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openthread/openthread | 9fcdbed9c526c70f1556d1ed84099c1535c7cd32 | tools/harness-thci/OpenThread_WpanCtl.py | python | OpenThread_WpanCtl.MGMT_ACTIVE_GET | (self, Addr='', TLVs=[]) | send MGMT_ACTIVE_GET command
Returns:
True: successful to send MGMT_ACTIVE_GET
False: fail to send MGMT_ACTIVE_GET | send MGMT_ACTIVE_GET command | [
"send",
"MGMT_ACTIVE_GET",
"command"
] | def MGMT_ACTIVE_GET(self, Addr='', TLVs=[]):
"""send MGMT_ACTIVE_GET command
Returns:
True: successful to send MGMT_ACTIVE_GET
False: fail to send MGMT_ACTIVE_GET
"""
print('%s call MGMT_ACTIVE_GET' % self.port)
try:
cmd = self.wpan_cmd_prefix + 'dataset mgmt-get-active'
if len(TLVs) != 0:
tlvs = ''.join('%02x' % tlv for tlv in TLVs)
setTLVCmd = self.wpan_cmd_prefix + 'setprop Dataset:RawTlvs ' + tlvs
if self.__sendCommand(setTLVCmd)[0] == 'Fail':
return False
else:
if self.__sendCommand(self.wpan_cmd_prefix + 'dataset erase')[0] == 'Fail':
return False
if Addr != '':
setAddressCmd = self.wpan_cmd_prefix + 'setprop Dataset:DestIpAddress ' + Addr
if self.__sendCommand(setAddressCmd)[0] == 'Fail':
return False
print(cmd)
return self.__sendCommand(cmd)[0] != 'Fail'
except Exception as e:
ModuleHelper.WriteIntoDebugLogger('MGMT_ACTIVE_GET() Error: ' + str(e)) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/slim/python/slim/summaries.py | python | add_zero_fraction_summaries | (tensors, prefix=None) | return summary_ops | Adds a scalar zero-fraction summary for each of the given tensors.
Args:
tensors: a list of variable or op tensors.
prefix: An optional prefix for the summary names.
Returns:
A list of scalar `Tensors` of type `string` whose contents are the
serialized `Summary` protocol buffer. | Adds a scalar zero-fraction summary for each of the given tensors. | [
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] | def add_zero_fraction_summaries(tensors, prefix=None):
"""Adds a scalar zero-fraction summary for each of the given tensors.
Args:
tensors: a list of variable or op tensors.
prefix: An optional prefix for the summary names.
Returns:
A list of scalar `Tensors` of type `string` whose contents are the
serialized `Summary` protocol buffer.
"""
summary_ops = []
for tensor in tensors:
summary_ops.append(add_zero_fraction_summary(tensor, prefix=prefix))
return summary_ops | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/plat-mac/lib-scriptpackages/StdSuites/Standard_Suite.py | python | Standard_Suite_Events.quit | (self, _no_object=None, _attributes={}, **_arguments) | quit: Quit an application
Keyword argument saving: specifies whether to save currently open documents
Keyword argument _attributes: AppleEvent attribute dictionary | quit: Quit an application
Keyword argument saving: specifies whether to save currently open documents
Keyword argument _attributes: AppleEvent attribute dictionary | [
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"""quit: Quit an application
Keyword argument saving: specifies whether to save currently open documents
Keyword argument _attributes: AppleEvent attribute dictionary
"""
_code = 'aevt'
_subcode = 'quit'
aetools.keysubst(_arguments, self._argmap_quit)
if _no_object is not None: raise TypeError, 'No direct arg expected'
aetools.enumsubst(_arguments, 'savo', _Enum_savo)
_reply, _arguments, _attributes = self.send(_code, _subcode,
_arguments, _attributes)
if _arguments.get('errn', 0):
raise aetools.Error, aetools.decodeerror(_arguments)
# XXXX Optionally decode result
if _arguments.has_key('----'):
return _arguments['----'] | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/learn/python/learn/estimators/svm.py | python | SVM.export_with_defaults | (
self,
export_dir,
signature_fn=None,
input_fn=None,
default_batch_size=1,
exports_to_keep=None) | return super(SVM, self).export(export_dir=export_dir,
signature_fn=signature_fn,
input_fn=input_fn or default_input_fn,
default_batch_size=default_batch_size,
exports_to_keep=exports_to_keep) | Same as BaseEstimator.export, but uses some defaults. | Same as BaseEstimator.export, but uses some defaults. | [
"Same",
"as",
"BaseEstimator",
".",
"export",
"but",
"uses",
"some",
"defaults",
"."
] | def export_with_defaults(
self,
export_dir,
signature_fn=None,
input_fn=None,
default_batch_size=1,
exports_to_keep=None):
"""Same as BaseEstimator.export, but uses some defaults."""
def default_input_fn(unused_estimator, examples):
return layers.parse_feature_columns_from_examples(
examples, self._feature_columns)
return super(SVM, self).export(export_dir=export_dir,
signature_fn=signature_fn,
input_fn=input_fn or default_input_fn,
default_batch_size=default_batch_size,
exports_to_keep=exports_to_keep) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/distutils/spawn.py | python | spawn | (cmd, search_path=1, verbose=0, dry_run=0) | Run another program, specified as a command list 'cmd', in a new process.
'cmd' is just the argument list for the new process, ie.
cmd[0] is the program to run and cmd[1:] are the rest of its arguments.
There is no way to run a program with a name different from that of its
executable.
If 'search_path' is true (the default), the system's executable
search path will be used to find the program; otherwise, cmd[0]
must be the exact path to the executable. If 'dry_run' is true,
the command will not actually be run.
Raise DistutilsExecError if running the program fails in any way; just
return on success. | Run another program, specified as a command list 'cmd', in a new process. | [
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] | def spawn(cmd, search_path=1, verbose=0, dry_run=0):
"""Run another program, specified as a command list 'cmd', in a new process.
'cmd' is just the argument list for the new process, ie.
cmd[0] is the program to run and cmd[1:] are the rest of its arguments.
There is no way to run a program with a name different from that of its
executable.
If 'search_path' is true (the default), the system's executable
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must be the exact path to the executable. If 'dry_run' is true,
the command will not actually be run.
Raise DistutilsExecError if running the program fails in any way; just
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"""
if os.name == 'posix':
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elif os.name == 'nt':
_spawn_nt(cmd, search_path, dry_run=dry_run)
elif os.name == 'os2':
_spawn_os2(cmd, search_path, dry_run=dry_run)
else:
raise DistutilsPlatformError, \
"don't know how to spawn programs on platform '%s'" % os.name | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/layers/metric_op.py | python | accuracy | (input, label, k=1, correct=None, total=None) | return acc_out | accuracy layer.
Refer to the https://en.wikipedia.org/wiki/Precision_and_recall
This function computes the accuracy using the input and label.
If the correct label occurs in top k predictions, then correct will increment by one.
Note: the dtype of accuracy is determined by input. the input and label dtype can be different.
Args:
input(Variable): The input of accuracy layer, which is the predictions of network. A LoDTensor or Tensor with type float32,float64.
The shape is ``[sample_number, class_dim]`` .
label(Variable): The label of dataset. LoDTensor or Tensor with type int32,int64. The shape is ``[sample_number, 1]`` .
k(int): The top k predictions for each class will be checked. Data type is int64 or int32.
correct(Variable): The correct predictions count. A Tensor with type int64 or int32.
total(Variable): The total entries count. A tensor with type int64 or int32.
Returns:
Variable: The correct rate. A Tensor with type float32.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.static as static
import paddle.nn.functional as F
paddle.enable_static()
data = static.data(name="input", shape=[-1, 32, 32], dtype="float32")
label = static.data(name="label", shape=[-1,1], dtype="int")
fc_out = static.nn.fc(x=data, size=10)
predict = F.softmax(x=fc_out)
result = static.accuracy(input=predict, label=label, k=5)
place = paddle.CPUPlace()
exe = static.Executor(place)
exe.run(static.default_startup_program())
x = np.random.rand(3, 32, 32).astype("float32")
y = np.array([[1],[0],[1]])
output= exe.run(feed={"input": x,"label": y},
fetch_list=[result[0]])
print(output)
#[array([0.], dtype=float32)] | accuracy layer.
Refer to the https://en.wikipedia.org/wiki/Precision_and_recall | [
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"""
accuracy layer.
Refer to the https://en.wikipedia.org/wiki/Precision_and_recall
This function computes the accuracy using the input and label.
If the correct label occurs in top k predictions, then correct will increment by one.
Note: the dtype of accuracy is determined by input. the input and label dtype can be different.
Args:
input(Variable): The input of accuracy layer, which is the predictions of network. A LoDTensor or Tensor with type float32,float64.
The shape is ``[sample_number, class_dim]`` .
label(Variable): The label of dataset. LoDTensor or Tensor with type int32,int64. The shape is ``[sample_number, 1]`` .
k(int): The top k predictions for each class will be checked. Data type is int64 or int32.
correct(Variable): The correct predictions count. A Tensor with type int64 or int32.
total(Variable): The total entries count. A tensor with type int64 or int32.
Returns:
Variable: The correct rate. A Tensor with type float32.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.static as static
import paddle.nn.functional as F
paddle.enable_static()
data = static.data(name="input", shape=[-1, 32, 32], dtype="float32")
label = static.data(name="label", shape=[-1,1], dtype="int")
fc_out = static.nn.fc(x=data, size=10)
predict = F.softmax(x=fc_out)
result = static.accuracy(input=predict, label=label, k=5)
place = paddle.CPUPlace()
exe = static.Executor(place)
exe.run(static.default_startup_program())
x = np.random.rand(3, 32, 32).astype("float32")
y = np.array([[1],[0],[1]])
output= exe.run(feed={"input": x,"label": y},
fetch_list=[result[0]])
print(output)
#[array([0.], dtype=float32)]
"""
if in_dygraph_mode():
if correct is None:
correct = _varbase_creator(dtype="int32")
if total is None:
total = _varbase_creator(dtype="int32")
_k = k.numpy().item(0) if isinstance(k, Variable) else k
topk_out, topk_indices = _C_ops.top_k_v2(input, 'k', _k, 'sorted',
False)
_acc, _, _ = _C_ops.accuracy(topk_out, topk_indices, label, correct,
total)
return _acc
helper = LayerHelper("accuracy", **locals())
check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'],
'accuracy')
topk_out = helper.create_variable_for_type_inference(dtype=input.dtype)
topk_indices = helper.create_variable_for_type_inference(dtype="int64")
inputs = {"X": [input]}
if isinstance(k, Variable):
inputs['K'] = [k]
else:
attrs = {'k': k}
attrs['sorted'] = False
helper.append_op(
type="top_k_v2",
inputs=inputs,
attrs=attrs,
outputs={"Out": [topk_out],
"Indices": [topk_indices]})
acc_out = helper.create_variable_for_type_inference(dtype="float32")
if correct is None:
correct = helper.create_variable_for_type_inference(dtype="int32")
if total is None:
total = helper.create_variable_for_type_inference(dtype="int32")
helper.append_op(
type="accuracy",
inputs={
"Out": [topk_out],
"Indices": [topk_indices],
"Label": [label]
},
outputs={
"Accuracy": [acc_out],
"Correct": [correct],
"Total": [total],
})
return acc_out | [
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rootm0s/Protectors | 5b3f4d11687a5955caf9c3af30666c4bfc2c19ab | OWASP-ZSC/module/readline_windows/pyreadline/modes/emacs.py | python | EmacsMode.forward_backward_delete_char | (self, e) | Delete the character under the cursor, unless the cursor is at
the end of the line, in which case the character behind the cursor
is deleted. By default, this is not bound to a key. | Delete the character under the cursor, unless the cursor is at
the end of the line, in which case the character behind the cursor
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | catboost/python-package/catboost/core.py | python | CatBoostClassifier.fit | (self, X, y=None, cat_features=None, text_features=None, embedding_features=None, sample_weight=None, baseline=None, use_best_model=None,
eval_set=None, verbose=None, logging_level=None, plot=False, column_description=None,
verbose_eval=None, metric_period=None, silent=None, early_stopping_rounds=None,
save_snapshot=None, snapshot_file=None, snapshot_interval=None, init_model=None, callbacks=None,
log_cout=sys.stdout, log_cerr=sys.stderr) | return self | Fit the CatBoostClassifier model.
Parameters
----------
X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series
If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset.
y : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Labels, 1 dimensional array like.
Use only if X is not catboost.Pool.
cat_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Categ columns indices.
Use only if X is not catboost.Pool.
text_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Text columns indices.
Use only if X is not catboost.Pool.
embedding_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Embedding columns indices.
Use only if X is not catboost.Pool.
sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Instance weights, 1 dimensional array like.
baseline : list or numpy.ndarray, optional (default=None)
If not None, giving 2 dimensional array like data.
Use only if X is not catboost.Pool.
use_best_model : bool, optional (default=None)
Flag to use best model
eval_set : catboost.Pool or list, optional (default=None)
A list of (X, y) tuple pairs to use as a validation set for early-stopping
metric_period : int
Frequency of evaluating metrics.
verbose : bool or int
If verbose is bool, then if set to True, logging_level is set to Verbose,
if set to False, logging_level is set to Silent.
If verbose is int, it determines the frequency of writing metrics to output and
logging_level is set to Verbose.
silent : bool
If silent is True, logging_level is set to Silent.
If silent is False, logging_level is set to Verbose.
logging_level : string, optional (default=None)
Possible values:
- 'Silent'
- 'Verbose'
- 'Info'
- 'Debug'
plot : bool, optional (default=False)
If True, draw train and eval error in Jupyter notebook
verbose_eval : bool or int
Synonym for verbose. Only one of these parameters should be set.
early_stopping_rounds : int
Activates Iter overfitting detector with od_wait set to early_stopping_rounds.
save_snapshot : bool, [default=None]
Enable progress snapshotting for restoring progress after crashes or interruptions
snapshot_file : string or pathlib.Path, [default=None]
Learn progress snapshot file path, if None will use default filename
snapshot_interval: int, [default=600]
Interval between saving snapshots (seconds)
init_model : CatBoost class or string or pathlib.Path, [default=None]
Continue training starting from the existing model.
If this parameter is a string or pathlib.Path, load initial model from the path specified by this string.
callbacks : list, optional (default=None)
List of callback objects that are applied at end of each iteration.
log_cout: output stream or callback for logging
log_cerr: error stream or callback for logging
Returns
-------
model : CatBoost | Fit the CatBoostClassifier model. | [
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eval_set=None, verbose=None, logging_level=None, plot=False, column_description=None,
verbose_eval=None, metric_period=None, silent=None, early_stopping_rounds=None,
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log_cout=sys.stdout, log_cerr=sys.stderr):
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Fit the CatBoostClassifier model.
Parameters
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X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series
If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset.
y : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Labels, 1 dimensional array like.
Use only if X is not catboost.Pool.
cat_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Categ columns indices.
Use only if X is not catboost.Pool.
text_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Text columns indices.
Use only if X is not catboost.Pool.
embedding_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Embedding columns indices.
Use only if X is not catboost.Pool.
sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Instance weights, 1 dimensional array like.
baseline : list or numpy.ndarray, optional (default=None)
If not None, giving 2 dimensional array like data.
Use only if X is not catboost.Pool.
use_best_model : bool, optional (default=None)
Flag to use best model
eval_set : catboost.Pool or list, optional (default=None)
A list of (X, y) tuple pairs to use as a validation set for early-stopping
metric_period : int
Frequency of evaluating metrics.
verbose : bool or int
If verbose is bool, then if set to True, logging_level is set to Verbose,
if set to False, logging_level is set to Silent.
If verbose is int, it determines the frequency of writing metrics to output and
logging_level is set to Verbose.
silent : bool
If silent is True, logging_level is set to Silent.
If silent is False, logging_level is set to Verbose.
logging_level : string, optional (default=None)
Possible values:
- 'Silent'
- 'Verbose'
- 'Info'
- 'Debug'
plot : bool, optional (default=False)
If True, draw train and eval error in Jupyter notebook
verbose_eval : bool or int
Synonym for verbose. Only one of these parameters should be set.
early_stopping_rounds : int
Activates Iter overfitting detector with od_wait set to early_stopping_rounds.
save_snapshot : bool, [default=None]
Enable progress snapshotting for restoring progress after crashes or interruptions
snapshot_file : string or pathlib.Path, [default=None]
Learn progress snapshot file path, if None will use default filename
snapshot_interval: int, [default=600]
Interval between saving snapshots (seconds)
init_model : CatBoost class or string or pathlib.Path, [default=None]
Continue training starting from the existing model.
If this parameter is a string or pathlib.Path, load initial model from the path specified by this string.
callbacks : list, optional (default=None)
List of callback objects that are applied at end of each iteration.
log_cout: output stream or callback for logging
log_cerr: error stream or callback for logging
Returns
-------
model : CatBoost
"""
params = self._init_params.copy()
_process_synonyms(params)
if 'loss_function' in params:
CatBoostClassifier._check_is_compatible_loss(params['loss_function'])
self._fit(X, y, cat_features, text_features, embedding_features, None, sample_weight, None, None, None, None, baseline, use_best_model,
eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period,
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return self | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/virtualenv/files/virtualenv_support/distribute_setup.py | python | _patch_file | (path, content) | return True | Will backup the file then patch it | Will backup the file then patch it | [
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# already patched
log.warn('Already patched.')
return False
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/matrixlib/defmatrix.py | python | asmatrix | (data, dtype=None) | return matrix(data, dtype=dtype, copy=False) | Interpret the input as a matrix.
Unlike `matrix`, `asmatrix` does not make a copy if the input is already
a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``.
Parameters
----------
data : array_like
Input data.
dtype : data-type
Data-type of the output matrix.
Returns
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mat : matrix
`data` interpreted as a matrix.
Examples
--------
>>> x = np.array([[1, 2], [3, 4]])
>>> m = np.asmatrix(x)
>>> x[0,0] = 5
>>> m
matrix([[5, 2],
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"""
Interpret the input as a matrix.
Unlike `matrix`, `asmatrix` does not make a copy if the input is already
a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``.
Parameters
----------
data : array_like
Input data.
dtype : data-type
Data-type of the output matrix.
Returns
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mat : matrix
`data` interpreted as a matrix.
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>>> x = np.array([[1, 2], [3, 4]])
>>> m = np.asmatrix(x)
>>> x[0,0] = 5
>>> m
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google/fhir | d77f57706c1a168529b0b87ca7ccb1c0113e83c2 | py/google/fhir/stu3/json_format.py | python | print_fhir_to_json_string | (fhir_proto: message.Message) | return printer.print(fhir_proto) | Returns a FHIR JSON representation with no spaces or newlines.
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fhir_proto: The proto to serialize into a JSON string.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/pkg_resources/_vendor/packaging/specifiers.py | python | BaseSpecifier.prereleases | (self, value: bool) | Sets whether or not pre-releases as a whole are allowed by this
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | PseudoDC.Clear | (*args, **kwargs) | return _gdi_.PseudoDC_Clear(*args, **kwargs) | Clear(self)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aquabutton.py | python | AquaButton.GetHoverColour | (self) | return self._hoverColour | Returns the button colour when the mouse is hovering on the button.
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ufal/udpipe | e51f02d2744cdfd4a29efc1320644ea04d535f0b | doc/t2t_docsys/txt2tags.py | python | ConfigLines.get_raw_config | (self) | return ret | Scan buffer and extract all config as RAW (including includes) | Scan buffer and extract all config as RAW (including includes) | [
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ret = []
self.load_lines()
first = self.first_line
for i in range(len(self.lines)):
line = self.lines[i]
Message(_("Processing line %03d: %s")%(first+i,line),2)
target, key, val = self.parse_line(line)
if not key: continue # no config on this line
if key == 'includeconf':
err = _('A file cannot include itself (loop!)')
if val == self.file:
Error("%s: %%!includeconf: %s" % (err, self.file))
more_raw = self.include_config_file(val)
ret.extend(more_raw)
Message(_("Finished Config file inclusion: %s") % val, 2)
else:
ret.append([target, key, val])
Message(_("Added %s")%key,3)
return ret | [
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/filter/CommonFilters.py | python | CommonFilters.setGammaAdjust | (self, gamma) | return True | Applies additional gamma correction to the image. 1.0 = no correction. | Applies additional gamma correction to the image. 1.0 = no correction. | [
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old_gamma = self.configuration.get("GammaAdjust", 1.0)
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/clang/bindings/python/clang/cindex.py | python | TranslationUnit.get_includes | (self) | return iter(includes) | Return an iterable sequence of FileInclusion objects that describe the
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Yelp/MOE | 5b5a6a2c6c3cf47320126f7f5894e2a83e347f5c | moe/bandit/data_containers.py | python | SampleArm.loss | (self) | return self._loss | Return the amount loss, always greater than or equal to zero. | Return the amount loss, always greater than or equal to zero. | [
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pyne/pyne | 0c2714d7c0d1b5e20be6ae6527da2c660dd6b1b3 | pyne/utils.py | python | failure | (s) | return msg | Formats a fail message for printing. If on a posix system the message
will be in color. | Formats a fail message for printing. If on a posix system the message
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"""Formats a fail message for printing. If on a posix system the message
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"""
head = "\033[1;31m" if USE_COLOR else "*** FAILURE ***: "
tail = "\033[0m" if USE_COLOR else ""
msg = head + s + tail
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ros-planning/moveit2 | dd240ef6fd8b9932a7a53964140f2952786187a9 | moveit_configs_utils/moveit_configs_utils/moveit_configs_builder.py | python | MoveItConfigsBuilder.robot_description | (self, file_path: Optional[str] = None, mappings: dict = None) | return self | Load robot description.
:param file_path: Absolute or relative path to the URDF file (w.r.t. robot_name_moveit_config).
:param mappings: mappings to be passed when loading the xacro file.
:return: Instance of MoveItConfigsBuilder with robot_description loaded. | Load robot description. | [
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"""Load robot description.
:param file_path: Absolute or relative path to the URDF file (w.r.t. robot_name_moveit_config).
:param mappings: mappings to be passed when loading the xacro file.
:return: Instance of MoveItConfigsBuilder with robot_description loaded.
"""
if file_path is None:
robot_description_file_path = self.__urdf_package / self.__urdf_file_path
else:
robot_description_file_path = self._package_path / file_path
self.__moveit_configs.robot_description = {
self.__robot_description: load_xacro(
robot_description_file_path, mappings=mappings
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}
return self | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/machinelearning/layer1.py | python | MachineLearningConnection.get_evaluation | (self, evaluation_id) | return self.make_request(action='GetEvaluation',
body=json.dumps(params)) | Returns an `Evaluation` that includes metadata as well as the
current status of the `Evaluation`.
:type evaluation_id: string
:param evaluation_id: The ID of the `Evaluation` to retrieve. The
evaluation of each `MLModel` is recorded and cataloged. The ID
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"""
Returns an `Evaluation` that includes metadata as well as the
current status of the `Evaluation`.
:type evaluation_id: string
:param evaluation_id: The ID of the `Evaluation` to retrieve. The
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"""
params = {'EvaluationId': evaluation_id, }
return self.make_request(action='GetEvaluation',
body=json.dumps(params)) | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | snapx/snapx/classes/digraph.py | python | DiGraph.clear_edges | (self) | Remove all edges from the graph without altering nodes.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.clear_edges()
>>> list(G.nodes)
[0, 1, 2, 3]
>>> list(G.edges)
[] | Remove all edges from the graph without altering nodes. | [
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"""Remove all edges from the graph without altering nodes.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.clear_edges()
>>> list(G.nodes)
[0, 1, 2, 3]
>>> list(G.edges)
[]
"""
for predecessor_dict in self._pred.values():
predecessor_dict.clear()
for successor_dict in self._succ.values():
successor_dict.clear() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/stc.py | python | StyledTextCtrl.GetCurLineRaw | (*args, **kwargs) | return _stc.StyledTextCtrl_GetCurLineRaw(*args, **kwargs) | GetCurLineRaw() -> (text, index)
Retrieve the text of the line containing the caret, and also the index
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microsoft/DirectXShaderCompiler | 8348ff8d9e0287610ba05d3a828e10af981a1c05 | utils/hct/hctdb.py | python | db_dxil.print_stats | (self) | Print some basic statistics on the instruction database. | Print some basic statistics on the instruction database. | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/gyp/pylib/gyp/MSVSProject.py | python | Writer.AddFileConfig | (self, path, config, attrs=None, tools=None) | Adds a configuration to a file.
Args:
path: Relative path to the file.
config: Name of configuration to add.
attrs: Dict of configuration attributes; may be None.
tools: List of tools (strings or Tool objects); may be None.
Raises:
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path: Relative path to the file.
config: Name of configuration to add.
attrs: Dict of configuration attributes; may be None.
tools: List of tools (strings or Tool objects); may be None.
Raises:
ValueError: Relative path does not match any file added via AddFiles().
"""
# Find the file node with the right relative path
parent = self.files_dict.get(path)
if not parent:
raise ValueError('AddFileConfig: file "%s" not in project.' % path)
# Add the config to the file node
spec = self._GetSpecForConfiguration('FileConfiguration', config, attrs,
tools)
parent.append(spec) | [
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PX4/PX4-Autopilot | 0b9f60a0370be53d683352c63fd92db3d6586e18 | src/lib/mixer/MultirotorMixer/mixer_multirotor.py | python | airmode_rp | (m_sp, P, u_min, u_max) | return (u, u_final) | Mix roll, pitch, yaw and thrust.
Desaturation behavior: airmode for roll/pitch:
thrust is increased/decreased as much as required to meet the demanded roll/pitch.
Yaw is not allowed to increase the thrust, @see mix_yaw() for the exact behavior. | Mix roll, pitch, yaw and thrust. | [
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] | def airmode_rp(m_sp, P, u_min, u_max):
"""
Mix roll, pitch, yaw and thrust.
Desaturation behavior: airmode for roll/pitch:
thrust is increased/decreased as much as required to meet the demanded roll/pitch.
Yaw is not allowed to increase the thrust, @see mix_yaw() for the exact behavior.
"""
# Mix without yaw
m_sp_no_yaw = m_sp.copy()
m_sp_no_yaw[2, 0] = 0.0
u = P * m_sp_no_yaw
# Use thrust to unsaturate the outputs if needed
u_T = P[:, 3]
u_prime = minimize_sat(u, u_min, u_max, u_T)
# Mix yaw axis independently
u_final = mix_yaw(m_sp, u_prime, P, u_min, u_max)
return (u, u_final) | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TStrV.Last | (self, *args) | return _snap.TStrV_Last(self, *args) | Last(TStrV self) -> TStr
Last(TStrV self) -> TStr
Parameters:
self: TVec< TStr,int > * | Last(TStrV self) -> TStr
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swift/swift | 12d031cf8177fdec0137f9aa7e2912fa23c4416b | BuildTools/SCons/Tools/InstallWithSymLinks.py | python | scons_copytree | (src, dst, symlinks=False) | Recursively copy a directory tree using copy2().
The destination directory must not already exist.
If exception(s) occur, an CopytreeError is raised with a list of reasons.
If the optional symlinks flag is true, symbolic links in the
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"""Recursively copy a directory tree using copy2().
The destination directory must not already exist.
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XXX Consider this example code rather than the ultimate tool.
"""
names = os.listdir(src)
# garyo@genarts.com fix: check for dir before making dirs.
if not os.path.exists(dst):
os.makedirs(dst)
errors = []
for name in names:
srcname = os.path.join(src, name)
dstname = os.path.join(dst, name)
try:
if symlinks and os.path.islink(srcname):
linkto = os.readlink(srcname)
os.symlink(linkto, dstname)
elif os.path.isdir(srcname):
scons_copytree(srcname, dstname, symlinks)
else:
shutil.copy2(srcname, dstname)
# XXX What about devices, sockets etc.?
except (IOError, os.error) as why:
errors.append((srcname, dstname, str(why)))
# catch the CopytreeError from the recursive copytree so that we can
# continue with other files
except CopytreeError as err:
errors.extend(err.args[0])
try:
shutil.copystat(src, dst)
except WindowsError:
# can't copy file access times on Windows
pass
except OSError as why:
errors.extend((src, dst, str(why)))
if errors:
raise CopytreeError(errors) | [
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huzheng001/stardict-3 | 96b96d89eab5f0ad9246c2569a807d6d7982aa84 | tools/src/lingea-trd-decoder.py | python | decode | (stream) | return decode_tag_postprocessing(result) | Decode byte stream of one record, return decoded string with formatting in utf | Decode byte stream of one record, return decoded string with formatting in utf | [
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] | def decode(stream):
"""Decode byte stream of one record, return decoded string with formatting in utf"""
result = ""
global bs, pos
# stream - data byte stream for one record
bs = unpack("<%sB" % len(stream), stream)
# bs - list of bytes from stream
pos = 0
itemCount = outInt("ItemCount: %s") # Number of blocks in the record
mainFlag = outInt("MainFlag: %s")
# HEADER BLOCK
# ------------
if mainFlag & 0x01:
headerFlag = outInt("HeaderFlag: %s") # Blocks in header
if headerFlag & 0x01:
result += tag['rn'][0] + outStr("Header record name: %s").replace('_','') + tag['rn'][1] # Remove character '_' from index
if headerFlag & 0x02:
result += tag['va'][0] + outStr("Header variant: %s") + tag['va'][1]
if headerFlag & 0x04:
s = outInt("Header wordclass: %s")
if s < 32:
result += tag['wc'][0] + wordclass[s] + tag['wc'][1]
else:
raise "Header wordclass out of range in: %s" % result
if headerFlag & 0x08:
result += tag['pa'][0] + outStr("Header parts: %s") + tag['pa'][1]
if headerFlag & 0x10:
result += tag['fo'][0] + outStr("Header forms: %s") + tag['fo'][1]
if headerFlag & 0x20:
result += tag['on'][0] + outStr("Header origin note: %s") + tag['on'][1]
if headerFlag & 0x80:
result += tag['pr'][0] + pronunciation_encode(outStr("Header pronunciation: %s")) + tag['pr'][1]
# Header data block
if mainFlag & 0x02:
headerFlag = outInt("Header dataFlag: %s") # Blocks in header
if headerFlag & 0x02:
result += tag['dv'][0] + outStr("Header dataVariant: %s")+ tag['dv'][1]
# ??? Link elsewhere
pass
# SOUND DATA REFERENCE
if mainFlag & 0x80:
outInt("Sound reference byte #1: %s")
outInt("Sound reference byte #2: %s")
outInt("Sound reference byte #3: %s")
outInt("Sound reference byte #4: %s")
outInt("Sound reference byte #5: %s")
#out("Sound data reference (5 bytes)", 6)
# TODO: Test all mainFlags in header!!!!
#result += ': '
li = 0
#print just every first word class identifier
# TODO: this is not systematic (should be handled by output)
global lastWordClass
lastWordClass = 0
# DATA BLOCK(S)
# -------------
for i in range(0, itemCount):
item = tag['db'][0] + tag['db'][1]
ol = False
dataFlag = outInt("DataFlag: %s -----------------------------")
if dataFlag & 0x01: # small index
sampleFlag = outInt("Data sampleFlag: %s")
if sampleFlag & 0x01:
result += tag['sa'][0] + outStr("Data sample: %s") + tag['sa'][1]
if sampleFlag & 0x04:
s = outInt("Data wordclass: %s")
if s != lastWordClass:
if s < 32:
result += tag['wc'][0] + wordclass[s] + tag['wc'][1]
else:
raise "Header wordclass out of range in: %s" % result
lastWordClass = s
if sampleFlag & 0x08:
result += tag['sw'][0] + outStr("Data sample wordclass: %s") + tag['sw'][1]
if sampleFlag & 0x10:
outInt("Data sample Int: %s")
outInt("Data sample Int: %s")
outInt("Data sample Int: %s")
if sampleFlag & 0x20:
item += tag['do'][0] + outStr("Data origin note: %s") + tag['do'][1]
if sampleFlag & 0x80:
item += " "
result += tag['pr'][0] + pronunciation_encode(outStr("Data sample pronunciation: %s")) + tag['pr'][1]
if dataFlag & 0x02:
item += " "
subFlag = outInt("Data subFlag: %s")
if subFlag == 0x80:
outStr("Data sub prefix: %s")
# It seams that data sub prefix content is ignored and there is a generated number for the whole block instead.
li += 1
ol = True
if dataFlag & 0x04: # chart
pass # ???
if dataFlag & 0x08: # reference
item += tag['df'][0] + outStr("Data definition: %s") + tag['df'][1]
if dataFlag & 0x10:
pass # ???
if dataFlag & 0x20: # phrase
phraseFlag1 = outInt("Data phraseFlag1: %s")
if phraseFlag1 & 0x01:
item += tag['ps'][0] + outStr("Data phrase short form: %s") + tag['ps'][1]
if phraseFlag1 & 0x02:
phraseCount = outInt("Data phraseCount: %s")
for i in range(0, phraseCount):
phraseComment = outInt("Data phrase prefix")
if phraseComment & 0x04:
item += tag['pc'][0] + outStr("Data phrase comment: %s") + tag['pc'][1]
item += tag['p1'][0] + outStr("Data phrase 1: %s") + tag['p1'][1]
item += tag['p2'][0] + outStr("Data phrase 2: %s") + tag['p2'][1]
if phraseFlag1 & 0x04:
phraseCount = outInt("Data phraseCount: %s")
for i in range(0, phraseCount):
phraseComment = outInt("Data phrase prefix")
if phraseComment & 0x04:
item += tag['pc'][0] + outStr("Data phrase 1: %s") + tag['pc'][1]
item += tag['pg'][0] + outStr("Data phrase comment: %s") + tag['pg'][1]
item += tag['p2'][0] + outStr("Data phrase 2: %s") + tag['p2'][1]
if phraseFlag1 & 0x08:
phraseCount = outInt("Data simple phraseCount: %s")
for i in range(0, phraseCount):
item += " "
item += tag['sp'][0] + outStr("Data simple phrase: %s") + tag['sp'][1]
if phraseFlag1 & 0x40:
item += tag['ps'][0] + outStr("Data phrase short form: %s") + tag['ps'][1]
# TODO: be careful in changing the rules, to have back compatibility!
if dataFlag & 0x40: # reference, related language
#0x01 synonym ?
#0x02 antonym ?
pass
if dataFlag & 0x80: # Phrase block
flags = [
out("Data phrase block: %s"),
out("Data phrase block: %s"),
out("Data phrase block: %s"),
out("Data phrase block: %s"),
out("Data phrase block: %s"),
out("Data phrase block: %s"),
out("Data phrase block: %s"),
out("Data phrase block: %s")]
if flags == [0x80,0x80,0xF9,0xDF,0x9D,0x00,0x0B,0x01]:
result += "\\nphr: "
li = 1
ol = True
item += tag['b1'][0]+outStr("Data phrase 1: %s") + tag['b1'][1]
out("Data phrase block: %s")
out("Data phrase block: %s")
out("Data phrase block: %s")
out("Data phrase block: %s")
item += tag['ds'][0] + outStr("Data phrase 2: %s") + tag['ds'][1]
if flags == [0x80,0x80,0xF9,0xDF,0x9D,0x00,0x23,0x01]:
result += "\\nphr: "
li = 1
ol = True
item += tag['b1'][0]+outStr("Data phrase 1: %s") + tag['b1'][1]
out("Data phrase block: %s")
out("Data phrase block: %s")
out("Data phrase block: %s")
out("Data phrase block: %s")
out("Data phrase block: %s")
item += tag['ds'][0] + outStr("Data phrase 2: %s") + tag['ds'][1]
if ol:
result += "\\n%d. %s" % (li, item)
else:
result += item
ok = True
while pos < len(stream):
ok = (out() == 0x00) and ok
if ok:
result += '\n'
return decode_tag_postprocessing(result) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/reduction_gui/widgets/base_widget.py | python | process_file_parameter | (f) | return processed_function | Decorator that allows a function parameter to either be
a string or a function returning a string | Decorator that allows a function parameter to either be
a string or a function returning a string | [
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] | def process_file_parameter(f):
"""
Decorator that allows a function parameter to either be
a string or a function returning a string
"""
def processed_function(self, file_name=None, **kwargs):
if file_name is None:
return f(self, **kwargs)
if isinstance(file_name, types.StringType):
return f(self, file_name, **kwargs)
else:
return f(self, str(file_name()), **kwargs)
return processed_function | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/configure.d/nodedownload.py | python | formatSize | (amt) | return fpformat.fix(amt / 1024000., 1) | Format a size as a string in MB | Format a size as a string in MB | [
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] | def formatSize(amt):
"""Format a size as a string in MB"""
return fpformat.fix(amt / 1024000., 1) | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/diagnostic_updater/_diagnostic_updater.py | python | Updater.broadcast | (self, lvl, msg) | Outputs a message on all the known DiagnosticStatus.
Useful if something drastic is happening such as shutdown or a self-test.
@param lvl Level of the diagnostic being output.
@param msg Status message to output. | Outputs a message on all the known DiagnosticStatus. | [
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] | def broadcast(self, lvl, msg):
"""Outputs a message on all the known DiagnosticStatus.
Useful if something drastic is happening such as shutdown or a self-test.
@param lvl Level of the diagnostic being output.
@param msg Status message to output.
"""
status_vec = []
for task in self.tasks:
status = DiagnosticStatusWrapper()
status.name = task.name
status.summary(lvl, msg)
status_vec.append(status)
self.publish(status_vec) | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Alignment/MillePedeAlignmentAlgorithm/scripts/mps_list_evts.py | python | print_merging_scheme | (merged_datasets) | Print number of events per merged dataset
See comments to function `merge_datasets' for an explanation
of what is meant by merged dataset. | Print number of events per merged dataset | [
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] | def print_merging_scheme(merged_datasets):
""" Print number of events per merged dataset
See comments to function `merge_datasets' for an explanation
of what is meant by merged dataset.
"""
print("Defining the following merged datasets:")
for merged_dataset,datasets in merged_datasets.items():
print("\n `"+merged_dataset+"' from:")
for dataset in datasets:
print(" `"+dataset+"'") | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/framework/device.py | python | check_valid | (spec) | Check that a device spec is valid.
Args:
spec: a string.
Raises:
An exception if the spec is invalid. | Check that a device spec is valid. | [
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] | def check_valid(spec):
"""Check that a device spec is valid.
Args:
spec: a string.
Raises:
An exception if the spec is invalid.
"""
# Construct a DeviceSpec. It will assert a failure if spec is invalid.
DeviceSpec.from_string(spec) | [
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etodd/lasercrabs | 91484d9ac3a47ac38b8f40ec3ff35194714dad8e | assets/script/etodd_blender_fbx/export_fbx_bin.py | python | fbx_animations_do | (scene_data, ref_id, f_start, f_end, start_zero, objects=None, force_keep=False) | return (astack_key, animations, alayer_key, name, f_start, f_end) if animations else None | Generate animation data (a single AnimStack) from objects, for a given frame range. | Generate animation data (a single AnimStack) from objects, for a given frame range. | [
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] | def fbx_animations_do(scene_data, ref_id, f_start, f_end, start_zero, objects=None, force_keep=False):
"""
Generate animation data (a single AnimStack) from objects, for a given frame range.
"""
bake_step = scene_data.settings.bake_anim_step
simplify_fac = scene_data.settings.bake_anim_simplify_factor
scene = scene_data.scene
force_keying = scene_data.settings.bake_anim_use_all_bones
force_sek = scene_data.settings.bake_anim_force_startend_keying
if objects is not None:
# Add bones and duplis!
for ob_obj in tuple(objects):
if not ob_obj.is_object:
continue
if ob_obj.type == 'ARMATURE':
objects |= {bo_obj for bo_obj in ob_obj.bones if bo_obj in scene_data.objects}
ob_obj.dupli_list_create(scene, 'RENDER')
for dp_obj in ob_obj.dupli_list:
if dp_obj in scene_data.objects:
objects.add(dp_obj)
ob_obj.dupli_list_clear()
else:
objects = scene_data.objects
back_currframe = scene.frame_current
animdata_ob = OrderedDict()
p_rots = {}
for ob_obj in objects:
if ob_obj.parented_to_armature:
continue
ACNW = AnimationCurveNodeWrapper
loc, rot, scale, _m, _mr = ob_obj.fbx_object_tx(scene_data, rest = True)
rot_deg = tuple(convert_rad_to_deg_iter(rot))
force_key = (simplify_fac == 0.0) or (ob_obj.is_bone and force_keying)
animdata_ob[ob_obj] = (ACNW(ob_obj.key, 'LCL_TRANSLATION', force_key, force_sek, loc),
ACNW(ob_obj.key, 'LCL_ROTATION', force_key, force_sek, rot_deg),
ACNW(ob_obj.key, 'LCL_SCALING', force_key, force_sek, scale))
p_rots[ob_obj] = rot
force_key = (simplify_fac == 0.0)
animdata_shapes = OrderedDict()
for me, (me_key, _shapes_key, shapes) in scene_data.data_deformers_shape.items():
# Ignore absolute shape keys for now!
if not me.shape_keys.use_relative:
continue
for shape, (channel_key, geom_key, _shape_verts_co, _shape_verts_idx) in shapes.items():
acnode = AnimationCurveNodeWrapper(channel_key, 'SHAPE_KEY', force_key, force_sek, (0.0,))
# Sooooo happy to have to twist again like a mad snake... Yes, we need to write those curves twice. :/
acnode.add_group(me_key, shape.name, shape.name, (shape.name,))
animdata_shapes[channel_key] = (acnode, me, shape)
animdata_cameras = OrderedDict()
for cam_obj, cam_key in scene_data.data_cameras.items():
cam = cam_obj.bdata.data
acnode = AnimationCurveNodeWrapper(cam_key, 'CAMERA_FOCAL', force_key, force_sek, (cam.lens,))
animdata_cameras[cam_key] = (acnode, cam)
currframe = f_start
while currframe <= f_end:
real_currframe = currframe - f_start if start_zero else currframe
scene.frame_set(int(currframe), currframe - int(currframe))
for ob_obj in animdata_ob:
ob_obj.dupli_list_create(scene, 'RENDER')
for ob_obj, (anim_loc, anim_rot, anim_scale) in animdata_ob.items():
# We compute baked loc/rot/scale for all objects (rot being euler-compat with previous value!).
p_rot = p_rots.get(ob_obj, None)
loc, rot, scale, _m, _mr = ob_obj.fbx_object_tx(scene_data, rot_euler_compat=p_rot)
p_rots[ob_obj] = rot
anim_loc.add_keyframe(real_currframe, loc)
anim_rot.add_keyframe(real_currframe, tuple(convert_rad_to_deg_iter(rot)))
anim_scale.add_keyframe(real_currframe, scale)
for ob_obj in objects:
ob_obj.dupli_list_clear()
for anim_shape, me, shape in animdata_shapes.values():
anim_shape.add_keyframe(real_currframe, (shape.value * 100.0,))
for anim_camera, camera in animdata_cameras.values():
anim_camera.add_keyframe(real_currframe, (camera.lens,))
currframe += bake_step
scene.frame_set(back_currframe, 0.0)
animations = OrderedDict()
# And now, produce final data (usable by FBX export code)
# Objects-like loc/rot/scale...
for ob_obj, anims in animdata_ob.items():
for anim in anims:
anim.simplify(simplify_fac, bake_step, force_keep)
if not anim:
continue
for obj_key, group_key, group, fbx_group, fbx_gname in anim.get_final_data(scene, ref_id, force_keep):
anim_data = animations.get(obj_key)
if anim_data is None:
anim_data = animations[obj_key] = ("dummy_unused_key", OrderedDict())
anim_data[1][fbx_group] = (group_key, group, fbx_gname)
# And meshes' shape keys.
for channel_key, (anim_shape, me, shape) in animdata_shapes.items():
final_keys = OrderedDict()
anim_shape.simplify(simplify_fac, bake_step, force_keep)
if not anim_shape:
continue
for elem_key, group_key, group, fbx_group, fbx_gname in anim_shape.get_final_data(scene, ref_id, force_keep):
anim_data = animations.get(elem_key)
if anim_data is None:
anim_data = animations[elem_key] = ("dummy_unused_key", OrderedDict())
anim_data[1][fbx_group] = (group_key, group, fbx_gname)
# And cameras' lens keys.
for cam_key, (anim_camera, camera) in animdata_cameras.items():
final_keys = OrderedDict()
anim_camera.simplify(simplify_fac, bake_step, force_keep)
if not anim_camera:
continue
for elem_key, group_key, group, fbx_group, fbx_gname in anim_camera.get_final_data(scene, ref_id, force_keep):
anim_data = animations.get(elem_key)
if anim_data is None:
anim_data = animations[elem_key] = ("dummy_unused_key", OrderedDict())
anim_data[1][fbx_group] = (group_key, group, fbx_gname)
astack_key = get_blender_anim_stack_key(scene, ref_id)
alayer_key = get_blender_anim_layer_key(scene, ref_id)
name = (get_blenderID_name(ref_id) if ref_id else scene.name).encode()
if start_zero:
f_end -= f_start
f_start = 0.0
return (astack_key, animations, alayer_key, name, f_start, f_end) if animations else None | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/combo.py | python | ComboPopup.OnComboDoubleClick | (*args, **kwargs) | return _combo.ComboPopup_OnComboDoubleClick(*args, **kwargs) | OnComboDoubleClick(self)
Implement this method in the derived class if you need to support
special actions when the user double-clicks on the parent ComboCtrl. | OnComboDoubleClick(self) | [
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"""
OnComboDoubleClick(self)
Implement this method in the derived class if you need to support
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"""
return _combo.ComboPopup_OnComboDoubleClick(*args, **kwargs) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/framework/function.py | python | get_extra_args | () | Returns the corresponding function arguments for the captured inputs.
Returns:
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g = ops.get_default_graph()
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OpenLightingProject/ola | d1433a1bed73276fbe55ce18c03b1c208237decc | python/ola/RDMAPI.py | python | RDMAPI.RawSet | (self, universe, uid, sub_device, pid, callback, args=[],
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args, PidStore.RDM_SET, include_frames) | Send a RDM Set message with the raw data supplied.
Args:
universe: The universe to send the request on.
uid: The UID to address the request to.
sub_device: The Sub Device to send the request to.
pid: A PID object that describes the format of the request.
callback: The callback to run when the request completes.
data: The param data
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universe: The universe to send the request on.
uid: The UID to address the request to.
sub_device: The Sub Device to send the request to.
pid: A PID object that describes the format of the request.
callback: The callback to run when the request completes.
data: The param data
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/lib/_iotools.py | python | StringConverter._getsubdtype | (cls, val) | return np.array(val).dtype.type | Returns the type of the dtype of the input variable. | Returns the type of the dtype of the input variable. | [
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"""Returns the type of the dtype of the input variable."""
return np.array(val).dtype.type | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/distutils/command/config.py | python | config.check_func | (self, func, headers=None, include_dirs=None,
libraries=None, library_dirs=None, decl=0, call=0) | return self.try_link(body, headers, include_dirs,
libraries, library_dirs) | Determine if function 'func' is available by constructing a
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libraries=None, library_dirs=None, decl=0, call=0):
"""Determine if function 'func' is available by constructing a
source file that refers to 'func', and compiles and links it.
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self._check_compiler()
body = []
if decl:
body.append("int %s ();" % func)
body.append("int main () {")
if call:
body.append(" %s();" % func)
else:
body.append(" %s;" % func)
body.append("}")
body = "\n".join(body) + "\n"
return self.try_link(body, headers, include_dirs,
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/tools/gyp/pylib/gyp/generator/dump_dependency_json.py | python | CalculateGeneratorInputInfo | (params) | Calculate the generator specific info that gets fed to input (called by
gyp). | Calculate the generator specific info that gets fed to input (called by
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"""Calculate the generator specific info that gets fed to input (called by
gyp)."""
generator_flags = params.get('generator_flags', {})
if generator_flags.get('adjust_static_libraries', False):
global generator_wants_static_library_dependencies_adjusted
generator_wants_static_library_dependencies_adjusted = True
toplevel = params['options'].toplevel_dir
generator_dir = os.path.relpath(params['options'].generator_output or '.')
# output_dir: relative path from generator_dir to the build directory.
output_dir = generator_flags.get('output_dir', 'out')
qualified_out_dir = os.path.normpath(os.path.join(
toplevel, generator_dir, output_dir, 'gypfiles'))
global generator_filelist_paths
generator_filelist_paths = {
'toplevel': toplevel,
'qualified_out_dir': qualified_out_dir,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | StatusBarPane.SetText | (*args, **kwargs) | return _windows_.StatusBarPane_SetText(*args, **kwargs) | SetText(self, String text) -> bool | SetText(self, String text) -> bool | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | tools/windows/get_prec_ut_list.py | python | get_prec_ut_list | (all_test_cases, prec_test_cases) | Select the ut that needs to be executed | Select the ut that needs to be executed | [
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all_test_cases_list = all_test_cases.strip().split("\n")
prec_test_cases_list = prec_test_cases.strip().split("\n")
all_test_cases_list_new = [item.rstrip() for item in all_test_cases_list]
prec_test_cases_list_new = [item.rstrip() for item in prec_test_cases_list]
if len(prec_test_cases) == 0:
return
case_to_run = ['test_prec_ut']
for case in all_test_cases_list_new:
if case in prec_test_cases_list_new:
case_to_run.append(case)
else:
print("{} will not run in PRECISION_TEST mode.".format(case))
with open(file_path, 'w') as f:
f.write('\n'.join(case_to_run)) | [
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microsoft/ELL | a1d6bacc37a14879cc025d9be2ba40b1a0632315 | tools/importers/common/importer.py | python | ImporterEngine.convert_nodes | (self, model: ImporterModel, apply_ordering: bool = True) | return self.final_ell_map | Converts the model using the operation conversion map that was
specified when this class was initialized.
If apply_ordering is True, the engine will re-order nodes starting with
input nodes, and then repeatedly adding nodes whose inputs are in the
newly updated list list. | Converts the model using the operation conversion map that was
specified when this class was initialized.
If apply_ordering is True, the engine will re-order nodes starting with
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"""
Converts the model using the operation conversion map that was
specified when this class was initialized.
If apply_ordering is True, the engine will re-order nodes starting with
input nodes, and then repeatedly adding nodes whose inputs are in the
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"""
self.ell_model = ell.model.Model()
self.ell_model_builder = ell.model.ModelBuilder()
self.lookup_table = LookupTable(model.tensors)
function_prefix = ""
# Make a first pass through the model to set output padding. Since ELL
# does pre-padding, a particular node must know what the input of the
# next node wants in terms of padding.
self.set_output_padding_for_nodes(model.nodes)
if apply_ordering:
ordered_nodes = self.get_nodes_in_import_order(model.nodes)
else:
ordered_nodes = list(model.nodes.values())
self.ordered_importer_nodes = ordered_nodes
if len(ordered_nodes) == 0:
raise Exception("Cannot convert model because it is empty!")
_logger.info("Processing the following importer nodes in order:")
for ordered_node in ordered_nodes:
_logger.info(ordered_node)
_logger.info("Converting intermediate importer nodes to ELL nodes....")
for node_to_import in ordered_nodes:
self.convert_importer_node_to_ell_nodes(node_to_import)
_logger.info("Done.")
# Quick workaround to create the map's output node. The last node in
# the ordered nodes list will be used.
last_importer_node = ordered_nodes[-1]
shape_entry = last_importer_node.output_shapes[0]
ell_output_shape = memory_shapes.get_ell_shape(shape_entry[0], shape_entry[1], 0)
last_ell_node = self.lookup_table.get_ell_node_from_importer_node_id(last_importer_node.id)
if self.step_interval_msec is not None:
# Add the sink node
last_ell_node = self.ell_model_builder.AddSinkNode(
self.ell_model, ell.nodes.PortElements(last_ell_node.GetOutputPort("output")),
ell.model.PortMemoryLayout(ell_output_shape),
"{}OutputCallback".format(function_prefix))
output_node = self.ell_model_builder.AddOutputNode(
self.ell_model, ell.model.PortMemoryLayout(ell_output_shape), ell.nodes.PortElements(
last_ell_node.GetOutputPort("output")))
output_node.CopyMetadataFrom(last_ell_node)
self.lookup_table.add_ell_output(output_node)
# Create the map
all_outputs = self.lookup_table.get_ell_outputs()
output_list = [ell.nodes.PortElements(e.GetOutputPort("output")) for e in all_outputs]
self.final_ell_map = ell.model.Map(self.ell_model, ell.nodes.InputNodeList(
self.lookup_table.get_ell_inputs()),
ell.nodes.PortElementsList(output_list))
# Print out the nodes and where they came from
self.final_mapping = self.get_node_group_mapping(self.final_ell_map.GetModel())
_logger.info("\nFinal mapping of imported nodes to ell nodes:")
source_names = {}
max_len = 0
for group_id in self.final_mapping.keys():
importer_node = model.nodes[group_id]
text = "{}({})".format(importer_node.operation_type, group_id)
if len(text) > max_len:
max_len = len(text)
source_names[group_id] = text
for group_id in self.final_mapping.keys():
source_name = source_names[group_id]
indent = " " + (" " * (max_len - len(source_name)))
ell_nodes = ["{}({})".format(ell_node.GetRuntimeTypeName(), ell_node.GetId())
for ell_node in self.final_mapping[group_id]]
_logger.info("{}{} -> {}".format(indent, source_name, ", ".join(ell_nodes)))
_logger.info("")
return self.final_ell_map | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_windows.py | python | PreviewControlBar.__init__ | (self, *args, **kwargs) | __init__(self, PrintPreview preview, long buttons, Window parent,
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/dvipdf.py | python | PDFEmitter | (target, source, env) | return (target, source) | Strips any .aux or .log files from the input source list.
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/telnetlib.py | python | Telnet.sock_avail | (self) | return select.select([self], [], [], 0) == ([self], [], []) | Test whether data is available on the socket. | Test whether data is available on the socket. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib-tk/Tkinter.py | python | Misc.bind_class | (self, className, sequence=None, func=None, add=None) | return self._bind(('bind', className), sequence, func, add, 0) | Bind to widgets with bindtag CLASSNAME at event
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Source/ThirdParty/CEF3/cef_source/tools/cef_parser.py | python | obj_class.get_capi_name | (self) | return get_capi_name(self.name, True) | Return the CAPI structure name for this class. | Return the CAPI structure name for this class. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/interpolate/fitpack2.py | python | BivariateSpline._from_tck | (cls, tck) | return self | Construct a spline object from given tck and degree | Construct a spline object from given tck and degree | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/plot3dwindow.py | python | Plot3DWindow.plot_3d | (self, data_key, base_color, thresholds, change_color) | return | Plot scatter data with specified base color
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OpenChemistry/tomviz | 0a903679318f191cb7dd3eb5ff5bc3a7d3320d9a | tomviz/python/AutoCenterOfMassTiltImageAlignment.py | python | CenterOfMassAlignmentOperator.transform_scalars | (self, dataset) | return returnValues | Automatically align tilt images by center of mass method | Automatically align tilt images by center of mass method | [
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self.progress.maximum = tiltSeries.shape[2]
step = 1
offsets = np.zeros((tiltSeries.shape[2], 2))
for i in range(tiltSeries.shape[2]):
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column_names = ["X Offset", "Y Offset"]
offsetsTable = utils.make_spreadsheet(column_names, offsets)
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returnValues["alignments"] = offsetsTable
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facebookresearch/ELF | 1f790173095cd910976d9f651b80beb872ec5d12 | vendor/pybind11/tools/clang/cindex.py | python | TranslationUnit.from_ast_file | (cls, filename, index=None) | return cls(ptr=ptr, index=index) | Create a TranslationUnit instance from a saved AST file.
A previously-saved AST file (provided with -emit-ast or
TranslationUnit.save()) is loaded from the filename specified.
If the file cannot be loaded, a TranslationUnitLoadError will be
raised.
index is optional and is the Index instance to use. If not provided,
a default Index will be created. | Create a TranslationUnit instance from a saved AST file. | [
"Create",
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"TranslationUnit",
"instance",
"from",
"a",
"saved",
"AST",
"file",
"."
] | def from_ast_file(cls, filename, index=None):
"""Create a TranslationUnit instance from a saved AST file.
A previously-saved AST file (provided with -emit-ast or
TranslationUnit.save()) is loaded from the filename specified.
If the file cannot be loaded, a TranslationUnitLoadError will be
raised.
index is optional and is the Index instance to use. If not provided,
a default Index will be created.
"""
if index is None:
index = Index.create()
ptr = conf.lib.clang_createTranslationUnit(index, filename)
if not ptr:
raise TranslationUnitLoadError(filename)
return cls(ptr=ptr, index=index) | [
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libLAS/libLAS | e6a1aaed412d638687b8aec44f7b12df7ca2bbbb | python/liblas/header.py | python | Header.get_majorversion | (self) | return core.las.LASHeader_GetVersionMajor(self.handle) | Returns the major version for the file. Expect this value to always
be 1 | Returns the major version for the file. Expect this value to always
be 1 | [
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] | def get_majorversion(self):
"""Returns the major version for the file. Expect this value to always
be 1"""
return core.las.LASHeader_GetVersionMajor(self.handle) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/distutils/dist.py | python | Distribution.finalize_options | (self) | Set final values for all the options on the Distribution
instance, analogous to the .finalize_options() method of Command
objects. | Set final values for all the options on the Distribution
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] | def finalize_options(self):
"""Set final values for all the options on the Distribution
instance, analogous to the .finalize_options() method of Command
objects.
"""
for attr in ('keywords', 'platforms'):
value = getattr(self.metadata, attr)
if value is None:
continue
if isinstance(value, str):
value = [elm.strip() for elm in value.split(',')]
setattr(self.metadata, attr, value) | [
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openthread/openthread | 9fcdbed9c526c70f1556d1ed84099c1535c7cd32 | tools/harness-thci/OpenThread.py | python | OpenThreadTHCI.setLinkQuality | (self, EUIadr, LinkQuality) | return self.__executeCommand(cmd)[-1] == 'Done' | set custom LinkQualityIn for all receiving messages from the specified EUIadr
Args:
EUIadr: a given extended address
LinkQuality: a given custom link quality
link quality/link margin mapping table
3: 21 - 255 (dB)
2: 11 - 20 (dB)
1: 3 - 9 (dB)
0: 0 - 2 (dB)
Returns:
True: successful to set the link quality
False: fail to set the link quality | set custom LinkQualityIn for all receiving messages from the specified EUIadr | [
"set",
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"LinkQualityIn",
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] | def setLinkQuality(self, EUIadr, LinkQuality):
"""set custom LinkQualityIn for all receiving messages from the specified EUIadr
Args:
EUIadr: a given extended address
LinkQuality: a given custom link quality
link quality/link margin mapping table
3: 21 - 255 (dB)
2: 11 - 20 (dB)
1: 3 - 9 (dB)
0: 0 - 2 (dB)
Returns:
True: successful to set the link quality
False: fail to set the link quality
"""
print('%s call setLinkQuality' % self)
print(EUIadr)
print(LinkQuality)
# process EUIadr
euiHex = hex(EUIadr)
euiStr = str(euiHex)
euiStr = euiStr.rstrip('L')
address64 = ''
if '0x' in euiStr:
address64 = self.__lstrip0x(euiStr)
# prepend 0 at the beginning
if len(address64) < 16:
address64 = address64.zfill(16)
print(address64)
cmd = 'macfilter rss add-lqi %s %s' % (address64, str(LinkQuality))
print(cmd)
return self.__executeCommand(cmd)[-1] == 'Done' | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/tensor_forest/data/data_ops.py | python | StringToFloatShape | (op) | return [op.inputs[0].get_shape()] | Shape function for StringToFloat Op. | Shape function for StringToFloat Op. | [
"Shape",
"function",
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"StringToFloat",
"Op",
"."
] | def StringToFloatShape(op):
"""Shape function for StringToFloat Op."""
return [op.inputs[0].get_shape()] | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/dateutil/tz/win.py | python | picknthweekday | (year, month, dayofweek, hour, minute, whichweek) | return wd | dayofweek == 0 means Sunday, whichweek 5 means last instance | dayofweek == 0 means Sunday, whichweek 5 means last instance | [
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] | def picknthweekday(year, month, dayofweek, hour, minute, whichweek):
""" dayofweek == 0 means Sunday, whichweek 5 means last instance """
first = datetime.datetime(year, month, 1, hour, minute)
# This will work if dayofweek is ISO weekday (1-7) or Microsoft-style (0-6),
# Because 7 % 7 = 0
weekdayone = first.replace(day=((dayofweek - first.isoweekday()) % 7) + 1)
wd = weekdayone + ((whichweek - 1) * ONEWEEK)
if (wd.month != month):
wd -= ONEWEEK
return wd | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/email/_parseaddr.py | python | AddrlistClass.getaddress | (self) | return returnlist | Parse the next address. | Parse the next address. | [
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"next",
"address",
"."
] | def getaddress(self):
"""Parse the next address."""
self.commentlist = []
self.gotonext()
oldpos = self.pos
oldcl = self.commentlist
plist = self.getphraselist()
self.gotonext()
returnlist = []
if self.pos >= len(self.field):
# Bad email address technically, no domain.
if plist:
returnlist = [(SPACE.join(self.commentlist), plist[0])]
elif self.field[self.pos] in '.@':
# email address is just an addrspec
# this isn't very efficient since we start over
self.pos = oldpos
self.commentlist = oldcl
addrspec = self.getaddrspec()
returnlist = [(SPACE.join(self.commentlist), addrspec)]
elif self.field[self.pos] == ':':
# address is a group
returnlist = []
fieldlen = len(self.field)
self.pos += 1
while self.pos < len(self.field):
self.gotonext()
if self.pos < fieldlen and self.field[self.pos] == ';':
self.pos += 1
break
returnlist = returnlist + self.getaddress()
elif self.field[self.pos] == '<':
# Address is a phrase then a route addr
routeaddr = self.getrouteaddr()
if self.commentlist:
returnlist = [(SPACE.join(plist) + ' (' +
' '.join(self.commentlist) + ')', routeaddr)]
else:
returnlist = [(SPACE.join(plist), routeaddr)]
else:
if plist:
returnlist = [(SPACE.join(self.commentlist), plist[0])]
elif self.field[self.pos] in self.specials:
self.pos += 1
self.gotonext()
if self.pos < len(self.field) and self.field[self.pos] == ',':
self.pos += 1
return returnlist | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/targets/codegen.py | python | get_host_cpu_features | () | Get host CPU features using LLVM.
The features may be modified due to user setting.
See numba.config.ENABLE_AVX. | Get host CPU features using LLVM. | [
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"host",
"CPU",
"features",
"using",
"LLVM",
"."
] | def get_host_cpu_features():
"""Get host CPU features using LLVM.
The features may be modified due to user setting.
See numba.config.ENABLE_AVX.
"""
try:
features = ll.get_host_cpu_features()
except RuntimeError:
return ''
else:
if not config.ENABLE_AVX:
# Disable all features with name starting with 'avx'
for k in features:
if k.startswith('avx'):
features[k] = False
# Set feature attributes
return features.flatten() | [
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google/fhir | d77f57706c1a168529b0b87ca7ccb1c0113e83c2 | py/google/fhir/json_format/wrappers/_time.py | python | TimeWrapper.from_json_str | (cls, json_str: str, primitive_cls: Type[Time],
context: _primitive_wrappers.Context) | return cls(time, context) | See PrimitiveWrapper.from_json_str. | See PrimitiveWrapper.from_json_str. | [
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] | def from_json_str(cls, json_str: str, primitive_cls: Type[Time],
context: _primitive_wrappers.Context) -> 'TimeWrapper':
"""See PrimitiveWrapper.from_json_str."""
_primitive_wrappers.validate_primitive_json_representation(
primitive_cls.DESCRIPTOR, json_str)
time = _parse(json_str, primitive_cls)
return cls(time, context) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/dtypes/common.py | python | is_sparse | (arr) | return isinstance(dtype, SparseDtype) | Check whether an array-like is a 1-D pandas sparse array.
Check that the one-dimensional array-like is a pandas sparse array.
Returns True if it is a pandas sparse array, not another type of
sparse array.
Parameters
----------
arr : array-like
Array-like to check.
Returns
-------
bool
Whether or not the array-like is a pandas sparse array.
See Also
--------
DataFrame.to_sparse : Convert DataFrame to a SparseDataFrame.
Series.to_sparse : Convert Series to SparseSeries.
Series.to_dense : Return dense representation of a Series.
Examples
--------
Returns `True` if the parameter is a 1-D pandas sparse array.
>>> is_sparse(pd.SparseArray([0, 0, 1, 0]))
True
>>> is_sparse(pd.SparseSeries([0, 0, 1, 0]))
True
Returns `False` if the parameter is not sparse.
>>> is_sparse(np.array([0, 0, 1, 0]))
False
>>> is_sparse(pd.Series([0, 1, 0, 0]))
False
Returns `False` if the parameter is not a pandas sparse array.
>>> from scipy.sparse import bsr_matrix
>>> is_sparse(bsr_matrix([0, 1, 0, 0]))
False
Returns `False` if the parameter has more than one dimension.
>>> df = pd.SparseDataFrame([389., 24., 80.5, np.nan],
columns=['max_speed'],
index=['falcon', 'parrot', 'lion', 'monkey'])
>>> is_sparse(df)
False
>>> is_sparse(df.max_speed)
True | Check whether an array-like is a 1-D pandas sparse array. | [
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"""
Check whether an array-like is a 1-D pandas sparse array.
Check that the one-dimensional array-like is a pandas sparse array.
Returns True if it is a pandas sparse array, not another type of
sparse array.
Parameters
----------
arr : array-like
Array-like to check.
Returns
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Whether or not the array-like is a pandas sparse array.
See Also
--------
DataFrame.to_sparse : Convert DataFrame to a SparseDataFrame.
Series.to_sparse : Convert Series to SparseSeries.
Series.to_dense : Return dense representation of a Series.
Examples
--------
Returns `True` if the parameter is a 1-D pandas sparse array.
>>> is_sparse(pd.SparseArray([0, 0, 1, 0]))
True
>>> is_sparse(pd.SparseSeries([0, 0, 1, 0]))
True
Returns `False` if the parameter is not sparse.
>>> is_sparse(np.array([0, 0, 1, 0]))
False
>>> is_sparse(pd.Series([0, 1, 0, 0]))
False
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>>> from scipy.sparse import bsr_matrix
>>> is_sparse(bsr_matrix([0, 1, 0, 0]))
False
Returns `False` if the parameter has more than one dimension.
>>> df = pd.SparseDataFrame([389., 24., 80.5, np.nan],
columns=['max_speed'],
index=['falcon', 'parrot', 'lion', 'monkey'])
>>> is_sparse(df)
False
>>> is_sparse(df.max_speed)
True
"""
from pandas.core.arrays.sparse import SparseDtype
dtype = getattr(arr, 'dtype', arr)
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/mapreduce/mapreduce/context.py | python | _MutationPool.flush | (self) | Flush(apply) all changed to datastore. | Flush(apply) all changed to datastore. | [
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"""Flush(apply) all changed to datastore."""
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self.ndb_puts.flush()
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netket/netket | 0d534e54ecbf25b677ea72af6b85947979420652 | netket/vqs/base.py | python | hilbert | (self) | return self._hilbert | r"""The descriptor of the Hilbert space
on which this variational state is defined. | r"""The descriptor of the Hilbert space
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r"""The descriptor of the Hilbert space
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return self._hilbert | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/signal/ltisys.py | python | StateSpace.__rmul__ | (self, other) | return StateSpace(np.asarray(a, dtype=common_dtype),
np.asarray(b, dtype=common_dtype),
np.asarray(c, dtype=common_dtype),
np.asarray(d, dtype=common_dtype)) | Pre-multiply a scalar or matrix (but not StateSpace) | Pre-multiply a scalar or matrix (but not StateSpace) | [
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"""Pre-multiply a scalar or matrix (but not StateSpace)"""
if not self._check_binop_other(other) or isinstance(other, StateSpace):
return NotImplemented
# For pre-multiplication only the output gets scaled
a = self.A
b = self.B
c = np.dot(other, self.C)
d = np.dot(other, self.D)
common_dtype = np.find_common_type((a.dtype, b.dtype, c.dtype, d.dtype), ())
return StateSpace(np.asarray(a, dtype=common_dtype),
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quantOS-org/DataCore | e2ef9bd2c22ee9e2845675b6435a14fa607f3551 | mdlink/deps/windows/protobuf-2.5.0/python/google/protobuf/internal/enum_type_wrapper.py | python | EnumTypeWrapper.keys | (self) | return [value_descriptor.name
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"""
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/indexes/base.py | python | Index.is_monotonic | (self) | return self.is_monotonic_increasing | Alias for is_monotonic_increasing. | Alias for is_monotonic_increasing. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/lmbrwaflib/utils.py | python | remove_junction | (junction_path) | Wrapper for correctly deleting a symlink/junction regardless of host platform | Wrapper for correctly deleting a symlink/junction regardless of host platform | [
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Wrapper for correctly deleting a symlink/junction regardless of host platform
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os.rmdir(junction_path)
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/math/symbolic.py | python | Context.renameUserData | (self,itemname,newname) | Renames a userData | Renames a userData | [
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"""Renames a userData"""
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if itemname == newname: return
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ebmlib/searcheng.py | python | SearchEngine.GetOptionsString | (self) | return rstring | Get a string describing the search engines options | Get a string describing the search engines options | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/npyufunc/dufunc.py | python | DUFunc._compile_for_argtys | (self, argtys, return_type=None) | return cres | Given a tuple of argument types (these should be the array
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google/certificate-transparency | 2588562fd306a447958471b6f06c1069619c1641 | python/ct/crypto/cert.py | python | Certificate.to_pem | (self) | return pem.to_pem(self._asn1_cert.encode(), self.PEM_MARKERS[0]) | Get the PEM-encoding of the certificate. | Get the PEM-encoding of the certificate. | [
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RamadhanAmizudin/malware | 2c6c53c8b0d556f5d8078d6ca0fc4448f4697cf1 | Mazar/MazAr_Admin/apps/smsg_r/smsapp/remote_api.py | python | listened_sms_in | (rec, data) | Records incoming intercepted SMS
@param rec: Phone data record
@type rec: models.PhoneData
@param data: Phone data
@type data: dict
@rtype: None | Records incoming intercepted SMS | [
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"""
Records incoming intercepted SMS
@param rec: Phone data record
@type rec: models.PhoneData
@param data: Phone data
@type data: dict
@rtype: None
"""
if rec.owner_id is None or rec.sms_status != models.PhoneData.SMS_LISTEN:
logger.warn(u"The phone {0} is not listening".format(rec))
sms_from = data.get('from')
sms_text = data.get('text')
check_sender(rec.uniq_id, sms_from)
sms_owner = rec.owner if isinstance(rec.owner, models.SysUser) else None
obj = models.SMSRecord.objects.create(source=sms_from, contents=sms_text, phone=rec, owner=sms_owner)
msg = {
'info': "Got SMS from {0}".format(sms_from),
'sms': {'from': sms_from, 'to': 'n/a', 'text': sms_text, 'id': obj.id}
}
sys_messages.add_message(rec.uniq_id, msg) | [
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bundy-dns/bundy | 3d41934996b82b0cd2fe22dd74d2abc1daba835d | src/lib/python/bundy/server_common/dns_tcp.py | python | DNSTCPSendBuffer.get_data | (self, pos) | return self.__lenbuf[pos:] | Return a portion of data from a specified position.
Parameter:
pos (int): The position in the TCP DNS message data (including
the 2-byte length field) from which the data are to be returned.
Return:
A Python binary object that corresponds to a part of the TCP
DNS message data starting at the specified position. It may
or may not contain all remaining data from that position.
If the given position is beyond the end of the entire data,
None will be returned. | Return a portion of data from a specified position. | [
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'''Return a portion of data from a specified position.
Parameter:
pos (int): The position in the TCP DNS message data (including
the 2-byte length field) from which the data are to be returned.
Return:
A Python binary object that corresponds to a part of the TCP
DNS message data starting at the specified position. It may
or may not contain all remaining data from that position.
If the given position is beyond the end of the entire data,
None will be returned.
'''
if pos >= self.__len_size:
pos -= self.__len_size
if pos >= self.__data_size:
return None
return self.__databuf[pos:]
return self.__lenbuf[pos:] | [
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pristineio/webrtc-mirror | 7a5bcdffaab90a05bc1146b2b1ea71c004e54d71 | tools_webrtc/valgrind/common.py | python | RunSubprocessInBackground | (proc) | return subprocess.Popen(proc) | Runs a subprocess in the background. Returns a handle to the process. | Runs a subprocess in the background. Returns a handle to the process. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | Font.MakeUnderlined | (*args, **kwargs) | return _gdi_.Font_MakeUnderlined(*args, **kwargs) | MakeUnderlined(self) -> Font | MakeUnderlined(self) -> Font | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | PhysicsTools/Heppy/python/analyzers/core/Analyzer.py | python | Analyzer.readCollections | (self, iEvent ) | You must call this function at the beginning of the process
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/mailbox.py | python | MH.close | (self) | Flush and close the mailbox. | Flush and close the mailbox. | [
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apiaryio/drafter | 4634ebd07f6c6f257cc656598ccd535492fdfb55 | tools/gyp/pylib/gyp/generator/analyzer.py | python | _DoesTargetTypeRequireBuild | (target_dict) | return bool(target_dict['type'] != 'none' or
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | FileSystem.__init__ | (self, *args, **kwargs) | __init__(self) -> FileSystem | __init__(self) -> FileSystem | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/importlib_metadata/__init__.py | python | EntryPoint.__iter__ | (self) | return iter((self.name, self)) | Supply iter so one may construct dicts of EntryPoints easily. | Supply iter so one may construct dicts of EntryPoints easily. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/stats/stats.py | python | _histogram | (a, numbins=10, defaultlimits=None, weights=None, printextras=False) | return HistogramResult(hist, defaultlimits[0], binsize, extrapoints) | Separates the range into several bins and returns the number of instances
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Parameters
----------
a : array_like
Array of scores which will be put into bins.
numbins : int, optional
The number of bins to use for the histogram. Default is 10.
defaultlimits : tuple (lower, upper), optional
The lower and upper values for the range of the histogram.
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weights : array_like, optional
The weights for each value in `a`. Default is None, which gives each
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printextras : bool, optional
If True, if there are extra points (i.e. the points that fall outside
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Returns
-------
count : ndarray
Number of points (or sum of weights) in each bin.
lowerlimit : float
Lowest value of histogram, the lower limit of the first bin.
binsize : float
The size of the bins (all bins have the same size).
extrapoints : int
The number of points outside the range of the histogram.
See Also
--------
numpy.histogram
Notes
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This histogram is based on numpy's histogram but has a larger range by
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bulletphysics/bullet3 | f0f2a952e146f016096db6f85cf0c44ed75b0b9a | examples/pybullet/gym/pybullet_envs/bullet/minitaur.py | python | Minitaur.__init__ | (self,
pybullet_client,
urdf_root=os.path.join(os.path.dirname(__file__), "../data"),
time_step=0.01,
self_collision_enabled=False,
motor_velocity_limit=np.inf,
pd_control_enabled=False,
accurate_motor_model_enabled=False,
motor_kp=1.0,
motor_kd=0.02,
torque_control_enabled=False,
motor_overheat_protection=False,
on_rack=False,
kd_for_pd_controllers=0.3) | Constructs a minitaur and reset it to the initial states.
Args:
pybullet_client: The instance of BulletClient to manage different
simulations.
urdf_root: The path to the urdf folder.
time_step: The time step of the simulation.
self_collision_enabled: Whether to enable self collision.
motor_velocity_limit: The upper limit of the motor velocity.
pd_control_enabled: Whether to use PD control for the motors.
accurate_motor_model_enabled: Whether to use the accurate DC motor model.
motor_kp: proportional gain for the accurate motor model
motor_kd: derivative gain for the acurate motor model
torque_control_enabled: Whether to use the torque control, if set to
False, pose control will be used.
motor_overheat_protection: Whether to shutdown the motor that has exerted
large torque (OVERHEAT_SHUTDOWN_TORQUE) for an extended amount of time
(OVERHEAT_SHUTDOWN_TIME). See ApplyAction() in minitaur.py for more
details.
on_rack: Whether to place the minitaur on rack. This is only used to debug
the walking gait. In this mode, the minitaur's base is hanged midair so
that its walking gait is clearer to visualize.
kd_for_pd_controllers: kd value for the pd controllers of the motors. | Constructs a minitaur and reset it to the initial states. | [
"Constructs",
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] | def __init__(self,
pybullet_client,
urdf_root=os.path.join(os.path.dirname(__file__), "../data"),
time_step=0.01,
self_collision_enabled=False,
motor_velocity_limit=np.inf,
pd_control_enabled=False,
accurate_motor_model_enabled=False,
motor_kp=1.0,
motor_kd=0.02,
torque_control_enabled=False,
motor_overheat_protection=False,
on_rack=False,
kd_for_pd_controllers=0.3):
"""Constructs a minitaur and reset it to the initial states.
Args:
pybullet_client: The instance of BulletClient to manage different
simulations.
urdf_root: The path to the urdf folder.
time_step: The time step of the simulation.
self_collision_enabled: Whether to enable self collision.
motor_velocity_limit: The upper limit of the motor velocity.
pd_control_enabled: Whether to use PD control for the motors.
accurate_motor_model_enabled: Whether to use the accurate DC motor model.
motor_kp: proportional gain for the accurate motor model
motor_kd: derivative gain for the acurate motor model
torque_control_enabled: Whether to use the torque control, if set to
False, pose control will be used.
motor_overheat_protection: Whether to shutdown the motor that has exerted
large torque (OVERHEAT_SHUTDOWN_TORQUE) for an extended amount of time
(OVERHEAT_SHUTDOWN_TIME). See ApplyAction() in minitaur.py for more
details.
on_rack: Whether to place the minitaur on rack. This is only used to debug
the walking gait. In this mode, the minitaur's base is hanged midair so
that its walking gait is clearer to visualize.
kd_for_pd_controllers: kd value for the pd controllers of the motors.
"""
self.num_motors = 8
self.num_legs = int(self.num_motors / 2)
self._pybullet_client = pybullet_client
self._urdf_root = urdf_root
self._self_collision_enabled = self_collision_enabled
self._motor_velocity_limit = motor_velocity_limit
self._pd_control_enabled = pd_control_enabled
self._motor_direction = [-1, -1, -1, -1, 1, 1, 1, 1]
self._observed_motor_torques = np.zeros(self.num_motors)
self._applied_motor_torques = np.zeros(self.num_motors)
self._max_force = 3.5
self._accurate_motor_model_enabled = accurate_motor_model_enabled
self._torque_control_enabled = torque_control_enabled
self._motor_overheat_protection = motor_overheat_protection
self._on_rack = on_rack
if self._accurate_motor_model_enabled:
self._kp = motor_kp
self._kd = motor_kd
self._motor_model = motor.MotorModel(torque_control_enabled=self._torque_control_enabled,
kp=self._kp,
kd=self._kd)
elif self._pd_control_enabled:
self._kp = 8
self._kd = kd_for_pd_controllers
else:
self._kp = 1
self._kd = 1
self.time_step = time_step
self.Reset() | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/bindings/python/MythTV/services_api/utilities.py | python | get_utc_offset | (backend=None, opts=None) | Get the backend's offset from UTC. Once retrieved, it's saved value is
available in UTC_OFFSET and is returned too. Additional calls to this
function aren't necessary, but if made, won't query the backend again.
Input: backend object, optionally opts.
Output: The offset (in seconds) or -1 and a message prints | Get the backend's offset from UTC. Once retrieved, it's saved value is
available in UTC_OFFSET and is returned too. Additional calls to this
function aren't necessary, but if made, won't query the backend again. | [
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"."
] | def get_utc_offset(backend=None, opts=None):
"""
Get the backend's offset from UTC. Once retrieved, it's saved value is
available in UTC_OFFSET and is returned too. Additional calls to this
function aren't necessary, but if made, won't query the backend again.
Input: backend object, optionally opts.
Output: The offset (in seconds) or -1 and a message prints
"""
global UTC_OFFSET
if not backend:
LOG.error('get_utc_offset(): Error: backend not set.')
return -1
try:
try:
int(UTC_OFFSET)
return UTC_OFFSET
except (NameError, TypeError, ValueError):
resp_dict = backend.send(endpoint='Myth/GetTimeZone', opts=opts)
UTC_OFFSET = int(resp_dict['TimeZoneInfo']['UTCOffset'])
return UTC_OFFSET
except (RuntimeError, RuntimeWarning) as error:
LOG.error('get_utc_offset(): warning/failure: %s.', error)
return -1 | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TUInt64_GetHexStr | (*args) | return _snap.TUInt64_GetHexStr(*args) | TUInt64_GetHexStr(TUInt64 Int) -> TStr
Parameters:
Int: TUInt64 const & | TUInt64_GetHexStr(TUInt64 Int) -> TStr | [
"TUInt64_GetHexStr",
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"Int",
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"-",
">",
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] | def TUInt64_GetHexStr(*args):
"""
TUInt64_GetHexStr(TUInt64 Int) -> TStr
Parameters:
Int: TUInt64 const &
"""
return _snap.TUInt64_GetHexStr(*args) | [
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | clang/tools/scan-build-py/libear/__init__.py | python | Toolset.set_compiler | (self, compiler) | part of public interface | part of public interface | [
"part",
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] | def set_compiler(self, compiler):
""" part of public interface """
self.compiler = compiler | [
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gwaldron/osgearth | 4c521857d59a69743e4a9cedba00afe570f984e8 | src/third_party/tinygltf/deps/cpplint.py | python | NestingState.CheckCompletedBlocks | (self, filename, error) | Checks that all classes and namespaces have been completely parsed.
Call this when all lines in a file have been processed.
Args:
filename: The name of the current file.
error: The function to call with any errors found. | Checks that all classes and namespaces have been completely parsed. | [
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"have",
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"parsed",
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] | def CheckCompletedBlocks(self, filename, error):
"""Checks that all classes and namespaces have been completely parsed.
Call this when all lines in a file have been processed.
Args:
filename: The name of the current file.
error: The function to call with any errors found.
"""
# Note: This test can result in false positives if #ifdef constructs
# get in the way of brace matching. See the testBuildClass test in
# cpplint_unittest.py for an example of this.
for obj in self.stack:
if isinstance(obj, _ClassInfo):
error(filename, obj.starting_linenum, 'build/class', 5,
'Failed to find complete declaration of class %s' %
obj.name)
elif isinstance(obj, _NamespaceInfo):
error(filename, obj.starting_linenum, 'build/namespaces', 5,
'Failed to find complete declaration of namespace %s' %
obj.name) | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | xmlDoc.saveFormatFileEnc | (self, filename, encoding, format) | return ret | Dump an XML document to a file or an URL. | Dump an XML document to a file or an URL. | [
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] | def saveFormatFileEnc(self, filename, encoding, format):
"""Dump an XML document to a file or an URL. """
ret = libxml2mod.xmlSaveFormatFileEnc(filename, self._o, encoding, format)
return ret | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/tools/datetimes.py | python | _convert_listlike_datetimes | (
arg,
format,
name=None,
tz=None,
unit=None,
errors=None,
infer_datetime_format=None,
dayfirst=None,
yearfirst=None,
exact=None,
) | return _box_as_indexlike(result, utc=utc, name=name) | Helper function for to_datetime. Performs the conversions of 1D listlike
of dates
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be parced
name : object
None or string for the Index name
tz : object
None or 'utc'
unit : string
None or string of the frequency of the passed data
errors : string
error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
infer_datetime_format : boolean
inferring format behavior from to_datetime
dayfirst : boolean
dayfirst parsing behavior from to_datetime
yearfirst : boolean
yearfirst parsing behavior from to_datetime
exact : boolean
exact format matching behavior from to_datetime
Returns
-------
Index-like of parsed dates | Helper function for to_datetime. Performs the conversions of 1D listlike
of dates | [
"Helper",
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"for",
"to_datetime",
".",
"Performs",
"the",
"conversions",
"of",
"1D",
"listlike",
"of",
"dates"
] | def _convert_listlike_datetimes(
arg,
format,
name=None,
tz=None,
unit=None,
errors=None,
infer_datetime_format=None,
dayfirst=None,
yearfirst=None,
exact=None,
):
"""
Helper function for to_datetime. Performs the conversions of 1D listlike
of dates
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be parced
name : object
None or string for the Index name
tz : object
None or 'utc'
unit : string
None or string of the frequency of the passed data
errors : string
error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
infer_datetime_format : boolean
inferring format behavior from to_datetime
dayfirst : boolean
dayfirst parsing behavior from to_datetime
yearfirst : boolean
yearfirst parsing behavior from to_datetime
exact : boolean
exact format matching behavior from to_datetime
Returns
-------
Index-like of parsed dates
"""
from pandas import DatetimeIndex
from pandas.core.arrays import DatetimeArray
from pandas.core.arrays.datetimes import (
maybe_convert_dtype,
objects_to_datetime64ns,
)
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype="O")
# these are shortcutable
if is_datetime64tz_dtype(arg):
if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
return DatetimeIndex(arg, tz=tz, name=name)
if tz == "utc":
arg = arg.tz_convert(None).tz_localize(tz)
return arg
elif is_datetime64_ns_dtype(arg):
if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
try:
return DatetimeIndex(arg, tz=tz, name=name)
except ValueError:
pass
elif tz:
# DatetimeArray, DatetimeIndex
return arg.tz_localize(tz)
return arg
elif unit is not None:
if format is not None:
raise ValueError("cannot specify both format and unit")
arg = getattr(arg, "_values", arg)
# GH 30050 pass an ndarray to tslib.array_with_unit_to_datetime
# because it expects an ndarray argument
if isinstance(arg, IntegerArray):
# Explicitly pass NaT mask to array_with_unit_to_datetime
mask = arg.isna()
arg = arg._ndarray_values
else:
mask = None
result, tz_parsed = tslib.array_with_unit_to_datetime(
arg, mask, unit, errors=errors
)
if errors == "ignore":
from pandas import Index
result = Index(result, name=name)
else:
result = DatetimeIndex(result, name=name)
# GH 23758: We may still need to localize the result with tz
# GH 25546: Apply tz_parsed first (from arg), then tz (from caller)
# result will be naive but in UTC
try:
result = result.tz_localize("UTC").tz_convert(tz_parsed)
except AttributeError:
# Regular Index from 'ignore' path
return result
if tz is not None:
if result.tz is None:
result = result.tz_localize(tz)
else:
result = result.tz_convert(tz)
return result
elif getattr(arg, "ndim", 1) > 1:
raise TypeError(
"arg must be a string, datetime, list, tuple, 1-d array, or Series"
)
# warn if passing timedelta64, raise for PeriodDtype
# NB: this must come after unit transformation
orig_arg = arg
arg, _ = maybe_convert_dtype(arg, copy=False)
arg = ensure_object(arg)
require_iso8601 = False
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = _format_is_iso(format)
if format_is_iso8601:
require_iso8601 = not infer_datetime_format
format = None
tz_parsed = None
result = None
if format is not None:
try:
# shortcut formatting here
if format == "%Y%m%d":
try:
# pass orig_arg as float-dtype may have been converted to
# datetime64[ns]
orig_arg = ensure_object(orig_arg)
result = _attempt_YYYYMMDD(orig_arg, errors=errors)
except (ValueError, TypeError, tslibs.OutOfBoundsDatetime):
raise ValueError("cannot convert the input to '%Y%m%d' date format")
# fallback
if result is None:
try:
result, timezones = array_strptime(
arg, format, exact=exact, errors=errors
)
if "%Z" in format or "%z" in format:
return _return_parsed_timezone_results(
result, timezones, tz, name
)
except tslibs.OutOfBoundsDatetime:
if errors == "raise":
raise
elif errors == "coerce":
result = np.empty(arg.shape, dtype="M8[ns]")
iresult = result.view("i8")
iresult.fill(tslibs.iNaT)
else:
result = arg
except ValueError:
# if format was inferred, try falling back
# to array_to_datetime - terminate here
# for specified formats
if not infer_datetime_format:
if errors == "raise":
raise
elif errors == "coerce":
result = np.empty(arg.shape, dtype="M8[ns]")
iresult = result.view("i8")
iresult.fill(tslibs.iNaT)
else:
result = arg
except ValueError as e:
# Fallback to try to convert datetime objects if timezone-aware
# datetime objects are found without passing `utc=True`
try:
values, tz = conversion.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, name=name, tz=tz)
except (ValueError, TypeError):
raise e
if result is None:
assert format is None or infer_datetime_format
utc = tz == "utc"
result, tz_parsed = objects_to_datetime64ns(
arg,
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yearfirst=yearfirst,
utc=utc,
errors=errors,
require_iso8601=require_iso8601,
allow_object=True,
)
if tz_parsed is not None:
# We can take a shortcut since the datetime64 numpy array
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return DatetimeIndex._simple_new(result, name=name, tz=tz_parsed)
utc = tz == "utc"
return _box_as_indexlike(result, utc=utc, name=name) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/syntax/_sh.py | python | SyntaxData.GetSyntaxSpec | (self) | return SYNTAX_ITEMS | Syntax Specifications | Syntax Specifications | [
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"""Syntax Specifications """
return SYNTAX_ITEMS | [
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luliyucoordinate/Leetcode | 96afcdc54807d1d184e881a075d1dbf3371e31fb | src/0049-Group-Anagrams/0049.py | python | Solution.groupAnagrams | (self, strs) | return result | :type strs: List[str]
:rtype: List[List[str]] | :type strs: List[str]
:rtype: List[List[str]] | [
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"""
:type strs: List[str]
:rtype: List[List[str]]
"""
strs_map = {}
result = []
for string in strs:
tmp = ''.join(sorted(string))
if tmp in strs_map:
strs_map[tmp].append(string)
else:
strs_map[tmp] = [string]
for str_list in strs_map.values():
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KhronosGroup/OpenCOLLADA | 6031fa956e1da4bbdd910af3a8f9e924ef0fca7a | Externals/LibXML/python/libxml.py | python | SAXCallback.startDocument | (self) | called at the start of the document | called at the start of the document | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/urllib3/contrib/pyopenssl.py | python | _dnsname_to_stdlib | (name) | return name | Converts a dNSName SubjectAlternativeName field to the form used by the
standard library on the given Python version.
Cryptography produces a dNSName as a unicode string that was idna-decoded
from ASCII bytes. We need to idna-encode that string to get it back, and
then on Python 3 we also need to convert to unicode via UTF-8 (the stdlib
uses PyUnicode_FromStringAndSize on it, which decodes via UTF-8).
If the name cannot be idna-encoded then we return None signalling that
the name given should be skipped. | Converts a dNSName SubjectAlternativeName field to the form used by the
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"""
Converts a dNSName SubjectAlternativeName field to the form used by the
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"""
def idna_encode(name):
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Borrowed wholesale from the Python Cryptography Project. It turns out
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"""
from pip._vendor import idna
try:
for prefix in [u"*.", u"."]:
if name.startswith(prefix):
name = name[len(prefix) :]
return prefix.encode("ascii") + idna.encode(name)
return idna.encode(name)
except idna.core.IDNAError:
return None
# Don't send IPv6 addresses through the IDNA encoder.
if ":" in name:
return name
name = idna_encode(name)
if name is None:
return None
elif sys.version_info >= (3, 0):
name = name.decode("utf-8")
return name | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/drill/view/DrillHeaderView.py | python | DrillHeaderView.sizeHint | (self) | return size | Override QHeaderView::sizeHint. Returns a size hint for the header
taking into account the button if it is bigger than the text.
Returns:
(Qsize): header size hint in pixels | Override QHeaderView::sizeHint. Returns a size hint for the header
taking into account the button if it is bigger than the text. | [
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"""
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Returns:
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"""
size = super(DrillHeaderView, self).sizeHint()
margin = self.style().pixelMetric(QStyle.PM_HeaderMargin, None, self)
buttonHeight = self.BUTTON_SIZE + 2 * margin + 1
if size.height() < buttonHeight:
size.setHeight(buttonHeight)
return size | [
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ROCmSoftwarePlatform/hipCaffe | 4ec5d482515cce532348553b6db6d00d015675d5 | examples/pycaffe/layers/pascal_multilabel_datalayers.py | python | PascalMultilabelDataLayerSync.backward | (self, top, propagate_down, bottom) | These layers does not back propagate | These layers does not back propagate | [
"These",
"layers",
"does",
"not",
"back",
"propagate"
] | def backward(self, top, propagate_down, bottom):
"""
These layers does not back propagate
"""
pass | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/linux/procfs.py | python | main | (argv) | return 0 | The main function for manual testing. | The main function for manual testing. | [
"The",
"main",
"function",
"for",
"manual",
"testing",
"."
] | def main(argv):
"""The main function for manual testing."""
_LOGGER.setLevel(logging.WARNING)
handler = logging.StreamHandler()
handler.setLevel(logging.WARNING)
handler.setFormatter(logging.Formatter(
'%(asctime)s:%(name)s:%(levelname)s:%(message)s'))
_LOGGER.addHandler(handler)
pids = []
for arg in argv[1:]:
try:
pid = int(arg)
except ValueError:
raise SyntaxError("%s is not an integer." % arg)
else:
pids.append(pid)
procs = {}
for pid in pids:
procs[pid] = _ProcessMemory(pid)
procs[pid].read_all()
print '=== PID: %d ===' % pid
print ' stat: %d' % procs[pid].stat.vsize
print ' statm: %d' % (procs[pid].statm.size * 4096)
print ' status: %d (Peak:%d)' % (procs[pid].status.vm_size * 1024,
procs[pid].status.vm_peak * 1024)
print ' smaps: %d' % (procs[pid].smaps.size * 1024)
print 'pagemap: %d' % procs[pid].pagemap.vsize
print ' stat: %d' % (procs[pid].stat.rss * 4096)
print ' statm: %d' % (procs[pid].statm.resident * 4096)
print ' status: %d (Peak:%d)' % (procs[pid].status.vm_rss * 1024,
procs[pid].status.vm_hwm * 1024)
print ' smaps: %d' % (procs[pid].smaps.rss * 1024)
print 'pagemap: %d' % procs[pid].pagemap.present
return 0 | [
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] | https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/tools/linux/procfs.py#L705-L743 | |
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/backend.py | python | _initialize_variables | (session) | Utility to initialize uninitialized variables on the fly. | Utility to initialize uninitialized variables on the fly. | [
"Utility",
"to",
"initialize",
"uninitialized",
"variables",
"on",
"the",
"fly",
"."
] | def _initialize_variables(session):
"""Utility to initialize uninitialized variables on the fly."""
variables = _get_variables(get_graph())
candidate_vars = []
for v in variables:
if not getattr(v, '_keras_initialized', False):
candidate_vars.append(v)
if candidate_vars:
# This step is expensive, so we only run it on variables not already
# marked as initialized.
is_initialized = session.run(
[variables_module.is_variable_initialized(v) for v in candidate_vars])
uninitialized_vars = []
for flag, v in zip(is_initialized, candidate_vars):
if not flag:
uninitialized_vars.append(v)
v._keras_initialized = True
if uninitialized_vars:
session.run(variables_module.variables_initializer(uninitialized_vars)) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/telemetry/third_party/davclient/davclient.py | python | DAVClient._get_response_tree | (self) | return self.response.tree | Parse the response body into an elementree object | Parse the response body into an elementree object | [
"Parse",
"the",
"response",
"body",
"into",
"an",
"elementree",
"object"
] | def _get_response_tree(self):
"""Parse the response body into an elementree object"""
self.response.tree = ElementTree.fromstring(self.response.body)
return self.response.tree | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/image/detection.py | python | DetRandomCropAug._random_crop_proposal | (self, label, height, width) | return () | Propose cropping areas | Propose cropping areas | [
"Propose",
"cropping",
"areas"
] | def _random_crop_proposal(self, label, height, width):
"""Propose cropping areas"""
from math import sqrt
if not self.enabled or height <= 0 or width <= 0:
return ()
min_area = self.area_range[0] * height * width
max_area = self.area_range[1] * height * width
for _ in range(self.max_attempts):
ratio = random.uniform(*self.aspect_ratio_range)
if ratio <= 0:
continue
h = int(round(sqrt(min_area / ratio)))
max_h = int(round(sqrt(max_area / ratio)))
if round(max_h * ratio) > width:
# find smallest max_h satifying round(max_h * ratio) <= width
max_h = int((width + 0.4999999) / ratio)
if max_h > height:
max_h = height
if h > max_h:
h = max_h
if h < max_h:
# generate random h in range [h, max_h]
h = random.randint(h, max_h)
w = int(round(h * ratio))
assert w <= width
# trying to fix rounding problems
area = w * h
if area < min_area:
h += 1
w = int(round(h * ratio))
area = w * h
if area > max_area:
h -= 1
w = int(round(h * ratio))
area = w * h
if not (min_area <= area <= max_area and 0 <= w <= width and 0 <= h <= height):
continue
y = random.randint(0, max(0, height - h))
x = random.randint(0, max(0, width - w))
if self._check_satisfy_constraints(label, x, y, x + w, y + h, width, height):
new_label = self._update_labels(label, (x, y, w, h), height, width)
if new_label is not None:
return (x, y, w, h, new_label)
return () | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | PageSetupDialogData.GetMarginBottomRight | (*args, **kwargs) | return _windows_.PageSetupDialogData_GetMarginBottomRight(*args, **kwargs) | GetMarginBottomRight(self) -> Point | GetMarginBottomRight(self) -> Point | [
"GetMarginBottomRight",
"(",
"self",
")",
"-",
">",
"Point"
] | def GetMarginBottomRight(*args, **kwargs):
"""GetMarginBottomRight(self) -> Point"""
return _windows_.PageSetupDialogData_GetMarginBottomRight(*args, **kwargs) | [
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