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Wangler2333/tcp_udp_web_tools-pyqt5 | 791df73791e3e6f61643f10613c84810cdf2ffc2 | tcp_udp_web_tools_all_in_one.py | python | Ui_TCP.click_clear | (self) | pushbutton_clear控件点击触发的槽
:return: None | pushbutton_clear控件点击触发的槽
:return: None | [
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"""
pushbutton_clear控件点击触发的槽
:return: None
"""
self.textBrowser_recv.clear() | [
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/weakref.py | python | finalize.peek | (self) | If alive then return (obj, func, args, kwargs);
otherwise return None | If alive then return (obj, func, args, kwargs);
otherwise return None | [
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martinRenou/ipycanvas | 53348e3f63153fc80459c9f1e76ce73dc75dcd49 | ipycanvas/canvas.py | python | Canvas.ellipse | (self, x, y, radius_x, radius_y, rotation, start_angle, end_angle, anticlockwise=False) | Add an ellipse centered at ``(x, y)`` with the radii ``radius_x`` and ``radius_y`` to the current path.
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HiKapok/X-Detector | 1b19e15709635e007494648c4fb519b703a29d84 | light_head_rfcn_train.py | python | parse_comma_list | (args) | return [float(s.strip()) for s in args.split(',')] | [] | def parse_comma_list(args):
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and3rson/clay | c271cecf6b6ea6465abcdd2444171b1a565a60a3 | clay/clipboard.py | python | copy | (text) | return False | Copy text to clipboard.
Return True on success. | Copy text to clipboard. | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/tasmota/config_flow.py | python | FlowHandler.async_step_confirm | (
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data = {CONF_DISCOVERY_PREFIX: self._prefix}
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jkehler/awslambda-psycopg2 | c7b1b2f6382bbe5893d95c4e7f4b5ffdf05ab3b4 | with_ssl_support/psycopg2/extras.py | python | register_uuid | (oids=None, conn_or_curs=None) | return _ext.UUID | Create the UUID type and an uuid.UUID adapter.
:param oids: oid for the PostgreSQL :sql:`uuid` type, or 2-items sequence
with oids of the type and the array. If not specified, use PostgreSQL
standard oids.
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"""Create the UUID type and an uuid.UUID adapter.
:param oids: oid for the PostgreSQL :sql:`uuid` type, or 2-items sequence
with oids of the type and the array. If not specified, use PostgreSQL
standard oids.
:param conn_or_curs: where to register the typecaster. If not specified,
register it globally.
"""
import uuid
if not oids:
oid1 = 2950
oid2 = 2951
elif isinstance(oids, (list, tuple)):
oid1, oid2 = oids
else:
oid1 = oids
oid2 = 2951
_ext.UUID = _ext.new_type((oid1, ), "UUID",
lambda data, cursor: data and uuid.UUID(data) or None)
_ext.UUIDARRAY = _ext.new_array_type((oid2,), "UUID[]", _ext.UUID)
_ext.register_type(_ext.UUID, conn_or_curs)
_ext.register_type(_ext.UUIDARRAY, conn_or_curs)
_ext.register_adapter(uuid.UUID, UUID_adapter)
return _ext.UUID | [
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sympy/sympy | d822fcba181155b85ff2b29fe525adbafb22b448 | sympy/functions/elementary/hyperbolic.py | python | sinh.as_real_imag | (self, deep=True, **hints) | return (sinh(re)*cos(im), cosh(re)*sin(im)) | Returns this function as a complex coordinate. | Returns this function as a complex coordinate. | [
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from sympy.functions.elementary.trigonometric import (cos, sin)
if self.args[0].is_extended_real:
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return (self.expand(deep, **hints), S.Zero)
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return (self, S.Zero)
if deep:
re, im = self.args[0].expand(deep, **hints).as_real_imag()
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re, im = self.args[0].as_real_imag()
return (sinh(re)*cos(im), cosh(re)*sin(im)) | [
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yhat/ggpy | b6d23c22d52557b983da8ce7a3a6992501dadcd6 | ggplot/colors/palettes.py | python | dark_palette | (color, n_colors=6, reverse=False, as_cmap=False) | return blend_palette(colors, n_colors, as_cmap) | Make a palette that blends from a deep gray to `color`.
Parameters
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color : matplotlib color
hex, rgb-tuple, or html color name
n_colors : int, optional
number of colors in the palette
reverse : bool, optional
if True, reverse the direction of the blend
as_cmap : bool, optional
if True, return as a matplotlib colormap instead of list
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"""Make a palette that blends from a deep gray to `color`.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
n_colors : int, optional
number of colors in the palette
reverse : bool, optional
if True, reverse the direction of the blend
as_cmap : bool, optional
if True, return as a matplotlib colormap instead of list
Returns
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palette : list or colormap
"""
gray = "#222222"
colors = [color, gray] if reverse else [gray, color]
return blend_palette(colors, n_colors, as_cmap) | [
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WerWolv/EdiZon_CheatsConfigsAndScripts | d16d36c7509c01dca770f402babd83ff2e9ae6e7 | Scripts/lib/python3.5/logging/handlers.py | python | SocketHandler.handleError | (self, record) | Handle an error during logging.
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/mpmath/functions/theta.py | python | _djacobi_theta3a | (ctx, z, q, nd) | return (2*ctx.j)**nd * s | case ctx._im(z) != 0
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max term for minimum n*abs(log(q).real) + ctx._im(z) | case ctx._im(z) != 0
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | stock/ctp/ApiStruct.py | python | RspQueryAccount.__init__ | (self, TradeCode='', BankID='', BankBranchID='', BrokerID='', BrokerBranchID='', TradeDate='', TradeTime='', BankSerial='', TradingDay='', PlateSerial=0, LastFragment=LF_Yes, SessionID=0, CustomerName='', IdCardType=ICT_EID, IdentifiedCardNo='', CustType=CUSTT_Person, BankAccount='', BankPassWord='', AccountID='', Password='', FutureSerial=0, InstallID=0, UserID='', VerifyCertNoFlag=YNI_Yes, CurrencyID='', Digest='', BankAccType=BAT_BankBook, DeviceID='', BankSecuAccType=BAT_BankBook, BrokerIDByBank='', BankSecuAcc='', BankPwdFlag=BPWDF_NoCheck, SecuPwdFlag=BPWDF_NoCheck, OperNo='', RequestID=0, TID=0, BankUseAmount=0.0, BankFetchAmount=0.0) | [] | def __init__(self, TradeCode='', BankID='', BankBranchID='', BrokerID='', BrokerBranchID='', TradeDate='', TradeTime='', BankSerial='', TradingDay='', PlateSerial=0, LastFragment=LF_Yes, SessionID=0, CustomerName='', IdCardType=ICT_EID, IdentifiedCardNo='', CustType=CUSTT_Person, BankAccount='', BankPassWord='', AccountID='', Password='', FutureSerial=0, InstallID=0, UserID='', VerifyCertNoFlag=YNI_Yes, CurrencyID='', Digest='', BankAccType=BAT_BankBook, DeviceID='', BankSecuAccType=BAT_BankBook, BrokerIDByBank='', BankSecuAcc='', BankPwdFlag=BPWDF_NoCheck, SecuPwdFlag=BPWDF_NoCheck, OperNo='', RequestID=0, TID=0, BankUseAmount=0.0, BankFetchAmount=0.0):
self.TradeCode = '' #业务功能码, char[7]
self.BankID = '' #银行代码, char[4]
self.BankBranchID = 'BankBrchID' #银行分支机构代码, char[5]
self.BrokerID = '' #期商代码, char[11]
self.BrokerBranchID = 'FutureBranchID' #期商分支机构代码, char[31]
self.TradeDate = '' #交易日期, char[9]
self.TradeTime = '' #交易时间, char[9]
self.BankSerial = '' #银行流水号, char[13]
self.TradingDay = 'TradeDate' #交易系统日期 , char[9]
self.PlateSerial = 'Serial' #银期平台消息流水号, int
self.LastFragment = '' #最后分片标志, char
self.SessionID = '' #会话号, int
self.CustomerName = 'IndividualName' #客户姓名, char[51]
self.IdCardType = '' #证件类型, char
self.IdentifiedCardNo = '' #证件号码, char[51]
self.CustType = '' #客户类型, char
self.BankAccount = '' #银行帐号, char[41]
self.BankPassWord = 'Password' #银行密码, char[41]
self.AccountID = '' #投资者帐号, char[15]
self.Password = '' #期货密码, char[41]
self.FutureSerial = '' #期货公司流水号, int
self.InstallID = '' #安装编号, int
self.UserID = '' #用户标识, char[16]
self.VerifyCertNoFlag = 'YesNoIndicator' #验证客户证件号码标志, char
self.CurrencyID = '' #币种代码, char[4]
self.Digest = '' #摘要, char[36]
self.BankAccType = '' #银行帐号类型, char
self.DeviceID = '' #渠道标志, char[3]
self.BankSecuAccType = 'BankAccType' #期货单位帐号类型, char
self.BrokerIDByBank = 'BankCodingForFuture' #期货公司银行编码, char[33]
self.BankSecuAcc = 'BankAccount' #期货单位帐号, char[41]
self.BankPwdFlag = 'PwdFlag' #银行密码标志, char
self.SecuPwdFlag = 'PwdFlag' #期货资金密码核对标志, char
self.OperNo = '' #交易柜员, char[17]
self.RequestID = '' #请求编号, int
self.TID = '' #交易ID, int
self.BankUseAmount = 'TradeAmount' #银行可用金额, double
self.BankFetchAmount = 'TradeAmount' | [
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YaoZeyuan/ZhihuHelp_archived | a0e4a7acd4512452022ce088fff2adc6f8d30195 | src/lib/oauth/zhihu_oauth/exception.py | python | NeedCaptchaException.__init__ | (self) | 登录过程需要验证码 | 登录过程需要验证码 | [
"登录过程需要验证码"
] | def __init__(self):
"""
登录过程需要验证码
"""
pass | [
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Cadene/tensorflow-model-zoo.torch | 990b10ffc22d4c8eacb2a502f20415b4f70c74c2 | models/research/neural_gpu/neural_gpu.py | python | quantize | (t, quant_scale, max_value=1.0) | return res | Quantize a tensor t with each element in [-max_value, max_value]. | Quantize a tensor t with each element in [-max_value, max_value]. | [
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"""Quantize a tensor t with each element in [-max_value, max_value]."""
t = tf.minimum(max_value, tf.maximum(t, -max_value))
big = quant_scale * (t + max_value) + 0.5
with tf.get_default_graph().gradient_override_map({"Floor": "CustomIdG"}):
res = (tf.floor(big) / quant_scale) - max_value
return res | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/returners/__init__.py | python | _fetch_ret_config | (ret) | return str(ret["ret_config"]) | Fetches 'ret_config' if available.
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Fetches 'ret_config' if available.
@see :func:`get_returner_options`
"""
if not ret:
return None
if "ret_config" not in ret:
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return str(ret["ret_config"]) | [
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mitre-attack/attack-website | 446748b71f412f7125d596a5eae0869559c89f05 | modules/util/stixhelpers.py | python | datasource_of | () | return datasource_of | Builds map from data component STIX ID to data source STIX object | Builds map from data component STIX ID to data source STIX object | [
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"""
Builds map from data component STIX ID to data source STIX object
"""
datacomponents = relationshipgetters.get_datacomponent_list()
datasource_of = {}
for datacomponent in datacomponents:
if not datasource_of.get(datacomponent['id']):
datasource = get_datasource_from_list(datacomponent['x_mitre_data_source_ref'])
if datasource:
datasource_of[datacomponent['id']] = datasource
return datasource_of | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/lib-tk/ttk.py | python | Button.__init__ | (self, master=None, **kw) | Construct a Ttk Button widget with the parent master.
STANDARD OPTIONS
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WIDGET-SPECIFIC OPTIONS
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"""Construct a Ttk Button widget with the parent master.
STANDARD OPTIONS
class, compound, cursor, image, state, style, takefocus,
text, textvariable, underline, width
WIDGET-SPECIFIC OPTIONS
command, default, width
"""
Widget.__init__(self, master, "ttk::button", kw) | [
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TesterlifeRaymond/doraemon | d5cb6e34bd5f2aa97273ce0c0c9303e32beaa333 | venv/lib/python3.6/site-packages/pip/_vendor/requests/packages/urllib3/connectionpool.py | python | HTTPConnectionPool._new_conn | (self) | return conn | Return a fresh :class:`HTTPConnection`. | Return a fresh :class:`HTTPConnection`. | [
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] | def _new_conn(self):
"""
Return a fresh :class:`HTTPConnection`.
"""
self.num_connections += 1
log.info("Starting new HTTP connection (%d): %s",
self.num_connections, self.host)
conn = self.ConnectionCls(host=self.host, port=self.port,
timeout=self.timeout.connect_timeout,
strict=self.strict, **self.conn_kw)
return conn | [
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gpodder/mygpo | 7a028ad621d05d4ca0d58fd22fb92656c8835e43 | mygpo/users/views/registration.py | python | ResendActivationView.form_valid | (self, form) | return super(ResendActivationView, self).form_valid(form) | called whene the form was POSTed and its contents were valid | called whene the form was POSTed and its contents were valid | [
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"""called whene the form was POSTed and its contents were valid"""
try:
user = UserProxy.objects.all().by_username_or_email(
form.cleaned_data["username"], form.cleaned_data["email"]
)
except UserProxy.DoesNotExist:
messages.error(self.request, _("User does not exist."))
return HttpResponseRedirect(reverse("resend-activation"))
if user.profile.activation_key is None:
messages.success(
self.request,
_("Your account already has been " "activated. Go ahead and log in."),
)
send_activation_email(user, self.request)
return super(ResendActivationView, self).form_valid(form) | [
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jmsdnns/microarmy | 09bcd535eae75e96661636e30a0ca0fac60c7192 | microarmy/firepower.py | python | parse_responses | (responses) | return aggregate_dict | Quick and dirty. | Quick and dirty. | [
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"""Quick and dirty."""
aggregate_dict = {
'num_trans': [],
'elapsed': [],
'tran_rate': [],
}
for response in responses:
try:
num_trans = response[4].split('\t')[2].strip()[:-5]
elapsed = response[6].split('\t')[2].strip()[:-5]
tran_rate = response[9].split('\t')[1].strip()[:-10]
except IndexError:
raise UnparsableData(response)
aggregate_dict['num_trans'].append(num_trans)
aggregate_dict['elapsed'].append(elapsed)
aggregate_dict['tran_rate'].append(tran_rate)
return aggregate_dict | [
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yu4u/noise2noise | c25d5a81cd2c7077e801b42e1dd05442fd19d8c2 | model.py | python | L0Loss.__init__ | (self) | [] | def __init__(self):
self.gamma = K.variable(2.) | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/sympy/physics/mechanics/point.py | python | Point.locatenew | (self, name, value) | return p | Creates a new point with a position defined from this point.
Parameters
==========
name : str
The name for the new point
value : Vector
The position of the new point relative to this point
Examples
========
>>> from sympy.physics.mechanics import ReferenceFrame, Point
>>> N = ReferenceFrame('N')
>>> P1 = Point('P1')
>>> P2 = P1.locatenew('P2', 10 * N.x) | Creates a new point with a position defined from this point. | [
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] | def locatenew(self, name, value):
"""Creates a new point with a position defined from this point.
Parameters
==========
name : str
The name for the new point
value : Vector
The position of the new point relative to this point
Examples
========
>>> from sympy.physics.mechanics import ReferenceFrame, Point
>>> N = ReferenceFrame('N')
>>> P1 = Point('P1')
>>> P2 = P1.locatenew('P2', 10 * N.x)
"""
if not isinstance(name, str):
raise TypeError('Must supply a valid name')
if value == 0:
value = Vector(0)
value = _check_vector(value)
p = Point(name)
p.set_pos(self, value)
self.set_pos(p, -value)
return p | [
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tianzhi0549/FCOS | 0eb8ee0b7114a3ca42ad96cd89e0ac63a205461e | fcos_core/structures/bounding_box.py | python | BoxList.resize | (self, size, *args, **kwargs) | return bbox.convert(self.mode) | Returns a resized copy of this bounding box
:param size: The requested size in pixels, as a 2-tuple:
(width, height). | Returns a resized copy of this bounding box | [
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"""
Returns a resized copy of this bounding box
:param size: The requested size in pixels, as a 2-tuple:
(width, height).
"""
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size))
if ratios[0] == ratios[1]:
ratio = ratios[0]
scaled_box = self.bbox * ratio
bbox = BoxList(scaled_box, size, mode=self.mode)
# bbox._copy_extra_fields(self)
for k, v in self.extra_fields.items():
if not isinstance(v, torch.Tensor):
v = v.resize(size, *args, **kwargs)
bbox.add_field(k, v)
return bbox
ratio_width, ratio_height = ratios
xmin, ymin, xmax, ymax = self._split_into_xyxy()
scaled_xmin = xmin * ratio_width
scaled_xmax = xmax * ratio_width
scaled_ymin = ymin * ratio_height
scaled_ymax = ymax * ratio_height
scaled_box = torch.cat(
(scaled_xmin, scaled_ymin, scaled_xmax, scaled_ymax), dim=-1
)
bbox = BoxList(scaled_box, size, mode="xyxy")
# bbox._copy_extra_fields(self)
for k, v in self.extra_fields.items():
if not isinstance(v, torch.Tensor):
v = v.resize(size, *args, **kwargs)
bbox.add_field(k, v)
return bbox.convert(self.mode) | [
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mahmoud/lithoxyl | b4bfa92c54df85b4bd5935fe270e2aa3fb25c412 | lithoxyl/logger.py | python | Logger.on_begin | (self, begin_event) | return | Publish *begin_event* to all sinks with ``on_begin()`` hooks. | Publish *begin_event* to all sinks with ``on_begin()`` hooks. | [
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if self.async_mode:
self.event_queue.append(('begin', begin_event))
else:
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begin_hook(begin_event)
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KalleHallden/AutoTimer | 2d954216700c4930baa154e28dbddc34609af7ce | env/lib/python2.7/site-packages/objc/_convenience_mapping.py | python | contains_objectForKey_ | (self, key) | return res is not None | [] | def contains_objectForKey_(self, key):
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return res is not None | [
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feisuzhu/thbattle | ac0dee1b2d86de7664289cf432b157ef25427ba1 | tools/THB.app/Contents/Resources/pycparser.egg/pycparser/c_parser.py | python | CParser.p_pointer | (self, p) | pointer : TIMES type_qualifier_list_opt
| TIMES type_qualifier_list_opt pointer | pointer : TIMES type_qualifier_list_opt
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] | def p_pointer(self, p):
""" pointer : TIMES type_qualifier_list_opt
| TIMES type_qualifier_list_opt pointer
"""
coord = self._coord(p.lineno(1))
# Pointer decls nest from inside out. This is important when different
# levels have different qualifiers. For example:
#
# char * const * p;
#
# Means "pointer to const pointer to char"
#
# While:
#
# char ** const p;
#
# Means "const pointer to pointer to char"
#
# So when we construct PtrDecl nestings, the leftmost pointer goes in
# as the most nested type.
nested_type = c_ast.PtrDecl(quals=p[2] or [], type=None, coord=coord)
if len(p) > 3:
tail_type = p[3]
while tail_type.type is not None:
tail_type = tail_type.type
tail_type.type = nested_type
p[0] = p[3]
else:
p[0] = nested_type | [
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robotlearn/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | pyrobolearn/optimizers/torch_optimizer.py | python | Adam.reset | (self) | Reset the optimizer. | Reset the optimizer. | [
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google/grr | 8ad8a4d2c5a93c92729206b7771af19d92d4f915 | grr/client/grr_response_client/comms.py | python | GRRHTTPClient.Run | (self) | The main run method of the client.
This method does not normally return. Only if there have been more than
connection_error_limit failures, the method returns and allows the
client to exit. | The main run method of the client. | [
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] | def Run(self):
"""The main run method of the client.
This method does not normally return. Only if there have been more than
connection_error_limit failures, the method returns and allows the
client to exit.
"""
while True:
if self.http_manager.ErrorLimitReached():
return
# Check if there is a message from the nanny to be sent.
self.client_worker.SendNannyMessage()
now = time.time()
# Check with the foreman if we need to
if (now > self.last_foreman_check +
config.CONFIG["Client.foreman_check_frequency"]):
# We must not queue messages from the comms thread with blocking=True
# or we might deadlock. If the output queue is full, we can't accept
# more work from the foreman anyways so it's ok to drop the message.
try:
self.client_worker.SendReply(
rdf_protodict.DataBlob(),
session_id=rdfvalue.FlowSessionID(flow_name="Foreman"),
require_fastpoll=False,
blocking=False)
self.last_foreman_check = now
except queue.Full:
pass
try:
self.RunOnce()
except Exception: # pylint: disable=broad-except
# Catch everything, yes, this is terrible but necessary
logging.warning("Uncaught exception caught: %s", traceback.format_exc())
if flags.FLAGS.pdb_post_mortem:
pdb.post_mortem()
# We suicide if our memory is exceeded, and there is no more work to do
# right now. Our death should not result in loss of messages since we are
# not holding any requests in our input queues.
if (self.client_worker.MemoryExceeded() and
not self.client_worker.IsActive() and
self.client_worker.InQueueSize() == 0 and
self.client_worker.OutQueueSize() == 0):
logging.warning("Memory exceeded - exiting.")
self.client_worker.SendClientAlert("Memory limit exceeded, exiting.")
# Make sure this will return True so we don't get more work.
# pylint: disable=g-bad-name
self.client_worker.MemoryExceeded = lambda: True
# pylint: enable=g-bad-name
# Now send back the client message.
self.RunOnce()
# And done for now.
sys.exit(-1)
self.timer.Wait()
self.client_worker.Heartbeat() | [
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nosmokingbandit/Watcher3 | 0217e75158b563bdefc8e01c3be7620008cf3977 | lib/transmissionrpc/client.py | python | Client.verify_torrent | (self, ids, timeout=None) | verify torrent(s) with provided id(s) | verify torrent(s) with provided id(s) | [
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scikit-learn/scikit-learn | 1d1aadd0711b87d2a11c80aad15df6f8cf156712 | sklearn/preprocessing/_discretization.py | python | KBinsDiscretizer._validate_n_bins | (self, n_features) | return n_bins | Returns n_bins_, the number of bins per feature. | Returns n_bins_, the number of bins per feature. | [
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"""Returns n_bins_, the number of bins per feature."""
orig_bins = self.n_bins
if isinstance(orig_bins, numbers.Number):
if not isinstance(orig_bins, numbers.Integral):
raise ValueError(
"{} received an invalid n_bins type. "
"Received {}, expected int.".format(
KBinsDiscretizer.__name__, type(orig_bins).__name__
)
)
if orig_bins < 2:
raise ValueError(
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KBinsDiscretizer.__name__, orig_bins
)
)
return np.full(n_features, orig_bins, dtype=int)
n_bins = check_array(orig_bins, dtype=int, copy=True, ensure_2d=False)
if n_bins.ndim > 1 or n_bins.shape[0] != n_features:
raise ValueError("n_bins must be a scalar or array of shape (n_features,).")
bad_nbins_value = (n_bins < 2) | (n_bins != orig_bins)
violating_indices = np.where(bad_nbins_value)[0]
if violating_indices.shape[0] > 0:
indices = ", ".join(str(i) for i in violating_indices)
raise ValueError(
"{} received an invalid number "
"of bins at indices {}. Number of bins "
"must be at least 2, and must be an int.".format(
KBinsDiscretizer.__name__, indices
)
)
return n_bins | [
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aws/sagemaker-python-sdk | 9d259b316f7f43838c16f35c10e98a110b56735b | src/sagemaker/amazon/amazon_estimator.py | python | get_image_uri | (region_name, repo_name, repo_version=1) | return image_uris.retrieve(
framework=repo_name,
region=region_name,
version=repo_version,
) | Deprecated method. Please use sagemaker.image_uris.retrieve().
Args:
region_name: name of the region
repo_name: name of the repo (e.g. xgboost)
repo_version: version of the repo
Returns:
the image uri | Deprecated method. Please use sagemaker.image_uris.retrieve(). | [
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"""Deprecated method. Please use sagemaker.image_uris.retrieve().
Args:
region_name: name of the region
repo_name: name of the repo (e.g. xgboost)
repo_version: version of the repo
Returns:
the image uri
"""
renamed_warning("The method get_image_uri")
return image_uris.retrieve(
framework=repo_name,
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cmbruns/pyopenvr | ac4847a8a05cda0d4bcf7c4f243008b2a191c7a5 | src/openvr/__init__.py | python | IVRApplications.getApplicationKeyByIndex | (self, applicationIndex) | return bytes(appKeyBuffer.value).decode('utf-8') | Returns the key of the specified application. The index is at least 0 and is less than the return
value of GetApplicationCount(). The buffer should be at least k_unMaxApplicationKeyLength in order to
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appKeyBuffer = ctypes.create_string_buffer(appKeyBufferLen)
error = fn(applicationIndex, appKeyBuffer, appKeyBufferLen)
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/pip/_vendor/ipaddress.py | python | IPv6Address.is_loopback | (self) | return self._ip == 1 | Test if the address is a loopback address.
Returns:
A boolean, True if the address is a loopback address as defined in
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Returns:
A boolean, True if the address is a loopback address as defined in
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"""
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urschrei/pyzotero | ed4175e0f1a62f10984b311864c13ac453fa9ebe | pyzotero/zotero.py | python | Zotero.saved_search | (self, name, conditions) | return req.json() | Create a saved search. conditions is a list of dicts
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condition, operator, value | Create a saved search. conditions is a list of dicts
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"""Create a saved search. conditions is a list of dicts
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"""
self.savedsearch._validate(conditions)
payload = [{"name": name, "conditions": conditions}]
headers = {"Zotero-Write-Token": token()}
headers.update(self.default_headers())
self._check_backoff()
req = requests.post(
url=build_url(
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headers=headers,
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self.request = req
try:
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error_handler(self, req)
backoff = self.request.headers.get("backoff") or self.request.headers.get(
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if backoff:
self._set_backoff(backoff)
return req.json() | [
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entropy1337/infernal-twin | 10995cd03312e39a48ade0f114ebb0ae3a711bb8 | Modules/build/pip/build/lib.linux-i686-2.7/pip/_vendor/distlib/manifest.py | python | Manifest.__init__ | (self, base=None) | Initialise an instance.
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Initialise an instance.
:param base: The base directory to explore under.
"""
self.base = os.path.abspath(os.path.normpath(base or os.getcwd()))
self.prefix = self.base + os.sep
self.allfiles = None
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giantbranch/python-hacker-code | addbc8c73e7e6fb9e4fcadcec022fa1d3da4b96d | 我手敲的代码(中文注释)/chapter9/pycrypto-2.6.1/lib/Crypto/PublicKey/RSA.py | python | RSAImplementation.__init__ | (self, **kwargs) | Create a new RSA key factory.
:Keywords:
use_fast_math : bool
Specify which mathematic library to use:
- *None* (default). Use fastest math available.
- *True* . Use fast math.
- *False* . Use slow math.
default_randfunc : callable
Specify how to collect random data:
- *None* (default). Use Random.new().read().
- not *None* . Use the specified function directly.
:Raise RuntimeError:
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- *True* . Use fast math.
- *False* . Use slow math.
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:Raise RuntimeError:
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"""
use_fast_math = kwargs.get('use_fast_math', None)
if use_fast_math is None: # Automatic
if _fastmath is not None:
self._math = _fastmath
else:
self._math = _slowmath
elif use_fast_math: # Explicitly select fast math
if _fastmath is not None:
self._math = _fastmath
else:
raise RuntimeError("fast math module not available")
else: # Explicitly select slow math
self._math = _slowmath
self.error = self._math.error
self._default_randfunc = kwargs.get('default_randfunc', None)
self._current_randfunc = None | [
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d2l-ai/d2l-zh | 1c2e25a557db446b5691c18e595e5664cc254730 | contrib/to-rm-mx-contrib-text/d2lzh/utils.py | python | data_iter_consecutive | (corpus_indices, batch_size, num_steps, ctx=None) | Sample mini-batches in a consecutive order from sequential data. | Sample mini-batches in a consecutive order from sequential data. | [
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data_len = len(corpus_indices)
batch_len = data_len // batch_size
indices = corpus_indices[0 : batch_size * batch_len].reshape((
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epoch_size = (batch_len - 1) // num_steps
for i in range(epoch_size):
i = i * num_steps
X = indices[:, i : i + num_steps]
Y = indices[:, i + 1 : i + num_steps + 1]
yield X, Y | [
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khamidou/kite | c049faf8522c8346c22c70f2a35a35db6b4a155d | src/back/kite/bottle.py | python | run | (app=None, server='wsgiref', host='127.0.0.1', port=8080,
interval=1, reloader=False, quiet=False, plugins=None,
debug=None, **kargs) | Start a server instance. This method blocks until the server terminates.
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:param server: Server adapter to use. See :data:`server_names` keys
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:param interval: Auto-reloader interval in seconds (default: 1)
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debug=None, **kargs):
""" Start a server instance. This method blocks until the server terminates.
:param app: WSGI application or target string supported by
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:param server: Server adapter to use. See :data:`server_names` keys
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:param host: Server address to bind to. Pass ``0.0.0.0`` to listens on
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:param options: Options passed to the server adapter.
"""
if NORUN: return
if reloader and not os.environ.get('BOTTLE_CHILD'):
try:
lockfile = None
fd, lockfile = tempfile.mkstemp(prefix='bottle.', suffix='.lock')
os.close(fd) # We only need this file to exist. We never write to it
while os.path.exists(lockfile):
args = [sys.executable] + sys.argv
environ = os.environ.copy()
environ['BOTTLE_CHILD'] = 'true'
environ['BOTTLE_LOCKFILE'] = lockfile
p = subprocess.Popen(args, env=environ)
while p.poll() is None: # Busy wait...
os.utime(lockfile, None) # I am alive!
time.sleep(interval)
if p.poll() != 3:
if os.path.exists(lockfile): os.unlink(lockfile)
sys.exit(p.poll())
except KeyboardInterrupt:
pass
finally:
if os.path.exists(lockfile):
os.unlink(lockfile)
return
try:
if debug is not None: _debug(debug)
app = app or default_app()
if isinstance(app, basestring):
app = load_app(app)
if not callable(app):
raise ValueError("Application is not callable: %r" % app)
for plugin in plugins or []:
app.install(plugin)
if server in server_names:
server = server_names.get(server)
if isinstance(server, basestring):
server = load(server)
if isinstance(server, type):
server = server(host=host, port=port, **kargs)
if not isinstance(server, ServerAdapter):
raise ValueError("Unknown or unsupported server: %r" % server)
server.quiet = server.quiet or quiet
if not server.quiet:
_stderr("Bottle v%s server starting up (using %s)...\n" % (__version__, repr(server)))
_stderr("Listening on http://%s:%d/\n" % (server.host, server.port))
_stderr("Hit Ctrl-C to quit.\n\n")
if reloader:
lockfile = os.environ.get('BOTTLE_LOCKFILE')
bgcheck = FileCheckerThread(lockfile, interval)
with bgcheck:
server.run(app)
if bgcheck.status == 'reload':
sys.exit(3)
else:
server.run(app)
except KeyboardInterrupt:
pass
except (SystemExit, MemoryError):
raise
except:
if not reloader: raise
if not getattr(server, 'quiet', quiet):
print_exc()
time.sleep(interval)
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alephsecurity/abootool | 4117b82d07e6b3a80eeab560d2140ae2dcfe2463 | image.py | python | BlobArchive.__init__ | (self, path, oem, device, build) | [] | def __init__(self, path, oem, device, build):
self.oem = oem
self.device = device
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DxCx/plugin.video.9anime | 34358c2f701e5ddf19d3276926374a16f63f7b6a | resources/lib/ui/js2py/legecy_translators/objects.py | python | remove_arrays | (code, count=1) | return res, replacements, count | removes arrays and replaces them with ARRAY_LVALS
returns new code and replacement dict
*NOTE* has to be called AFTER remove objects | removes arrays and replaces them with ARRAY_LVALS
returns new code and replacement dict
*NOTE* has to be called AFTER remove objects | [
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"""removes arrays and replaces them with ARRAY_LVALS
returns new code and replacement dict
*NOTE* has to be called AFTER remove objects"""
res = ''
last = ''
replacements = {}
for e in bracket_split(code, ['[]']):
if e[0]=='[':
if is_array(last):
name = ARRAY_LVAL % count
res += ' ' + name
replacements[name] = e
count += 1
else: # pseudo array. But pseudo array can contain true array. for example a[['d'][3]] has 2 pseudo and 1 true array
cand, new_replacements, count = remove_arrays(e[1:-1], count)
res += '[%s]' % cand
replacements.update(new_replacements)
else:
res += e
last = e
return res, replacements, count | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/distutils/filelist.py | python | _find_all_simple | (path) | return filter(os.path.isfile, results) | Find all files under 'path' | Find all files under 'path' | [
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"under",
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] | def _find_all_simple(path):
"""
Find all files under 'path'
"""
results = (
os.path.join(base, file)
for base, dirs, files in os.walk(path, followlinks=True)
for file in files
)
return filter(os.path.isfile, results) | [
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phaethon/kamene | bf679a65d456411942ee4a907818ba3d6a183bfe | kamene/layers/bluetooth.py | python | srbt | (peer, pkts, inter=0.1, *args, **kargs) | return a,b | send and receive using a bluetooth socket | send and receive using a bluetooth socket | [
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] | def srbt(peer, pkts, inter=0.1, *args, **kargs):
"""send and receive using a bluetooth socket"""
s = conf.BTsocket(peer=peer)
a,b = sndrcv(s,pkts,inter=inter,*args,**kargs)
s.close()
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titusjan/argos | 5a9c31a8a9a2ca825bbf821aa1e685740e3682d7 | argos/widgets/argostableview.py | python | ArgosTableView.copySelectionToClipboard | (self) | Copies selected cells to clipboard.
Only works for ContiguousSelection | Copies selected cells to clipboard. | [
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"to",
"clipboard",
"."
] | def copySelectionToClipboard(self):
""" Copies selected cells to clipboard.
Only works for ContiguousSelection
"""
if not self.model():
logger.warning("Table contains no data. Copy to clipboard aborted.")
return
if self.selectionMode() not in [QtWidgets.QTableView.SingleSelection,
QtWidgets.QTableView.ContiguousSelection]:
logger.warning("Copy to clipboard does not work for current selection mode: {}"
.format(self.selectionMode()))
return
selectedIndices = self.selectionModel().selectedIndexes()
logger.info("Copying {} selected cells to clipboard.".format(len(selectedIndices)))
# selectedIndexes() can return unsorted list so we sort it here to be sure.
selectedIndices.sort(key=lambda idx: (idx.row(), idx.column()))
# Unflatten indices into a list of list of indicides
allIndices = []
allLines = []
lineIndices = [] # indices of current line
prevRow = None
for selIdx in selectedIndices:
if prevRow != selIdx.row() and prevRow is not None: # new line
allIndices.append(lineIndices)
lineIndices = []
lineIndices.append(selIdx)
prevRow = selIdx.row()
allIndices.append(lineIndices)
del lineIndices
# Convert to tab-separated lines so it can be pasted in Excel.
lines = []
for lineIndices in allIndices:
line = '\t'.join([str(idx.data()) for idx in lineIndices])
lines.append(line)
txt = '\n'.join(lines)
#print(txt)
QtWidgets.QApplication.clipboard().setText(txt) | [
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/lib-tk/Tkinter.py | python | Text.tag_lower | (self, tagName, belowThis=None) | Change the priority of tag TAGNAME such that it is lower
than the priority of BELOWTHIS. | Change the priority of tag TAGNAME such that it is lower
than the priority of BELOWTHIS. | [
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] | def tag_lower(self, tagName, belowThis=None):
"""Change the priority of tag TAGNAME such that it is lower
than the priority of BELOWTHIS."""
self.tk.call(self._w, 'tag', 'lower', tagName, belowThis) | [
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guildai/guildai | 1665985a3d4d788efc1a3180ca51cc417f71ca78 | guild/external/pip/_vendor/pkg_resources/__init__.py | python | ZipProvider._is_current | (self, file_path, zip_path) | return zip_contents == file_contents | Return True if the file_path is current for this zip_path | Return True if the file_path is current for this zip_path | [
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] | def _is_current(self, file_path, zip_path):
"""
Return True if the file_path is current for this zip_path
"""
timestamp, size = self._get_date_and_size(self.zipinfo[zip_path])
if not os.path.isfile(file_path):
return False
stat = os.stat(file_path)
if stat.st_size != size or stat.st_mtime != timestamp:
return False
# check that the contents match
zip_contents = self.loader.get_data(zip_path)
with open(file_path, 'rb') as f:
file_contents = f.read()
return zip_contents == file_contents | [
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wucng/TensorExpand | 4ea58f64f5c5082b278229b799c9f679536510b7 | TensorExpand/图片项目/11、对抗样本/main.py | python | adversary_example | (image_path, cls_target,
noise_limit, required_score) | Find and plot adversarial noise for the given image.
image_path: File-path to the input-image (must be *.jpg).
cls_target: Target class-number (integer between 1-1000).
noise_limit: Limit for pixel-values in the noise.
required_score: Stop when target-class score reaches this. | Find and plot adversarial noise for the given image. | [
"Find",
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"plot",
"adversarial",
"noise",
"for",
"the",
"given",
"image",
"."
] | def adversary_example(image_path, cls_target,
noise_limit, required_score):
"""
Find and plot adversarial noise for the given image.
image_path: File-path to the input-image (must be *.jpg).
cls_target: Target class-number (integer between 1-1000).
noise_limit: Limit for pixel-values in the noise.
required_score: Stop when target-class score reaches this.
"""
# Find the adversarial noise.
image, noisy_image, noise, \
name_source, name_target, \
score_source, score_source_org, score_target = \
find_adversary_noise(image_path=image_path,
cls_target=cls_target,
noise_limit=noise_limit,
required_score=required_score)
# Plot the image and the noise.
plot_images(image=image, noise=noise, noisy_image=noisy_image,
name_source=name_source, name_target=name_target,
score_source=score_source,
score_source_org=score_source_org,
score_target=score_target)
# Print some statistics for the noise.
msg = "Noise min: {0:.3f}, max: {1:.3f}, mean: {2:.3f}, std: {3:.3f}"
print(msg.format(noise.min(), noise.max(),
noise.mean(), noise.std())) | [
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mgear-dev/mgear | 06ddc26c5adb5eab07ca470c7fafa77404c8a1de | scripts/mgear/maya/shifter/component/spine_ik_01/__init__.py | python | Component.addAttributes | (self) | Create the anim and setupr rig attributes for the component | Create the anim and setupr rig attributes for the component | [
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"and",
"setupr",
"rig",
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"for",
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"component"
] | def addAttributes(self):
"""Create the anim and setupr rig attributes for the component"""
# Anim -------------------------------------------
self.position_att = self.addAnimParam("position",
"Position",
"double",
self.settings["position"],
0,
1)
self.maxstretch_att = self.addAnimParam("maxstretch",
"Max Stretch",
"double",
self.settings["maxstretch"],
1)
self.maxsquash_att = self.addAnimParam("maxsquash",
"Max Squash",
"double",
self.settings["maxsquash"],
0,
1)
self.softness_att = self.addAnimParam("softness",
"Softness",
"double",
self.settings["softness"],
0,
1)
self.lock_ori0_att = self.addAnimParam("lock_ori0",
"Lock Ori 0",
"double",
self.settings["lock_ori"],
0,
1)
self.lock_ori1_att = self.addAnimParam("lock_ori1",
"Lock Ori 1",
"double",
self.settings["lock_ori"],
0,
1)
self.tan0_att = self.addAnimParam("tan0", "Tangent 0", "double", 1, 0)
self.tan1_att = self.addAnimParam("tan1", "Tangent 1", "double", 1, 0)
# Volume
self.volume_att = self.addAnimParam(
"volume", "Volume", "double", 1, 0, 1)
if self.settings["autoBend"]:
self.sideBend_att = self.addAnimParam(
"sideBend", "Side Bend", "double", .5, 0, 2)
self.frontBend_att = self.addAnimParam(
"frontBend", "Front Bend", "double", .5, 0, 2)
# Setup ------------------------------------------
# Eval Fcurve
self.st_value = fcurve.getFCurveValues(
self.settings["st_profile"], self.settings["division"])
self.sq_value = fcurve.getFCurveValues(
self.settings["sq_profile"], self.settings["division"])
self.st_att = [self.addSetupParam("stretch_%s" % i,
"Stretch %s" % i,
"double",
self.st_value[i],
-1,
0)
for i in range(self.settings["division"])]
self.sq_att = [self.addSetupParam("squash_%s" % i,
"Squash %s" % i,
"double",
self.sq_value[i],
0,
1)
for i in range(self.settings["division"])] | [
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fortharris/Pcode | 147962d160a834c219e12cb456abc130826468e4 | rope/base/evaluate.py | python | eval_node2 | (scope, node) | return evaluator.old_result, evaluator.result | [] | def eval_node2(scope, node):
evaluator = StatementEvaluator(scope)
ast.walk(node, evaluator)
return evaluator.old_result, evaluator.result | [
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-linux/x64/mako/_ast_util.py | python | SourceGenerator.visit_BinOp | (self, node) | [] | def visit_BinOp(self, node):
self.write("(")
self.visit(node.left)
self.write(" %s " % BINOP_SYMBOLS[type(node.op)])
self.visit(node.right)
self.write(")") | [
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CLUEbenchmark/CLUE | 5bd39732734afecb490cf18a5212e692dbf2c007 | baselines/models/bert/run_classifier.py | python | model_fn_builder | (bert_config, num_labels, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu,
use_one_hot_embeddings) | return model_fn | Returns `model_fn` closure for TPUEstimator. | Returns `model_fn` closure for TPUEstimator. | [
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] | def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu,
use_one_hot_embeddings):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
tf.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
label_ids = features["label_ids"]
is_real_example = None
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
(total_loss, per_example_loss, logits, probabilities) = create_model(
bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
num_labels, use_one_hot_embeddings)
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(per_example_loss, label_ids, logits, is_real_example):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
accuracy = tf.metrics.accuracy(
labels=label_ids, predictions=predictions, weights=is_real_example)
loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
return {
"eval_accuracy": accuracy,
"eval_loss": loss,
}
eval_metrics = (metric_fn,
[per_example_loss, label_ids, logits, is_real_example])
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
predictions={"probabilities": probabilities},
scaffold_fn=scaffold_fn)
return output_spec
return model_fn | [
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great-expectations/great_expectations | 45224cb890aeae725af25905923d0dbbab2d969d | great_expectations/_version.py | python | render_pep440_old | (pieces) | return rendered | TAG[.postDISTANCE[.dev0]] .
The ".dev0" means dirty.
Eexceptions:
1: no tags. 0.postDISTANCE[.dev0] | TAG[.postDISTANCE[.dev0]] . | [
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"dev0",
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] | def render_pep440_old(pieces):
"""TAG[.postDISTANCE[.dev0]] .
The ".dev0" means dirty.
Eexceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
return rendered | [
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... | https://github.com/great-expectations/great_expectations/blob/45224cb890aeae725af25905923d0dbbab2d969d/great_expectations/_version.py#L407-L426 | |
CGATOxford/cgat | 326aad4694bdfae8ddc194171bb5d73911243947 | CGAT/Timeseries/__init__.py | python | drawVennDiagram | (deg_dict, header, out_dir) | Take a dictionary of gene IDs, with keys corresponding
to timepoints/differential expression analyses and
generate a Venn diagram. Maximum of 5 overlapping sets
possible using R package:: VennDiagram. | Take a dictionary of gene IDs, with keys corresponding
to timepoints/differential expression analyses and
generate a Venn diagram. Maximum of 5 overlapping sets
possible using R package:: VennDiagram. | [
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... | def drawVennDiagram(deg_dict, header, out_dir):
'''
Take a dictionary of gene IDs, with keys corresponding
to timepoints/differential expression analyses and
generate a Venn diagram. Maximum of 5 overlapping sets
possible using R package:: VennDiagram.
'''
keys = deg_dict.keys()
try:
keys = sorted(keys, key=lambda x: int(x.split("_")[1].rstrip("-time")))
except IndexError:
pass
venn_size = len(keys)
R('''suppressPackageStartupMessages(library("VennDiagram"))''')
n1 = set(deg_dict[keys[0]])
n2 = set(deg_dict[keys[1]])
area1 = len(n1)
area2 = len(n2)
n12 = len(n1.intersection(n2))
# for Venn > 2 sets
if venn_size == 3:
n3 = set(deg_dict[keys[2]])
area3 = len(n3)
n13 = len(n1.intersection(n3))
n23 = len(n2.intersection(n3))
n123 = len((n1.intersection(n2)).intersection(n3))
cat1, cat2, cat3 = keys
R('''png("%(out_dir)s/%(header)s-venn.png", '''
'''width=480, height=480)''' % locals())
R('''draw.triple.venn(%(area1)d, %(area2)d, %(area3)d, '''
'''%(n12)d, %(n23)d, %(n13)d, %(n123)d, '''
'''c('%(cat1)s', '%(cat2)s', '%(cat3)s'), '''
'''col=c('red', 'yellow', 'skyblue'), '''
'''fill=c('red', 'yellow', 'skyblue'), '''
'''margin=0.05, alpha=0.5)''' % locals())
R('''dev.off()''')
elif venn_size == 4:
n3 = set(deg_dict[keys[2]])
area3 = len(n3)
n13 = len(n1.intersection(n3))
n23 = len(n2.intersection(n3))
n123 = len((n1.intersection(n2)).intersection(n3))
n4 = set(deg_dict[keys[3]])
area4 = len(n4)
n14 = len(n1.intersection(n4))
n24 = len(n2.intersection(n4))
n34 = len(n3.intersection(n4))
n124 = len((n1.intersection(n2)).intersection(n4))
n134 = len((n1.intersection(n3)).intersection(n4))
n234 = len((n2.intersection(n3)).intersection(n4))
n1234 = len(((n1.intersection(n2)).intersection(n3)).intersection(n4))
cat1, cat2, cat3, cat4 = keys
R('''png("%(out_dir)s/%(header)s-venn.png",'''
'''width=480, height=480)''' % locals())
R('''draw.quad.venn(%(area1)d, %(area2)d, %(area3)d, %(area4)d,'''
'''%(n12)d, %(n13)d, %(n14)d, %(n23)d, %(n24)d, %(n34)d,'''
'''%(n123)d, %(n124)d, %(n134)d, %(n234)d, %(n1234)d,'''
'''c('%(cat1)s', '%(cat2)s', '%(cat3)s', '%(cat4)s'), '''
'''col=c("red", "yellow", "skyblue", "orange"), '''
'''fill=c("red", "yellow", "skyblue", "orange"), '''
'''margin=0.05, alpha=0.5)''' % locals())
R('''dev.off()''')
elif venn_size == 5:
n3 = set(deg_dict[keys[2]])
area3 = len(n3)
n13 = len(n1.intersection(n3))
n23 = len(n2.intersection(n3))
n123 = len((n1.intersection(n2)).intersection(n3))
n4 = set(deg_dict[keys[3]])
area4 = len(n4)
n14 = len(n1.intersection(n4))
n24 = len(n2.intersection(n4))
n34 = len(n3.intersection(n4))
n124 = len((n1.intersection(n2)).intersection(n4))
n134 = len((n1.intersection(n3)).intersection(n4))
n234 = len((n2.intersection(n3)).intersection(n4))
n1234 = len(((n1.intersection(n2)).intersection(n3)).intersection(n4))
n5 = set(deg_dict[keys[4]])
area5 = len(n5)
n15 = len(n1.intersection(n5))
n25 = len(n2.intersection(n5))
n35 = len(n3.intersection(n5))
n45 = len(n4.intersection(n5))
n125 = len((n1.intersection(n2)).intersection(n5))
n135 = len((n1.intersection(n3)).intersection(n5))
n145 = len((n1.intersection(n4)).intersection(n5))
n235 = len((n2.intersection(n3)).intersection(n5))
n245 = len((n2.intersection(n4)).intersection(n5))
n345 = len((n3.intersection(n4)).intersection(n5))
n1235 = len(((n1.intersection(n2)).intersection(n3)).intersection(n5))
n1245 = len(((n1.intersection(n2)).intersection(n4)).intersection(n5))
n1345 = len(((n1.intersection(n3)).intersection(n4)).intersection(n5))
n2345 = len(((n2.intersection(n3)).intersection(n4)).intersection(n5))
nstep = ((n1.intersection(n2)).intersection(n3))
n12345 = len((nstep.intersection(n4)).intersection(n5))
cat1, cat2, cat3, cat4, cat5 = keys
R('''png("%(out_dir)s/%(header)s-venn.png", '''
'''height=480, width=480)''' % locals())
R('''draw.quintuple.venn(%(area1)d, %(area2)d, %(area3)d, '''
'''%(area4)d, %(area5)d, %(n12)d, %(n13)d, %(n14)d,'''
'''%(n15)d, %(n23)d, %(n24)d, %(n25)d, %(n34)d, %(n35)d,'''
'''%(n45)d, %(n123)d, %(n124)d, %(n125)d, %(n134)d,'''
'''%(n135)d, %(n145)d, %(n234)d, %(n235)d, %(n245)d,'''
'''%(n345)d, %(n1234)d, %(n1235)d, %(n1245)d, %(n1345)d,'''
'''%(n2345)d, %(n12345)d, '''
'''c('%(cat1)s', '%(cat2)s', '%(cat3)s', '%(cat4)s', '%(cat5)s'),'''
'''col=c("red", "yellow", "skyblue", "orange", "purple"),'''
'''fill=c("red", "yellow", "skyblue", "orange", "purple"),'''
'''alpha=0.05, margin=0.05, cex=rep(0.8, 31))''' % locals())
R('''dev.off()''')
elif venn_size > 5:
raise ValueError("Illegal venn diagram size, must be <= 5") | [
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... | https://github.com/CGATOxford/cgat/blob/326aad4694bdfae8ddc194171bb5d73911243947/CGAT/Timeseries/__init__.py#L686-L809 | ||
lad1337/XDM | 0c1b7009fe00f06f102a6f67c793478f515e7efe | site-packages/logilab/astng/rebuilder.py | python | TreeRebuilder.visit_tryfinally | (self, node, parent) | return newnode | visit a TryFinally node by returning a fresh instance of it | visit a TryFinally node by returning a fresh instance of it | [
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] | def visit_tryfinally(self, node, parent):
"""visit a TryFinally node by returning a fresh instance of it"""
newnode = new.TryFinally()
_lineno_parent(node, newnode, parent)
newnode.body = [self.visit(child, newnode) for child in node.body]
newnode.finalbody = [self.visit(n, newnode) for n in node.finalbody]
newnode.set_line_info(newnode.last_child())
return newnode | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/djangosaml2-0.16.11/djangosaml2/views.py | python | metadata | (request, config_loader_path=None, valid_for=None) | return HttpResponse(content=text_type(metadata).encode('utf-8'),
content_type="text/xml; charset=utf8") | Returns an XML with the SAML 2.0 metadata for this
SP as configured in the settings.py file. | Returns an XML with the SAML 2.0 metadata for this
SP as configured in the settings.py file. | [
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] | def metadata(request, config_loader_path=None, valid_for=None):
"""Returns an XML with the SAML 2.0 metadata for this
SP as configured in the settings.py file.
"""
conf = get_config(config_loader_path, request)
metadata = entity_descriptor(conf)
return HttpResponse(content=text_type(metadata).encode('utf-8'),
content_type="text/xml; charset=utf8") | [
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FederatedAI/FATE | 32540492623568ecd1afcb367360133616e02fa3 | python/fate_arch/federation/pulsar/_federation.py | python | Federation.remote | (
self,
v,
name: str,
tag: str,
parties: typing.List[Party],
gc: GarbageCollectionABC,
) | [] | def remote(
self,
v,
name: str,
tag: str,
parties: typing.List[Party],
gc: GarbageCollectionABC,
) -> typing.NoReturn:
log_str = f"[pulsar.remote](name={name}, tag={tag}, parties={parties})"
_name_dtype_keys = [
_SPLIT_.join([party.role, party.party_id, name, tag, "remote"])
for party in parties
]
# tell the receiver what sender is going to send.
if _name_dtype_keys[0] not in self._name_dtype_map:
party_topic_infos = self._get_party_topic_infos(
parties, dtype=NAME_DTYPE_TAG
)
channel_infos = self._get_channels(
party_topic_infos=party_topic_infos)
if isinstance(v, Table):
body = {"dtype": FederationDataType.TABLE,
"partitions": v.partitions}
else:
body = {"dtype": FederationDataType.OBJECT}
LOGGER.debug(
f"[pulsar.remote] _name_dtype_keys: {_name_dtype_keys}, dtype: {body}"
)
self._send_obj(
name=name,
tag=_SPLIT_.join([tag, NAME_DTYPE_TAG]),
data=p_dumps(body),
channel_infos=channel_infos,
)
for k in _name_dtype_keys:
if k not in self._name_dtype_map:
self._name_dtype_map[k] = body
if isinstance(v, Table):
total_size = v.count()
partitions = v.partitions
LOGGER.debug(
f"[{log_str}]start to remote RDD, total_size={total_size}, partitions={partitions}"
)
party_topic_infos = self._get_party_topic_infos(
parties, name, partitions=partitions
)
# add gc
gc.add_gc_action(tag, v, "__del__", {})
send_func = self._get_partition_send_func(
name,
tag,
partitions,
party_topic_infos,
mq=self._mq,
maximun_message_size=self._max_message_size,
conf=self._pulsar_manager.runtime_config,
)
# noinspection PyProtectedMember
v._rdd.mapPartitionsWithIndex(send_func).count()
else:
LOGGER.debug(f"[{log_str}]start to remote obj")
party_topic_infos = self._get_party_topic_infos(parties, name)
channel_infos = self._get_channels(
party_topic_infos=party_topic_infos)
self._send_obj(
name=name, tag=tag, data=p_dumps(v), channel_infos=channel_infos
)
LOGGER.debug(f"[{log_str}]finish to remote") | [
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GiulioRossetti/cdlib | b2c6311b99725bb2b029556f531d244a2af14a2a | cdlib/algorithms/crisp_partition.py | python | agdl | (g_original: object, number_communities: int, kc: int) | return NodeClustering(
coms,
g_original,
"AGDL",
method_parameters={"number_communities": number_communities, "kc": kc},
) | AGDL is a graph-based agglomerative algorithm, for clustering high-dimensional data.
The algorithm uses the indegree and outdegree to characterize the affinity between two clusters.
**Supported Graph Types**
========== ======== ========
Undirected Directed Weighted
========== ======== ========
Yes Yes Yes
========== ======== ========
:param g_original: a networkx/igraph object
:param number_communities: number of communities
:param kc: size of the neighbor set for each cluster
:return: NodeClustering object
:Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> com = algorithms.agdl(g, number_communities=3, kc=4)
:References:
Zhang, W., Wang, X., Zhao, D., & Tang, X. (2012, October). `Graph degree linkage: Agglomerative clustering on a directed graph. <https://link.springer.com/chapter/10.1007/978-3-642-33718-5_31/>`_ In European Conference on Computer Vision (pp. 428-441). Springer, Berlin, Heidelberg.
.. note:: Reference implementation: https://github.com/myungjoon/GDL | AGDL is a graph-based agglomerative algorithm, for clustering high-dimensional data.
The algorithm uses the indegree and outdegree to characterize the affinity between two clusters. | [
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... | def agdl(g_original: object, number_communities: int, kc: int) -> NodeClustering:
"""
AGDL is a graph-based agglomerative algorithm, for clustering high-dimensional data.
The algorithm uses the indegree and outdegree to characterize the affinity between two clusters.
**Supported Graph Types**
========== ======== ========
Undirected Directed Weighted
========== ======== ========
Yes Yes Yes
========== ======== ========
:param g_original: a networkx/igraph object
:param number_communities: number of communities
:param kc: size of the neighbor set for each cluster
:return: NodeClustering object
:Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> com = algorithms.agdl(g, number_communities=3, kc=4)
:References:
Zhang, W., Wang, X., Zhao, D., & Tang, X. (2012, October). `Graph degree linkage: Agglomerative clustering on a directed graph. <https://link.springer.com/chapter/10.1007/978-3-642-33718-5_31/>`_ In European Conference on Computer Vision (pp. 428-441). Springer, Berlin, Heidelberg.
.. note:: Reference implementation: https://github.com/myungjoon/GDL
"""
g = convert_graph_formats(g_original, nx.Graph)
communities = Agdl(g, number_communities, kc)
nodes = {k: v for k, v in enumerate(g.nodes())}
coms = []
for com in communities:
coms.append([nodes[n] for n in com])
return NodeClustering(
coms,
g_original,
"AGDL",
method_parameters={"number_communities": number_communities, "kc": kc},
) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/PIL/TiffImagePlugin.py | python | ImageFileDirectory_v2.write_string | (self, value) | return b"" + value.encode('ascii', 'replace') + b"\0" | [] | def write_string(self, value):
# remerge of https://github.com/python-pillow/Pillow/pull/1416
if sys.version_info[0] == 2:
value = value.decode('ascii', 'replace')
return b"" + value.encode('ascii', 'replace') + b"\0" | [
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sentinel-hub/sentinelhub-py | d7ad283cf9d4bd4c8c1a8b169cdbe37c5bc8208a | sentinelhub/time_utils.py | python | is_valid_time | (time) | Check if input string represents a valid time/date stamp
:param time: a string containing a time/date stamp
:type time: str
:return: `True` is string is a valid time/date stamp, `False` otherwise
:rtype: bool | Check if input string represents a valid time/date stamp | [
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"/",
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] | def is_valid_time(time):
""" Check if input string represents a valid time/date stamp
:param time: a string containing a time/date stamp
:type time: str
:return: `True` is string is a valid time/date stamp, `False` otherwise
:rtype: bool
"""
try:
dateutil.parser.parse(time)
return True
except dateutil.parser.ParserError:
return False | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/template/engine.py | python | Engine.template_context_processors | (self) | return tuple(import_string(path) for path in context_processors) | [] | def template_context_processors(self):
context_processors = _builtin_context_processors
context_processors += tuple(self.context_processors)
return tuple(import_string(path) for path in context_processors) | [
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ppizarror/pygame-menu | da5827a1ad0686e8ff2aa536b74bbfba73967bcf | pygame_menu/widgets/core/selection.py | python | Selection.copy | (self) | return copy.deepcopy(self) | Creates a deep copy of the object.
:return: Copied selection effect | Creates a deep copy of the object. | [
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] | def copy(self) -> 'Selection':
"""
Creates a deep copy of the object.
:return: Copied selection effect
"""
return copy.deepcopy(self) | [
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Theano/Theano | 8fd9203edfeecebced9344b0c70193be292a9ade | theano/gradient.py | python | consider_constant | (x) | return consider_constant_(x) | DEPRECATED: use zero_grad() or disconnected_grad() instead.
Consider an expression constant when computing gradients.
The expression itself is unaffected, but when its gradient is
computed, or the gradient of another expression that this
expression is a subexpression of, it will not be backpropagated
through. In other words, the gradient of the expression is
truncated to 0.
:param x: A Theano expression whose gradient should be truncated.
:return: The expression is returned unmodified, but its gradient
is now truncated to 0.
.. versionadded:: 0.7 | DEPRECATED: use zero_grad() or disconnected_grad() instead. | [
"DEPRECATED",
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] | def consider_constant(x):
"""
DEPRECATED: use zero_grad() or disconnected_grad() instead.
Consider an expression constant when computing gradients.
The expression itself is unaffected, but when its gradient is
computed, or the gradient of another expression that this
expression is a subexpression of, it will not be backpropagated
through. In other words, the gradient of the expression is
truncated to 0.
:param x: A Theano expression whose gradient should be truncated.
:return: The expression is returned unmodified, but its gradient
is now truncated to 0.
.. versionadded:: 0.7
"""
warnings.warn((
"consider_constant() is deprecated, use zero_grad() or "
"disconnected_grad() instead."), stacklevel=3)
return consider_constant_(x) | [
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facebookresearch/SpanBERT | 0670d8b6a38f6714b85ea7a033f16bd8cc162676 | pretraining/fairseq/data/no_nsp_span_bert_dataset.py | python | NoNSPSpanBertDataset.get_dummy_batch | (self, num_tokens, max_positions, tgt_len=12) | return self.collater([
{
'id': i,
'source': source,
'segment_labels': segment_labels,
'lm_target': lm_target,
'pair_targets': pair_targets
}
for i in range(bsz)
]) | Return a dummy batch with a given number of tokens. | Return a dummy batch with a given number of tokens. | [
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] | def get_dummy_batch(self, num_tokens, max_positions, tgt_len=12):
"""Return a dummy batch with a given number of tokens."""
if isinstance(max_positions, float) or isinstance(max_positions, int):
tgt_len = min(tgt_len, max_positions)
source = self.vocab.dummy_sentence(tgt_len)
segment_labels = torch.zeros(tgt_len, dtype=torch.long)
pair_targets = torch.zeros((1, self.args.max_pair_targets + 2), dtype=torch.long)
lm_target = source
bsz = num_tokens // tgt_len
return self.collater([
{
'id': i,
'source': source,
'segment_labels': segment_labels,
'lm_target': lm_target,
'pair_targets': pair_targets
}
for i in range(bsz)
]) | [
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peterbrittain/asciimatics | 9a490faddf484ee5b9b845316f921f5888b23b18 | asciimatics/utilities.py | python | BoxTool.style | (self) | return self._style | The line drawing style used to draw boxes. Possible styles are set
in :mod:`asciimatics.constants`.
:param style: One of ``ASCII_LINE``, ``SINGLE_LINE``, or ``DOUBLE_LINE`` | The line drawing style used to draw boxes. Possible styles are set
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"""
The line drawing style used to draw boxes. Possible styles are set
in :mod:`asciimatics.constants`.
:param style: One of ``ASCII_LINE``, ``SINGLE_LINE``, or ``DOUBLE_LINE``
"""
return self._style | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/data/data_collator.py | python | numpy_default_data_collator | (features: List[InputDataClass]) | return batch | [] | def numpy_default_data_collator(features: List[InputDataClass]) -> Dict[str, Any]:
import numpy as np
if not isinstance(features[0], (dict, BatchEncoding)):
features = [vars(f) for f in features]
first = features[0]
batch = {}
# Special handling for labels.
# Ensure that tensor is created with the correct type
# (it should be automatically the case, but let's make sure of it.)
if "label" in first and first["label"] is not None:
label = first["label"].item() if isinstance(first["label"], np.ndarray) else first["label"]
dtype = np.int64 if isinstance(label, int) else np.float32
batch["labels"] = np.array([f["label"] for f in features], dtype=dtype)
elif "label_ids" in first and first["label_ids"] is not None:
if isinstance(first["label_ids"], np.ndarray):
batch["labels"] = np.stack([f["label_ids"] for f in features])
else:
dtype = np.int64 if type(first["label_ids"][0]) is int else np.float32
batch["labels"] = np.array([f["label_ids"] for f in features], dtype=dtype)
# Handling of all other possible keys.
# Again, we will use the first element to figure out which key/values are not None for this model.
for k, v in first.items():
if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
if isinstance(v, np.ndarray):
batch[k] = np.stack([f[k] for f in features])
else:
batch[k] = np.array([f[k] for f in features])
return batch | [
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rushter/MLAlgorithms | 3c8e16b8de3baf131395ae57edd479e59566a7c6 | mla/fm.py | python | FMRegressor.fit | (self, X, y=None) | [] | def fit(self, X, y=None):
super(FMRegressor, self).fit(X, y)
self.loss = mean_squared_error
self.loss_grad = elementwise_grad(mean_squared_error) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Tools/gdb/libpython.py | python | PyUnicodeObjectPtr.proxyval | (self, visited) | return result | [] | def proxyval(self, visited):
# From unicodeobject.h:
# Py_ssize_t length; /* Length of raw Unicode data in buffer */
# Py_UNICODE *str; /* Raw Unicode buffer */
field_length = long(self.field('length'))
field_str = self.field('str')
# Gather a list of ints from the Py_UNICODE array; these are either
# UCS-2 or UCS-4 code points:
if self.char_width() > 2:
Py_UNICODEs = [int(field_str[i]) for i in safe_range(field_length)]
else:
# A more elaborate routine if sizeof(Py_UNICODE) is 2 in the
# inferior process: we must join surrogate pairs.
Py_UNICODEs = []
i = 0
limit = safety_limit(field_length)
while i < limit:
ucs = int(field_str[i])
i += 1
if ucs < 0xD800 or ucs >= 0xDC00 or i == field_length:
Py_UNICODEs.append(ucs)
continue
# This could be a surrogate pair.
ucs2 = int(field_str[i])
if ucs2 < 0xDC00 or ucs2 > 0xDFFF:
continue
code = (ucs & 0x03FF) << 10
code |= ucs2 & 0x03FF
code += 0x00010000
Py_UNICODEs.append(code)
i += 1
# Convert the int code points to unicode characters, and generate a
# local unicode instance.
# This splits surrogate pairs if sizeof(Py_UNICODE) is 2 here (in gdb).
result = u''.join([_unichr(ucs) for ucs in Py_UNICODEs])
return result | [
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HonglinChu/SiamTrackers | 8471660b14f970578a43f077b28207d44a27e867 | SiamFCpp/SiamFCpp-video_analyst/siamfcpp/data/utils/filter_box.py | python | filter_unreasonable_training_boxes | (im: np.array, bbox,
config: Dict) | return filter_flag | r"""
Filter too small,too large objects and objects with extreme ratio
No input check. Assume that all imput (im, bbox) are valid object
Arguments
---------
im: np.array
image, formate=(H, W, C)
bbox: np.array or indexable object
bounding box annotation in (x, y, w, h) format | r"""
Filter too small,too large objects and objects with extreme ratio
No input check. Assume that all imput (im, bbox) are valid object | [
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config: Dict) -> bool:
r"""
Filter too small,too large objects and objects with extreme ratio
No input check. Assume that all imput (im, bbox) are valid object
Arguments
---------
im: np.array
image, formate=(H, W, C)
bbox: np.array or indexable object
bounding box annotation in (x, y, w, h) format
"""
eps = 1e-6
im_area = im.shape[0] * im.shape[1]
_, _, w, h = bbox
bbox_area = w * h
bbox_area_rate = bbox_area / im_area
bbox_ratio = h / (w + eps)
# valid trainng box condition
conds = [(config["min_area_rate"] < bbox_area_rate,
bbox_area_rate < config["max_area_rate"]),
max(bbox_ratio, 1.0 / max(bbox_ratio, eps)) < config["max_ratio"]]
# if not all conditions are satisfied, filter the sample
filter_flag = not all(conds)
return filter_flag | [
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pypa/bandersnatch | 2e3eb53029ddb8f205f85242d724ae492040c1ce | src/bandersnatch/mirror.py | python | BandersnatchMirror.process_package | (self, package: Package) | [] | async def process_package(self, package: Package) -> None:
loop = asyncio.get_running_loop()
# Don't save anything if our metadata filters all fail.
if not package.filter_metadata(self.filters.filter_metadata_plugins()):
return None
# save the metadata before filtering releases
# (dalley): why? the original author does not remember, and it doesn't seem
# to make a lot of sense.
# https://github.com/pypa/bandersnatch/commit/2a8cf8441b97f28eb817042a65a042d680fa527e#r39676370
if self.json_save:
json_saved = await loop.run_in_executor(
self.storage_backend.executor,
self.save_json_metadata,
package.metadata,
package.name,
)
assert json_saved
package.filter_all_releases_files(self.filters.filter_release_file_plugins())
package.filter_all_releases(self.filters.filter_release_plugins())
if self.release_files_save:
await self.sync_release_files(package)
await loop.run_in_executor(
self.storage_backend.executor, self.sync_simple_page, package
)
# XMLRPC PyPI Endpoint stores raw_name so we need to provide it
await loop.run_in_executor(
self.storage_backend.executor,
self.record_finished_package,
package.raw_name,
)
# Cleanup old legacy non PEP 503 Directories created for the Simple API
await self.cleanup_non_pep_503_paths(package) | [
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Abjad/abjad | d0646dfbe83db3dc5ab268f76a0950712b87b7fd | abjad/makers.py | python | LeafMaker.tag | (self) | return self._tag | r"""
Gets tag.
.. container:: example
Integer and string elements in ``pitches`` result in notes:
>>> maker = abjad.LeafMaker(tag=abjad.Tag("leaf_maker"))
>>> pitches = [2, 4, "F#5", "G#5"]
>>> duration = abjad.Duration(1, 4)
>>> leaves = maker(pitches, duration)
>>> staff = abjad.Staff(leaves)
>>> abjad.show(staff) # doctest: +SKIP
.. docs::
>>> string = abjad.lilypond(staff, tags=True)
>>> print(string)
\new Staff
{
%! leaf_maker
d'4
%! leaf_maker
e'4
%! leaf_maker
fs''4
%! leaf_maker
gs''4
} | r"""
Gets tag. | [
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"Gets",
"tag",
"."
] | def tag(self) -> typing.Optional[Tag]:
r"""
Gets tag.
.. container:: example
Integer and string elements in ``pitches`` result in notes:
>>> maker = abjad.LeafMaker(tag=abjad.Tag("leaf_maker"))
>>> pitches = [2, 4, "F#5", "G#5"]
>>> duration = abjad.Duration(1, 4)
>>> leaves = maker(pitches, duration)
>>> staff = abjad.Staff(leaves)
>>> abjad.show(staff) # doctest: +SKIP
.. docs::
>>> string = abjad.lilypond(staff, tags=True)
>>> print(string)
\new Staff
{
%! leaf_maker
d'4
%! leaf_maker
e'4
%! leaf_maker
fs''4
%! leaf_maker
gs''4
}
"""
return self._tag | [
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deepset-ai/haystack | 79fdda8a7cf393d774803608a4874f2a6e63cf6f | haystack/document_stores/weaviate.py | python | WeaviateDocumentStore._check_document | (self, cur_props: List[str], doc: dict) | return [item for item in doc.keys() if item not in cur_props] | Find the properties in the document that don't exist in the existing schema. | Find the properties in the document that don't exist in the existing schema. | [
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] | def _check_document(self, cur_props: List[str], doc: dict) -> List[str]:
"""
Find the properties in the document that don't exist in the existing schema.
"""
return [item for item in doc.keys() if item not in cur_props] | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | tools/find/ast.py | python | _links | (n_str) | return asdl.expr.StatTest(asdl.statAccessor_e.LinkCount, parse_number_predicate(n_str)) | [] | def _links(n_str):
return asdl.expr.StatTest(asdl.statAccessor_e.LinkCount, parse_number_predicate(n_str)) | [
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tensorflow/estimator | edb6e18703a0fa00182bcc72a056da6f5ce45e70 | tensorflow_estimator/python/estimator/canned/linear.py | python | _sdca_model_fn | (features, labels, mode, head, feature_columns, optimizer) | A model_fn for linear models that use the SDCA optimizer.
Args:
features: dict of `Tensor`.
labels: `Tensor` of shape `[batch_size]`.
mode: Defines whether this is training, evaluation or prediction. See
`ModeKeys`.
head: A `Head` instance.
feature_columns: An iterable containing all the feature columns used by the
model.
optimizer: a `LinearSDCA` instance.
Returns:
An `EstimatorSpec` instance.
Raises:
ValueError: mode or params are invalid, or features has the wrong type. | A model_fn for linear models that use the SDCA optimizer. | [
"A",
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"for",
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"use",
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] | def _sdca_model_fn(features, labels, mode, head, feature_columns, optimizer):
"""A model_fn for linear models that use the SDCA optimizer.
Args:
features: dict of `Tensor`.
labels: `Tensor` of shape `[batch_size]`.
mode: Defines whether this is training, evaluation or prediction. See
`ModeKeys`.
head: A `Head` instance.
feature_columns: An iterable containing all the feature columns used by the
model.
optimizer: a `LinearSDCA` instance.
Returns:
An `EstimatorSpec` instance.
Raises:
ValueError: mode or params are invalid, or features has the wrong type.
"""
assert feature_column_lib.is_feature_column_v2(feature_columns)
if isinstance(head,
(binary_class_head.BinaryClassHead,
head_lib._BinaryLogisticHeadWithSigmoidCrossEntropyLoss)): # pylint: disable=protected-access
loss_type = 'logistic_loss'
elif isinstance(head, (regression_head.RegressionHead,
head_lib._RegressionHeadWithMeanSquaredErrorLoss)): # pylint: disable=protected-access
assert head.logits_dimension == 1
loss_type = 'squared_loss'
else:
raise ValueError('Unsupported head type: {}'.format(head))
# The default name for LinearModel.
linear_model_name = 'linear_model'
# Name scope has no effect on variables in LinearModel, as it uses
# tf.get_variables() for variable creation. So we modify the model name to
# keep the variable names the same for checkpoint backward compatibility in
# canned Linear v2.
if isinstance(
head,
(binary_class_head.BinaryClassHead, regression_head.RegressionHead)):
linear_model_name = 'linear/linear_model'
linear_model = LinearModel(
feature_columns=feature_columns,
units=1,
sparse_combiner='sum',
name=linear_model_name)
logits = linear_model(features)
# We'd like to get all the non-bias variables associated with this
# LinearModel.
# TODO(rohanj): Figure out how to get shared embedding weights variable
# here.
bias = linear_model.bias
variables = linear_model.variables
# Expand (potential) Partitioned variables
bias = _get_expanded_variable_list([bias])
variables = _get_expanded_variable_list(variables)
variables = [var for var in variables if var not in bias]
tf.compat.v1.summary.scalar('bias', bias[0][0])
tf.compat.v1.summary.scalar('fraction_of_zero_weights',
_compute_fraction_of_zero(variables))
if mode == ModeKeys.TRAIN:
sdca_model, train_op = optimizer.get_train_step(
linear_model.layer._state_manager, # pylint: disable=protected-access
head._weight_column, # pylint: disable=protected-access
loss_type,
feature_columns,
features,
labels,
linear_model.bias,
tf.compat.v1.train.get_global_step())
update_weights_hook = _SDCAUpdateWeightsHook(sdca_model, train_op)
model_fn_ops = head.create_estimator_spec(
features=features,
mode=mode,
labels=labels,
train_op_fn=lambda unused_loss_fn: train_op,
logits=logits)
return model_fn_ops._replace(
training_chief_hooks=(model_fn_ops.training_chief_hooks +
(update_weights_hook,)))
else:
return head.create_estimator_spec(
features=features, mode=mode, labels=labels, logits=logits) | [
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/numpy/matrixlib/defmatrix.py | python | matrix.prod | (self, axis=None, dtype=None, out=None) | return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis) | Return the product of the array elements over the given axis.
Refer to `prod` for full documentation.
See Also
--------
prod, ndarray.prod
Notes
-----
Same as `ndarray.prod`, except, where that returns an `ndarray`, this
returns a `matrix` object instead.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.prod()
0
>>> x.prod(0)
matrix([[ 0, 45, 120, 231]])
>>> x.prod(1)
matrix([[ 0],
[ 840],
[7920]]) | Return the product of the array elements over the given axis. | [
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"""
Return the product of the array elements over the given axis.
Refer to `prod` for full documentation.
See Also
--------
prod, ndarray.prod
Notes
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Same as `ndarray.prod`, except, where that returns an `ndarray`, this
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Examples
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>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.prod()
0
>>> x.prod(0)
matrix([[ 0, 45, 120, 231]])
>>> x.prod(1)
matrix([[ 0],
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zhirongw/lemniscate.pytorch | f7cfe298357cb2b169cd59eb540aca24bed1f9b8 | lib/custom_transforms.py | python | RandomGaussianBlurring.__call__ | (self, image) | return image | [] | def __call__(self, image):
if isinstance(self.sigma, collections.Sequence):
sigma = random_num_generator(
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/chart-studio/chart_studio/api/v2/dashboards.py | python | retrieve | (fid) | return request("get", url) | Retrieve a dashboard from Plotly. | Retrieve a dashboard from Plotly. | [
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modelop/hadrian | 7c63e539d79e6e3cad959792d313dfc8b0c523ea | titus/titus/pfaast.py | python | SymbolTable.__call__ | (self, name) | Get a symbol's type from this scope or a parent's and raise a ``KeyError`` if not defined
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hubblestack/hubble | 763142474edcecdec5fd25591dc29c3536e8f969 | hubblestack/files/hubblestack_nova/misc.py | python | check_all_users_home_directory | (max_system_uid) | return True if not error else str(error) | Ensure all users' home directories exist | Ensure all users' home directories exist | [
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"""
Ensure all users' home directories exist
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max_system_uid = int(max_system_uid)
users_uids_dirs = _execute_shell_command("cat /etc/passwd | awk -F: '{ print $1 \" \" $3 \" \" $6 \" \" $7}'", python_shell=True).strip()
users_uids_dirs = users_uids_dirs.split('\n') if users_uids_dirs else []
error = []
for user_data in users_uids_dirs:
user_uid_dir = user_data.strip().split(" ")
if len(user_uid_dir) < 4:
user_uid_dir = user_uid_dir + [''] * (4 - len(user_uid_dir))
if user_uid_dir[1].isdigit():
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error += ["Either home directory " + user_uid_dir[2] + " of user " + user_uid_dir[0] + " is invalid or does not exist."]
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error += ["User " + user_uid_dir[0] + " has invalid uid " + user_uid_dir[1]]
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | medusa/config.py | python | change_NZB_DIR | (nzb_dir) | return True | Change NZB Folder
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Change NZB Folder
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"""
if not nzb_dir:
app._NZB_DIR = ''
return True
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if app_nzb_dir != os.path.normpath(nzb_dir):
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app._NZB_DIR = os.path.normpath(nzb_dir)
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baowenbo/DAIN | 9d9c0d7b3718dfcda9061c85efec472478a3aa86 | MegaDepth/util/png.py | python | encode | (buf, width, height) | return b''.join(
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""" buf: must be bytes or a bytearray in py3, a regular string in py2. formatted RGBRGB... """
assert (width * height * 3 == len(buf))
bpp = 3
def raw_data():
# reverse the vertical line order and add null bytes at the start
row_bytes = width * bpp
for row_start in range((height - 1) * width * bpp, -1, -row_bytes):
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yield buf[row_start:row_start + row_bytes]
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COLOR_TYPE_RGBA = 6
bit_depth = 8
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_deployment_status.py | python | V1DeploymentStatus.ready_replicas | (self, ready_replicas) | Sets the ready_replicas of this V1DeploymentStatus.
Total number of ready pods targeted by this deployment. # noqa: E501
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suavecode/SUAVE | 4f83c467c5662b6cc611ce2ab6c0bdd25fd5c0a5 | trunk/SUAVE/Attributes/Gases/Air.py | python | Air.compute_absolute_viscosity | (self,T=300.,p=101325.) | return C1*(T**(1.5))/(T + S) | Compute the absolute (dynamic) viscosity
Assumptions:
Ideal gas
Source:
https://www.cfd-online.com/Wiki/Sutherland's_law
Inputs:
T [K] - Temperature
Outputs:
absolute viscosity [kg/(m-s)]
Properties Used:
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Assumptions:
Ideal gas
Source:
https://www.cfd-online.com/Wiki/Sutherland's_law
Inputs:
T [K] - Temperature
Outputs:
absolute viscosity [kg/(m-s)]
Properties Used:
None
"""
S = 110.4 # constant in deg K (Sutherland's Formula)
C1 = 1.458e-6 # kg/m-s-sqrt(K), constant (Sutherland's Formula)
return C1*(T**(1.5))/(T + S) | [
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deanishe/alfred-fixum | 34cc2232789af5373befcffe8cd50536c88b20bf | src/workflow/workflow.py | python | Workflow.reset | (self) | Delete workflow settings, cache and data.
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File :attr:`settings <settings_path>` and directories
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"""
self.clear_cache()
self.clear_data()
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deepjyoti30/ytmdl | 0227541f303739a01e27a6d74499229d9bf44f84 | ytmdl/core.py | python | download | (link, yt_title, args) | return path | Download the song by using the passed link.
The song will be saved with the passed title.
Return the saved path of the song. | Download the song by using the passed link. | [
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"""Download the song by using the passed link.
The song will be saved with the passed title.
Return the saved path of the song.
"""
logger.info('Downloading {}{}{} in {}{}kbps{}'.format(
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yt_title,
Style.RESET_ALL,
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defaults.DEFAULT.SONG_QUALITY,
Style.RESET_ALL
))
path = yt.dw(link, args.proxy, yt_title,
args.format, no_progress=args.quiet)
if type(path) is not str:
# Probably an error occured
raise DownloadError(link, path)
logger.info('Downloaded!')
return path | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/bin/x86/Debug/Lib/mailbox.py | python | _singlefileMailbox._post_message_hook | (self, f) | return | Called after writing each message to file f. | Called after writing each message to file f. | [
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postgres/pgadmin4 | 374c5e952fa594d749fadf1f88076c1cba8c5f64 | web/pgadmin/dashboard/__init__.py | python | config | (sid=None) | return get_data(sid, None, 'config.sql') | This function returns server config information
:param sid: server id
:return: | This function returns server config information
:param sid: server id
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"""
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return get_data(sid, None, 'config.sql') | [
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pypa/pip | 7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4 | src/pip/_vendor/distlib/locators.py | python | AggregatingLocator.__init__ | (self, *locators, **kwargs) | Initialise an instance.
:param locators: The list of locators to search.
:param kwargs: Passed to the superclass constructor,
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Initialise an instance.
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* merge - if False (the default), the first successful
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self.merge = kwargs.pop('merge', False)
self.locators = locators
super(AggregatingLocator, self).__init__(**kwargs) | [
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youtify/youtify | 82cbc4a4ca6283f14f7179d4aeba30ed1ee1fea8 | dropbox/oauth.py | python | OAuthServer.authorize_token | (self, token, user) | return self.data_store.authorize_request_token(token, user) | Authorize a request token. | Authorize a request token. | [
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"."
] | def authorize_token(self, token, user):
"""Authorize a request token."""
return self.data_store.authorize_request_token(token, user) | [
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")"
] | https://github.com/youtify/youtify/blob/82cbc4a4ca6283f14f7179d4aeba30ed1ee1fea8/dropbox/oauth.py#L437-L439 | |
ustayready/CredKing | 68b612e4cdf01d2b65b14ab2869bb8a5531056ee | plugins/gmail/requests/cookies.py | python | RequestsCookieJar.__setstate__ | (self, state) | Unlike a normal CookieJar, this class is pickleable. | Unlike a normal CookieJar, this class is pickleable. | [
"Unlike",
"a",
"normal",
"CookieJar",
"this",
"class",
"is",
"pickleable",
"."
] | def __setstate__(self, state):
"""Unlike a normal CookieJar, this class is pickleable."""
self.__dict__.update(state)
if '_cookies_lock' not in self.__dict__:
self._cookies_lock = threading.RLock() | [
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... | https://github.com/ustayready/CredKing/blob/68b612e4cdf01d2b65b14ab2869bb8a5531056ee/plugins/gmail/requests/cookies.py#L409-L413 | ||
SebKuzminsky/pycam | 55e3129f518e470040e79bb00515b4bfcf36c172 | pycam/Utils/threading.py | python | ProcessStatistics.__init__ | (self, timeout=120) | [] | def __init__(self, timeout=120):
self.processes = {}
self.queues = {}
self.workers = {}
self.timeout = timeout | [
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] | https://github.com/SebKuzminsky/pycam/blob/55e3129f518e470040e79bb00515b4bfcf36c172/pycam/Utils/threading.py#L691-L695 | ||||
rigetti/pyquil | 36987ecb78d5dc85d299dd62395b7669a1cedd5a | pyquil/api/_quantum_computer.py | python | QuantumComputer.qubit_topology | (self) | return self.compiler.quantum_processor.qubit_topology() | Return a NetworkX graph representation of this QuantumComputer's quantum_processor's qubit
connectivity.
See :py:func:`AbstractQuantumProcessor.qubit_topology` for more. | Return a NetworkX graph representation of this QuantumComputer's quantum_processor's qubit
connectivity. | [
"Return",
"a",
"NetworkX",
"graph",
"representation",
"of",
"this",
"QuantumComputer",
"s",
"quantum_processor",
"s",
"qubit",
"connectivity",
"."
] | def qubit_topology(self) -> nx.graph:
"""
Return a NetworkX graph representation of this QuantumComputer's quantum_processor's qubit
connectivity.
See :py:func:`AbstractQuantumProcessor.qubit_topology` for more.
"""
return self.compiler.quantum_processor.qubit_topology() | [
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")"
] | https://github.com/rigetti/pyquil/blob/36987ecb78d5dc85d299dd62395b7669a1cedd5a/pyquil/api/_quantum_computer.py#L117-L124 | |
kdexd/virtex | 2baba8a4f3a4d80d617b3bc59e4be25b1052db57 | virtex/utils/metrics.py | python | TopkAccuracy.reset | (self) | r"""Reset counters; to be used at the start of new epoch/validation. | r"""Reset counters; to be used at the start of new epoch/validation. | [
"r",
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"counters",
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"/",
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"."
] | def reset(self):
r"""Reset counters; to be used at the start of new epoch/validation."""
self.num_total = 0.0
self.num_correct = 0.0 | [
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] | https://github.com/kdexd/virtex/blob/2baba8a4f3a4d80d617b3bc59e4be25b1052db57/virtex/utils/metrics.py#L41-L44 | ||
polakowo/vectorbt | 6638735c131655760474d72b9f045d1dbdbd8fe9 | vectorbt/generic/nb.py | python | bshift_1d_nb | (a: tp.Array1d, n: int = 1, fill_value: tp.Scalar = np.nan) | return _bshift_1d_nb | Shift backward by `n` positions.
Numba equivalent to `pd.Series(a).shift(n)`.
!!! warning
This operation looks ahead. | Shift backward by `n` positions. | [
"Shift",
"backward",
"by",
"n",
"positions",
"."
] | def bshift_1d_nb(a: tp.Array1d, n: int = 1, fill_value: tp.Scalar = np.nan) -> tp.Array1d:
"""Shift backward by `n` positions.
Numba equivalent to `pd.Series(a).shift(n)`.
!!! warning
This operation looks ahead."""
nb_enabled = not isinstance(a, np.ndarray)
if nb_enabled:
a_dtype = as_dtype(a.dtype)
if isinstance(fill_value, Omitted):
fill_value_dtype = np.asarray(fill_value.value).dtype
else:
fill_value_dtype = as_dtype(fill_value)
else:
a_dtype = a.dtype
fill_value_dtype = np.array(fill_value).dtype
dtype = np.promote_types(a_dtype, fill_value_dtype)
def _bshift_1d_nb(a, n, fill_value):
out = np.empty_like(a, dtype=dtype)
out[-n:] = fill_value
out[:-n] = a[n:]
return out
if not nb_enabled:
return _bshift_1d_nb(a, n, fill_value)
return _bshift_1d_nb | [
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"a"... | https://github.com/polakowo/vectorbt/blob/6638735c131655760474d72b9f045d1dbdbd8fe9/vectorbt/generic/nb.py#L173-L201 | |
saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/kubernetesmod.py | python | show_deployment | (name, namespace="default", **kwargs) | Return the kubernetes deployment defined by name and namespace
CLI Example:
.. code-block:: bash
salt '*' kubernetes.show_deployment my-nginx default
salt '*' kubernetes.show_deployment name=my-nginx namespace=default | Return the kubernetes deployment defined by name and namespace | [
"Return",
"the",
"kubernetes",
"deployment",
"defined",
"by",
"name",
"and",
"namespace"
] | def show_deployment(name, namespace="default", **kwargs):
"""
Return the kubernetes deployment defined by name and namespace
CLI Example:
.. code-block:: bash
salt '*' kubernetes.show_deployment my-nginx default
salt '*' kubernetes.show_deployment name=my-nginx namespace=default
"""
cfg = _setup_conn(**kwargs)
try:
api_instance = kubernetes.client.ExtensionsV1beta1Api()
api_response = api_instance.read_namespaced_deployment(name, namespace)
return api_response.to_dict()
except (ApiException, HTTPError) as exc:
if isinstance(exc, ApiException) and exc.status == 404:
return None
else:
log.exception(
"Exception when calling "
"ExtensionsV1beta1Api->read_namespaced_deployment"
)
raise CommandExecutionError(exc)
finally:
_cleanup(**cfg) | [
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"ExtensionsV1beta1Ap... | https://github.com/saltstack/salt/blob/fae5bc757ad0f1716483ce7ae180b451545c2058/salt/modules/kubernetesmod.py#L597-L624 | ||
freewym/espresso | 6671c507350295269e38add57dbe601dcb8e6ecf | espresso/data/asr_dataset.py | python | AsrDataset.collater | (self, samples, pad_to_length=None) | return res | Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
pad_to_length (dict, optional): a dictionary of
{"source": source_pad_to_length, "target": target_pad_to_length}
to indicate the max length to pad to in source and target respectively.
Returns:
dict: a mini-batch with the following keys:
- `id` (LongTensor): example IDs in the original input order
- `utt_id` (List[str]): list of utterance ids
- `nsentences` (int): batch size
- `ntokens` (int): total number of tokens in the batch
- `net_input` (dict): the input to the Model, containing keys:
- `src_tokens` (FloatTensor): a padded 3D Tensor of features in
the source of shape `(bsz, src_len, feat_dim)`. Padding will
appear on the left if *left_pad_source* is ``True``.
- `src_lengths` (IntTensor): 1D Tensor of the unpadded
lengths of each source sequence of shape `(bsz)`
- `prev_output_tokens` (LongTensor): a padded 2D Tensor of
tokens in the target sentence, shifted right by one
position for teacher forcing, of shape `(bsz, tgt_len)`.
This key will not be present if *input_feeding* is
``False``. Padding will appear on the left if
*left_pad_target* is ``True``.
- `src_lang_id` (LongTensor): a long Tensor which contains source
language IDs of each sample in the batch
- `target` (LongTensor): a padded 2D Tensor of tokens in the
target sentence of shape `(bsz, tgt_len)`. Padding will appear
on the left if *left_pad_target* is ``True``.
- `text` (List[str]): list of original text
- `tgt_lang_id` (LongTensor): a long Tensor which contains target language
IDs of each sample in the batch | Merge a list of samples to form a mini-batch. | [
"Merge",
"a",
"list",
"of",
"samples",
"to",
"form",
"a",
"mini",
"-",
"batch",
"."
] | def collater(self, samples, pad_to_length=None):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
pad_to_length (dict, optional): a dictionary of
{"source": source_pad_to_length, "target": target_pad_to_length}
to indicate the max length to pad to in source and target respectively.
Returns:
dict: a mini-batch with the following keys:
- `id` (LongTensor): example IDs in the original input order
- `utt_id` (List[str]): list of utterance ids
- `nsentences` (int): batch size
- `ntokens` (int): total number of tokens in the batch
- `net_input` (dict): the input to the Model, containing keys:
- `src_tokens` (FloatTensor): a padded 3D Tensor of features in
the source of shape `(bsz, src_len, feat_dim)`. Padding will
appear on the left if *left_pad_source* is ``True``.
- `src_lengths` (IntTensor): 1D Tensor of the unpadded
lengths of each source sequence of shape `(bsz)`
- `prev_output_tokens` (LongTensor): a padded 2D Tensor of
tokens in the target sentence, shifted right by one
position for teacher forcing, of shape `(bsz, tgt_len)`.
This key will not be present if *input_feeding* is
``False``. Padding will appear on the left if
*left_pad_target* is ``True``.
- `src_lang_id` (LongTensor): a long Tensor which contains source
language IDs of each sample in the batch
- `target` (LongTensor): a padded 2D Tensor of tokens in the
target sentence of shape `(bsz, tgt_len)`. Padding will appear
on the left if *left_pad_target* is ``True``.
- `text` (List[str]): list of original text
- `tgt_lang_id` (LongTensor): a long Tensor which contains target language
IDs of each sample in the batch
"""
res = collate(
samples,
pad_idx=self.dictionary.pad(),
eos_idx=self.dictionary.eos(),
left_pad_source=self.left_pad_source,
left_pad_target=self.left_pad_target,
input_feeding=self.input_feeding,
pad_to_length=pad_to_length,
pad_to_multiple=self.pad_to_multiple,
src_bucketed=(self.buckets is not None),
)
if self.src_lang_id is not None or self.tgt_lang_id is not None:
src_tokens = res["net_input"]["src_tokens"]
bsz = src_tokens.size(0)
if self.src_lang_id is not None:
res["net_input"]["src_lang_id"] = (
torch.LongTensor([[self.src_lang_id]]).expand(bsz, 1).to(src_tokens)
)
if self.tgt_lang_id is not None:
res["tgt_lang_id"] = (
torch.LongTensor([[self.tgt_lang_id]]).expand(bsz, 1).to(src_tokens)
)
return res | [
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... | https://github.com/freewym/espresso/blob/6671c507350295269e38add57dbe601dcb8e6ecf/espresso/data/asr_dataset.py#L291-L352 | |
angr/angr | 4b04d56ace135018083d36d9083805be8146688b | angr/analyses/identifier/functions/atoi.py | python | atoi.__init__ | (self) | [] | def __init__(self):
super(atoi, self).__init__()
self.skips_whitespace = False
self.allows_negative = True | [
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] | https://github.com/angr/angr/blob/4b04d56ace135018083d36d9083805be8146688b/angr/analyses/identifier/functions/atoi.py#L9-L12 | ||||
allegro/ralph | 1e4a9e1800d5f664abaef2624b8bf7512df279ce | src/ralph/data_center/admin.py | python | DataCenterAssetChangeList.get_ordering | (self, request, queryset) | return ordering | Adds extra ordering params for ordering by location. | Adds extra ordering params for ordering by location. | [
"Adds",
"extra",
"ordering",
"params",
"for",
"ordering",
"by",
"location",
"."
] | def get_ordering(self, request, queryset):
"""Adds extra ordering params for ordering by location."""
# NOTE(romcheg): slot_no is added by Django Admin automatically.
location_fields = [
'rack__server_room__data_center__name',
'rack__server_room__name',
'rack__name',
'position',
]
ordering = super(DataCenterAssetChangeList, self).get_ordering(
request, queryset
)
params = self.params
if ORDER_VAR in params:
order_params = params[ORDER_VAR].split('.')
for insert_index, p in enumerate(order_params):
try:
none, pfx, idx = p.rpartition('-')
if self.list_display[int(idx)] == 'show_location':
ordering[insert_index:insert_index] = [
'{}{}'.format(pfx, field)
for field in location_fields
]
except (IndexError, ValueError):
continue # Invalid ordering specified, skip it.
return ordering | [
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... | https://github.com/allegro/ralph/blob/1e4a9e1800d5f664abaef2624b8bf7512df279ce/src/ralph/data_center/admin.py#L328-L359 | |
scrapy/scrapy | b04cfa48328d5d5749dca6f50fa34e0cfc664c89 | scrapy/core/downloader/handlers/http10.py | python | HTTP10DownloadHandler.__init__ | (self, settings, crawler=None) | [] | def __init__(self, settings, crawler=None):
self.HTTPClientFactory = load_object(settings['DOWNLOADER_HTTPCLIENTFACTORY'])
self.ClientContextFactory = load_object(settings['DOWNLOADER_CLIENTCONTEXTFACTORY'])
self._settings = settings
self._crawler = crawler | [
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"]",
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"load_object... | https://github.com/scrapy/scrapy/blob/b04cfa48328d5d5749dca6f50fa34e0cfc664c89/scrapy/core/downloader/handlers/http10.py#L10-L14 | ||||
spyder-ide/spyder | 55da47c032dfcf519600f67f8b30eab467f965e7 | spyder/plugins/outlineexplorer/widgets.py | python | OutlineExplorerTreeWidget.clicked | (self, item) | Click event | Click event | [
"Click",
"event"
] | def clicked(self, item):
"""Click event"""
if isinstance(item, FileRootItem):
self.root_item_selected(item)
self.activated(item) | [
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cakebread/yolk | ee8c9f529a542d9c5eff4fe69b9c7906c802e4d8 | yolk/cli.py | python | Yolk.pypi_search | (self) | return 0 | Search PyPI by metadata keyword
e.g. yolk -S name=yolk AND license=GPL
@param spec: Cheese Shop search spec
@type spec: list of strings
spec examples:
["name=yolk"]
["license=GPL"]
["name=yolk", "AND", "license=GPL"]
@returns: 0 on success or 1 if mal-formed search spec | Search PyPI by metadata keyword
e.g. yolk -S name=yolk AND license=GPL | [
"Search",
"PyPI",
"by",
"metadata",
"keyword",
"e",
".",
"g",
".",
"yolk",
"-",
"S",
"name",
"=",
"yolk",
"AND",
"license",
"=",
"GPL"
] | def pypi_search(self):
"""
Search PyPI by metadata keyword
e.g. yolk -S name=yolk AND license=GPL
@param spec: Cheese Shop search spec
@type spec: list of strings
spec examples:
["name=yolk"]
["license=GPL"]
["name=yolk", "AND", "license=GPL"]
@returns: 0 on success or 1 if mal-formed search spec
"""
spec = self.pkg_spec
#Add remainging cli arguments to options.pypi_search
search_arg = self.options.pypi_search
spec.insert(0, search_arg.strip())
(spec, operator) = self.parse_search_spec(spec)
if not spec:
return 1
for pkg in self.pypi.search(spec, operator):
if pkg['summary']:
summary = pkg['summary'].encode('utf-8')
else:
summary = ""
print("""%s (%s):
%s
""" % (pkg['name'].encode('utf-8'), pkg["version"],
summary))
return 0 | [
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"(",
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"st... | https://github.com/cakebread/yolk/blob/ee8c9f529a542d9c5eff4fe69b9c7906c802e4d8/yolk/cli.py#L801-L834 | |
TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/tke/v20180525/models.py | python | SyncPrometheusTemplateRequest.__init__ | (self) | r"""
:param TemplateId: 实例id
:type TemplateId: str
:param Targets: 同步目标
:type Targets: list of PrometheusTemplateSyncTarget | r"""
:param TemplateId: 实例id
:type TemplateId: str
:param Targets: 同步目标
:type Targets: list of PrometheusTemplateSyncTarget | [
"r",
":",
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":",
"同步目标",
":",
"type",
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":",
"list",
"of",
"PrometheusTemplateSyncTarget"
] | def __init__(self):
r"""
:param TemplateId: 实例id
:type TemplateId: str
:param Targets: 同步目标
:type Targets: list of PrometheusTemplateSyncTarget
"""
self.TemplateId = None
self.Targets = None | [
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] | https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/tke/v20180525/models.py#L9778-L9786 |
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