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spesmilo/electrum | bdbd59300fbd35b01605e66145458e5f396108e8 | electrum/lnchannel.py | python | AbstractChannel.sweep_address | (self) | return addr | [] | def sweep_address(self) -> str:
# TODO: in case of unilateral close with pending HTLCs, this address will be reused
addr = None
if self.is_static_remotekey_enabled():
our_payment_pubkey = self.config[LOCAL].payment_basepoint.pubkey
addr = make_commitment_output_to_remote_address(our_payment_pubkey)
if addr is None:
addr = self._fallback_sweep_address
assert addr
if self.lnworker:
assert self.lnworker.wallet.is_mine(addr)
return addr | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/twisted/twisted/words/protocols/jabber/xmlstream.py | python | StreamManager.addHandler | (self, handler) | Add protocol handler.
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Add protocol handler.
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"""
XMPPHandlerCollection.addHandler(self, handler)
# get protocol handler up to speed when a connection has already
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wxWidgets/Phoenix | b2199e299a6ca6d866aa6f3d0888499136ead9d6 | wx/lib/docview.py | python | PathOnly | (path) | return os.path.split(path)[0] | Returns the path of a full path without the filename. | Returns the path of a full path without the filename. | [
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cms-dev/cms | 0401c5336b34b1731736045da4877fef11889274 | cms/server/jinja2_toolbox.py | python | today | (ctx, dt) | return dt.replace(tzinfo=utc).astimezone(timezone).date() \
== now.replace(tzinfo=utc).astimezone(timezone).date() | Returns whether the given datetime is today.
ctx (Context): a Jinja2 context, needed to retrieve the current
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dt (datetime): a datetime.
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datetime and the timezone to use when comparing.
dt (datetime): a datetime.
return (bool): whether dt occurred today in the timezone.
"""
now = ctx.get("now", make_datetime())
timezone = ctx.get("timezone", local_tz)
return dt.replace(tzinfo=utc).astimezone(timezone).date() \
== now.replace(tzinfo=utc).astimezone(timezone).date() | [
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/cookielib.py | python | CookieJar.clear_session_cookies | (self) | Discard all session cookies.
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"""
self._cookies_lock.acquire()
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/plat-irix6/FILE.py | python | BBTOB | (bbs) | return ((bbs) << BBSHIFT) | [] | def BBTOB(bbs): return ((bbs) << BBSHIFT) | [
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lzx1413/PytorchSSD | 320fe34f394f40aaa3b8a34d1ceed46e7ffecd46 | models/RFB_Net_vgg.py | python | BasicRFB_a.__init__ | (self, in_planes, out_planes, stride=1, scale=0.1) | self.branch3 = nn.Sequential(
BasicConv(in_planes, inter_planes, kernel_size=1, stride=1),
BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=1),
BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=3, dilation=3, relu=False)
) | self.branch3 = nn.Sequential(
BasicConv(in_planes, inter_planes, kernel_size=1, stride=1),
BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=1),
BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=3, dilation=3, relu=False)
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super(BasicRFB_a, self).__init__()
self.scale = scale
self.out_channels = out_planes
inter_planes = in_planes // 4
self.branch0 = nn.Sequential(
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BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=1, relu=False)
)
self.branch1 = nn.Sequential(
BasicConv(in_planes, inter_planes, kernel_size=1, stride=1),
BasicConv(inter_planes, inter_planes, kernel_size=(3, 1), stride=1, padding=(1, 0)),
BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=3, dilation=3, relu=False)
)
self.branch2 = nn.Sequential(
BasicConv(in_planes, inter_planes, kernel_size=1, stride=1),
BasicConv(inter_planes, inter_planes, kernel_size=(1, 3), stride=stride, padding=(0, 1)),
BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=3, dilation=3, relu=False)
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'''
self.branch3 = nn.Sequential(
BasicConv(in_planes, inter_planes, kernel_size=1, stride=1),
BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=1),
BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=3, dilation=3, relu=False)
)
'''
self.branch3 = nn.Sequential(
BasicConv(in_planes, inter_planes // 2, kernel_size=1, stride=1),
BasicConv(inter_planes // 2, (inter_planes // 4) * 3, kernel_size=(1, 3), stride=1, padding=(0, 1)),
BasicConv((inter_planes // 4) * 3, inter_planes, kernel_size=(3, 1), stride=stride, padding=(1, 0)),
BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=5, dilation=5, relu=False)
)
self.ConvLinear = BasicConv(4 * inter_planes, out_planes, kernel_size=1, stride=1, relu=False)
self.shortcut = BasicConv(in_planes, out_planes, kernel_size=1, stride=stride, relu=False)
self.relu = nn.ReLU(inplace=False) | [
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jython/jython3 | def4f8ec47cb7a9c799ea4c745f12badf92c5769 | lib-python/3.5.1/tkinter/__init__.py | python | PanedWindow.panecget | (self, child, option) | return self.tk.call(
(self._w, 'panecget') + (child, '-'+option)) | Query a management option for window.
Option may be any value allowed by the paneconfigure subcommand | Query a management option for window. | [
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return self.tk.call(
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CvvT/dumpDex | 92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1 | python/idaapi.py | python | var_ref_t.__ne__ | (self, *args) | return _idaapi.var_ref_t___ne__(self, *args) | __ne__(self, r) -> bool | __ne__(self, r) -> bool | [
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return _idaapi.var_ref_t___ne__(self, *args) | [
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apache/tvm | 6eb4ed813ebcdcd9558f0906a1870db8302ff1e0 | python/tvm/relay/op/contrib/ethosu.py | python | qnn_maxpool2d_pattern | () | return pattern | This function creates the pattern for nn.max_pool2d with optional fused RELU activation. | This function creates the pattern for nn.max_pool2d with optional fused RELU activation. | [
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"""
This function creates the pattern for nn.max_pool2d with optional fused RELU activation.
"""
pattern = is_op("nn.max_pool2d")(wildcard())
pattern = pattern.optional(is_op("clip"))
return pattern | [
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e2nIEE/pandapower | 12bd83d7c4e1bf3fa338dab2db649c3cd3db0cfb | pandapower/control/run_control.py | python | prepare_run_ctrl | (net, ctrl_variables, **kwargs) | return ctrl_variables | Prepares run control functions. Internal variables needed:
**controller_order** (list) - Order in which controllers in net.controller will be called
**runpp** (function) - the runpp function (for time series a faster version is possible)
**initial_run** (bool) - some controllers need an initial run of the powerflow prior to the control step | Prepares run control functions. Internal variables needed: | [
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**runpp** (function) - the runpp function (for time series a faster version is possible)
**initial_run** (bool) - some controllers need an initial run of the powerflow prior to the control step
"""
# sort controller_order by order if not already done
ctrl_var = ctrl_variables
if ctrl_variables is None:
ctrl_variables = ctrl_variables_default(net)
if ('continue_on_divergence') in kwargs and (ctrl_var is None or 'continue_on_divergence' not in ctrl_var.keys()):
div = kwargs.pop('continue_on_divergence')
ctrl_variables['continue_on_divergence'] = div
if ('check_each_level') in kwargs and (ctrl_var is None or 'continue_on_divergence' not in ctrl_var.keys()):
check = kwargs.pop('check_each_level')
ctrl_variables['check_each_level'] = check
ctrl_variables["errors"] = (LoadflowNotConverged, OPFNotConverged, NetCalculationNotConverged)
return ctrl_variables | [
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cdhigh/KindleEar | 7c4ecf9625239f12a829210d1760b863ef5a23aa | lib/cssutils/tokenize2.py | python | Tokenizer.__init__ | (self, macros=None, productions=None, doComments=True) | inits tokenizer with given macros and productions which default to
cssutils own macros and productions | inits tokenizer with given macros and productions which default to
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"""
inits tokenizer with given macros and productions which default to
cssutils own macros and productions
"""
if isinstance(macros, dict):
macros_hash_key = sorted(macros.items())
else:
macros_hash_key = macros
hash_key = str((macros_hash_key, productions))
if hash_key in _TOKENIZER_CACHE:
(tokenmatches, commentmatcher, urimatcher) = _TOKENIZER_CACHE[hash_key]
else:
if not macros:
macros = MACROS
if not productions:
productions = PRODUCTIONS
tokenmatches = self._compile_productions(self._expand_macros(macros,
productions))
commentmatcher = [x[1] for x in tokenmatches if x[0] == 'COMMENT'][0]
urimatcher = [x[1] for x in tokenmatches if x[0] == 'URI'][0]
_TOKENIZER_CACHE[hash_key] = (tokenmatches, commentmatcher, urimatcher)
self.tokenmatches = tokenmatches
self.commentmatcher = commentmatcher
self.urimatcher = urimatcher
self._doComments = doComments
self._pushed = [] | [
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lululxvi/deepxde | 730c97282636e86c845ce2ba3253482f2178469e | deepxde/data/fpde.py | python | FractionalTime.get_matrix_dynamic | (self, sparse) | return self.fracx.get_matrix(sparse) | [] | def get_matrix_dynamic(self, sparse):
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/boto/datapipeline/layer1.py | python | DataPipelineConnection.report_task_runner_heartbeat | (self, taskrunner_id, worker_group=None,
hostname=None) | return self.make_request(action='ReportTaskRunnerHeartbeat',
body=json.dumps(params)) | Task runners call ReportTaskRunnerHeartbeat every 15 minutes
to indicate that they are operational. In the case of AWS Data
Pipeline Task Runner launched on a resource managed by AWS
Data Pipeline, the web service can use this call to detect
when the task runner application has failed and restart a new
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:param worker_group: Indicates the type of task the task runner is
configured to accept and process. The worker group is set as a
field on objects in the pipeline when they are created. You can
only specify a single value for `workerGroup` in the call to
ReportTaskRunnerHeartbeat. There are no wildcard values permitted
in `workerGroup`, the string must be an exact, case-sensitive,
match.
:type hostname: string
:param hostname: The public DNS name of the calling task runner. | Task runners call ReportTaskRunnerHeartbeat every 15 minutes
to indicate that they are operational. In the case of AWS Data
Pipeline Task Runner launched on a resource managed by AWS
Data Pipeline, the web service can use this call to detect
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hostname=None):
"""
Task runners call ReportTaskRunnerHeartbeat every 15 minutes
to indicate that they are operational. In the case of AWS Data
Pipeline Task Runner launched on a resource managed by AWS
Data Pipeline, the web service can use this call to detect
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:type taskrunner_id: string
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in `workerGroup`, the string must be an exact, case-sensitive,
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:param hostname: The public DNS name of the calling task runner.
"""
params = {'taskrunnerId': taskrunner_id, }
if worker_group is not None:
params['workerGroup'] = worker_group
if hostname is not None:
params['hostname'] = hostname
return self.make_request(action='ReportTaskRunnerHeartbeat',
body=json.dumps(params)) | [
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LoRexxar/Kunlun-M | 06a68cf308f3d38be223d1d891465abcac9db88a | utils/file.py | python | File.lines | (self, line_rule) | return content | 获取指定行内容
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result = get_line(self.file_path, line_rule)
result = "\n".join(result)
if len(result):
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if content == '':
content = False
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sagemath/sagenb | 67a73cbade02639bc08265f28f3165442113ad4d | sagenb/notebook/cell.py | python | Cell.next_compute_id | (self) | r"""
Returns the ID of the next compute cell in this compute cell's
worksheet object. If this cell is *not* in the worksheet, it
returns the ID of the worksheet's *first* compute cell. If
this *is* the last compute cell, it returns its *own* ID.
OUTPUT:
- an integer or string
EXAMPLES::
sage: nb = sagenb.notebook.notebook.Notebook(tmp_dir(ext='.sagenb'))
sage: nb.user_manager().add_user('sage','sage','sage@sagemath.org',force=True)
sage: W = nb.create_new_worksheet('Test', 'sage')
sage: W.edit_save('foo\n{{{\n2+3\n///\n5\n}}}bar\n{{{\n2+8\n///\n10\n}}}')
sage: W.new_cell_after(1, "2^2")
Cell 4: in=2^2, out=
sage: [W.get_cell_with_id(i).next_compute_id() for i in [1, 4, 3]]
[4, 3, 3] | r"""
Returns the ID of the next compute cell in this compute cell's
worksheet object. If this cell is *not* in the worksheet, it
returns the ID of the worksheet's *first* compute cell. If
this *is* the last compute cell, it returns its *own* ID. | [
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r"""
Returns the ID of the next compute cell in this compute cell's
worksheet object. If this cell is *not* in the worksheet, it
returns the ID of the worksheet's *first* compute cell. If
this *is* the last compute cell, it returns its *own* ID.
OUTPUT:
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EXAMPLES::
sage: nb = sagenb.notebook.notebook.Notebook(tmp_dir(ext='.sagenb'))
sage: nb.user_manager().add_user('sage','sage','sage@sagemath.org',force=True)
sage: W = nb.create_new_worksheet('Test', 'sage')
sage: W.edit_save('foo\n{{{\n2+3\n///\n5\n}}}bar\n{{{\n2+8\n///\n10\n}}}')
sage: W.new_cell_after(1, "2^2")
Cell 4: in=2^2, out=
sage: [W.get_cell_with_id(i).next_compute_id() for i in [1, 4, 3]]
[4, 3, 3]
"""
L = self.worksheet().compute_cell_list()
try:
k = L.index(self)
except ValueError:
print("Warning -- cell %s no longer exists" % self.id())
return L[0].id()
try:
return L[k + 1].id()
except IndexError:
return self.id() | [
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scikit-learn/scikit-learn | 1d1aadd0711b87d2a11c80aad15df6f8cf156712 | sklearn/cluster/_spectral.py | python | cluster_qr | (vectors) | return vectors.argmax(axis=1) | Find the discrete partition closest to the eigenvector embedding.
This implementation was proposed in [1]_.
.. versionadded:: 1.1
Parameters
----------
vectors : array-like, shape: (n_samples, n_clusters)
The embedding space of the samples.
Returns
-------
labels : array of integers, shape: n_samples
The cluster labels of vectors.
References
----------
.. [1] `Simple, direct, and efficient multi-way spectral clustering, 2019
Anil Damle, Victor Minden, Lexing Ying
<:doi:`10.1093/imaiai/iay008`>`_ | Find the discrete partition closest to the eigenvector embedding. | [
"Find",
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"eigenvector",
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] | def cluster_qr(vectors):
"""Find the discrete partition closest to the eigenvector embedding.
This implementation was proposed in [1]_.
.. versionadded:: 1.1
Parameters
----------
vectors : array-like, shape: (n_samples, n_clusters)
The embedding space of the samples.
Returns
-------
labels : array of integers, shape: n_samples
The cluster labels of vectors.
References
----------
.. [1] `Simple, direct, and efficient multi-way spectral clustering, 2019
Anil Damle, Victor Minden, Lexing Ying
<:doi:`10.1093/imaiai/iay008`>`_
"""
k = vectors.shape[1]
_, _, piv = qr(vectors.T, pivoting=True)
ut, _, v = svd(vectors[piv[:k], :].T)
vectors = abs(np.dot(vectors, np.dot(ut, v.conj())))
return vectors.argmax(axis=1) | [
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | golismero/managers/pluginmanager.py | python | PluginInfo._fix_classname | (self, plugin_class) | Protected method to update the class name if found during plugin load.
(Assumes it's always valid, so no sanitization is performed).
.. warning: This method is called internally by GoLismero,
do not call it yourself!
:param plugin_class: Plugin class name.
:type plugin_class: str | Protected method to update the class name if found during plugin load.
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"""
Protected method to update the class name if found during plugin load.
(Assumes it's always valid, so no sanitization is performed).
.. warning: This method is called internally by GoLismero,
do not call it yourself!
:param plugin_class: Plugin class name.
:type plugin_class: str
"""
self.__plugin_class = plugin_class | [
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colour-science/colour | 38782ac059e8ddd91939f3432bf06811c16667f0 | colour/utilities/array.py | python | MixinDataclassArray.__array__ | (self, *args, **kwargs) | return tstack([
value if value is not None else default
for value in field_values.values()
], *args, **kwargs) | Implements support for *dataclass_like* conversion to :class:`ndarray`
class.
A field set to *None* will be filled with `np.nan` according to the
shape of the first field not set with *None*.
Other Parameters
----------------
\\*args : list, optional
Arguments.
\\**kwargs : dict, optional
Keywords arguments.
Returns
-------
ndarray
*dataclass_like* converted to `ndarray`. | Implements support for *dataclass_like* conversion to :class:`ndarray`
class. | [
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"""
Implements support for *dataclass_like* conversion to :class:`ndarray`
class.
A field set to *None* will be filled with `np.nan` according to the
shape of the first field not set with *None*.
Other Parameters
----------------
\\*args : list, optional
Arguments.
\\**kwargs : dict, optional
Keywords arguments.
Returns
-------
ndarray
*dataclass_like* converted to `ndarray`.
"""
field_values = {
field.name: getattr(self, field.name)
for field in fields(self)
}
default = None
for field, value in field_values.items():
if value is not None:
default = full(as_float_array(value).shape, np.nan)
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return tstack([
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microsoft/SDNet | adef46911c4ef4161938e0dcc8a2b435cd680582 | Models/Bert/modeling.py | python | PreTrainedBertModel.init_bert_weights | (self, module) | Initialize the weights. | Initialize the weights. | [
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""" Initialize the weights.
"""
if isinstance(module, (nn.Linear, nn.Embedding)):
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# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_() | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/irc/irc/bot.py | python | SingleServerIRCBot._connect | (self) | Establish a connection to the server at the front of the server_list. | Establish a connection to the server at the front of the server_list. | [
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self.connect(server.host, server.port, self._nickname,
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scrapinghub/spidermon | f2b21e45e70796f583bbb97f39b823c31d242b17 | spidermon/results/monitor.py | python | MonitorResult.addFailure | (self, test, error) | [] | def addFailure(self, test, error):
super().addFailure(test, error)
self.step[test].status = settings.MONITOR.STATUS.FAILURE
self.step[test].error = self._exc_info_to_string(error, test)
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xtiankisutsa/MARA_Framework | ac4ac88bfd38f33ae8780a606ed09ab97177c562 | tools/AndroBugs/tools/modified/androguard/core/bytecodes/dvm.py | python | DCode.pretty_show | (self, m_a) | Display (with a pretty print) this object
:param m_a: :class:`MethodAnalysis` object | Display (with a pretty print) this object | [
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"""
Display (with a pretty print) this object
:param m_a: :class:`MethodAnalysis` object
"""
bytecode.PrettyShow(m_a, m_a.basic_blocks.gets(), self.notes)
bytecode.PrettyShowEx(m_a.exceptions.gets()) | [
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TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | 5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e | tensorflow_dl_models/research/syntaxnet/dragnn/python/digraph_ops.py | python | LabelPotentialsFromTokens | (tokens, weights) | return labels_bxnxl | r"""Computes label potentials from tokens and weights.
For each batch of token activations, computes a scalar potential for each
label as the product between the activations of the source token and the
|weights|. Specifically,
labels[b,t,l] = \sum_{i} weights[l,i] * tokens[b,t,i]
Args:
tokens: [B,N,T] tensor of batched token activations.
weights: [L,T] matrix of weights.
B,N may be dynamic, but L,T must be static. The dtype of all arguments must
be compatible.
Returns:
[B,N,L] tensor of label potentials as defined above, with the same dtype as
the arguments. | r"""Computes label potentials from tokens and weights. | [
"r",
"Computes",
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"potentials",
"from",
"tokens",
"and",
"weights",
"."
] | def LabelPotentialsFromTokens(tokens, weights):
r"""Computes label potentials from tokens and weights.
For each batch of token activations, computes a scalar potential for each
label as the product between the activations of the source token and the
|weights|. Specifically,
labels[b,t,l] = \sum_{i} weights[l,i] * tokens[b,t,i]
Args:
tokens: [B,N,T] tensor of batched token activations.
weights: [L,T] matrix of weights.
B,N may be dynamic, but L,T must be static. The dtype of all arguments must
be compatible.
Returns:
[B,N,L] tensor of label potentials as defined above, with the same dtype as
the arguments.
"""
check.Eq(tokens.get_shape().ndims, 3, 'tokens must be rank 3')
check.Eq(weights.get_shape().ndims, 2, 'weights must be a matrix')
num_labels = weights.get_shape().as_list()[0]
num_activations = weights.get_shape().as_list()[1]
check.NotNone(num_labels, 'unknown number of labels')
check.NotNone(num_activations, 'unknown activation dimension')
check.Eq(tokens.get_shape().as_list()[2], num_activations,
'activation mismatch between weights and tokens')
tokens_shape = tf.shape(tokens)
batch_size = tokens_shape[0]
num_tokens = tokens_shape[1]
check.Same([tokens.dtype.base_dtype,
weights.dtype.base_dtype],
'dtype mismatch')
# Flatten out the batch dimension so we can use one big matmul().
tokens_bnxt = tf.reshape(tokens, [-1, num_activations])
labels_bnxl = tf.matmul(tokens_bnxt, weights, transpose_b=True)
# Restore the batch dimension in the output.
labels_bxnxl = tf.reshape(labels_bnxl, [batch_size, num_tokens, num_labels])
return labels_bxnxl | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/schemes/hyperelliptic_curves/invariants.py | python | igusa_clebsch_invariants | (f) | return clebsch_to_igusa(*clebsch_invariants(f)) | r"""
Given a sextic form `f`, return the Igusa-Clebsch invariants `I_2, I_4,
I_6, I_{10}` of Igusa and Clebsch [IJ1960]_.
`f` may be homogeneous in two variables or inhomogeneous in one.
EXAMPLES::
sage: from sage.schemes.hyperelliptic_curves.invariants import igusa_clebsch_invariants
sage: R.<x, y> = QQ[]
sage: igusa_clebsch_invariants(x^6 + y^6)
(-240, 1620, -119880, -46656)
sage: R.<x> = QQ[]
sage: igusa_clebsch_invariants(x^6 + x^5 + x^4 + x^2 + 2)
(-496, 6220, -955932, -1111784)
sage: magma(x^6 + 1).IgusaClebschInvariants() # optional - magma
[ -240, 1620, -119880, -46656 ]
sage: magma(x^6 + x^5 + x^4 + x^2 + 2).IgusaClebschInvariants() # optional - magma
[ -496, 6220, -955932, -1111784 ]
TESTS:
Let's check a symbolic example::
sage: R.<a, b, c, d, e> = QQ[]
sage: S.<x> = R[]
sage: igusa_clebsch_invariants(x^5 + a*x^4 + b*x^3 + c*x^2 + d*x + e)[0]
6*b^2 - 16*a*c + 40*d
sage: absolute_igusa_invariants_wamelen(GF(5)['x'](x^6 - 2*x))
Traceback (most recent call last):
...
NotImplementedError: Invariants of binary sextics/genus 2 hyperelliptic curves not implemented in characteristics 2, 3, and 5 | r"""
Given a sextic form `f`, return the Igusa-Clebsch invariants `I_2, I_4,
I_6, I_{10}` of Igusa and Clebsch [IJ1960]_. | [
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r"""
Given a sextic form `f`, return the Igusa-Clebsch invariants `I_2, I_4,
I_6, I_{10}` of Igusa and Clebsch [IJ1960]_.
`f` may be homogeneous in two variables or inhomogeneous in one.
EXAMPLES::
sage: from sage.schemes.hyperelliptic_curves.invariants import igusa_clebsch_invariants
sage: R.<x, y> = QQ[]
sage: igusa_clebsch_invariants(x^6 + y^6)
(-240, 1620, -119880, -46656)
sage: R.<x> = QQ[]
sage: igusa_clebsch_invariants(x^6 + x^5 + x^4 + x^2 + 2)
(-496, 6220, -955932, -1111784)
sage: magma(x^6 + 1).IgusaClebschInvariants() # optional - magma
[ -240, 1620, -119880, -46656 ]
sage: magma(x^6 + x^5 + x^4 + x^2 + 2).IgusaClebschInvariants() # optional - magma
[ -496, 6220, -955932, -1111784 ]
TESTS:
Let's check a symbolic example::
sage: R.<a, b, c, d, e> = QQ[]
sage: S.<x> = R[]
sage: igusa_clebsch_invariants(x^5 + a*x^4 + b*x^3 + c*x^2 + d*x + e)[0]
6*b^2 - 16*a*c + 40*d
sage: absolute_igusa_invariants_wamelen(GF(5)['x'](x^6 - 2*x))
Traceback (most recent call last):
...
NotImplementedError: Invariants of binary sextics/genus 2 hyperelliptic curves not implemented in characteristics 2, 3, and 5
"""
return clebsch_to_igusa(*clebsch_invariants(f)) | [
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doraemonext/wechat-python-sdk | bf6f6f3d4a5440feb73a51937059d7feddc335a0 | wechat_sdk/basic.py | python | WechatBasic.get_access_token | (self) | return self.conf.get_access_token() | 获取 Access Token 及 Access Token 过期日期, 仅供缓存使用, 如果希望得到原生的 Access Token 请求数据请使用 :func:`grant_token`
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:return: dict 对象, key 包括 `access_token` 及 `access_token_expires_at` | 获取 Access Token 及 Access Token 过期日期, 仅供缓存使用, 如果希望得到原生的 Access Token 请求数据请使用 :func:`grant_token`
**仅为兼容 v0.6.0 以前版本使用, 自行维护 access_token 请使用 access_token_setfunc 和 access_token_getfunc 进行操作**
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isl-org/MiDaS | b7fbf07a5d687653ec053757152f8f87efe49b4d | midas/midas_net.py | python | MidasNet.__init__ | (self, path=None, features=256, non_negative=True) | Init.
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path (str, optional): Path to saved model. Defaults to None.
features (int, optional): Number of features. Defaults to 256.
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path (str, optional): Path to saved model. Defaults to None.
features (int, optional): Number of features. Defaults to 256.
backbone (str, optional): Backbone network for encoder. Defaults to resnet50
"""
print("Loading weights: ", path)
super(MidasNet, self).__init__()
use_pretrained = False if path is None else True
self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
self.scratch.refinenet4 = FeatureFusionBlock(features)
self.scratch.refinenet3 = FeatureFusionBlock(features)
self.scratch.refinenet2 = FeatureFusionBlock(features)
self.scratch.refinenet1 = FeatureFusionBlock(features)
self.scratch.output_conv = nn.Sequential(
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nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
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nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
)
if path:
self.load(path) | [
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kedro-org/kedro | e78990c6b606a27830f0d502afa0f639c0830950 | kedro/versioning/journal.py | python | JournalFileHandler.emit | (self, record: logging.LogRecord) | Overriding emit function in logging.Handler, which will output the record to
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message = json.loads(record.getMessage())
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tianzhi0549/FCOS | 0eb8ee0b7114a3ca42ad96cd89e0ac63a205461e | fcos_core/modeling/rpn/retinanet/loss.py | python | RetinaNetLossComputation.__call__ | (self, anchors, box_cls, box_regression, targets) | return retinanet_cls_loss, retinanet_regression_loss | Arguments:
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box_cls (list[Tensor])
box_regression (list[Tensor])
targets (list[BoxList])
Returns:
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retinanet_regression_loss (Tensor | Arguments:
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box_cls (list[Tensor])
box_regression (list[Tensor])
targets (list[BoxList])
Returns:
retinanet_cls_loss (Tensor)
retinanet_regression_loss (Tensor
"""
anchors = [cat_boxlist(anchors_per_image) for anchors_per_image in anchors]
labels, regression_targets = self.prepare_targets(anchors, targets)
N = len(labels)
box_cls, box_regression = \
concat_box_prediction_layers(box_cls, box_regression)
labels = torch.cat(labels, dim=0)
regression_targets = torch.cat(regression_targets, dim=0)
pos_inds = torch.nonzero(labels > 0).squeeze(1)
retinanet_regression_loss = smooth_l1_loss(
box_regression[pos_inds],
regression_targets[pos_inds],
beta=self.bbox_reg_beta,
size_average=False,
) / (max(1, pos_inds.numel() * self.regress_norm))
labels = labels.int()
retinanet_cls_loss = self.box_cls_loss_func(
box_cls,
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return retinanet_cls_loss, retinanet_regression_loss | [
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/beets/art.py | python | clear | (log, lib, query) | [] | def clear(log, lib, query):
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log.info(u'Clearing album art from {0} items', len(items))
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/volvooncall/lock.py | python | VolvoLock.async_lock | (self, **kwargs) | Lock the car. | Lock the car. | [
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jupyterhub/kubespawner | 81e6059a1481138d27d0d3c32f403ecd21d576d8 | kubespawner/spawner.py | python | KubeSpawner._start_watching_pods | (self, replace=False) | return self._start_reflector(
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vitruvianscience/OpenDeep | e96efc449101094354b615cf15afe6d03644fc36 | opendeep/models/model.py | python | Model.get_loss | (self) | return None | Helper function for defining model-specific loss functions. Normally, you would pass an instance of
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xavierd/clang_complete | 293a1062274a06be61797612034bd8d87851406e | plugin/clang/cindex.py | python | TranslationUnit.get_file | (self, filename) | return File.from_name(self, filename) | Obtain a File from this translation unit. | Obtain a File from this translation unit. | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/amqp/transport.py | python | TCPTransport._setup_transport | (self) | [] | def _setup_transport(self):
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eltonlaw/impyute | b76a6b4bd3da36515d5f1fa87f35d0c3f4209c83 | impyute/ops/wrapper.py | python | _shape_3d | (data) | return len(np.shape(data)) == 3 | True if array is 3D | True if array is 3D | [
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ARISE-Initiative/robosuite | a5dfaf03cd769170881a1931d8f19c8eb72f531a | robosuite/utils/mjmod.py | python | CameraModder.get_pos | (self, name) | return self.model.cam_pos[camid] | Grabs position of a specific camera
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elfgzp/wechat_mall | b2a39bcf7b3d459e594751ed3525d71fcb27acb8 | weixin/pay.py | python | WeixinPay.order_query | (self, transaction_id='', out_trade_no='') | return self.make_request(method, url, kwargs) | 订单查询接口
https://pay.weixin.qq.com/wiki/doc/api/jsapi.php?chapter=9_2
:param out_trade_no: 可选，商户订单号，默认自动生成
:param transaction_id: 可选，微信订单号 和out_trade_no 二选一
:return: 返回的结果数据
-----
trade_state 订单状态
SUCCESS—支付成功
REFUND—转入退款
NOTPAY—未支付
CLOSED—已关闭
REVOKED—已撤销（刷卡支付）
USERPAYING--用户支付中
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trade_state 订单状态
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REFUND—转入退款
NOTPAY—未支付
CLOSED—已关闭
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-----
trade_state 订单状态
SUCCESS—支付成功
REFUND—转入退款
NOTPAY—未支付
CLOSED—已关闭
REVOKED—已撤销（刷卡支付）
USERPAYING--用户支付中
PAYERROR--支付失败(其他原因，如银行返回失败)
"""
path = 'pay/orderquery'
params = dict(
transaction_id=transaction_id,
out_trade_no=out_trade_no
)
method, url, kwargs = self.prepare_request('POST', path, params)
return self.make_request(method, url, kwargs) | [
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asyml/texar-pytorch | b83d3ec17e19da08fc5f81996d02f91176e55e54 | texar/torch/utils/rnn.py | python | bidirectional_dynamic_rnn | (
cell_fw: RNNCellBase[State],
cell_bw: RNNCellBase[State],
inputs: torch.Tensor,
sequence_length: Optional[Union[torch.LongTensor, List[int]]] = None,
initial_state_fw: Optional[State] = None,
initial_state_bw: Optional[State] = None,
time_major: bool = False) | return outputs, output_states | r"""Creates a dynamic version of bidirectional recurrent neural network.
Takes input and builds independent forward and backward RNNs. The
input_size of forward and backward cell must match. The initial state for
both directions is zero by default (but can be set optionally) and no
intermediate states are ever returned -- the network is fully unrolled
for the given (passed in) length(s) of the sequence(s) or completely
unrolled if length(s) is not given.
Args:
cell_fw: An instance of RNNCell, to be used for forward direction.
cell_bw: An instance of RNNCell, to be used for backward direction.
inputs: The RNN inputs.
If time_major == False (default), this must be a tensor of shape:
``[batch_size, max_time, ...]``, or a nested tuple of such elements.
If time_major == True, this must be a tensor of shape:
``[max_time, batch_size, ...]``, or a nested tuple of such elements.
sequence_length: (optional) An int32/int64 tensor, size
``[batch_size]``, containing the actual lengths for each of the
sequences in
the batch. If not provided, all batch entries are assumed
to be full sequences; and time reversal is applied from time
``0`` to ``max_time`` for each sequence.
initial_state_fw: (optional) An initial state for the forward RNN.
This must be a tensor of appropriate type and shape
``[batch_size, cell_fw.state_size]``.
If ``cell_fw.state_size`` is a tuple, this should be a tuple of
tensors having shapes ``[batch_size, s]``
for ``s`` in ``cell_fw.state_size``.
initial_state_bw: (optional) Same as for ``initial_state_fw``, but using
the corresponding properties of ``cell_bw``.
time_major: The shape format of the ``inputs`` and ``outputs`` Tensors.
If true, these ``Tensors`` must be shaped
``[max_time, batch_size, depth]``.
If false, these ``Tensors`` must be shaped
``[batch_size, max_time, depth]``.
Using ``time_major = True`` is a bit more efficient because it
avoids transposes at the beginning and end of the RNN calculation.
However, most TensorFlow data is batch-major, so by
default this function accepts input and emits output
in batch-major form.
Returns:
A tuple (outputs, output_states) where:
outputs: A tuple (output_fw, output_bw) containing the forward and
the backward rnn output ``Tensor``.
If time_major == False (default),
output_fw will be a ``Tensor`` shaped:
``[batch_size, max_time, cell_fw.output_size]``
and output_bw will be a ``Tensor`` shaped:
``[batch_size, max_time, cell_bw.output_size]``.
If time_major == True,
output_fw will be a ``Tensor`` shaped:
``[max_time, batch_size, cell_fw.output_size]``
and output_bw will be a ``Tensor`` shaped:
``[max_time, batch_size, cell_bw.output_size]``.
It returns a tuple instead of a single concatenated ``Tensor``,
unlike in the ``bidirectional_rnn``. If the concatenated
one is preferred, the forward and backward outputs can
be concatenated as ``tf.concat(outputs, 2)``.
output_states: A tuple (output_state_fw, output_state_bw) containing
the forward and the backward final states of bidirectional rnn. | r"""Creates a dynamic version of bidirectional recurrent neural network. | [
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] | def bidirectional_dynamic_rnn(
cell_fw: RNNCellBase[State],
cell_bw: RNNCellBase[State],
inputs: torch.Tensor,
sequence_length: Optional[Union[torch.LongTensor, List[int]]] = None,
initial_state_fw: Optional[State] = None,
initial_state_bw: Optional[State] = None,
time_major: bool = False) -> Tuple[Tuple[torch.Tensor, torch.Tensor],
Tuple[State, State]]:
r"""Creates a dynamic version of bidirectional recurrent neural network.
Takes input and builds independent forward and backward RNNs. The
input_size of forward and backward cell must match. The initial state for
both directions is zero by default (but can be set optionally) and no
intermediate states are ever returned -- the network is fully unrolled
for the given (passed in) length(s) of the sequence(s) or completely
unrolled if length(s) is not given.
Args:
cell_fw: An instance of RNNCell, to be used for forward direction.
cell_bw: An instance of RNNCell, to be used for backward direction.
inputs: The RNN inputs.
If time_major == False (default), this must be a tensor of shape:
``[batch_size, max_time, ...]``, or a nested tuple of such elements.
If time_major == True, this must be a tensor of shape:
``[max_time, batch_size, ...]``, or a nested tuple of such elements.
sequence_length: (optional) An int32/int64 tensor, size
``[batch_size]``, containing the actual lengths for each of the
sequences in
the batch. If not provided, all batch entries are assumed
to be full sequences; and time reversal is applied from time
``0`` to ``max_time`` for each sequence.
initial_state_fw: (optional) An initial state for the forward RNN.
This must be a tensor of appropriate type and shape
``[batch_size, cell_fw.state_size]``.
If ``cell_fw.state_size`` is a tuple, this should be a tuple of
tensors having shapes ``[batch_size, s]``
for ``s`` in ``cell_fw.state_size``.
initial_state_bw: (optional) Same as for ``initial_state_fw``, but using
the corresponding properties of ``cell_bw``.
time_major: The shape format of the ``inputs`` and ``outputs`` Tensors.
If true, these ``Tensors`` must be shaped
``[max_time, batch_size, depth]``.
If false, these ``Tensors`` must be shaped
``[batch_size, max_time, depth]``.
Using ``time_major = True`` is a bit more efficient because it
avoids transposes at the beginning and end of the RNN calculation.
However, most TensorFlow data is batch-major, so by
default this function accepts input and emits output
in batch-major form.
Returns:
A tuple (outputs, output_states) where:
outputs: A tuple (output_fw, output_bw) containing the forward and
the backward rnn output ``Tensor``.
If time_major == False (default),
output_fw will be a ``Tensor`` shaped:
``[batch_size, max_time, cell_fw.output_size]``
and output_bw will be a ``Tensor`` shaped:
``[batch_size, max_time, cell_bw.output_size]``.
If time_major == True,
output_fw will be a ``Tensor`` shaped:
``[max_time, batch_size, cell_fw.output_size]``
and output_bw will be a ``Tensor`` shaped:
``[max_time, batch_size, cell_bw.output_size]``.
It returns a tuple instead of a single concatenated ``Tensor``,
unlike in the ``bidirectional_rnn``. If the concatenated
one is preferred, the forward and backward outputs can
be concatenated as ``tf.concat(outputs, 2)``.
output_states: A tuple (output_state_fw, output_state_bw) containing
the forward and the backward final states of bidirectional rnn.
"""
output_fw, output_state_fw = dynamic_rnn(cell=cell_fw,
inputs=inputs,
sequence_length=sequence_length,
initial_state=initial_state_fw,
time_major=time_major)
if time_major:
time_steps = inputs.shape[0]
batch_size = inputs.shape[1]
else:
time_steps = inputs.shape[1]
batch_size = inputs.shape[0]
if sequence_length is None:
sequence_length = torch.tensor([time_steps] * batch_size,
dtype=torch.int32,
device=inputs.device)
# Backward direction
inputs_reverse = reverse_sequence(inputs=inputs,
seq_lengths=sequence_length,
time_major=time_major)
tmp, output_state_bw = dynamic_rnn(cell=cell_bw,
inputs=inputs_reverse,
sequence_length=sequence_length,
initial_state=initial_state_bw,
time_major=time_major)
output_bw = reverse_sequence(inputs=tmp,
seq_lengths=sequence_length,
time_major=time_major)
outputs = (output_fw, output_bw)
output_states = (output_state_fw, output_state_bw)
return outputs, output_states | [
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spectralpython/spectral | e1cd919f5f66abddc219b76926450240feaaed8f | spectral/graphics/spypylab.py | python | KeyParser.mods_are | (self, *args) | return True | Return True if modifiers are exactly the ones specified. | Return True if modifiers are exactly the ones specified. | [
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'''Return True if modifiers are exactly the ones specified.'''
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avrae/avrae | 6ebe46a1ec3d4dfaa2f9b18fac948325f39f87de | aliasing/api/statblock.py | python | AliasSkill.prof | (self) | return self._skill.prof | The proficiency multiplier in this skill. 0 = no proficiency, 0.5 = JoAT, 1 = proficiency, 2 = expertise.
:rtype: float or int | The proficiency multiplier in this skill. 0 = no proficiency, 0.5 = JoAT, 1 = proficiency, 2 = expertise. | [
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The proficiency multiplier in this skill. 0 = no proficiency, 0.5 = JoAT, 1 = proficiency, 2 = expertise.
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return self._skill.prof | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/iot/v20180123/models.py | python | AssociateSubDeviceToGatewayProductResponse.__init__ | (self) | r"""
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str | r"""
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str | [
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r"""
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str
"""
self.RequestId = None | [
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/pip/pip/_internal/req/req_install.py | python | InstallRequirement.__repr__ | (self) | return '<{} object: {} editable={!r}>'.format(
self.__class__.__name__, str(self), self.editable) | [] | def __repr__(self):
# type: () -> str
return '<{} object: {} editable={!r}>'.format(
self.__class__.__name__, str(self), self.editable) | [
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pazz/alot | 52f11f089df19cf336ad0983368e880dc5364149 | alot/settings/theme.py | python | Theme.__init__ | (self, path) | :param path: path to theme file
:type path: str
:raises: :class:`~alot.settings.errors.ConfigError` | :param path: path to theme file
:type path: str
:raises: :class:`~alot.settings.errors.ConfigError` | [
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"""
:param path: path to theme file
:type path: str
:raises: :class:`~alot.settings.errors.ConfigError`
"""
self._spec = os.path.join(DEFAULTSPATH, 'theme.spec')
self._config = read_config(path, self._spec, report_extra=True,
checks={'align': checks.align_mode,
'widthtuple': checks.width_tuple,
'force_list': checks.force_list,
'attrtriple': checks.attr_triple})
self._colours = [1, 16, 256]
# make sure every entry in 'order' lists have their own subsections
threadline = self._config['search']['threadline']
for sec in self._config['search']:
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if tline['parts'] is not None:
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here = set(tline.sections)
indefault = set(threadline.sections)
diff = listed.difference(here.union(indefault))
if diff:
msg = 'missing threadline parts: %s' % ', '.join(diff)
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googleapis/google-auth-library-python | 87cd2455aca96ac3fbc4a8b2f8e4c0bba568a70b | google/oauth2/id_token.py | python | verify_oauth2_token | (id_token, request, audience=None, clock_skew_in_seconds=0) | return idinfo | Verifies an ID Token issued by Google's OAuth 2.0 authorization server.
Args:
id_token (Union[str, bytes]): The encoded token.
request (google.auth.transport.Request): The object used to make
HTTP requests.
audience (str): The audience that this token is intended for. This is
typically your application's OAuth 2.0 client ID. If None then the
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clock_skew_in_seconds (int): The clock skew used for `iat` and `exp`
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Returns:
Mapping[str, Any]: The decoded token.
Raises:
exceptions.GoogleAuthError: If the issuer is invalid.
ValueError: If token verification fails | Verifies an ID Token issued by Google's OAuth 2.0 authorization server. | [
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"""Verifies an ID Token issued by Google's OAuth 2.0 authorization server.
Args:
id_token (Union[str, bytes]): The encoded token.
request (google.auth.transport.Request): The object used to make
HTTP requests.
audience (str): The audience that this token is intended for. This is
typically your application's OAuth 2.0 client ID. If None then the
audience is not verified.
clock_skew_in_seconds (int): The clock skew used for `iat` and `exp`
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Returns:
Mapping[str, Any]: The decoded token.
Raises:
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"""
idinfo = verify_token(
id_token,
request,
audience=audience,
certs_url=_GOOGLE_OAUTH2_CERTS_URL,
clock_skew_in_seconds=clock_skew_in_seconds,
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if idinfo["iss"] not in _GOOGLE_ISSUERS:
raise exceptions.GoogleAuthError(
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return idinfo | [
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romanz/trezor-agent | 23f8ef09a5b8eda2187b6883487d7ce0d9bd0b5e | libagent/gpg/keyring.py | python | parse | (s) | return parse_term(s) | Parse full s-expr from bytes. | Parse full s-expr from bytes. | [
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name, s = parse_term(s)
values = [name]
while not s.startswith(b')'):
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values.append(value)
return values, s[1:]
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donnemartin/gitsome | d7c57abc7cb66e9c910a844f15d4536866da3310 | xonsh/platform.py | python | pathbasename | (p) | return pathsplit(p)[-1] | This is a safe version of os.path.basename(), which does not work on
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chb/indivo_server | 9826c67ab17d7fc0df935db327344fb0c7d237e5 | indivo/views/documents/document_delete.py | python | _document_delete | (document_id, pha=None, record=None) | return DONE | Delete a document.
**ARGUMENTS:**
* *document_id*: The internal identifier of the document to delete.
* *pha*: If the document to delete is scoped to an app, this
:py:class:`~indivo.models.apps.PHA` instance refers to the app.
* *record*: If the document to delete is scoped to a record, this
:py:class:`~indivo.models.records_and_documents.Record`
instance refers to the record.
**RETURNS:**
* :http:statuscode:`200` on success.
**RAISES:**
* :py:exc:`django.http.Http404` if the arguments don't identify an existing
document. | Delete a document. | [
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] | def _document_delete(document_id, pha=None, record=None):
""" Delete a document.
**ARGUMENTS:**
* *document_id*: The internal identifier of the document to delete.
* *pha*: If the document to delete is scoped to an app, this
:py:class:`~indivo.models.apps.PHA` instance refers to the app.
* *record*: If the document to delete is scoped to a record, this
:py:class:`~indivo.models.records_and_documents.Record`
instance refers to the record.
**RETURNS:**
* :http:statuscode:`200` on success.
**RAISES:**
* :py:exc:`django.http.Http404` if the arguments don't identify an existing
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"""
document = _get_document(record=record, pha=pha, document_id=document_id)
if not document:
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prody/ProDy | b24bbf58aa8fffe463c8548ae50e3955910e5b7f | prody/dynamics/plotting.py | python | showContactMap | (enm, **kwargs) | return show | Show contact map using :func:`showAtomicMatrix`. *enm* can be
either a :class:`.GNM` or :class:`.Atomic` object. | Show contact map using :func:`showAtomicMatrix`. *enm* can be
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import matplotlib.pyplot as plt
#if SETTINGS['auto_show']:
# plt.figure()
cmap = kwargs.pop('cmap', 'Greys')
if isinstance(enm, GNMBase):
K = enm.getKirchhoff()
atoms = kwargs.pop('atoms', None)
elif isinstance(enm, Atomic):
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A = -(K - D)
show = showAtomicMatrix(A, atoms=atoms, cmap=cmap, **kwargs)
plt.title('{0} contact map'.format(enm.getTitle()))
plt.xlabel('Residue')
plt.ylabel('Residue')
#if SETTINGS['auto_show']:
# showFigure()
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colour-science/colour | 38782ac059e8ddd91939f3432bf06811c16667f0 | colour/models/rgb/transfer_functions/filmic_pro.py | python | _log_decoding_FilmicPro6_interpolator | () | return _LOG_DECODING_FILMICPRO_INTERPOLATOR_CACHE | Returns the *FiLMiC Pro 6* log decoding curve / electro-optical transfer
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Extrapolator
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twisted/twisted | dee676b040dd38b847ea6fb112a712cb5e119490 | src/twisted/web/vhost.py | python | NameVirtualHost._getResourceForRequest | (self, request) | return self.hosts.get(host, self.default) or resource.NoResource(
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hostHeader = request.getHeader(b"host")
if hostHeader == None:
return self.default or resource.NoResource()
else:
host = hostHeader.lower().split(b":", 1)[0]
return self.hosts.get(host, self.default) or resource.NoResource(
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jython/frozen-mirror | b8d7aa4cee50c0c0fe2f4b235dd62922dd0f3f99 | lib-python/2.7/stringold.py | python | rfind | (s, *args) | return _apply(s.rfind, args) | rfind(s, sub [,start [,end]]) -> int
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arguments start and end are interpreted as in slice notation.
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twitter/zktraffic | 82db04d9aafa13f694d4f5c7265069db42c0307c | zktraffic/zab/quorum_packet.py | python | AckEpoch.__init__ | (self, timestamp, src, dst, ptype, zxid, length, epoch) | [] | def __init__(self, timestamp, src, dst, ptype, zxid, length, epoch):
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NTMC-Community/MatchZoo | 8a487ee5a574356fc91e4f48e219253dc11bcff2 | matchzoo/utils/bert_utils.py | python | whitespace_tokenize | (text) | return tokens | Runs basic whitespace cleaning and splitting on a piece of text. | Runs basic whitespace cleaning and splitting on a piece of text. | [
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"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
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oxwhirl/smac | 456d133f40030e60f27bc7a85d2c5bdf96f6ad56 | smac/examples/rllib/env.py | python | RLlibStarCraft2Env.reset | (self) | return return_obs | Resets the env and returns observations from ready agents.
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"""
obs_list, state_list = self._env.reset()
return_obs = {}
for i, obs in enumerate(obs_list):
return_obs[i] = {
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self._ready_agents = list(range(len(obs_list)))
return return_obs | [
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OpenAgricultureFoundation/openag-device-software | a51d2de399c0a6781ae51d0dcfaae1583d75f346 | device/utilities/logger.py | python | Logger.exception | (self, message: str) | Reports standard logging exception message if in normal runtime
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self.logger.exception(self.name + ": " + str(message))
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | lib-python/2.7/lib-tk/tkFont.py | python | Font.actual | (self, option=None) | Return actual font attributes | Return actual font attributes | [
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if option:
return self._call("font", "actual", self.name, "-"+option)
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return self._mkdict(
self._split(self._call("font", "actual", self.name))
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yt-project/yt | dc7b24f9b266703db4c843e329c6c8644d47b824 | yt/utilities/parameter_file_storage.py | python | ParameterFileStore.insert_ds | (self, ds) | This will insert a new *ds* and flush the database to disk. | This will insert a new *ds* and flush the database to disk. | [
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self._records[ds._hash()] = self._adapt_ds(ds)
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Ultimaker/Uranium | 66da853cd9a04edd3a8a03526fac81e83c03f5aa | UM/VersionUpgrade.py | python | FormatException.__str__ | (self) | return "Exception parsing " + self._file + ": " + self._message | Gives a human-readable representation of this exception.
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/core/data/dc/utils/token.py | python | DataToken.__str__ | (self) | return smart_str(self._value, errors='ignore') | [] | def __str__(self):
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/_pydecimal.py | python | Decimal.logical_or | (self, other, context=None) | return _dec_from_triple(0, result.lstrip('0') or '0', 0) | Applies an 'or' operation between self and other's digits. | Applies an 'or' operation between self and other's digits. | [
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if context is None:
context = getcontext()
other = _convert_other(other, raiseit=True)
if not self._islogical() or not other._islogical():
return context._raise_error(InvalidOperation)
# fill to context.prec
(opa, opb) = self._fill_logical(context, self._int, other._int)
# make the operation, and clean starting zeroes
result = "".join([str(int(a)|int(b)) for a,b in zip(opa,opb)])
return _dec_from_triple(0, result.lstrip('0') or '0', 0) | [
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calmevtime/DCTNet | bd7c669b478e47fde230119045133d10e135de97 | classification/datasets/cvtransforms.py | python | RandomPerspective.get_params | (fov_range, anglex_ranges, angley_ranges, anglez_ranges, shear_ranges,
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"""Get parameters for perspective transformation
Returns:
sequence: params to be passed to the perspective transformation
"""
fov = 90 + random.uniform(-fov_range, fov_range)
anglex = random.uniform(anglex_ranges[0], anglex_ranges[1])
angley = random.uniform(angley_ranges[0], angley_ranges[1])
anglez = random.uniform(anglez_ranges[0], anglez_ranges[1])
shear = random.uniform(shear_ranges[0], shear_ranges[1])
max_dx = translate[0] * img_size[1]
max_dy = translate[1] * img_size[0]
translations = (np.round(random.uniform(-max_dx, max_dx)),
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scale = (random.uniform(1 / scale_ranges[0], scale_ranges[0]),
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return fov, anglex, angley, anglez, shear, translations, scale | [
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tensorflow/models | 6b8bb0cbeb3e10415c7a87448f08adc3c484c1d3 | official/legacy/image_classification/resnet/resnet_runnable.py | python | ResnetRunnable.train_step | (self, iterator) | See base class. | See base class. | [
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loss = tf.reduce_sum(prediction_loss) * (1.0 /
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if self.flags_obj.enable_xla:
step_fn = tf.function(step_fn, jit_compile=True)
self.strategy.run(step_fn, args=(next(iterator),)) | [
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KenT2/pipresents-gapless | 31a347bb8b45898a3fe08b1daf765e31d47b7a87 | remi/gui.py | python | TextInput.onenter | (self, new_value) | return self.eventManager.propagate(self.EVENT_ONENTER, (new_value,)) | Called when the user types an ENTER into the TextInput.
Note: This event can't be registered together with Widget.onkeydown.
Args:
new_value (str): the new string content of the TextInput. | Called when the user types an ENTER into the TextInput.
Note: This event can't be registered together with Widget.onkeydown. | [
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"""Called when the user types an ENTER into the TextInput.
Note: This event can't be registered together with Widget.onkeydown.
Args:
new_value (str): the new string content of the TextInput.
"""
self.set_value(new_value)
return self.eventManager.propagate(self.EVENT_ONENTER, (new_value,)) | [
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rkhleics/wagtailmodeladmin | 7fddc853bab2ff3868b8c7a03329308c55f16358 | wagtailmodeladmin/options.py | python | ModelAdmin.get_extra_attrs_for_field_col | (self, obj, field_name) | return {} | Return a dictionary of additional HTML attributes to be added to a
table cell when rendering the output of `field_name` for `obj` in
`index_view`.
Must always return a dictionary. | Return a dictionary of additional HTML attributes to be added to a
table cell when rendering the output of `field_name` for `obj` in
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Return a dictionary of additional HTML attributes to be added to a
table cell when rendering the output of `field_name` for `obj` in
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/tarfile.py | python | TarFile.makedir | (self, tarinfo, targetpath) | Make a directory called targetpath. | Make a directory called targetpath. | [
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] | def makedir(self, tarinfo, targetpath):
"""Make a directory called targetpath.
"""
try:
# Use a safe mode for the directory, the real mode is set
# later in _extract_member().
os.mkdir(targetpath, 0o700)
except FileExistsError:
pass | [
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phonopy/phonopy | 816586d0ba8177482ecf40e52f20cbdee2260d51 | phonopy/phonon/dos.py | python | plot_partial_dos | (
ax,
frequency_points,
partial_dos,
indices=None,
legend=None,
xlabel=None,
ylabel=None,
draw_grid=True,
flip_xy=False,
) | Plot partial DOS. | Plot partial DOS. | [
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] | def plot_partial_dos(
ax,
frequency_points,
partial_dos,
indices=None,
legend=None,
xlabel=None,
ylabel=None,
draw_grid=True,
flip_xy=False,
):
"""Plot partial DOS."""
warnings.warn(
"plot_partial_dos() is deprecated. Use plot_projected_dos() instead.",
DeprecationWarning,
)
plot_projected_dos(
ax,
frequency_points,
partial_dos,
indices=indices,
legend=legend,
xlabel=xlabel,
ylabel=ylabel,
draw_grid=draw_grid,
flip_xy=flip_xy,
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gleeda/memtriage | c24f4859995cccb9d88ccc0118d90693019cc1d5 | volatility/volatility/renderers/html.py | python | JSONRenderer.render | (self, outfd, data) | return outfd.write(json.dumps(json_input,ensure_ascii=False)) | Renderers a treegrid as columns/row items in JSON format | Renderers a treegrid as columns/row items in JSON format | [
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"""Renderers a treegrid as columns/row items in JSON format"""
# TODO: Implement tree structure in JSON
if data.max_depth() > 1:
raise NotImplementedError("JSON output for trees has not yet been implemented")
# TODO: Output (basic) type information in JSON
json_input = {"columns": [column.name for column in data.columns], "rows": data.visit(None, self.render_row, [])}
return outfd.write(json.dumps(json_input,ensure_ascii=False)) | [
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mikedh/trimesh | 6b1e05616b44e6dd708d9bc748b211656ebb27ec | trimesh/scene/scene.py | python | Scene.rezero | (self) | Move the current scene so that the AABB of the whole
scene is centered at the origin.
Does this by changing the base frame to a new, offset
base frame. | Move the current scene so that the AABB of the whole
scene is centered at the origin. | [
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] | def rezero(self):
"""
Move the current scene so that the AABB of the whole
scene is centered at the origin.
Does this by changing the base frame to a new, offset
base frame.
"""
if self.is_empty or np.allclose(self.centroid, 0.0):
# early exit since what we want already exists
return
# the transformation to move the overall scene to AABB centroid
matrix = np.eye(4)
matrix[:3, 3] = -self.centroid
# we are going to change the base frame
new_base = str(self.graph.base_frame) + '_I'
self.graph.update(frame_from=new_base,
frame_to=self.graph.base_frame,
matrix=matrix)
self.graph.base_frame = new_base | [
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tribe29/checkmk | 6260f2512e159e311f426e16b84b19d0b8e9ad0c | cmk/gui/wsgi/applications/utils.py | python | load_single_global_wato_setting | (varname: str, deflt: Any = None) | return settings.get(varname, deflt) | Load a single config option from WATO globals (Only for special use)
This is a small hack to get access to the current configuration without
the need to load the whole GUI config.
The problem is: The profiling setting is needed before the GUI config
is loaded regularly. This is needed, because we want to be able to
profile our whole WSGI app, including the config loading logic.
We only process the WATO written global settings file to get the WATO
settings. Which should be enough for the most cases. | Load a single config option from WATO globals (Only for special use) | [
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] | def load_single_global_wato_setting(varname: str, deflt: Any = None) -> Any:
"""Load a single config option from WATO globals (Only for special use)
This is a small hack to get access to the current configuration without
the need to load the whole GUI config.
The problem is: The profiling setting is needed before the GUI config
is loaded regularly. This is needed, because we want to be able to
profile our whole WSGI app, including the config loading logic.
We only process the WATO written global settings file to get the WATO
settings. Which should be enough for the most cases.
"""
settings = cmk.utils.store.load_mk_file(
cmk.utils.paths.default_config_dir + "/multisite.d/wato/global.mk", default={}
)
return settings.get(varname, deflt) | [
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CalebBell/fluids | dbbdd1d5ea3c458bd68dd8e21cf6281a0ec3fc80 | fluids/core.py | python | C2K | (C) | return C + zero_Celsius | Convert Celsius to Kelvin.
Parameters
----------
C : float
Celsius temperature to be converted, [degC]
Returns
-------
K : float
Equivalent Kelvin temperature, [K]
Notes
-----
Computes ``K = C + zero_Celsius`` where `zero_Celsius` = 273.15, i.e.,
(the absolute value of) temperature "absolute zero" as measured in Celsius.
Examples
--------
>>> C2K(-40)
233.14999999999998 | Convert Celsius to Kelvin. | [
"Convert",
"Celsius",
"to",
"Kelvin",
"."
] | def C2K(C):
"""Convert Celsius to Kelvin.
Parameters
----------
C : float
Celsius temperature to be converted, [degC]
Returns
-------
K : float
Equivalent Kelvin temperature, [K]
Notes
-----
Computes ``K = C + zero_Celsius`` where `zero_Celsius` = 273.15, i.e.,
(the absolute value of) temperature "absolute zero" as measured in Celsius.
Examples
--------
>>> C2K(-40)
233.14999999999998
"""
return C + zero_Celsius | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/functions/min_max.py | python | MinMax_base.__call__ | (self, *args, **kwds) | EXAMPLES::
sage: max_symbolic(3, 5, x)
max(x, 5)
sage: max_symbolic(3, 5, x, hold=True)
max(3, 5, x)
sage: max_symbolic([3, 5, x])
max(x, 5)
::
sage: min_symbolic(3, 5, x)
min(x, 3)
sage: min_symbolic(3, 5, x, hold=True)
min(3, 5, x)
sage: min_symbolic([3, 5, x])
min(x, 3)
TESTS:
We get an exception if no arguments are given::
sage: max_symbolic()
Traceback (most recent call last):
...
ValueError: number of arguments must be > 0
Check if a single argument which is not iterable works::
sage: max_symbolic(None)
Traceback (most recent call last):
...
TypeError: 'NoneType' object is not iterable
sage: max_symbolic(5)
Traceback (most recent call last):
...
TypeError: 'sage.rings.integer.Integer' object is not iterable
sage: max_symbolic(x)
Traceback (most recent call last):
...
TypeError: 'sage.symbolic.expression.Expression' object is not iterable
sage: min_symbolic(5)
Traceback (most recent call last):
...
TypeError: 'sage.rings.integer.Integer' object is not iterable
sage: min_symbolic(x)
Traceback (most recent call last):
...
TypeError: 'sage.symbolic.expression.Expression' object is not iterable | EXAMPLES:: | [
"EXAMPLES",
"::"
] | def __call__(self, *args, **kwds):
"""
EXAMPLES::
sage: max_symbolic(3, 5, x)
max(x, 5)
sage: max_symbolic(3, 5, x, hold=True)
max(3, 5, x)
sage: max_symbolic([3, 5, x])
max(x, 5)
::
sage: min_symbolic(3, 5, x)
min(x, 3)
sage: min_symbolic(3, 5, x, hold=True)
min(3, 5, x)
sage: min_symbolic([3, 5, x])
min(x, 3)
TESTS:
We get an exception if no arguments are given::
sage: max_symbolic()
Traceback (most recent call last):
...
ValueError: number of arguments must be > 0
Check if a single argument which is not iterable works::
sage: max_symbolic(None)
Traceback (most recent call last):
...
TypeError: 'NoneType' object is not iterable
sage: max_symbolic(5)
Traceback (most recent call last):
...
TypeError: 'sage.rings.integer.Integer' object is not iterable
sage: max_symbolic(x)
Traceback (most recent call last):
...
TypeError: 'sage.symbolic.expression.Expression' object is not iterable
sage: min_symbolic(5)
Traceback (most recent call last):
...
TypeError: 'sage.rings.integer.Integer' object is not iterable
sage: min_symbolic(x)
Traceback (most recent call last):
...
TypeError: 'sage.symbolic.expression.Expression' object is not iterable
"""
if len(args) == 0:
raise ValueError("number of arguments must be > 0")
if len(args) == 1:
try:
args = (SR._force_pyobject(iter(args[0])),)
except TypeError:
raise
try:
return BuiltinFunction.__call__(self, *args, **kwds)
except ValueError:
pass | [
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entropy1337/infernal-twin | 10995cd03312e39a48ade0f114ebb0ae3a711bb8 | Modules/build/pip/build/lib.linux-i686-2.7/pip/_vendor/ipaddress.py | python | IPv6Address.sixtofour | (self) | return IPv4Address((self._ip >> 80) & 0xFFFFFFFF) | Return the IPv4 6to4 embedded address.
Returns:
The IPv4 6to4-embedded address if present or None if the
address doesn't appear to contain a 6to4 embedded address. | Return the IPv4 6to4 embedded address. | [
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] | def sixtofour(self):
"""Return the IPv4 6to4 embedded address.
Returns:
The IPv4 6to4-embedded address if present or None if the
address doesn't appear to contain a 6to4 embedded address.
"""
if (self._ip >> 112) != 0x2002:
return None
return IPv4Address((self._ip >> 80) & 0xFFFFFFFF) | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/tools/sqli/thirdparty/pagerank/pagerank.py | python | int_str | (string_, integer, factor) | return integer | [] | def int_str(string_, integer, factor):
for i in xrange(len(string_)) :
integer *= factor
integer &= 0xFFFFFFFF
integer += ord(string_[i])
return integer | [
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eggnogdb/eggnog-mapper | d6e6cdf0a829f2bd85480f3f3f16e38c213cd091 | eggnogmapper/annotation/orthologs.py | python | __load_orthology | (member, orthology) | return all_orthologs | [] | def __load_orthology(member, orthology):
all_orthologs = {
"one2one": set(),
"one2many": set(),
"many2many": set(),
"many2one": set(),
"all": set(),
} # each set contains a list of taxa.sequence items
# member
# e.g. 1041607.K0KSN3
# orthology
# k: (species, (tuple_of_co-orthologs))
# v: set of species with orthologs: set((species1, list_orths), (species2, list_orths), ...)
# e.g. of many2one relationship
# {
# ('1041607', ('1041607.K0KPV8', '1041607.K0KSN3')): {
# ('27289', ('27289.XP_003672691.1',)),
# ('4956', ('4956.XP_002498479.1',)),
# ('381046', ('381046.XP_002553862.1',)),
# ('28985', ('28985.XP_453001.1',)),
# ('113608', ('113608.XP_003687705.1',)),
# ('588726', ('588726.J7S748',)),
# ('27288', ('27288.XP_003673468.1',)),
# ('36033', ('36033.XP_001645185.1',)),
# ('4932', ('4932.YGR015C',)),
# ('5478', ('5478.XP_446354.1',)),
# ('432096', ('432096.XP_003958079.1',)),
# ('4950', ('4950.XP_003681095.1',))
# }
# }
for k, v in orthology.items():
all_orthologs['all'].update(k[1])
if len(k[1]) == 1:
otype_prefix = "one2"
else:
otype_prefix = "many2"
for t2, co2 in v:
all_orthologs['all'].update(co2)
if len(co2) == 1:
otype = otype_prefix + "one"
else:
otype = otype_prefix + "many"
all_orthologs[otype].update(k[1])
all_orthologs[otype].update(co2)
return all_orthologs | [
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cea-sec/Sibyl | 14866eb8ef3a65fcc4535faaf76eb42faf64d313 | sibyl/learn/learn.py | python | TestCreator.create_test | (self) | return self.create_test_from_trace() | Main function of the trace that is in charge of calling other methods in the right order
Return a string that correspong to the code of the test class | Main function of the trace that is in charge of calling other methods in the right order
Return a string that correspong to the code of the test class | [
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"""
Main function of the trace that is in charge of calling other methods in the right order
Return a string that correspong to the code of the test class
"""
self.parse_types()
self.create_trace()
self.prune_snapshots()
self.clean_trace()
self.test_trace()
assert len(self.trace) > 0
self.extract_refs()
return self.create_test_from_trace() | [
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ssokolow/quicktile | ca8105dcdd08c26de0ce6b987958c15a88489de2 | quicktile/util.py | python | Rectangle.to_relative | (self, other_rect: 'Rectangle') | return self._replace(x=self.x - other_rect.x,
y=self.y - other_rect.y) | Interpret self as absolute and make it relative to ``other_rect``.
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(eg. Convert a window position that's relative to the top-left corner
of the desktop as a whole into one that's relative to a single monitor)
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:param other_rect: The reference frame to make this rectangle's
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:returns: A relative-coordinates version of this rectangle.
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Pylons/webob | 259230aa2b8b9cf675c996e157c5cf021c256059 | src/webob/acceptparse.py | python | AcceptLanguageInvalidHeader.__str__ | (self) | return "<invalid header value>" | Return the ``str`` ``'<invalid header value>'``. | Return the ``str`` ``'<invalid header value>'``. | [
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lanmaster53/recon-ng | 9e907dfe09fce2997f0301d746796408e01a60b7 | recon/core/framework.py | python | Framework._do_script_status | (self, params) | Provides the status of command recording | Provides the status of command recording | [
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] | def _do_script_status(self, params):
'''Provides the status of command recording'''
status = 'started' if Framework._record else 'stopped'
self.output(f"Command recording is {status}.") | [
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TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | 5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e | deep-learning/CapsNET/TensorFlow_Implementation/capsLayer.py | python | conv2d | (inputs,
filters,
vec_len,
kernel_size,
strides=(1, 1),
with_routing=False,
reuse=None) | return(layer(inputs, kernel_size=kernel_size, stride=strides)) | A capsule convolutional layer.(Note: not tested yet)
Args:
inputs: A tensor.
Returns:
...
Raises:
... | A capsule convolutional layer.(Note: not tested yet)
Args:
inputs: A tensor.
Returns:
...
Raises:
... | [
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vec_len,
kernel_size,
strides=(1, 1),
with_routing=False,
reuse=None):
'''A capsule convolutional layer.(Note: not tested yet)
Args:
inputs: A tensor.
Returns:
...
Raises:
...
'''
layer = CapsLayer(num_outputs=filters,
vec_len=vec_len,
with_routing=with_routing,
layer_type='CONV')
return(layer(inputs, kernel_size=kernel_size, stride=strides)) | [
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pytrainer/pytrainer | 66f3e2b30b48c66e03111248faffc43b8e31c583 | extensions/gpx2garmin/gpx2garmin.py | python | gpx2garmin.exportGPX | (self) | return subprocess.call(cmd) | export the GPX file using gpsbabel | export the GPX file using gpsbabel | [
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' export the GPX file using gpsbabel '
cmd = ["gpsbabel", "-t", "-i", "gpx", "-f", self.tmpgpxfile, "-o", "garmin"]
if self.limit is not None:
cmd = cmd + ["-x", "simplify,count=%s" % self.limit]
cmd = cmd + ["-F", self.device]
return subprocess.call(cmd) | [
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PowerScript/KatanaFramework | 0f6ad90a88de865d58ec26941cb4460501e75496 | lib/future/src/future/types/newstr.py | python | newstr.encode | (self, encoding='utf-8', errors='strict') | return newbytes(super(newstr, self).encode(encoding, errors)) | Returns bytes
Encode S using the codec registered for encoding. Default encoding
is 'utf-8'. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
'xmlcharrefreplace' as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors. | Returns bytes | [
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] | def encode(self, encoding='utf-8', errors='strict'):
"""
Returns bytes
Encode S using the codec registered for encoding. Default encoding
is 'utf-8'. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
'xmlcharrefreplace' as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors.
"""
from future.types.newbytes import newbytes
# Py2 unicode.encode() takes encoding and errors as optional parameter,
# not keyword arguments as in Python 3 str.
# For the surrogateescape error handling mechanism, the
# codecs.register_error() function seems to be inadequate for an
# implementation of it when encoding. (Decoding seems fine, however.)
# For example, in the case of
# u'\udcc3'.encode('ascii', 'surrogateescape_handler')
# after registering the ``surrogateescape_handler`` function in
# future.utils.surrogateescape, both Python 2.x and 3.x raise an
# exception anyway after the function is called because the unicode
# string it has to return isn't encodable strictly as ASCII.
if errors == 'surrogateescape':
if encoding == 'utf-16':
# Known to fail here. See test_encoding_works_normally()
raise NotImplementedError('FIXME: surrogateescape handling is '
'not yet implemented properly')
# Encode char by char, building up list of byte-strings
mybytes = []
for c in self:
code = ord(c)
if 0xD800 <= code <= 0xDCFF:
mybytes.append(newbytes([code - 0xDC00]))
else:
mybytes.append(c.encode(encoding=encoding))
return newbytes(b'').join(mybytes)
return newbytes(super(newstr, self).encode(encoding, errors)) | [
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mpenning/ciscoconfparse | a6a176e6ceac7c5f3e974272fa70273476ba84a3 | ciscoconfparse/models_asa.py | python | BaseASAIntfLine.__repr__ | (self) | [] | def __repr__(self):
if not self.is_switchport:
if self.ipv4_addr_object == self.default_ipv4_addr_object:
addr = "No IPv4"
else:
addr = self.ipv4_addr_object
return "<%s # %s '%s' info: '%s'>" % (
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return "<%s # %s '%s' info: 'switchport'>" % (
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sqall01/alertR | e1d1a83e54f876cc4cd7bd87387e05cb75d4dc13 | sensorClientWeatherService/lib/client/core.py | python | Connection.recv | (self,
buffsize: int,
timeout: Optional[float] = 20.0) | Receives data on the connection.
:param buffsize: how much data at max is received
:param timeout: timeout of the receiving call | Receives data on the connection.
:param buffsize: how much data at max is received
:param timeout: timeout of the receiving call | [
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buffsize: int,
timeout: Optional[float] = 20.0) -> bytes:
"""
Receives data on the connection.
:param buffsize: how much data at max is received
:param timeout: timeout of the receiving call
"""
raise NotImplementedError("Abstract class.") | [
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realpython/book2-exercises | cde325eac8e6d8cff2316601c2e5b36bb46af7d0 | py2manager/gluon/highlight.py | python | Highlighter.highlight | (self, data) | return ''.join(self.output).expandtabs(4) | Syntax highlight some python code.
Returns html version of code. | Syntax highlight some python code.
Returns html version of code. | [
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"""
Syntax highlight some python code.
Returns html version of code.
"""
i = 0
mode = self.mode
while i < len(data):
for (token, o_re, style) in Highlighter.all_styles[mode][1]:
if not token in self.suppress_tokens:
match = o_re.match(data, i)
if match:
if style:
new_mode = \
Highlighter.all_styles[mode][0](self,
token, match, style
% dict(link=self.link))
else:
new_mode = \
Highlighter.all_styles[mode][0](self,
token, match, style)
if not new_mode is None:
mode = new_mode
i += max(1, len(match.group()))
break
else:
self.change_style(None, None)
self.output.append(data[i])
i += 1
self.change_style(None, None)
return ''.join(self.output).expandtabs(4) | [
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django-oscar/django-oscar-accounts | 8a6dc3b42306979779f048b4d3ed0a9fd4a2f794 | src/oscar_accounts/api/views.py | python | TransferRefundsView.valid_payload | (self, payload) | return self.created(
reverse('oscar_accounts_api:transfer', kwargs={'reference': transfer.reference}),
transfer.as_dict()) | [] | def valid_payload(self, payload):
to_refund = get_object_or_404(Transfer,
reference=self.kwargs['reference'])
amount = payload['amount']
max_refund = to_refund.max_refund()
if amount > max_refund:
return self.forbidden(
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"is %.2f") % max_refund)
if not to_refund.source.is_active():
raise ValidationError(errors.ACCOUNT_INACTIVE)
try:
transfer = facade.transfer(
to_refund.destination, to_refund.source,
payload['amount'], parent=to_refund,
merchant_reference=payload.get('merchant_reference', None))
except exceptions.AccountException as e:
return self.forbidden(
code=errors.CANNOT_CREATE_TRANSFER,
msg=e.message)
return self.created(
reverse('oscar_accounts_api:transfer', kwargs={'reference': transfer.reference}),
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explosion/thinc | 95d418925b25277a92291005318710b86b2bbb41 | thinc/model.py | python | Model.__truediv__ | (self, other: Any) | return self._context_operators.get()["/"](self, other) | Apply the function bound to the '/' operator. | Apply the function bound to the '/' operator. | [
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avatartwo/avatar2 | 21ba70e1aec71dc87cf948dc5f3644c96fe437c0 | avatar2/avatar2.py | python | Avatar.stop | (self) | Stop the thread which manages the asynchronous messages. | Stop the thread which manages the asynchronous messages. | [
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sympy/sympy | d822fcba181155b85ff2b29fe525adbafb22b448 | sympy/combinatorics/rewritingsystem_fsm.py | python | StateMachine.add_state | (self, state_name, state_type=None, rh_rule=None) | Instantiate a state object and stores it in the 'states' dictionary.
Arguments:
state_name (instance of FreeGroupElement or string) -- name of the new states.
state_type (string) -- Denotes the type (accept/start/dead) of the state added.
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'''
Instantiate a state object and stores it in the 'states' dictionary.
Arguments:
state_name (instance of FreeGroupElement or string) -- name of the new states.
state_type (string) -- Denotes the type (accept/start/dead) of the state added.
rh_rule (instance of FreeGroupElement) -- right hand rule for dead state.
'''
new_state = State(state_name, self, state_type, rh_rule)
self.states[state_name] = new_state | [
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learningequality/ka-lite | 571918ea668013dcf022286ea85eff1c5333fb8b | kalite/facility/views.py | python | facility_user_signup | (request) | return _facility_user(request, new_user=True, title=title) | Anyone can sign up, unless we have set the restricted flag | Anyone can sign up, unless we have set the restricted flag | [
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"""
Anyone can sign up, unless we have set the restricted flag
"""
if getattr(request, "is_logged_in", False):
return HttpResponseRedirect(reverse("homepage"))
if settings.DISABLE_SELF_ADMIN:
# Users cannot create/edit their own data when UserRestricted
raise PermissionDenied(_("Please contact a coach or administrator to receive login information to this installation."))
if settings.CENTRAL_SERVER:
raise Http404(_("You may not sign up as a facility user on the central server."))
title = _("Sign up for an account")
return _facility_user(request, new_user=True, title=title) | [
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openstack/trove | be86b79119d16ee77f596172f43b0c97cb2617bd | trove/cluster/models.py | python | validate_volume_size | (size) | Verify the volume size is within the maximum limit for Trove volumes. | Verify the volume size is within the maximum limit for Trove volumes. | [
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] | def validate_volume_size(size):
"""Verify the volume size is within the maximum limit for Trove volumes."""
if size is None:
raise exception.VolumeSizeNotSpecified()
max_size = CONF.max_accepted_volume_size
if int(size) > max_size:
msg = ("Volume 'size' cannot exceed maximum "
"of %d Gb, %s cannot be accepted."
% (max_size, size))
raise exception.VolumeQuotaExceeded(msg) | [
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":... | https://github.com/openstack/trove/blob/be86b79119d16ee77f596172f43b0c97cb2617bd/trove/cluster/models.py#L646-L655 | ||
PyThaiNLP/pythainlp | de38b8507bf0934540aa5094e5f7f57d7f67e2dc | pythainlp/augment/word2vec/thai2fit.py | python | Thai2fitAug.augment | (
self,
sentence: str,
n_sent: int = 1,
p: float = 0.7
) | return self.aug.augment(sentence, n_sent, p) | Text Augment using word2vec from Thai2Fit
:param str sentence: thai sentence
:param int n_sent: number sentence
:param float p: Probability of word
:return: list of text augment
:rtype: List[Tuple[str]]
:Example:
::
from pythainlp.augment.word2vec import Thai2fitAug
aug = Thai2fitAug()
aug.augment("ผมเรียน", n_sent=2, p=0.5)
# output: [('พวกเรา', 'เรียน'), ('ฉัน', 'เรียน')] | Text Augment using word2vec from Thai2Fit | [
"Text",
"Augment",
"using",
"word2vec",
"from",
"Thai2Fit"
] | def augment(
self,
sentence: str,
n_sent: int = 1,
p: float = 0.7
) -> List[Tuple[str]]:
"""
Text Augment using word2vec from Thai2Fit
:param str sentence: thai sentence
:param int n_sent: number sentence
:param float p: Probability of word
:return: list of text augment
:rtype: List[Tuple[str]]
:Example:
::
from pythainlp.augment.word2vec import Thai2fitAug
aug = Thai2fitAug()
aug.augment("ผมเรียน", n_sent=2, p=0.5)
# output: [('พวกเรา', 'เรียน'), ('ฉัน', 'เรียน')]
"""
return self.aug.augment(sentence, n_sent, p) | [
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elastic/elasticsearch-py | 6ef1adfa3c840a84afda7369cd8e43ae7dc45cdb | elasticsearch/_sync/client/security.py | python | SecurityClient.clear_cached_roles | (
self,
*,
name: Any,
error_trace: Optional[bool] = None,
filter_path: Optional[Union[List[str], str]] = None,
human: Optional[bool] = None,
pretty: Optional[bool] = None,
) | return self._perform_request("POST", __target, headers=__headers) | Evicts roles from the native role cache.
`<https://www.elastic.co/guide/en/elasticsearch/reference/current/security-api-clear-role-cache.html>`_
:param name: Role name | Evicts roles from the native role cache. | [
"Evicts",
"roles",
"from",
"the",
"native",
"role",
"cache",
"."
] | def clear_cached_roles(
self,
*,
name: Any,
error_trace: Optional[bool] = None,
filter_path: Optional[Union[List[str], str]] = None,
human: Optional[bool] = None,
pretty: Optional[bool] = None,
) -> ObjectApiResponse[Any]:
"""
Evicts roles from the native role cache.
`<https://www.elastic.co/guide/en/elasticsearch/reference/current/security-api-clear-role-cache.html>`_
:param name: Role name
"""
if name in SKIP_IN_PATH:
raise ValueError("Empty value passed for parameter 'name'")
__path = f"/_security/role/{_quote(name)}/_clear_cache"
__query: Dict[str, Any] = {}
if error_trace is not None:
__query["error_trace"] = error_trace
if filter_path is not None:
__query["filter_path"] = filter_path
if human is not None:
__query["human"] = human
if pretty is not None:
__query["pretty"] = pretty
if __query:
__target = f"{__path}?{_quote_query(__query)}"
else:
__target = __path
__headers = {"accept": "application/json"}
return self._perform_request("POST", __target, headers=__headers) | [
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... | https://github.com/elastic/elasticsearch-py/blob/6ef1adfa3c840a84afda7369cd8e43ae7dc45cdb/elasticsearch/_sync/client/security.py#L232-L265 | |
SiCKRAGE/SiCKRAGE | 45fb67c0c730fc22a34c695b5a62b11970621c53 | sickrage/libs/adba/aniDBmaper.py | python | AniDBMaper._getBitChain | (self, map, wanted) | return bit | Return an hex string with the correct bit set corresponding to the wanted fields in the map | Return an hex string with the correct bit set corresponding to the wanted fields in the map | [
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"to",
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"the",
"map"
] | def _getBitChain(self, map, wanted):
"""Return an hex string with the correct bit set corresponding to the wanted fields in the map
"""
bit = 0
for index, field in enumerate(map):
if field in wanted and field not in self.blacklist:
bit ^= 1 << len(map) - index - 1
bit = str(hex(bit)).lstrip("0x").rstrip("L")
bit = ''.join(["0" for unused in range(int(len(map) / 4) - len(bit))]) + bit
return bit | [
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simpeg/simpeg | d93145d768b5512621cdd75566b4a8175fee9ed3 | SimPEG/electromagnetics/natural_source/fields.py | python | Fields3DPrimarySecondary._e_px | (self, e_pxSolution, source_list) | return self._e_pxPrimary(e_pxSolution, source_list) + self._e_pxSecondary(
e_pxSolution, source_list
) | px polarization of electric field from source
:param numpy.ndarray e_pxSolution: px polarization that was solved for
:param list source_list: list of sources
:rtype: numpy.ndarray
:return: electric field as defined by the sources | px polarization of electric field from source | [
"px",
"polarization",
"of",
"electric",
"field",
"from",
"source"
] | def _e_px(self, e_pxSolution, source_list):
"""
px polarization of electric field from source
:param numpy.ndarray e_pxSolution: px polarization that was solved for
:param list source_list: list of sources
:rtype: numpy.ndarray
:return: electric field as defined by the sources
"""
return self._e_pxPrimary(e_pxSolution, source_list) + self._e_pxSecondary(
e_pxSolution, source_list
) | [
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IntelLabs/coach | dea46ae0d22b0a0cd30b9fc138a4a2642e1b9d9d | rl_coach/architectures/network_wrapper.py | python | NetworkWrapper.update_target_network | (self, rate=1.0) | Copy weights: online network >>> target network
:param rate: the rate of copying the weights - 1 for copying exactly | Copy weights: online network >>> target network | [
"Copy",
"weights",
":",
"online",
"network",
">>>",
"target",
"network"
] | def update_target_network(self, rate=1.0):
"""
Copy weights: online network >>> target network
:param rate: the rate of copying the weights - 1 for copying exactly
"""
if self.target_network:
self.target_network.set_weights(self.online_network.get_weights(), rate) | [
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... | https://github.com/IntelLabs/coach/blob/dea46ae0d22b0a0cd30b9fc138a4a2642e1b9d9d/rl_coach/architectures/network_wrapper.py#L109-L116 | ||
jwkvam/bowtie | 220cd41367a70f2e206db846278cb7b6fd3649eb | bowtie/_component.py | python | Event.__init__ | (self, name: str, uuid: int, getter: Optional[str] = None) | Create an event.
Parameters
----------
name : str
uuid : int
getter : str, optional | Create an event. | [
"Create",
"an",
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"."
] | def __init__(self, name: str, uuid: int, getter: Optional[str] = None) -> None:
"""Create an event.
Parameters
----------
name : str
uuid : int
getter : str, optional
"""
self.name = name
self.uuid = uuid
self.getter = getter | [
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... | https://github.com/jwkvam/bowtie/blob/220cd41367a70f2e206db846278cb7b6fd3649eb/bowtie/_component.py#L30-L42 | ||
yuxiaokui/Intranet-Penetration | f57678a204840c83cbf3308e3470ae56c5ff514b | proxy/XX-Net/code/default/gae_proxy/server/lib/google/appengine/tools/bulkloader.py | python | RestoreLoader._translate_entity_proto | (self, entity_proto) | return entity_proto | Transform the ReferenceProperties of the given entity to fix app_id. | Transform the ReferenceProperties of the given entity to fix app_id. | [
"Transform",
"the",
"ReferenceProperties",
"of",
"the",
"given",
"entity",
"to",
"fix",
"app_id",
"."
] | def _translate_entity_proto(self, entity_proto):
"""Transform the ReferenceProperties of the given entity to fix app_id."""
entity_key = entity_proto.mutable_key()
entity_key.set_app(self.app_id)
original_entity_namespace = entity_key.name_space()
if self.namespace:
entity_key.set_name_space(self.namespace)
else:
entity_key.clear_name_space()
for prop in entity_proto.property_list():
prop_value = prop.mutable_value()
if prop_value.has_referencevalue():
self.rewrite_reference_proto(original_entity_namespace,
prop_value.mutable_referencevalue())
for prop in entity_proto.raw_property_list():
prop_value = prop.mutable_value()
if prop_value.has_referencevalue():
self.rewrite_reference_proto(original_entity_namespace,
prop_value.mutable_referencevalue())
return entity_proto | [
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TEag1e/BurpCollector | 3ab69c472c3f74828eda5f6f9ad7c7a15cf2c179 | pymysql/protocol.py | python | MysqlPacket.read | (self, size) | return result | Read the first 'size' bytes in packet and advance cursor past them. | Read the first 'size' bytes in packet and advance cursor past them. | [
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"the",
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"size",
"bytes",
"in",
"packet",
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"past",
"them",
"."
] | def read(self, size):
"""Read the first 'size' bytes in packet and advance cursor past them."""
result = self._data[self._position:(self._position+size)]
if len(result) != size:
error = ('Result length not requested length:\n'
'Expected=%s. Actual=%s. Position: %s. Data Length: %s'
% (size, len(result), self._position, len(self._data)))
if DEBUG:
print(error)
self.dump()
raise AssertionError(error)
self._position += size
return result | [
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OctoPrint/OctoPrint | 4b12b0e6f06c3abfb31b1840a0605e2de8e911d2 | src/octoprint/_version.py | python | git_pieces_from_lookup | (lookup, root, verbose, run_command=run_command) | Extract version information based on provided lookup data. | Extract version information based on provided lookup data. | [
"Extract",
"version",
"information",
"based",
"on",
"provided",
"lookup",
"data",
"."
] | def git_pieces_from_lookup(lookup, root, verbose, run_command=run_command):
"""Extract version information based on provided lookup data."""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
stdout = run_command(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root)
if stdout is None:
raise NotThisMethod("git rev-parse --abbrev-ref HEAD failed")
current_branch = stdout.strip()
if current_branch == "HEAD":
raise NotThisMethod("not on a branch")
for matcher, render, tag, ref_commit in lookup:
if matcher.match(current_branch):
if tag is None or ref_commit is None:
raise NotThisMethod("tag or ref_commit is unset for " "this branch")
stdout = run_command(
GITS, ["rev-list", "%s..HEAD" % ref_commit, "--count"], cwd=root
)
if stdout is None:
raise NotThisMethod(
"git rev-list %s..HEAD " "--count failed" % ref_commit
)
try:
num_commits = int(stdout.strip())
except ValueError:
raise NotThisMethod(
"git rev-list %s..HEAD --count didn't "
"return a valid number" % ref_commit
)
stdout = run_command(GITS, ["rev-parse", "--short", "HEAD"], cwd=root)
if stdout is None:
raise NotThisMethod("git describe rev-parse " "--short HEAD failed")
short_hash = stdout.strip()
stdout = run_command(
GITS, ["describe", "--tags", "--dirty", "--always"], cwd=root
)
if stdout is None:
raise NotThisMethod("git describe --tags --dirty " "--always failed")
dirty = stdout.strip().endswith("-dirty")
stdout = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
if stdout is None:
raise NotThisMethod("git rev-parse HEAD failed")
full = stdout.strip()
return {
"long": full,
"short": short_hash,
"dirty": dirty,
"branch": current_branch,
"closest-tag": tag,
"distance": num_commits,
"error": None,
"render": render,
}
raise NotThisMethod("no matching lookup definition found") | [
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"... | https://github.com/OctoPrint/OctoPrint/blob/4b12b0e6f06c3abfb31b1840a0605e2de8e911d2/src/octoprint/_version.py#L377-L439 |
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