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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/syntax/_ruby.py | python | KeywordString | (option=0) | return RUBY_KW[1] | Returns the specified Keyword String
@note: not used by most modules | Returns the specified Keyword String
@note: not used by most modules | [
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"""Returns the specified Keyword String
@note: not used by most modules
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/module/sequential_module.py | python | SequentialModule.get_params | (self) | return (arg_params, aux_params) | Gets current parameters.
Returns
-------
(arg_params, aux_params)
A pair of dictionaries each mapping parameter names to NDArray values. This
is a merged dictionary of all the parameters in the modules. | Gets current parameters. | [
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"""Gets current parameters.
Returns
-------
(arg_params, aux_params)
A pair of dictionaries each mapping parameter names to NDArray values. This
is a merged dictionary of all the parameters in the modules.
"""
assert self.binded and self.params_initialized
arg_params = dict()
aux_params = dict()
for module in self._modules:
arg, aux = module.get_params()
arg_params.update(arg)
aux_params.update(aux)
return (arg_params, aux_params) | [
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/tools/gyp/pylib/gyp/generator/make.py | python | MakefileWriter.Write | (self, qualified_target, base_path, output_filename, spec, configs,
part_of_all) | The main entry point: writes a .mk file for a single target.
Arguments:
qualified_target: target we're generating
base_path: path relative to source root we're building in, used to resolve
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output_filename: output .mk file name to write
spec, configs: gyp info
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"""The main entry point: writes a .mk file for a single target.
Arguments:
qualified_target: target we're generating
base_path: path relative to source root we're building in, used to resolve
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output_filename: output .mk file name to write
spec, configs: gyp info
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"""
gyp.common.EnsureDirExists(output_filename)
self.fp = open(output_filename, 'w')
self.fp.write(header)
self.qualified_target = qualified_target
self.path = base_path
self.target = spec['target_name']
self.type = spec['type']
self.toolset = spec['toolset']
self.is_mac_bundle = gyp.xcode_emulation.IsMacBundle(self.flavor, spec)
if self.flavor == 'mac':
self.xcode_settings = gyp.xcode_emulation.XcodeSettings(spec)
else:
self.xcode_settings = None
deps, link_deps = self.ComputeDeps(spec)
# Some of the generation below can add extra output, sources, or
# link dependencies. All of the out params of the functions that
# follow use names like extra_foo.
extra_outputs = []
extra_sources = []
extra_link_deps = []
extra_mac_bundle_resources = []
mac_bundle_deps = []
if self.is_mac_bundle:
self.output = self.ComputeMacBundleOutput(spec)
self.output_binary = self.ComputeMacBundleBinaryOutput(spec)
else:
self.output = self.output_binary = self.ComputeOutput(spec)
self.is_standalone_static_library = bool(
spec.get('standalone_static_library', 0))
self._INSTALLABLE_TARGETS = ('executable', 'loadable_module',
'shared_library')
if (self.is_standalone_static_library or
self.type in self._INSTALLABLE_TARGETS):
self.alias = os.path.basename(self.output)
install_path = self._InstallableTargetInstallPath()
else:
self.alias = self.output
install_path = self.output
self.WriteLn("TOOLSET := " + self.toolset)
self.WriteLn("TARGET := " + self.target)
# Actions must come first, since they can generate more OBJs for use below.
if 'actions' in spec:
self.WriteActions(spec['actions'], extra_sources, extra_outputs,
extra_mac_bundle_resources, part_of_all)
# Rules must be early like actions.
if 'rules' in spec:
self.WriteRules(spec['rules'], extra_sources, extra_outputs,
extra_mac_bundle_resources, part_of_all)
if 'copies' in spec:
self.WriteCopies(spec['copies'], extra_outputs, part_of_all)
# Bundle resources.
if self.is_mac_bundle:
all_mac_bundle_resources = (
spec.get('mac_bundle_resources', []) + extra_mac_bundle_resources)
self.WriteMacBundleResources(all_mac_bundle_resources, mac_bundle_deps)
self.WriteMacInfoPlist(mac_bundle_deps)
# Sources.
all_sources = spec.get('sources', []) + extra_sources
if all_sources:
if self.flavor == 'mac':
# libtool on OS X generates warnings for duplicate basenames in the same
# target.
_ValidateSourcesForOSX(spec, all_sources)
self.WriteSources(
configs, deps, all_sources, extra_outputs,
extra_link_deps, part_of_all,
gyp.xcode_emulation.MacPrefixHeader(
self.xcode_settings, lambda p: Sourceify(self.Absolutify(p)),
self.Pchify))
sources = filter(Compilable, all_sources)
if sources:
self.WriteLn(SHARED_HEADER_SUFFIX_RULES_COMMENT1)
extensions = set([os.path.splitext(s)[1] for s in sources])
for ext in extensions:
if ext in self.suffix_rules_srcdir:
self.WriteLn(self.suffix_rules_srcdir[ext])
self.WriteLn(SHARED_HEADER_SUFFIX_RULES_COMMENT2)
for ext in extensions:
if ext in self.suffix_rules_objdir1:
self.WriteLn(self.suffix_rules_objdir1[ext])
for ext in extensions:
if ext in self.suffix_rules_objdir2:
self.WriteLn(self.suffix_rules_objdir2[ext])
self.WriteLn('# End of this set of suffix rules')
# Add dependency from bundle to bundle binary.
if self.is_mac_bundle:
mac_bundle_deps.append(self.output_binary)
self.WriteTarget(spec, configs, deps, extra_link_deps + link_deps,
mac_bundle_deps, extra_outputs, part_of_all)
# Update global list of target outputs, used in dependency tracking.
target_outputs[qualified_target] = install_path
# Update global list of link dependencies.
if self.type in ('static_library', 'shared_library'):
target_link_deps[qualified_target] = self.output_binary
# Currently any versions have the same effect, but in future the behavior
# could be different.
if self.generator_flags.get('android_ndk_version', None):
self.WriteAndroidNdkModuleRule(self.target, all_sources, link_deps)
self.fp.close() | [
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daijifeng001/caffe-rfcn | 543f8f6a4b7c88256ea1445ae951a12d1ad9cffd | python/caffe/classifier.py | python | Classifier.predict | (self, inputs, oversample=True) | return predictions | Predict classification probabilities of inputs.
Parameters
----------
inputs : iterable of (H x W x K) input ndarrays.
oversample : boolean
average predictions across center, corners, and mirrors
when True (default). Center-only prediction when False.
Returns
-------
predictions: (N x C) ndarray of class probabilities for N images and C
classes. | Predict classification probabilities of inputs. | [
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] | def predict(self, inputs, oversample=True):
"""
Predict classification probabilities of inputs.
Parameters
----------
inputs : iterable of (H x W x K) input ndarrays.
oversample : boolean
average predictions across center, corners, and mirrors
when True (default). Center-only prediction when False.
Returns
-------
predictions: (N x C) ndarray of class probabilities for N images and C
classes.
"""
# Scale to standardize input dimensions.
input_ = np.zeros((len(inputs),
self.image_dims[0],
self.image_dims[1],
inputs[0].shape[2]),
dtype=np.float32)
for ix, in_ in enumerate(inputs):
input_[ix] = caffe.io.resize_image(in_, self.image_dims)
if oversample:
# Generate center, corner, and mirrored crops.
input_ = caffe.io.oversample(input_, self.crop_dims)
else:
# Take center crop.
center = np.array(self.image_dims) / 2.0
crop = np.tile(center, (1, 2))[0] + np.concatenate([
-self.crop_dims / 2.0,
self.crop_dims / 2.0
])
crop = crop.astype(int)
input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
# Classify
caffe_in = np.zeros(np.array(input_.shape)[[0, 3, 1, 2]],
dtype=np.float32)
for ix, in_ in enumerate(input_):
caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
out = self.forward_all(**{self.inputs[0]: caffe_in})
predictions = out[self.outputs[0]]
# For oversampling, average predictions across crops.
if oversample:
predictions = predictions.reshape((len(predictions) / 10, 10, -1))
predictions = predictions.mean(1)
return predictions | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | DC.SetClippingRegionAsRegion | (*args, **kwargs) | return _gdi_.DC_SetClippingRegionAsRegion(*args, **kwargs) | SetClippingRegionAsRegion(self, Region region)
Sets the clipping region for this device context to the intersection
of the given region described by the parameters of this method and the
previously set clipping region. You should call `DestroyClippingRegion`
if you want to set the clipping region exactly to the region
specified.
The clipping region is an area to which drawing is
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"""
SetClippingRegionAsRegion(self, Region region)
Sets the clipping region for this device context to the intersection
of the given region described by the parameters of this method and the
previously set clipping region. You should call `DestroyClippingRegion`
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The clipping region is an area to which drawing is
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ideawu/ssdb-rocks | a3cbb322cafb2f493252829c608e2239df98c9ac | deps/cpy/antlr3/streams.py | python | CommonTokenStream.get | (self, i) | return self.tokens[i] | Return absolute token i; ignore which channel the tokens are on;
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | ListView.ClearColumnImage | (*args, **kwargs) | return _controls_.ListView_ClearColumnImage(*args, **kwargs) | ClearColumnImage(self, int col) | ClearColumnImage(self, int col) | [
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return _controls_.ListView_ClearColumnImage(*args, **kwargs) | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | python | get_guided_grad_cam | (cam, imggrad) | return np.multiply(cam, imggrad) | Compute Guided Grad-CAM. Refer section 3 of https://arxiv.org/abs/1610.02391 for details | Compute Guided Grad-CAM. Refer section 3 of https://arxiv.org/abs/1610.02391 for details | [
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spencer-project/spencer_people_tracking | 09b256ba4bc22c5cae8a5ae88960de1a387cfd7f | tracking/people/spencer_tracking_metrics/src/spencer_tracking_metrics/pymot/rect.py | python | Rect.__init__ | (self, entity) | Constructor from dict with keys width, height, x, y, dco and id | Constructor from dict with keys width, height, x, y, dco and id | [
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self.x_ = entity["x"]
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self.dco_ = entity.get("dco",False)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/richtext.py | python | RichTextBuffer.GetHandlerFlags | (*args, **kwargs) | return _richtext.RichTextBuffer_GetHandlerFlags(*args, **kwargs) | GetHandlerFlags(self) -> int | GetHandlerFlags(self) -> int | [
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wywu/LAB | 4b6debd302ae109fd104d4dd04dccc3418ae7471 | examples/pycaffe/tools.py | python | SimpleTransformer.set_scale | (self, scale) | Set the data scaling. | Set the data scaling. | [
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MVIG-SJTU/RMPE | 5188c230ec800c12be7369c3619615bc9b020aa4 | scripts/cpp_lint.py | python | RemoveMultiLineComments | (filename, lines, error) | Removes multiline (c-style) comments from lines. | Removes multiline (c-style) comments from lines. | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/elastictranscoder/layer1.py | python | ElasticTranscoderConnection.update_pipeline_notifications | (self, id=None, notifications=None) | return self.make_request('POST', uri, expected_status=200,
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When you update notifications for a pipeline, Elastic
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:type id: string
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:param notifications:
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To receive notifications, you must also subscribe to the new topic in
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+ **Progressing**: The topic ARN for the Amazon Simple Notification
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+ **Completed**: The topic ARN for the Amazon SNS topic that you want
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+ **Warning**: The topic ARN for the Amazon SNS topic that you want to
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is the ARN that Amazon SNS returned when you created the topic.
+ **Error**: The topic ARN for the Amazon SNS topic that you want to
notify when Elastic Transcoder encounters an error condition. This
is the ARN that Amazon SNS returned when you created the topic. | With the UpdatePipelineNotifications operation, you can update
Amazon Simple Notification Service (Amazon SNS) notifications
for a pipeline. | [
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"""
With the UpdatePipelineNotifications operation, you can update
Amazon Simple Notification Service (Amazon SNS) notifications
for a pipeline.
When you update notifications for a pipeline, Elastic
Transcoder returns the values that you specified in the
request.
:type id: string
:param id: The identifier of the pipeline for which you want to change
notification settings.
:type notifications: dict
:param notifications:
The topic ARN for the Amazon Simple Notification Service (Amazon SNS)
topic that you want to notify to report job status.
To receive notifications, you must also subscribe to the new topic in
the Amazon SNS console.
+ **Progressing**: The topic ARN for the Amazon Simple Notification
Service (Amazon SNS) topic that you want to notify when Elastic
Transcoder has started to process jobs that are added to this
pipeline. This is the ARN that Amazon SNS returned when you created
the topic.
+ **Completed**: The topic ARN for the Amazon SNS topic that you want
to notify when Elastic Transcoder has finished processing a job.
This is the ARN that Amazon SNS returned when you created the
topic.
+ **Warning**: The topic ARN for the Amazon SNS topic that you want to
notify when Elastic Transcoder encounters a warning condition. This
is the ARN that Amazon SNS returned when you created the topic.
+ **Error**: The topic ARN for the Amazon SNS topic that you want to
notify when Elastic Transcoder encounters an error condition. This
is the ARN that Amazon SNS returned when you created the topic.
"""
uri = '/2012-09-25/pipelines/{0}/notifications'.format(id)
params = {}
if id is not None:
params['Id'] = id
if notifications is not None:
params['Notifications'] = notifications
return self.make_request('POST', uri, expected_status=200,
data=json.dumps(params)) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/debug/wrappers/framework.py | python | BaseDebugWrapperSession.__init__ | (self, sess, thread_name_filter=None) | Constructor of `BaseDebugWrapperSession`.
Args:
sess: An (unwrapped) TensorFlow session instance.
thread_name_filter: Regular-expression filter (whitelist) for name(s) of
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sess: An (unwrapped) TensorFlow session instance.
thread_name_filter: Regular-expression filter (whitelist) for name(s) of
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Raises:
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"""
_check_type(sess, session.BaseSession)
# The session being wrapped.
self._sess = sess
self._thread_name_filter_pattern = (re.compile(thread_name_filter)
if thread_name_filter else None)
# Keeps track of number of run calls that have been performed on this
# debug-wrapper session.
self._run_call_count = 0
# Invoke on-session-init callback.
response = self.on_session_init(OnSessionInitRequest(self._sess))
_check_type(response, OnSessionInitResponse)
if response.action == OnSessionInitAction.PROCEED:
pass
elif response.action == OnSessionInitAction.REMOTE_INSTR_LOOP:
# TODO(cais): Implement REMOTE_INSTR_LOOP
raise NotImplementedError(
"OnSessionInitAction REMOTE_INSTR_LOOP has not been "
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else:
raise ValueError(
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/propgrid.py | python | PGProperty.InsertChild | (*args, **kwargs) | return _propgrid.PGProperty_InsertChild(*args, **kwargs) | InsertChild(self, int index, PGProperty childProperty) -> PGProperty | InsertChild(self, int index, PGProperty childProperty) -> PGProperty | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/rsa/rsa/_version133.py | python | ceil | (x) | return int(math.ceil(x)) | ceil(x) -> int(math.ceil(x)) | ceil(x) -> int(math.ceil(x)) | [
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"""ceil(x) -> int(math.ceil(x))"""
return int(math.ceil(x)) | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/generate_stubs/generate_stubs.py | python | PosixStubWriter.CStyleIdentifier | (cls, identifier) | return string.capwords(re.sub(INVALID_C_IDENT_CHARS, '', identifier)) | Generates a C style identifier.
The module_name has all invalid identifier characters removed (anything
that's not [_a-zA-Z0-9]) and is run through string.capwords to try
and approximate camel case.
Args:
identifier: The string with the module name to turn to C-style.
Returns:
A string that can be used as part of a C identifier. | Generates a C style identifier. | [
"Generates",
"a",
"C",
"style",
"identifier",
"."
] | def CStyleIdentifier(cls, identifier):
"""Generates a C style identifier.
The module_name has all invalid identifier characters removed (anything
that's not [_a-zA-Z0-9]) and is run through string.capwords to try
and approximate camel case.
Args:
identifier: The string with the module name to turn to C-style.
Returns:
A string that can be used as part of a C identifier.
"""
return string.capwords(re.sub(INVALID_C_IDENT_CHARS, '', identifier)) | [
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llvm-mirror/lldb | d01083a850f577b85501a0902b52fd0930de72c7 | utils/vim-lldb/python-vim-lldb/vim_signs.py | python | VimSign.define | (self, sign_text, highlight_colour) | return sign_name | Defines sign and highlight (if highlight_colour is not None). | Defines sign and highlight (if highlight_colour is not None). | [
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] | def define(self, sign_text, highlight_colour):
""" Defines sign and highlight (if highlight_colour is not None). """
sign_name = "sign%d" % VimSign.name_id
if highlight_colour is None:
vim.command("sign define %s text=%s" % (sign_name, sign_text))
else:
self.highlight_name = "highlight%d" % VimSign.name_id
vim.command(
"highlight %s ctermbg=%s guibg=%s" %
(self.highlight_name, highlight_colour, highlight_colour))
vim.command(
"sign define %s text=%s linehl=%s texthl=%s" %
(sign_name, sign_text, self.highlight_name, self.highlight_name))
VimSign.defined_signs[(sign_text, highlight_colour)] = sign_name
VimSign.name_id += 1
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/boosted_trees/examples/binary_mnist.py | python | _get_tfbt | (output_dir) | return estimator | Configures TF Boosted Trees estimator based on flags. | Configures TF Boosted Trees estimator based on flags. | [
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"Boosted",
"Trees",
"estimator",
"based",
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"."
] | def _get_tfbt(output_dir):
"""Configures TF Boosted Trees estimator based on flags."""
learner_config = learner_pb2.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = FLAGS.learning_rate
learner_config.regularization.l1 = 0.0
learner_config.regularization.l2 = FLAGS.l2 / FLAGS.examples_per_layer
learner_config.constraints.max_tree_depth = FLAGS.depth
growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER
learner_config.growing_mode = growing_mode
run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)
# Create a TF Boosted trees estimator that can take in custom loss.
estimator = GradientBoostedDecisionTreeClassifier(
learner_config=learner_config,
examples_per_layer=FLAGS.examples_per_layer,
model_dir=output_dir,
num_trees=FLAGS.num_trees,
center_bias=False,
config=run_config)
return estimator | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_misc.py | python | ConfigBase.Set | (*args, **kwargs) | return _misc_.ConfigBase_Set(*args, **kwargs) | Set(ConfigBase config) -> ConfigBase
Sets the global config object (the one returned by Get) and returns a
reference to the previous global config object. | Set(ConfigBase config) -> ConfigBase | [
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"""
Set(ConfigBase config) -> ConfigBase
Sets the global config object (the one returned by Get) and returns a
reference to the previous global config object.
"""
return _misc_.ConfigBase_Set(*args, **kwargs) | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/BeautifulSoup.py | python | PageElement.findPrevious | (self, name=None, attrs={}, text=None, **kwargs) | return self._findOne(self.findAllPrevious, name, attrs, text, **kwargs) | Returns the first item that matches the given criteria and
appears before this Tag in the document. | Returns the first item that matches the given criteria and
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] | def findPrevious(self, name=None, attrs={}, text=None, **kwargs):
"""Returns the first item that matches the given criteria and
appears before this Tag in the document."""
return self._findOne(self.findAllPrevious, name, attrs, text, **kwargs) | [
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/libraries/ioss/src/visualization/catalyst/phactori/phactori.py | python | PhactoriOperationSpecifics.GetListOfInputOperationNamesForThisOperationType | (self) | return retList | operations which depend on additional inputs besides the default single
input need to override this method and return their dependencies as a
list of names | operations which depend on additional inputs besides the default single
input need to override this method and return their dependencies as a
list of names | [
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"""operations which depend on additional inputs besides the default single
input need to override this method and return their dependencies as a
list of names"""
retList = []
return retList | [
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KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | kratos/python_scripts/gid_output_process.py | python | GiDOutputProcess.ExecuteFinalize | (self) | Finalize files and free resources. | Finalize files and free resources. | [
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] | def ExecuteFinalize(self):
'''Finalize files and free resources.'''
if self.multifile_flag == MultiFileFlag.SingleFile:
self.__finalize_results()
if self.point_output_process is not None:
self.point_output_process.ExecuteFinalize()
for freq,f in self.volume_list_files:
f.close()
for freq,f in self.cut_list_files:
f.close()
# Note: it is important to call the GidIO destructor, since it closes output files
# Since Python's garbage colletion DOES NOT ensure that the destructor will be called,
# I'm deallocating the GidIO instances explicitly. This is VERY BAD PRACTICE
# and effectively breaks the class if called after this point, but we haven't found
# a better solution yet (jcotela 12/V/2016)
del self.body_io
del self.cut_io | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/winpython/wppm.py | python | Distribution.remove_directory | (self, path) | Try to remove directory -- on WindowsError, remove it later | Try to remove directory -- on WindowsError, remove it later | [
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"""Try to remove directory -- on WindowsError, remove it later"""
try:
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except WindowsError:
self.to_be_removed.append(path) | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/cmark/bench/statistics.py | python | _sum | (data, start=0) | return T(total) | _sum(data [, start]) -> value
Return a high-precision sum of the given numeric data. If optional
argument ``start`` is given, it is added to the total. If ``data`` is
empty, ``start`` (defaulting to 0) is returned.
Examples
--------
>>> _sum([3, 2.25, 4.5, -0.5, 1.0], 0.75)
11.0
Some sources of round-off error will be avoided:
>>> _sum([1e50, 1, -1e50] * 1000) # Built-in sum returns zero.
1000.0
Fractions and Decimals are also supported:
>>> from fractions import Fraction as F
>>> _sum([F(2, 3), F(7, 5), F(1, 4), F(5, 6)])
Fraction(63, 20)
>>> from decimal import Decimal as D
>>> data = [D("0.1375"), D("0.2108"), D("0.3061"), D("0.0419")]
>>> _sum(data)
Decimal('0.6963')
Mixed types are currently treated as an error, except that int is
allowed. | _sum(data [, start]) -> value | [
"_sum",
"(",
"data",
"[",
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] | def _sum(data, start=0):
"""_sum(data [, start]) -> value
Return a high-precision sum of the given numeric data. If optional
argument ``start`` is given, it is added to the total. If ``data`` is
empty, ``start`` (defaulting to 0) is returned.
Examples
--------
>>> _sum([3, 2.25, 4.5, -0.5, 1.0], 0.75)
11.0
Some sources of round-off error will be avoided:
>>> _sum([1e50, 1, -1e50] * 1000) # Built-in sum returns zero.
1000.0
Fractions and Decimals are also supported:
>>> from fractions import Fraction as F
>>> _sum([F(2, 3), F(7, 5), F(1, 4), F(5, 6)])
Fraction(63, 20)
>>> from decimal import Decimal as D
>>> data = [D("0.1375"), D("0.2108"), D("0.3061"), D("0.0419")]
>>> _sum(data)
Decimal('0.6963')
Mixed types are currently treated as an error, except that int is
allowed.
"""
# We fail as soon as we reach a value that is not an int or the type of
# the first value which is not an int. E.g. _sum([int, int, float, int])
# is okay, but sum([int, int, float, Fraction]) is not.
allowed_types = set([int, type(start)])
n, d = _exact_ratio(start)
partials = {d: n} # map {denominator: sum of numerators}
# Micro-optimizations.
exact_ratio = _exact_ratio
partials_get = partials.get
# Add numerators for each denominator.
for x in data:
_check_type(type(x), allowed_types)
n, d = exact_ratio(x)
partials[d] = partials_get(d, 0) + n
# Find the expected result type. If allowed_types has only one item, it
# will be int; if it has two, use the one which isn't int.
assert len(allowed_types) in (1, 2)
if len(allowed_types) == 1:
assert allowed_types.pop() is int
T = int
else:
T = (allowed_types - set([int])).pop()
if None in partials:
assert issubclass(T, (float, Decimal))
assert not math.isfinite(partials[None])
return T(partials[None])
total = Fraction()
for d, n in sorted(partials.items()):
total += Fraction(n, d)
if issubclass(T, int):
assert total.denominator == 1
return T(total.numerator)
if issubclass(T, Decimal):
return T(total.numerator)/total.denominator
return T(total) | [
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alibaba/MNN | c4d9566171d589c3ded23aa18ffb197016995a12 | pymnn/pip_package/MNN/expr/__init__.py | python | greater | (x, y) | return _F.greater(x, y) | greater(x, y)
Return the ``x > y``, element-wise.
Parameters
----------
x : var_like, input value.
y : var_like, input value.
Returns
-------
z : Var. The ``x > y`` of `x` and `y`, dtype is int32.
Example:
-------
>>> expr.greater([-9., 0.5], [1.2, -3.0])
var([0, 1]) | greater(x, y)
Return the ``x > y``, element-wise. | [
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'''
greater(x, y)
Return the ``x > y``, element-wise.
Parameters
----------
x : var_like, input value.
y : var_like, input value.
Returns
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z : Var. The ``x > y`` of `x` and `y`, dtype is int32.
Example:
-------
>>> expr.greater([-9., 0.5], [1.2, -3.0])
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'''
x = _to_var(x)
y = _to_var(y)
x, y = _match_dtype(x, y)
return _F.greater(x, y) | [
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CanalTP/navitia | cb84ce9859070187e708818b058e6a7e0b7f891b | source/jormungandr/jormungandr/scenarios/helper_classes/helper_utils.py | python | _update_fallback_sections | (journey, fallback_dp, fallback_period_extremity, fallback_type, via_access_point) | Replace journey's fallback sections with the given fallback_dp.
Note: the replacement is done in place of the journey | Replace journey's fallback sections with the given fallback_dp. | [
"Replace",
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] | def _update_fallback_sections(journey, fallback_dp, fallback_period_extremity, fallback_type, via_access_point):
"""
Replace journey's fallback sections with the given fallback_dp.
Note: the replacement is done in place of the journey
"""
aligned_fallback = _align_fallback_direct_path_datetime(fallback_dp, fallback_period_extremity)
fallback_sections = aligned_fallback.journeys[0].sections
# update the 'id' which isn't set
_rename_fallback_sections_ids(fallback_sections)
if fallback_type == StreetNetworkPathType.BEGINNING_FALLBACK:
section_to_replace = journey.sections[0]
else:
section_to_replace = journey.sections[-1]
journey.sections.remove(section_to_replace)
# We have to create the link between the fallback and the pt part manually here
if fallback_type == StreetNetworkPathType.BEGINNING_FALLBACK:
fallback_sections[-1].destination.CopyFrom(journey.sections[0].origin)
else:
fallback_sections[0].origin.CopyFrom(journey.sections[-1].destination)
if (
isinstance(via_access_point, type_pb2.PtObject)
and via_access_point.embedded_type == type_pb2.ACCESS_POINT
):
if fallback_type == StreetNetworkPathType.BEGINNING_FALLBACK:
fallback_sections[-1].vias.add().CopyFrom(via_access_point.access_point)
else:
fallback_sections[0].vias.add().CopyFrom(via_access_point.access_point)
journey.sections.extend(fallback_sections)
journey.sections.sort(key=cmp_to_key(SectionSorter())) | [
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wujian16/Cornell-MOE | df299d1be882d2af9796d7a68b3f9505cac7a53e | moe/optimal_learning/python/data_containers.py | python | HistoricalData.num_derivatives | (self) | return self._num_derivatives | Return the number of derivatives' observationsof a point in ``self.points_sampled``. | Return the number of derivatives' observationsof a point in ``self.points_sampled``. | [
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"""Return the number of derivatives' observationsof a point in ``self.points_sampled``."""
return self._num_derivatives | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/clang/bindings/python/clang/cindex.py | python | Type.get_canonical | (self) | return conf.lib.clang_getCanonicalType(self) | Return the canonical type for a Type.
Clang's type system explicitly models typedefs and all the
ways a specific type can be represented. The canonical type
is the underlying type with all the "sugar" removed. For
example, if 'T' is a typedef for 'int', the canonical type for
'T' would be 'int'. | Return the canonical type for a Type. | [
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] | def get_canonical(self):
"""
Return the canonical type for a Type.
Clang's type system explicitly models typedefs and all the
ways a specific type can be represented. The canonical type
is the underlying type with all the "sugar" removed. For
example, if 'T' is a typedef for 'int', the canonical type for
'T' would be 'int'.
"""
return conf.lib.clang_getCanonicalType(self) | [
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Ubpa/RenderLab | 71db49aa03de4fb258f9171691c8d570216e5e05 | bin/NN_Trainer/util.py | python | Hyperbox.GenSpiltHyperbox | (self, spiltAxis) | return leftHyperbox, rightHyperbox, spiltVal | 按轴 spiltAxis 分割超盒
@param
spiltAxis: 分割轴，要求 spiltAxis in self.GetSpiltableAxis()
@return
leftHyperbox: 左超盒
rightHyperbox: 右超盒
spiltVal: 分割值 | 按轴 spiltAxis 分割超盒 | [
"按轴",
"spiltAxis",
"分割超盒"
] | def GenSpiltHyperbox(self, spiltAxis):
"""
按轴 spiltAxis 分割超盒
@param
spiltAxis: 分割轴，要求 spiltAxis in self.GetSpiltableAxis()
@return
leftHyperbox: 左超盒
rightHyperbox: 右超盒
spiltVal: 分割值
"""
if spiltAxis not in self.GetSpiltableAxis():
raise RuntimeError("spiltAxis not in self.GetSpiltableAxis()")
hyperbox = self.__box
spiltVal = (hyperbox[spiltAxis][1] - hyperbox[spiltAxis][0]) / 2
leftHyperbox = self.GenCopy()
rightHyperbox = self.GenCopy()
leftHyperbox.__box[spiltAxis][1] = spiltVal
rightHyperbox.__box[spiltAxis][0] = spiltVal
return leftHyperbox, rightHyperbox, spiltVal | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/distribute/values.py | python | DistributedVariable._get_on_device_or_primary | (self) | Returns value in same replica or device if possible, else the _primary. | Returns value in same replica or device if possible, else the _primary. | [
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"""Returns value in same replica or device if possible, else the _primary."""
if values_util.is_saving_non_distributed():
return self._primary
replica_id = values_util.get_current_replica_id_as_int()
if replica_id is None:
# Try to find a value on the current device.
current_device = device_util.canonicalize(device_util.current())
for i, value in enumerate(self._values):
if device_util.canonicalize(value.device) == current_device:
return self._get_replica(i)
return self._get_replica(0)
else:
return self._get_replica(replica_id) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/command/install.py | python | install.create_path_file | (self) | Creates the .pth file | Creates the .pth file | [
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] | def create_path_file(self):
"""Creates the .pth file"""
filename = os.path.join(self.install_libbase,
self.path_file + ".pth")
if self.install_path_file:
self.execute(write_file,
(filename, [self.extra_dirs]),
"creating %s" % filename)
else:
self.warn("path file '%s' not created" % filename) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/tensor_array_ops.py | python | TensorArray.close | (self, name=None) | Close the current TensorArray. | Close the current TensorArray. | [
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"TensorArray",
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] | def close(self, name=None):
"""Close the current TensorArray."""
with ops.colocate_with(self._handle):
return gen_data_flow_ops._tensor_array_close(
handle=self._handle, name=name) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/pkg_resources/_vendor/pyparsing.py | python | ParserElement.__add__ | (self, other ) | return And( [ self, other ] ) | Implementation of + operator - returns C{L{And}}. Adding strings to a ParserElement
converts them to L{Literal}s by default.
Example::
greet = Word(alphas) + "," + Word(alphas) + "!"
hello = "Hello, World!"
print (hello, "->", greet.parseString(hello))
Prints::
Hello, World! -> ['Hello', ',', 'World', '!'] | Implementation of + operator - returns C{L{And}}. Adding strings to a ParserElement
converts them to L{Literal}s by default.
Example::
greet = Word(alphas) + "," + Word(alphas) + "!"
hello = "Hello, World!"
print (hello, "->", greet.parseString(hello))
Prints::
Hello, World! -> ['Hello', ',', 'World', '!'] | [
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"""
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Example::
greet = Word(alphas) + "," + Word(alphas) + "!"
hello = "Hello, World!"
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Prints::
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"""
if isinstance( other, basestring ):
other = ParserElement._literalStringClass( other )
if not isinstance( other, ParserElement ):
warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
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return None
return And( [ self, other ] ) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/computation/scope.py | python | Scope.has_resolvers | (self) | return bool(len(self.resolvers)) | Return whether we have any extra scope.
For example, DataFrames pass Their columns as resolvers during calls to
``DataFrame.eval()`` and ``DataFrame.query()``.
Returns
-------
hr : bool | Return whether we have any extra scope. | [
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"""
Return whether we have any extra scope.
For example, DataFrames pass Their columns as resolvers during calls to
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Returns
-------
hr : bool
"""
return bool(len(self.resolvers)) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/ftplib.py | python | FTP.set_pasv | (self, val) | Use passive or active mode for data transfers.
With a false argument, use the normal PORT mode,
With a true argument, use the PASV command. | Use passive or active mode for data transfers.
With a false argument, use the normal PORT mode,
With a true argument, use the PASV command. | [
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] | def set_pasv(self, val):
'''Use passive or active mode for data transfers.
With a false argument, use the normal PORT mode,
With a true argument, use the PASV command.'''
self.passiveserver = val | [
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microsoft/CNTK | e9396480025b9ca457d26b6f33dd07c474c6aa04 | bindings/python/cntk/ops/sequence/__init__.py | python | past_value | (x, initial_state=None, time_step=1, name='') | return past_value(x, initial_state, time_step, name) | This function returns the past value w.r.t. ``x``. It is most often used when
creating RNNs. The resulting tensor has the same shape as the input but is
the previous logical sample. The ``time_step`` parameter is the number of steps
to look into the past and is 1 by default. If there is no past value (i.e.
the current sample is the first one in the tensor) then the ``initial_state``
value is returned.
The initial state can be a constant (scalar or tensor), a learnable tensor
or input data (which has a batch dimension, as needed for sequence-to-sequence models).
Example:
>>> # create example input: one sequence with 4 tensors of shape (3, 2)
>>> from cntk.layers.typing import Tensor, Sequence
>>> x = C.sequence.input_variable((3,2))
>>> x0 = np.reshape(np.arange(24,dtype=np.float32),(1,4,3,2))
>>> x0
array([[[[ 0., 1.],
[ 2., 3.],
[ 4., 5.]],
<BLANKLINE>
[[ 6., 7.],
[ 8., 9.],
[ 10., 11.]],
<BLANKLINE>
[[ 12., 13.],
[ 14., 15.],
[ 16., 17.]],
<BLANKLINE>
[[ 18., 19.],
[ 20., 21.],
[ 22., 23.]]]], dtype=float32)
>>> # this demonstrates how past_value shifts the sequence by one, padding with initial_state
>>> y = C.sequence.past_value(x) # initial_state is 0 by default
>>> y.eval({x:x0})
[array([[[ 0., 0.],
[ 0., 0.],
[ 0., 0.]],
<BLANKLINE>
[[ 0., 1.],
[ 2., 3.],
[ 4., 5.]],
<BLANKLINE>
[[ 6., 7.],
[ 8., 9.],
[ 10., 11.]],
<BLANKLINE>
[[ 12., 13.],
[ 14., 15.],
[ 16., 17.]]], dtype=float32)]
>>> # here, we pass a the initial_state as input data (e.g. sequence-to-sequence)
>>> s = C.input_variable((3,2)) # not a sequence, e.g. a final encoder hidden state
>>> s0 = np.reshape(np.arange(6,dtype=np.float32)/2,(1,3,2))
>>> s0
array([[[ 0. , 0.5],
[ 1. , 1.5],
[ 2. , 2.5]]], dtype=float32)
>>> y = C.sequence.past_value(x, initial_state=s)
>>> y.eval({x:x0, s:s0}) # same as the previous example except for the first time step
[array([[[ 0. , 0.5],
[ 1. , 1.5],
[ 2. , 2.5]],
<BLANKLINE>
[[ 0. , 1. ],
[ 2. , 3. ],
[ 4. , 5. ]],
<BLANKLINE>
[[ 6. , 7. ],
[ 8. , 9. ],
[ 10. , 11. ]],
<BLANKLINE>
[[ 12. , 13. ],
[ 14. , 15. ],
[ 16. , 17. ]]], dtype=float32)]
Args:
x: the tensor (or its name) from which the past value is obtained
initial_state: tensor or scalar representing the initial value to be used when the input tensor is shifted in time.
time_step (int): the number of time steps to look into the past (default 1)
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function` | This function returns the past value w.r.t. ``x``. It is most often used when
creating RNNs. The resulting tensor has the same shape as the input but is
the previous logical sample. The ``time_step`` parameter is the number of steps
to look into the past and is 1 by default. If there is no past value (i.e.
the current sample is the first one in the tensor) then the ``initial_state``
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'''
This function returns the past value w.r.t. ``x``. It is most often used when
creating RNNs. The resulting tensor has the same shape as the input but is
the previous logical sample. The ``time_step`` parameter is the number of steps
to look into the past and is 1 by default. If there is no past value (i.e.
the current sample is the first one in the tensor) then the ``initial_state``
value is returned.
The initial state can be a constant (scalar or tensor), a learnable tensor
or input data (which has a batch dimension, as needed for sequence-to-sequence models).
Example:
>>> # create example input: one sequence with 4 tensors of shape (3, 2)
>>> from cntk.layers.typing import Tensor, Sequence
>>> x = C.sequence.input_variable((3,2))
>>> x0 = np.reshape(np.arange(24,dtype=np.float32),(1,4,3,2))
>>> x0
array([[[[ 0., 1.],
[ 2., 3.],
[ 4., 5.]],
<BLANKLINE>
[[ 6., 7.],
[ 8., 9.],
[ 10., 11.]],
<BLANKLINE>
[[ 12., 13.],
[ 14., 15.],
[ 16., 17.]],
<BLANKLINE>
[[ 18., 19.],
[ 20., 21.],
[ 22., 23.]]]], dtype=float32)
>>> # this demonstrates how past_value shifts the sequence by one, padding with initial_state
>>> y = C.sequence.past_value(x) # initial_state is 0 by default
>>> y.eval({x:x0})
[array([[[ 0., 0.],
[ 0., 0.],
[ 0., 0.]],
<BLANKLINE>
[[ 0., 1.],
[ 2., 3.],
[ 4., 5.]],
<BLANKLINE>
[[ 6., 7.],
[ 8., 9.],
[ 10., 11.]],
<BLANKLINE>
[[ 12., 13.],
[ 14., 15.],
[ 16., 17.]]], dtype=float32)]
>>> # here, we pass a the initial_state as input data (e.g. sequence-to-sequence)
>>> s = C.input_variable((3,2)) # not a sequence, e.g. a final encoder hidden state
>>> s0 = np.reshape(np.arange(6,dtype=np.float32)/2,(1,3,2))
>>> s0
array([[[ 0. , 0.5],
[ 1. , 1.5],
[ 2. , 2.5]]], dtype=float32)
>>> y = C.sequence.past_value(x, initial_state=s)
>>> y.eval({x:x0, s:s0}) # same as the previous example except for the first time step
[array([[[ 0. , 0.5],
[ 1. , 1.5],
[ 2. , 2.5]],
<BLANKLINE>
[[ 0. , 1. ],
[ 2. , 3. ],
[ 4. , 5. ]],
<BLANKLINE>
[[ 6. , 7. ],
[ 8. , 9. ],
[ 10. , 11. ]],
<BLANKLINE>
[[ 12. , 13. ],
[ 14. , 15. ],
[ 16. , 17. ]]], dtype=float32)]
Args:
x: the tensor (or its name) from which the past value is obtained
initial_state: tensor or scalar representing the initial value to be used when the input tensor is shifted in time.
time_step (int): the number of time steps to look into the past (default 1)
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.internal import sanitize_dtype_cntk
from cntk.cntk_py import Constant, past_value
if initial_state is None:
initial_state = Constant.scalar(sanitize_dtype_cntk(x.dtype), 0.0)
else:
initial_state = sanitize_input(initial_state)
x = sanitize_input(x)
return past_value(x, initial_state, time_step, name) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/ttk.py | python | Progressbar.step | (self, amount=None) | Increments the value option by amount.
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ppizarro/coursera | b39847928df4d9d5986b801085c025e8e9122b6a | stanford-algorithms1/programming6/2sum/2SUM.py | python | TwoSum_Naive | (lst, target) | return None | Naive 2-SUM algorithm.
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'''
Naive 2-SUM algorithm.
O(n^2) time.
'''
for x in lst:
for y in lst:
if x != y and x+y == target:
return (x, y)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | Image.ConvertToGreyscale | (*args) | return _core_.Image_ConvertToGreyscale(*args) | ConvertToGreyscale(self) -> Image
ConvertToGreyscale(self, double lr, double lg, double lb) -> Image
Convert to greyscale image. Uses the luminance component (Y) of the
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pyne/pyne | 0c2714d7c0d1b5e20be6ae6527da2c660dd6b1b3 | pyne/xs/data_source.py | python | EAFDataSource._load_group_structure | (self) | Loads the EAF energy bounds array, E_g, from nuc_data. | Loads the EAF energy bounds array, E_g, from nuc_data. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_pyio.py | python | IOBase.truncate | (self, pos=None) | Truncate file to size bytes.
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wujian16/Cornell-MOE | df299d1be882d2af9796d7a68b3f9505cac7a53e | moe/optimal_learning/python/cpp_wrappers/knowledge_gradient_mcmc.py | python | PosteriorMeanMCMC.set_current_point | (self, points_to_sample) | Set current_point to the specified point; ordering must match.
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:type points_to_sample: array of float64 with shape (problem_size) | Set current_point to the specified point; ordering must match.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/lib2to3/fixer_util.py | python | does_tree_import | (package, name, node) | return bool(binding) | Returns true if name is imported from package at the
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generalized-intelligence/GAAS | 29ab17d3e8a4ba18edef3a57c36d8db6329fac73 | deprecated/algorithms/sfm/dataset.py | python | DataSet._undistorted_segmentation_file | (self, image) | return os.path.join(self._undistorted_segmentation_path(), image + '.png') | Path of undistorted version of a segmentation. | Path of undistorted version of a segmentation. | [
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wiseio/paratext | 2e28660b48e61e7aa172129507206fcea6e57446 | python/paratext/core.py | python | baseline_disk_to_mem | (filename, *args, **kwargs) | return count | This function copies the contents of a file into a collection of buffers.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | BoxSizer.GetOrientation | (*args, **kwargs) | return _core_.BoxSizer_GetOrientation(*args, **kwargs) | GetOrientation(self) -> int
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/android/shared_android_state.py | python | SharedAndroidState.TearDownState | (self) | Tear down anything created in the __init__ method that is not needed.
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/clang_format.py | python | format_func | (clang_format) | Format files command entry point. | Format files command entry point. | [
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/affine/unary_operators.py | python | NegExpression.sign_from_args | (self) | return (self.args[0].is_nonpos(), self.args[0].is_nonneg()) | Returns sign (is positive, is negative) of the expression. | Returns sign (is positive, is negative) of the expression. | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBBroadcaster.EventTypeHasListeners | (self, *args) | return _lldb.SBBroadcaster_EventTypeHasListeners(self, *args) | EventTypeHasListeners(self, uint32_t event_type) -> bool | EventTypeHasListeners(self, uint32_t event_type) -> bool | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/Inelastic/Direct/RunDescriptor.py | python | RunDescriptor._split_ws_name | (self,ws_name) | Method to split existing workspace name
into parts, in such a way that _build_name would restore the same name | Method to split existing workspace name
into parts, in such a way that _build_name would restore the same name | [
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] | def _split_ws_name(self,ws_name):
"""Method to split existing workspace name
into parts, in such a way that _build_name would restore the same name
"""
# Remove suffix
name = self.rremove(ws_name,self._ws_suffix)
if self._run_list:
summed = RunDescriptor._holder.sum_runs
sumExt = self._run_list.sum_ext(summed)
else:
sumExt = ''
if len(sumExt) > 0:
name = self.rremove(ws_name,sumExt)
# remove _prop_name:
name = name.replace(self._prop_name,'',1)
try:
part_ind = re.search('#(.+?)#', name).group(0)
name = name.replace(part_ind,'',1)
except AttributeError:
part_ind = ''
if self._run_number:
instr_name = self._instr_name()
name = name.replace(instr_name,'',1)
# Hell knows how to redefine these warnings or if they are valid or not
#pylint: disable=W0141
#pylint: disable=W0110
self._ws_cname = part_ind + ''.join(re.findall(r'\D+', name))
else:
#pylint: disable=attribute-defined-outside-init
self._ws_cname = part_ind + name | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/PIL/ImageFile.py | python | Parser.close | (self) | return self.image | (Consumer) Close the stream.
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"""
(Consumer) Close the stream.
:returns: An image object.
:exception IOError: If the parser failed to parse the image file either
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"""
# finish decoding
if self.decoder:
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# incremental parsing not possible; reopen the file
# not that we have all data
with io.BytesIO(self.data) as fp:
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/distutils/misc_util.py | python | is_local_src_dir | (directory) | return os.path.isdir(new_dir) | Return true if directory is local directory. | Return true if directory is local directory. | [
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"""Return true if directory is local directory.
"""
if not is_string(directory):
return False
abs_dir = os.path.abspath(directory)
c = os.path.commonprefix([os.getcwd(), abs_dir])
new_dir = abs_dir[len(c):].split(os.sep)
if new_dir and not new_dir[0]:
new_dir = new_dir[1:]
if new_dir and new_dir[0]=='build':
return False
new_dir = os.sep.join(new_dir)
return os.path.isdir(new_dir) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/nn_ops.py | python | _convert_padding | (padding) | return padding, explicit_paddings | Converts Python padding to C++ padding for ops which take EXPLICIT padding.
Args:
padding: the `padding` argument for a Python op which supports EXPLICIT
padding.
Returns:
(padding, explicit_paddings) pair, which should be passed as attributes to a
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Raises:
ValueError: If padding is invalid. | Converts Python padding to C++ padding for ops which take EXPLICIT padding. | [
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] | def _convert_padding(padding):
"""Converts Python padding to C++ padding for ops which take EXPLICIT padding.
Args:
padding: the `padding` argument for a Python op which supports EXPLICIT
padding.
Returns:
(padding, explicit_paddings) pair, which should be passed as attributes to a
C++ op.
Raises:
ValueError: If padding is invalid.
"""
explicit_paddings = []
if padding == "EXPLICIT":
# Give a better error message if EXPLICIT is passed.
raise ValueError('"EXPLICIT" is not a valid value for the padding '
"parameter. To use explicit padding, the padding "
"parameter must be a list.")
if isinstance(padding, (list, tuple)):
for i, dim_paddings in enumerate(padding):
if not isinstance(dim_paddings, (list, tuple)):
raise ValueError("When padding is a list, each element of padding must "
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if len(dim_paddings) != 2:
raise ValueError("When padding is a list, each element of padding must "
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explicit_paddings.extend(dim_paddings)
if len(padding) != 4:
raise ValueError("When padding is a list, it must be of size 4. Got "
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padding = "EXPLICIT"
return padding, explicit_paddings | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/flatmenu.py | python | FlatMenuBar.AddTool | (self, toolId, label="", bitmap1=wx.NullBitmap, bitmap2=wx.NullBitmap,
kind=wx.ITEM_NORMAL, shortHelp="", longHelp="") | Adds a tool to the toolbar.
:param integer `toolId`: an integer by which the tool may be identified in subsequent
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:param string `label`: the tool label string;
:param integer `kind`: may be ``wx.ITEM_NORMAL`` for a normal button (default),
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:param `bitmap1`: the primary tool bitmap, an instance of :class:`Bitmap`;
:param `bitmap2`: the bitmap used when the tool is disabled. If it is equal to
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:param string `shortHelp`: a string used for the tools tooltip;
:param string `longHelp`: this string is shown in the :class:`StatusBar` (if any) of the
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:param integer `toolId`: an integer by which the tool may be identified in subsequent
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:param string `label`: the tool label string;
:param integer `kind`: may be ``wx.ITEM_NORMAL`` for a normal button (default),
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toggled) or ``wx.ITEM_RADIO`` for a checkable tool which makes part of a radio
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:param `bitmap1`: the primary tool bitmap, an instance of :class:`Bitmap`;
:param `bitmap2`: the bitmap used when the tool is disabled. If it is equal to
:class:`NullBitmap`, the disabled bitmap is automatically generated by greing out
the normal one;
:param string `shortHelp`: a string used for the tools tooltip;
:param string `longHelp`: this string is shown in the :class:`StatusBar` (if any) of the
parent frame when the mouse pointer is inside the tool. | [
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"tool"... | def AddTool(self, toolId, label="", bitmap1=wx.NullBitmap, bitmap2=wx.NullBitmap,
kind=wx.ITEM_NORMAL, shortHelp="", longHelp=""):
"""
Adds a tool to the toolbar.
:param integer `toolId`: an integer by which the tool may be identified in subsequent
operations;
:param string `label`: the tool label string;
:param integer `kind`: may be ``wx.ITEM_NORMAL`` for a normal button (default),
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:param string `shortHelp`: a string used for the tools tooltip;
:param string `longHelp`: this string is shown in the :class:`StatusBar` (if any) of the
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"""
self._tbButtons.append(ToolBarItem(FlatToolbarItem(bitmap1, toolId, label, bitmap2, kind, shortHelp, longHelp), wx.Rect(), ControlNormal)) | [
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/mfg/algorithms/policy_value.py | python | PolicyValue.evaluate | (self) | Evaluate the value over states of self._policy. | Evaluate the value over states of self._policy. | [
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"""Evaluate the value over states of self._policy."""
for state in self._root_states:
self.eval_state(state) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/control/robotinterfaceutils.py | python | make_from_file | (fn : str, robotModel : RobotModel, *args,**kwargs) | return maker(robotModel,*args,**kwargs) | Create a RobotInterfaceBase from a Python file or module containing the
``make()`` function.
args and kwargs will be passed to ``make``.
Example::
iface = make_from_file('klampt.control.simrobotcontroller', robot) | Create a RobotInterfaceBase from a Python file or module containing the
``make()`` function. | [
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] | def make_from_file(fn : str, robotModel : RobotModel, *args,**kwargs) -> RobotInterfaceBase:
"""Create a RobotInterfaceBase from a Python file or module containing the
``make()`` function.
args and kwargs will be passed to ``make``.
Example::
iface = make_from_file('klampt.control.simrobotcontroller', robot)
"""
import importlib
if fn.endswith('py') or fn.endswith('pyc'):
import os
import sys
path,base = os.path.split(fn)
mod_name,file_ext = os.path.splitext(base)
sys.path.append(os.path.abspath(path))
mod = importlib.import_module(mod_name,base)
sys.path.pop(-1)
else:
mod = importlib.import_module(fn)
try:
maker = mod.make
except AttributeError:
print("Module",mod.__name__,"must have a make() method")
raise
return maker(robotModel,*args,**kwargs) | [
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Ifsttar/I-Simpa | 2283385f4cac769a92e265edabb9c79cb6c42d03 | currentRelease/SystemScript/graphy/backends/google_chart_api/encoders.py | python | BaseChartEncoder.Url | (self, width, height, use_html_entities=False) | return util.EncodeUrl(self.url_base, params, self.escape_url,
use_html_entities) | Get the URL for our graph.
Args:
use_html_entities: If True, reserved HTML characters (&, <, >, ") in the
URL are replaced with HTML entities (&, <, etc.). Default is False. | Get the URL for our graph. | [
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] | def Url(self, width, height, use_html_entities=False):
"""Get the URL for our graph.
Args:
use_html_entities: If True, reserved HTML characters (&, <, >, ") in the
URL are replaced with HTML entities (&, <, etc.). Default is False.
"""
self._width = width
self._height = height
params = self._Params(self.chart)
return util.EncodeUrl(self.url_base, params, self.escape_url,
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/distributions/python/ops/shape.py | python | _DistributionShape._introspect_ndims | (self, ndims) | return None, math_ops.equal(ndims, 0) | Helper to establish some properties of input ndims args. | Helper to establish some properties of input ndims args. | [
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] | def _introspect_ndims(self, ndims):
"""Helper to establish some properties of input ndims args."""
if self._is_all_constant_helper(ndims):
return (tensor_util.constant_value(ndims),
tensor_util.constant_value(ndims) == 0)
return None, math_ops.equal(ndims, 0) | [
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facebook/openr | ed38bdfd6bf290084bfab4821b59f83e7b59315d | openr/py/openr/cli/clis/prefix_mgr.py | python | AdvertisedRoutesCli.rejected_on_area | (
cli_opts: bunch.Bunch, area: str, prefix: List[str] # noqa: B902
) | Show routes rejected by area policy on advertisement | Show routes rejected by area policy on advertisement | [
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"on",
"advertisement"
] | def rejected_on_area(
cli_opts: bunch.Bunch, area: str, prefix: List[str] # noqa: B902
) -> None:
"""
Show routes rejected by area policy on advertisement
"""
opts = cli_opts.advertised_routes_options
prefix_mgr.AreaAdvertisedRoutesCmd(cli_opts).run(
area,
ctrl_types.RouteFilterType.REJECTED_ON_ADVERTISE,
prefix,
opts.prefix_type,
opts.json,
opts.detail,
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2.py | python | xmlDoc.xpathNewContext | (self) | return __tmp | Create a new xmlXPathContext | Create a new xmlXPathContext | [
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] | def xpathNewContext(self):
"""Create a new xmlXPathContext """
ret = libxml2mod.xmlXPathNewContext(self._o)
if ret is None:raise xpathError('xmlXPathNewContext() failed')
__tmp = xpathContext(_obj=ret)
return __tmp | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_misc.py | python | LogDebug | (*args, **kwargs) | return _misc_.LogDebug(*args, **kwargs) | LogDebug(String msg) | LogDebug(String msg) | [
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"""LogDebug(String msg)"""
return _misc_.LogDebug(*args, **kwargs) | [
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trailofbits/sienna-locomotive | 09bc1a0bea7d7a33089422c62e0d3c715ecb7ce0 | sl2/gui/__main__.py | python | MainWindow.handle_new_crash | (self, thread, run_id) | Updates the crash counter and pauses other threads if specified | Updates the crash counter and pauses other threads if specified | [
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""" Updates the crash counter and pauses other threads if specified """
self.crashes_model.update()
self.crashes_table.resizeColumnsToContents()
self.stats_widget.update()
self.crash_counter.increment()
crash = db.Crash.factory(run_id, get_target_slug(config.config))
if not crash:
return None
self.crashes.append(crash)
if not thread.should_fuzz:
self.pause_all_threads()
# self.triage_output.append(str(crash))
self.crashes.append(crash) | [
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | Bool | (name, ctx=None) | return BoolRef(Z3_mk_const(ctx.ref(), to_symbol(name, ctx), BoolSort(ctx).ast), ctx) | Return a Boolean constant named `name`. If `ctx=None`, then the global context is used.
>>> p = Bool('p')
>>> q = Bool('q')
>>> And(p, q)
And(p, q) | Return a Boolean constant named `name`. If `ctx=None`, then the global context is used. | [
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"""Return a Boolean constant named `name`. If `ctx=None`, then the global context is used.
>>> p = Bool('p')
>>> q = Bool('q')
>>> And(p, q)
And(p, q)
"""
ctx = _get_ctx(ctx)
return BoolRef(Z3_mk_const(ctx.ref(), to_symbol(name, ctx), BoolSort(ctx).ast), ctx) | [
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facebook/watchman | 0917460c71b000b96be9b9575d77f06f2f6053bb | build/fbcode_builder/getdeps/cargo.py | python | CargoBuilder._resolve_crate_to_path | (crate, git_conf) | Tries to find <crate> in git_conf["inst_dir"] by searching a [package]
keyword followed by name = "<crate>". | Tries to find <crate> in git_conf["inst_dir"] by searching a [package]
keyword followed by name = "<crate>". | [
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"""
Tries to find <crate> in git_conf["inst_dir"] by searching a [package]
keyword followed by name = "<crate>".
"""
source_dir = git_conf["source_dir"]
search_pattern = '[package]\nname = "{}"'.format(crate)
for root, _, files in os.walk(source_dir):
for fname in files:
if fname == "Cargo.toml":
with open(os.path.join(root, fname), "r") as f:
if search_pattern in f.read():
return root
raise Exception("Failed to found crate {} in path {}".format(crate, source_dir)) | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TNEANet.NodeAttrIsStrDeleted | (self, *args) | return _snap.TNEANet_NodeAttrIsStrDeleted(self, *args) | NodeAttrIsStrDeleted(TNEANet self, int const & NId, TStrIntPrH::TIter const & NodeHI) -> bool
Parameters:
NId: int const &
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"""
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Parameters:
NId: int const &
NodeHI: TStrIntPrH::TIter const &
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/mo/openvino/tools/mo/front/tf/extractors/utils.py | python | collect_tf_attrs | (attrs) | return ret_attrs | Function generates map for attributes and parsing functions
param: attrs - TF proto message with attributes
return: mapping attributes and parsing functions ready for use in update_node_stat function | Function generates map for attributes and parsing functions
param: attrs - TF proto message with attributes
return: mapping attributes and parsing functions ready for use in update_node_stat function | [
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"""
Function generates map for attributes and parsing functions
param: attrs - TF proto message with attributes
return: mapping attributes and parsing functions ready for use in update_node_stat function
"""
ret_attrs = {}
type_parsers = {
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'i': lambda x: x.i,
'f': lambda x: x.f,
'b': lambda x: x.b,
'type': lambda x: tf_dtype_extractor(x.type),
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for a in attrs:
t = check_attr_type(attrs[a])
a_l = attrs[a]
while t == 'list':
a_l = type_parsers[t](attrs[a])
t = check_attr_type(a_l)
ret_attrs[a] = type_parsers[t](a_l)
return ret_attrs | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/tools/inspector_protocol/jinja2/lexer.py | python | count_newlines | (value) | return len(newline_re.findall(value)) | Count the number of newline characters in the string. This is
useful for extensions that filter a stream. | Count the number of newline characters in the string. This is
useful for extensions that filter a stream. | [
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] | def count_newlines(value):
"""Count the number of newline characters in the string. This is
useful for extensions that filter a stream.
"""
return len(newline_re.findall(value)) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/fractions.py | python | Fraction._sub | (a, b) | return Fraction(a.numerator * b.denominator -
b.numerator * a.denominator,
a.denominator * b.denominator) | a - b | a - b | [
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"""a - b"""
return Fraction(a.numerator * b.denominator -
b.numerator * a.denominator,
a.denominator * b.denominator) | [
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MVIG-SJTU/RMPE | 5188c230ec800c12be7369c3619615bc9b020aa4 | scripts/cpp_lint.py | python | PrintUsage | (message) | Prints a brief usage string and exits, optionally with an error message.
Args:
message: The optional error message. | Prints a brief usage string and exits, optionally with an error message. | [
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] | def PrintUsage(message):
"""Prints a brief usage string and exits, optionally with an error message.
Args:
message: The optional error message.
"""
sys.stderr.write(_USAGE)
if message:
sys.exit('\nFATAL ERROR: ' + message)
else:
sys.exit(1) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/targets/linalg.py | python | matrix_rank_impl | (a, tol=None) | return _get_matrix_rank_impl(a, tol) | Computes rank for matrices and vectors.
The only issue that may arise is that because numpy uses double
precision lapack calls whereas numba uses type specific lapack
calls, some singular values may differ and therefore counting the
number of them above a tolerance may lead to different counts,
and therefore rank, in some cases. | Computes rank for matrices and vectors.
The only issue that may arise is that because numpy uses double
precision lapack calls whereas numba uses type specific lapack
calls, some singular values may differ and therefore counting the
number of them above a tolerance may lead to different counts,
and therefore rank, in some cases. | [
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"""
Computes rank for matrices and vectors.
The only issue that may arise is that because numpy uses double
precision lapack calls whereas numba uses type specific lapack
calls, some singular values may differ and therefore counting the
number of them above a tolerance may lead to different counts,
and therefore rank, in some cases.
"""
ensure_lapack()
_check_linalg_1_or_2d_matrix(a, "matrix_rank")
def _2d_matrix_rank_impl(a, tol):
# handle the tol==None case separately for type inference to work
if tol in (None, types.none):
nb_type = getattr(a.dtype, "underlying_float", a.dtype)
np_type = np_support.as_dtype(nb_type)
eps_val = np.finfo(np_type).eps
def _2d_tol_none_impl(a, tol=None):
s = _compute_singular_values(a)
# replicate numpy default tolerance calculation
r = a.shape[0]
c = a.shape[1]
l = max(r, c)
t = s[0] * l * eps_val
return _get_rank_from_singular_values(s, t)
return _2d_tol_none_impl
else:
def _2d_tol_not_none_impl(a, tol=None):
s = _compute_singular_values(a)
return _get_rank_from_singular_values(s, tol)
return _2d_tol_not_none_impl
def _get_matrix_rank_impl(a, tol):
ndim = a.ndim
if ndim == 1:
# NOTE: Technically, the numpy implementation could be argued as
# incorrect for the case of a vector (1D matrix). If a tolerance
# is provided and a vector with a singular value below tolerance is
# encountered this should report a rank of zero, the numpy
# implementation does not do this and instead elects to report that
# if any value in the vector is nonzero then the rank is 1.
# An example would be [0, 1e-15, 0, 2e-15] which numpy reports as
# rank 1 invariant of `tol`. The singular value for this vector is
# obviously sqrt(5)*1e-15 and so a tol of e.g. sqrt(6)*1e-15 should
# lead to a reported rank of 0 whereas a tol of 1e-15 should lead
# to a reported rank of 1, numpy reports 1 regardless.
# The code below replicates the numpy behaviour.
def _1d_matrix_rank_impl(a, tol=None):
for k in range(len(a)):
if a[k] != 0.:
return 1
return 0
return _1d_matrix_rank_impl
elif ndim == 2:
return _2d_matrix_rank_impl(a, tol)
else:
assert 0 # unreachable
return _get_matrix_rank_impl(a, tol) | [
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stellar-deprecated/stellard | 67eabb2217bdfa9a6ea317f62338fb6bca458c90 | src/protobuf/python/mox.py | python | MockMethod.AndRaise | (self, exception) | Set the exception to raise when this method is called.
Args:
# exception: the exception to raise when this method is called.
exception: Exception | Set the exception to raise when this method is called. | [
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"""Set the exception to raise when this method is called.
Args:
# exception: the exception to raise when this method is called.
exception: Exception
"""
self._exception = exception | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | src/third_party/scons-3.1.2/scons-local-3.1.2/SCons/Tool/pdflatex.py | python | generate | (env) | Add Builders and construction variables for pdflatex to an Environment. | Add Builders and construction variables for pdflatex to an Environment. | [
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] | def generate(env):
"""Add Builders and construction variables for pdflatex to an Environment."""
global PDFLaTeXAction
if PDFLaTeXAction is None:
PDFLaTeXAction = SCons.Action.Action('$PDFLATEXCOM', '$PDFLATEXCOMSTR')
global PDFLaTeXAuxAction
if PDFLaTeXAuxAction is None:
PDFLaTeXAuxAction = SCons.Action.Action(PDFLaTeXAuxFunction,
strfunction=SCons.Tool.tex.TeXLaTeXStrFunction)
env.AppendUnique(LATEXSUFFIXES=SCons.Tool.LaTeXSuffixes)
from . import pdf
pdf.generate(env)
bld = env['BUILDERS']['PDF']
bld.add_action('.ltx', PDFLaTeXAuxAction)
bld.add_action('.latex', PDFLaTeXAuxAction)
bld.add_emitter('.ltx', SCons.Tool.tex.tex_pdf_emitter)
bld.add_emitter('.latex', SCons.Tool.tex.tex_pdf_emitter)
SCons.Tool.tex.generate_common(env) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/richtext.py | python | RichTextObject.DeleteRange | (*args, **kwargs) | return _richtext.RichTextObject_DeleteRange(*args, **kwargs) | DeleteRange(self, RichTextRange range) -> bool | DeleteRange(self, RichTextRange range) -> bool | [
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"""DeleteRange(self, RichTextRange range) -> bool"""
return _richtext.RichTextObject_DeleteRange(*args, **kwargs) | [
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H-uru/Plasma | c2140ea046e82e9c199e257a7f2e7edb42602871 | Scripts/Python/xSimpleImager.py | python | xSimpleImager.OnNotify | (self,state,id,events) | They've entered into the imager's region... inform them thru the KI | They've entered into the imager's region... inform them thru the KI | [
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"They've entered into the imager's region... inform them thru the KI"
global CurrentDisplayedElementID
global RegionMembers
#~ PtDebugPrint("xSimpleImager: Notify event state=%f,id=%d,events=" % (state,id),events)
# is this our activator notifying us?
if id == ImagerRegion.id:
if PtWasLocallyNotified(self.key):
ageMgr = ptVault()
if PtIsInternalRelease() or (not ImagerMembersOnly.value or ageMgr.amOwnerOfCurrentAge()):
if id == ImagerRegion.id:
for event in events:
if event[0] == kCollisionEvent:
kiLevel = PtDetermineKILevel()
if (kiLevel < kNormalKI):
return
if ImagerPelletUpload.value:
messagetoki = str(ImagerName.value) + "<p>"
else:
messagetoki = ImagerName.value
if event[1]:
PtDebugPrint("xSimpleImager: add imager %s" % (ImagerName.value),level=kDebugDumpLevel)
PtSendKIMessage(kAddPlayerDevice,messagetoki)
RegionMembers = RegionMembers + 1
if (RegionMembers == 1):
ImagerButtonResp.run(self.key,state='buttonOn')
else:
PtDebugPrint("xSimpleImager: remove imager %s" % (ImagerName.value),level=kDebugDumpLevel)
PtSendKIMessage(kRemovePlayerDevice,messagetoki)
RegionMembers = RegionMembers - 1
if (RegionMembers == -1):
RegionMembers = 0
if (RegionMembers == 0):
ImagerButtonResp.run(self.key,state='buttonOff')
else:
# else it must be a notification back to ourselves...
# ...telling us to display a certain element
for event in events:
if event[0] == kVariableEvent:
if event[1][:7] == "dispID=":
newID = int(event[1][7:])
if newID != CurrentDisplayedElementID:
CurrentDisplayedElementID = newID
self.IShowCurrentContent()
elif event[1][:7] == "Update=":
newID = int(event[1][7:])
if not self.sceneobject.isLocallyOwned():
PtForceVaultNodeUpdate(newID)
self.IShowCurrentContent()
if newID == CurrentDisplayedElementID:
self.IShowCurrentContent()
elif event[1][:9] == "Uploaded=":
newID = int(event[1][9:])
if newID == CurrentDisplayedElementID:
self.IShowCurrentContent()
elif event[1][:7] == "Upload=":
deviceName = event[1][7:]
nodeId = int(event[3])
if deviceName == ImagerName.value:
ageVault = ptAgeVault()
folder = ageVault.getDeviceInbox(ImagerName.value)
if folder and PtWasLocallyNotified(self.key):
folder.linkToNode(nodeId)
selfnotify = ptNotify(self.key)
selfnotify.clearReceivers()
selfnotify.addReceiver(self.key)
selfnotify.netPropagate(True)
selfnotify.netForce(True)
selfnotify.setActivate(1.0)
sname = f"Uploaded={nodeId}"
selfnotify.addVarNumber(sname, 1.0)
selfnotify.send() | [
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htcondor/htcondor | 4829724575176d1d6c936e4693dfd78a728569b0 | src/blahp/src/scripts/pbs_status.py | python | log | (msg) | A very lightweight log - not meant to be used in production, but helps
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"""
A very lightweight log - not meant to be used in production, but helps
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"""
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D-X-Y/caffe-faster-rcnn | eb50c97ff48f3df115d0e85fe0a32b0c7e2aa4cb | scripts/cpp_lint.py | python | _CppLintState.SetVerboseLevel | (self, level) | return last_verbose_level | Sets the module's verbosity, and returns the previous setting. | Sets the module's verbosity, and returns the previous setting. | [
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"""Sets the module's verbosity, and returns the previous setting."""
last_verbose_level = self.verbose_level
self.verbose_level = level
return last_verbose_level | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/urllib3/response.py | python | HTTPResponse._init_length | (self, request_method) | return length | Set initial length value for Response content if available. | Set initial length value for Response content if available. | [
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"""
Set initial length value for Response content if available.
"""
length = self.headers.get("content-length")
if length is not None:
if self.chunked:
# This Response will fail with an IncompleteRead if it can't be
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# the response before raising an exception.
log.warning(
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return None
try:
# RFC 7230 section 3.3.2 specifies multiple content lengths can
# be sent in a single Content-Length header
# (e.g. Content-Length: 42, 42). This line ensures the values
# are all valid ints and that as long as the `set` length is 1,
# all values are the same. Otherwise, the header is invalid.
lengths = set([int(val) for val in length.split(",")])
if len(lengths) > 1:
raise InvalidHeader(
"Content-Length contained multiple "
"unmatching values (%s)" % length
)
length = lengths.pop()
except ValueError:
length = None
else:
if length < 0:
length = None
# Convert status to int for comparison
# In some cases, httplib returns a status of "_UNKNOWN"
try:
status = int(self.status)
except ValueError:
status = 0
# Check for responses that shouldn't include a body
if status in (204, 304) or 100 <= status < 200 or request_method == "HEAD":
length = 0
return length | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/learn/python/learn/estimators/estimator.py | python | Estimator.__init__ | (self,
model_fn=None,
model_dir=None,
config=None,
params=None,
feature_engineering_fn=None) | Constructs an Estimator instance.
Args:
model_fn: Model function, takes features and targets tensors or dicts of
tensors and returns predictions and loss tensors.
Supports next three signatures for the function:
* `(features, targets) -> (predictions, loss, train_op)`
* `(features, targets, mode) -> (predictions, loss, train_op)`
* `(features, targets, mode, params) -> (predictions, loss, train_op)`
Where
* `features` are single `Tensor` or `dict` of `Tensor`s
(depending on data passed to `fit`),
* `targets` are `Tensor` or `dict` of `Tensor`s (for multi-head
models). If mode is `ModeKeys.INFER`, `targets=None` will be
passed. If the `model_fn`'s signature does not accept
`mode`, the `model_fn` must still be able to handle
`targets=None`.
* `mode` represents if this training, evaluation or
prediction. See `ModeKeys`.
* `params` is a `dict` of hyperparameters. Will receive what
is passed to Estimator in `params` parameter. This allows
to configure Estimators from hyper parameter tunning.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator to
continue training a previously saved model.
config: Configuration object.
params: `dict` of hyper parameters that will be passed into `model_fn`.
Keys are names of parameters, values are basic python types.
feature_engineering_fn: Feature engineering function. Takes features and
targets which are the output of `input_fn` and
returns features and targets which will be fed
into `model_fn`. Please check `model_fn` for
a definition of features and targets.
Raises:
ValueError: parameters of `model_fn` don't match `params`. | Constructs an Estimator instance. | [
"Constructs",
"an",
"Estimator",
"instance",
"."
] | def __init__(self,
model_fn=None,
model_dir=None,
config=None,
params=None,
feature_engineering_fn=None):
"""Constructs an Estimator instance.
Args:
model_fn: Model function, takes features and targets tensors or dicts of
tensors and returns predictions and loss tensors.
Supports next three signatures for the function:
* `(features, targets) -> (predictions, loss, train_op)`
* `(features, targets, mode) -> (predictions, loss, train_op)`
* `(features, targets, mode, params) -> (predictions, loss, train_op)`
Where
* `features` are single `Tensor` or `dict` of `Tensor`s
(depending on data passed to `fit`),
* `targets` are `Tensor` or `dict` of `Tensor`s (for multi-head
models). If mode is `ModeKeys.INFER`, `targets=None` will be
passed. If the `model_fn`'s signature does not accept
`mode`, the `model_fn` must still be able to handle
`targets=None`.
* `mode` represents if this training, evaluation or
prediction. See `ModeKeys`.
* `params` is a `dict` of hyperparameters. Will receive what
is passed to Estimator in `params` parameter. This allows
to configure Estimators from hyper parameter tunning.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator to
continue training a previously saved model.
config: Configuration object.
params: `dict` of hyper parameters that will be passed into `model_fn`.
Keys are names of parameters, values are basic python types.
feature_engineering_fn: Feature engineering function. Takes features and
targets which are the output of `input_fn` and
returns features and targets which will be fed
into `model_fn`. Please check `model_fn` for
a definition of features and targets.
Raises:
ValueError: parameters of `model_fn` don't match `params`.
"""
super(Estimator, self).__init__(model_dir=model_dir, config=config)
if model_fn is not None:
# Check number of arguments of the given function matches requirements.
model_fn_args = _get_arguments(model_fn)
if params is not None and 'params' not in model_fn_args:
raise ValueError('Estimator\'s model_fn (%s) has less than 4 '
'arguments, but not None params (%s) are passed.' %
(model_fn, params))
if params is None and 'params' in model_fn_args:
logging.warning('Estimator\'s model_fn (%s) has includes params '
'argument, but params are not passed to Estimator.',
model_fn)
self._model_fn = model_fn
self.params = params
self._feature_engineering_fn = (
feature_engineering_fn or _identity_feature_engineering_fn) | [
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xlgames-inc/XLE | cdd8682367d9e9fdbdda9f79d72bb5b1499cec46 | Foreign/FreeType/src/tools/docmaker/tohtml.py | python | HtmlFormatter.make_html_items | ( self, items ) | return string.join( lines, '\n' ) | Convert a field's content into HTML. | Convert a field's content into HTML. | [
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] | def make_html_items( self, items ):
"""Convert a field's content into HTML."""
lines = []
for item in items:
if item.lines:
lines.append( self.make_html_code( item.lines ) )
else:
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return string.join( lines, '\n' ) | [
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fifengine/fifengine | 4b62c42e85bec19893cef8e63e6855927cff2c47 | engine/python/fife/extensions/serializers/xml_loader_tools.py | python | root_subfile | (masterfile, subfile) | return pathstr | Returns new path for given subfile (path), which is rooted against masterfile
E.g. if masterfile is ./../foo/bar.xml and subfile is ./../foo2/subfoo.xml,
returned path is ../foo2/subfoo.xml
NOTE: masterfile is expected to be *file*, not directory. subfile can be either | Returns new path for given subfile (path), which is rooted against masterfile
E.g. if masterfile is ./../foo/bar.xml and subfile is ./../foo2/subfoo.xml,
returned path is ../foo2/subfoo.xml
NOTE: masterfile is expected to be *file*, not directory. subfile can be either | [
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NOTE: masterfile is expected to be *file*, not directory. subfile can be either
"""
s = '/'
masterfile = norm_path(os.path.abspath(masterfile))
subfile = norm_path(os.path.abspath(subfile))
master_fragments = masterfile.split(s)
sub_fragments = subfile.split(s)
master_leftovers = []
sub_leftovers = []
for i in range(len(master_fragments)):
try:
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master_leftovers = master_fragments[i+1:]
sub_leftovers = sub_fragments[i+1:]
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break
pathstr = ''
for f in master_leftovers[:-1]:
pathstr += '..' + s
pathstr += s.join(sub_leftovers)
return pathstr | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/coverage/coverage/annotate.py | python | AnnotateReporter.report | (self, morfs, directory=None) | Run the report.
See `coverage.report()` for arguments. | Run the report. | [
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See `coverage.report()` for arguments.
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self.report_files(self.annotate_file, morfs, directory) | [
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vtraag/louvain-igraph | 124ea1be49ee74eec2eaca8006599d7fc5560db6 | src/louvain/functions.py | python | find_partition_temporal | (graphs, partition_type,
interslice_weight=1,
slice_attr='slice', vertex_id_attr='id',
edge_type_attr='type', weight_attr='weight',
seed=None,
**kwargs) | return membership_time_slices, improvement | Detect communities for temporal graphs.
Each graph is considered to represent a time slice and does not necessarily
need to be defined on the same set of vertices. Nodes in two consecutive
slices are identified on the basis of the ``vertex_id_attr``, i.e. if two
nodes in two consecutive slices have an identical value of the
``vertex_id_attr`` they are coupled. The ``vertex_id_attr`` should hence be
unique in each slice. The nodes are then coupled with a weight of
``interslice_weight`` which is set in the edge attribute ``weight_attr``. No
weight is set if the ``interslice_weight`` is None (i.e. corresponding in
practice with a weight of 1). See :func:`time_slices_to_layers` for
a more detailed explanation.
Parameters
----------
graphs : list of :class:`ig.Graph`
List of :class:`louvain.VertexPartition` layers to optimise.
partition_type : type of :class:`VertexPartition.MutableVertexPartition`
The type of partition to use for optimisation (identical for all graphs).
interslice_weight : float
The weight of the coupling between two consecutive time slices.
slice_attr : string
The vertex attribute to use for indicating the slice of a node.
vertex_id_attr : string
The vertex to use to identify nodes.
edge_type_attr : string
The edge attribute to use for indicating the type of link (`interslice` or
`intraslice`).
weight_attr : string
The edge attribute used to indicate the weight.
seed : int
Seed for the random number generator. By default uses a random seed
if nothing is specified.
**kwargs
Remaining keyword arguments, passed on to constructor of
``partition_type``.
Returns
-------
list of membership
list containing for each slice the membership vector.
float
Improvement in quality of combined partitions, see
:func:`Optimiser.optimise_partition_multiplex`.
See Also
--------
:func:`time_slices_to_layers`
:func:`slices_to_layers`
Examples
--------
>>> n = 100
>>> G_1 = ig.Graph.Lattice([n], 1)
>>> G_1.vs['id'] = range(n)
>>> G_2 = ig.Graph.Lattice([n], 1)
>>> G_2.vs['id'] = range(n)
>>> membership, improvement = louvain.find_partition_temporal([G_1, G_2],
... louvain.ModularityVertexPartition,
... interslice_weight=1) | Detect communities for temporal graphs. | [
"Detect",
"communities",
"for",
"temporal",
"graphs",
"."
] | def find_partition_temporal(graphs, partition_type,
interslice_weight=1,
slice_attr='slice', vertex_id_attr='id',
edge_type_attr='type', weight_attr='weight',
seed=None,
**kwargs):
""" Detect communities for temporal graphs.
Each graph is considered to represent a time slice and does not necessarily
need to be defined on the same set of vertices. Nodes in two consecutive
slices are identified on the basis of the ``vertex_id_attr``, i.e. if two
nodes in two consecutive slices have an identical value of the
``vertex_id_attr`` they are coupled. The ``vertex_id_attr`` should hence be
unique in each slice. The nodes are then coupled with a weight of
``interslice_weight`` which is set in the edge attribute ``weight_attr``. No
weight is set if the ``interslice_weight`` is None (i.e. corresponding in
practice with a weight of 1). See :func:`time_slices_to_layers` for
a more detailed explanation.
Parameters
----------
graphs : list of :class:`ig.Graph`
List of :class:`louvain.VertexPartition` layers to optimise.
partition_type : type of :class:`VertexPartition.MutableVertexPartition`
The type of partition to use for optimisation (identical for all graphs).
interslice_weight : float
The weight of the coupling between two consecutive time slices.
slice_attr : string
The vertex attribute to use for indicating the slice of a node.
vertex_id_attr : string
The vertex to use to identify nodes.
edge_type_attr : string
The edge attribute to use for indicating the type of link (`interslice` or
`intraslice`).
weight_attr : string
The edge attribute used to indicate the weight.
seed : int
Seed for the random number generator. By default uses a random seed
if nothing is specified.
**kwargs
Remaining keyword arguments, passed on to constructor of
``partition_type``.
Returns
-------
list of membership
list containing for each slice the membership vector.
float
Improvement in quality of combined partitions, see
:func:`Optimiser.optimise_partition_multiplex`.
See Also
--------
:func:`time_slices_to_layers`
:func:`slices_to_layers`
Examples
--------
>>> n = 100
>>> G_1 = ig.Graph.Lattice([n], 1)
>>> G_1.vs['id'] = range(n)
>>> G_2 = ig.Graph.Lattice([n], 1)
>>> G_2.vs['id'] = range(n)
>>> membership, improvement = louvain.find_partition_temporal([G_1, G_2],
... louvain.ModularityVertexPartition,
... interslice_weight=1)
"""
# Create layers
G_layers, G_interslice, G = time_slices_to_layers(graphs,
interslice_weight,
slice_attr=slice_attr,
vertex_id_attr=vertex_id_attr,
edge_type_attr=edge_type_attr,
weight_attr=weight_attr)
# Optimise partitions
arg_dict = {}
if 'node_sizes' in partition_type.__init__.__code__.co_varnames:
arg_dict['node_sizes'] = 'node_size'
if 'weights' in partition_type.__init__.__code__.co_varnames:
arg_dict['weights'] = 'weight'
arg_dict.update(kwargs)
partitions = []
for H in G_layers:
arg_dict['graph'] = H
partitions.append(partition_type(**arg_dict))
# We can always take the same interslice partition, as this should have no
# cost in the optimisation.
partition_interslice = CPMVertexPartition(G_interslice, resolution_parameter=0,
node_sizes='node_size', weights=weight_attr)
optimiser = Optimiser()
if (not seed is None):
optimiser.set_rng_seed(seed)
improvement = optimiser.optimise_partition_multiplex(partitions + [partition_interslice])
# Transform results back into original form.
membership = {(v[slice_attr], v[vertex_id_attr]): m for v, m in zip(G.vs, partitions[0].membership)}
membership_time_slices = []
for slice_idx, H in enumerate(graphs):
membership_slice = [membership[(slice_idx, v[vertex_id_attr])] for v in H.vs]
membership_time_slices.append(list(membership_slice))
return membership_time_slices, improvement | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/internals/blocks.py | python | Block._try_coerce_args | (self, values, other) | return values, other | provide coercion to our input arguments | provide coercion to our input arguments | [
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""" provide coercion to our input arguments """
if np.any(notna(other)) and not self._can_hold_element(other):
# coercion issues
# let higher levels handle
raise TypeError("cannot convert {} to an {}".format(
type(other).__name__,
type(self).__name__.lower().replace('Block', '')))
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/weights_broadcast_ops.py | python | assert_broadcastable | (weights, values) | Asserts `weights` can be broadcast to `values`.
In `tf.losses` and `tf.metrics`, we support limited weight broadcasting. We
let weights be either scalar, or the same rank as the target values, with each
dimension either 1, or the same as the corresponding values dimension.
Args:
weights: `Tensor` of weights.
values: `Tensor` of values to which weights are applied.
Returns:
`Operation` raising `InvalidArgumentError` if `weights` has incorrect shape.
`no_op` if static checks determine `weights` has correct shape.
Raises:
ValueError: If static checks determine `weights` has incorrect shape. | Asserts `weights` can be broadcast to `values`. | [
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] | def assert_broadcastable(weights, values):
"""Asserts `weights` can be broadcast to `values`.
In `tf.losses` and `tf.metrics`, we support limited weight broadcasting. We
let weights be either scalar, or the same rank as the target values, with each
dimension either 1, or the same as the corresponding values dimension.
Args:
weights: `Tensor` of weights.
values: `Tensor` of values to which weights are applied.
Returns:
`Operation` raising `InvalidArgumentError` if `weights` has incorrect shape.
`no_op` if static checks determine `weights` has correct shape.
Raises:
ValueError: If static checks determine `weights` has incorrect shape.
"""
with ops.name_scope(None, "assert_broadcastable", (weights, values)) as scope:
with ops.name_scope(None, "weights", (weights,)) as weights_scope:
weights = ops.convert_to_tensor(weights, name=weights_scope)
weights_shape = array_ops.shape(weights, name="shape")
weights_rank = array_ops.rank(weights, name="rank")
weights_rank_static = tensor_util.constant_value(weights_rank)
with ops.name_scope(None, "values", (values,)) as values_scope:
values = ops.convert_to_tensor(values, name=values_scope)
values_shape = array_ops.shape(values, name="shape")
values_rank = array_ops.rank(values, name="rank")
values_rank_static = tensor_util.constant_value(values_rank)
# Try static checks.
if weights_rank_static is not None and values_rank_static is not None:
if weights_rank_static == 0:
return control_flow_ops.no_op(name="static_scalar_check_success")
if weights_rank_static != values_rank_static:
raise ValueError(
"%s values.rank=%s. weights.rank=%s."
" values.shape=%s. weights.shape=%s." % (
_ASSERT_BROADCASTABLE_ERROR_PREFIX, values_rank_static,
weights_rank_static, values.shape, weights.shape))
weights_shape_static = tensor_util.constant_value(weights_shape)
values_shape_static = tensor_util.constant_value(values_shape)
if weights_shape_static is not None and values_shape_static is not None:
# Sanity check, this should always be true since we checked rank above.
ndims = len(values_shape_static)
assert ndims == len(weights_shape_static)
for i in range(ndims):
if weights_shape_static[i] not in (1, values_shape_static[i]):
raise ValueError(
"%s Mismatch at dim %s. values.shape=%s weights.shape=%s." % (
_ASSERT_BROADCASTABLE_ERROR_PREFIX, i, values_shape_static,
weights_shape_static))
return control_flow_ops.no_op(name="static_dims_check_success")
# Dynamic checks.
is_scalar = math_ops.equal(0, weights_rank, name="is_scalar")
data = (
_ASSERT_BROADCASTABLE_ERROR_PREFIX,
"weights.shape=", weights.name, weights_shape,
"values.shape=", values.name, values_shape,
"is_scalar=", is_scalar,
)
is_valid_shape = control_flow_ops.cond(
is_scalar,
lambda: is_scalar,
lambda: _has_valid_nonscalar_shape( # pylint: disable=g-long-lambda
weights_rank, weights_shape, values_rank, values_shape),
name="is_valid_shape")
return control_flow_ops.Assert(is_valid_shape, data, name=scope) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/pkg_resources/__init__.py | python | register_loader_type | (loader_type, provider_factory) | Register `provider_factory` to make providers for `loader_type`
`loader_type` is the type or class of a PEP 302 ``module.__loader__``,
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`loader_type` is the type or class of a PEP 302 ``module.__loader__``,
and `provider_factory` is a function that, passed a *module* object,
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"""
_provider_factories[loader_type] = provider_factory | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/tempfile.py | python | _candidate_tempdir_list | () | return dirlist | Generate a list of candidate temporary directories which
_get_default_tempdir will try. | Generate a list of candidate temporary directories which
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"""Generate a list of candidate temporary directories which
_get_default_tempdir will try."""
dirlist = []
# First, try the environment.
for envname in 'TMPDIR', 'TEMP', 'TMP':
dirname = _os.getenv(envname)
if dirname: dirlist.append(dirname)
# Failing that, try OS-specific locations.
if _os.name == 'nt':
dirlist.extend([ _os.path.expanduser(r'~\AppData\Local\Temp'),
_os.path.expandvars(r'%SYSTEMROOT%\Temp'),
r'c:\temp', r'c:\tmp', r'\temp', r'\tmp' ])
else:
dirlist.extend([ '/tmp', '/var/tmp', '/usr/tmp' ])
# As a last resort, the current directory.
try:
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gv22ga/dlib-face-recognition-android | 42d6305cbd85833f2b85bb79b70ab9ab004153c9 | tools/lint/cpplint.py | python | GetLineWidth | (line) | Determines the width of the line in column positions.
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Returns:
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Args:
line: A string, which may be a Unicode string.
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The width of the line in column positions, accounting for Unicode
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The width of the line in column positions, accounting for Unicode
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcode_emulation.py | python | XcodeArchsDefault.ActiveArchs | (self, archs, valid_archs, sdkroot) | return expanded_archs | Expands variables references in ARCHS, and filter by VALID_ARCHS if it
is defined (if not set, Xcode accept any value in ARCHS, otherwise, only
values present in VALID_ARCHS are kept). | Expands variables references in ARCHS, and filter by VALID_ARCHS if it
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expanded_archs = self._ExpandArchs(archs or self._default, sdkroot or '')
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exodus2.in.py | python | basename | (file_name) | return base_name | Extract base name from file_name.
basename("test.e") -> "test" | Extract base name from file_name.
basename("test.e") -> "test" | [
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"""
Extract base name from file_name.
basename("test.e") -> "test"
"""
fileParts = file_name.split(".")
base_name = ".".join(fileParts[:-1])
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TStr.PutFExt | (*args) | return _snap.TStr_PutFExt(*args) | PutFExt(TStr FNm, TStr FExt) -> TStr
Parameters:
FNm: TStr const &
FExt: TStr const & | PutFExt(TStr FNm, TStr FExt) -> TStr | [
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"""
PutFExt(TStr FNm, TStr FExt) -> TStr
Parameters:
FNm: TStr const &
FExt: TStr const &
"""
return _snap.TStr_PutFExt(*args) | [
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GoSSIP-SJTU/Armariris | ad5d868482956b2194a77b39c8d543c7c2318200 | tools/clang/bindings/python/clang/cindex.py | python | FileInclusion.is_input_file | (self) | return self.depth == 0 | True if the included file is the input file. | True if the included file is the input file. | [
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RobotLocomotion/drake | 0e18a34604c45ed65bc9018a54f7610f91cdad5b | common/proto/call_python_client.py | python | CallPythonClient.handle_messages | (self, max_count=None, record=True, execute=True) | return (count, msgs) | Handle all messages sent (e.g., through IPython).
@param max_count Maximum number of messages to handle.
@param record Record all messages and return them.
@param execute Execute the given message upon receiving it.
@return (count, msgs) where `count` is how many messages were processed
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@param max_count Maximum number of messages to handle.
@param record Record all messages and return them.
@param execute Execute the given message upon receiving it.
@return (count, msgs) where `count` is how many messages were processed
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"""
assert record or execute, "Not doing anything useful?"
count = 0
msgs = []
for msg in self._read_next_message():
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self._execute_message(msg)
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if record:
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return (count, msgs) | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/distutils/ccompiler.py | python | CCompiler.set_runtime_library_dirs | (self, dirs) | Set the list of directories to search for shared libraries at
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/collections.py | python | OrderedDict.__eq__ | (self, other) | return dict.__eq__(self, other) | od.__eq__(y) <==> od==y. Comparison to another OD is order-sensitive
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if isinstance(other, OrderedDict):
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/eclib/pstatbar.py | python | ProgressStatusBar.GetGauge | (self) | return self.prog | Return the wx.Gauge used by this window
@return: wx.Gauge | Return the wx.Gauge used by this window
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/jsonschema/validators.py | python | RefResolver.resolve_from_url | (self, url) | return self.resolve_fragment(document, fragment) | Resolve the given remote URL. | Resolve the given remote URL. | [
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"""
Resolve the given remote URL.
"""
url, fragment = urldefrag(url)
try:
document = self.store[url]
except KeyError:
try:
document = self.resolve_remote(url)
except Exception as exc:
raise exceptions.RefResolutionError(exc)
return self.resolve_fragment(document, fragment) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/urllib3/contrib/securetransport.py | python | _read_callback | (connection_id, data_buffer, data_length_pointer) | SecureTransport read callback. This is called by ST to request that data
be returned from the socket. | SecureTransport read callback. This is called by ST to request that data
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"""
SecureTransport read callback. This is called by ST to request that data
be returned from the socket.
"""
wrapped_socket = None
try:
wrapped_socket = _connection_refs.get(connection_id)
if wrapped_socket is None:
return SecurityConst.errSSLInternal
base_socket = wrapped_socket.socket
requested_length = data_length_pointer[0]
timeout = wrapped_socket.gettimeout()
error = None
read_count = 0
try:
while read_count < requested_length:
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remaining = requested_length - read_count
buffer = (ctypes.c_char * remaining).from_address(
data_buffer + read_count
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chunk_size = base_socket.recv_into(buffer, remaining)
read_count += chunk_size
if not chunk_size:
if not read_count:
return SecurityConst.errSSLClosedGraceful
break
except (socket.error) as e:
error = e.errno
if error is not None and error != errno.EAGAIN:
data_length_pointer[0] = read_count
if error == errno.ECONNRESET or error == errno.EPIPE:
return SecurityConst.errSSLClosedAbort
raise
data_length_pointer[0] = read_count
if read_count != requested_length:
return SecurityConst.errSSLWouldBlock
return 0
except Exception as e:
if wrapped_socket is not None:
wrapped_socket._exception = e
return SecurityConst.errSSLInternal | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/operator_pd_vdvt_update.py | python | OperatorPDSqrtVDVTUpdate.get_shape | (self) | return self._operator.get_shape() | Static `TensorShape` of entire operator.
If this operator represents the batch matrix `A` with
`A.shape = [N1,...,Nn, k, k]`, then this returns
`TensorShape([N1,...,Nn, k, k])`
Returns:
`TensorShape`, statically determined, may be undefined. | Static `TensorShape` of entire operator. | [
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] | def get_shape(self):
"""Static `TensorShape` of entire operator.
If this operator represents the batch matrix `A` with
`A.shape = [N1,...,Nn, k, k]`, then this returns
`TensorShape([N1,...,Nn, k, k])`
Returns:
`TensorShape`, statically determined, may be undefined.
"""
return self._operator.get_shape() | [
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