|
(self, i, j)
|
!
@brief Couples two specified oscillators in the network with dynamic connections.
@param[in] i (uint): index of an oscillator that should be coupled with oscillator 'j' in the network.
@param[in] j (uint): index of an oscillator that should be coupled with oscillator 'i' in the network.
@note This method can be used only in case of DYNAMIC connections, otherwise it throws expection.
|
!
|
[
"!"
] |
def set_connection(self, i, j):
"""!
@brief Couples two specified oscillators in the network with dynamic connections.
@param[in] i (uint): index of an oscillator that should be coupled with oscillator 'j' in the network.
@param[in] j (uint): index of an oscillator that should be coupled with oscillator 'i' in the network.
@note This method can be used only in case of DYNAMIC connections, otherwise it throws expection.
"""
if (self.structure != conn_type.DYNAMIC):
raise NameError("Connection between oscillators can be changed only in case of dynamic type.");
if (self._conn_represent == conn_represent.MATRIX):
self._osc_conn[i][j] = True;
self._osc_conn[j][i] = True;
else:
self._osc_conn[i].append(j);
self._osc_conn[j].append(i);
|
[
"def",
"set_connection",
"(",
"self",
",",
"i",
",",
"j",
")",
":",
"if",
"(",
"self",
".",
"structure",
"!=",
"conn_type",
".",
"DYNAMIC",
")",
":",
"raise",
"NameError",
"(",
"\"Connection between oscillators can be changed only in case of dynamic type.\"",
")",
"if",
"(",
"self",
".",
"_conn_represent",
"==",
"conn_represent",
".",
"MATRIX",
")",
":",
"self",
".",
"_osc_conn",
"[",
"i",
"]",
"[",
"j",
"]",
"=",
"True",
"self",
".",
"_osc_conn",
"[",
"j",
"]",
"[",
"i",
"]",
"=",
"True",
"else",
":",
"self",
".",
"_osc_conn",
"[",
"i",
"]",
".",
"append",
"(",
"j",
")",
"self",
".",
"_osc_conn",
"[",
"j",
"]",
".",
"append",
"(",
"i",
")"
] |
https://github.com/annoviko/pyclustering/blob/bf4f51a472622292627ec8c294eb205585e50f52/pyclustering/nnet/__init__.py#L372-L391
|
||
plotly/plotly.py
|
cfad7862594b35965c0e000813bd7805e8494a5b
|
packages/python/plotly/plotly/graph_objs/scattergl/_unselected.py
|
python
|
Unselected.marker
|
(self)
|
return self["marker"]
|
The 'marker' property is an instance of Marker
that may be specified as:
- An instance of :class:`plotly.graph_objs.scattergl.unselected.Marker`
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
color
Sets the marker color of unselected points,
applied only when a selection exists.
opacity
Sets the marker opacity of unselected points,
applied only when a selection exists.
size
Sets the marker size of unselected points,
applied only when a selection exists.
Returns
-------
plotly.graph_objs.scattergl.unselected.Marker
|
The 'marker' property is an instance of Marker
that may be specified as:
- An instance of :class:`plotly.graph_objs.scattergl.unselected.Marker`
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
color
Sets the marker color of unselected points,
applied only when a selection exists.
opacity
Sets the marker opacity of unselected points,
applied only when a selection exists.
size
Sets the marker size of unselected points,
applied only when a selection exists.
|
[
"The",
"marker",
"property",
"is",
"an",
"instance",
"of",
"Marker",
"that",
"may",
"be",
"specified",
"as",
":",
"-",
"An",
"instance",
"of",
":",
"class",
":",
"plotly",
".",
"graph_objs",
".",
"scattergl",
".",
"unselected",
".",
"Marker",
"-",
"A",
"dict",
"of",
"string",
"/",
"value",
"properties",
"that",
"will",
"be",
"passed",
"to",
"the",
"Marker",
"constructor",
"Supported",
"dict",
"properties",
":",
"color",
"Sets",
"the",
"marker",
"color",
"of",
"unselected",
"points",
"applied",
"only",
"when",
"a",
"selection",
"exists",
".",
"opacity",
"Sets",
"the",
"marker",
"opacity",
"of",
"unselected",
"points",
"applied",
"only",
"when",
"a",
"selection",
"exists",
".",
"size",
"Sets",
"the",
"marker",
"size",
"of",
"unselected",
"points",
"applied",
"only",
"when",
"a",
"selection",
"exists",
"."
] |
def marker(self):
"""
The 'marker' property is an instance of Marker
that may be specified as:
- An instance of :class:`plotly.graph_objs.scattergl.unselected.Marker`
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
color
Sets the marker color of unselected points,
applied only when a selection exists.
opacity
Sets the marker opacity of unselected points,
applied only when a selection exists.
size
Sets the marker size of unselected points,
applied only when a selection exists.
Returns
-------
plotly.graph_objs.scattergl.unselected.Marker
"""
return self["marker"]
|
[
"def",
"marker",
"(",
"self",
")",
":",
"return",
"self",
"[",
"\"marker\"",
"]"
] |
https://github.com/plotly/plotly.py/blob/cfad7862594b35965c0e000813bd7805e8494a5b/packages/python/plotly/plotly/graph_objs/scattergl/_unselected.py#L16-L40
|
|
YingZhangDUT/Deep-Mutual-Learning
|
34a20583debe4e0dab1d9856db69bed278c5c011
|
datasets/convert_to_tfrecords.py
|
python
|
run
|
(image_dir, output_dir, split_name)
|
Convert images to tfrecords.
Args:
image_dir: The image directory where the raw images are stored.
output_dir: The directory where the lists and tfrecords are stored.
split_name: The split name of dataset.
|
Convert images to tfrecords.
Args:
image_dir: The image directory where the raw images are stored.
output_dir: The directory where the lists and tfrecords are stored.
split_name: The split name of dataset.
|
[
"Convert",
"images",
"to",
"tfrecords",
".",
"Args",
":",
"image_dir",
":",
"The",
"image",
"directory",
"where",
"the",
"raw",
"images",
"are",
"stored",
".",
"output_dir",
":",
"The",
"directory",
"where",
"the",
"lists",
"and",
"tfrecords",
"are",
"stored",
".",
"split_name",
":",
"The",
"split",
"name",
"of",
"dataset",
"."
] |
def run(image_dir, output_dir, split_name):
"""Convert images to tfrecords.
Args:
image_dir: The image directory where the raw images are stored.
output_dir: The directory where the lists and tfrecords are stored.
split_name: The split name of dataset.
"""
list_filename = os.path.join(output_dir, '%s.txt' % split_name)
tf_filename = os.path.join(output_dir, '%s.tfrecord' % split_name)
if tf.gfile.Exists(tf_filename):
print('Dataset files already exist. Exiting without re-creating them.')
return
with tf.python_io.TFRecordWriter(tf_filename) as tfrecord_writer:
_add_to_tfrecord(image_dir, list_filename, tfrecord_writer, split_name)
print(" Done! \n")
|
[
"def",
"run",
"(",
"image_dir",
",",
"output_dir",
",",
"split_name",
")",
":",
"list_filename",
"=",
"os",
".",
"path",
".",
"join",
"(",
"output_dir",
",",
"'%s.txt'",
"%",
"split_name",
")",
"tf_filename",
"=",
"os",
".",
"path",
".",
"join",
"(",
"output_dir",
",",
"'%s.tfrecord'",
"%",
"split_name",
")",
"if",
"tf",
".",
"gfile",
".",
"Exists",
"(",
"tf_filename",
")",
":",
"print",
"(",
"'Dataset files already exist. Exiting without re-creating them.'",
")",
"return",
"with",
"tf",
".",
"python_io",
".",
"TFRecordWriter",
"(",
"tf_filename",
")",
"as",
"tfrecord_writer",
":",
"_add_to_tfrecord",
"(",
"image_dir",
",",
"list_filename",
",",
"tfrecord_writer",
",",
"split_name",
")",
"print",
"(",
"\" Done! \\n\"",
")"
] |
https://github.com/YingZhangDUT/Deep-Mutual-Learning/blob/34a20583debe4e0dab1d9856db69bed278c5c011/datasets/convert_to_tfrecords.py#L50-L67
|
||
nate-parrott/Flashlight
|
c3a7c7278a1cccf8918e7543faffc68e863ff5ab
|
flashlightplugins/cloudstorage/common.py
|
python
|
posix_time_to_http
|
(posix_time)
|
Convert posix time to HTML header time format.
Args:
posix_time: unix time.
Returns:
A datatime str in RFC 2616 format.
|
Convert posix time to HTML header time format.
|
[
"Convert",
"posix",
"time",
"to",
"HTML",
"header",
"time",
"format",
"."
] |
def posix_time_to_http(posix_time):
"""Convert posix time to HTML header time format.
Args:
posix_time: unix time.
Returns:
A datatime str in RFC 2616 format.
"""
if posix_time:
return email_utils.formatdate(posix_time, usegmt=True)
|
[
"def",
"posix_time_to_http",
"(",
"posix_time",
")",
":",
"if",
"posix_time",
":",
"return",
"email_utils",
".",
"formatdate",
"(",
"posix_time",
",",
"usegmt",
"=",
"True",
")"
] |
https://github.com/nate-parrott/Flashlight/blob/c3a7c7278a1cccf8918e7543faffc68e863ff5ab/flashlightplugins/cloudstorage/common.py#L331-L341
|
||
sagemath/sagecell
|
f654176956af8ebd83a0769b916116d600785398
|
namespace.py
|
python
|
InstrumentedNamespace.__init__
|
(self, *args, **kwargs)
|
Set up a namespace id
|
Set up a namespace id
|
[
"Set",
"up",
"a",
"namespace",
"id"
] |
def __init__(self, *args, **kwargs):
"""
Set up a namespace id
"""
dict.__init__(self,*args,**kwargs)
self.events = defaultdict(lambda: defaultdict(list))
|
[
"def",
"__init__",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"dict",
".",
"__init__",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
"self",
".",
"events",
"=",
"defaultdict",
"(",
"lambda",
":",
"defaultdict",
"(",
"list",
")",
")"
] |
https://github.com/sagemath/sagecell/blob/f654176956af8ebd83a0769b916116d600785398/namespace.py#L5-L10
|
||
securesystemslab/zippy
|
ff0e84ac99442c2c55fe1d285332cfd4e185e089
|
zippy/benchmarks/src/benchmarks/sympy/sympy/core/numbers.py
|
python
|
NaN.__mul__
|
(self, other)
|
return self
|
[] |
def __mul__(self, other):
return self
|
[
"def",
"__mul__",
"(",
"self",
",",
"other",
")",
":",
"return",
"self"
] |
https://github.com/securesystemslab/zippy/blob/ff0e84ac99442c2c55fe1d285332cfd4e185e089/zippy/benchmarks/src/benchmarks/sympy/sympy/core/numbers.py#L2524-L2525
|
|||
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/whoosh/util/varints.py
|
python
|
varint_to_int
|
(vi)
|
return i
|
[] |
def varint_to_int(vi):
b = ord(vi[0])
p = 1
i = b & 0x7f
shift = 7
while b & 0x80 != 0:
b = ord(vi[p])
p += 1
i |= (b & 0x7F) << shift
shift += 7
return i
|
[
"def",
"varint_to_int",
"(",
"vi",
")",
":",
"b",
"=",
"ord",
"(",
"vi",
"[",
"0",
"]",
")",
"p",
"=",
"1",
"i",
"=",
"b",
"&",
"0x7f",
"shift",
"=",
"7",
"while",
"b",
"&",
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"!=",
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https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/whoosh/util/varints.py#L63-L73
|
|||
oracle/oci-python-sdk
|
3c1604e4e212008fb6718e2f68cdb5ef71fd5793
|
src/oci/core/virtual_network_client.py
|
python
|
VirtualNetworkClient.remove_drg_route_distribution_statements
|
(self, drg_route_distribution_id, remove_drg_route_distribution_statements_details, **kwargs)
|
Removes one or more route distribution statements from the specified route distribution's map.
:param str drg_route_distribution_id: (required)
The `OCID`__ of the route distribution.
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm
:param oci.core.models.RemoveDrgRouteDistributionStatementsDetails remove_drg_route_distribution_statements_details: (required)
Request with one or more route distribution statements to remove from the route distribution.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type None
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/core/remove_drg_route_distribution_statements.py.html>`__ to see an example of how to use remove_drg_route_distribution_statements API.
|
Removes one or more route distribution statements from the specified route distribution's map.
|
[
"Removes",
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"more",
"route",
"distribution",
"statements",
"from",
"the",
"specified",
"route",
"distribution",
"s",
"map",
"."
] |
def remove_drg_route_distribution_statements(self, drg_route_distribution_id, remove_drg_route_distribution_statements_details, **kwargs):
"""
Removes one or more route distribution statements from the specified route distribution's map.
:param str drg_route_distribution_id: (required)
The `OCID`__ of the route distribution.
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm
:param oci.core.models.RemoveDrgRouteDistributionStatementsDetails remove_drg_route_distribution_statements_details: (required)
Request with one or more route distribution statements to remove from the route distribution.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type None
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/core/remove_drg_route_distribution_statements.py.html>`__ to see an example of how to use remove_drg_route_distribution_statements API.
"""
resource_path = "/drgRouteDistributions/{drgRouteDistributionId}/actions/removeDrgRouteDistributionStatements"
method = "POST"
expected_kwargs = ["retry_strategy"]
extra_kwargs = [_key for _key in six.iterkeys(kwargs) if _key not in expected_kwargs]
if extra_kwargs:
raise ValueError(
"remove_drg_route_distribution_statements got unknown kwargs: {!r}".format(extra_kwargs))
path_params = {
"drgRouteDistributionId": drg_route_distribution_id
}
path_params = {k: v for (k, v) in six.iteritems(path_params) if v is not missing}
for (k, v) in six.iteritems(path_params):
if v is None or (isinstance(v, six.string_types) and len(v.strip()) == 0):
raise ValueError('Parameter {} cannot be None, whitespace or empty string'.format(k))
header_params = {
"accept": "application/json",
"content-type": "application/json"
}
retry_strategy = self.base_client.get_preferred_retry_strategy(
operation_retry_strategy=kwargs.get('retry_strategy'),
client_retry_strategy=self.retry_strategy
)
if retry_strategy:
if not isinstance(retry_strategy, retry.NoneRetryStrategy):
self.base_client.add_opc_client_retries_header(header_params)
retry_strategy.add_circuit_breaker_callback(self.circuit_breaker_callback)
return retry_strategy.make_retrying_call(
self.base_client.call_api,
resource_path=resource_path,
method=method,
path_params=path_params,
header_params=header_params,
body=remove_drg_route_distribution_statements_details)
else:
return self.base_client.call_api(
resource_path=resource_path,
method=method,
path_params=path_params,
header_params=header_params,
body=remove_drg_route_distribution_statements_details)
|
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https://github.com/oracle/oci-python-sdk/blob/3c1604e4e212008fb6718e2f68cdb5ef71fd5793/src/oci/core/virtual_network_client.py#L17750-L17823
|
||
yzhao062/combo
|
229d578de498b47ae03cf2580472aceebf8c2766
|
combo/models/base.py
|
python
|
BaseAggregator._detector_predict_proba
|
(self, X, proba_method='linear')
|
Predict the probability of a sample being outlier. Two approaches
are possible:
1. simply use Min-max conversion to linearly transform the outlier
scores into the range of [0,1]. The model must be
fitted first.
2. use unifying scores, see :cite:`kriegel2011interpreting`.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
proba_method : str, optional (default='linear')
Probability conversion method. It must be one of
'linear' or 'unify'.
Returns
-------
outlier_labels : numpy array of shape (n_samples,)
For each observation, tells whether or not
it should be considered as an outlier according to the
fitted model. Return the outlier probability, ranging
in [0,1].
|
Predict the probability of a sample being outlier. Two approaches
are possible:
|
[
"Predict",
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"probability",
"of",
"a",
"sample",
"being",
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".",
"Two",
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"are",
"possible",
":"
] |
def _detector_predict_proba(self, X, proba_method='linear'):
"""Predict the probability of a sample being outlier. Two approaches
are possible:
1. simply use Min-max conversion to linearly transform the outlier
scores into the range of [0,1]. The model must be
fitted first.
2. use unifying scores, see :cite:`kriegel2011interpreting`.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
proba_method : str, optional (default='linear')
Probability conversion method. It must be one of
'linear' or 'unify'.
Returns
-------
outlier_labels : numpy array of shape (n_samples,)
For each observation, tells whether or not
it should be considered as an outlier according to the
fitted model. Return the outlier probability, ranging
in [0,1].
"""
check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
train_scores = self.decision_scores_
test_scores = self.decision_function(X)
probs = np.zeros([X.shape[0], int(self._classes)])
if proba_method == 'linear':
scaler = MinMaxScaler().fit(train_scores.reshape(-1, 1))
probs[:, 1] = scaler.transform(
test_scores.reshape(-1, 1)).ravel().clip(0, 1)
probs[:, 0] = 1 - probs[:, 1]
return probs
elif proba_method == 'unify':
# turn output into probability
pre_erf_score = (test_scores - self._mu) / (
self._sigma * np.sqrt(2))
erf_score = erf(pre_erf_score)
probs[:, 1] = erf_score.clip(0, 1).ravel()
probs[:, 0] = 1 - probs[:, 1]
return probs
else:
raise ValueError(proba_method,
'is not a valid probability conversion method')
|
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] |
https://github.com/yzhao062/combo/blob/229d578de498b47ae03cf2580472aceebf8c2766/combo/models/base.py#L165-L215
|
||
bruderstein/PythonScript
|
df9f7071ddf3a079e3a301b9b53a6dc78cf1208f
|
PythonLib/min/site.py
|
python
|
setcopyright
|
()
|
Set 'copyright' and 'credits' in builtins
|
Set 'copyright' and 'credits' in builtins
|
[
"Set",
"copyright",
"and",
"credits",
"in",
"builtins"
] |
def setcopyright():
"""Set 'copyright' and 'credits' in builtins"""
builtins.copyright = _sitebuiltins._Printer("copyright", sys.copyright)
if sys.platform[:4] == 'java':
builtins.credits = _sitebuiltins._Printer(
"credits",
"Jython is maintained by the Jython developers (www.jython.org).")
else:
builtins.credits = _sitebuiltins._Printer("credits", """\
Thanks to CWI, CNRI, BeOpen.com, Zope Corporation and a cast of thousands
for supporting Python development. See www.python.org for more information.""")
files, dirs = [], []
# Not all modules are required to have a __file__ attribute. See
# PEP 420 for more details.
if hasattr(os, '__file__'):
here = os.path.dirname(os.__file__)
files.extend(["LICENSE.txt", "LICENSE"])
dirs.extend([os.path.join(here, os.pardir), here, os.curdir])
builtins.license = _sitebuiltins._Printer(
"license",
"See https://www.python.org/psf/license/",
files, dirs)
|
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",",
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",",
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")"
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https://github.com/bruderstein/PythonScript/blob/df9f7071ddf3a079e3a301b9b53a6dc78cf1208f/PythonLib/min/site.py#L407-L428
|
||
benoitc/couchdbkit
|
6be148640c00b54ee87a2f2d502e9d67fa5b45a8
|
couchdbkit/external.py
|
python
|
External.send_response
|
(self, code=200, body="", headers={})
|
[] |
def send_response(self, code=200, body="", headers={}):
resp = {
'code': code,
'body': body,
'headers': headers
}
self.write(json.dumps(resp))
|
[
"def",
"send_response",
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")"
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https://github.com/benoitc/couchdbkit/blob/6be148640c00b54ee87a2f2d502e9d67fa5b45a8/couchdbkit/external.py#L52-L58
|
||||
cuthbertLab/music21
|
bd30d4663e52955ed922c10fdf541419d8c67671
|
music21/abcFormat/__init__.py
|
python
|
ABCMetadata.getDefaultQuarterLength
|
(self)
|
r'''
If there is a quarter length representation available, return it as a floating point value
>>> am = abcFormat.ABCMetadata('L:1/2')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
2.0
>>> am = abcFormat.ABCMetadata('L:1/8')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
0.5
>>> am = abcFormat.ABCMetadata('M:C|')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
0.5
If taking from meter, find the "fraction" and if < 0.75 use sixteenth notes.
If >= 0.75 use eighth notes.
>>> am = abcFormat.ABCMetadata('M:2/4')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
0.25
>>> am = abcFormat.ABCMetadata('M:3/4')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
0.5
>>> am = abcFormat.ABCMetadata('M:6/8')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
0.5
Meter is only used for default length if there is no L:
>>> x = 'L:1/4\nM:3/4\n\nf'
>>> sc = converter.parse(x, format='abc')
>>> sc.recurse().notes.first().duration.type
'quarter'
|
r'''
If there is a quarter length representation available, return it as a floating point value
|
[
"r",
"If",
"there",
"is",
"a",
"quarter",
"length",
"representation",
"available",
"return",
"it",
"as",
"a",
"floating",
"point",
"value"
] |
def getDefaultQuarterLength(self) -> float:
r'''
If there is a quarter length representation available, return it as a floating point value
>>> am = abcFormat.ABCMetadata('L:1/2')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
2.0
>>> am = abcFormat.ABCMetadata('L:1/8')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
0.5
>>> am = abcFormat.ABCMetadata('M:C|')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
0.5
If taking from meter, find the "fraction" and if < 0.75 use sixteenth notes.
If >= 0.75 use eighth notes.
>>> am = abcFormat.ABCMetadata('M:2/4')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
0.25
>>> am = abcFormat.ABCMetadata('M:3/4')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
0.5
>>> am = abcFormat.ABCMetadata('M:6/8')
>>> am.preParse()
>>> am.getDefaultQuarterLength()
0.5
Meter is only used for default length if there is no L:
>>> x = 'L:1/4\nM:3/4\n\nf'
>>> sc = converter.parse(x, format='abc')
>>> sc.recurse().notes.first().duration.type
'quarter'
'''
# environLocal.printDebug(['getDefaultQuarterLength', self.data])
if self.isDefaultNoteLength() and '/' in self.data:
# should be in L:1/4 form
n, d = self.data.split('/')
n = int(n.strip())
# the notation L: 1/G is found in some essen files
# this is extremely uncommon and might be an error
if d == 'G':
d = 4 # assume a default
else:
d = int(d.strip())
# 1/4 is 1, 1/8 is 0.5
return n * 4 / d
elif self.isMeter():
# if meter auto-set a default not length
parameters = self.getTimeSignatureParameters()
if parameters is None:
return 0.5 # TODO: assume default, need to configure
n, d, unused_symbol = parameters
if n / d < 0.75:
return 0.25 # less than 0.75 the default is a sixteenth note
else:
return 0.5 # otherwise it is an eighth note
else: # pragma: no cover
raise ABCTokenException(
f'no quarter length associated with this metadata: {self.data}')
|
[
"def",
"getDefaultQuarterLength",
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"self",
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"->",
"float",
":",
"# environLocal.printDebug(['getDefaultQuarterLength', self.data])",
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".",
"isDefaultNoteLength",
"(",
")",
"and",
"'/'",
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".",
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"# should be in L:1/4 form",
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",",
"d",
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"strip",
"(",
")",
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"# the notation L: 1/G is found in some essen files",
"# this is extremely uncommon and might be an error",
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"==",
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"=",
"4",
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"int",
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"strip",
"(",
")",
")",
"# 1/4 is 1, 1/8 is 0.5",
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"n",
"*",
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"/",
"d",
"elif",
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"isMeter",
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"return",
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"n",
",",
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",",
"unused_symbol",
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"/",
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"0.75",
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"return",
"0.25",
"# less than 0.75 the default is a sixteenth note",
"else",
":",
"return",
"0.5",
"# otherwise it is an eighth note",
"else",
":",
"# pragma: no cover",
"raise",
"ABCTokenException",
"(",
"f'no quarter length associated with this metadata: {self.data}'",
")"
] |
https://github.com/cuthbertLab/music21/blob/bd30d4663e52955ed922c10fdf541419d8c67671/music21/abcFormat/__init__.py#L677-L750
|
||
great-expectations/great_expectations
|
45224cb890aeae725af25905923d0dbbab2d969d
|
great_expectations/render/renderer/notebook_renderer.py
|
python
|
BaseNotebookRenderer.render_to_disk
|
(
self,
notebook_file_path: str,
)
|
Render a notebook to disk from arguments
|
Render a notebook to disk from arguments
|
[
"Render",
"a",
"notebook",
"to",
"disk",
"from",
"arguments"
] |
def render_to_disk(
self,
notebook_file_path: str,
) -> None:
"""
Render a notebook to disk from arguments
"""
raise NotImplementedError
|
[
"def",
"render_to_disk",
"(",
"self",
",",
"notebook_file_path",
":",
"str",
",",
")",
"->",
"None",
":",
"raise",
"NotImplementedError"
] |
https://github.com/great-expectations/great_expectations/blob/45224cb890aeae725af25905923d0dbbab2d969d/great_expectations/render/renderer/notebook_renderer.py#L77-L84
|
||
Net-ng/kansha
|
85b5816da126b1c7098707c98f217d8b2e524ff2
|
kansha/board/comp.py
|
python
|
Board.get_available_user_ids
|
(self)
|
return set(dbm.user.id for dbm in self.data.board_members)
|
Return list of member
Return:
- list of members
|
Return list of member
|
[
"Return",
"list",
"of",
"member"
] |
def get_available_user_ids(self):
"""Return list of member
Return:
- list of members
"""
return set(dbm.user.id for dbm in self.data.board_members)
|
[
"def",
"get_available_user_ids",
"(",
"self",
")",
":",
"return",
"set",
"(",
"dbm",
".",
"user",
".",
"id",
"for",
"dbm",
"in",
"self",
".",
"data",
".",
"board_members",
")"
] |
https://github.com/Net-ng/kansha/blob/85b5816da126b1c7098707c98f217d8b2e524ff2/kansha/board/comp.py#L748-L754
|
|
F8LEFT/DecLLVM
|
d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c
|
python/idaapi.py
|
python
|
lvar_t.is_floating_var
|
(self, *args)
|
return _idaapi.lvar_t_is_floating_var(self, *args)
|
is_floating_var(self) -> bool
|
is_floating_var(self) -> bool
|
[
"is_floating_var",
"(",
"self",
")",
"-",
">",
"bool"
] |
def is_floating_var(self, *args):
"""
is_floating_var(self) -> bool
"""
return _idaapi.lvar_t_is_floating_var(self, *args)
|
[
"def",
"is_floating_var",
"(",
"self",
",",
"*",
"args",
")",
":",
"return",
"_idaapi",
".",
"lvar_t_is_floating_var",
"(",
"self",
",",
"*",
"args",
")"
] |
https://github.com/F8LEFT/DecLLVM/blob/d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c/python/idaapi.py#L35928-L35932
|
|
Arelle/Arelle
|
20f3d8a8afd41668e1520799acd333349ce0ba17
|
arelle/TkTableWrapper.py
|
python
|
Table.spans
|
(self, index=None, **kwargs)
|
Manipulate row/col spans.
When called with no arguments, all known spans are returned as a dict.
When called with only the index, the span for that index only is
returned, if any. Otherwise kwargs is assumed to contain keys/values
pairs used to set spans. A span starts at the row,col defined by a key
and continues for the specified number of rows,cols specified by
its value. A span of 0,0 unsets any span on that cell.
|
Manipulate row/col spans.
|
[
"Manipulate",
"row",
"/",
"col",
"spans",
"."
] |
def spans(self, index=None, **kwargs):
"""Manipulate row/col spans.
When called with no arguments, all known spans are returned as a dict.
When called with only the index, the span for that index only is
returned, if any. Otherwise kwargs is assumed to contain keys/values
pairs used to set spans. A span starts at the row,col defined by a key
and continues for the specified number of rows,cols specified by
its value. A span of 0,0 unsets any span on that cell."""
if kwargs:
args = tkinter._flatten(list(kwargs.items()))
self.tk.call(self._w, 'spans', *args)
else:
return self.tk.call(self._w, 'spans', index)
|
[
"def",
"spans",
"(",
"self",
",",
"index",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"kwargs",
":",
"args",
"=",
"tkinter",
".",
"_flatten",
"(",
"list",
"(",
"kwargs",
".",
"items",
"(",
")",
")",
")",
"self",
".",
"tk",
".",
"call",
"(",
"self",
".",
"_w",
",",
"'spans'",
",",
"*",
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")",
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".",
"tk",
".",
"call",
"(",
"self",
".",
"_w",
",",
"'spans'",
",",
"index",
")"
] |
https://github.com/Arelle/Arelle/blob/20f3d8a8afd41668e1520799acd333349ce0ba17/arelle/TkTableWrapper.py#L374-L387
|
||
nopernik/mpDNS
|
b17dc39e7068406df82cb3431b3042e74e520cf9
|
dnslib/dns.py
|
python
|
DNSQuestion.__ne__
|
(self,other)
|
return not(self.__eq__(other))
|
[] |
def __ne__(self,other):
return not(self.__eq__(other))
|
[
"def",
"__ne__",
"(",
"self",
",",
"other",
")",
":",
"return",
"not",
"(",
"self",
".",
"__eq__",
"(",
"other",
")",
")"
] |
https://github.com/nopernik/mpDNS/blob/b17dc39e7068406df82cb3431b3042e74e520cf9/dnslib/dns.py#L678-L679
|
|||
zachwill/flask-engine
|
7c8ad4bfe36382a8c9286d873ec7b785715832a4
|
libs/pkg_resources.py
|
python
|
Environment.add
|
(self,dist)
|
Add `dist` if we ``can_add()`` it and it isn't already added
|
Add `dist` if we ``can_add()`` it and it isn't already added
|
[
"Add",
"dist",
"if",
"we",
"can_add",
"()",
"it",
"and",
"it",
"isn",
"t",
"already",
"added"
] |
def add(self,dist):
"""Add `dist` if we ``can_add()`` it and it isn't already added"""
if self.can_add(dist) and dist.has_version():
dists = self._distmap.setdefault(dist.key,[])
if dist not in dists:
dists.append(dist)
if dist.key in self._cache:
_sort_dists(self._cache[dist.key])
|
[
"def",
"add",
"(",
"self",
",",
"dist",
")",
":",
"if",
"self",
".",
"can_add",
"(",
"dist",
")",
"and",
"dist",
".",
"has_version",
"(",
")",
":",
"dists",
"=",
"self",
".",
"_distmap",
".",
"setdefault",
"(",
"dist",
".",
"key",
",",
"[",
"]",
")",
"if",
"dist",
"not",
"in",
"dists",
":",
"dists",
".",
"append",
"(",
"dist",
")",
"if",
"dist",
".",
"key",
"in",
"self",
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"_cache",
":",
"_sort_dists",
"(",
"self",
".",
"_cache",
"[",
"dist",
".",
"key",
"]",
")"
] |
https://github.com/zachwill/flask-engine/blob/7c8ad4bfe36382a8c9286d873ec7b785715832a4/libs/pkg_resources.py#L751-L758
|
||
kubernetes-client/python
|
47b9da9de2d02b2b7a34fbe05afb44afd130d73a
|
kubernetes/client/models/admissionregistration_v1_webhook_client_config.py
|
python
|
AdmissionregistrationV1WebhookClientConfig.service
|
(self, service)
|
Sets the service of this AdmissionregistrationV1WebhookClientConfig.
:param service: The service of this AdmissionregistrationV1WebhookClientConfig. # noqa: E501
:type: AdmissionregistrationV1ServiceReference
|
Sets the service of this AdmissionregistrationV1WebhookClientConfig.
|
[
"Sets",
"the",
"service",
"of",
"this",
"AdmissionregistrationV1WebhookClientConfig",
"."
] |
def service(self, service):
"""Sets the service of this AdmissionregistrationV1WebhookClientConfig.
:param service: The service of this AdmissionregistrationV1WebhookClientConfig. # noqa: E501
:type: AdmissionregistrationV1ServiceReference
"""
self._service = service
|
[
"def",
"service",
"(",
"self",
",",
"service",
")",
":",
"self",
".",
"_service",
"=",
"service"
] |
https://github.com/kubernetes-client/python/blob/47b9da9de2d02b2b7a34fbe05afb44afd130d73a/kubernetes/client/models/admissionregistration_v1_webhook_client_config.py#L102-L110
|
||
nltk/nltk
|
3f74ac55681667d7ef78b664557487145f51eb02
|
nltk/corpus/reader/propbank.py
|
python
|
PropbankInstance.predid
|
(self)
|
return "rel"
|
Identifier of the predicate.
|
Identifier of the predicate.
|
[
"Identifier",
"of",
"the",
"predicate",
"."
] |
def predid(self):
"""Identifier of the predicate."""
return "rel"
|
[
"def",
"predid",
"(",
"self",
")",
":",
"return",
"\"rel\""
] |
https://github.com/nltk/nltk/blob/3f74ac55681667d7ef78b664557487145f51eb02/nltk/corpus/reader/propbank.py#L232-L234
|
|
bisohns/search-engine-parser
|
ede1355a1f63398d9217b8e502fbd6c52b53bf09
|
search_engine_parser/core/engines/stackoverflow.py
|
python
|
Search.parse_single_result
|
(self, single_result, return_type=ReturnType.FULL, **kwargs)
|
return rdict
|
Parses the source code to return
:param single_result: single result found in <div class="summary">
:type single_result: `bs4.element.ResultSet`
:return: parsed title, link and description of single result
:rtype: dict
|
Parses the source code to return
|
[
"Parses",
"the",
"source",
"code",
"to",
"return"
] |
def parse_single_result(self, single_result, return_type=ReturnType.FULL, **kwargs):
"""
Parses the source code to return
:param single_result: single result found in <div class="summary">
:type single_result: `bs4.element.ResultSet`
:return: parsed title, link and description of single result
:rtype: dict
"""
rdict = SearchItem()
h3 = single_result.find('h3') # pylint: disable=invalid-name
link_tag = h3.find('a')
if return_type in (ReturnType.FULL, return_type.TITLE):
# Get the text and link
rdict["titles"] = link_tag.text
if return_type in (ReturnType.FULL, return_type.LINK):
ref_link = link_tag.get('href')
link = self.base_url + ref_link
rdict["links"] = link
if return_type in (ReturnType.FULL, return_type.DESCRIPTION):
caption = single_result.find('div', class_='excerpt')
rdict["descriptions"] = caption.text
return rdict
|
[
"def",
"parse_single_result",
"(",
"self",
",",
"single_result",
",",
"return_type",
"=",
"ReturnType",
".",
"FULL",
",",
"*",
"*",
"kwargs",
")",
":",
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"h3",
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"find",
"(",
"'h3'",
")",
"# pylint: disable=invalid-name",
"link_tag",
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"TITLE",
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"get",
"(",
"'href'",
")",
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"+",
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"]",
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",",
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"DESCRIPTION",
")",
":",
"caption",
"=",
"single_result",
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"'div'",
",",
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")",
"rdict",
"[",
"\"descriptions\"",
"]",
"=",
"caption",
".",
"text",
"return",
"rdict"
] |
https://github.com/bisohns/search-engine-parser/blob/ede1355a1f63398d9217b8e502fbd6c52b53bf09/search_engine_parser/core/engines/stackoverflow.py#L35-L59
|
|
apache/tvm
|
6eb4ed813ebcdcd9558f0906a1870db8302ff1e0
|
python/tvm/contrib/miopen.py
|
python
|
conv2d_forward
|
(
x,
w,
stride_h=1,
stride_w=1,
pad_h=0,
pad_w=0,
dilation_h=1,
dilation_w=1,
conv_mode=0,
data_type=1,
group_count=1,
)
|
return te.extern(
list(oshape),
[x, w],
lambda ins, outs: tvm.tir.call_packed(
"tvm.contrib.miopen.conv2d.forward",
conv_mode,
data_type,
pad_h,
pad_w,
stride_h,
stride_w,
dilation_h,
dilation_w,
algo,
ins[0],
ins[1],
outs[0],
),
name="y",
)
|
Create an extern op that compute 2D convolution with MIOpen
Parameters
----------
x: Tensor
input feature map
w: Tensor
convolution weight
stride_h: int
height stride
stride_w: int
width stride
pad_h: int
height pad
pad_w: int
weight pad
dilation_h: int
height dilation
dilation_w: int
width dilation
conv_mode: int
0: miopenConvolution
1: miopenTranspose
data_type: int
0: miopenHalf (fp16)
1: miopenFloat (fp32)
group_count: int
number of groups
Returns
-------
y: Tensor
The result tensor
|
Create an extern op that compute 2D convolution with MIOpen
|
[
"Create",
"an",
"extern",
"op",
"that",
"compute",
"2D",
"convolution",
"with",
"MIOpen"
] |
def conv2d_forward(
x,
w,
stride_h=1,
stride_w=1,
pad_h=0,
pad_w=0,
dilation_h=1,
dilation_w=1,
conv_mode=0,
data_type=1,
group_count=1,
):
"""Create an extern op that compute 2D convolution with MIOpen
Parameters
----------
x: Tensor
input feature map
w: Tensor
convolution weight
stride_h: int
height stride
stride_w: int
width stride
pad_h: int
height pad
pad_w: int
weight pad
dilation_h: int
height dilation
dilation_w: int
width dilation
conv_mode: int
0: miopenConvolution
1: miopenTranspose
data_type: int
0: miopenHalf (fp16)
1: miopenFloat (fp32)
group_count: int
number of groups
Returns
-------
y: Tensor
The result tensor
"""
assert 0 <= conv_mode <= 2, "0: miopenConvolution / 1: miopenTranspose / 2: miopenGroupConv"
if group_count > 1:
conv_mode = 2
oshape = np.zeros((len(x.shape)), dtype=np.int32)
xshape = x.shape
wshape = w.shape
setup_func = tvm._ffi.get_global_func("tvm.contrib.miopen.conv2d.setup")
algo = setup_func(
conv_mode,
data_type,
pad_h,
pad_w,
stride_h,
stride_w,
dilation_h,
dilation_w,
xshape[0].value,
xshape[1].value,
xshape[2].value,
xshape[3].value,
wshape[0].value,
wshape[1].value,
wshape[2].value,
wshape[3].value,
group_count,
_get_np_int32_array_handle(oshape),
)
return te.extern(
list(oshape),
[x, w],
lambda ins, outs: tvm.tir.call_packed(
"tvm.contrib.miopen.conv2d.forward",
conv_mode,
data_type,
pad_h,
pad_w,
stride_h,
stride_w,
dilation_h,
dilation_w,
algo,
ins[0],
ins[1],
outs[0],
),
name="y",
)
|
[
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"conv2d_forward",
"(",
"x",
",",
"w",
",",
"stride_h",
"=",
"1",
",",
"stride_w",
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"1",
",",
"pad_h",
"=",
"0",
",",
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"=",
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",",
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",",
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">",
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https://github.com/apache/tvm/blob/6eb4ed813ebcdcd9558f0906a1870db8302ff1e0/python/tvm/contrib/miopen.py#L45-L138
|
|
TencentCloud/tencentcloud-sdk-python
|
3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2
|
tencentcloud/dts/v20180330/dts_client.py
|
python
|
DtsClient.DescribeMigrateCheckJob
|
(self, request)
|
本接口用于创建校验后,获取校验的结果. 能查询到当前校验的状态和进度.
若通过校验, 则可调用'StartMigrateJob' 开始迁移.
若未通过校验, 则能查询到校验失败的原因. 请按照报错, 通过'ModifyMigrateJob'修改迁移配置或是调整源/目标实例的相关参数.
:param request: Request instance for DescribeMigrateCheckJob.
:type request: :class:`tencentcloud.dts.v20180330.models.DescribeMigrateCheckJobRequest`
:rtype: :class:`tencentcloud.dts.v20180330.models.DescribeMigrateCheckJobResponse`
|
本接口用于创建校验后,获取校验的结果. 能查询到当前校验的状态和进度.
若通过校验, 则可调用'StartMigrateJob' 开始迁移.
若未通过校验, 则能查询到校验失败的原因. 请按照报错, 通过'ModifyMigrateJob'修改迁移配置或是调整源/目标实例的相关参数.
|
[
"本接口用于创建校验后",
"获取校验的结果",
".",
"能查询到当前校验的状态和进度",
".",
"若通过校验",
"则可调用",
"StartMigrateJob",
"开始迁移",
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"若未通过校验",
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"请按照报错",
"通过",
"ModifyMigrateJob",
"修改迁移配置或是调整源",
"/",
"目标实例的相关参数",
"."
] |
def DescribeMigrateCheckJob(self, request):
"""本接口用于创建校验后,获取校验的结果. 能查询到当前校验的状态和进度.
若通过校验, 则可调用'StartMigrateJob' 开始迁移.
若未通过校验, 则能查询到校验失败的原因. 请按照报错, 通过'ModifyMigrateJob'修改迁移配置或是调整源/目标实例的相关参数.
:param request: Request instance for DescribeMigrateCheckJob.
:type request: :class:`tencentcloud.dts.v20180330.models.DescribeMigrateCheckJobRequest`
:rtype: :class:`tencentcloud.dts.v20180330.models.DescribeMigrateCheckJobResponse`
"""
try:
params = request._serialize()
body = self.call("DescribeMigrateCheckJob", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.DescribeMigrateCheckJobResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise
else:
raise TencentCloudSDKException(e.message, e.message)
|
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https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/dts/v20180330/dts_client.py#L231-L258
|
||
rll/rllab
|
ba78e4c16dc492982e648f117875b22af3965579
|
rllab/envs/box2d/box2d_viewer.py
|
python
|
Box2DViewer.setCenter
|
(self, value)
|
Updates the view offset based on the center of the screen.
Tells the debug draw to update its values also.
|
Updates the view offset based on the center of the screen.
|
[
"Updates",
"the",
"view",
"offset",
"based",
"on",
"the",
"center",
"of",
"the",
"screen",
"."
] |
def setCenter(self, value):
"""
Updates the view offset based on the center of the screen.
Tells the debug draw to update its values also.
"""
self._viewCenter = b2Vec2(*value)
self._viewCenter *= self._viewZoom
self._viewOffset = self._viewCenter - self.screenSize / 2
|
[
"def",
"setCenter",
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",",
"value",
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"_viewCenter",
"=",
"b2Vec2",
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"=",
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".",
"_viewCenter",
"-",
"self",
".",
"screenSize",
"/",
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] |
https://github.com/rll/rllab/blob/ba78e4c16dc492982e648f117875b22af3965579/rllab/envs/box2d/box2d_viewer.py#L182-L190
|
||
liquidctl/liquidctl
|
73962574632f94050c2a75f517e929a29797b5e2
|
liquidctl/cli.py
|
python
|
_device_set_color
|
(dev, args, **opts)
|
[] |
def _device_set_color(dev, args, **opts):
color = map(color_from_str, args['<color>'])
dev.set_color(args['<channel>'].lower(), args['<mode>'].lower(), color, **opts)
|
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"lower",
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https://github.com/liquidctl/liquidctl/blob/73962574632f94050c2a75f517e929a29797b5e2/liquidctl/cli.py#L274-L276
|
||||
facebookresearch/neuralvolumes
|
8c5fad49b2b05b4b2e79917ee87299e7c1676d59
|
data/dryice1.py
|
python
|
load_krt
|
(path)
|
return cameras
|
Load KRT file containing intrinsic and extrinsic parameters.
|
Load KRT file containing intrinsic and extrinsic parameters.
|
[
"Load",
"KRT",
"file",
"containing",
"intrinsic",
"and",
"extrinsic",
"parameters",
"."
] |
def load_krt(path):
"""Load KRT file containing intrinsic and extrinsic parameters."""
cameras = {}
with open(path, "r") as f:
while True:
name = f.readline()
if name == "":
break
intrin = [[float(x) for x in f.readline().split()] for i in range(3)]
dist = [float(x) for x in f.readline().split()]
extrin = [[float(x) for x in f.readline().split()] for i in range(3)]
f.readline()
cameras[name[:-1]] = {
"intrin": np.array(intrin),
"dist": np.array(dist),
"extrin": np.array(extrin)}
return cameras
|
[
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"load_krt",
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https://github.com/facebookresearch/neuralvolumes/blob/8c5fad49b2b05b4b2e79917ee87299e7c1676d59/data/dryice1.py#L13-L33
|
|
ask/mode
|
a104009f0c96790b9f6140179b4968da07a38c81
|
mode/worker.py
|
python
|
Worker.carp
|
(self, msg: str)
|
Write warning to standard err.
|
Write warning to standard err.
|
[
"Write",
"warning",
"to",
"standard",
"err",
"."
] |
def carp(self, msg: str) -> None:
"""Write warning to standard err."""
self._say(msg, file=self.stderr)
|
[
"def",
"carp",
"(",
"self",
",",
"msg",
":",
"str",
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"None",
":",
"self",
".",
"_say",
"(",
"msg",
",",
"file",
"=",
"self",
".",
"stderr",
")"
] |
https://github.com/ask/mode/blob/a104009f0c96790b9f6140179b4968da07a38c81/mode/worker.py#L153-L155
|
||
facebookresearch/FaderNetworks
|
cdd9e50659b635a6e04311e1cf4b9a6e6683319b
|
src/training.py
|
python
|
Trainer.save_best_periodic
|
(self, to_log)
|
Save the best models / periodically save the models.
|
Save the best models / periodically save the models.
|
[
"Save",
"the",
"best",
"models",
"/",
"periodically",
"save",
"the",
"models",
"."
] |
def save_best_periodic(self, to_log):
"""
Save the best models / periodically save the models.
"""
if to_log['ae_loss'] < self.best_loss:
self.best_loss = to_log['ae_loss']
logger.info('Best reconstruction loss: %.5f' % self.best_loss)
self.save_model('best_rec')
if self.params.eval_clf and np.mean(to_log['clf_accu']) > self.best_accu:
self.best_accu = np.mean(to_log['clf_accu'])
logger.info('Best evaluation accuracy: %.5f' % self.best_accu)
self.save_model('best_accu')
if to_log['n_epoch'] % 5 == 0 and to_log['n_epoch'] > 0:
self.save_model('periodic-%i' % to_log['n_epoch'])
|
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https://github.com/facebookresearch/FaderNetworks/blob/cdd9e50659b635a6e04311e1cf4b9a6e6683319b/src/training.py#L245-L258
|
||
abhik/pebl
|
5e7d694eb1e4f90e0f1410000b958ba62698a268
|
src/pebl/config.py
|
python
|
write
|
(filename, include_defaults=False)
|
Writes parameters to config file.
If include_default is True, writes all parameters. Else, only writes
parameters that were specifically set (via config file, command line, etc).
|
Writes parameters to config file.
|
[
"Writes",
"parameters",
"to",
"config",
"file",
"."
] |
def write(filename, include_defaults=False):
"""Writes parameters to config file.
If include_default is True, writes all parameters. Else, only writes
parameters that were specifically set (via config file, command line, etc).
"""
config = ConfigParser()
params = _parameters.values() if include_defaults \
else [p for p in _parameters.values() if p.source]
for param in params:
config.set(param.section, param.option, param.value)
with file(filename, 'w') as f:
config.write(f)
|
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https://github.com/abhik/pebl/blob/5e7d694eb1e4f90e0f1410000b958ba62698a268/src/pebl/config.py#L183-L199
|
||
kuri65536/python-for-android
|
26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891
|
python-build/python-libs/gdata/src/gdata/service.py
|
python
|
GDataService.ClientLogin
|
(self, username, password, account_type=None, service=None,
auth_service_url=None, source=None, captcha_token=None,
captcha_response=None)
|
Convenience method for authenticating using ProgrammaticLogin.
Sets values for email, password, and other optional members.
Args:
username:
password:
account_type: string (optional)
service: string (optional)
auth_service_url: string (optional)
captcha_token: string (optional)
captcha_response: string (optional)
|
Convenience method for authenticating using ProgrammaticLogin.
Sets values for email, password, and other optional members.
|
[
"Convenience",
"method",
"for",
"authenticating",
"using",
"ProgrammaticLogin",
".",
"Sets",
"values",
"for",
"email",
"password",
"and",
"other",
"optional",
"members",
"."
] |
def ClientLogin(self, username, password, account_type=None, service=None,
auth_service_url=None, source=None, captcha_token=None,
captcha_response=None):
"""Convenience method for authenticating using ProgrammaticLogin.
Sets values for email, password, and other optional members.
Args:
username:
password:
account_type: string (optional)
service: string (optional)
auth_service_url: string (optional)
captcha_token: string (optional)
captcha_response: string (optional)
"""
self.email = username
self.password = password
if account_type:
self.account_type = account_type
if service:
self.service = service
if source:
self.source = source
if auth_service_url:
self.auth_service_url = auth_service_url
self.ProgrammaticLogin(captcha_token, captcha_response)
|
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"=",
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"self",
".",
"ProgrammaticLogin",
"(",
"captcha_token",
",",
"captcha_response",
")"
] |
https://github.com/kuri65536/python-for-android/blob/26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891/python-build/python-libs/gdata/src/gdata/service.py#L815-L843
|
||
spectacles/CodeComplice
|
8ca8ee4236f72b58caa4209d2fbd5fa56bd31d62
|
libs/codeintel2/css_linter.py
|
python
|
_CSSLexerClassifier.is_tag
|
(self, tok)
|
return (tok['style'] == ScintillaConstants.SCE_CSS_TAG
or self.is_operator(tok, "*"))
|
[] |
def is_tag(self, tok):
return (tok['style'] == ScintillaConstants.SCE_CSS_TAG
or self.is_operator(tok, "*"))
|
[
"def",
"is_tag",
"(",
"self",
",",
"tok",
")",
":",
"return",
"(",
"tok",
"[",
"'style'",
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"SCE_CSS_TAG",
"or",
"self",
".",
"is_operator",
"(",
"tok",
",",
"\"*\"",
")",
")"
] |
https://github.com/spectacles/CodeComplice/blob/8ca8ee4236f72b58caa4209d2fbd5fa56bd31d62/libs/codeintel2/css_linter.py#L122-L124
|
|||
knownsec/Pocsuite
|
877d1b1604629b8dcd6e53b167c3c98249e5e94f
|
pocsuite/thirdparty/pyparsing/pyparsing.py
|
python
|
upcaseTokens
|
(s,l,t)
|
return [ tt.upper() for tt in map(_ustr,t) ]
|
Helper parse action to convert tokens to upper case.
|
Helper parse action to convert tokens to upper case.
|
[
"Helper",
"parse",
"action",
"to",
"convert",
"tokens",
"to",
"upper",
"case",
"."
] |
def upcaseTokens(s,l,t):
"""Helper parse action to convert tokens to upper case."""
return [ tt.upper() for tt in map(_ustr,t) ]
|
[
"def",
"upcaseTokens",
"(",
"s",
",",
"l",
",",
"t",
")",
":",
"return",
"[",
"tt",
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"upper",
"(",
")",
"for",
"tt",
"in",
"map",
"(",
"_ustr",
",",
"t",
")",
"]"
] |
https://github.com/knownsec/Pocsuite/blob/877d1b1604629b8dcd6e53b167c3c98249e5e94f/pocsuite/thirdparty/pyparsing/pyparsing.py#L3406-L3408
|
|
google/trax
|
d6cae2067dedd0490b78d831033607357e975015
|
trax/data/inputs.py
|
python
|
generate_sequential_chunks
|
(max_length=None)
|
return _f
|
Returns a function that generates chunks of atmost max_length length.
|
Returns a function that generates chunks of atmost max_length length.
|
[
"Returns",
"a",
"function",
"that",
"generates",
"chunks",
"of",
"atmost",
"max_length",
"length",
"."
] |
def generate_sequential_chunks(max_length=None):
"""Returns a function that generates chunks of atmost max_length length."""
def _f(generator):
for example in generator:
n_tokens = len(example)
if n_tokens <= max_length:
yield example
else:
n_segments = int(math.ceil(float(n_tokens) / float(max_length)))
for i in range(n_segments):
start = max_length * i
end = min(start + max_length, n_tokens)
yield example[start:end]
return _f
|
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https://github.com/google/trax/blob/d6cae2067dedd0490b78d831033607357e975015/trax/data/inputs.py#L918-L931
|
|
tomplus/kubernetes_asyncio
|
f028cc793e3a2c519be6a52a49fb77ff0b014c9b
|
kubernetes_asyncio/client/api/apps_v1beta2_api.py
|
python
|
AppsV1beta2Api.delete_namespaced_deployment_with_http_info
|
(self, name, namespace, **kwargs)
|
return self.api_client.call_api(
'/apis/apps/v1beta2/namespaces/{namespace}/deployments/{name}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1Status', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
|
delete_namespaced_deployment # noqa: E501
delete a Deployment # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_namespaced_deployment_with_http_info(name, namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str name: name of the Deployment (required)
:param str namespace: object name and auth scope, such as for teams and projects (required)
:param str pretty: If 'true', then the output is pretty printed.
:param str dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed
:param int grace_period_seconds: The duration in seconds before the object should be deleted. Value must be non-negative integer. The value zero indicates delete immediately. If this value is nil, the default grace period for the specified type will be used. Defaults to a per object value if not specified. zero means delete immediately.
:param bool orphan_dependents: Deprecated: please use the PropagationPolicy, this field will be deprecated in 1.7. Should the dependent objects be orphaned. If true/false, the \"orphan\" finalizer will be added to/removed from the object's finalizers list. Either this field or PropagationPolicy may be set, but not both.
:param str propagation_policy: Whether and how garbage collection will be performed. Either this field or OrphanDependents may be set, but not both. The default policy is decided by the existing finalizer set in the metadata.finalizers and the resource-specific default policy. Acceptable values are: 'Orphan' - orphan the dependents; 'Background' - allow the garbage collector to delete the dependents in the background; 'Foreground' - a cascading policy that deletes all dependents in the foreground.
:param V1DeleteOptions body:
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1Status, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
|
delete_namespaced_deployment # noqa: E501
|
[
"delete_namespaced_deployment",
"#",
"noqa",
":",
"E501"
] |
def delete_namespaced_deployment_with_http_info(self, name, namespace, **kwargs): # noqa: E501
"""delete_namespaced_deployment # noqa: E501
delete a Deployment # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_namespaced_deployment_with_http_info(name, namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str name: name of the Deployment (required)
:param str namespace: object name and auth scope, such as for teams and projects (required)
:param str pretty: If 'true', then the output is pretty printed.
:param str dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed
:param int grace_period_seconds: The duration in seconds before the object should be deleted. Value must be non-negative integer. The value zero indicates delete immediately. If this value is nil, the default grace period for the specified type will be used. Defaults to a per object value if not specified. zero means delete immediately.
:param bool orphan_dependents: Deprecated: please use the PropagationPolicy, this field will be deprecated in 1.7. Should the dependent objects be orphaned. If true/false, the \"orphan\" finalizer will be added to/removed from the object's finalizers list. Either this field or PropagationPolicy may be set, but not both.
:param str propagation_policy: Whether and how garbage collection will be performed. Either this field or OrphanDependents may be set, but not both. The default policy is decided by the existing finalizer set in the metadata.finalizers and the resource-specific default policy. Acceptable values are: 'Orphan' - orphan the dependents; 'Background' - allow the garbage collector to delete the dependents in the background; 'Foreground' - a cascading policy that deletes all dependents in the foreground.
:param V1DeleteOptions body:
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1Status, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'name',
'namespace',
'pretty',
'dry_run',
'grace_period_seconds',
'orphan_dependents',
'propagation_policy',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_namespaced_deployment" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `delete_namespaced_deployment`") # noqa: E501
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `delete_namespaced_deployment`") # noqa: E501
collection_formats = {}
path_params = {}
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
query_params = []
if 'pretty' in local_var_params and local_var_params['pretty'] is not None: # noqa: E501
query_params.append(('pretty', local_var_params['pretty'])) # noqa: E501
if 'dry_run' in local_var_params and local_var_params['dry_run'] is not None: # noqa: E501
query_params.append(('dryRun', local_var_params['dry_run'])) # noqa: E501
if 'grace_period_seconds' in local_var_params and local_var_params['grace_period_seconds'] is not None: # noqa: E501
query_params.append(('gracePeriodSeconds', local_var_params['grace_period_seconds'])) # noqa: E501
if 'orphan_dependents' in local_var_params and local_var_params['orphan_dependents'] is not None: # noqa: E501
query_params.append(('orphanDependents', local_var_params['orphan_dependents'])) # noqa: E501
if 'propagation_policy' in local_var_params and local_var_params['propagation_policy'] is not None: # noqa: E501
query_params.append(('propagationPolicy', local_var_params['propagation_policy'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'application/yaml', 'application/vnd.kubernetes.protobuf']) # noqa: E501
# Authentication setting
auth_settings = ['BearerToken'] # noqa: E501
return self.api_client.call_api(
'/apis/apps/v1beta2/namespaces/{namespace}/deployments/{name}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1Status', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
|
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https://github.com/tomplus/kubernetes_asyncio/blob/f028cc793e3a2c519be6a52a49fb77ff0b014c9b/kubernetes_asyncio/client/api/apps_v1beta2_api.py#L1937-L2056
|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/sqlalchemy/orm/identity.py
|
python
|
StrongInstanceDict.add
|
(self, state)
|
[] |
def add(self, state):
if state.key in self:
if attributes.instance_state(self._dict[state.key]) is not state:
raise sa_exc.InvalidRequestError(
"Can't attach instance "
"%s; another instance with key %s is already "
"present in this session." % (
orm_util.state_str(state), state.key))
return False
else:
self._dict[state.key] = state.obj()
self._manage_incoming_state(state)
return True
|
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".",
"state_str",
"(",
"state",
")",
",",
"state",
".",
"key",
")",
")",
"return",
"False",
"else",
":",
"self",
".",
"_dict",
"[",
"state",
".",
"key",
"]",
"=",
"state",
".",
"obj",
"(",
")",
"self",
".",
"_manage_incoming_state",
"(",
"state",
")",
"return",
"True"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/sqlalchemy/orm/identity.py#L300-L312
|
||||
hyperledger/aries-cloudagent-python
|
2f36776e99f6053ae92eed8123b5b1b2e891c02a
|
aries_cloudagent/protocols/present_proof/dif/pres_exch_handler.py
|
python
|
DIFPresExchHandler.process_constraint_holders
|
(
self,
subject_ids: Sequence[str],
)
|
Check if holder or subject of claim still controls the identifier.
|
Check if holder or subject of claim still controls the identifier.
|
[
"Check",
"if",
"holder",
"or",
"subject",
"of",
"claim",
"still",
"controls",
"the",
"identifier",
"."
] |
async def process_constraint_holders(
self,
subject_ids: Sequence[str],
) -> bool:
"""Check if holder or subject of claim still controls the identifier."""
async with self.profile.session() as session:
wallet = session.inject(BaseWallet)
try:
for subject_id in subject_ids:
await wallet.get_local_did(subject_id.replace("did:sov:", ""))
self.is_holder = True
return True
except (WalletError, WalletNotFoundError):
return False
|
[
"async",
"def",
"process_constraint_holders",
"(",
"self",
",",
"subject_ids",
":",
"Sequence",
"[",
"str",
"]",
",",
")",
"->",
"bool",
":",
"async",
"with",
"self",
".",
"profile",
".",
"session",
"(",
")",
"as",
"session",
":",
"wallet",
"=",
"session",
".",
"inject",
"(",
"BaseWallet",
")",
"try",
":",
"for",
"subject_id",
"in",
"subject_ids",
":",
"await",
"wallet",
".",
"get_local_did",
"(",
"subject_id",
".",
"replace",
"(",
"\"did:sov:\"",
",",
"\"\"",
")",
")",
"self",
".",
"is_holder",
"=",
"True",
"return",
"True",
"except",
"(",
"WalletError",
",",
"WalletNotFoundError",
")",
":",
"return",
"False"
] |
https://github.com/hyperledger/aries-cloudagent-python/blob/2f36776e99f6053ae92eed8123b5b1b2e891c02a/aries_cloudagent/protocols/present_proof/dif/pres_exch_handler.py#L437-L450
|
||
albertz/music-player
|
d23586f5bf657cbaea8147223be7814d117ae73d
|
mac/pyobjc-framework-Quartz/Examples/Programming with Quartz/BasicDrawing/ImageMasking.py
|
python
|
getMaskData2
|
()
|
return _data2
|
[] |
def getMaskData2():
return _data2
|
[
"def",
"getMaskData2",
"(",
")",
":",
"return",
"_data2"
] |
https://github.com/albertz/music-player/blob/d23586f5bf657cbaea8147223be7814d117ae73d/mac/pyobjc-framework-Quartz/Examples/Programming with Quartz/BasicDrawing/ImageMasking.py#L230-L231
|
|||
stanfordnlp/cocoa
|
26bfea048f00241eb4dbff861b2d9260bde52636
|
mutualfriends/web/third_party_backend.py
|
python
|
BackendConnection.create_user_if_necessary
|
(self, userid)
|
Adds a new user to the database if necessary
:param :
:return:
|
Adds a new user to the database if necessary
:param :
:return:
|
[
"Adds",
"a",
"new",
"user",
"to",
"the",
"database",
"if",
"necessary",
":",
"param",
":",
":",
"return",
":"
] |
def create_user_if_necessary(self, userid):
"""
Adds a new user to the database if necessary
:param :
:return:
"""
with self.conn:
cursor = self.conn.cursor()
now = get_timestamp()
cursor.execute("INSERT OR IGNORE INTO ActiveUsers VALUES (?,?,?,?,?)",
(userid, json.dumps([]), json.dumps([]), 0, now))
|
[
"def",
"create_user_if_necessary",
"(",
"self",
",",
"userid",
")",
":",
"with",
"self",
".",
"conn",
":",
"cursor",
"=",
"self",
".",
"conn",
".",
"cursor",
"(",
")",
"now",
"=",
"get_timestamp",
"(",
")",
"cursor",
".",
"execute",
"(",
"\"INSERT OR IGNORE INTO ActiveUsers VALUES (?,?,?,?,?)\"",
",",
"(",
"userid",
",",
"json",
".",
"dumps",
"(",
"[",
"]",
")",
",",
"json",
".",
"dumps",
"(",
"[",
"]",
")",
",",
"0",
",",
"now",
")",
")"
] |
https://github.com/stanfordnlp/cocoa/blob/26bfea048f00241eb4dbff861b2d9260bde52636/mutualfriends/web/third_party_backend.py#L31-L41
|
||
pymc-devs/pymc
|
38867dd19e96afb0ceccc8ccd74a9795f118dfe3
|
pymc/distributions/continuous.py
|
python
|
Gumbel.logcdf
|
(
value: Union[float, np.ndarray, TensorVariable],
mu: Union[float, np.ndarray, TensorVariable],
beta: Union[float, np.ndarray, TensorVariable],
)
|
return check_parameters(res, 0 < beta, msg="beta > 0")
|
Compute the log of the cumulative distribution function for Gumbel distribution
at the specified value.
Parameters
----------
value: numeric or np.ndarray or aesara.tensor
Value(s) for which log CDF is calculated. If the log CDF for multiple
values are desired the values must be provided in a numpy array or Aesara tensor.
Returns
-------
TensorVariable
|
Compute the log of the cumulative distribution function for Gumbel distribution
at the specified value.
|
[
"Compute",
"the",
"log",
"of",
"the",
"cumulative",
"distribution",
"function",
"for",
"Gumbel",
"distribution",
"at",
"the",
"specified",
"value",
"."
] |
def logcdf(
value: Union[float, np.ndarray, TensorVariable],
mu: Union[float, np.ndarray, TensorVariable],
beta: Union[float, np.ndarray, TensorVariable],
) -> TensorVariable:
"""
Compute the log of the cumulative distribution function for Gumbel distribution
at the specified value.
Parameters
----------
value: numeric or np.ndarray or aesara.tensor
Value(s) for which log CDF is calculated. If the log CDF for multiple
values are desired the values must be provided in a numpy array or Aesara tensor.
Returns
-------
TensorVariable
"""
res = -at.exp(-(value - mu) / beta)
return check_parameters(res, 0 < beta, msg="beta > 0")
|
[
"def",
"logcdf",
"(",
"value",
":",
"Union",
"[",
"float",
",",
"np",
".",
"ndarray",
",",
"TensorVariable",
"]",
",",
"mu",
":",
"Union",
"[",
"float",
",",
"np",
".",
"ndarray",
",",
"TensorVariable",
"]",
",",
"beta",
":",
"Union",
"[",
"float",
",",
"np",
".",
"ndarray",
",",
"TensorVariable",
"]",
",",
")",
"->",
"TensorVariable",
":",
"res",
"=",
"-",
"at",
".",
"exp",
"(",
"-",
"(",
"value",
"-",
"mu",
")",
"/",
"beta",
")",
"return",
"check_parameters",
"(",
"res",
",",
"0",
"<",
"beta",
",",
"msg",
"=",
"\"beta > 0\"",
")"
] |
https://github.com/pymc-devs/pymc/blob/38867dd19e96afb0ceccc8ccd74a9795f118dfe3/pymc/distributions/continuous.py#L3311-L3332
|
|
bruderstein/PythonScript
|
df9f7071ddf3a079e3a301b9b53a6dc78cf1208f
|
PythonLib/full/filecmp.py
|
python
|
_filter
|
(flist, skip)
|
return list(filterfalse(skip.__contains__, flist))
|
[] |
def _filter(flist, skip):
return list(filterfalse(skip.__contains__, flist))
|
[
"def",
"_filter",
"(",
"flist",
",",
"skip",
")",
":",
"return",
"list",
"(",
"filterfalse",
"(",
"skip",
".",
"__contains__",
",",
"flist",
")",
")"
] |
https://github.com/bruderstein/PythonScript/blob/df9f7071ddf3a079e3a301b9b53a6dc78cf1208f/PythonLib/full/filecmp.py#L294-L295
|
|||
cloudera/hue
|
23f02102d4547c17c32bd5ea0eb24e9eadd657a4
|
apps/beeswax/gen-py/hive_metastore/ThriftHiveMetastore.py
|
python
|
Client.delete_partition_column_statistics
|
(self, db_name, tbl_name, part_name, col_name)
|
return self.recv_delete_partition_column_statistics()
|
Parameters:
- db_name
- tbl_name
- part_name
- col_name
|
Parameters:
- db_name
- tbl_name
- part_name
- col_name
|
[
"Parameters",
":",
"-",
"db_name",
"-",
"tbl_name",
"-",
"part_name",
"-",
"col_name"
] |
def delete_partition_column_statistics(self, db_name, tbl_name, part_name, col_name):
"""
Parameters:
- db_name
- tbl_name
- part_name
- col_name
"""
self.send_delete_partition_column_statistics(db_name, tbl_name, part_name, col_name)
return self.recv_delete_partition_column_statistics()
|
[
"def",
"delete_partition_column_statistics",
"(",
"self",
",",
"db_name",
",",
"tbl_name",
",",
"part_name",
",",
"col_name",
")",
":",
"self",
".",
"send_delete_partition_column_statistics",
"(",
"db_name",
",",
"tbl_name",
",",
"part_name",
",",
"col_name",
")",
"return",
"self",
".",
"recv_delete_partition_column_statistics",
"(",
")"
] |
https://github.com/cloudera/hue/blob/23f02102d4547c17c32bd5ea0eb24e9eadd657a4/apps/beeswax/gen-py/hive_metastore/ThriftHiveMetastore.py#L4946-L4956
|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_hxb2/lib/python3.5/site-packages/PIL/ImageSequence.py
|
python
|
Iterator.next
|
(self)
|
return self.__next__()
|
[] |
def next(self):
return self.__next__()
|
[
"def",
"next",
"(",
"self",
")",
":",
"return",
"self",
".",
"__next__",
"(",
")"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_hxb2/lib/python3.5/site-packages/PIL/ImageSequence.py#L55-L56
|
|||
SilenceDut/nbaplus-server
|
368067f7a1de958c12fb35462afe2713760b52ae
|
beautifulsoup4-4.3.2/bs4/dammit.py
|
python
|
UnicodeDammit._to_unicode
|
(self, data, encoding, errors="strict")
|
return unicode(data, encoding, errors)
|
Given a string and its encoding, decodes the string into Unicode.
%encoding is a string recognized by encodings.aliases
|
Given a string and its encoding, decodes the string into Unicode.
%encoding is a string recognized by encodings.aliases
|
[
"Given",
"a",
"string",
"and",
"its",
"encoding",
"decodes",
"the",
"string",
"into",
"Unicode",
".",
"%encoding",
"is",
"a",
"string",
"recognized",
"by",
"encodings",
".",
"aliases"
] |
def _to_unicode(self, data, encoding, errors="strict"):
'''Given a string and its encoding, decodes the string into Unicode.
%encoding is a string recognized by encodings.aliases'''
return unicode(data, encoding, errors)
|
[
"def",
"_to_unicode",
"(",
"self",
",",
"data",
",",
"encoding",
",",
"errors",
"=",
"\"strict\"",
")",
":",
"return",
"unicode",
"(",
"data",
",",
"encoding",
",",
"errors",
")"
] |
https://github.com/SilenceDut/nbaplus-server/blob/368067f7a1de958c12fb35462afe2713760b52ae/beautifulsoup4-4.3.2/bs4/dammit.py#L425-L428
|
|
freedombox/FreedomBox
|
335a7f92cc08f27981f838a7cddfc67740598e54
|
plinth/modules/networks/__init__.py
|
python
|
set_internet_connection_type
|
(internet_connection_type)
|
return kvstore.set('networks_internet_type', internet_connection_type)
|
Store the internet connection type.
|
Store the internet connection type.
|
[
"Store",
"the",
"internet",
"connection",
"type",
"."
] |
def set_internet_connection_type(internet_connection_type):
"""Store the internet connection type."""
return kvstore.set('networks_internet_type', internet_connection_type)
|
[
"def",
"set_internet_connection_type",
"(",
"internet_connection_type",
")",
":",
"return",
"kvstore",
".",
"set",
"(",
"'networks_internet_type'",
",",
"internet_connection_type",
")"
] |
https://github.com/freedombox/FreedomBox/blob/335a7f92cc08f27981f838a7cddfc67740598e54/plinth/modules/networks/__init__.py#L110-L112
|
|
FederatedAI/FATE
|
32540492623568ecd1afcb367360133616e02fa3
|
python/fate_arch/federation/pulsar/_pulsar_manager.py
|
python
|
PulsarManager.delete_namespace
|
(self, tenant: str, namespace: str, force: bool = False)
|
return response
|
[] |
def delete_namespace(self, tenant: str, namespace: str, force: bool = False):
session = self._create_session()
response = session.delete(
self.service_url +
'namespace/{}/{}?force={}'.format(tenant,
namespace, str(force).lower())
)
return response
|
[
"def",
"delete_namespace",
"(",
"self",
",",
"tenant",
":",
"str",
",",
"namespace",
":",
"str",
",",
"force",
":",
"bool",
"=",
"False",
")",
":",
"session",
"=",
"self",
".",
"_create_session",
"(",
")",
"response",
"=",
"session",
".",
"delete",
"(",
"self",
".",
"service_url",
"+",
"'namespace/{}/{}?force={}'",
".",
"format",
"(",
"tenant",
",",
"namespace",
",",
"str",
"(",
"force",
")",
".",
"lower",
"(",
")",
")",
")",
"return",
"response"
] |
https://github.com/FederatedAI/FATE/blob/32540492623568ecd1afcb367360133616e02fa3/python/fate_arch/federation/pulsar/_pulsar_manager.py#L161-L168
|
|||
albertz/music-player
|
d23586f5bf657cbaea8147223be7814d117ae73d
|
mac/pyobjc-framework-Quartz/Examples/Programming with Quartz/BasicDrawing/Utilities.py
|
python
|
getRGBOpaqueRedColor
|
()
|
return _rgbRed
|
[] |
def getRGBOpaqueRedColor():
global _rgbRed
if _rgbRed is None:
opaqueRed = (0.663, 0, 0.031, 1)
_rgbRed = CGColorCreate(
getTheCalibratedRGBColorSpace(), opaqueRed)
return _rgbRed
|
[
"def",
"getRGBOpaqueRedColor",
"(",
")",
":",
"global",
"_rgbRed",
"if",
"_rgbRed",
"is",
"None",
":",
"opaqueRed",
"=",
"(",
"0.663",
",",
"0",
",",
"0.031",
",",
"1",
")",
"_rgbRed",
"=",
"CGColorCreate",
"(",
"getTheCalibratedRGBColorSpace",
"(",
")",
",",
"opaqueRed",
")",
"return",
"_rgbRed"
] |
https://github.com/albertz/music-player/blob/d23586f5bf657cbaea8147223be7814d117ae73d/mac/pyobjc-framework-Quartz/Examples/Programming with Quartz/BasicDrawing/Utilities.py#L120-L126
|
|||
saltstack/salt
|
fae5bc757ad0f1716483ce7ae180b451545c2058
|
salt/modules/bigip.py
|
python
|
delete_pool
|
(hostname, username, password, name)
|
A function to connect to a bigip device and delete a specific pool.
hostname
The host/address of the bigip device
username
The iControl REST username
password
The iControl REST password
name
The name of the pool which will be deleted
CLI Example
.. code-block:: bash
salt '*' bigip.delete_node bigip admin admin my-pool
|
A function to connect to a bigip device and delete a specific pool.
|
[
"A",
"function",
"to",
"connect",
"to",
"a",
"bigip",
"device",
"and",
"delete",
"a",
"specific",
"pool",
"."
] |
def delete_pool(hostname, username, password, name):
"""
A function to connect to a bigip device and delete a specific pool.
hostname
The host/address of the bigip device
username
The iControl REST username
password
The iControl REST password
name
The name of the pool which will be deleted
CLI Example
.. code-block:: bash
salt '*' bigip.delete_node bigip admin admin my-pool
"""
# build session
bigip_session = _build_session(username, password)
# delete to REST
try:
response = bigip_session.delete(
BIG_IP_URL_BASE.format(host=hostname) + "/ltm/pool/{name}".format(name=name)
)
except requests.exceptions.ConnectionError as e:
return _load_connection_error(hostname, e)
if _load_response(response) == "":
return True
else:
return _load_response(response)
|
[
"def",
"delete_pool",
"(",
"hostname",
",",
"username",
",",
"password",
",",
"name",
")",
":",
"# build session",
"bigip_session",
"=",
"_build_session",
"(",
"username",
",",
"password",
")",
"# delete to REST",
"try",
":",
"response",
"=",
"bigip_session",
".",
"delete",
"(",
"BIG_IP_URL_BASE",
".",
"format",
"(",
"host",
"=",
"hostname",
")",
"+",
"\"/ltm/pool/{name}\"",
".",
"format",
"(",
"name",
"=",
"name",
")",
")",
"except",
"requests",
".",
"exceptions",
".",
"ConnectionError",
"as",
"e",
":",
"return",
"_load_connection_error",
"(",
"hostname",
",",
"e",
")",
"if",
"_load_response",
"(",
"response",
")",
"==",
"\"\"",
":",
"return",
"True",
"else",
":",
"return",
"_load_response",
"(",
"response",
")"
] |
https://github.com/saltstack/salt/blob/fae5bc757ad0f1716483ce7ae180b451545c2058/salt/modules/bigip.py#L1004-L1038
|
||
pyansys/pymapdl
|
c07291fc062b359abf0e92b95a92d753a95ef3d7
|
ansys/mapdl/core/_commands/preproc/meshing.py
|
python
|
Meshing.desize
|
(
self,
minl="",
minh="",
mxel="",
angl="",
angh="",
edgmn="",
edgmx="",
adjf="",
adjm="",
**kwargs,
)
|
return self.run(command, **kwargs)
|
Controls default element sizes.
APDL Command: DESIZE
Parameters
----------
minl
Minimum number of elements that will be attached to a line when
using lower-order elements (defaults to 3 elements per line). If
MINL = DEFA, all arguments will be set back to default values. If
MINL = STAT, list status of command (Including on/off status). If
MINL = OFF, deactivate default element sizing. If MINL = ON,
reactivate default element sizing.
minh
Minimum number of elements that will be attached to a line when
using higher-order elements. Defaults to 2 elements per line.
mxel
Maximum number of elements that will be attached to a single line
(lower or higher-order elements). Defaults to 15 elements per line
for h-elements. To deactivate this limit, specify a large number
(such as 9999).
angl
Maximum spanned angle per lower-order element for curved lines.
Defaults to 15 degrees per element.
angh
Maximum spanned angle per higher-order element for curved lines.
Defaults to 28 degrees per element.
edgmn
Minimum element edge length. Defaults to no minimum edge length.
The MINL or MINH argument can override this value.
edgmx
Maximum element edge length. Defaults to no maximum edge length.
The MXEL argument can override this value.
adjf
Target aspect ratio for adjacent line. Used only when free
meshing. Defaults to 1.0, which attempts to create equal-sided
h-elements.
adjm
Target aspect ratio for adjacent line. Used only when map meshing.
Defaults to 4.0, which attempts to create rectangular h-elements.
Notes
-----
DESIZE settings are usually used for mapped meshing. They are also
used for free meshing if SmartSizing is turned off [SMRTSIZE,OFF],
which is the default. Even when SmartSizing is on, some DESIZE
settings (such as maximum and minimum element edge length) can affect
free mesh density. The default settings of the DESIZE command are used
only when no other element size specifications [KESIZE, LESIZE, ESIZE]
exist for a certain line.
This command is also valid for rezoning.
|
Controls default element sizes.
|
[
"Controls",
"default",
"element",
"sizes",
"."
] |
def desize(
self,
minl="",
minh="",
mxel="",
angl="",
angh="",
edgmn="",
edgmx="",
adjf="",
adjm="",
**kwargs,
):
"""Controls default element sizes.
APDL Command: DESIZE
Parameters
----------
minl
Minimum number of elements that will be attached to a line when
using lower-order elements (defaults to 3 elements per line). If
MINL = DEFA, all arguments will be set back to default values. If
MINL = STAT, list status of command (Including on/off status). If
MINL = OFF, deactivate default element sizing. If MINL = ON,
reactivate default element sizing.
minh
Minimum number of elements that will be attached to a line when
using higher-order elements. Defaults to 2 elements per line.
mxel
Maximum number of elements that will be attached to a single line
(lower or higher-order elements). Defaults to 15 elements per line
for h-elements. To deactivate this limit, specify a large number
(such as 9999).
angl
Maximum spanned angle per lower-order element for curved lines.
Defaults to 15 degrees per element.
angh
Maximum spanned angle per higher-order element for curved lines.
Defaults to 28 degrees per element.
edgmn
Minimum element edge length. Defaults to no minimum edge length.
The MINL or MINH argument can override this value.
edgmx
Maximum element edge length. Defaults to no maximum edge length.
The MXEL argument can override this value.
adjf
Target aspect ratio for adjacent line. Used only when free
meshing. Defaults to 1.0, which attempts to create equal-sided
h-elements.
adjm
Target aspect ratio for adjacent line. Used only when map meshing.
Defaults to 4.0, which attempts to create rectangular h-elements.
Notes
-----
DESIZE settings are usually used for mapped meshing. They are also
used for free meshing if SmartSizing is turned off [SMRTSIZE,OFF],
which is the default. Even when SmartSizing is on, some DESIZE
settings (such as maximum and minimum element edge length) can affect
free mesh density. The default settings of the DESIZE command are used
only when no other element size specifications [KESIZE, LESIZE, ESIZE]
exist for a certain line.
This command is also valid for rezoning.
"""
command = (
f"DESIZE,{minl},{minh},{mxel},{angl},{angh},{edgmn},{edgmx},{adjf},{adjm}"
)
return self.run(command, **kwargs)
|
[
"def",
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"minl",
"=",
"\"\"",
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"minh",
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"\"\"",
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"mxel",
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"\"\"",
",",
"angl",
"=",
"\"\"",
",",
"angh",
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"\"\"",
",",
"edgmn",
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"\"\"",
",",
"edgmx",
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"\"\"",
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"f\"DESIZE,{minl},{minh},{mxel},{angl},{angh},{edgmn},{edgmx},{adjf},{adjm}\"",
")",
"return",
"self",
".",
"run",
"(",
"command",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/pyansys/pymapdl/blob/c07291fc062b359abf0e92b95a92d753a95ef3d7/ansys/mapdl/core/_commands/preproc/meshing.py#L456-L533
|
|
googleads/google-ads-python
|
2a1d6062221f6aad1992a6bcca0e7e4a93d2db86
|
google/ads/googleads/v7/services/services/google_ads_service/client.py
|
python
|
GoogleAdsServiceClient.hotel_performance_view_path
|
(customer_id: str,)
|
return "customers/{customer_id}/hotelPerformanceView".format(
customer_id=customer_id,
)
|
Return a fully-qualified hotel_performance_view string.
|
Return a fully-qualified hotel_performance_view string.
|
[
"Return",
"a",
"fully",
"-",
"qualified",
"hotel_performance_view",
"string",
"."
] |
def hotel_performance_view_path(customer_id: str,) -> str:
"""Return a fully-qualified hotel_performance_view string."""
return "customers/{customer_id}/hotelPerformanceView".format(
customer_id=customer_id,
)
|
[
"def",
"hotel_performance_view_path",
"(",
"customer_id",
":",
"str",
",",
")",
"->",
"str",
":",
"return",
"\"customers/{customer_id}/hotelPerformanceView\"",
".",
"format",
"(",
"customer_id",
"=",
"customer_id",
",",
")"
] |
https://github.com/googleads/google-ads-python/blob/2a1d6062221f6aad1992a6bcca0e7e4a93d2db86/google/ads/googleads/v7/services/services/google_ads_service/client.py#L1720-L1724
|
|
JaniceWuo/MovieRecommend
|
4c86db64ca45598917d304f535413df3bc9fea65
|
movierecommend/venv1/Lib/site-packages/django/core/serializers/__init__.py
|
python
|
unregister_serializer
|
(format)
|
Unregister a given serializer. This is not a thread-safe operation.
|
Unregister a given serializer. This is not a thread-safe operation.
|
[
"Unregister",
"a",
"given",
"serializer",
".",
"This",
"is",
"not",
"a",
"thread",
"-",
"safe",
"operation",
"."
] |
def unregister_serializer(format):
"Unregister a given serializer. This is not a thread-safe operation."
if not _serializers:
_load_serializers()
if format not in _serializers:
raise SerializerDoesNotExist(format)
del _serializers[format]
|
[
"def",
"unregister_serializer",
"(",
"format",
")",
":",
"if",
"not",
"_serializers",
":",
"_load_serializers",
"(",
")",
"if",
"format",
"not",
"in",
"_serializers",
":",
"raise",
"SerializerDoesNotExist",
"(",
"format",
")",
"del",
"_serializers",
"[",
"format",
"]"
] |
https://github.com/JaniceWuo/MovieRecommend/blob/4c86db64ca45598917d304f535413df3bc9fea65/movierecommend/venv1/Lib/site-packages/django/core/serializers/__init__.py#L86-L92
|
||
python/cpython
|
e13cdca0f5224ec4e23bdd04bb3120506964bc8b
|
Lib/pipes.py
|
python
|
Template.__repr__
|
(self)
|
return '<Template instance, steps=%r>' % (self.steps,)
|
t.__repr__() implements repr(t).
|
t.__repr__() implements repr(t).
|
[
"t",
".",
"__repr__",
"()",
"implements",
"repr",
"(",
"t",
")",
"."
] |
def __repr__(self):
"""t.__repr__() implements repr(t)."""
return '<Template instance, steps=%r>' % (self.steps,)
|
[
"def",
"__repr__",
"(",
"self",
")",
":",
"return",
"'<Template instance, steps=%r>'",
"%",
"(",
"self",
".",
"steps",
",",
")"
] |
https://github.com/python/cpython/blob/e13cdca0f5224ec4e23bdd04bb3120506964bc8b/Lib/pipes.py#L90-L92
|
|
Project-MONAI/MONAI
|
83f8b06372a3803ebe9281300cb794a1f3395018
|
monai/transforms/utility/array.py
|
python
|
AsChannelLast.__call__
|
(self, img: NdarrayOrTensor)
|
return moveaxis(img, self.channel_dim, -1)
|
Apply the transform to `img`.
|
Apply the transform to `img`.
|
[
"Apply",
"the",
"transform",
"to",
"img",
"."
] |
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Apply the transform to `img`.
"""
return moveaxis(img, self.channel_dim, -1)
|
[
"def",
"__call__",
"(",
"self",
",",
"img",
":",
"NdarrayOrTensor",
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"->",
"NdarrayOrTensor",
":",
"return",
"moveaxis",
"(",
"img",
",",
"self",
".",
"channel_dim",
",",
"-",
"1",
")"
] |
https://github.com/Project-MONAI/MONAI/blob/83f8b06372a3803ebe9281300cb794a1f3395018/monai/transforms/utility/array.py#L159-L163
|
|
terrifyzhao/bert-utils
|
1d5f3eb649b4ee8a059f7050da483d0cd6d7fff4
|
modeling.py
|
python
|
attention_layer
|
(from_tensor,
to_tensor,
attention_mask=None,
num_attention_heads=1,
size_per_head=512,
query_act=None,
key_act=None,
value_act=None,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
do_return_2d_tensor=False,
batch_size=None,
from_seq_length=None,
to_seq_length=None)
|
return context_layer
|
Performs multi-headed attention from `from_tensor` to `to_tensor`.
This is an implementation of multi-headed attention based on "Attention
is all you Need". If `from_tensor` and `to_tensor` are the same, then
this is self-attention. Each timestep in `from_tensor` attends to the
corresponding sequence in `to_tensor`, and returns a fixed-with vector.
This function first projects `from_tensor` into a "query" tensor and
`to_tensor` into "key" and "value" tensors. These are (effectively) a list
of tensors of length `num_attention_heads`, where each tensor is of shape
[batch_size, seq_length, size_per_head].
Then, the query and key tensors are dot-producted and scaled. These are
softmaxed to obtain attention probabilities. The value tensors are then
interpolated by these probabilities, then concatenated back to a single
tensor and returned.
In practice, the multi-headed attention are done with transposes and
reshapes rather than actual separate tensors.
Args:
from_tensor: float Tensor of shape [batch_size, from_seq_length,
from_width].
to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width].
attention_mask: (optional) int32 Tensor of shape [batch_size,
from_seq_length, to_seq_length]. The values should be 1 or 0. The
attention scores will effectively be set to -infinity for any positions in
the mask that are 0, and will be unchanged for positions that are 1.
num_attention_heads: int. Number of attention heads.
size_per_head: int. Size of each attention head.
query_act: (optional) Activation function for the query transform.
key_act: (optional) Activation function for the key transform.
value_act: (optional) Activation function for the value transform.
attention_probs_dropout_prob: (optional) float. Dropout probability of the
attention probabilities.
initializer_range: float. Range of the weight initializer.
do_return_2d_tensor: bool. If True, the output will be of shape [batch_size
* from_seq_length, num_attention_heads * size_per_head]. If False, the
output will be of shape [batch_size, from_seq_length, num_attention_heads
* size_per_head].
batch_size: (Optional) int. If the input is 2D, this might be the batch size
of the 3D version of the `from_tensor` and `to_tensor`.
from_seq_length: (Optional) If the input is 2D, this might be the seq length
of the 3D version of the `from_tensor`.
to_seq_length: (Optional) If the input is 2D, this might be the seq length
of the 3D version of the `to_tensor`.
Returns:
float Tensor of shape [batch_size, from_seq_length,
num_attention_heads * size_per_head]. (If `do_return_2d_tensor` is
true, this will be of shape [batch_size * from_seq_length,
num_attention_heads * size_per_head]).
Raises:
ValueError: Any of the arguments or tensor shapes are invalid.
|
Performs multi-headed attention from `from_tensor` to `to_tensor`.
|
[
"Performs",
"multi",
"-",
"headed",
"attention",
"from",
"from_tensor",
"to",
"to_tensor",
"."
] |
def attention_layer(from_tensor,
to_tensor,
attention_mask=None,
num_attention_heads=1,
size_per_head=512,
query_act=None,
key_act=None,
value_act=None,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
do_return_2d_tensor=False,
batch_size=None,
from_seq_length=None,
to_seq_length=None):
"""Performs multi-headed attention from `from_tensor` to `to_tensor`.
This is an implementation of multi-headed attention based on "Attention
is all you Need". If `from_tensor` and `to_tensor` are the same, then
this is self-attention. Each timestep in `from_tensor` attends to the
corresponding sequence in `to_tensor`, and returns a fixed-with vector.
This function first projects `from_tensor` into a "query" tensor and
`to_tensor` into "key" and "value" tensors. These are (effectively) a list
of tensors of length `num_attention_heads`, where each tensor is of shape
[batch_size, seq_length, size_per_head].
Then, the query and key tensors are dot-producted and scaled. These are
softmaxed to obtain attention probabilities. The value tensors are then
interpolated by these probabilities, then concatenated back to a single
tensor and returned.
In practice, the multi-headed attention are done with transposes and
reshapes rather than actual separate tensors.
Args:
from_tensor: float Tensor of shape [batch_size, from_seq_length,
from_width].
to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width].
attention_mask: (optional) int32 Tensor of shape [batch_size,
from_seq_length, to_seq_length]. The values should be 1 or 0. The
attention scores will effectively be set to -infinity for any positions in
the mask that are 0, and will be unchanged for positions that are 1.
num_attention_heads: int. Number of attention heads.
size_per_head: int. Size of each attention head.
query_act: (optional) Activation function for the query transform.
key_act: (optional) Activation function for the key transform.
value_act: (optional) Activation function for the value transform.
attention_probs_dropout_prob: (optional) float. Dropout probability of the
attention probabilities.
initializer_range: float. Range of the weight initializer.
do_return_2d_tensor: bool. If True, the output will be of shape [batch_size
* from_seq_length, num_attention_heads * size_per_head]. If False, the
output will be of shape [batch_size, from_seq_length, num_attention_heads
* size_per_head].
batch_size: (Optional) int. If the input is 2D, this might be the batch size
of the 3D version of the `from_tensor` and `to_tensor`.
from_seq_length: (Optional) If the input is 2D, this might be the seq length
of the 3D version of the `from_tensor`.
to_seq_length: (Optional) If the input is 2D, this might be the seq length
of the 3D version of the `to_tensor`.
Returns:
float Tensor of shape [batch_size, from_seq_length,
num_attention_heads * size_per_head]. (If `do_return_2d_tensor` is
true, this will be of shape [batch_size * from_seq_length,
num_attention_heads * size_per_head]).
Raises:
ValueError: Any of the arguments or tensor shapes are invalid.
"""
def transpose_for_scores(input_tensor, batch_size, num_attention_heads,
seq_length, width):
output_tensor = tf.reshape(
input_tensor, [batch_size, seq_length, num_attention_heads, width])
output_tensor = tf.transpose(output_tensor, [0, 2, 1, 3])
return output_tensor
from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
to_shape = get_shape_list(to_tensor, expected_rank=[2, 3])
if len(from_shape) != len(to_shape):
raise ValueError(
"The rank of `from_tensor` must match the rank of `to_tensor`.")
if len(from_shape) == 3:
batch_size = from_shape[0]
from_seq_length = from_shape[1]
to_seq_length = to_shape[1]
elif len(from_shape) == 2:
if (batch_size is None or from_seq_length is None or to_seq_length is None):
raise ValueError(
"When passing in rank 2 tensors to attention_layer, the values "
"for `batch_size`, `from_seq_length`, and `to_seq_length` "
"must all be specified.")
# Scalar dimensions referenced here:
# B = batch size (number of sequences)
# F = `from_tensor` sequence length
# T = `to_tensor` sequence length
# N = `num_attention_heads`
# H = `size_per_head`
from_tensor_2d = reshape_to_matrix(from_tensor)
to_tensor_2d = reshape_to_matrix(to_tensor)
# `query_layer` = [B*F, N*H]
query_layer = tf.layers.dense(
from_tensor_2d,
num_attention_heads * size_per_head,
activation=query_act,
name="query",
kernel_initializer=create_initializer(initializer_range))
# `key_layer` = [B*T, N*H]
key_layer = tf.layers.dense(
to_tensor_2d,
num_attention_heads * size_per_head,
activation=key_act,
name="key",
kernel_initializer=create_initializer(initializer_range))
# `value_layer` = [B*T, N*H]
value_layer = tf.layers.dense(
to_tensor_2d,
num_attention_heads * size_per_head,
activation=value_act,
name="value",
kernel_initializer=create_initializer(initializer_range))
# `query_layer` = [B, N, F, H]
query_layer = transpose_for_scores(query_layer, batch_size,
num_attention_heads, from_seq_length,
size_per_head)
# `key_layer` = [B, N, T, H]
key_layer = transpose_for_scores(key_layer, batch_size, num_attention_heads,
to_seq_length, size_per_head)
# Take the dot product between "query" and "key" to get the raw
# attention scores.
# `attention_scores` = [B, N, F, T]
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
attention_scores = tf.multiply(attention_scores,
1.0 / math.sqrt(float(size_per_head)))
if attention_mask is not None:
# `attention_mask` = [B, 1, F, T]
attention_mask = tf.expand_dims(attention_mask, axis=[1])
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
adder = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_scores += adder
# Normalize the attention scores to probabilities.
# `attention_probs` = [B, N, F, T]
attention_probs = tf.nn.softmax(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = dropout(attention_probs, attention_probs_dropout_prob)
# `value_layer` = [B, T, N, H]
value_layer = tf.reshape(
value_layer,
[batch_size, to_seq_length, num_attention_heads, size_per_head])
# `value_layer` = [B, N, T, H]
value_layer = tf.transpose(value_layer, [0, 2, 1, 3])
# `context_layer` = [B, N, F, H]
context_layer = tf.matmul(attention_probs, value_layer)
# `context_layer` = [B, F, N, H]
context_layer = tf.transpose(context_layer, [0, 2, 1, 3])
if do_return_2d_tensor:
# `context_layer` = [B*F, N*V]
context_layer = tf.reshape(
context_layer,
[batch_size * from_seq_length, num_attention_heads * size_per_head])
else:
# `context_layer` = [B, F, N*V]
context_layer = tf.reshape(
context_layer,
[batch_size, from_seq_length, num_attention_heads * size_per_head])
return context_layer
|
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] |
https://github.com/terrifyzhao/bert-utils/blob/1d5f3eb649b4ee8a059f7050da483d0cd6d7fff4/modeling.py#L560-L753
|
|
jhpyle/docassemble
|
b90c84e57af59aa88b3404d44d0b125c70f832cc
|
docassemble_base/docassemble/base/util.py
|
python
|
DADict._trigger_gather
|
(self)
|
Triggers the gathering process.
|
Triggers the gathering process.
|
[
"Triggers",
"the",
"gathering",
"process",
"."
] |
def _trigger_gather(self):
"""Triggers the gathering process."""
if docassemble.base.functions.get_gathering_mode(self.instanceName) is False:
if self.auto_gather:
self.gather()
else:
self.gathered
|
[
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":",
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] |
https://github.com/jhpyle/docassemble/blob/b90c84e57af59aa88b3404d44d0b125c70f832cc/docassemble_base/docassemble/base/util.py#L2250-L2256
|
||
truenas/middleware
|
b11ec47d6340324f5a32287ffb4012e5d709b934
|
src/middlewared/middlewared/plugins/chart_releases_linux/upgrade.py
|
python
|
ChartReleaseService.upgrade_summary
|
(self, release_name, options)
|
return {
'container_images_to_update': {
k: v for k, v in release['resources']['container_images'].items() if v['update_available']
},
'changelog': changelog,
'available_versions_for_upgrade': all_newer_versions,
'item_update_available': release['update_available'],
'image_update_available': release['container_images_update_available'],
**version_info,
}
|
Retrieve upgrade summary for `release_name` which will include which container images will be updated
and changelog for `options.item_version` chart version specified if applicable. If only container images
need to be updated, changelog will be `null`.
If chart release `release_name` does not require an upgrade, an error will be raised.
|
Retrieve upgrade summary for `release_name` which will include which container images will be updated
and changelog for `options.item_version` chart version specified if applicable. If only container images
need to be updated, changelog will be `null`.
|
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"null",
"."
] |
async def upgrade_summary(self, release_name, options):
"""
Retrieve upgrade summary for `release_name` which will include which container images will be updated
and changelog for `options.item_version` chart version specified if applicable. If only container images
need to be updated, changelog will be `null`.
If chart release `release_name` does not require an upgrade, an error will be raised.
"""
release = await self.middleware.call(
'chart.release.query', [['id', '=', release_name]], {'extra': {'retrieve_resources': True}, 'get': True}
)
if not release['update_available'] and not release['container_images_update_available']:
raise CallError('No update is available for chart release', errno=errno.ENOENT)
version_info = {
'latest_version': release['chart_metadata']['version'],
'upgrade_version': release['chart_metadata']['version'],
'latest_human_version': release['human_version'],
'upgrade_human_version': release['human_version'],
}
changelog = None
all_newer_versions = []
if release['update_available']:
available_items = await self.get_versions(release, options)
latest_item = available_items['latest_version']
upgrade_version = available_items['specified_version']
version_info.update({
'latest_version': latest_item['version'],
'latest_human_version': latest_item['human_version'],
'upgrade_version': upgrade_version['version'],
'upgrade_human_version': upgrade_version['human_version'],
})
changelog = upgrade_version['changelog']
all_newer_versions = [
{
'version': v['version'],
'human_version': v['human_version'],
} for v in available_items['versions'].values()
if parse_version(v['version']) > parse_version(release['chart_metadata']['version'])
]
return {
'container_images_to_update': {
k: v for k, v in release['resources']['container_images'].items() if v['update_available']
},
'changelog': changelog,
'available_versions_for_upgrade': all_newer_versions,
'item_update_available': release['update_available'],
'image_update_available': release['container_images_update_available'],
**version_info,
}
|
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"[",
"'container_images_update_available'",
"]",
",",
"*",
"*",
"version_info",
",",
"}"
] |
https://github.com/truenas/middleware/blob/b11ec47d6340324f5a32287ffb4012e5d709b934/src/middlewared/middlewared/plugins/chart_releases_linux/upgrade.py#L154-L204
|
|
sfepy/sfepy
|
02ec7bb2ab39ee1dfe1eb4cd509f0ffb7dcc8b25
|
sfepy/discrete/problem.py
|
python
|
Problem.set_mesh_coors
|
(self, coors, update_fields=False, actual=False,
clear_all=True, extra_dofs=False)
|
Set mesh coordinates.
Parameters
----------
coors : array
The new coordinates.
update_fields : bool
If True, update also coordinates of fields.
actual : bool
If True, update the actual configuration coordinates,
otherwise the undeformed configuration ones.
|
Set mesh coordinates.
|
[
"Set",
"mesh",
"coordinates",
"."
] |
def set_mesh_coors(self, coors, update_fields=False, actual=False,
clear_all=True, extra_dofs=False):
"""
Set mesh coordinates.
Parameters
----------
coors : array
The new coordinates.
update_fields : bool
If True, update also coordinates of fields.
actual : bool
If True, update the actual configuration coordinates,
otherwise the undeformed configuration ones.
"""
set_mesh_coors(self.domain, self.fields, coors,
update_fields=update_fields, actual=actual,
clear_all=clear_all, extra_dofs=extra_dofs)
|
[
"def",
"set_mesh_coors",
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"self",
",",
"coors",
",",
"update_fields",
"=",
"False",
",",
"actual",
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",",
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"=",
"actual",
",",
"clear_all",
"=",
"clear_all",
",",
"extra_dofs",
"=",
"extra_dofs",
")"
] |
https://github.com/sfepy/sfepy/blob/02ec7bb2ab39ee1dfe1eb4cd509f0ffb7dcc8b25/sfepy/discrete/problem.py#L739-L756
|
||
plotly/plotly.py
|
cfad7862594b35965c0e000813bd7805e8494a5b
|
packages/python/plotly/plotly/graph_objs/_table.py
|
python
|
Table.customdatasrc
|
(self)
|
return self["customdatasrc"]
|
Sets the source reference on Chart Studio Cloud for
`customdata`.
The 'customdatasrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
|
Sets the source reference on Chart Studio Cloud for
`customdata`.
The 'customdatasrc' property must be specified as a string or
as a plotly.grid_objs.Column object
|
[
"Sets",
"the",
"source",
"reference",
"on",
"Chart",
"Studio",
"Cloud",
"for",
"customdata",
".",
"The",
"customdatasrc",
"property",
"must",
"be",
"specified",
"as",
"a",
"string",
"or",
"as",
"a",
"plotly",
".",
"grid_objs",
".",
"Column",
"object"
] |
def customdatasrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`customdata`.
The 'customdatasrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["customdatasrc"]
|
[
"def",
"customdatasrc",
"(",
"self",
")",
":",
"return",
"self",
"[",
"\"customdatasrc\"",
"]"
] |
https://github.com/plotly/plotly.py/blob/cfad7862594b35965c0e000813bd7805e8494a5b/packages/python/plotly/plotly/graph_objs/_table.py#L224-L236
|
|
jython/jython3
|
def4f8ec47cb7a9c799ea4c745f12badf92c5769
|
lib-python/3.5.1/importlib/abc.py
|
python
|
PathEntryFinder.invalidate_caches
|
(self)
|
An optional method for clearing the finder's cache, if any.
This method is used by PathFinder.invalidate_caches().
|
An optional method for clearing the finder's cache, if any.
This method is used by PathFinder.invalidate_caches().
|
[
"An",
"optional",
"method",
"for",
"clearing",
"the",
"finder",
"s",
"cache",
"if",
"any",
".",
"This",
"method",
"is",
"used",
"by",
"PathFinder",
".",
"invalidate_caches",
"()",
"."
] |
def invalidate_caches(self):
"""An optional method for clearing the finder's cache, if any.
This method is used by PathFinder.invalidate_caches().
"""
|
[
"def",
"invalidate_caches",
"(",
"self",
")",
":"
] |
https://github.com/jython/jython3/blob/def4f8ec47cb7a9c799ea4c745f12badf92c5769/lib-python/3.5.1/importlib/abc.py#L116-L119
|
||
makerbot/ReplicatorG
|
d6f2b07785a5a5f1e172fb87cb4303b17c575d5d
|
skein_engines/skeinforge-35/fabmetheus_utilities/euclidean.py
|
python
|
XIntersectionIndex.__cmp__
|
(self, other)
|
return int( self.x > other.x )
|
Get comparison in order to sort x intersections in ascending order of x.
|
Get comparison in order to sort x intersections in ascending order of x.
|
[
"Get",
"comparison",
"in",
"order",
"to",
"sort",
"x",
"intersections",
"in",
"ascending",
"order",
"of",
"x",
"."
] |
def __cmp__(self, other):
'Get comparison in order to sort x intersections in ascending order of x.'
if self.x < other.x:
return - 1
return int( self.x > other.x )
|
[
"def",
"__cmp__",
"(",
"self",
",",
"other",
")",
":",
"if",
"self",
".",
"x",
"<",
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"x",
":",
"return",
"-",
"1",
"return",
"int",
"(",
"self",
".",
"x",
">",
"other",
".",
"x",
")"
] |
https://github.com/makerbot/ReplicatorG/blob/d6f2b07785a5a5f1e172fb87cb4303b17c575d5d/skein_engines/skeinforge-35/fabmetheus_utilities/euclidean.py#L2423-L2427
|
|
dropbox/dropbox-sdk-python
|
015437429be224732990041164a21a0501235db1
|
dropbox/team_log.py
|
python
|
EventDetails.get_sso_error_details
|
(self)
|
return self._value
|
Only call this if :meth:`is_sso_error_details` is true.
:rtype: SsoErrorDetails
|
Only call this if :meth:`is_sso_error_details` is true.
|
[
"Only",
"call",
"this",
"if",
":",
"meth",
":",
"is_sso_error_details",
"is",
"true",
"."
] |
def get_sso_error_details(self):
"""
Only call this if :meth:`is_sso_error_details` is true.
:rtype: SsoErrorDetails
"""
if not self.is_sso_error_details():
raise AttributeError("tag 'sso_error_details' not set")
return self._value
|
[
"def",
"get_sso_error_details",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"is_sso_error_details",
"(",
")",
":",
"raise",
"AttributeError",
"(",
"\"tag 'sso_error_details' not set\"",
")",
"return",
"self",
".",
"_value"
] |
https://github.com/dropbox/dropbox-sdk-python/blob/015437429be224732990041164a21a0501235db1/dropbox/team_log.py#L18489-L18497
|
|
aws-quickstart/quickstart-redhat-openshift
|
2b87dd38b72e7e4c439a606c5a9ea458d72da612
|
functions/source/DeleteBucketContents/requests/utils.py
|
python
|
guess_filename
|
(obj)
|
Tries to guess the filename of the given object.
|
Tries to guess the filename of the given object.
|
[
"Tries",
"to",
"guess",
"the",
"filename",
"of",
"the",
"given",
"object",
"."
] |
def guess_filename(obj):
"""Tries to guess the filename of the given object."""
name = getattr(obj, 'name', None)
if (name and isinstance(name, basestring) and name[0] != '<' and
name[-1] != '>'):
return os.path.basename(name)
|
[
"def",
"guess_filename",
"(",
"obj",
")",
":",
"name",
"=",
"getattr",
"(",
"obj",
",",
"'name'",
",",
"None",
")",
"if",
"(",
"name",
"and",
"isinstance",
"(",
"name",
",",
"basestring",
")",
"and",
"name",
"[",
"0",
"]",
"!=",
"'<'",
"and",
"name",
"[",
"-",
"1",
"]",
"!=",
"'>'",
")",
":",
"return",
"os",
".",
"path",
".",
"basename",
"(",
"name",
")"
] |
https://github.com/aws-quickstart/quickstart-redhat-openshift/blob/2b87dd38b72e7e4c439a606c5a9ea458d72da612/functions/source/DeleteBucketContents/requests/utils.py#L219-L224
|
||
PaddlePaddle/ERNIE
|
15eddb022ce1beb281777e9ab8807a1bdfa7a76e
|
propeller/paddle/train/metrics.py
|
python
|
Mrr.__init__
|
(self, qid, label, pred)
|
doc
|
doc
|
[
"doc"
] |
def __init__(self, qid, label, pred):
"""doc"""
if label.shape != pred.shape:
raise ValueError(
'expect label shape == pred shape, got: label.shape=%s, pred.shape = %s'
% (repr(label), repr(pred)))
self.qid = qid
self.label = label
self.pred = pred
self.reset()
|
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"def",
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"(",
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",",
"qid",
",",
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",",
"pred",
")",
":",
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https://github.com/PaddlePaddle/ERNIE/blob/15eddb022ce1beb281777e9ab8807a1bdfa7a76e/propeller/paddle/train/metrics.py#L447-L457
|
||
dipu-bd/lightnovel-crawler
|
eca7a71f217ce7a6b0a54d2e2afb349571871880
|
sources/en/n/novelmtl.py
|
python
|
NovelMTLCrawler.read_novel_info
|
(self)
|
Get novel title, autor, cover etc
|
Get novel title, autor, cover etc
|
[
"Get",
"novel",
"title",
"autor",
"cover",
"etc"
] |
def read_novel_info(self):
'''Get novel title, autor, cover etc'''
logger.debug('Visiting %s', self.novel_url)
soup = self.get_soup(self.novel_url)
self.novel_title = soup.select_one('.novel-info .novel-title').text.strip()
logger.info('Novel title: %s', self.novel_title)
try:
self.novel_cover = self.absolute_url(
soup.select_one('#novel figure.cover img')['data-src'])
logger.info('Novel cover: %s', self.novel_cover)
except Exception as e:
logger.debug('Failed to get novel cover: %s', e)
try:
self.novel_author = soup.select_one(
'.novel-info .author span[itemprop="author"]').text.strip()
logger.info('%s', self.novel_author)
except Exception as e:
logger.debug('Failed to parse novel author. Error: %s', e)
last_page = soup.select('#chapters .pagination li a')[-1]['href']
logger.debug('Last page: %s', last_page)
last_page_qs = parse_qs(urlparse(last_page).query)
max_page = int(last_page_qs['page'][0])
wjm = last_page_qs['wjm'][0]
logger.debug('Max page: %d, wjm = %s', max_page, wjm)
futures = []
for i in range(max_page + 1):
payload = {
'page': i,
'wjm': wjm,
'_': self.cur_time,
'X-Requested-With': 'XMLHttpRequest',
}
url = chapter_list_url + '?' + '&'.join([
'%s=%s' % (k, v) for k, v in payload.items()
])
logger.debug('Fetching chapters from %s', url)
futures.append(self.executor.submit(self.get_soup, url))
# end for
for page, f in enumerate(futures):
soup = f.result()
vol_id = page + 1
self.volumes.append({'id': vol_id})
for a in soup.select('ul.chapter-list li a'):
chap_id = len(self.chapters) + 1
self.chapters.append({
'id': chap_id,
'volume': vol_id,
'url': self.absolute_url(a['href']),
'title': a.select_one('.chapter-title').text.strip(),
})
|
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"}",
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] |
https://github.com/dipu-bd/lightnovel-crawler/blob/eca7a71f217ce7a6b0a54d2e2afb349571871880/sources/en/n/novelmtl.py#L55-L111
|
||
ricequant/rqalpha-mod-ctp
|
bfd40801f9a182226a911cac74660f62993eb6db
|
rqalpha_mod_ctp/ctp/pyctp/linux64_35/__init__.py
|
python
|
TraderApi.OnRspQryCombAction
|
(self, pCombAction, pRspInfo, nRequestID, bIsLast)
|
请求查询申请组合响应
|
请求查询申请组合响应
|
[
"请求查询申请组合响应"
] |
def OnRspQryCombAction(self, pCombAction, pRspInfo, nRequestID, bIsLast):
"""请求查询申请组合响应"""
|
[
"def",
"OnRspQryCombAction",
"(",
"self",
",",
"pCombAction",
",",
"pRspInfo",
",",
"nRequestID",
",",
"bIsLast",
")",
":"
] |
https://github.com/ricequant/rqalpha-mod-ctp/blob/bfd40801f9a182226a911cac74660f62993eb6db/rqalpha_mod_ctp/ctp/pyctp/linux64_35/__init__.py#L660-L661
|
||
chrysn/aiocoap
|
1f03d4ceb969b2b443c288c312d44c3b7c3e2031
|
aiocoap/cli/common.py
|
python
|
add_server_arguments
|
(parser)
|
Add the --bind option to an argparse parser
|
Add the --bind option to an argparse parser
|
[
"Add",
"the",
"--",
"bind",
"option",
"to",
"an",
"argparse",
"parser"
] |
def add_server_arguments(parser):
"""Add the --bind option to an argparse parser"""
def hostportsplit_helper(arg):
"""Wrapper around hostportsplit that gives better error messages than
'invalid hostportsplit value'"""
try:
return hostportsplit(arg)
except ValueError:
raise parser.error("Invalid argument to --bind." +
" Did you mean --bind '[%s]'?" % arg
if arg.count(':') >= 2 and '[' not in arg
else " See --help-bind for details.")
parser.add_argument('--bind', help="Host and/or port to bind to (see --help-bind for details)", type=hostportsplit_helper, default=None)
parser.add_argument('--credentials', help="JSON file pointing to credentials for the server's identity/ies.", type=Path)
# These are to be eventually migrated into credentials
parser.add_argument('--tls-server-certificate', help="TLS certificate (chain) to present to connecting clients (in PEM format)", metavar="CRT")
parser.add_argument('--tls-server-key', help="TLS key to load that supports the server certificate", metavar="KEY")
parser.add_argument('--help-bind', help=argparse.SUPPRESS, action=_HelpBind)
|
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] |
https://github.com/chrysn/aiocoap/blob/1f03d4ceb969b2b443c288c312d44c3b7c3e2031/aiocoap/cli/common.py#L59-L82
|
||
omz/PythonistaAppTemplate
|
f560f93f8876d82a21d108977f90583df08d55af
|
PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/parsedatetime/__init__.py
|
python
|
_initSymbols
|
(ptc)
|
Initialize symbols and single character constants.
|
Initialize symbols and single character constants.
|
[
"Initialize",
"symbols",
"and",
"single",
"character",
"constants",
"."
] |
def _initSymbols(ptc):
"""
Initialize symbols and single character constants.
"""
# build am and pm lists to contain
# original case, lowercase and first-char
# versions of the meridian text
if len(ptc.locale.meridian) > 0:
am = ptc.locale.meridian[0]
ptc.am = [ am ]
if len(am) > 0:
ptc.am.append(am[0])
am = am.lower()
ptc.am.append(am)
ptc.am.append(am[0])
else:
am = ''
ptc.am = [ '', '' ]
if len(ptc.locale.meridian) > 1:
pm = ptc.locale.meridian[1]
ptc.pm = [ pm ]
if len(pm) > 0:
ptc.pm.append(pm[0])
pm = pm.lower()
ptc.pm.append(pm)
ptc.pm.append(pm[0])
else:
pm = ''
ptc.pm = [ '', '' ]
|
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https://github.com/omz/PythonistaAppTemplate/blob/f560f93f8876d82a21d108977f90583df08d55af/PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/parsedatetime/__init__.py#L1930-L1962
|
||
kanzure/nanoengineer
|
874e4c9f8a9190f093625b267f9767e19f82e6c4
|
cad/src/command_support/EditCommand.py
|
python
|
EditCommand._removeStructure
|
(self)
|
Remove this structure.
@see: L{self.cancelStructure}
|
Remove this structure.
|
[
"Remove",
"this",
"structure",
"."
] |
def _removeStructure(self):
"""
Remove this structure.
@see: L{self.cancelStructure}
"""
if self.struct is not None:
self.struct.kill_with_contents()
self.struct = None
self._revertNumber()
self.win.win_update()
|
[
"def",
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"_revertNumber",
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https://github.com/kanzure/nanoengineer/blob/874e4c9f8a9190f093625b267f9767e19f82e6c4/cad/src/command_support/EditCommand.py#L358-L367
|
||
diegma/graph-2-text
|
56e19d3066116c26464acde46c1b52ec46f319e6
|
onmt/Models.py
|
python
|
RNNEncoder.forward
|
(self, src, lengths=None, encoder_state=None)
|
return encoder_final, memory_bank
|
See :obj:`EncoderBase.forward()`
|
See :obj:`EncoderBase.forward()`
|
[
"See",
":",
"obj",
":",
"EncoderBase",
".",
"forward",
"()"
] |
def forward(self, src, lengths=None, encoder_state=None):
"See :obj:`EncoderBase.forward()`"
self._check_args(src, lengths, encoder_state)
emb = self.embeddings(src)
s_len, batch, emb_dim = emb.size()
packed_emb = emb
if lengths is not None and not self.no_pack_padded_seq:
# Lengths data is wrapped inside a Variable.
lengths = lengths.view(-1).tolist()
packed_emb = pack(emb, lengths)
memory_bank, encoder_final = self.rnn(packed_emb, encoder_state)
if lengths is not None and not self.no_pack_padded_seq:
memory_bank = unpack(memory_bank)[0]
if self.use_bridge:
encoder_final = self._bridge(encoder_final)
return encoder_final, memory_bank
|
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"=",
"lengths",
".",
"view",
"(",
"-",
"1",
")",
".",
"tolist",
"(",
")",
"packed_emb",
"=",
"pack",
"(",
"emb",
",",
"lengths",
")",
"memory_bank",
",",
"encoder_final",
"=",
"self",
".",
"rnn",
"(",
"packed_emb",
",",
"encoder_state",
")",
"if",
"lengths",
"is",
"not",
"None",
"and",
"not",
"self",
".",
"no_pack_padded_seq",
":",
"memory_bank",
"=",
"unpack",
"(",
"memory_bank",
")",
"[",
"0",
"]",
"if",
"self",
".",
"use_bridge",
":",
"encoder_final",
"=",
"self",
".",
"_bridge",
"(",
"encoder_final",
")",
"return",
"encoder_final",
",",
"memory_bank"
] |
https://github.com/diegma/graph-2-text/blob/56e19d3066116c26464acde46c1b52ec46f319e6/onmt/Models.py#L134-L154
|
|
firedm/FireDM
|
d27d3d27c869625f75520ca2bacfa9ebd11caf2f
|
firedm/controller.py
|
python
|
Controller._update_playlist_menu
|
(self, pl_menu)
|
update playlist menu and send notification to view
|
update playlist menu and send notification to view
|
[
"update",
"playlist",
"menu",
"and",
"send",
"notification",
"to",
"view"
] |
def _update_playlist_menu(self, pl_menu):
"""update playlist menu and send notification to view"""
self.playlist_menu = pl_menu
self._update_view(command='playlist_menu', playlist_menu=pl_menu)
|
[
"def",
"_update_playlist_menu",
"(",
"self",
",",
"pl_menu",
")",
":",
"self",
".",
"playlist_menu",
"=",
"pl_menu",
"self",
".",
"_update_view",
"(",
"command",
"=",
"'playlist_menu'",
",",
"playlist_menu",
"=",
"pl_menu",
")"
] |
https://github.com/firedm/FireDM/blob/d27d3d27c869625f75520ca2bacfa9ebd11caf2f/firedm/controller.py#L586-L589
|
||
coderedcorp/coderedcms
|
14bd43ffa418279986658dde93005bd6dfbd763c
|
coderedcms/models/page_models.py
|
python
|
CoderedEventIndexPage.fullcalendar_view
|
(self)
|
return {
self.CalendarViews.NONE: '',
self.CalendarViews.MONTH: 'dayGridMonth',
self.CalendarViews.AGENDA_WEEK: 'timeGridWeek',
self.CalendarViews.AGENDA_DAY: 'timeGridDay',
self.CalendarViews.LIST_MONTH: 'listMonth',
}[self.default_calendar_view]
|
Translate calendar views to fullcalendar.js identifiers.
|
Translate calendar views to fullcalendar.js identifiers.
|
[
"Translate",
"calendar",
"views",
"to",
"fullcalendar",
".",
"js",
"identifiers",
"."
] |
def fullcalendar_view(self) -> str:
"""
Translate calendar views to fullcalendar.js identifiers.
"""
return {
self.CalendarViews.NONE: '',
self.CalendarViews.MONTH: 'dayGridMonth',
self.CalendarViews.AGENDA_WEEK: 'timeGridWeek',
self.CalendarViews.AGENDA_DAY: 'timeGridDay',
self.CalendarViews.LIST_MONTH: 'listMonth',
}[self.default_calendar_view]
|
[
"def",
"fullcalendar_view",
"(",
"self",
")",
"->",
"str",
":",
"return",
"{",
"self",
".",
"CalendarViews",
".",
"NONE",
":",
"''",
",",
"self",
".",
"CalendarViews",
".",
"MONTH",
":",
"'dayGridMonth'",
",",
"self",
".",
"CalendarViews",
".",
"AGENDA_WEEK",
":",
"'timeGridWeek'",
",",
"self",
".",
"CalendarViews",
".",
"AGENDA_DAY",
":",
"'timeGridDay'",
",",
"self",
".",
"CalendarViews",
".",
"LIST_MONTH",
":",
"'listMonth'",
",",
"}",
"[",
"self",
".",
"default_calendar_view",
"]"
] |
https://github.com/coderedcorp/coderedcms/blob/14bd43ffa418279986658dde93005bd6dfbd763c/coderedcms/models/page_models.py#L972-L982
|
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