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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/PathGeom.py | python | Side.toString | (cls, side) | return "On" | toString(side)
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"""toString(side)
Returns a string representation of the enum value."""
if side == cls.Left:
return "Left"
if side == cls.Right:
return "Right"
return "On" | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/setuptools/command/build_py.py | python | build_py.__getattr__ | (self, attr) | return orig.build_py.__getattr__(self, attr) | lazily compute data files | lazily compute data files | [
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kevinlin311tw/cvpr16-deepbit | c60fb3233d7d534cfcee9d3ed47d77af437ee32a | scripts/cpp_lint.py | python | ReplaceAll | (pattern, rep, s) | return _regexp_compile_cache[pattern].sub(rep, s) | Replaces instances of pattern in a string with a replacement.
The compiled regex is kept in a cache shared by Match and Search.
Args:
pattern: regex pattern
rep: replacement text
s: search string
Returns:
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The compiled regex is kept in a cache shared by Match and Search.
Args:
pattern: regex pattern
rep: replacement text
s: search string
Returns:
string with replacements made (or original string if no replacements)
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if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py | python | OptionMenu.__init__ | (self, master, variable, value, *values, **kwargs) | Construct an optionmenu widget with the parent MASTER, with
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value VALUE, the other menu values VALUES and an additional
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menu = self.__menu = Menu(self, name="menu", tearoff=0)
self.menuname = menu._w
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callback = kwargs.get('command')
if 'command' in kwargs:
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menu.add_command(label=value,
command=_setit(variable, value, callback))
for v in values:
menu.add_command(label=v,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/labelbook.py | python | LabelContainer.Resize | (self, event) | Actually resizes the tab area.
:param `event`: an instance of :class:`SizeEvent`. | Actually resizes the tab area. | [
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"""
Actually resizes the tab area.
:param `event`: an instance of :class:`SizeEvent`.
"""
# Resize our size
self._tabAreaSize = self.GetSize()
newWidth = self._tabAreaSize.x
x = event.GetX()
if self.HasAGWFlag(INB_BOTTOM) or self.HasAGWFlag(INB_RIGHT):
newWidth -= event.GetX()
else:
newWidth = x
if newWidth < 100: # Dont allow width to be lower than that
newWidth = 100
self.SetSizeHints(newWidth, self._tabAreaSize.y)
# Update the tab new area width
self._nTabAreaWidth = newWidth
self.GetParent().Freeze()
self.GetParent().GetSizer().Layout()
self.GetParent().Thaw() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | Wm.wm_iconposition | (self, x=None, y=None) | return self._getints(self.tk.call(
'wm', 'iconposition', self._w, x, y)) | Set the position of the icon of this widget to X and Y. Return
a tuple of the current values of X and X if None is given. | Set the position of the icon of this widget to X and Y. Return
a tuple of the current values of X and X if None is given. | [
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"""Set the position of the icon of this widget to X and Y. Return
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return self._getints(self.tk.call(
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/pkg_resources/__init__.py | python | WorkingSet.add_entry | (self, entry) | Add a path item to ``.entries``, finding any distributions on it
``find_distributions(entry, True)`` is used to find distributions
corresponding to the path entry, and they are added. `entry` is
always appended to ``.entries``, even if it is already present.
(This is because ``sys.path`` can contain the same value more than
once, and the ``.entries`` of the ``sys.path`` WorkingSet should always
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"""Add a path item to ``.entries``, finding any distributions on it
``find_distributions(entry, True)`` is used to find distributions
corresponding to the path entry, and they are added. `entry` is
always appended to ``.entries``, even if it is already present.
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"""
self.entry_keys.setdefault(entry, [])
self.entries.append(entry)
for dist in find_distributions(entry, True):
self.add(dist, entry, False) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/botocore/vendored/requests/auth.py | python | HTTPDigestAuth.handle_redirect | (self, r, **kwargs) | Reset num_401_calls counter on redirects. | Reset num_401_calls counter on redirects. | [
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"""Reset num_401_calls counter on redirects."""
if r.is_redirect:
self.num_401_calls = 1 | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/io/pytables.py | python | IndexCol.__eq__ | (self, other: Any) | return all(
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) | compare 2 col items | compare 2 col items | [
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""" compare 2 col items """
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/gradients_impl.py | python | _VerifyGeneratedGradients | (grads, op) | Verify that gradients are valid in number and type.
Args:
grads: List of generated gradients.
op: Operation for which the gradients where generated.
Raises:
ValueError: if sizes of gradients and inputs don't match.
TypeError: if type of any gradient is not valid for its input. | Verify that gradients are valid in number and type. | [
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] | def _VerifyGeneratedGradients(grads, op):
"""Verify that gradients are valid in number and type.
Args:
grads: List of generated gradients.
op: Operation for which the gradients where generated.
Raises:
ValueError: if sizes of gradients and inputs don't match.
TypeError: if type of any gradient is not valid for its input.
"""
if len(grads) != len(op.inputs):
raise ValueError("Num gradients %d generated for op %s do not match num "
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/decimal.py | python | Context.compare_total | (self, a, b) | return a.compare_total(b) | Compares two operands using their abstract representation.
This is not like the standard compare, which use their numerical
value. Note that a total ordering is defined for all possible abstract
representations.
>>> ExtendedContext.compare_total(Decimal('12.73'), Decimal('127.9'))
Decimal('-1')
>>> ExtendedContext.compare_total(Decimal('-127'), Decimal('12'))
Decimal('-1')
>>> ExtendedContext.compare_total(Decimal('12.30'), Decimal('12.3'))
Decimal('-1')
>>> ExtendedContext.compare_total(Decimal('12.30'), Decimal('12.30'))
Decimal('0')
>>> ExtendedContext.compare_total(Decimal('12.3'), Decimal('12.300'))
Decimal('1')
>>> ExtendedContext.compare_total(Decimal('12.3'), Decimal('NaN'))
Decimal('-1')
>>> ExtendedContext.compare_total(1, 2)
Decimal('-1')
>>> ExtendedContext.compare_total(Decimal(1), 2)
Decimal('-1')
>>> ExtendedContext.compare_total(1, Decimal(2))
Decimal('-1') | Compares two operands using their abstract representation. | [
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"""Compares two operands using their abstract representation.
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representations.
>>> ExtendedContext.compare_total(Decimal('12.73'), Decimal('127.9'))
Decimal('-1')
>>> ExtendedContext.compare_total(Decimal('-127'), Decimal('12'))
Decimal('-1')
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Decimal('-1')
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Decimal('0')
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>>> ExtendedContext.compare_total(Decimal('12.3'), Decimal('NaN'))
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>>> ExtendedContext.compare_total(1, 2)
Decimal('-1')
>>> ExtendedContext.compare_total(Decimal(1), 2)
Decimal('-1')
>>> ExtendedContext.compare_total(1, Decimal(2))
Decimal('-1')
"""
a = _convert_other(a, raiseit=True)
return a.compare_total(b) | [
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/affine/binary_operators.py | python | multiply.numeric | (self, values) | Multiplies the values elementwise. | Multiplies the values elementwise. | [
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] | def numeric(self, values):
"""Multiplies the values elementwise.
"""
if sp.issparse(values[0]):
return values[0].multiply(values[1])
elif sp.issparse(values[1]):
return values[1].multiply(values[0])
else:
return np.multiply(values[0], values[1]) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/odr/odrpack.py | python | ODR.restart | (self, iter=None) | return self.run() | Restarts the run with iter more iterations.
Parameters
----------
iter : int, optional
ODRPACK's default for the number of new iterations is 10.
Returns
-------
output : Output instance
This object is also assigned to the attribute .output . | Restarts the run with iter more iterations. | [
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""" Restarts the run with iter more iterations.
Parameters
----------
iter : int, optional
ODRPACK's default for the number of new iterations is 10.
Returns
-------
output : Output instance
This object is also assigned to the attribute .output .
"""
if self.output is None:
raise OdrError("cannot restart: run() has not been called before")
self.set_job(restart=1)
self.work = self.output.work
self.iwork = self.output.iwork
self.maxit = iter
return self.run() | [
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rdiankov/openrave | d1a23023fd4b58f077d2ca949ceaf1b91f3f13d7 | python/ikfast.py | python | IKFastSolver.iterateThreeNonIntersectingAxes | (self, solvejointvars, Links, LinksInv) | check for three consecutive non-intersecting axes.
if several points exist, so have to choose one that is least complex? | check for three consecutive non-intersecting axes.
if several points exist, so have to choose one that is least complex? | [
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"""check for three consecutive non-intersecting axes.
if several points exist, so have to choose one that is least complex?
"""
ilinks = [i for i,Tlink in enumerate(Links) if self.has(Tlink,*solvejointvars)]
usedindices = []
for imode in range(2):
for i in range(len(ilinks)-2):
if i in usedindices:
continue
startindex = ilinks[i]
endindex = ilinks[i+2]+1
p0 = self.multiplyMatrix(Links[ilinks[i]:ilinks[i+1]])[0:3,3]
p1 = self.multiplyMatrix(Links[ilinks[i+1]:ilinks[i+2]])[0:3,3]
has0 = self.has(p0,*solvejointvars)
has1 = self.has(p1,*solvejointvars)
if (imode == 0 and has0 and has1) or (imode == 1 and (has0 or has1)):
T0links = Links[startindex:endindex]
T1links = LinksInv[:startindex][::-1]
T1links.append(self.Tee)
T1links += LinksInv[endindex:][::-1]
usedindices.append(i)
usedvars = [var for var in solvejointvars if any([self.has(T0,var) for T0 in T0links])]
log.info('found 3 consecutive non-intersecting axes links[%d:%d], vars=%s',startindex,endindex,str(usedvars))
yield T0links, T1links | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | net/tools/quic/benchmark/run_client.py | python | PageloadExperiment.__init__ | (self, use_wget, quic_binary_dir, quic_server_address,
quic_server_port) | Initialize PageloadExperiment.
Args:
use_wget: Whether to use wget.
quic_binary_dir: Directory for quic_binary.
quic_server_address: IP address of quic server.
quic_server_port: Port of the quic server. | Initialize PageloadExperiment. | [
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] | def __init__(self, use_wget, quic_binary_dir, quic_server_address,
quic_server_port):
"""Initialize PageloadExperiment.
Args:
use_wget: Whether to use wget.
quic_binary_dir: Directory for quic_binary.
quic_server_address: IP address of quic server.
quic_server_port: Port of the quic server.
"""
self.use_wget = use_wget
self.quic_binary_dir = quic_binary_dir
self.quic_server_address = quic_server_address
self.quic_server_port = quic_server_port
if not use_wget and not os.path.isfile(quic_binary_dir + '/quic_client'):
raise IOError('There is no quic_client in the given dir: %s.'
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NVIDIA/TensorRT | 42805f078052daad1a98bc5965974fcffaad0960 | tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/tensor.py | python | LazyValues.load | (self) | return np.array(onnx.numpy_helper.to_array(self.tensor)) | Load a numpy array from the underlying tensor values.
Returns:
np.array: A numpy array containing the values of the tensor. | Load a numpy array from the underlying tensor values. | [
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] | def load(self):
"""
Load a numpy array from the underlying tensor values.
Returns:
np.array: A numpy array containing the values of the tensor.
"""
import onnx
import onnx.numpy_helper
return np.array(onnx.numpy_helper.to_array(self.tensor)) | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | CondCore/Utilities/python/CondDBFW/uploads.py | python | uploader.get_hashes_to_send | (self, upload_session_id) | return response | Get the hashes of the payloads we want to send that the server doesn't have yet. | Get the hashes of the payloads we want to send that the server doesn't have yet. | [
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Get the hashes of the payloads we want to send that the server doesn't have yet.
"""
self._outputter.write("Getting list of hashes that the server does not have Payloads for, to send to server.")
post_data = json.dumps(self.get_all_hashes())
url_data = {"database" : self.data_to_send["destinationDatabase"], "upload_session_id" : upload_session_id}
query = url_query(url=self._SERVICE_URL + "check_hashes/", url_data=url_data, body=post_data)
response = query.send()
return response | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/decomposition/nmf.py | python | _update_projected_gradient_h | (X, W, H, tolH, nls_max_iter, alpha, l1_ratio,
sparseness, beta, eta) | return H, gradH, iterH | Helper function for _fit_projected_gradient | Helper function for _fit_projected_gradient | [
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] | def _update_projected_gradient_h(X, W, H, tolH, nls_max_iter, alpha, l1_ratio,
sparseness, beta, eta):
"""Helper function for _fit_projected_gradient"""
n_samples, n_features = X.shape
n_components_ = W.shape[1]
if sparseness is None:
H, gradH, iterH = _nls_subproblem(X, W, H, tolH, nls_max_iter,
alpha=alpha, l1_ratio=l1_ratio)
elif sparseness == 'data':
H, gradH, iterH = _nls_subproblem(
safe_vstack([X, np.zeros((n_components_, n_features))]),
safe_vstack([W,
np.sqrt(eta) * np.eye(n_components_)]),
H, tolH, nls_max_iter, alpha=alpha, l1_ratio=l1_ratio)
elif sparseness == 'components':
H, gradH, iterH = _nls_subproblem(
safe_vstack([X, np.zeros((1, n_features))]),
safe_vstack([W, np.sqrt(beta) * np.ones((1, n_components_))]),
H, tolH, nls_max_iter, alpha=alpha, l1_ratio=l1_ratio)
return H, gradH, iterH | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | Pen.GetDashes | (*args, **kwargs) | return _gdi_.Pen_GetDashes(*args, **kwargs) | GetDashes(self) -> PyObject | GetDashes(self) -> PyObject | [
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"""GetDashes(self) -> PyObject"""
return _gdi_.Pen_GetDashes(*args, **kwargs) | [
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larroy/clearskies_core | 3574ddf0edc8555454c7044126e786a6c29444dc | tools/gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings.AdjustIncludeDirs | (self, include_dirs, config) | return [self.ConvertVSMacros(p, config=config) for p in includes] | Updates include_dirs to expand VS specific paths, and adds the system
include dirs used for platform SDK and similar. | Updates include_dirs to expand VS specific paths, and adds the system
include dirs used for platform SDK and similar. | [
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"""Updates include_dirs to expand VS specific paths, and adds the system
include dirs used for platform SDK and similar."""
config = self._TargetConfig(config)
includes = include_dirs + self.msvs_system_include_dirs[config]
includes.extend(self._Setting(
('VCCLCompilerTool', 'AdditionalIncludeDirectories'), config, default=[]))
return [self.ConvertVSMacros(p, config=config) for p in includes] | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | build/android/android_commands.py | python | AndroidCommands.SearchLogcatRecord | (self, record, message, thread_id=None, proc_id=None,
log_level=None, component=None) | return results | Searches the specified logcat output and returns results.
This method searches through the logcat output specified by record for a
certain message, narrowing results by matching them against any other
specified criteria. It returns all matching lines as described below.
Args:
record: A string generated by Start/StopRecordingLogcat to search.
message: An output string to search for.
thread_id: The thread id that is the origin of the message.
proc_id: The process that is the origin of the message.
log_level: The log level of the message.
component: The name of the component that would create the message.
Returns:
A list of dictionaries represeting matching entries, each containing keys
thread_id, proc_id, log_level, component, and message. | Searches the specified logcat output and returns results. | [
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] | def SearchLogcatRecord(self, record, message, thread_id=None, proc_id=None,
log_level=None, component=None):
"""Searches the specified logcat output and returns results.
This method searches through the logcat output specified by record for a
certain message, narrowing results by matching them against any other
specified criteria. It returns all matching lines as described below.
Args:
record: A string generated by Start/StopRecordingLogcat to search.
message: An output string to search for.
thread_id: The thread id that is the origin of the message.
proc_id: The process that is the origin of the message.
log_level: The log level of the message.
component: The name of the component that would create the message.
Returns:
A list of dictionaries represeting matching entries, each containing keys
thread_id, proc_id, log_level, component, and message.
"""
if thread_id:
thread_id = str(thread_id)
if proc_id:
proc_id = str(proc_id)
results = []
reg = re.compile('(\d+)\s+(\d+)\s+([A-Z])\s+([A-Za-z]+)\s*:(.*)$',
re.MULTILINE)
log_list = reg.findall(record)
for (tid, pid, log_lev, comp, msg) in log_list:
if ((not thread_id or thread_id == tid) and
(not proc_id or proc_id == pid) and
(not log_level or log_level == log_lev) and
(not component or component == comp) and msg.find(message) > -1):
match = dict({'thread_id': tid, 'proc_id': pid,
'log_level': log_lev, 'component': comp,
'message': msg})
results.append(match)
return results | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/setuptools/sandbox.py | python | AbstractSandbox._remap_output | (self, operation, path) | return self._validate_path(path) | Called for path outputs | Called for path outputs | [
"Called",
"for",
"path",
"outputs"
] | def _remap_output(self, operation, path):
"""Called for path outputs"""
return self._validate_path(path) | [
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Kronuz/Xapiand | a71570859dcfc9f48090d845053f359b07f4f78c | contrib/python/xapiand-py/xapiand/client/documents.py | python | DocumentsClient.upsert | (self, index, id, body=None, content_type=None, params=None) | return self.transport.perform_request('UPSERT', make_url(index, id=id),
params=params, body=body,
headers=content_type and {'content-type': content_type}) | Update or create a document based on a partial data provided.
:arg index: The name of the index
:arg id: Document ID
:arg body: The request definition using a partial `doc`
:arg selector: A comma-separated list of fields to return in the response
:arg timeout: Explicit operation timeout | Update or create a document based on a partial data provided. | [
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] | def upsert(self, index, id, body=None, content_type=None, params=None):
"""
Update or create a document based on a partial data provided.
:arg index: The name of the index
:arg id: Document ID
:arg body: The request definition using a partial `doc`
:arg selector: A comma-separated list of fields to return in the response
:arg timeout: Explicit operation timeout
"""
for param in (index, id):
if param in SKIP_IN_PATH:
raise ValueError("Empty value passed for a required argument.")
return self.transport.perform_request('UPSERT', make_url(index, id=id),
params=params, body=body,
headers=content_type and {'content-type': content_type}) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/eager/context.py | python | get_log_device_placement | () | return context().log_device_placement | Get if device placements are logged.
Returns:
If device placements are logged. | Get if device placements are logged. | [
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] | def get_log_device_placement():
"""Get if device placements are logged.
Returns:
If device placements are logged.
"""
return context().log_device_placement | [
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GoSSIP-SJTU/TripleDoggy | 03648d6b19c812504b14e8b98c8c7b3f443f4e54 | tools/clang/tools/scan-build-py/libear/__init__.py | python | Toolset.set_language_standard | (self, standard) | part of public interface | part of public interface | [
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] | def set_language_standard(self, standard):
""" part of public interface """
self.c_flags.append('-std=' + standard) | [
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albertz/openlierox | d316c14a8eb57848ef56e9bfa7b23a56f694a51b | tools/DedicatedServerVideo/gdata/apps/service.py | python | AppsService.GetGeneratorFromLinkFinder | (self, link_finder, func,
num_retries=gdata.service.DEFAULT_NUM_RETRIES,
delay=gdata.service.DEFAULT_DELAY,
backoff=gdata.service.DEFAULT_BACKOFF) | returns a generator for pagination | returns a generator for pagination | [
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num_retries=gdata.service.DEFAULT_NUM_RETRIES,
delay=gdata.service.DEFAULT_DELAY,
backoff=gdata.service.DEFAULT_BACKOFF):
"""returns a generator for pagination"""
yield link_finder
next = link_finder.GetNextLink()
while next is not None:
next_feed = func(str(self.GetWithRetries(
next.href, num_retries=num_retries, delay=delay, backoff=backoff)))
yield next_feed
next = next_feed.GetNextLink() | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/agilepy/lib_wx/objpanel.py | python | NaviPanelMixin.refresh_panel | (self, obj=None, id=None, ids=None, **kwargs) | return None | Deletes previous conents
Builds panel for obj
Updates path window and history | Deletes previous conents
Builds panel for obj
Updates path window and history | [
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] | def refresh_panel(self, obj=None, id=None, ids=None, **kwargs):
"""
Deletes previous conents
Builds panel for obj
Updates path window and history
"""
if obj is None:
if self.get_obj() == self._defaultobj:
return None # no changes
else:
obj = self._defaultobj
# print 'Navicanvas.refresh_panel with',obj.ident,kwargs
if id is not None:
self.path.SetValue(obj.format_ident_row_abs(id))
elif ids is not None:
# no id provided, just show identification string
self.path.SetValue(obj.format_ident_abs())
else: # object and id provided: compose string with id
self.path.SetValue(obj.format_ident_abs())
# self.path.SetSize((len(self.path.GetValue())*10,-1))
# self.path.SetSize((-1,-1))
# remove previous obj panel
sizer = self.GetSizer()
sizer.Remove(1)
self.objpanel.Destroy()
#del self.objpanel
# build new obj panel
# self.objpanel=ObjPanel(self,obj,id=id,attrs=self.attrs,groups=self.groups,
# func_change_obj=self.change_obj,
# is_modal=self.is_modal,
# mainframe=self.mainframe,
# immediate_apply=self.immediate_apply,
# **self.buttonargs)
attrconfigs = obj.get_attrsman().get_configs()
# for attrconfig in obj.get_configs()#attrconfigs:
# if '_private' not in attrconfig.groups:
# attrconfigs.append(attrconfig)
args = {'attrconfigs': attrconfigs,
# 'tables' : None,
# 'table' : None,
'id': id, 'ids': ids,
'groupnames': self.groupnames,
'func_change_obj': self.change_obj,
'show_groupnames': False, 'show_title': True, 'is_modal': self.is_modal,
'mainframe': self.mainframe, 'pos': wx.DefaultPosition, 'size': wx.DefaultSize,
'immediate_apply': self.immediate_apply, 'panelstyle': 'default'
}
args.update(self.buttonargs)
args.update(kwargs)
# print ' make ObjPanel\n args =',args
self.objpanel = ObjPanel(self, obj=obj, **args)
# if id is not None:
# self.objpanel=ObjPanel(self,obj,id=id,func_change_obj=self.change_obj)
# else:
# self.objpanel=ObjPanel(self,obj,func_change_obj=self.change_obj)
sizer.Add(self.objpanel, 1, wx.GROW)
self.Refresh()
# sizer.Fit(self)
sizer.Layout()
# add to history
return None | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/six.py | python | remove_move | (name) | Remove item from six.moves. | Remove item from six.moves. | [
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"""Remove item from six.moves."""
try:
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except AttributeError:
try:
del moves.__dict__[name]
except KeyError:
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vgteam/vg | cf4d516a5e9ee5163c783e4437ddf16b18a4b561 | scripts/filter_variants_on_repeats.py | python | entrypoint | () | 0-argument entry point for setuptools to call. | 0-argument entry point for setuptools to call. | [
"0",
"-",
"argument",
"entry",
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"for",
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] | def entrypoint():
"""
0-argument entry point for setuptools to call.
"""
# Provide main with its arguments and handle exit codes
sys.exit(main(sys.argv)) | [
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Yelp/MOE | 5b5a6a2c6c3cf47320126f7f5894e2a83e347f5c | moe/optimal_learning/python/python_version/expected_improvement.py | python | ExpectedImprovement.__init__ | (
self,
gaussian_process,
points_to_sample=None,
points_being_sampled=None,
num_mc_iterations=DEFAULT_EXPECTED_IMPROVEMENT_MC_ITERATIONS,
randomness=None,
mvndst_parameters=None
) | Construct an ExpectedImprovement object that supports q,p-EI.
TODO(GH-56): Allow callers to pass in a source of randomness.
:param gaussian_process: GaussianProcess describing
:type gaussian_process: interfaces.gaussian_process_interface.GaussianProcessInterface subclass
:param points_to_sample: points at which to evaluate EI and/or its gradient to check their value in future experiments (i.e., "q" in q,p-EI)
:type points_to_sample: array of float64 with shape (num_to_sample, dim)
:param points_being_sampled: points being sampled in concurrent experiments (i.e., "p" in q,p-EI)
:type points_being_sampled: array of float64 with shape (num_being_sampled, dim)
:param num_mc_iterations: number of monte-carlo iterations to use (when monte-carlo integration is used to compute EI)
:type num_mc_iterations: int > 0
:param randomness: random source(s) used for monte-carlo integration (when applicable) (UNUSED)
:type randomness: (UNUSED) | Construct an ExpectedImprovement object that supports q,p-EI. | [
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"that",
"supports",
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"-",
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] | def __init__(
self,
gaussian_process,
points_to_sample=None,
points_being_sampled=None,
num_mc_iterations=DEFAULT_EXPECTED_IMPROVEMENT_MC_ITERATIONS,
randomness=None,
mvndst_parameters=None
):
"""Construct an ExpectedImprovement object that supports q,p-EI.
TODO(GH-56): Allow callers to pass in a source of randomness.
:param gaussian_process: GaussianProcess describing
:type gaussian_process: interfaces.gaussian_process_interface.GaussianProcessInterface subclass
:param points_to_sample: points at which to evaluate EI and/or its gradient to check their value in future experiments (i.e., "q" in q,p-EI)
:type points_to_sample: array of float64 with shape (num_to_sample, dim)
:param points_being_sampled: points being sampled in concurrent experiments (i.e., "p" in q,p-EI)
:type points_being_sampled: array of float64 with shape (num_being_sampled, dim)
:param num_mc_iterations: number of monte-carlo iterations to use (when monte-carlo integration is used to compute EI)
:type num_mc_iterations: int > 0
:param randomness: random source(s) used for monte-carlo integration (when applicable) (UNUSED)
:type randomness: (UNUSED)
"""
self._num_mc_iterations = num_mc_iterations
self._gaussian_process = gaussian_process
if gaussian_process._points_sampled_value.size > 0:
self._best_so_far = numpy.amin(gaussian_process._points_sampled_value)
else:
self._best_so_far = numpy.finfo(numpy.float64).max
if points_being_sampled is None:
self._points_being_sampled = numpy.array([])
else:
self._points_being_sampled = numpy.copy(points_being_sampled)
if points_to_sample is None:
self.current_point = numpy.zeros((1, gaussian_process.dim))
else:
self.current_point = points_to_sample
if mvndst_parameters is None:
self._mvndst_parameters = DEFAULT_MVNDST_PARAMS
else:
self._mvndst_parameters = mvndst_parameters
self.log = logging.getLogger(__name__) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/eager/function.py | python | _shape_relaxed_type_for_composite_tensor | (x) | Returns a shape-relaxed TypeSpec for x (if composite) or x (if not). | Returns a shape-relaxed TypeSpec for x (if composite) or x (if not). | [
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] | def _shape_relaxed_type_for_composite_tensor(x):
"""Returns a shape-relaxed TypeSpec for x (if composite) or x (if not)."""
if isinstance(x, composite_tensor.CompositeTensor):
# pylint: disable=protected-access
return x._type_spec._with_tensor_ranks_only()
else:
return x | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/tensor_forest/python/tensor_forest.py | python | RandomForestGraphs.inference_graph | (self, input_data, **inference_args) | Constructs a TF graph for evaluating a random forest.
Args:
input_data: A tensor or dict of string->Tensor for the input data.
This input_data must generate the same spec as the
input_data used in training_graph: the dict must have
the same keys, for example, and all tensors must have
the same size in their first dimension.
**inference_args: Keyword arguments to pass through to each tree.
Returns:
A tuple of (probabilities, tree_paths, variance), where variance
is the variance over all the trees for regression problems only.
Raises:
NotImplementedError: If trying to use feature bagging with sparse
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] | def inference_graph(self, input_data, **inference_args):
"""Constructs a TF graph for evaluating a random forest.
Args:
input_data: A tensor or dict of string->Tensor for the input data.
This input_data must generate the same spec as the
input_data used in training_graph: the dict must have
the same keys, for example, and all tensors must have
the same size in their first dimension.
**inference_args: Keyword arguments to pass through to each tree.
Returns:
A tuple of (probabilities, tree_paths, variance), where variance
is the variance over all the trees for regression problems only.
Raises:
NotImplementedError: If trying to use feature bagging with sparse
features.
"""
processed_dense_features, processed_sparse_features, data_spec = (
data_ops.ParseDataTensorOrDict(input_data))
probabilities = []
paths = []
for i in range(self.params.num_trees):
with ops.device(self.variables.device_dummies[i].device):
tree_data = processed_dense_features
if self.params.bagged_features:
if processed_sparse_features is not None:
raise NotImplementedError(
'Feature bagging not supported with sparse features.')
tree_data = self._bag_features(i, tree_data)
probs, path = self.trees[i].inference_graph(
tree_data,
data_spec,
sparse_features=processed_sparse_features,
**inference_args)
probabilities.append(probs)
paths.append(path)
with ops.device(self.variables.device_dummies[0].device):
# shape of all_predict should be [batch_size, num_trees, num_outputs]
all_predict = array_ops.stack(probabilities, axis=1)
average_values = math_ops.div(
math_ops.reduce_sum(all_predict, 1),
self.params.num_trees,
name='probabilities')
tree_paths = array_ops.stack(paths, axis=1)
regression_variance = None
if self.params.regression:
expected_squares = math_ops.div(
math_ops.reduce_sum(all_predict * all_predict, 1),
self.params.num_trees)
regression_variance = math_ops.maximum(
0., expected_squares - average_values * average_values)
return average_values, tree_paths, regression_variance | [
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OAID/Tengine | 66b2c22ad129d25e2fc6de3b22a608bb54dd90db | tools/optimize/yolov3-opt.py | python | cut_nodes_input_output | (node, cut_names) | del nodes from input nodes
Args:
node: the nodes of ONNX model
Returns:
optimized graph nodes(inplace) | del nodes from input nodes
Args:
node: the nodes of ONNX model
Returns:
optimized graph nodes(inplace) | [
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"""
del nodes from input nodes
Args:
node: the nodes of ONNX model
Returns:
optimized graph nodes(inplace)
"""
for i in range(len(node) - 1, -1, -1):
if node[i].name in cut_names:
node.pop(i) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/closure_linter/closure_linter/tokenutil.py | python | GetIdentifierForToken | (token) | Get the symbol specified by a token.
Given a token, this function additionally concatenates any parts of an
identifying symbol being identified that are split by whitespace or a
newline.
The function will return None if the token is not the first token of an
identifier.
Args:
token: The first token of a symbol.
Returns:
The whole symbol, as a string. | Get the symbol specified by a token. | [
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] | def GetIdentifierForToken(token):
"""Get the symbol specified by a token.
Given a token, this function additionally concatenates any parts of an
identifying symbol being identified that are split by whitespace or a
newline.
The function will return None if the token is not the first token of an
identifier.
Args:
token: The first token of a symbol.
Returns:
The whole symbol, as a string.
"""
# Search backward to determine if this token is the first token of the
# identifier. If it is not the first token, return None to signal that this
# token should be ignored.
prev_token = token.previous
while prev_token:
if (prev_token.IsType(JavaScriptTokenType.IDENTIFIER) or
IsDot(prev_token)):
return None
if (prev_token.IsType(tokens.TokenType.WHITESPACE) or
prev_token.IsAnyType(JavaScriptTokenType.COMMENT_TYPES)):
prev_token = prev_token.previous
else:
break
# A "function foo()" declaration.
if token.type is JavaScriptTokenType.FUNCTION_NAME:
return token.string
# A "var foo" declaration (if the previous token is 'var')
previous_code_token = GetPreviousCodeToken(token)
if previous_code_token and previous_code_token.IsKeyword('var'):
return token.string
# Otherwise, this is potentially a namespaced (goog.foo.bar) identifier that
# could span multiple lines or be broken up by whitespace. We need
# to concatenate.
identifier_types = set([
JavaScriptTokenType.IDENTIFIER,
JavaScriptTokenType.SIMPLE_LVALUE
])
assert token.type in identifier_types
# Start with the first token
symbol_tokens = [token]
if token.next:
for t in token.next:
last_symbol_token = symbol_tokens[-1]
# A dot is part of the previous symbol.
if IsDot(t):
symbol_tokens.append(t)
continue
# An identifier is part of the previous symbol if the previous one was a
# dot.
if t.type in identifier_types:
if IsDot(last_symbol_token):
symbol_tokens.append(t)
continue
else:
break
# Skip any whitespace
if t.type in JavaScriptTokenType.NON_CODE_TYPES:
continue
# This is the end of the identifier. Stop iterating.
break
if symbol_tokens:
return ''.join([t.string for t in symbol_tokens]) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py | python | Misc.grab_current | (self) | return self._nametowidget(name) | Return widget which has currently the grab in this application
or None. | Return widget which has currently the grab in this application
or None. | [
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] | def grab_current(self):
"""Return widget which has currently the grab in this application
or None."""
name = self.tk.call('grab', 'current', self._w)
if not name: return None
return self._nametowidget(name) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/_differentialevolution.py | python | DifferentialEvolutionSolver._select_samples | (self, candidate, number_samples) | return idxs | obtain random integers from range(self.num_population_members),
without replacement. You can't have the original candidate either. | obtain random integers from range(self.num_population_members),
without replacement. You can't have the original candidate either. | [
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] | def _select_samples(self, candidate, number_samples):
"""
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"""
idxs = list(range(self.num_population_members))
idxs.remove(candidate)
self.random_number_generator.shuffle(idxs)
idxs = idxs[:number_samples]
return idxs | [
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wujian16/Cornell-MOE | df299d1be882d2af9796d7a68b3f9505cac7a53e | moe/optimal_learning/python/cpp_wrappers/log_likelihood.py | python | GaussianProcessLogLikelihood.set_hyperparameters | (self, hyperparameters) | Set hyperparameters to the specified hyperparameters; ordering must match.
:param hyperparameters: hyperparameters at which to evaluate the log likelihood (objective function), ``f(x)``
:type hyperparameters: array of float64 with shape (num_hyperparameters) | Set hyperparameters to the specified hyperparameters; ordering must match. | [
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] | def set_hyperparameters(self, hyperparameters):
"""Set hyperparameters to the specified hyperparameters; ordering must match.
:param hyperparameters: hyperparameters at which to evaluate the log likelihood (objective function), ``f(x)``
:type hyperparameters: array of float64 with shape (num_hyperparameters)
"""
self._covariance.hyperparameters = hyperparameters[:self._covariance.num_hyperparameters]
self._noise_variance = hyperparameters[self._covariance.num_hyperparameters:] | [
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h0x91b/redis-v8 | ac8b9d49701d75bcee3719892a2a6a50b437e47a | redis/deps/v8/tools/stats-viewer.py | python | UiCounter.__init__ | (self, var, format) | Creates a new ui counter.
Args:
var: the Tkinter string variable for updating the ui
format: the format string used to format this counter | Creates a new ui counter. | [
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] | def __init__(self, var, format):
"""Creates a new ui counter.
Args:
var: the Tkinter string variable for updating the ui
format: the format string used to format this counter
"""
self.var = var
self.format = format
self.last_value = None | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/maximum-number-of-balloons.py | python | Solution.maxNumberOfBalloons | (self, text) | return min(source_count[c]//target_count[c] for c in target_count.iterkeys()) | :type text: str
:rtype: int | :type text: str
:rtype: int | [
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"""
:type text: str
:rtype: int
"""
TARGET = "balloon"
source_count = collections.Counter(text)
target_count = collections.Counter(TARGET)
return min(source_count[c]//target_count[c] for c in target_count.iterkeys()) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/distribute/distribute_coordinator.py | python | _WorkerContext.experimental_should_init | (self) | return self._strategy.extended.experimental_should_init | Whether to run init ops. | Whether to run init ops. | [
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] | def experimental_should_init(self):
"""Whether to run init ops."""
return self._strategy.extended.experimental_should_init | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcode_emulation.py | python | MacPrefixHeader._Gch | (self, lang, arch) | return self._CompiledHeader(lang, arch) + '.gch' | Returns the actual file name of the prefix header for language |lang|. | Returns the actual file name of the prefix header for language |lang|. | [
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"""Returns the actual file name of the prefix header for language |lang|."""
assert self.compile_headers
return self._CompiledHeader(lang, arch) + '.gch' | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | PanedWindow.proxy_forget | (self) | return self.proxy("forget") | Remove the proxy from the display. | Remove the proxy from the display. | [
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] | def proxy_forget(self):
"""Remove the proxy from the display.
"""
return self.proxy("forget") | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/packaging/py3/packaging/_manylinux.py | python | _parse_glibc_version | (version_str: str) | return int(m.group("major")), int(m.group("minor")) | Parse glibc version.
We use a regexp instead of str.split because we want to discard any
random junk that might come after the minor version -- this might happen
in patched/forked versions of glibc (e.g. Linaro's version of glibc
uses version strings like "2.20-2014.11"). See gh-3588. | Parse glibc version. | [
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"version",
"."
] | def _parse_glibc_version(version_str: str) -> Tuple[int, int]:
"""Parse glibc version.
We use a regexp instead of str.split because we want to discard any
random junk that might come after the minor version -- this might happen
in patched/forked versions of glibc (e.g. Linaro's version of glibc
uses version strings like "2.20-2014.11"). See gh-3588.
"""
m = re.match(r"(?P<major>[0-9]+)\.(?P<minor>[0-9]+)", version_str)
if not m:
warnings.warn(
"Expected glibc version with 2 components major.minor,"
" got: %s" % version_str,
RuntimeWarning,
)
return -1, -1
return int(m.group("major")), int(m.group("minor")) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/requests/cookies.py | python | RequestsCookieJar.itervalues | (self) | Dict-like itervalues() that returns an iterator of values of cookies
from the jar.
.. seealso:: iterkeys() and iteritems(). | Dict-like itervalues() that returns an iterator of values of cookies
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] | def itervalues(self):
"""Dict-like itervalues() that returns an iterator of values of cookies
from the jar.
.. seealso:: iterkeys() and iteritems().
"""
for cookie in iter(self):
yield cookie.value | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBTypeMemberFunction.GetDemangledName | (self) | return _lldb.SBTypeMemberFunction_GetDemangledName(self) | GetDemangledName(SBTypeMemberFunction self) -> char const * | GetDemangledName(SBTypeMemberFunction self) -> char const * | [
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"""GetDemangledName(SBTypeMemberFunction self) -> char const *"""
return _lldb.SBTypeMemberFunction_GetDemangledName(self) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/distutils/line_endings.py | python | unix2dos | (file) | Replace LF with CRLF in argument files. Print names of changed files. | Replace LF with CRLF in argument files. Print names of changed files. | [
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] | def unix2dos(file):
"Replace LF with CRLF in argument files. Print names of changed files."
if os.path.isdir(file):
print(file, "Directory!")
return
data = open(file, "rb").read()
if '\0' in data:
print(file, "Binary!")
return
newdata = re.sub("\r\n", "\n", data)
newdata = re.sub("\n", "\r\n", newdata)
if newdata != data:
print('unix2dos:', file)
f = open(file, "wb")
f.write(newdata)
f.close()
return file
else:
print(file, 'ok') | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/VBox/VMM/VMMAll/IEMAllInstructionsPython.py | python | InstructionMap.getDisasTableName | (self) | return sName | Returns the disassembler table name for this map. | Returns the disassembler table name for this map. | [
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"""
Returns the disassembler table name for this map.
"""
sName = 'g_aDisas';
for sWord in self.sName.split('_'):
if sWord == 'm': # suffix indicating modrm.mod==mem
sName += '_m';
elif sWord == 'r': # suffix indicating modrm.mod==reg
sName += '_r';
elif len(sWord) == 2 and re.match('^[a-f0-9][a-f0-9]$', sWord):
sName += '_' + sWord;
else:
sWord = sWord.replace('grp', 'Grp');
sWord = sWord.replace('map', 'Map');
sName += sWord[0].upper() + sWord[1:];
return sName; | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-time.py | python | SConsTimer.get_debug_times | (self, file, time_string=None) | return result | Fetch times from the --debug=time strings in the specified file. | Fetch times from the --debug=time strings in the specified file. | [
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"""
Fetch times from the --debug=time strings in the specified file.
"""
if time_string is None:
search_string = self.time_string_all
else:
search_string = time_string
contents = open(file).read()
if not contents:
sys.stderr.write('file %s has no contents!\n' % repr(file))
return None
result = re.findall(r'%s: ([\d\.]*)' % search_string, contents)[-4:]
result = [ float(r) for r in result ]
if not time_string is None:
try:
result = result[0]
except IndexError:
sys.stderr.write('file %s has no results!\n' % repr(file))
return None
return result | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/peacock/Execute/ConsoleOutputViewerPlugin.py | python | ConsoleOutputViewerPlugin.viewPositionChanged | (self, val) | The view changed.
This can be due to adding additional text
or if the user scroll around the text window.
If we are at the bottom then we turn on auto scrolling.
Input:
val[int]: The current position of the vertical bar slider | The view changed.
This can be due to adding additional text
or if the user scroll around the text window.
If we are at the bottom then we turn on auto scrolling.
Input:
val[int]: The current position of the vertical bar slider | [
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KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | applications/FemToDemApplication/python_scripts/MainCouplingPfemFemDemAitkenSubstepping.py | python | KratosPrintInfo | (message) | This function prints info on screen | This function prints info on screen | [
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google/usd_from_gltf | 6d288cce8b68744494a226574ae1d7ba6a9c46eb | tools/ufgbatch/ufgcommon/process.py | python | get_process_command | (cmd_args) | return cmd | Get command-line text for task process. | Get command-line text for task process. | [
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if arg_index > 0:
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cmd += '"' + arg + '"'
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cmd += arg
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/neural_network/multilayer_perceptron.py | python | BaseMultilayerPerceptron.fit | (self, X, y) | return self._fit(X, y, incremental=False) | Fit the model to data matrix X and target y.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
y : array-like, shape (n_samples,)
The target values.
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Parameters
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The input data.
y : array-like, shape (n_samples,)
The target values.
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return self._fit(X, y, incremental=False) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/third_party/mlir/utils/spirv/gen_spirv_dialect.py | python | map_spec_operand_to_ods_argument | (operand) | return '{}:${}'.format(arg_type, name) | Maps a operand in SPIR-V JSON spec to an op argument in ODS.
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- A dict containing the operand's kind, quantifier, and name
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assert kind != 'IdResultType', 'unexpected to handle "IdResultType" kind'
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arg_type = 'I32'
elif kind == 'LiteralInteger':
if quantifier == '':
arg_type = 'I32Attr'
elif quantifier == '?':
arg_type = 'OptionalAttr<I32Attr>'
else:
arg_type = 'OptionalAttr<I32ArrayAttr>'
elif kind == 'LiteralString' or \
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assert False, '"{}" kind unimplemented'.format(kind)
else:
# The rest are all enum operands that we represent with op attributes.
assert quantifier != '*', 'unexpected to have variadic enum attribute'
arg_type = 'SPV_{}Attr'.format(kind)
if quantifier == '?':
arg_type = 'OptionalAttr<{}>'.format(arg_type)
name = operand.get('name', '')
name = snake_casify(name) if name else kind.lower()
return '{}:${}'.format(arg_type, name) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/pip/vendor/html5lib/html5parser.py | python | HTMLParser.parse | (self, stream, encoding=None, parseMeta=True, useChardet=True) | return self.tree.getDocument() | Parse a HTML document into a well-formed tree
stream - a filelike object or string containing the HTML to be parsed
The optional encoding parameter must be a string that indicates
the encoding. If specified, that encoding will be used,
regardless of any BOM or later declaration (such as in a meta
element) | Parse a HTML document into a well-formed tree | [
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"""Parse a HTML document into a well-formed tree
stream - a filelike object or string containing the HTML to be parsed
The optional encoding parameter must be a string that indicates
the encoding. If specified, that encoding will be used,
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self._parse(stream, innerHTML=False, encoding=encoding,
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return self.tree.getDocument() | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Source/bindings/scripts/interface_dependency_resolver.py | python | InterfaceDependencyResolver.__init__ | (self, interfaces_info, reader) | Initialize dependency resolver.
Args:
interfaces_info:
dict of interfaces information, from compute_dependencies.py
reader:
IdlReader, used for reading dependency files | Initialize dependency resolver. | [
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Args:
interfaces_info:
dict of interfaces information, from compute_dependencies.py
reader:
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self.interfaces_info = interfaces_info
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/compat/numpy/function.py | python | validate_argmax_with_skipna | (skipna, args, kwargs) | return skipna | If 'Series.argmax' is called via the 'numpy' library,
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'skipna' parameter is either an instance of ndarray or
is None, since 'skipna' itself should be a boolean | [
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skipna, args = process_skipna(skipna, args)
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synfig/synfig | a5ec91db5b751dc12e4400ccfb5c063fd6d2d928 | synfig-studio/plugins/lottie-exporter/properties/shapePropKeyframe/helper.py | python | convert_tangent_to_lottie | (t1, t2) | return t1, t2 | Converts tangent from Synfig format to lottie format
Args:
t1 (common.Vector.Vector) : tangent 1 of a point
t2 (common.Vector.Vector) : tangent 2 of a point
Returns:
(common.Vector.Vector) : Converted tangent 1
(common.Vector.Vector) : Converted tangent 2 | Converts tangent from Synfig format to lottie format | [
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"""
Converts tangent from Synfig format to lottie format
Args:
t1 (common.Vector.Vector) : tangent 1 of a point
t2 (common.Vector.Vector) : tangent 2 of a point
Returns:
(common.Vector.Vector) : Converted tangent 1
(common.Vector.Vector) : Converted tangent 2
"""
# Convert to Lottie format
t1 /= 3
t2 /= 3
# Synfig and Lottie use different in tangents SEE DOCUMENTATION
t1 *= -1
# Important: t1 and t2 have to be relative
# The y-axis is different in lottie
t1[1] = -t1[1]
t2[1] = -t2[1]
return t1, t2 | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/framework/ops.py | python | SparseTensor.op | (self) | return self.values.op | The `Operation` that produces `values` as an output. | The `Operation` that produces `values` as an output. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/decimal.py | python | Decimal.is_subnormal | (self, context=None) | return self.adjusted() < context.Emin | Return True if self is subnormal; otherwise return False. | Return True if self is subnormal; otherwise return False. | [
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"""Return True if self is subnormal; otherwise return False."""
if self._is_special or not self:
return False
if context is None:
context = getcontext()
return self.adjusted() < context.Emin | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/importlib/_bootstrap.py | python | BuiltinImporter.get_code | (cls, fullname) | return None | Return None as built-in modules do not have code objects. | Return None as built-in modules do not have code objects. | [
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QMCPACK/qmcpack | d0948ab455e38364458740cc8e2239600a14c5cd | utils/afqmctools/afqmctools/analysis/average.py | python | average_two_rdm | (filename, estimator='back_propagated', eqlb=1, skip=1, ix=None) | Average AFQMC 2RDM.
Returns a list of 2RDMS, where
2RDM[s1s2,i,k,j,l] = <c_{i}^+ c_{j}^+ c_{l} c_{k}>.
For closed shell systems, returns [(a,a,a,a),(a,a,b,b)]
For collinear systems, returns [(a,a,a,a),(a,a,b,b),(b,b,b,b)]
Parameters
----------
filename : string
QMCPACK output containing density matrix (*.h5 file).
estimator : string
Estimator type to analyse. Options: back_propagated or mixed.
Default: back_propagated.
eqlb : int
Number of blocks for equilibration. Default 1.
skip : int
Number of blocks to skip in between measurements equilibration.
Default 1 (use all data).
ix : int
Back propagation path length to average. Optional.
Default: None (chooses longest path).
Returns
-------
two_rdm : :class:`numpy.ndarray`
List of averaged 2RDM.
two_rdm_err : :class:`numpy.ndarray`
List of error bars for 2RDM elements. | Average AFQMC 2RDM. | [
"Average",
"AFQMC",
"2RDM",
"."
] | def average_two_rdm(filename, estimator='back_propagated', eqlb=1, skip=1, ix=None):
"""Average AFQMC 2RDM.
Returns a list of 2RDMS, where
2RDM[s1s2,i,k,j,l] = <c_{i}^+ c_{j}^+ c_{l} c_{k}>.
For closed shell systems, returns [(a,a,a,a),(a,a,b,b)]
For collinear systems, returns [(a,a,a,a),(a,a,b,b),(b,b,b,b)]
Parameters
----------
filename : string
QMCPACK output containing density matrix (*.h5 file).
estimator : string
Estimator type to analyse. Options: back_propagated or mixed.
Default: back_propagated.
eqlb : int
Number of blocks for equilibration. Default 1.
skip : int
Number of blocks to skip in between measurements equilibration.
Default 1 (use all data).
ix : int
Back propagation path length to average. Optional.
Default: None (chooses longest path).
Returns
-------
two_rdm : :class:`numpy.ndarray`
List of averaged 2RDM.
two_rdm_err : :class:`numpy.ndarray`
List of error bars for 2RDM elements.
"""
md = get_metadata(filename)
mean, err = average_observable(filename, 'two_rdm', eqlb=eqlb, skip=skip,
estimator=estimator, ix=ix)
nbasis = md['NMO']
wt = md['WalkerType']
try:
walker = WALKER_TYPE[wt]
except IndexError:
print('Unknown walker type {}'.format(wt))
if walker == 'closed':
return mean.reshape(2,nbasis,nbasis,nbasis,nbasis), err.reshape(2,nbasis,nbasis,nbasis,nbasis)
elif walker == 'collinear':
return mean.reshape(3,nbasis,nbasis,nbasis,nbasis), err.reshape(3,nbasis,nbasis,nbasis,nbasis)
elif walker == 'non_collinear':
return mean.reshape(2*nbasis,2*nbasis,2*nbasis,2*nbasis), err.reshape(2*nbasis,2*nbasis,2*nbasis, 2*nbasis)
else:
print('Unknown walker type.')
return None | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/reduce4circleControl.py | python | CWSCDReductionControl.get_single_scan_pt_model | (self, exp_number, scan_number, pt_number, roi_name, integration_direction) | return vec_x, vec_model | get a single-pt scan summed 1D data either vertically or horizontally with model data
:param exp_number:
:param scan_number:
:param pt_number:
:param roi_name:
:param integration_direction:
:return: 2-tuple.. vector model | get a single-pt scan summed 1D data either vertically or horizontally with model data
:param exp_number:
:param scan_number:
:param pt_number:
:param roi_name:
:param integration_direction:
:return: 2-tuple.. vector model | [
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""" get a single-pt scan summed 1D data either vertically or horizontally with model data
:param exp_number:
:param scan_number:
:param pt_number:
:param roi_name:
:param integration_direction:
:return: 2-tuple.. vector model
"""
# get record key
ws_record_key = self._current_single_pt_integration_key
print('[DB...BAT] Retrieve ws record key: {}'.format(ws_record_key))
# TODO - 20180814 - Check pt number, rio name and integration direction
if ws_record_key in self._single_pt_matrix_dict:
# check integration manager
integration_manager = self._single_pt_matrix_dict[ws_record_key]
assert integration_manager.exp_number == exp_number, 'blabla'
else:
raise RuntimeError('Last single-pt integration manager (key) {} does not exist.'
.format(ws_record_key))
matrix_ws = AnalysisDataService.retrieve(integration_manager.get_model_workspace())
ws_index = integration_manager.get_spectrum_number(scan_number, from_zero=True)
vec_x = matrix_ws.readX(ws_index)
vec_model = matrix_ws.readY(ws_index)
return vec_x, vec_model | [
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | clang/bindings/python/clang/cindex.py | python | SourceLocation.column | (self) | return self._get_instantiation()[2] | Get the column represented by this source location. | Get the column represented by this source location. | [
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"""Get the column represented by this source location."""
return self._get_instantiation()[2] | [
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crosslife/OpenBird | 9e0198a1a2295f03fa1e8676e216e22c9c7d380b | cocos2d/tools/bindings-generator/backup/clang-llvm-3.3-pybinding/cindex.py | python | Type.translation_unit | (self) | return self._tu | The TranslationUnit to which this Type is associated. | The TranslationUnit to which this Type is associated. | [
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] | def translation_unit(self):
"""The TranslationUnit to which this Type is associated."""
# If this triggers an AttributeError, the instance was not properly
# instantiated.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | BookCtrlBase_GetClassDefaultAttributes | (*args, **kwargs) | return _core_.BookCtrlBase_GetClassDefaultAttributes(*args, **kwargs) | BookCtrlBase_GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
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user's system, especially if it uses themes.
The variant parameter is only relevant under Mac currently and is
ignore under other platforms. Under Mac, it will change the size of
the returned font. See `wx.Window.SetWindowVariant` for more about
this. | BookCtrlBase_GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes | [
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"""
BookCtrlBase_GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
control -- which is a much better idea than hard coding specific
colours or fonts which might look completely out of place on the
user's system, especially if it uses themes.
The variant parameter is only relevant under Mac currently and is
ignore under other platforms. Under Mac, it will change the size of
the returned font. See `wx.Window.SetWindowVariant` for more about
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"""
return _core_.BookCtrlBase_GetClassDefaultAttributes(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/setuptools/command/bdist_egg.py | python | iter_symbols | (code) | Yield names and strings used by `code` and its nested code objects | Yield names and strings used by `code` and its nested code objects | [
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"""Yield names and strings used by `code` and its nested code objects"""
for name in code.co_names:
yield name
for const in code.co_consts:
if isinstance(const, six.string_types):
yield const
elif isinstance(const, CodeType):
for name in iter_symbols(const):
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/qcdb/libmintscoordentry.py | python | NumberValue.rset | (self, val) | Resets value of coordinate if not fixed | Resets value of coordinate if not fixed | [
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"""Resets value of coordinate if not fixed"""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/customtreectrl.py | python | GenericTreeItem.Set3StateValue | (self, state) | Sets the checkbox item to the given `state`.
:param integer `state`: can be one of: ``wx.CHK_UNCHECKED`` (check is off), ``wx.CHK_CHECKED``
(check is on) or ``wx.CHK_UNDETERMINED`` (check is mixed).
:raise: `Exception` when the item is not a 3-state checkbox item.
:note: This method raises an exception when the checkbox item is a 2-state checkbox
and setting the state to ``wx.CHK_UNDETERMINED``.
:note: This method is meaningful only for checkbox-like items. | Sets the checkbox item to the given `state`. | [
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"""
Sets the checkbox item to the given `state`.
:param integer `state`: can be one of: ``wx.CHK_UNCHECKED`` (check is off), ``wx.CHK_CHECKED``
(check is on) or ``wx.CHK_UNDETERMINED`` (check is mixed).
:raise: `Exception` when the item is not a 3-state checkbox item.
:note: This method raises an exception when the checkbox item is a 2-state checkbox
and setting the state to ``wx.CHK_UNDETERMINED``.
:note: This method is meaningful only for checkbox-like items.
"""
if not self._is3State and state == wx.CHK_UNDETERMINED:
raise Exception("Set3StateValue can only be used with 3-state checkbox items.")
self._checked = state | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/richtext.py | python | RichTextCtrl.ApplyItalicToSelection | (*args, **kwargs) | return _richtext.RichTextCtrl_ApplyItalicToSelection(*args, **kwargs) | ApplyItalicToSelection(self) -> bool
Apply italic to the selection | ApplyItalicToSelection(self) -> bool | [
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"""
ApplyItalicToSelection(self) -> bool
Apply italic to the selection
"""
return _richtext.RichTextCtrl_ApplyItalicToSelection(*args, **kwargs) | [
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | clang-tools-extra/clang-tidy/tool/run-clang-tidy.py | python | find_compilation_database | (path) | return os.path.realpath(result) | Adjusts the directory until a compilation database is found. | Adjusts the directory until a compilation database is found. | [
"Adjusts",
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"found",
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] | def find_compilation_database(path):
"""Adjusts the directory until a compilation database is found."""
result = './'
while not os.path.isfile(os.path.join(result, path)):
if os.path.realpath(result) == '/':
print('Error: could not find compilation database.')
sys.exit(1)
result += '../'
return os.path.realpath(result) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/boto3/dynamodb/conditions.py | python | Attr.ne | (self, value) | return NotEquals(self, value) | Creates a condition where the attribute is not equal to the value
:param value: The value that the attribute is not equal to. | Creates a condition where the attribute is not equal to the value | [
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"""Creates a condition where the attribute is not equal to the value
:param value: The value that the attribute is not equal to.
"""
return NotEquals(self, value) | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/v8/third_party/jinja2/ext.py | python | Extension.bind | (self, environment) | return rv | Create a copy of this extension bound to another environment. | Create a copy of this extension bound to another environment. | [
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"""Create a copy of this extension bound to another environment."""
rv = object.__new__(self.__class__)
rv.__dict__.update(self.__dict__)
rv.environment = environment
return rv | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/serialize.py | python | _get_function_globals_for_reduction | (func) | return globs | Analyse *func* and return a dictionary of global values suitable for
reduction. | Analyse *func* and return a dictionary of global values suitable for
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] | def _get_function_globals_for_reduction(func):
"""
Analyse *func* and return a dictionary of global values suitable for
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"""
func_id = bytecode.FunctionIdentity.from_function(func)
bc = bytecode.ByteCode(func_id)
globs = bc.get_used_globals()
for k, v in globs.items():
# Make modules picklable by name
if isinstance(v, ModuleType):
globs[k] = _ModuleRef(v.__name__)
# Remember the module name so that the function gets a proper __module__
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globs['__name__'] = func.__module__
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/eager/context.py | python | set_log_device_placement | (enabled) | Turns logging for device placement decisions on or off.
Operations execute on a particular device, producing and consuming tensors on
that device. This may change the performance of the operation or require
TensorFlow to copy data to or from an accelerator, so knowing where operations
execute is useful for debugging performance issues.
For more advanced profiling, use the [TensorFlow
profiler](https://www.tensorflow.org/guide/profiler).
Device placement for operations is typically controlled by a `tf.device`
scope, but there are exceptions, for example operations on a `tf.Variable`
which follow the initial placement of the variable. Turning off soft device
placement (with `tf.config.set_soft_device_placement`) provides more explicit
control.
>>> tf.debugging.set_log_device_placement(True)
>>> tf.ones([])
>>> # [...] op Fill in device /job:localhost/replica:0/task:0/device:GPU:0
>>> with tf.device("CPU"):
... tf.ones([])
>>> # [...] op Fill in device /job:localhost/replica:0/task:0/device:CPU:0
>>> tf.debugging.set_log_device_placement(False)
Turning on `tf.debugging.set_log_device_placement` also logs the placement of
ops inside `tf.function` when the function is called.
Args:
enabled: Whether to enabled device placement logging. | Turns logging for device placement decisions on or off. | [
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] | def set_log_device_placement(enabled):
"""Turns logging for device placement decisions on or off.
Operations execute on a particular device, producing and consuming tensors on
that device. This may change the performance of the operation or require
TensorFlow to copy data to or from an accelerator, so knowing where operations
execute is useful for debugging performance issues.
For more advanced profiling, use the [TensorFlow
profiler](https://www.tensorflow.org/guide/profiler).
Device placement for operations is typically controlled by a `tf.device`
scope, but there are exceptions, for example operations on a `tf.Variable`
which follow the initial placement of the variable. Turning off soft device
placement (with `tf.config.set_soft_device_placement`) provides more explicit
control.
>>> tf.debugging.set_log_device_placement(True)
>>> tf.ones([])
>>> # [...] op Fill in device /job:localhost/replica:0/task:0/device:GPU:0
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... tf.ones([])
>>> # [...] op Fill in device /job:localhost/replica:0/task:0/device:CPU:0
>>> tf.debugging.set_log_device_placement(False)
Turning on `tf.debugging.set_log_device_placement` also logs the placement of
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Args:
enabled: Whether to enabled device placement logging.
"""
context().log_device_placement = enabled | [
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ValveSoftware/source-sdk-2013 | 0d8dceea4310fde5706b3ce1c70609d72a38efdf | sp/src/thirdparty/protobuf-2.3.0/python/google/protobuf/reflection.py | python | _AddIsInitializedMethod | (message_descriptor, cls) | Adds the IsInitialized and FindInitializationError methods to the
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"""Adds the IsInitialized and FindInitializationError methods to the
protocol message class."""
required_fields = [field for field in message_descriptor.fields
if field.label == _FieldDescriptor.LABEL_REQUIRED]
def IsInitialized(self, errors=None):
"""Checks if all required fields of a message are set.
Args:
errors: A list which, if provided, will be populated with the field
paths of all missing required fields.
Returns:
True iff the specified message has all required fields set.
"""
# Performance is critical so we avoid HasField() and ListFields().
for field in required_fields:
if (field not in self._fields or
(field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE and
not self._fields[field]._is_present_in_parent)):
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errors.extend(self.FindInitializationErrors())
return False
for field, value in self._fields.iteritems():
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errors.extend(self.FindInitializationErrors())
return False
elif value._is_present_in_parent and not value.IsInitialized():
if errors is not None:
errors.extend(self.FindInitializationErrors())
return False
return True
cls.IsInitialized = IsInitialized
def FindInitializationErrors(self):
"""Finds required fields which are not initialized.
Returns:
A list of strings. Each string is a path to an uninitialized field from
the top-level message, e.g. "foo.bar[5].baz".
"""
errors = [] # simplify things
for field in required_fields:
if not self.HasField(field.name):
errors.append(field.name)
for field, value in self.ListFields():
if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE:
if field.is_extension:
name = "(%s)" % field.full_name
else:
name = field.name
if field.label == _FieldDescriptor.LABEL_REPEATED:
for i in xrange(len(value)):
element = value[i]
prefix = "%s[%d]." % (name, i)
sub_errors = element.FindInitializationErrors()
errors += [ prefix + error for error in sub_errors ]
else:
prefix = name + "."
sub_errors = value.FindInitializationErrors()
errors += [ prefix + error for error in sub_errors ]
return errors
cls.FindInitializationErrors = FindInitializationErrors | [
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Komnomnomnom/swigibpy | cfd307fdbfaffabc69a2dc037538d7e34a8b8daf | swigibpy.py | python | EClientSocketBase.subscribeToGroupEvents | (self, reqId, groupId) | return _swigibpy.EClientSocketBase_subscribeToGroupEvents(self, reqId, groupId) | subscribeToGroupEvents(EClientSocketBase self, int reqId, int groupId) | subscribeToGroupEvents(EClientSocketBase self, int reqId, int groupId) | [
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] | def subscribeToGroupEvents(self, reqId, groupId):
"""subscribeToGroupEvents(EClientSocketBase self, int reqId, int groupId)"""
return _swigibpy.EClientSocketBase_subscribeToGroupEvents(self, reqId, groupId) | [
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fatih/subvim | 241b6d170597857105da219c9b7d36059e9f11fb | vim/base/YouCompleteMe/third_party/jedi/jedi/api_classes.py | python | BaseDefinition.__init__ | (self, definition, start_pos) | An instance of :class:`jedi.parsing_representation.Base` subclass. | An instance of :class:`jedi.parsing_representation.Base` subclass. | [
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self.is_keyword = isinstance(definition, keywords.Keyword)
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facebookincubator/BOLT | 88c70afe9d388ad430cc150cc158641701397f70 | lldb/utils/lui/lldbutil.py | python | get_description | (obj, option=None) | return stream.GetData() | Calls lldb_obj.GetDescription() and returns a string, or None.
For SBTarget, SBBreakpointLocation, and SBWatchpoint lldb objects, an extra
option can be passed in to describe the detailed level of description
desired:
o lldb.eDescriptionLevelBrief
o lldb.eDescriptionLevelFull
o lldb.eDescriptionLevelVerbose | Calls lldb_obj.GetDescription() and returns a string, or None. | [
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"""Calls lldb_obj.GetDescription() and returns a string, or None.
For SBTarget, SBBreakpointLocation, and SBWatchpoint lldb objects, an extra
option can be passed in to describe the detailed level of description
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o lldb.eDescriptionLevelFull
o lldb.eDescriptionLevelVerbose
"""
method = getattr(obj, 'GetDescription')
if not method:
return None
tuple = (lldb.SBTarget, lldb.SBBreakpointLocation, lldb.SBWatchpoint)
if isinstance(obj, tuple):
if option is None:
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stream = lldb.SBStream()
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smartdevicelink/sdl_core | 68f082169e0a40fccd9eb0db3c83911c28870f07 | tools/InterfaceGenerator/generator/parsers/JSONRPC.py | python | Parser._provide_enum_element_for_function | (self, enum_name, element_name) | return element | Provide enum element for functions.
This implementation replaces the underscore separating interface and
function name with dot and sets it as name of enum element leaving
the name with underscore as internal_name. For enums other than
FunctionID the base implementation is called.
Returns EnumElement. | Provide enum element for functions. | [
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the name with underscore as internal_name. For enums other than
FunctionID the base implementation is called.
Returns EnumElement.
"""
name = element_name
internal_name = None
if "FunctionID" == enum_name:
prefix_length = len(self._interface_name) + 1
if element_name[:prefix_length] != self._interface_name + '_':
raise ParseError(
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/hfctables.py | python | ProcessTableWidget.get_mask | (self, row_index) | return self.get_cell_value(row_index, self._colIndexMask) | get the mask/ROI name that this integration is based on
:param row_index:
:return: | get the mask/ROI name that this integration is based on
:param row_index:
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/decimal.py | python | Context._regard_flags | (self, *flags) | Stop ignoring the flags, if they are raised | Stop ignoring the flags, if they are raised | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | xmlTextReader.Prefix | (self) | return ret | A shorthand reference to the namespace associated with the
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/locale.py | python | _build_localename | (localetuple) | Builds a locale code from the given tuple (language code,
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/turtle.py | python | TurtleScreenBase._resize | (self, canvwidth=None, canvheight=None, bg=None) | Resize the canvas the turtles are drawing on. Does
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# needs amendment
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emlid/Navio | 14b96c83ad57a10580655e3af49c9be1f5fc0ad2 | Python/navio/adafruit_ads1x15.py | python | ADS1x15.stopContinuousConversion | (self) | return True | Stops the ADC's conversions when in continuous mode \
and resets the configuration to its default value. | Stops the ADC's conversions when in continuous mode \
and resets the configuration to its default value. | [
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# Write the default config register to the ADC
# Once we write, the ADC will do a single conversion and
# enter power-off mode.
config = 0x8583 # Page 18 datasheet.
bytes = [(config >> 8) & 0xFF, config & 0xFF]
self.i2c.writeList(self.__ADS1015_REG_POINTER_CONFIG, bytes)
return True | [
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livecode/livecode | 4606a10ea10b16d5071d0f9f263ccdd7ede8b31d | gyp/pylib/gyp/generator/analyzer.py | python | _GetBuildTargets | (matching_targets, roots) | return result | Returns the set of Targets that require a build.
matching_targets: targets that changed and need to be built.
roots: set of root targets in the build files to search from. | Returns the set of Targets that require a build.
matching_targets: targets that changed and need to be built.
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neopenx/Dragon | 0e639a7319035ddc81918bd3df059230436ee0a1 | Dragon/python/dragon/vm/caffe/solver.py | python | Solver.iter | (self) | return self._iter | Return or Set the current iteration. [**PyCaffe Style**]
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iter : int
The value of iteration to set.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pkg_resources/__init__.py | python | WorkingSet.__init__ | (self, entries=None) | Create working set from list of path entries (default=sys.path) | Create working set from list of path entries (default=sys.path) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | GraphicsGradientStops.SetEndColour | (*args, **kwargs) | return _gdi_.GraphicsGradientStops_SetEndColour(*args, **kwargs) | SetEndColour(self, Colour col) | SetEndColour(self, Colour col) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/base.py | python | IndexOpsMixin.to_numpy | (self, dtype=None, copy=False, na_value=lib.no_default, **kwargs) | return result | A NumPy ndarray representing the values in this Series or Index.
.. versionadded:: 0.24.0
Parameters
----------
dtype : str or numpy.dtype, optional
The dtype to pass to :meth:`numpy.asarray`.
copy : bool, default False
Whether to ensure that the returned value is a not a view on
another array. Note that ``copy=False`` does not *ensure* that
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a copy is made, even if not strictly necessary.
na_value : Any, optional
The value to use for missing values. The default value depends
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.. versionadded:: 1.0.0
**kwargs
Additional keywords passed through to the ``to_numpy`` method
of the underlying array (for extension arrays).
.. versionadded:: 1.0.0
Returns
-------
numpy.ndarray
See Also
--------
Series.array : Get the actual data stored within.
Index.array : Get the actual data stored within.
DataFrame.to_numpy : Similar method for DataFrame.
Notes
-----
The returned array will be the same up to equality (values equal
in `self` will be equal in the returned array; likewise for values
that are not equal). When `self` contains an ExtensionArray, the
dtype may be different. For example, for a category-dtype Series,
``to_numpy()`` will return a NumPy array and the categorical dtype
will be lost.
For NumPy dtypes, this will be a reference to the actual data stored
in this Series or Index (assuming ``copy=False``). Modifying the result
in place will modify the data stored in the Series or Index (not that
we recommend doing that).
For extension types, ``to_numpy()`` *may* require copying data and
coercing the result to a NumPy type (possibly object), which may be
expensive. When you need a no-copy reference to the underlying data,
:attr:`Series.array` should be used instead.
This table lays out the different dtypes and default return types of
``to_numpy()`` for various dtypes within pandas.
================== ================================
dtype array type
================== ================================
category[T] ndarray[T] (same dtype as input)
period ndarray[object] (Periods)
interval ndarray[object] (Intervals)
IntegerNA ndarray[object]
datetime64[ns] datetime64[ns]
datetime64[ns, tz] ndarray[object] (Timestamps)
================== ================================
Examples
--------
>>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.to_numpy()
array(['a', 'b', 'a'], dtype=object)
Specify the `dtype` to control how datetime-aware data is represented.
Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`
objects, each with the correct ``tz``.
>>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
>>> ser.to_numpy(dtype=object)
array([Timestamp('2000-01-01 00:00:00+0100', tz='CET', freq='D'),
Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],
dtype=object)
Or ``dtype='datetime64[ns]'`` to return an ndarray of native
datetime64 values. The values are converted to UTC and the timezone
info is dropped.
>>> ser.to_numpy(dtype="datetime64[ns]")
... # doctest: +ELLIPSIS
array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'],
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"""
A NumPy ndarray representing the values in this Series or Index.
.. versionadded:: 0.24.0
Parameters
----------
dtype : str or numpy.dtype, optional
The dtype to pass to :meth:`numpy.asarray`.
copy : bool, default False
Whether to ensure that the returned value is a not a view on
another array. Note that ``copy=False`` does not *ensure* that
``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
a copy is made, even if not strictly necessary.
na_value : Any, optional
The value to use for missing values. The default value depends
on `dtype` and the type of the array.
.. versionadded:: 1.0.0
**kwargs
Additional keywords passed through to the ``to_numpy`` method
of the underlying array (for extension arrays).
.. versionadded:: 1.0.0
Returns
-------
numpy.ndarray
See Also
--------
Series.array : Get the actual data stored within.
Index.array : Get the actual data stored within.
DataFrame.to_numpy : Similar method for DataFrame.
Notes
-----
The returned array will be the same up to equality (values equal
in `self` will be equal in the returned array; likewise for values
that are not equal). When `self` contains an ExtensionArray, the
dtype may be different. For example, for a category-dtype Series,
``to_numpy()`` will return a NumPy array and the categorical dtype
will be lost.
For NumPy dtypes, this will be a reference to the actual data stored
in this Series or Index (assuming ``copy=False``). Modifying the result
in place will modify the data stored in the Series or Index (not that
we recommend doing that).
For extension types, ``to_numpy()`` *may* require copying data and
coercing the result to a NumPy type (possibly object), which may be
expensive. When you need a no-copy reference to the underlying data,
:attr:`Series.array` should be used instead.
This table lays out the different dtypes and default return types of
``to_numpy()`` for various dtypes within pandas.
================== ================================
dtype array type
================== ================================
category[T] ndarray[T] (same dtype as input)
period ndarray[object] (Periods)
interval ndarray[object] (Intervals)
IntegerNA ndarray[object]
datetime64[ns] datetime64[ns]
datetime64[ns, tz] ndarray[object] (Timestamps)
================== ================================
Examples
--------
>>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.to_numpy()
array(['a', 'b', 'a'], dtype=object)
Specify the `dtype` to control how datetime-aware data is represented.
Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`
objects, each with the correct ``tz``.
>>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
>>> ser.to_numpy(dtype=object)
array([Timestamp('2000-01-01 00:00:00+0100', tz='CET', freq='D'),
Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],
dtype=object)
Or ``dtype='datetime64[ns]'`` to return an ndarray of native
datetime64 values. The values are converted to UTC and the timezone
info is dropped.
>>> ser.to_numpy(dtype="datetime64[ns]")
... # doctest: +ELLIPSIS
array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'],
dtype='datetime64[ns]')
"""
if is_extension_array_dtype(self.dtype):
return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)
else:
if kwargs:
msg = "to_numpy() got an unexpected keyword argument '{}'".format(
list(kwargs.keys())[0]
)
raise TypeError(msg)
result = np.asarray(self._values, dtype=dtype)
# TODO(GH-24345): Avoid potential double copy
if copy or na_value is not lib.no_default:
result = result.copy()
if na_value is not lib.no_default:
result[self.isna()] = na_value
return result | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/urllib3/connection.py | python | HTTPConnection.request_chunked | (self, method, url, body=None, headers=None) | Alternative to the common request method, which sends the
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skip_accept_encoding = "accept-encoding" in headers
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self.send(len_str.encode("utf-8"))
self.send(b"\r\n")
self.send(chunk)
self.send(b"\r\n")
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self.send(b"0\r\n\r\n") | [
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naver/sling | 5671cd445a2caae0b4dd0332299e4cfede05062c | webkit/Tools/Scripts/webkitpy/thirdparty/mod_pywebsocket/standalone.py | python | WebSocketServer.get_request | (self) | return accepted_socket, client_address | Override TCPServer.get_request to wrap OpenSSL.SSL.Connection
object with _StandaloneSSLConnection to provide makefile method. We
cannot substitute OpenSSL.SSL.Connection.makefile since it's readonly
attribute. | Override TCPServer.get_request to wrap OpenSSL.SSL.Connection
object with _StandaloneSSLConnection to provide makefile method. We
cannot substitute OpenSSL.SSL.Connection.makefile since it's readonly
attribute. | [
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"""Override TCPServer.get_request to wrap OpenSSL.SSL.Connection
object with _StandaloneSSLConnection to provide makefile method. We
cannot substitute OpenSSL.SSL.Connection.makefile since it's readonly
attribute.
"""
accepted_socket, client_address = self.socket.accept()
server_options = self.websocket_server_options
if server_options.use_tls:
if server_options.tls_module == _TLS_BY_STANDARD_MODULE:
try:
accepted_socket.do_handshake()
except ssl.SSLError, e:
self._logger.debug('%r', e)
raise
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elif server_options.tls_module == _TLS_BY_PYOPENSSL:
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ctx = OpenSSL.SSL.Context(OpenSSL.SSL.SSLv23_METHOD)
ctx.use_privatekey_file(server_options.private_key)
ctx.use_certificate_file(server_options.certificate)
def default_callback(conn, cert, errnum, errdepth, ok):
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ctx.load_verify_locations(server_options.tls_client_ca,
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ctx.set_verify(OpenSSL.SSL.VERIFY_NONE, default_callback)
accepted_socket = OpenSSL.SSL.Connection(ctx, accepted_socket)
accepted_socket.set_accept_state()
# Convert SSL related error into socket.error so that
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#
# TODO(tyoshino): Convert all kinds of errors.
try:
accepted_socket.do_handshake()
except OpenSSL.SSL.Error, e:
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self._logger.debug('%r', e)
raise socket.error(1, '%r' % e)
cert = accepted_socket.get_peer_certificate()
self._logger.debug('Client cert subject: %r',
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accepted_socket = _StandaloneSSLConnection(accepted_socket)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aui/auibook.py | python | AuiTabCtrl.OnMiddleDown | (self, event) | Handles the ``wx.EVT_MIDDLE_DOWN`` event for :class:`AuiTabCtrl`.
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self.StopTooltipTimer()
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/setuptools/msvc.py | python | EnvironmentInfo.NetFxSDKLibraries | (self) | return [join(self.si.NetFxSdkDir, r'lib\um%s' % arch_subdir)] | Microsoft .Net Framework SDK Libraries.
Return
------
list of str
paths | Microsoft .Net Framework SDK Libraries. | [
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"""
Microsoft .Net Framework SDK Libraries.
Return
------
list of str
paths
"""
if self.vs_ver < 14.0 or not self.si.NetFxSdkDir:
return []
arch_subdir = self.pi.target_dir(x64=True)
return [join(self.si.NetFxSdkDir, r'lib\um%s' % arch_subdir)] | [
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] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/setuptools/msvc.py#L1538-L1551 | |
PolygonTek/BlueshiftEngine | fbc374cbc391e1147c744649f405a66a27c35d89 | Source/ThirdParty/freetype/src/tools/glnames.py | python | main | () | main program body | main program body | [
"main",
"program",
"body"
] | def main():
"""main program body"""
if len( sys.argv ) != 2:
print __doc__ % sys.argv[0]
sys.exit( 1 )
file = open( sys.argv[1], "wb" )
write = file.write
count_sid = len( sid_standard_names )
# `mac_extras' contains the list of glyph names in the Macintosh standard
# encoding which are not in the SID Standard Names.
#
mac_extras = filter_glyph_names( mac_standard_names, sid_standard_names )
# `base_list' contains the names of our final glyph names table.
# It consists of the `mac_extras' glyph names, followed by the SID
# standard names.
#
mac_extras_count = len( mac_extras )
base_list = mac_extras + sid_standard_names
write( "/****************************************************************************\n" )
write( " *\n" )
write( " * %-71s\n" % os.path.basename( sys.argv[1] ) )
write( " *\n" )
write( " * PostScript glyph names.\n" )
write( " *\n" )
write( " * Copyright 2005-2019 by\n" )
write( " * David Turner, Robert Wilhelm, and Werner Lemberg.\n" )
write( " *\n" )
write( " * This file is part of the FreeType project, and may only be used,\n" )
write( " * modified, and distributed under the terms of the FreeType project\n" )
write( " * license, LICENSE.TXT. By continuing to use, modify, or distribute\n" )
write( " * this file you indicate that you have read the license and\n" )
write( " * understand and accept it fully.\n" )
write( " *\n" )
write( " */\n" )
write( "\n" )
write( "\n" )
write( " /* This file has been generated automatically -- do not edit! */\n" )
write( "\n" )
write( "\n" )
# dump final glyph list (mac extras + sid standard names)
#
st = StringTable( base_list, "ft_standard_glyph_names" )
st.dump( file )
st.dump_sublist( file, "ft_mac_names",
"FT_NUM_MAC_NAMES", mac_standard_names )
st.dump_sublist( file, "ft_sid_names",
"FT_NUM_SID_NAMES", sid_standard_names )
dump_encoding( file, "t1_standard_encoding", t1_standard_encoding )
dump_encoding( file, "t1_expert_encoding", t1_expert_encoding )
# dump the AGL in its compressed form
#
agl_glyphs, agl_values = adobe_glyph_values()
dict = StringNode( "", 0 )
for g in range( len( agl_glyphs ) ):
dict.add( agl_glyphs[g], eval( "0x" + agl_values[g] ) )
dict = dict.optimize()
dict_len = dict.locate( 0 )
dict_array = dict.store( "" )
write( """\
/*
* This table is a compressed version of the Adobe Glyph List (AGL),
* optimized for efficient searching. It has been generated by the
* `glnames.py' python script located in the `src/tools' directory.
*
* The lookup function to get the Unicode value for a given string
* is defined below the table.
*/
#ifdef FT_CONFIG_OPTION_ADOBE_GLYPH_LIST
""" )
dump_array( dict_array, write, "ft_adobe_glyph_list" )
# write the lookup routine now
#
write( """\
#ifdef DEFINE_PS_TABLES
/*
* This function searches the compressed table efficiently.
*/
static unsigned long
ft_get_adobe_glyph_index( const char* name,
const char* limit )
{
int c = 0;
int count, min, max;
const unsigned char* p = ft_adobe_glyph_list;
if ( name == 0 || name >= limit )
goto NotFound;
c = *name++;
count = p[1];
p += 2;
min = 0;
max = count;
while ( min < max )
{
int mid = ( min + max ) >> 1;
const unsigned char* q = p + mid * 2;
int c2;
q = ft_adobe_glyph_list + ( ( (int)q[0] << 8 ) | q[1] );
c2 = q[0] & 127;
if ( c2 == c )
{
p = q;
goto Found;
}
if ( c2 < c )
min = mid + 1;
else
max = mid;
}
goto NotFound;
Found:
for (;;)
{
/* assert (*p & 127) == c */
if ( name >= limit )
{
if ( (p[0] & 128) == 0 &&
(p[1] & 128) != 0 )
return (unsigned long)( ( (int)p[2] << 8 ) | p[3] );
goto NotFound;
}
c = *name++;
if ( p[0] & 128 )
{
p++;
if ( c != (p[0] & 127) )
goto NotFound;
continue;
}
p++;
count = p[0] & 127;
if ( p[0] & 128 )
p += 2;
p++;
for ( ; count > 0; count--, p += 2 )
{
int offset = ( (int)p[0] << 8 ) | p[1];
const unsigned char* q = ft_adobe_glyph_list + offset;
if ( c == ( q[0] & 127 ) )
{
p = q;
goto NextIter;
}
}
goto NotFound;
NextIter:
;
}
NotFound:
return 0;
}
#endif /* DEFINE_PS_TABLES */
#endif /* FT_CONFIG_OPTION_ADOBE_GLYPH_LIST */
""" )
if 0: # generate unit test, or don't
#
# now write the unit test to check that everything works OK
#
write( "#ifdef TEST\n\n" )
write( "static const char* const the_names[] = {\n" )
for name in agl_glyphs:
write( ' "' + name + '",\n' )
write( " 0\n};\n" )
write( "static const unsigned long the_values[] = {\n" )
for val in agl_values:
write( ' 0x' + val + ',\n' )
write( " 0\n};\n" )
write( """
#include <stdlib.h>
#include <stdio.h>
int
main( void )
{
int result = 0;
const char* const* names = the_names;
const unsigned long* values = the_values;
for ( ; *names; names++, values++ )
{
const char* name = *names;
unsigned long reference = *values;
unsigned long value;
value = ft_get_adobe_glyph_index( name, name + strlen( name ) );
if ( value != reference )
{
result = 1;
fprintf( stderr, "name '%s' => %04x instead of %04x\\n",
name, value, reference );
}
}
return result;
}
""" )
write( "#endif /* TEST */\n" )
write("\n/* END */\n") | [
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")",
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")"
] | https://github.com/PolygonTek/BlueshiftEngine/blob/fbc374cbc391e1147c744649f405a66a27c35d89/Source/ThirdParty/freetype/src/tools/glnames.py#L5289-L5532 | ||
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/eager/function.py | python | ConcreteFunction.set_external_captures | (self, captures) | Updates the function capture values.
The new values must have tensor types and shapes consistent with the
original captures of the concrete function, but it is allowed to change a
value captured with a deferred one and vice-versa.
Args:
captures: A list of tensors or closures. Tensors are value captures, and
closures are call-time (deferred captures). | Updates the function capture values. | [
"Updates",
"the",
"function",
"capture",
"values",
"."
] | def set_external_captures(self, captures):
"""Updates the function capture values.
The new values must have tensor types and shapes consistent with the
original captures of the concrete function, but it is allowed to change a
value captured with a deferred one and vice-versa.
Args:
captures: A list of tensors or closures. Tensors are value captures, and
closures are call-time (deferred captures).
"""
# TODO(wxinyi): 1. verify that the new captures' type spec is compatible
# with the original's. However, doing so requires MirroredVariable captures
# initialized. 2. replace the original/new captures/deferred
# captures in the wrapped graph. Doing such for a capture-to-deferred
# capture replacement requires more arguments than the deferred capture
# itself, e.g. default value, spec.
self._captured_inputs = captures | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/examples/learn/text_classification_character_rnn.py | python | char_rnn_model | (features, labels, mode) | return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops) | Character level recurrent neural network model to predict classes. | Character level recurrent neural network model to predict classes. | [
"Character",
"level",
"recurrent",
"neural",
"network",
"model",
"to",
"predict",
"classes",
"."
] | def char_rnn_model(features, labels, mode):
"""Character level recurrent neural network model to predict classes."""
byte_vectors = tf.one_hot(features[CHARS_FEATURE], 256, 1., 0.)
byte_list = tf.unstack(byte_vectors, axis=1)
cell = tf.contrib.rnn.GRUCell(HIDDEN_SIZE)
_, encoding = tf.contrib.rnn.static_rnn(cell, byte_list, dtype=tf.float32)
logits = tf.layers.dense(encoding, MAX_LABEL, activation=None)
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions={
'class': predicted_classes,
'prob': tf.nn.softmax(logits)
})
onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0)
loss = tf.losses.softmax_cross_entropy(
onehot_labels=onehot_labels, logits=logits)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(
labels=labels, predictions=predicted_classes)
}
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops) | [
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priyankchheda/algorithms | c361aa9071573fa9966d5b02d05e524815abcf2b | dynamic_connectivity/quick_find.py | python | QuickFind.union | (self, elem1, elem2) | When connecting two objects elem1 and elem2, change the id of all
objects that have the id of elem1 to that of elem2, or vice-versa.
Time Complexity is O(n), which is too expensive.
:param elem1: element 1
:param elem2: element 2 | When connecting two objects elem1 and elem2, change the id of all
objects that have the id of elem1 to that of elem2, or vice-versa.
Time Complexity is O(n), which is too expensive. | [
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""" When connecting two objects elem1 and elem2, change the id of all
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Time Complexity is O(n), which is too expensive.
:param elem1: element 1
:param elem2: element 2
"""
pid = self.data[elem1]
qid = self.data[elem2]
for i in range(len(self.data)):
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self.data[i] = qid | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/mailbox.py | python | mbox._post_message_hook | (self, f) | Called after writing each message to file f. | Called after writing each message to file f. | [
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"""Called after writing each message to file f."""
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google/mysql-protobuf | 467cda676afaa49e762c5c9164a43f6ad31a1fbf | protobuf/objectivec/DevTools/pddm.py | python | MacroCollection.Expand | (self, macro_ref_str) | return self._Expand(match, [], macro_ref_str) | Expands the macro reference.
Args:
macro_ref_str: String of a macro reference (i.e. foo(a, b)).
Returns:
The text from the expansion.
Raises:
PDDMError if there are any issues. | Expands the macro reference. | [
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"""Expands the macro reference.
Args:
macro_ref_str: String of a macro reference (i.e. foo(a, b)).
Returns:
The text from the expansion.
Raises:
PDDMError if there are any issues.
"""
match = _MACRO_RE.match(macro_ref_str)
if match is None or match.group(0) != macro_ref_str:
raise PDDMError('Failed to parse macro reference: "%s"' % macro_ref_str)
if match.group('name') not in self._macros:
raise PDDMError('No macro named "%s".' % match.group('name'))
return self._Expand(match, [], macro_ref_str) | [
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