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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/setuptools/_distutils/command/install.py | python | install.finalize_unix | (self) | Finalizes options for posix platforms. | Finalizes options for posix platforms. | [
"Finalizes",
"options",
"for",
"posix",
"platforms",
"."
] | def finalize_unix(self):
"""Finalizes options for posix platforms."""
if self.install_base is not None or self.install_platbase is not None:
if ((self.install_lib is None and
self.install_purelib is None and
self.install_platlib is None) or
self.install_headers is None or
self.install_scripts is None or
self.install_data is None):
raise DistutilsOptionError(
"install-base or install-platbase supplied, but "
"installation scheme is incomplete")
return
if self.user:
if self.install_userbase is None:
raise DistutilsPlatformError(
"User base directory is not specified")
self.install_base = self.install_platbase = self.install_userbase
self.select_scheme("posix_user")
elif self.home is not None:
self.install_base = self.install_platbase = self.home
self.select_scheme("posix_home")
else:
if self.prefix is None:
if self.exec_prefix is not None:
raise DistutilsOptionError(
"must not supply exec-prefix without prefix")
# Allow Fedora to add components to the prefix
_prefix_addition = getattr(sysconfig, '_prefix_addition', "")
self.prefix = (
os.path.normpath(sys.prefix) + _prefix_addition)
self.exec_prefix = (
os.path.normpath(sys.exec_prefix) + _prefix_addition)
else:
if self.exec_prefix is None:
self.exec_prefix = self.prefix
self.install_base = self.prefix
self.install_platbase = self.exec_prefix
self.select_scheme("posix_prefix") | [
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Slicer/SlicerGitSVNArchive | 65e92bb16c2b32ea47a1a66bee71f238891ee1ca | Base/Python/slicer/util.py | python | saveScene | (filename, properties={}) | return app.coreIOManager().saveNodes(filetype, properties) | Save the current scene.
Based on the value of 'filename', the current scene is saved either
as a MRML file, MRB file or directory.
If filename ends with '.mrml', the scene is saved as a single file
without associated data.
If filename ends with '.mrb', the scene is saved as a MRML bundle (Zip
archive with scene and data files).
In every other case, the scene is saved in the directory
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will be written to disk. If needed, directories and sub-directories
will be created. | Save the current scene. | [
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"""Save the current scene.
Based on the value of 'filename', the current scene is saved either
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If filename ends with '.mrml', the scene is saved as a single file
without associated data.
If filename ends with '.mrb', the scene is saved as a MRML bundle (Zip
archive with scene and data files).
In every other case, the scene is saved in the directory
specified by 'filename'. Both MRML scene file and data
will be written to disk. If needed, directories and sub-directories
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"""
from slicer import app
filetype = 'SceneFile'
properties['fileName'] = filename
return app.coreIOManager().saveNodes(filetype, properties) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/indexes/base.py | python | _maybe_cast_data_without_dtype | (subarr: np.ndarray) | return result | If we have an arraylike input but no passed dtype, try to infer
a supported dtype.
Parameters
----------
subarr : np.ndarray[object]
Returns
-------
np.ndarray or ExtensionArray | If we have an arraylike input but no passed dtype, try to infer
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"""
If we have an arraylike input but no passed dtype, try to infer
a supported dtype.
Parameters
----------
subarr : np.ndarray[object]
Returns
-------
np.ndarray or ExtensionArray
"""
result = lib.maybe_convert_objects(
subarr,
convert_datetime=True,
convert_timedelta=True,
convert_period=True,
convert_interval=True,
dtype_if_all_nat=np.dtype("datetime64[ns]"),
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if result.dtype.kind in ["b", "c"]:
return subarr
result = ensure_wrapped_if_datetimelike(result)
return result | [
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/elementwise/minimum.py | python | minimum.is_decr | (self, idx) | return False | Is the composition non-increasing in argument idx? | Is the composition non-increasing in argument idx? | [
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acbull/Unbiased_LambdaMart | 7c39abe5caa18ca07df2d23c2db392916d92956c | Unbias_LightGBM/python-package/lightgbm/basic.py | python | _InnerPredictor.__get_num_preds | (self, num_iteration, nrow, predict_type) | return n_preds.value | Get size of prediction result | Get size of prediction result | [
"Get",
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] | def __get_num_preds(self, num_iteration, nrow, predict_type):
"""
Get size of prediction result
"""
n_preds = ctypes.c_int64(0)
_safe_call(_LIB.LGBM_BoosterCalcNumPredict(
self.handle,
ctypes.c_int(nrow),
ctypes.c_int(predict_type),
ctypes.c_int(num_iteration),
ctypes.byref(n_preds)))
return n_preds.value | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/gestures.py | python | MouseGestures.OnMotion | (self, event) | Internal. Used if Start() has been run | Internal. Used if Start() has been run | [
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'''Internal. Used if Start() has been run'''
if self.recording:
currentposition = event.GetPosition()
if self.lastposition != (-1, -1):
self.rawgesture += self.GetDirection(self.lastposition, currentposition)
if self.showgesture:
#Draw it!
px1, py1 = self.parent.ClientToScreen(self.lastposition)
px2, py2 = self.parent.ClientToScreen(currentposition)
self.dc.DrawLine(px1, py1, px2, py2)
self.lastposition = currentposition
event.Skip() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/stc.py | python | StyledTextCtrl.AutoCompSetMaxWidth | (*args, **kwargs) | return _stc.StyledTextCtrl_AutoCompSetMaxWidth(*args, **kwargs) | AutoCompSetMaxWidth(self, int characterCount)
Set the maximum width, in characters, of auto-completion and user lists.
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"""
AutoCompSetMaxWidth(self, int characterCount)
Set the maximum width, in characters, of auto-completion and user lists.
Set to 0 to autosize to fit longest item, which is the default.
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return _stc.StyledTextCtrl_AutoCompSetMaxWidth(*args, **kwargs) | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/MooseDocs/extensions/bibtex.py | python | BibtexExtension.preRead | (self, page) | Initialize the page citations list. | Initialize the page citations list. | [
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"""Initialize the page citations list."""
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/graph_editor/transform.py | python | Transformer._transform_sgv | (self, sgv) | return sgv_.remap(input_map_, output_map_) | Transform a subgraph view.
For convenience, a transform operation returns a subgraph view of the
transformed graph.
Args:
sgv: the subgraph to be transformed.
Returns:
The transformed subgraph. | Transform a subgraph view. | [
"Transform",
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"subgraph",
"view",
"."
] | def _transform_sgv(self, sgv):
"""Transform a subgraph view.
For convenience, a transform operation returns a subgraph view of the
transformed graph.
Args:
sgv: the subgraph to be transformed.
Returns:
The transformed subgraph.
"""
ops_ = [op_ for _, op_ in iteritems(self._info.transformed_ops)]
sgv_ = subgraph.SubGraphView(ops_)
sgv_inputs_ = sgv_.inputs
sgv_outputs_ = sgv_.outputs
# re-order inputs
input_map_ = []
for input_t in sgv.inputs:
if input_t not in self._info.transformed_ts:
continue
input_t_ = self._info.transformed_ts[input_t]
if input_t_ not in sgv_inputs_:
continue
input_t_index_ = sgv_.input_index(input_t_)
input_map_.append(input_t_index_)
# re-order outputs
output_map_ = []
for output_t in sgv.outputs:
if output_t not in self._info.transformed_ts:
continue
output_t_ = self._info.transformed_ts[output_t]
if output_t_ not in sgv_outputs_:
continue
output_t_index_ = sgv_.output_index(output_t_)
output_map_.append(output_t_index_)
return sgv_.remap(input_map_, output_map_) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/android/loading/cloud/frontend/clovis_frontend.py | python | Root | () | return flask.render_template('form.html') | Home page: show the new task form. | Home page: show the new task form. | [
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"""Home page: show the new task form."""
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/common/battor/battor/battor_wrapper.py | python | BattorWrapper._SendBattorCommandImpl | (self, cmd) | return self._battor_shell.stdout.readline() | Sends command to the BattOr. | Sends command to the BattOr. | [
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"."
] | def _SendBattorCommandImpl(self, cmd):
"""Sends command to the BattOr."""
self._battor_shell.stdin.write('%s\n' % cmd)
self._battor_shell.stdin.flush()
return self._battor_shell.stdout.readline() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/webkit.py | python | WebKitNewWindowEvent.__init__ | (self, *args, **kwargs) | __init__(self, Window win=None) -> WebKitNewWindowEvent | __init__(self, Window win=None) -> WebKitNewWindowEvent | [
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"""__init__(self, Window win=None) -> WebKitNewWindowEvent"""
_webkit.WebKitNewWindowEvent_swiginit(self,_webkit.new_WebKitNewWindowEvent(*args, **kwargs)) | [
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/v7.9.317/third_party/jinja2/filters.py | python | do_unique | (environment, value, case_sensitive=False, attribute=None) | Returns a list of unique items from the the given iterable.
.. sourcecode:: jinja
{{ ['foo', 'bar', 'foobar', 'FooBar']|unique }}
-> ['foo', 'bar', 'foobar']
The unique items are yielded in the same order as their first occurrence in
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:param case_sensitive: Treat upper and lower case strings as distinct.
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] | def do_unique(environment, value, case_sensitive=False, attribute=None):
"""Returns a list of unique items from the the given iterable.
.. sourcecode:: jinja
{{ ['foo', 'bar', 'foobar', 'FooBar']|unique }}
-> ['foo', 'bar', 'foobar']
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:param case_sensitive: Treat upper and lower case strings as distinct.
:param attribute: Filter objects with unique values for this attribute.
"""
getter = make_attrgetter(
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seen = set()
for item in value:
key = getter(item)
if key not in seen:
seen.add(key)
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/BeautifulSoup.py | python | Tag.__init__ | (self, parser, name, attrs=None, parent=None,
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# We don't actually store the parser object: that lets extracted
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self.parserClass = parser.__class__
self.isSelfClosing = parser.isSelfClosingTag(name)
self.name = name
if attrs is None:
attrs = []
elif isinstance(attrs, dict):
attrs = attrs.items()
self.attrs = attrs
self.contents = []
self.setup(parent, previous)
self.hidden = False
self.containsSubstitutions = False
self.convertHTMLEntities = parser.convertHTMLEntities
self.convertXMLEntities = parser.convertXMLEntities
self.escapeUnrecognizedEntities = parser.escapeUnrecognizedEntities
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convert = lambda(k, val): (k,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/distlib/metadata.py | python | LegacyMetadata.write_file | (self, fileobject, skip_unknown=False) | Write the PKG-INFO format data to a file object. | Write the PKG-INFO format data to a file object. | [
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] | def write_file(self, fileobject, skip_unknown=False):
"""Write the PKG-INFO format data to a file object."""
self.set_metadata_version()
for field in _version2fieldlist(self['Metadata-Version']):
values = self.get(field)
if skip_unknown and values in ('UNKNOWN', [], ['UNKNOWN']):
continue
if field in _ELEMENTSFIELD:
self._write_field(fileobject, field, ','.join(values))
continue
if field not in _LISTFIELDS:
if field == 'Description':
if self.metadata_version in ('1.0', '1.1'):
values = values.replace('\n', '\n ')
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fifengine/fifengine | 4b62c42e85bec19893cef8e63e6855927cff2c47 | engine/python/fife/extensions/pychan/attrs.py | python | UnicodeAttr.parse | (self,value) | return str(value) | Parses a value and checks for errors.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/grid.py | python | GridCellTextEditor.GetValue | (*args, **kwargs) | return _grid.GridCellTextEditor_GetValue(*args, **kwargs) | GetValue(self) -> String | GetValue(self) -> String | [
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v8mips/v8mips | f0c9cc0bbfd461c7f516799d9a58e9a7395f737e | tools/js2c.py | python | WriteStartupBlob | (sources, startup_blob) | Write a startup blob, as expected by V8 Initialize ...
TODO(vogelheim): Add proper method name.
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epam/Indigo | 30e40b4b1eb9bae0207435a26cfcb81ddcc42be1 | api/python/indigo/__init__.py | python | IndigoObject.smarts | (self) | return self.dispatcher._checkResultString(
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | third_party/python_gflags/gflags.py | python | FlagValues._AssertValidators | (self, validators) | Assert if all validators in the list are satisfied.
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message = validator.PrintFlagsWithValues(self)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/wheel/vendored/packaging/tags.py | python | parse_tag | (tag) | return frozenset(tags) | Parses the provided tag (e.g. `py3-none-any`) into a frozenset of Tag instances.
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tags = set()
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for interpreter in interpreters.split("."):
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tags.add(Tag(interpreter, abi, platform_))
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triton-inference-server/server | 11a11d9cb1e9734ed9fd305e752da70f07d1992f | qa/python_models/fini_error/model.py | python | TritonPythonModel.execute | (self, requests) | return responses | The body of this model doesn't matter. The main purpose of this model is
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input_tensor = pb_utils.get_input_tensor_by_name(request, "IN")
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/tools/inspector_protocol/jinja2/filters.py | python | do_tojson | (eval_ctx, value, indent=None) | return htmlsafe_json_dumps(value, dumper=dumper, **options) | Dumps a structure to JSON so that it's safe to use in ``<script>``
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NVIDIA/DALI | bf16cc86ba8f091b145f91962f21fe1b6aff243d | dali/python/nvidia/dali/external_source.py | python | _ExternalSourceGroup.get_batch | (self, pipeline, batch_size, epoch_idx) | return _ExternalDataBatch(self, pipeline, callback_out, batch_size) | Call the source callback and feed the results to the ExternalSource nodes in `pipeline`.
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microsoft/DirectXShaderCompiler | 8348ff8d9e0287610ba05d3a828e10af981a1c05 | tools/clang/utils/check_cfc/check_cfc.py | python | derive_output_file | (args) | Derive output file from the input file (if just one) or None
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fengbingchun/NN_Test | d6305825d5273e4569ccd1eda9ffa2a9c72e18d2 | src/tiny-dnn/third_party/cpplint.py | python | CheckPosixThreading | (filename, clean_lines, linenum, error) | Checks for calls to thread-unsafe functions.
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funnyzhou/Adaptive_Feeding | 9c78182331d8c0ea28de47226e805776c638d46f | lib/fast_rcnn/nms_wrapper.py | python | nms | (dets, thresh, force_cpu=False) | Dispatch to either CPU or GPU NMS implementations. | Dispatch to either CPU or GPU NMS implementations. | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/beta.py | python | Beta.dtype | (self) | return self._a_b_sum.dtype | dtype of samples from this distribution. | dtype of samples from this distribution. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | ComboBox.SetMark | (*args, **kwargs) | return _controls_.ComboBox_SetMark(*args, **kwargs) | SetMark(self, long from, long to)
Selects the text between the two positions in the combobox text field. | SetMark(self, long from, long to) | [
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] | def SetMark(*args, **kwargs):
"""
SetMark(self, long from, long to)
Selects the text between the two positions in the combobox text field.
"""
return _controls_.ComboBox_SetMark(*args, **kwargs) | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/incubate/fleet/base/role_maker.py | python | GeneralRoleMaker.is_worker | (self) | return self._role == Role.WORKER | whether current process is worker | whether current process is worker | [
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"""
whether current process is worker
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if not self._role_is_generated:
self.generate_role()
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | EvtHandler.Bind | (self, event, handler, source=None, id=wx.ID_ANY, id2=wx.ID_ANY) | Bind an event to an event handler.
:param event: One of the EVT_* objects that specifies the
type of event to bind,
:param handler: A callable object to be invoked when the
event is delivered to self. Pass None to
disconnect an event handler.
:param source: Sometimes the event originates from a
different window than self, but you still
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handling system is able to differentiate
between the same event type from different
controls.
:param id: Used to spcify the event source by ID instead
of instance.
:param id2: Used when it is desirable to bind a handler
to a range of IDs, such as with EVT_MENU_RANGE. | Bind an event to an event handler. | [
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"""
Bind an event to an event handler.
:param event: One of the EVT_* objects that specifies the
type of event to bind,
:param handler: A callable object to be invoked when the
event is delivered to self. Pass None to
disconnect an event handler.
:param source: Sometimes the event originates from a
different window than self, but you still
want to catch it in self. (For example, a
button event delivered to a frame.) By
passing the source of the event, the event
handling system is able to differentiate
between the same event type from different
controls.
:param id: Used to spcify the event source by ID instead
of instance.
:param id2: Used when it is desirable to bind a handler
to a range of IDs, such as with EVT_MENU_RANGE.
"""
assert isinstance(event, wx.PyEventBinder)
assert handler is None or callable(handler)
assert source is None or hasattr(source, 'GetId')
if source is not None:
id = source.GetId()
event.Bind(self, id, id2, handler) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/internals/blocks.py | python | Block.take_nd | (self, indexer, axis, new_mgr_locs=None, fill_tuple=None) | Take values according to indexer and return them as a block.bb | Take values according to indexer and return them as a block.bb | [
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"""
Take values according to indexer and return them as a block.bb
"""
# algos.take_nd dispatches for DatetimeTZBlock, CategoricalBlock
# so need to preserve types
# sparse is treated like an ndarray, but needs .get_values() shaping
values = self.values
if fill_tuple is None:
fill_value = self.fill_value
allow_fill = False
else:
fill_value = fill_tuple[0]
allow_fill = True
new_values = algos.take_nd(
values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value
)
# Called from three places in managers, all of which satisfy
# this assertion
assert not (axis == 0 and new_mgr_locs is None)
if new_mgr_locs is None:
new_mgr_locs = self.mgr_locs
if not is_dtype_equal(new_values.dtype, self.dtype):
return self.make_block(new_values, new_mgr_locs)
else:
return self.make_block_same_class(new_values, new_mgr_locs) | [
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/input.py | python | RemoveSelfDependencies | (targets) | Remove self dependencies from targets that have the prune_self_dependency
variable set. | Remove self dependencies from targets that have the prune_self_dependency
variable set. | [
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"""Remove self dependencies from targets that have the prune_self_dependency
variable set."""
for target_name, target_dict in targets.iteritems():
for dependency_key in dependency_sections:
dependencies = target_dict.get(dependency_key, [])
if dependencies:
for t in dependencies:
if t == target_name:
if targets[t].get('variables', {}).get('prune_self_dependency', 0):
target_dict[dependency_key] = Filter(dependencies, target_name) | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/find-kth-largest-xor-coordinate-value.py | python | Solution.kthLargestValue | (self, matrix, k) | return vals[k-1] | :type matrix: List[List[int]]
:type k: int
:rtype: int | :type matrix: List[List[int]]
:type k: int
:rtype: int | [
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"""
:type matrix: List[List[int]]
:type k: int
:rtype: int
"""
def nth_element(nums, n, compare=lambda a, b: a < b):
def tri_partition(nums, left, right, target, compare):
mid = left
while mid <= right:
if nums[mid] == target:
mid += 1
elif compare(nums[mid], target):
nums[left], nums[mid] = nums[mid], nums[left]
left += 1
mid += 1
else:
nums[mid], nums[right] = nums[right], nums[mid]
right -= 1
return left, right
left, right = 0, len(nums)-1
while left <= right:
pivot_idx = random.randint(left, right)
pivot_left, pivot_right = tri_partition(nums, left, right, nums[pivot_idx], compare)
if pivot_left <= n <= pivot_right:
return
elif pivot_left > n:
right = pivot_left-1
else: # pivot_right < n.
left = pivot_right+1
vals = []
for r in xrange(len(matrix)):
curr = 0
for c in xrange(len(matrix[0])):
curr = curr^matrix[r][c]
if r == 0:
matrix[r][c] = curr
else:
matrix[r][c] = curr^matrix[r-1][c]
vals.append(matrix[r][c])
nth_element(vals, k-1, compare=lambda a, b: a > b)
return vals[k-1] | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/ensemble/bagging.py | python | BaggingClassifier._validate_estimator | (self) | Check the estimator and set the base_estimator_ attribute. | Check the estimator and set the base_estimator_ attribute. | [
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"""Check the estimator and set the base_estimator_ attribute."""
super(BaggingClassifier, self)._validate_estimator(
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/closure_linter/closure_linter/typeannotation.py | python | TypeAnnotation.GetNullability | (self, modifiers=True) | return TypeAnnotation.NULLABILITY_UNKNOWN | Computes whether the type may be null.
Args:
modifiers: Whether the modifiers ? and ! should be considered in the
evaluation.
Returns:
True if the type allows null, False if the type is strictly non nullable
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"""Computes whether the type may be null.
Args:
modifiers: Whether the modifiers ? and ! should be considered in the
evaluation.
Returns:
True if the type allows null, False if the type is strictly non nullable
and NULLABILITY_UNKNOWN if the nullability cannot be determined.
"""
# Explicitly marked nullable types or 'null' are nullable.
if ((modifiers and self.or_null) or
self.identifier == TypeAnnotation.NULL_TYPE):
return True
# Explicitly marked non-nullable types or non-nullable base types:
if ((modifiers and self.not_null) or self.record_type
or self.identifier in TypeAnnotation.NON_NULLABLE):
return False
# A type group is nullable if any of its elements are nullable.
if self.type_group:
maybe_nullable = False
for sub_type in self.sub_types:
nullability = sub_type.GetNullability()
if nullability == self.NULLABILITY_UNKNOWN:
maybe_nullable = nullability
elif nullability:
return True
return maybe_nullable
# Whitelisted types are nullable.
if self.identifier.rstrip('.') in TypeAnnotation.NULLABLE_TYPE_WHITELIST:
return True
# All other types are unknown (most should be nullable, but
# enums are not and typedefs might not be).
return TypeAnnotation.NULLABILITY_UNKNOWN | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_stc.py | python | EditraStc.GetBookmarks | (self) | return [line for line in range(self.GetLineCount())
if MarkIsSet(self, line)] | Gets a list of all lines containing bookmarks
@return: list of line numbers | Gets a list of all lines containing bookmarks
@return: list of line numbers | [
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"""Gets a list of all lines containing bookmarks
@return: list of line numbers
"""
MarkIsSet = ed_marker.Bookmark.IsSet
return [line for line in range(self.GetLineCount())
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turi-code/SFrame | 796b9bdfb2fa1b881d82080754643c7e68629cd2 | oss_src/unity/python/sframe/data_structures/sframe.py | python | load_sframe | (filename) | return sf | Load an SFrame. The filename extension is used to determine the format
automatically. This function is particularly useful for SFrames previously
saved in binary format. For CSV imports the ``SFrame.read_csv`` function
provides greater control. If the SFrame is in binary format, ``filename`` is
actually a directory, created when the SFrame is saved.
Parameters
----------
filename : string
Location of the file to load. Can be a local path or a remote URL.
Returns
-------
out : SFrame
See Also
--------
SFrame.save, SFrame.read_csv
Examples
--------
>>> sf = graphlab.SFrame({'id':[1,2,3], 'val':['A','B','C']})
>>> sf.save('my_sframe') # 'my_sframe' is a directory
>>> sf_loaded = graphlab.load_sframe('my_sframe') | Load an SFrame. The filename extension is used to determine the format
automatically. This function is particularly useful for SFrames previously
saved in binary format. For CSV imports the ``SFrame.read_csv`` function
provides greater control. If the SFrame is in binary format, ``filename`` is
actually a directory, created when the SFrame is saved. | [
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"""
Load an SFrame. The filename extension is used to determine the format
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saved in binary format. For CSV imports the ``SFrame.read_csv`` function
provides greater control. If the SFrame is in binary format, ``filename`` is
actually a directory, created when the SFrame is saved.
Parameters
----------
filename : string
Location of the file to load. Can be a local path or a remote URL.
Returns
-------
out : SFrame
See Also
--------
SFrame.save, SFrame.read_csv
Examples
--------
>>> sf = graphlab.SFrame({'id':[1,2,3], 'val':['A','B','C']})
>>> sf.save('my_sframe') # 'my_sframe' is a directory
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sf = SFrame(data=filename)
return sf | [
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sslab-gatech/qsym | 78702ba8928519ffb9beb7859ec2f7ddce2b2fe4 | third_party/pin-2.14-71313-gcc.4.4.7-linux/source/tools/Utils/and-launch.py | python | PrintDebugInfo | (params, cmd_list) | Print debug info
@param params: Parameters.
@type params: ScriptData.
@param cmd_list: List of commands.
@type cmd_list: list of strings. | Print debug info | [
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"info"
] | def PrintDebugInfo(params, cmd_list):
"""
Print debug info
@param params: Parameters.
@type params: ScriptData.
@param cmd_list: List of commands.
@type cmd_list: list of strings.
"""
logging.debug('Remote working directory: %s', params.remote_dir)
logging.debug('Path to android-install.tar.gz: %s', params.and_install)
logging.debug('Path to busybox: %s', params.busybox)
logging.debug('Path to pintool on host: %s', params.toolpath)
logging.debug('Path to application: %s', params.app_path)
c_list = 'List of commands:'
for line in cmd_list: c_list += '\n ' + line
logging.debug(c_list)
logging.debug('Path to launch script: %s', params.launch_script_path)
logging.debug('Launch script:\n%s', params.script) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/urllib3/contrib/_securetransport/low_level.py | python | _is_cert | (item) | return CoreFoundation.CFGetTypeID(item) == expected | Returns True if a given CFTypeRef is a certificate. | Returns True if a given CFTypeRef is a certificate. | [
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"""
Returns True if a given CFTypeRef is a certificate.
"""
expected = Security.SecCertificateGetTypeID()
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Ewenwan/MVision | 97b394dfa48cb21c82cd003b1a952745e413a17f | CNN/SSD/coco_vgg16-ssd-300-300/ssd_detect.py | python | CaffeDetection.detect | (self, image_file, conf_thresh=0.5, topn=5) | return result | SSD detection | SSD detection | [
"SSD",
"detection"
] | def detect(self, image_file, conf_thresh=0.5, topn=5):
'''
SSD detection
'''
# set net to batch size of 1
# image_resize = 300 # 1张图片 3通道 300*300
self.net.blobs['data'].reshape(1, 3, self.image_resize, self.image_resize)
image = caffe.io.load_image(image_file)
# Run the net and examine the top_k results
transformed_image = self.transformer.preprocess('data', image)
self.net.blobs['data'].data[...] = transformed_image
# 模型前向传播 并且获取 detection_out 层的输出
detections = self.net.forward()['detection_out']
# 模型输出 解码
det_label = detections[0,0,:,1]# 标签索引
det_conf = detections[0,0,:,2] # 可信度
det_xmin = detections[0,0,:,3] # 坐标
det_ymin = detections[0,0,:,4]
det_xmax = detections[0,0,:,5]
det_ymax = detections[0,0,:,6]
# 获取可行度大于 0.5的索引
top_indices = [i for i, conf in enumerate(det_conf) if conf >= conf_thresh]
top_conf = det_conf[top_indices]# 可信度
top_label_indices = det_label[top_indices].tolist()# 标签索引
top_labels = get_labelname(self.labelmap, top_label_indices)# 标签字符串
top_xmin = det_xmin[top_indices]# 坐标 0~1小数
top_ymin = det_ymin[top_indices]
top_xmax = det_xmax[top_indices]
top_ymax = det_ymax[top_indices]
# 前5个
result = []
for i in xrange(min(topn, top_conf.shape[0])):# 前5个
xmin = top_xmin[i] # xmin = int(round(top_xmin[i] * image.shape[1]))
ymin = top_ymin[i] # ymin = int(round(top_ymin[i] * image.shape[0]))
xmax = top_xmax[i] # xmax = int(round(top_xmax[i] * image.shape[1]))
ymax = top_ymax[i] # ymax = int(round(top_ymax[i] * image.shape[0]))
score = top_conf[i]# 预测得分
label = int(top_label_indices[i])#标签id
label_name = top_labels[i]#标签字符串
result.append([xmin, ymin, xmax, ymax, label, score, label_name])
# result[i][0] xmin
# result[i][1] ymin
# result[i][2] xmax
# result[i][3] ymax
# result[i][4] label
# result[i][5] score
# result[i][6] label_name
return result | [
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/cudf/cudf/core/indexed_frame.py | python | IndexedFrame.drop_duplicates | (
self,
subset=None,
keep="first",
nulls_are_equal=True,
ignore_index=False,
) | return self._from_columns_like_self(
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keys=keys,
keep=keep,
nulls_are_equal=nulls_are_equal,
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subset : list, optional
List of columns to consider when dropping rows.
keep : ["first", "last", False]
"first" will keep the first duplicate entry, "last" will keep the
last duplicate entry, and False will drop all duplicates.
nulls_are_equal: bool, default True
Null elements are considered equal to other null elements.
ignore_index: bool, default False
If True, the resulting axis will be labeled 0, 1, ..., n - 1. | Drop duplicate rows in frame. | [
"Drop",
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] | def drop_duplicates(
self,
subset=None,
keep="first",
nulls_are_equal=True,
ignore_index=False,
):
"""
Drop duplicate rows in frame.
subset : list, optional
List of columns to consider when dropping rows.
keep : ["first", "last", False]
"first" will keep the first duplicate entry, "last" will keep the
last duplicate entry, and False will drop all duplicates.
nulls_are_equal: bool, default True
Null elements are considered equal to other null elements.
ignore_index: bool, default False
If True, the resulting axis will be labeled 0, 1, ..., n - 1.
"""
if subset is None:
subset = self._column_names
elif (
not np.iterable(subset)
or isinstance(subset, str)
or isinstance(subset, tuple)
and subset in self._data.names
):
subset = (subset,)
diff = set(subset) - set(self._data)
if len(diff) != 0:
raise KeyError(f"columns {diff} do not exist")
subset_cols = [name for name in self._column_names if name in subset]
if len(subset_cols) == 0:
return self.copy(deep=True)
keys = self._positions_from_column_names(
subset, offset_by_index_columns=not ignore_index
)
return self._from_columns_like_self(
libcudf.stream_compaction.drop_duplicates(
list(self._columns)
if ignore_index
else list(self._index._columns + self._columns),
keys=keys,
keep=keep,
nulls_are_equal=nulls_are_equal,
),
self._column_names,
self._index.names if not ignore_index else None,
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/data_flow_ops.py | python | QueueBase.name | (self) | return self._queue_ref.op.name | The name of the underlying queue. | The name of the underlying queue. | [
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"""The name of the underlying queue."""
return self._queue_ref.op.name | [
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tfwu/FaceDetection-ConvNet-3D | f9251c48eb40c5aec8fba7455115c355466555be | python/build/lib.linux-x86_64-2.7/mxnet/ndarray.py | python | NDArray.__setitem__ | (self, in_slice, value) | Set ndarray value | Set ndarray value | [
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] | def __setitem__(self, in_slice, value):
"""Set ndarray value"""
if not self.writable:
raise ValueError('trying to assign to a readonly NDArray')
if not isinstance(in_slice, slice) or in_slice.step is not None:
raise ValueError('NDArray only support continuous slicing on axis 0')
if in_slice.start is not None or in_slice.stop is not None:
sliced_arr = self._slice(in_slice.start, in_slice.stop)
sliced_arr[:] = value
return
if isinstance(value, NDArray):
if value.handle is not self.handle:
value.copyto(self)
elif isinstance(value, numeric_types):
NDArray._set_value(float(value), out=self)
elif isinstance(value, (np.ndarray, np.generic)):
self._sync_copyfrom(value)
else:
raise TypeError('type %s not supported' % str(type(value))) | [
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balloonwj/TeamTalk | dc79c40687e4c9d7bec07ff5c9782be586fd9b41 | win-client/3rdParty/src/json/devtools/batchbuild.py | python | BuildDesc.merged_with | ( self, build_desc ) | return BuildDesc( self.prepend_envs + build_desc.prepend_envs,
self.variables + build_desc.variables,
build_desc.build_type or self.build_type,
build_desc.generator or self.generator ) | Returns a new BuildDesc by merging field content.
Prefer build_desc fields to self fields for single valued field. | Returns a new BuildDesc by merging field content.
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"""Returns a new BuildDesc by merging field content.
Prefer build_desc fields to self fields for single valued field.
"""
return BuildDesc( self.prepend_envs + build_desc.prepend_envs,
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/keras/_impl/keras/models.py | python | Sequential.get_layer | (self, name=None, index=None) | return self.model.get_layer(name, index) | Retrieve a layer that is part of the model.
Returns a layer based on either its name (unique)
or its index in the graph. Indices are based on
order of horizontal graph traversal (bottom-up).
Arguments:
name: string, name of layer.
index: integer, index of layer.
Returns:
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] | def get_layer(self, name=None, index=None):
"""Retrieve a layer that is part of the model.
Returns a layer based on either its name (unique)
or its index in the graph. Indices are based on
order of horizontal graph traversal (bottom-up).
Arguments:
name: string, name of layer.
index: integer, index of layer.
Returns:
A layer instance.
"""
if not self.built:
self.build()
return self.model.get_layer(name, index) | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/skia/bench/tile_analyze.py | python | main | () | Parses flags and outputs expected Skia picture bench results. | Parses flags and outputs expected Skia picture bench results. | [
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] | def main():
"""Parses flags and outputs expected Skia picture bench results."""
parser = optparse.OptionParser(USAGE_STRING % '%prog' + HELP_STRING)
parser.add_option(OPTION_PLATFORM_SHORT, OPTION_PLATFORM,
dest='plat', default=DEFAULT_PLATFORM,
help='Platform to analyze. Set to DEFAULT_PLATFORM if not given.')
parser.add_option(OPTION_REVISION_SHORT, OPTION_REVISION,
dest='rev',
help='(Mandatory) revision number to analyze.')
parser.add_option(OPTION_DIR_SHORT, OPTION_DIR,
dest='log_dir', default='',
help=('(Optional) local directory where bench log files reside. If left '
'empty (by default), will try to read from Google Storage.'))
parser.add_option(OPTION_REPRESENTATION_ALG_SHORT, OPTION_REPRESENTATION_ALG,
dest='alg', default=REPRESENTATION_ALG,
help=('Bench representation algorithm. '
'Default to "%s".' % REPRESENTATION_ALG))
(options, args) = parser.parse_args()
if not (options.rev and options.rev.isdigit()):
parser.error('Please provide correct mandatory flag %s' % OPTION_REVISION)
return
rev = int(options.rev)
(js_codes, body_codes) = OutputTileAnalysis(
rev, options.alg, options.log_dir, options.plat)
print HTML_PREFIX + js_codes + body_codes + HTML_SUFFIX | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Source/bindings/scripts/v8_interface.py | python | common_key | (dicts, key) | return common(dicts, lambda d: key in d) | Returns common presence of a key across an iterable of dicts, or None.
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/tools/docs/parser.py | python | ReferenceResolver._cc_link | (self, string, link_text, unused_manual_link_text,
relative_path_to_root) | return '[`%s`](%s)' % (link_text, cc_relative_path) | Generate a link for a @{tensorflow::...} reference. | Generate a link for a | [
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] | def _cc_link(self, string, link_text, unused_manual_link_text,
relative_path_to_root):
"""Generate a link for a @{tensorflow::...} reference."""
# TODO(josh11b): Fix this hard-coding of paths.
if string == 'tensorflow::ClientSession':
ret = 'class/tensorflow/client-session.md'
elif string == 'tensorflow::Scope':
ret = 'class/tensorflow/scope.md'
elif string == 'tensorflow::Status':
ret = 'class/tensorflow/status.md'
elif string == 'tensorflow::Tensor':
ret = 'class/tensorflow/tensor.md'
elif string == 'tensorflow::ops::Const':
ret = 'namespace/tensorflow/ops.md#const'
else:
self.add_error('C++ reference not understood: "%s"' % string)
return 'TODO_C++:%s' % string
# relative_path_to_root gets you to api_docs/python, we go from there
# to api_docs/cc, and then add ret.
cc_relative_path = os.path.normpath(os.path.join(
relative_path_to_root, '../cc', ret))
return '[`%s`](%s)' % (link_text, cc_relative_path) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/compiler/pycodegen.py | python | CodeGenerator._implicitNameOp | (self, prefix, name) | Emit name ops for names generated implicitly by for loops
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"""Emit name ops for names generated implicitly by for loops
The interpreter generates names that start with a period or
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they aren't present in the program text.
"""
if self.optimized:
self.emit(prefix + '_FAST', name)
else:
self.emit(prefix + '_NAME', name) | [
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msftguy/ssh-rd | a5f3a79daeac5844edebf01916c9613563f1c390 | _3rd/boost_1_48_0/tools/build/v2/build/generators.py | python | Generator.run | (self, project, name, prop_set, sources) | Tries to invoke this generator on the given sources. Returns a
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project: Project for which the targets are generated.
name: Determines the name of 'name' attribute for
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prop_set: Desired properties for generated targets.
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""" Tries to invoke this generator on the given sources. Returns a
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project: Project for which the targets are generated.
name: Determines the name of 'name' attribute for
all generated targets. See 'generated_targets' method.
prop_set: Desired properties for generated targets.
sources: Source targets.
"""
if project.manager ().logger ().on ():
project.manager ().logger ().log (__name__, " generator '%s'" % self.id_)
project.manager ().logger ().log (__name__, " composing: '%s'" % self.composing_)
if not self.composing_ and len (sources) > 1 and len (self.source_types_) > 1:
raise BaseException ("Unsupported source/source_type combination")
# We don't run composing generators if no name is specified. The reason
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#
# This in effect, means that composing generators are runnable only
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# transofrmation CPP -> OBJ -> STATIC_LIB -> RSP -> EXE won't be allowed
# (the OBJ -> STATIC_LIB generator is composing)
if not self.composing_ or name:
return self.run_really (project, name, prop_set, sources)
else:
return [] | [
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scribusproject/scribus | 41ec7c775a060912cf251682a8b1437f753f80f4 | scribus/plugins/scripter/python/mikro.py | python | is_scripter_child | (qobj) | return found | walk up the object tree until Scripter or the root is found | walk up the object tree until Scripter or the root is found | [
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"""
walk up the object tree until Scripter or the root is found
"""
found = False
p = qobj.parent()
while p and not found:
if str(p.objectName()) == "Scripter":
found = True
break
else:
p = p.parent()
return found | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py3/prompt_toolkit/key_binding/bindings/emacs.py | python | load_emacs_shift_selection_bindings | () | return ConditionalKeyBindings(key_bindings, emacs_mode) | Bindings to select text with shift + cursor movements | Bindings to select text with shift + cursor movements | [
"Bindings",
"to",
"select",
"text",
"with",
"shift",
"+",
"cursor",
"movements"
] | def load_emacs_shift_selection_bindings() -> KeyBindingsBase:
"""
Bindings to select text with shift + cursor movements
"""
key_bindings = KeyBindings()
handle = key_bindings.add
def unshift_move(event: E) -> None:
"""
Used for the shift selection mode. When called with
a shift + movement key press event, moves the cursor
as if shift is not pressed.
"""
key = event.key_sequence[0].key
if key == Keys.ShiftUp:
event.current_buffer.auto_up(count=event.arg)
return
if key == Keys.ShiftDown:
event.current_buffer.auto_down(count=event.arg)
return
# the other keys are handled through their readline command
key_to_command: Dict[Union[Keys, str], str] = {
Keys.ShiftLeft: "backward-char",
Keys.ShiftRight: "forward-char",
Keys.ShiftHome: "beginning-of-line",
Keys.ShiftEnd: "end-of-line",
Keys.ControlShiftLeft: "backward-word",
Keys.ControlShiftRight: "forward-word",
Keys.ControlShiftHome: "beginning-of-buffer",
Keys.ControlShiftEnd: "end-of-buffer",
}
try:
# Both the dict lookup and `get_by_name` can raise KeyError.
binding = get_by_name(key_to_command[key])
except KeyError:
pass
else: # (`else` is not really needed here.)
if isinstance(binding, Binding):
# (It should always be a binding here)
binding.call(event)
@handle("s-left", filter=~has_selection)
@handle("s-right", filter=~has_selection)
@handle("s-up", filter=~has_selection)
@handle("s-down", filter=~has_selection)
@handle("s-home", filter=~has_selection)
@handle("s-end", filter=~has_selection)
@handle("c-s-left", filter=~has_selection)
@handle("c-s-right", filter=~has_selection)
@handle("c-s-home", filter=~has_selection)
@handle("c-s-end", filter=~has_selection)
def _start_selection(event: E) -> None:
"""
Start selection with shift + movement.
"""
# Take the current cursor position as the start of this selection.
buff = event.current_buffer
if buff.text:
buff.start_selection(selection_type=SelectionType.CHARACTERS)
if buff.selection_state is not None:
# (`selection_state` should never be `None`, it is created by
# `start_selection`.)
buff.selection_state.enter_shift_mode()
# Then move the cursor
original_position = buff.cursor_position
unshift_move(event)
if buff.cursor_position == original_position:
# Cursor didn't actually move - so cancel selection
# to avoid having an empty selection
buff.exit_selection()
@handle("s-left", filter=shift_selection_mode)
@handle("s-right", filter=shift_selection_mode)
@handle("s-up", filter=shift_selection_mode)
@handle("s-down", filter=shift_selection_mode)
@handle("s-home", filter=shift_selection_mode)
@handle("s-end", filter=shift_selection_mode)
@handle("c-s-left", filter=shift_selection_mode)
@handle("c-s-right", filter=shift_selection_mode)
@handle("c-s-home", filter=shift_selection_mode)
@handle("c-s-end", filter=shift_selection_mode)
def _extend_selection(event: E) -> None:
"""
Extend the selection
"""
# Just move the cursor, like shift was not pressed
unshift_move(event)
buff = event.current_buffer
if buff.selection_state is not None:
if buff.cursor_position == buff.selection_state.original_cursor_position:
# selection is now empty, so cancel selection
buff.exit_selection()
@handle(Keys.Any, filter=shift_selection_mode)
def _replace_selection(event: E) -> None:
"""
Replace selection by what is typed
"""
event.current_buffer.cut_selection()
get_by_name("self-insert").call(event)
@handle("enter", filter=shift_selection_mode & is_multiline)
def _newline(event: E) -> None:
"""
A newline replaces the selection
"""
event.current_buffer.cut_selection()
event.current_buffer.newline(copy_margin=not in_paste_mode())
@handle("backspace", filter=shift_selection_mode)
def _delete(event: E) -> None:
"""
Delete selection.
"""
event.current_buffer.cut_selection()
@handle("c-y", filter=shift_selection_mode)
def _yank(event: E) -> None:
"""
In shift selection mode, yanking (pasting) replace the selection.
"""
buff = event.current_buffer
if buff.selection_state:
buff.cut_selection()
get_by_name("yank").call(event)
# moving the cursor in shift selection mode cancels the selection
@handle("left", filter=shift_selection_mode)
@handle("right", filter=shift_selection_mode)
@handle("up", filter=shift_selection_mode)
@handle("down", filter=shift_selection_mode)
@handle("home", filter=shift_selection_mode)
@handle("end", filter=shift_selection_mode)
@handle("c-left", filter=shift_selection_mode)
@handle("c-right", filter=shift_selection_mode)
@handle("c-home", filter=shift_selection_mode)
@handle("c-end", filter=shift_selection_mode)
def _cancel(event: E) -> None:
"""
Cancel selection.
"""
event.current_buffer.exit_selection()
# we then process the cursor movement
key_press = event.key_sequence[0]
event.key_processor.feed(key_press, first=True)
return ConditionalKeyBindings(key_bindings, emacs_mode) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/corrections_tab_widget/background_corrections_model.py | python | BackgroundCorrectionsModel.__init__ | (self, corrections_model: CorrectionsModel, context: MuonContext) | Initialize the BackgroundCorrectionsModel with empty data. | Initialize the BackgroundCorrectionsModel with empty data. | [
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] | def __init__(self, corrections_model: CorrectionsModel, context: MuonContext):
"""Initialize the BackgroundCorrectionsModel with empty data."""
self._corrections_model = corrections_model
self._context = context
self._corrections_context = context.corrections_context | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/platform.py | python | _node | (default='') | Helper to determine the node name of this machine. | Helper to determine the node name of this machine. | [
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"."
] | def _node(default=''):
""" Helper to determine the node name of this machine.
"""
try:
import socket
except ImportError:
# No sockets...
return default
try:
return socket.gethostname()
except OSError:
# Still not working...
return default | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/boost/grad_freeze.py | python | GradientFreeze.freeze_generate | (self, network, optimizer) | return network, optimizer | r"""
Generate freeze network and optimizer.
Args:
network (Cell): The training network.
optimizer (Cell): Optimizer for updating the weights. | r"""
Generate freeze network and optimizer. | [
"r",
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"and",
"optimizer",
"."
] | def freeze_generate(self, network, optimizer):
r"""
Generate freeze network and optimizer.
Args:
network (Cell): The training network.
optimizer (Cell): Optimizer for updating the weights.
"""
train_para_groups = self.split_parameters_groups(
network, self._param_groups)
for i in range(self._param_groups):
train_para_groups[i] = self._param_processer.generate_group_params(train_para_groups[i],
optimizer.init_params['params'])
train_strategy = self.generate_freeze_index_sequence(
self._param_groups, self._freeze_type, self._freeze_p, self._total_steps)
optimizer = FreezeOpt(optimizer, train_para_groups, train_strategy)
return network, optimizer | [
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taskflow/taskflow | f423a100a70b275f6e7331bc96537a3fe172e8d7 | 3rd-party/tbb/python/tbb/pool.py | python | Pool.close | (self) | Prevents any more tasks from being submitted to the
pool. Once all the tasks have been completed the worker
processes will exit. | Prevents any more tasks from being submitted to the
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] | def close(self):
"""Prevents any more tasks from being submitted to the
pool. Once all the tasks have been completed the worker
processes will exit."""
# No lock here. We assume it's sufficiently atomic...
self._closed = True | [
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/solver/solver.py | python | parseArgToArray | (arg, nDof, mesh=None, userData={}) | Parse array related arguments to create a valid value array.
Parameters
----------
arg : float | int | iterable | callable
The target array value that will be converted to an array.
If arg is a callable with it must fulfill:
:: arg(cell|node|boundary, userData={})
Where MeshEntity is one of
:gimliapi:`GIMLI::Cell` ,
:gimliapi:`GIMLI::Node` or
:gimliapi:`GIMLI::Boundary`
depending on nDof, where nDof is mesh.cellCount(),
mesh.nodeCount() or mesh.boundaryCount(),
respectively.
nDof : int | [int]
Desired array size.
mesh : :gimliapi:`GIMLI::Mesh`
Used if arg is callable
userData : class
Used if arg is callable
Returns
-------
ret : :gimliapi:`GIMLI::RVector`
Array of desired length filled with the appropriate values. | Parse array related arguments to create a valid value array. | [
"Parse",
"array",
"related",
"arguments",
"to",
"create",
"a",
"valid",
"value",
"array",
"."
] | def parseArgToArray(arg, nDof, mesh=None, userData={}):
"""
Parse array related arguments to create a valid value array.
Parameters
----------
arg : float | int | iterable | callable
The target array value that will be converted to an array.
If arg is a callable with it must fulfill:
:: arg(cell|node|boundary, userData={})
Where MeshEntity is one of
:gimliapi:`GIMLI::Cell` ,
:gimliapi:`GIMLI::Node` or
:gimliapi:`GIMLI::Boundary`
depending on nDof, where nDof is mesh.cellCount(),
mesh.nodeCount() or mesh.boundaryCount(),
respectively.
nDof : int | [int]
Desired array size.
mesh : :gimliapi:`GIMLI::Mesh`
Used if arg is callable
userData : class
Used if arg is callable
Returns
-------
ret : :gimliapi:`GIMLI::RVector`
Array of desired length filled with the appropriate values.
"""
#pg.warn('check if obsolete: parseArgToArray')
if not hasattr(nDof, '__len__'):
nDof = [nDof]
try:
return pg.Vector(nDof[0], float(arg))
except BaseException as _:
pass
if hasattr(arg, '__len__'):
if isinstance(arg, np.ndarray):
if len(arg) == nDof[0]:
return arg
else:
raise Exception('Given array does not have requested (' +
str(nDof) + ') size (' +
str(len(arg)) + ')')
for n in nDof:
if len(arg) == n:
return arg
try:
# [marker, val] || [[marker, val]]
return parseMapToCellArray(arg, mesh)
except:
raise Exception("Array 'arg' has the wrong size: " +
str(len(arg)) + " != " + str(nDof))
elif hasattr(arg, '__call__'):
ret = pg.Vector(nDof[0], 0.0)
if not mesh:
raise Exception("Please provide a mesh for the callable"
"argument to parse ")
if nDof[0] == mesh.nodeCount():
for n in mesh.nodes():
if userData:
ret[n.id()] = arg(node=n, userData=userData)
else:
ret[n.id()] = arg(node=n)
elif nDof[0] == mesh.cellCount():
for c in mesh.cells():
if userData:
ret[c.id()] = arg(cell=c, userData=userData)
else:
ret[c.id()] = arg(cell=c)
elif nDof[0] == mesh.boundaryCount():
for b in mesh.boundaries():
if userData:
ret[b.id()] = arg(boundary=b, userData=userData)
else:
ret[b.id()] = arg(boundary=b)
else:
raise Exception("Cannot parse callable argument " + str(nDof) +
" nodes: " + str(mesh.nodeCount()) +
" cells: " + str(mesh.cellCount()))
return ret
raise Exception("Cannot parse argument type " + str(type(arg))) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/nntplib.py | python | NNTP.stat | (self, id) | return self.statcmd('STAT ' + id) | Process a STAT command. Argument:
- id: article number or message id
Returns:
- resp: server response if successful
- nr: the article number
- id: the message id | Process a STAT command. Argument:
- id: article number or message id
Returns:
- resp: server response if successful
- nr: the article number
- id: the message id | [
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"""Process a STAT command. Argument:
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- id: the message id"""
return self.statcmd('STAT ' + id) | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/agilepy/lib_wx/objpanel.py | python | ObjPanelMixin.on_restore | (self, event) | Copy object values to widget contents | Copy object values to widget contents | [
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"""
Copy object values to widget contents
"""
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py | python | Misc.unbind_all | (self, sequence) | Unbind for all widgets for event SEQUENCE all functions. | Unbind for all widgets for event SEQUENCE all functions. | [
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] | def unbind_all(self, sequence):
"""Unbind for all widgets for event SEQUENCE all functions."""
self.tk.call('bind', 'all' , sequence, '') | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/lib/arrayterator.py | python | Arrayterator.flat | (self) | A 1-D flat iterator for Arrayterator objects.
This iterator returns elements of the array to be iterated over in
`Arrayterator` one by one. It is similar to `flatiter`.
See Also
--------
`Arrayterator`
flatiter
Examples
--------
>>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
>>> a_itor = np.lib.arrayterator.Arrayterator(a, 2)
>>> for subarr in a_itor.flat:
... if not subarr:
... print subarr, type(subarr)
...
0 <type 'numpy.int32'> | A 1-D flat iterator for Arrayterator objects. | [
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"""
A 1-D flat iterator for Arrayterator objects.
This iterator returns elements of the array to be iterated over in
`Arrayterator` one by one. It is similar to `flatiter`.
See Also
--------
`Arrayterator`
flatiter
Examples
--------
>>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
>>> a_itor = np.lib.arrayterator.Arrayterator(a, 2)
>>> for subarr in a_itor.flat:
... if not subarr:
... print subarr, type(subarr)
...
0 <type 'numpy.int32'>
"""
for block in self:
for value in block.flat:
yield value | [
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apiaryio/snowcrash | b5b39faa85f88ee17459edf39fdc6fe4fc70d2e3 | tools/gyp/pylib/gyp/generator/eclipse.py | python | WriteIncludePaths | (out, eclipse_langs, include_dirs) | Write the includes section of a CDT settings export file. | Write the includes section of a CDT settings export file. | [
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] | def WriteIncludePaths(out, eclipse_langs, include_dirs):
"""Write the includes section of a CDT settings export file."""
out.write(' <section name="org.eclipse.cdt.internal.ui.wizards.' \
'settingswizards.IncludePaths">\n')
out.write(' <language name="holder for library settings"></language>\n')
for lang in eclipse_langs:
out.write(' <language name="%s">\n' % lang)
for include_dir in include_dirs:
out.write(' <includepath workspace_path="false">%s</includepath>\n' %
include_dir)
out.write(' </language>\n')
out.write(' </section>\n') | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/distributions/python/ops/mixture_same_family.py | python | MixtureSameFamily.__init__ | (self,
mixture_distribution,
components_distribution,
validate_args=False,
allow_nan_stats=True,
name="MixtureSameFamily") | Construct a `MixtureSameFamily` distribution.
Args:
mixture_distribution: `tfp.distributions.Categorical`-like instance.
Manages the probability of selecting components. The number of
categories must match the rightmost batch dimension of the
`components_distribution`. Must have either scalar `batch_shape` or
`batch_shape` matching `components_distribution.batch_shape[:-1]`.
components_distribution: `tfp.distributions.Distribution`-like instance.
Right-most batch dimension indexes components.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: `if not mixture_distribution.dtype.is_integer`.
ValueError: if mixture_distribution does not have scalar `event_shape`.
ValueError: if `mixture_distribution.batch_shape` and
`components_distribution.batch_shape[:-1]` are both fully defined and
the former is neither scalar nor equal to the latter.
ValueError: if `mixture_distribution` categories does not equal
`components_distribution` rightmost batch shape. | Construct a `MixtureSameFamily` distribution. | [
"Construct",
"a",
"MixtureSameFamily",
"distribution",
"."
] | def __init__(self,
mixture_distribution,
components_distribution,
validate_args=False,
allow_nan_stats=True,
name="MixtureSameFamily"):
"""Construct a `MixtureSameFamily` distribution.
Args:
mixture_distribution: `tfp.distributions.Categorical`-like instance.
Manages the probability of selecting components. The number of
categories must match the rightmost batch dimension of the
`components_distribution`. Must have either scalar `batch_shape` or
`batch_shape` matching `components_distribution.batch_shape[:-1]`.
components_distribution: `tfp.distributions.Distribution`-like instance.
Right-most batch dimension indexes components.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: `if not mixture_distribution.dtype.is_integer`.
ValueError: if mixture_distribution does not have scalar `event_shape`.
ValueError: if `mixture_distribution.batch_shape` and
`components_distribution.batch_shape[:-1]` are both fully defined and
the former is neither scalar nor equal to the latter.
ValueError: if `mixture_distribution` categories does not equal
`components_distribution` rightmost batch shape.
"""
parameters = dict(locals())
with ops.name_scope(name) as name:
self._mixture_distribution = mixture_distribution
self._components_distribution = components_distribution
self._runtime_assertions = []
s = components_distribution.event_shape_tensor()
s_dim0 = tensor_shape.dimension_value(s.shape[0])
self._event_ndims = (s_dim0
if s_dim0 is not None
else array_ops.shape(s)[0])
if not mixture_distribution.dtype.is_integer:
raise ValueError(
"`mixture_distribution.dtype` ({}) is not over integers".format(
mixture_distribution.dtype.name))
if (mixture_distribution.event_shape.ndims is not None
and mixture_distribution.event_shape.ndims != 0):
raise ValueError("`mixture_distribution` must have scalar `event_dim`s")
elif validate_args:
self._runtime_assertions += [
control_flow_ops.assert_has_rank(
mixture_distribution.event_shape_tensor(), 0,
message="`mixture_distribution` must have scalar `event_dim`s"),
]
mdbs = mixture_distribution.batch_shape
cdbs = components_distribution.batch_shape.with_rank_at_least(1)[:-1]
if mdbs.is_fully_defined() and cdbs.is_fully_defined():
if mdbs.ndims != 0 and mdbs != cdbs:
raise ValueError(
"`mixture_distribution.batch_shape` (`{}`) is not "
"compatible with `components_distribution.batch_shape` "
"(`{}`)".format(mdbs.as_list(), cdbs.as_list()))
elif validate_args:
mdbs = mixture_distribution.batch_shape_tensor()
cdbs = components_distribution.batch_shape_tensor()[:-1]
self._runtime_assertions += [
control_flow_ops.assert_equal(
distribution_util.pick_vector(
mixture_distribution.is_scalar_batch(), cdbs, mdbs),
cdbs,
message=(
"`mixture_distribution.batch_shape` is not "
"compatible with `components_distribution.batch_shape`"))]
km = tensor_shape.dimension_value(
mixture_distribution.logits.shape.with_rank_at_least(1)[-1])
kc = tensor_shape.dimension_value(
components_distribution.batch_shape.with_rank_at_least(1)[-1])
if km is not None and kc is not None and km != kc:
raise ValueError("`mixture_distribution components` ({}) does not "
"equal `components_distribution.batch_shape[-1]` "
"({})".format(km, kc))
elif validate_args:
km = array_ops.shape(mixture_distribution.logits)[-1]
kc = components_distribution.batch_shape_tensor()[-1]
self._runtime_assertions += [
control_flow_ops.assert_equal(
km, kc,
message=("`mixture_distribution components` does not equal "
"`components_distribution.batch_shape[-1:]`")),
]
elif km is None:
km = array_ops.shape(mixture_distribution.logits)[-1]
self._num_components = km
super(MixtureSameFamily, self).__init__(
dtype=self._components_distribution.dtype,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=(
self._mixture_distribution._graph_parents # pylint: disable=protected-access
+ self._components_distribution._graph_parents), # pylint: disable=protected-access
name=name) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/autograd/gradcheck.py | python | _get_analytical_jacobian_forward_ad | (fn, inputs, outputs, *, check_grad_dtypes=False,
all_u=None) | return jacobians | Computes the analytical Jacobian using forward mode AD of `fn(inputs)` using forward mode AD with respect
to `target`. Returns N * M Jacobians where N is the number of tensors in target that require grad and
M is the number of non-integral outputs.
Contrary to other functions here, this function requires "inputs" to actually be used by the function.
The computed value is expected to be wrong if the function captures the inputs by side effect instead of
using the passed ones (many torch.nn tests do this).
Args:
fn: the function to compute the jacobian for
inputs: inputs to `fn`
outputs: provide precomputed outputs to avoid one extra invocation of fn
check_grad_dtypes: if True, will check that the gradient dtype are valid
all_u (optional): if provided, the Jacobian will be right multiplied with this vector
Returns:
A tuple of M N-tuples of tensors | Computes the analytical Jacobian using forward mode AD of `fn(inputs)` using forward mode AD with respect
to `target`. Returns N * M Jacobians where N is the number of tensors in target that require grad and
M is the number of non-integral outputs.
Contrary to other functions here, this function requires "inputs" to actually be used by the function.
The computed value is expected to be wrong if the function captures the inputs by side effect instead of
using the passed ones (many torch.nn tests do this). | [
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all_u=None) -> Tuple[Tuple[torch.Tensor, ...], ...]:
"""Computes the analytical Jacobian using forward mode AD of `fn(inputs)` using forward mode AD with respect
to `target`. Returns N * M Jacobians where N is the number of tensors in target that require grad and
M is the number of non-integral outputs.
Contrary to other functions here, this function requires "inputs" to actually be used by the function.
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Args:
fn: the function to compute the jacobian for
inputs: inputs to `fn`
outputs: provide precomputed outputs to avoid one extra invocation of fn
check_grad_dtypes: if True, will check that the gradient dtype are valid
all_u (optional): if provided, the Jacobian will be right multiplied with this vector
Returns:
A tuple of M N-tuples of tensors
"""
# To avoid early import issues
fwAD = torch.autograd.forward_ad
tensor_inputs = tuple(i for i in inputs if is_tensor_like(i) and i.requires_grad)
if any(i.is_complex() for i in tensor_inputs):
raise ValueError("Expected inputs to be non-complex for _get_analytical_jacobian_forward_ad.")
if all_u:
jacobians = tuple(_allocate_jacobians_with_outputs(outputs, 1) for i in tensor_inputs)
else:
jacobians = tuple(_allocate_jacobians_with_outputs(outputs, i.numel()) for i in tensor_inputs)
with fwAD.dual_level():
fw_grads = []
dual_inputs = []
for i, inp in enumerate(inputs):
if is_tensor_like(inp) and inp.requires_grad:
if inp.layout == torch._mkldnn: # type: ignore[attr-defined]
raise ValueError("MKLDNN inputs are not support for forward AD gradcheck.")
inp = fwAD.make_dual(inp, torch.zeros_like(inp))
# If inp is a differentiable view, the dual might not be the tangent given to
# make_dual, so read it explicitly from the dual tensor
fw_grads.append(fwAD.unpack_dual(inp)[1])
dual_inputs.append(inp)
if all_u:
# Do the full reduction in one pass
# To be consistent with numerical evaluation, we actually compute one reduction per input
for i, (fw_grad, u) in enumerate(zip(fw_grads, all_u)):
fw_grad.copy_(u.view_as(fw_grad))
raw_outputs = _as_tuple(fn(*dual_inputs))
dual_outputs = filter(_is_float_or_complex_tensor, raw_outputs)
for index_o, d_o in enumerate(dual_outputs):
val, res = fwAD.unpack_dual(d_o)
if check_grad_dtypes and res is not None and val.is_complex() != res.is_complex():
raise GradcheckError('Forward AD gradient has dtype mismatch.')
# Remove extra dimension of size 1 corresponding to the reduced input
jacobians[i][index_o].squeeze_(0)
if res is None:
jacobians[i][index_o].zero_()
else:
jacobians[i][index_o].copy_(res.reshape(-1))
fw_grad.zero_()
else:
# Reconstruct the full Jacobian column by column
for i, fw_grad in enumerate(fw_grads):
for lin_idx, grad_idx in enumerate(product(*[range(m) for m in fw_grad.size()])):
fw_grad[grad_idx] = 1.
raw_outputs = _as_tuple(fn(*dual_inputs))
dual_outputs = filter(_is_float_or_complex_tensor, raw_outputs)
for index_o, d_o in enumerate(dual_outputs):
val, res = fwAD.unpack_dual(d_o)
if check_grad_dtypes and val.is_complex() != res.is_complex():
raise GradcheckError('Forward AD gradient has dtype mismatch.')
if res is None:
jacobians[i][index_o][lin_idx].zero_()
else:
jacobians[i][index_o][lin_idx].copy_(res.reshape(-1))
fw_grad[grad_idx] = 0.
return jacobians | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/LoadCIF.py | python | LoadCIF._data_with_space_group_keys | (self, cif_file) | Returns the cif data which contains at least one of the required SpaceGroupBuilder keys.
:param cif_file: The parsed cif file to check for keys.
:return: The Data section containing at least one of the required SpaceGroupBuilder keys. | Returns the cif data which contains at least one of the required SpaceGroupBuilder keys.
:param cif_file: The parsed cif file to check for keys.
:return: The Data section containing at least one of the required SpaceGroupBuilder keys. | [
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"""
Returns the cif data which contains at least one of the required SpaceGroupBuilder keys.
:param cif_file: The parsed cif file to check for keys.
:return: The Data section containing at least one of the required SpaceGroupBuilder keys.
"""
for data_key in cif_file.keys():
cif_data = cif_file[data_key]
if self._has_a_space_group_key(cif_data):
return cif_data
raise RuntimeError(f"Could not find any Space Group keys. Missing one of the following: "
f"{str(SpaceGroupBuilder.string_keys + SpaceGroupBuilder.number_keys)}") | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/indexes/multi.py | python | MultiIndex.nlevels | (self) | return len(self._levels) | Integer number of levels in this MultiIndex. | Integer number of levels in this MultiIndex. | [
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"""
Integer number of levels in this MultiIndex.
"""
return len(self._levels) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftguitools/gui_draft2sketch.py | python | Draft2Sketch.GetResources | (self) | return {'Pixmap': 'Draft_Draft2Sketch',
'MenuText': QT_TRANSLATE_NOOP("Draft_Draft2Sketch", "Draft to Sketch"),
'ToolTip': QT_TRANSLATE_NOOP("Draft_Draft2Sketch", "Convert bidirectionally between Draft objects and Sketches.\nMany Draft objects will be converted into a single non-constrained Sketch.\nHowever, a single sketch with disconnected traces will be converted into several individual Draft objects.")} | Set icon, menu and tooltip. | Set icon, menu and tooltip. | [
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] | def GetResources(self):
"""Set icon, menu and tooltip."""
return {'Pixmap': 'Draft_Draft2Sketch',
'MenuText': QT_TRANSLATE_NOOP("Draft_Draft2Sketch", "Draft to Sketch"),
'ToolTip': QT_TRANSLATE_NOOP("Draft_Draft2Sketch", "Convert bidirectionally between Draft objects and Sketches.\nMany Draft objects will be converted into a single non-constrained Sketch.\nHowever, a single sketch with disconnected traces will be converted into several individual Draft objects.")} | [
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facebookincubator/fizz | bd0ba1b80f72023cb7ede671a4caa85f6664d3f6 | build/fbcode_builder/getdeps/fetcher.py | python | ChangeStatus.__init__ | (self, all_changed=False) | Construct a ChangeStatus object. The default is to create
a status that indicates no changes, but passing all_changed=True
will create one that indicates that everything changed | Construct a ChangeStatus object. The default is to create
a status that indicates no changes, but passing all_changed=True
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"""Construct a ChangeStatus object. The default is to create
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will create one that indicates that everything changed"""
if all_changed:
self.source_files = 1
self.make_files = 1
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self.source_files = 0
self.make_files = 0 | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | src/third_party/scons-3.1.2/scons-local-3.1.2/SCons/Node/FS.py | python | File._add_strings_to_dependency_map | (self, dmap) | return dmap | In the case comparing node objects isn't sufficient, we'll add the strings for the nodes to the dependency map
:return: | In the case comparing node objects isn't sufficient, we'll add the strings for the nodes to the dependency map
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"""
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"""
first_string = str(next(iter(dmap)))
# print("DMAP:%s"%id(dmap))
if first_string not in dmap:
string_dict = {str(child): signature for child, signature in dmap.items()}
dmap.update(string_dict)
return dmap | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/debug/lib/grpc_debug_server.py | python | EventListenerBaseServicer.run_server | (self, blocking=True) | Start running the server.
Args:
blocking: If `True`, block until `stop_server()` is invoked.
Raises:
ValueError: If server stop has already been requested, or if the server
has already started running. | Start running the server. | [
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"""Start running the server.
Args:
blocking: If `True`, block until `stop_server()` is invoked.
Raises:
ValueError: If server stop has already been requested, or if the server
has already started running.
"""
self._server_lock.acquire()
try:
if self._stop_requested:
raise ValueError("Server has already stopped")
if self._server_started:
raise ValueError("Server has already started running")
no_max_message_sizes = [("grpc.max_receive_message_length", -1),
("grpc.max_send_message_length", -1)]
self.server = grpc.server(futures.ThreadPoolExecutor(max_workers=10),
options=no_max_message_sizes)
debug_service_pb2_grpc.add_EventListenerServicer_to_server(self,
self.server)
self.server.add_insecure_port("[::]:%d" % self._server_port)
self.server.start()
self._server_started = True
finally:
self._server_lock.release()
if blocking:
while not self._stop_requested:
time.sleep(1.0) | [
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | commonTools/build_stats/wrapper/WrapperOpTimer.py | python | WrapperOpTimer.time_op | (wcp) | return (csv_row, returncode) | evaluate 'op' with 'op_args', and gather stats into output_stats_file | evaluate 'op' with 'op_args', and gather stats into output_stats_file | [
"evaluate",
"op",
"with",
"op_args",
"and",
"gather",
"stats",
"into",
"output_stats_file"
] | def time_op(wcp):
"""
evaluate 'op' with 'op_args', and gather stats into output_stats_file
"""
# if os.path.exists(output_stats_file) and os.path.getsize(output_stats_file) > 0:
# print("WARNING: File '"+output_stats_file+"' exists and will be overwritten")
# print("op='"+op+"'")
# print("op_args='"+str(op_args)+"'")
# print("op_output_file='"+op_output_file+"'")
# initializing the titles and rows list
fields = []
csv_row = {}
cmdcount = 0
returncode = 0
for cmd in wcp.commands:
if cmdcount == 0:
cmd = [ wcp.time_cmd,
# '--append',
'--output=' + wcp.output_stats_file,
field_arg,
] + cmd
cmdcount += 1
returncode |= WrapperOpTimer.run_cmd(cmd)
# reading csv file
with open(wcp.output_stats_file, 'r') as csvfile:
# creating a csv reader object
csvreader = csv.reader(csvfile)
# extracting field names through first row
fields = next(csvreader)
# extracting each data row one by one
# we effectively retain only the last row.
# it isn't clear if we should expect multiple rows per file
#
# In the bash version of this I was able to handle multiple rows per file
# We could do that here, but it would require returning a list of csv maps
# On the system side of things, it is very murky. We would need to ensure
# file integrity (concurrent reads/writes). For now, it's
# best to enforce 1 file per operation performed. (which should happen if we
# name things correctly) - That is invalid is there is a cycle in the Build graph,
# but that is a larger problem.
for row in csvreader:
csv_row = dict(zip(fields, row))
# FileSize
csv_row['FileSize'] = WrapperOpTimer.get_file_size(wcp.op_output_file)
# add a field with the short op
csv_row['op'] = os.path.basename(wcp.op)
# FileName
if wcp.base_build_dir:
abs_base_build_dir = os.path.abspath(wcp.base_build_dir)
current_working_dir = os.path.abspath(os.getcwd())
rel_path_to_base_build_dir = os.path.relpath(
current_working_dir, start=abs_base_build_dir)
rel_op_output_file = os.path.join(rel_path_to_base_build_dir, wcp.op_output_file)
else:
rel_op_output_file = wcp.op_output_file
csv_row['FileName'] = rel_op_output_file
# Remove the build stats output file if the build failed
if returncode != 0 and os.path.exists(wcp.output_stats_file):
os.remove(wcp.output_stats_file)
return (csv_row, returncode) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/setuptools/command/easy_install.py | python | easy_install._set_fetcher_options | (self, base) | When easy_install is about to run bdist_egg on a source dist, that
source dist might have 'setup_requires' directives, requiring
additional fetching. Ensure the fetcher options given to easy_install
are available to that command as well. | When easy_install is about to run bdist_egg on a source dist, that
source dist might have 'setup_requires' directives, requiring
additional fetching. Ensure the fetcher options given to easy_install
are available to that command as well. | [
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"""
When easy_install is about to run bdist_egg on a source dist, that
source dist might have 'setup_requires' directives, requiring
additional fetching. Ensure the fetcher options given to easy_install
are available to that command as well.
"""
# find the fetch options from easy_install and write them out
# to the setup.cfg file.
ei_opts = self.distribution.get_option_dict('easy_install').copy()
fetch_directives = (
'find_links', 'site_dirs', 'index_url', 'optimize', 'allow_hosts',
)
fetch_options = {}
for key, val in ei_opts.items():
if key not in fetch_directives:
continue
fetch_options[key] = val[1]
# create a settings dictionary suitable for `edit_config`
settings = dict(easy_install=fetch_options)
cfg_filename = os.path.join(base, 'setup.cfg')
setopt.edit_config(cfg_filename, settings) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/math_ops.py | python | to_int32 | (x, name="ToInt32") | return cast(x, dtypes.int32, name=name) | Casts a tensor to type `int32`.
Args:
x: A `Tensor` or `SparseTensor` or `IndexedSlices`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor` or `IndexedSlices` with same shape as `x` with
type `int32`.
Raises:
TypeError: If `x` cannot be cast to the `int32`.
@compatibility(TF2)
This name was deprecated and removed in TF2, but has an exact replacement
`tf.cast(..., tf.int32)`. There are no further issues with eager execution or
tf.function.
Before:
>>> tf.compat.v1.to_int32(tf.constant(1, dtype=tf.int64))
<tf.Tensor: shape=(), dtype=int32, numpy=1>
After:
>>> tf.cast(tf.constant(1, dtype=tf.int64), tf.int32)
<tf.Tensor: shape=(), dtype=int32, numpy=1>
@end_compatibility | Casts a tensor to type `int32`. | [
"Casts",
"a",
"tensor",
"to",
"type",
"int32",
"."
] | def to_int32(x, name="ToInt32"):
"""Casts a tensor to type `int32`.
Args:
x: A `Tensor` or `SparseTensor` or `IndexedSlices`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor` or `IndexedSlices` with same shape as `x` with
type `int32`.
Raises:
TypeError: If `x` cannot be cast to the `int32`.
@compatibility(TF2)
This name was deprecated and removed in TF2, but has an exact replacement
`tf.cast(..., tf.int32)`. There are no further issues with eager execution or
tf.function.
Before:
>>> tf.compat.v1.to_int32(tf.constant(1, dtype=tf.int64))
<tf.Tensor: shape=(), dtype=int32, numpy=1>
After:
>>> tf.cast(tf.constant(1, dtype=tf.int64), tf.int32)
<tf.Tensor: shape=(), dtype=int32, numpy=1>
@end_compatibility
"""
return cast(x, dtypes.int32, name=name) | [
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GJDuck/LowFat | ecf6a0f0fa1b73a27a626cf493cc39e477b6faea | llvm-4.0.0.src/projects/compiler-rt/lib/sanitizer_common/scripts/cpplint.py | python | CheckMakePairUsesDeduction | (filename, clean_lines, linenum, error) | Check that make_pair's template arguments are deduced.
G++ 4.6 in C++0x mode fails badly if make_pair's template arguments are
specified explicitly, and such use isn't intended in any case.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Check that make_pair's template arguments are deduced. | [
"Check",
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"make_pair",
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"template",
"arguments",
"are",
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] | def CheckMakePairUsesDeduction(filename, clean_lines, linenum, error):
"""Check that make_pair's template arguments are deduced.
G++ 4.6 in C++0x mode fails badly if make_pair's template arguments are
specified explicitly, and such use isn't intended in any case.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
raw = clean_lines.raw_lines
line = raw[linenum]
match = _RE_PATTERN_EXPLICIT_MAKEPAIR.search(line)
if match:
error(filename, linenum, 'build/explicit_make_pair',
4, # 4 = high confidence
'For C++11-compatibility, omit template arguments from make_pair'
' OR use pair directly OR if appropriate, construct a pair directly') | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/lib-tk/Tkinter.py | python | Misc.colormodel | (self, value=None) | return self.tk.call('tk', 'colormodel', self._w, value) | Useless. Not implemented in Tk. | Useless. Not implemented in Tk. | [
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] | def colormodel(self, value=None):
"""Useless. Not implemented in Tk."""
return self.tk.call('tk', 'colormodel', self._w, value) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/pathlib.py | python | Path.read_text | (self, encoding=None, errors=None) | Open the file in text mode, read it, and close the file. | Open the file in text mode, read it, and close the file. | [
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] | def read_text(self, encoding=None, errors=None):
"""
Open the file in text mode, read it, and close the file.
"""
with self.open(mode='r', encoding=encoding, errors=errors) as f:
return f.read() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/enum.py | python | _is_sunder | (name) | return (len(name) > 2 and
name[0] == name[-1] == '_' and
name[1:2] != '_' and
name[-2:-1] != '_') | Returns True if a _sunder_ name, False otherwise. | Returns True if a _sunder_ name, False otherwise. | [
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] | def _is_sunder(name):
"""Returns True if a _sunder_ name, False otherwise."""
return (len(name) > 2 and
name[0] == name[-1] == '_' and
name[1:2] != '_' and
name[-2:-1] != '_') | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_misc.py | python | TimeSpan_Second | (*args) | return _misc_.TimeSpan_Second(*args) | TimeSpan_Second() -> TimeSpan | TimeSpan_Second() -> TimeSpan | [
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"""TimeSpan_Second() -> TimeSpan"""
return _misc_.TimeSpan_Second(*args) | [
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wujian16/Cornell-MOE | df299d1be882d2af9796d7a68b3f9505cac7a53e | moe/optimal_learning/python/data_containers.py | python | HistoricalData.validate_historical_data | (dim, points_sampled, points_sampled_value, points_sampled_noise_variance) | Check that the historical data components (dim, coordinates, values, noises) are consistent in dimension and all have finite values.
:param dim: number of (expected) spatial dimensions
:type dim: int > 0
:param points_sampled: already-sampled points
:type points_sampled: array of float64 with shape (num_sampled, dim)
:param points_sampled_value: function value measured at each point
:type points_sampled_value: array of float64 with shape (num_sampled)
:param points_sampled_noise_variance: noise variance associated with ``points_sampled_value``
:type points_sampled_noise_variance: array of float64 with shape (num_sampled)
:return: True if inputs are valid
:rtype: boolean | Check that the historical data components (dim, coordinates, values, noises) are consistent in dimension and all have finite values. | [
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] | def validate_historical_data(dim, points_sampled, points_sampled_value, points_sampled_noise_variance):
"""Check that the historical data components (dim, coordinates, values, noises) are consistent in dimension and all have finite values.
:param dim: number of (expected) spatial dimensions
:type dim: int > 0
:param points_sampled: already-sampled points
:type points_sampled: array of float64 with shape (num_sampled, dim)
:param points_sampled_value: function value measured at each point
:type points_sampled_value: array of float64 with shape (num_sampled)
:param points_sampled_noise_variance: noise variance associated with ``points_sampled_value``
:type points_sampled_noise_variance: array of float64 with shape (num_sampled)
:return: True if inputs are valid
:rtype: boolean
"""
if dim <= 0:
raise ValueError('Input dim = {0:d} is non-positive.'.format(dim))
# Check that all array leading dimensions are the same
if points_sampled.shape[0] != points_sampled_value.shape[0] or points_sampled.shape[0] != points_sampled_noise_variance.size:
raise ValueError('Input arrays do not have the same leading dimension: (points_sampled, value, noise) = ({0:d}, {1:d}, {2:d})'.format(points_sampled.shape[0], points_sampled_value.size, points_sampled_noise_variance.size))
if points_sampled.shape[0] > 0:
for i in range(points_sampled.shape[0]):
temp = SamplePoint(points_sampled[i], points_sampled_value[i], points_sampled_noise_variance[i])
temp.validate(dim=dim) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/dataset/engine/offload.py | python | check_concat_zip_dataset | (dataset) | Check if dataset is concatenated or zipped. | Check if dataset is concatenated or zipped. | [
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] | def check_concat_zip_dataset(dataset):
"""
Check if dataset is concatenated or zipped.
"""
while dataset:
if len(dataset.children) > 1:
raise RuntimeError("Offload module currently does not support concatenated or zipped datasets.")
if dataset.children:
dataset = dataset.children[0]
continue
dataset = dataset.children | [
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rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/ML/DecTree/Tree.py | python | TreeNode.GetLevel | (self) | return self.level | Returns the level of this node | Returns the level of this node | [
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""" Returns the level of this node
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | TreeEvent.GetKeyEvent | (*args, **kwargs) | return _controls_.TreeEvent_GetKeyEvent(*args, **kwargs) | GetKeyEvent(self) -> KeyEvent | GetKeyEvent(self) -> KeyEvent | [
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"""GetKeyEvent(self) -> KeyEvent"""
return _controls_.TreeEvent_GetKeyEvent(*args, **kwargs) | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/pkg_resources/__init__.py | python | IMetadataProvider.has_metadata | (name) | Does the package's distribution contain the named metadata? | Does the package's distribution contain the named metadata? | [
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"""Does the package's distribution contain the named metadata?""" | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/runpy.py | python | _run_code | (code, run_globals, init_globals=None,
mod_name=None, mod_fname=None,
mod_loader=None, pkg_name=None) | return run_globals | Helper to run code in nominated namespace | Helper to run code in nominated namespace | [
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] | def _run_code(code, run_globals, init_globals=None,
mod_name=None, mod_fname=None,
mod_loader=None, pkg_name=None):
"""Helper to run code in nominated namespace"""
if init_globals is not None:
run_globals.update(init_globals)
run_globals.update(__name__ = mod_name,
__file__ = mod_fname,
__loader__ = mod_loader,
__package__ = pkg_name)
exec code in run_globals
return run_globals | [
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/libraries/ioss/src/visualization/catalyst/phactori/PhactoriDriver.py | python | QuadInfoGQC.AddToInfoArrays | (self, outDoubleArray, outIntegerArray) | add the quad points, point average, and cellId to the argument arrays\n for purspose of doing mpi broadcast/reduce work | add the quad points, point average, and cellId to the argument arrays\n for purspose of doing mpi broadcast/reduce work | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/fsspec/spec.py | python | AbstractFileSystem.delete | (self, path, recursive=False, maxdepth=None) | return self.rm(path, recursive=recursive, maxdepth=maxdepth) | Alias of :ref:`FilesystemSpec.rm`. | Alias of :ref:`FilesystemSpec.rm`. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ribbon/toolbar.py | python | RibbonToolBar.SetToolDisabledBitmap | (self, tool_id, bitmap) | Sets the bitmap to be used by the tool with the given ID when the tool is in a disabled state.
:param `tool_id`: id of the tool in question, as passed to :meth:`~RibbonToolBar.AddTool`;
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | GenericDirCtrl.GetTreeCtrl | (*args, **kwargs) | return _controls_.GenericDirCtrl_GetTreeCtrl(*args, **kwargs) | GetTreeCtrl(self) -> TreeCtrl | GetTreeCtrl(self) -> TreeCtrl | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/drill/model/DrillSampleGroup.py | python | DrillSampleGroup.getSampleIndex | (self, sample) | return self._samples.index(sample) | Get the index of a sample in the group. Sample has to be in the group.
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/external/bazel_tools/third_party/py/gflags/gflags_validators.py | python | Validator.GetFlagsNames | (self) | Return the names of the flags checked by this validator.
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Netflix/NfWebCrypto | 499faf4eb9f9ccf0b21dc728e974970f54bd6c52 | plugin/ppapi/ppapi/generators/idl_parser.py | python | IDLParser.p_typedef_array | (self, p) | typedef_decl : modifiers TYPEDEF SYMBOL arrays SYMBOL ';' | typedef_decl : modifiers TYPEDEF SYMBOL arrays SYMBOL ';' | [
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typeref = self.BuildAttribute('TYPEREF', p[3])
children = ListFromConcat(p[1], typeref, p[4])
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/peacock/PostprocessorViewer/plugins/PostprocessorSelectPlugin.py | python | PostprocessorSelectPlugin.onSetData | (self, data) | Called when new data is being supplied to the widget.
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# Remove existing widgets
current_groups = {}
filenames = [d.filename() for d in data]
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group.clear()
if group.filename() not in filenames:
self.LineGroupsLayout.removeWidget(group)
group.setParent(None)
group.disconnect()
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current_groups[group.filename()] = group
self._groups = []
self.color_cycle = itertools.product(['-', '--', '-.', ':'], plt.cm.Paired(np.linspace(0, 1, 11)))
# Create the group widgets for each available variable
for d in data:
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group = current_groups[d.filename()]
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group.setParent(None)
group.disconnect()
self._newGroup(d)
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self.updateVariables()
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/skia/tools/buildbot_spec.py | python | build_targets_from_builder_dict | (builder_dict) | Return a list of targets to build, depending on the builder type. | Return a list of targets to build, depending on the builder type. | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/pydoc.py | python | isdata | (object) | return not (inspect.ismodule(object) or inspect.isclass(object) or
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shader-slang/slang | b8982fcf43b86c1e39dcc3dd19bff2821633eda6 | external/vulkan/registry/conventions.py | python | ConventionsBase._implMakeProseList | (self, elements, fmt, with_verb, comma_for_two_elts=False, serial_comma=True) | return ''.join(parts) | Internal-use implementation to make a (comma-separated) list for use in prose.
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/requests/cookies.py | python | RequestsCookieJar.items | (self) | return list(self.iteritems()) | Dict-like items() that returns a list of name-value tuples from the
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forkineye/ESPixelStick | 22926f1c0d1131f1369fc7cad405689a095ae3cb | dist/bin/esptool/serial/serialposix.py | python | Serial._update_rts_state | (self) | Set terminal status line: Request To Send | Set terminal status line: Request To Send | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemWebCommunicator/AWS/common-code/lib/AWSIoTPythonSDK/core/protocol/paho/client.py | python | Client.want_write | (self) | Call to determine if there is network data waiting to be written.
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] | def want_write(self):
"""Call to determine if there is network data waiting to be written.
Useful if you are calling select() yourself rather than using loop().
"""
if self._current_out_packet or len(self._out_packet) > 0:
return True
else:
return False | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/turtle.py | python | TNavigator.goto | (self, x, y=None) | Move turtle to an absolute position.
Aliases: setpos | setposition | goto:
Arguments:
x -- a number or a pair/vector of numbers
y -- a number None
call: goto(x, y) # two coordinates
--or: goto((x, y)) # a pair (tuple) of coordinates
--or: goto(vec) # e.g. as returned by pos()
Move turtle to an absolute position. If the pen is down,
a line will be drawn. The turtle's orientation does not change.
Example (for a Turtle instance named turtle):
>>> tp = turtle.pos()
>>> tp
(0.00, 0.00)
>>> turtle.setpos(60,30)
>>> turtle.pos()
(60.00,30.00)
>>> turtle.setpos((20,80))
>>> turtle.pos()
(20.00,80.00)
>>> turtle.setpos(tp)
>>> turtle.pos()
(0.00,0.00) | Move turtle to an absolute position. | [
"Move",
"turtle",
"to",
"an",
"absolute",
"position",
"."
] | def goto(self, x, y=None):
"""Move turtle to an absolute position.
Aliases: setpos | setposition | goto:
Arguments:
x -- a number or a pair/vector of numbers
y -- a number None
call: goto(x, y) # two coordinates
--or: goto((x, y)) # a pair (tuple) of coordinates
--or: goto(vec) # e.g. as returned by pos()
Move turtle to an absolute position. If the pen is down,
a line will be drawn. The turtle's orientation does not change.
Example (for a Turtle instance named turtle):
>>> tp = turtle.pos()
>>> tp
(0.00, 0.00)
>>> turtle.setpos(60,30)
>>> turtle.pos()
(60.00,30.00)
>>> turtle.setpos((20,80))
>>> turtle.pos()
(20.00,80.00)
>>> turtle.setpos(tp)
>>> turtle.pos()
(0.00,0.00)
"""
if y is None:
self._goto(Vec2D(*x))
else:
self._goto(Vec2D(x, y)) | [
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