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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2.py | python | cleanupOutputCallbacks | () | clears the entire output callback table. this includes the
compiled-in I/O callbacks. | clears the entire output callback table. this includes the
compiled-in I/O callbacks. | [
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"""clears the entire output callback table. this includes the
compiled-in I/O callbacks. """
libxml2mod.xmlCleanupOutputCallbacks() | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/external/bazel_tools/tools/android/stubify_manifest.py | python | StubifyInstantRun | (manifest_string) | return ElementTree.tostring(manifest) | Stubifies the manifest for Instant Run.
Args:
manifest_string: the input manifest as a string.
Returns:
The new manifest as a string.
Raises:
Exception: if somethign goes wrong | Stubifies the manifest for Instant Run. | [
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] | def StubifyInstantRun(manifest_string):
"""Stubifies the manifest for Instant Run.
Args:
manifest_string: the input manifest as a string.
Returns:
The new manifest as a string.
Raises:
Exception: if somethign goes wrong
"""
manifest, application = _ParseManifest(manifest_string)
old_application = application.get("{%s}name" % ANDROID)
if old_application:
application.set("name", old_application)
application.set("{%s}name" % ANDROID, INSTANT_RUN_BOOTSTRAP_APPLICATION)
return ElementTree.tostring(manifest) | [
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protocolbuffers/protobuf | b5ab0b7a18b7336c60130f4ddb2d97c51792f896 | python/mox.py | python | MockAnything.__nonzero__ | (self) | return 1 | Return 1 for nonzero so the mock can be used as a conditional. | Return 1 for nonzero so the mock can be used as a conditional. | [
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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/instrument_interval.py | python | InstrumentInterval.to_dict | (self) | return result | Returns the model properties as a dict | Returns the model properties as a dict | [
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"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(InstrumentInterval, dict):
for key, value in self.items():
result[key] = value
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/idlelib/configdialog.py | python | KeysPage.var_changed_keybinding | (self, *params) | Store change to a keybinding. | Store change to a keybinding. | [
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"Store change to a keybinding."
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event = self.bindingslist.get(ANCHOR).split()[0]
if idleConf.IsCoreBinding(event):
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ext_name = idleConf.GetExtnNameForEvent(event)
ext_keybind_section = ext_name + '_cfgBindings'
changes.add_option('extensions', ext_keybind_section, event, value) | [
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | BitVecRef.__div__ | (self, other) | return BitVecRef(Z3_mk_bvsdiv(self.ctx_ref(), a.as_ast(), b.as_ast()), self.ctx) | Create the Z3 expression (signed) division `self / other`.
Use the function UDiv() for unsigned division.
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>>> x / y
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>>> (x / y).sort()
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>>> UDiv(x, y).sexpr()
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>>> x = BitVec('x', 32)
>>> y = BitVec('y', 32)
>>> x / y
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>>> (x / y).sort()
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>>> UDiv(x, y).sexpr()
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"""
a, b = _coerce_exprs(self, other)
return BitVecRef(Z3_mk_bvsdiv(self.ctx_ref(), a.as_ast(), b.as_ast()), self.ctx) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/compiler/symbols.py | python | SymbolVisitor.visitAssign | (self, node, scope) | Propagate assignment flag down to child nodes.
The Assign node doesn't itself contains the variables being
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] | def visitAssign(self, node, scope):
"""Propagate assignment flag down to child nodes.
The Assign node doesn't itself contains the variables being
assigned to. Instead, the children in node.nodes are visited
with the assign flag set to true. When the names occur in
those nodes, they are marked as defs.
Some names that occur in an assignment target are not bound by
the assignment, e.g. a name occurring inside a slice. The
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"""
for n in node.nodes:
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/data_flow_ops.py | python | _as_shape_list | (shapes, dtypes, unknown_dim_allowed=False,
unknown_rank_allowed=False) | return shapes | Convert shapes to a list of tuples of int (or None). | Convert shapes to a list of tuples of int (or None). | [
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] | def _as_shape_list(shapes, dtypes, unknown_dim_allowed=False,
unknown_rank_allowed=False):
"""Convert shapes to a list of tuples of int (or None)."""
del dtypes
if unknown_dim_allowed:
if (not isinstance(shapes, collections.Sequence)
or not shapes
or any(shape is None or isinstance(shape, int) for shape in shapes)):
raise ValueError(
"When providing partial shapes, a list of shapes must be provided.")
if shapes is None: return None
if isinstance(shapes, tensor_shape.TensorShape):
shapes = [shapes]
if not isinstance(shapes, (tuple, list)):
raise TypeError(
"shapes must be a TensorShape or a list or tuple of TensorShapes.")
if all(shape is None or isinstance(shape, int) for shape in shapes):
# We have a single shape.
shapes = [shapes]
shapes = [tensor_shape.as_shape(shape) for shape in shapes]
if not unknown_dim_allowed:
if any([not shape.is_fully_defined() for shape in shapes]):
raise ValueError("All shapes must be fully defined: %s" % shapes)
if not unknown_rank_allowed:
if any([shape.dims is None for shape in shapes]):
raise ValueError("All shapes must have a defined rank: %s" % shapes)
return shapes | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/packaging/specifiers.py | python | BaseSpecifier.__ne__ | (self, other) | Returns a boolean representing whether or not the two Specifier like
objects are not equal. | Returns a boolean representing whether or not the two Specifier like
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | Pen.GetCap | (*args, **kwargs) | return _gdi_.Pen_GetCap(*args, **kwargs) | GetCap(self) -> int | GetCap(self) -> int | [
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"""GetCap(self) -> int"""
return _gdi_.Pen_GetCap(*args, **kwargs) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/eager/monitoring.py | python | SamplerCell.value | (self) | return histogram_proto | Retrieves the current distribution of samples.
Returns:
A HistogramProto describing the distribution of samples. | Retrieves the current distribution of samples. | [
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] | def value(self):
"""Retrieves the current distribution of samples.
Returns:
A HistogramProto describing the distribution of samples.
"""
with c_api_util.tf_buffer() as buffer_:
pywrap_tensorflow.TFE_MonitoringSamplerCellValue(self._cell, buffer_)
proto_data = pywrap_tensorflow.TF_GetBuffer(buffer_)
histogram_proto = summary_pb2.HistogramProto()
histogram_proto.ParseFromString(compat.as_bytes(proto_data))
return histogram_proto | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/learn/python/learn/estimators/head.py | python | _BinaryLogisticHead.create_model_fn_ops | (self,
features,
mode,
labels=None,
train_op_fn=None,
logits=None,
logits_input=None,
scope=None) | See `Head`. | See `Head`. | [
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scope=None):
"""See `Head`."""
with variable_scope.variable_scope(
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values=(tuple(six.itervalues(features)) +
(labels, logits, logits_input))):
labels = self._transform_labels(mode=mode, labels=labels)
logits = _logits(logits_input, logits, self.logits_dimension)
return _create_model_fn_ops(
features=features,
mode=mode,
loss_fn=self._loss_fn,
logits_to_predictions_fn=self._logits_to_predictions,
metrics_fn=self._metrics,
create_output_alternatives_fn=_classification_output_alternatives(
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labels=labels,
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logits=logits,
logits_dimension=self.logits_dimension,
head_name=self.head_name,
weight_column_name=self.weight_column_name,
enable_centered_bias=self._enable_centered_bias) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/training/session_manager.py | python | SessionManager.wait_for_session | (self, master, config=None, max_wait_secs=float("Inf")) | Creates a new `Session` and waits for model to be ready.
Creates a new `Session` on 'master'. Waits for the model to be
initialized or recovered from a checkpoint. It's expected that
another thread or process will make the model ready, and that this
is intended to be used by threads/processes that participate in a
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Args:
master: `String` representation of the TensorFlow master to use.
config: Optional ConfigProto proto used to configure the session.
max_wait_secs: Maximum time to wait for the session to become available.
Returns:
A `Session`. May be None if the operation exceeds the timeout
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Raises:
tf.DeadlineExceededError: if the session is not available after
max_wait_secs. | Creates a new `Session` and waits for model to be ready. | [
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"""Creates a new `Session` and waits for model to be ready.
Creates a new `Session` on 'master'. Waits for the model to be
initialized or recovered from a checkpoint. It's expected that
another thread or process will make the model ready, and that this
is intended to be used by threads/processes that participate in a
distributed training configuration where a different thread/process
is responsible for initializing or recovering the model being trained.
NB: The amount of time this method waits for the session is bounded
by max_wait_secs. By default, this function will wait indefinitely.
Args:
master: `String` representation of the TensorFlow master to use.
config: Optional ConfigProto proto used to configure the session.
max_wait_secs: Maximum time to wait for the session to become available.
Returns:
A `Session`. May be None if the operation exceeds the timeout
specified by config.operation_timeout_in_ms.
Raises:
tf.DeadlineExceededError: if the session is not available after
max_wait_secs.
"""
self._target = master
if max_wait_secs is None:
max_wait_secs = float("Inf")
timer = _CountDownTimer(max_wait_secs)
while True:
sess = session.Session(self._target, graph=self._graph, config=config)
if self._local_init_op:
sess.run([self._local_init_op])
not_ready = self._model_not_ready(sess)
if not not_ready:
return sess
self._safe_close(sess)
# Do we have enough time left to try again?
remaining_ms_after_wait = (
timer.secs_remaining() - self._recovery_wait_secs)
if remaining_ms_after_wait < 0:
raise errors.DeadlineExceededError(
None, None,
"Session was not ready after waiting %d secs." % (max_wait_secs,))
logging.info("Waiting for model to be ready: %s", not_ready)
time.sleep(self._recovery_wait_secs) | [
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Yaafe/Yaafe | f5ed847bdbf540b47e8fe1980dddfb5509ae7f9d | src_python/yaafelib/dataflow.py | python | DataFlow.display | (self) | Print the DataFlow to the standard output | Print the DataFlow to the standard output | [
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"""
Print the DataFlow to the standard output
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/boto3/resources/factory.py | python | ResourceFactory._load_attributes | (self, attrs, meta, resource_name, resource_model,
service_context) | Load resource attributes based on the resource shape. The shape
name is referenced in the resource JSON, but the shape itself
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3drobotics/ardupilot-solo | 05a123b002c11dccc905d4d7703a38e5f36ee723 | Tools/LogAnalyzer/DataflashLog.py | python | DataflashLogHelper.getTimeAtLine | (logdata, lineNumber) | return logdata.channels["GPS"][timeLabel].max() | returns the nearest GPS timestamp in milliseconds after the given line number | returns the nearest GPS timestamp in milliseconds after the given line number | [
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'''returns the nearest GPS timestamp in milliseconds after the given line number'''
if not "GPS" in logdata.channels:
raise Exception("no GPS log data found")
# older logs use 'TIme', newer logs use 'TimeMS'
timeLabel = "TimeMS"
if "Time" in logdata.channels["GPS"]:
timeLabel = "Time"
while lineNumber <= logdata.lineCount:
if lineNumber in logdata.channels["GPS"][timeLabel].dictData:
return logdata.channels["GPS"][timeLabel].dictData[lineNumber]
lineNumber = lineNumber + 1
sys.stderr.write("didn't find GPS data for " + str(lineNumber) + " - using maxtime\n")
return logdata.channels["GPS"][timeLabel].max() | [
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google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/tools/pssh/pssh-box.py | python | _parse_playready_data | (data) | return ret | Parses PlayReady PSSH data from the given binary string. | Parses PlayReady PSSH data from the given binary string. | [
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size = reader.read_int(4)
if size != len(data):
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while count > 0:
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record_len = reader.read_int(2)
record_data = reader.read_bytes(record_len)
ret.append('Record (size %d):' % record_len)
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ret.extend([
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ret.extend([
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | clang/tools/scan-build-py/lib/libscanbuild/report.py | python | create_counters | () | return predicate | Create counters for bug statistics.
Two entries are maintained: 'total' is an integer, represents the
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counters. The first level is 'bug category' the second is 'bug type'.
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""" Create counters for bug statistics.
Two entries are maintained: 'total' is an integer, represents the
number of bugs. The 'categories' is a two level categorisation of bug
counters. The first level is 'bug category' the second is 'bug type'.
Each entry in this classification is a dictionary of 'count', 'type'
and 'label'. """
def predicate(bug):
bug_category = bug['bug_category']
bug_type = bug['bug_type']
current_category = predicate.categories.get(bug_category, dict())
current_type = current_category.get(bug_type, {
'bug_type': bug_type,
'bug_type_class': category_type_name(bug),
'bug_count': 0
})
current_type.update({'bug_count': current_type['bug_count'] + 1})
current_category.update({bug_type: current_type})
predicate.categories.update({bug_category: current_category})
predicate.total += 1
predicate.total = 0
predicate.categories = dict()
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/_pydecimal.py | python | Decimal.__round__ | (self, n=None) | return int(self._rescale(0, ROUND_HALF_EVEN)) | Round self to the nearest integer, or to a given precision.
If only one argument is supplied, round a finite Decimal
instance self to the nearest integer. If self is infinite or
a NaN then a Python exception is raised. If self is finite
and lies exactly halfway between two integers then it is
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>>> round(Decimal('123.456'))
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>>> round(Decimal('-456.789'))
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>>> round(Decimal('-3.0'))
-3
>>> round(Decimal('2.5'))
2
>>> round(Decimal('3.5'))
4
>>> round(Decimal('Inf'))
Traceback (most recent call last):
...
OverflowError: cannot round an infinity
>>> round(Decimal('NaN'))
Traceback (most recent call last):
...
ValueError: cannot round a NaN
If a second argument n is supplied, self is rounded to n
decimal places using the rounding mode for the current
context.
For an integer n, round(self, -n) is exactly equivalent to
self.quantize(Decimal('1En')).
>>> round(Decimal('123.456'), 0)
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>>> round(Decimal('123.456'), 2)
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>>> round(Decimal('123.456'), -2)
Decimal('1E+2')
>>> round(Decimal('-Infinity'), 37)
Decimal('NaN')
>>> round(Decimal('sNaN123'), 0)
Decimal('NaN123') | Round self to the nearest integer, or to a given precision. | [
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"""Round self to the nearest integer, or to a given precision.
If only one argument is supplied, round a finite Decimal
instance self to the nearest integer. If self is infinite or
a NaN then a Python exception is raised. If self is finite
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>>> round(Decimal('123.456'))
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>>> round(Decimal('-456.789'))
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>>> round(Decimal('-3.0'))
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>>> round(Decimal('2.5'))
2
>>> round(Decimal('3.5'))
4
>>> round(Decimal('Inf'))
Traceback (most recent call last):
...
OverflowError: cannot round an infinity
>>> round(Decimal('NaN'))
Traceback (most recent call last):
...
ValueError: cannot round a NaN
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Decimal('123')
>>> round(Decimal('123.456'), 2)
Decimal('123.46')
>>> round(Decimal('123.456'), -2)
Decimal('1E+2')
>>> round(Decimal('-Infinity'), 37)
Decimal('NaN')
>>> round(Decimal('sNaN123'), 0)
Decimal('NaN123')
"""
if n is not None:
# two-argument form: use the equivalent quantize call
if not isinstance(n, int):
raise TypeError('Second argument to round should be integral')
exp = _dec_from_triple(0, '1', -n)
return self.quantize(exp)
# one-argument form
if self._is_special:
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raise ValueError("cannot round a NaN")
else:
raise OverflowError("cannot round an infinity")
return int(self._rescale(0, ROUND_HALF_EVEN)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | ComboBox.IsTextEmpty | (*args, **kwargs) | return _controls_.ComboBox_IsTextEmpty(*args, **kwargs) | IsTextEmpty(self) -> bool | IsTextEmpty(self) -> bool | [
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mozilla/DeepSpeech | aa1d28530d531d0d92289bf5f11a49fe516fdc86 | training/deepspeech_training/util/evaluate_tools.py | python | save_samples_json | (samples, output_path) | Save decoded tuples as JSON, converting NumPy floats to Python floats.
We set ensure_ascii=True to prevent json from escaping non-ASCII chars
in the texts. | Save decoded tuples as JSON, converting NumPy floats to Python floats. | [
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''' Save decoded tuples as JSON, converting NumPy floats to Python floats.
We set ensure_ascii=True to prevent json from escaping non-ASCII chars
in the texts.
'''
with open_remote(output_path, 'w') as fout:
json.dump(samples, fout, default=float, ensure_ascii=False, indent=2) | [
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exodus3.in.py | python | exodus.put_node_set_variable_values | (self, object_id, name, step, values) | return True | store a list of node set variable values for a specified node
set, node set variable name, and time step; the list has one
variable value per node in the set
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... exo.put_node_set_variable_values(node_set_id,
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<string> nsvar_name name of node set variable
<int> time_step 1-based index of time step
<list<float>> nsvar_vals
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-------
status : bool
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"""
store a list of node set variable values for a specified node
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variable value per node in the set
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/config.py | python | ConfigMetadataHandler._parse_version | (self, value) | return version | Parses `version` option value.
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version = self._parse_file(value)
if version != value:
version = version.strip()
# Be strict about versions loaded from file because it's easy to
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if isinstance(parse(version), LegacyVersion):
tmpl = (
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raise DistutilsOptionError(tmpl.format(**locals()))
return version
version = self._parse_attr(value, self.package_dir)
if callable(version):
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if not isinstance(version, string_types):
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/pot/openvino/tools/pot/utils/launcher.py | python | IELauncher._load_model | (self, path) | return self._ie.read_model(model=path['model'], weights=path['weights']) | Loads IT model from disk
:param path: dictionary:
'model': path to xml
'weights': path to bin
:return IE model instance | Loads IT model from disk
:param path: dictionary:
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""" Loads IT model from disk
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:return IE model instance
"""
return self._ie.read_model(model=path['model'], weights=path['weights']) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_style.py | python | StyleItem.SetSize | (self, size, ex=wx.EmptyString) | Sets the Font Size Value
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@keyword ex: extra attribute (i.e bold, italic, underline) | Sets the Font Size Value
@param size: font point size, or None to clear attribute
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@keyword ex: extra attribute (i.e bold, italic, underline)
"""
self.null = False
if size is None:
size = u''
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/protobuf/python/google/protobuf/internal/cpp_message.py | python | GetFieldDescriptor | (full_field_name) | return _pool.FindFieldByName(full_field_name) | Searches for a field descriptor given a full field name. | Searches for a field descriptor given a full field name. | [
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"""Searches for a field descriptor given a full field name."""
return _pool.FindFieldByName(full_field_name) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/framework/common_shapes.py | python | bias_add_shape | (op) | return [output_shape] | Shape function for a BiasAdd op. | Shape function for a BiasAdd op. | [
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] | def bias_add_shape(op):
"""Shape function for a BiasAdd op."""
input_shape = op.inputs[0].get_shape().with_rank_at_least(2)
bias_shape = op.inputs[1].get_shape().with_rank(1)
if input_shape.ndims is not None:
# Output has the same shape as input, and matches the length of
# bias in its bias dimension.
try:
data_format = op.get_attr("data_format")
except ValueError:
data_format = None
if data_format == b"NCHW":
# Merge the length of bias_shape into the third-to-last dimension.
output_shape = input_shape[0:-3].concatenate(input_shape[-3].merge_with(
bias_shape[0])).concatenate(input_shape[-2:])
else:
output_shape = input_shape[0:-1].concatenate(input_shape[-1].merge_with(
bias_shape[0]))
else:
output_shape = tensor_shape.unknown_shape()
return [output_shape] | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/graphy/graphy/bar_chart.py | python | BarChart.GetIndependentAxis | (self) | Get the main independendant axis, which depends on orientation. | Get the main independendant axis, which depends on orientation. | [
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"""Get the main independendant axis, which depends on orientation."""
if self.vertical:
return self.bottom
else:
return self.left | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/cookielib.py | python | unmatched | (match) | return match.string[:start]+match.string[end:] | Return unmatched part of re.Match object. | Return unmatched part of re.Match object. | [
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"""Return unmatched part of re.Match object."""
start, end = match.span(0)
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livecode/livecode | 4606a10ea10b16d5071d0f9f263ccdd7ede8b31d | gyp/pylib/gyp/mac_tool.py | python | MacTool._DetectInputEncoding | (self, file_name) | Reads the first few bytes from file_name and tries to guess the text
encoding. Returns None as a guess if it can't detect it. | Reads the first few bytes from file_name and tries to guess the text
encoding. Returns None as a guess if it can't detect it. | [
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"""Reads the first few bytes from file_name and tries to guess the text
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fp = open(file_name, 'rb')
try:
header = fp.read(3)
except e:
fp.close()
return None
fp.close()
if header.startswith("\xFE\xFF"):
return "UTF-16"
elif header.startswith("\xFF\xFE"):
return "UTF-16"
elif header.startswith("\xEF\xBB\xBF"):
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/distutils/command/config.py | python | config._check_compiler | (self) | Check that 'self.compiler' really is a CCompiler object;
if not, make it one. | Check that 'self.compiler' really is a CCompiler object;
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"""Check that 'self.compiler' really is a CCompiler object;
if not, make it one.
"""
# We do this late, and only on-demand, because this is an expensive
# import.
from distutils.ccompiler import CCompiler, new_compiler
if not isinstance(self.compiler, CCompiler):
self.compiler = new_compiler(compiler=self.compiler,
dry_run=self.dry_run, force=1)
customize_compiler(self.compiler)
if self.include_dirs:
self.compiler.set_include_dirs(self.include_dirs)
if self.libraries:
self.compiler.set_libraries(self.libraries)
if self.library_dirs:
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/gluon/parameter.py | python | Parameter.reset_ctx | (self, ctx) | Re-assign Parameter to other contexts.
Parameters
----------
ctx : Context or list of Context, default ``context.current_context()``.
Assign Parameter to given context. If ctx is a list of Context, a
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"""Re-assign Parameter to other contexts.
Parameters
----------
ctx : Context or list of Context, default ``context.current_context()``.
Assign Parameter to given context. If ctx is a list of Context, a
copy will be made for each context.
"""
if ctx is None:
ctx = [context.current_context()]
if isinstance(ctx, Context):
ctx = [ctx]
if self._data:
data = self._reduce()
with autograd.pause():
self._init_impl(data, ctx)
elif self._deferred_init:
init, _, default_init, data = self._deferred_init
self._deferred_init = (init, ctx, default_init, data)
else:
raise ValueError("Cannot reset context for Parameter '%s' because it "
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papyrussolution/OpenPapyrus | bbfb5ec2ea2109b8e2f125edd838e12eaf7b8b91 | Src/OSF/protobuf-3.19.1/python/google/protobuf/descriptor.py | python | DescriptorBase._SetOptions | (self, options, options_class_name) | Sets the descriptor's options
This function is used in generated proto2 files to update descriptor
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"""Sets the descriptor's options
This function is used in generated proto2 files to update descriptor
options. It must not be used outside proto2.
"""
self._options = options
self._options_class_name = options_class_name
# Does this descriptor have non-default options?
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/builder.py | python | NeuralNetworkBuilder.add_asin | (self, name, input_name, output_name) | return spec_layer | Add an asin layer to the model that computes element-wise arc-sine for
the input tensor.
Refer to the **AsinLayerParams** message in specification (NeuralNetwork.proto) for more details.
Parameters
----------
name: str
The name of this layer.
input_name: str
The input blob name of this layer.
output_name: str
The output blob name of this layer.
See Also
--------
add_sin, add_sinh, add_asinh | Add an asin layer to the model that computes element-wise arc-sine for
the input tensor.
Refer to the **AsinLayerParams** message in specification (NeuralNetwork.proto) for more details. | [
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"""
Add an asin layer to the model that computes element-wise arc-sine for
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Refer to the **AsinLayerParams** message in specification (NeuralNetwork.proto) for more details.
Parameters
----------
name: str
The name of this layer.
input_name: str
The input blob name of this layer.
output_name: str
The output blob name of this layer.
See Also
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"""
spec_layer = self._add_generic_layer(name, [input_name], [output_name])
spec_layer.asin.MergeFromString(b"")
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/construction.py | python | array | (
data: Sequence[object] | AnyArrayLike,
dtype: Dtype | None = None,
copy: bool = True,
) | return PandasArray._from_sequence(data, dtype=dtype, copy=copy) | Create an array.
Parameters
----------
data : Sequence of objects
The scalars inside `data` should be instances of the
scalar type for `dtype`. It's expected that `data`
represents a 1-dimensional array of data.
When `data` is an Index or Series, the underlying array
will be extracted from `data`.
dtype : str, np.dtype, or ExtensionDtype, optional
The dtype to use for the array. This may be a NumPy
dtype or an extension type registered with pandas using
:meth:`pandas.api.extensions.register_extension_dtype`.
If not specified, there are two possibilities:
1. When `data` is a :class:`Series`, :class:`Index`, or
:class:`ExtensionArray`, the `dtype` will be taken
from the data.
2. Otherwise, pandas will attempt to infer the `dtype`
from the data.
Note that when `data` is a NumPy array, ``data.dtype`` is
*not* used for inferring the array type. This is because
NumPy cannot represent all the types of data that can be
held in extension arrays.
Currently, pandas will infer an extension dtype for sequences of
============================== =======================================
Scalar Type Array Type
============================== =======================================
:class:`pandas.Interval` :class:`pandas.arrays.IntervalArray`
:class:`pandas.Period` :class:`pandas.arrays.PeriodArray`
:class:`datetime.datetime` :class:`pandas.arrays.DatetimeArray`
:class:`datetime.timedelta` :class:`pandas.arrays.TimedeltaArray`
:class:`int` :class:`pandas.arrays.IntegerArray`
:class:`float` :class:`pandas.arrays.FloatingArray`
:class:`str` :class:`pandas.arrays.StringArray` or
:class:`pandas.arrays.ArrowStringArray`
:class:`bool` :class:`pandas.arrays.BooleanArray`
============================== =======================================
The ExtensionArray created when the scalar type is :class:`str` is determined by
``pd.options.mode.string_storage`` if the dtype is not explicitly given.
For all other cases, NumPy's usual inference rules will be used.
.. versionchanged:: 1.0.0
Pandas infers nullable-integer dtype for integer data,
string dtype for string data, and nullable-boolean dtype
for boolean data.
.. versionchanged:: 1.2.0
Pandas now also infers nullable-floating dtype for float-like
input data
copy : bool, default True
Whether to copy the data, even if not necessary. Depending
on the type of `data`, creating the new array may require
copying data, even if ``copy=False``.
Returns
-------
ExtensionArray
The newly created array.
Raises
------
ValueError
When `data` is not 1-dimensional.
See Also
--------
numpy.array : Construct a NumPy array.
Series : Construct a pandas Series.
Index : Construct a pandas Index.
arrays.PandasArray : ExtensionArray wrapping a NumPy array.
Series.array : Extract the array stored within a Series.
Notes
-----
Omitting the `dtype` argument means pandas will attempt to infer the
best array type from the values in the data. As new array types are
added by pandas and 3rd party libraries, the "best" array type may
change. We recommend specifying `dtype` to ensure that
1. the correct array type for the data is returned
2. the returned array type doesn't change as new extension types
are added by pandas and third-party libraries
Additionally, if the underlying memory representation of the returned
array matters, we recommend specifying the `dtype` as a concrete object
rather than a string alias or allowing it to be inferred. For example,
a future version of pandas or a 3rd-party library may include a
dedicated ExtensionArray for string data. In this event, the following
would no longer return a :class:`arrays.PandasArray` backed by a NumPy
array.
>>> pd.array(['a', 'b'], dtype=str)
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
This would instead return the new ExtensionArray dedicated for string
data. If you really need the new array to be backed by a NumPy array,
specify that in the dtype.
>>> pd.array(['a', 'b'], dtype=np.dtype("<U1"))
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
Finally, Pandas has arrays that mostly overlap with NumPy
* :class:`arrays.DatetimeArray`
* :class:`arrays.TimedeltaArray`
When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
rather than a ``PandasArray``. This is for symmetry with the case of
timezone-aware data, which NumPy does not natively support.
>>> pd.array(['2015', '2016'], dtype='datetime64[ns]')
<DatetimeArray>
['2015-01-01 00:00:00', '2016-01-01 00:00:00']
Length: 2, dtype: datetime64[ns]
>>> pd.array(["1H", "2H"], dtype='timedelta64[ns]')
<TimedeltaArray>
['0 days 01:00:00', '0 days 02:00:00']
Length: 2, dtype: timedelta64[ns]
Examples
--------
If a dtype is not specified, pandas will infer the best dtype from the values.
See the description of `dtype` for the types pandas infers for.
>>> pd.array([1, 2])
<IntegerArray>
[1, 2]
Length: 2, dtype: Int64
>>> pd.array([1, 2, np.nan])
<IntegerArray>
[1, 2, <NA>]
Length: 3, dtype: Int64
>>> pd.array([1.1, 2.2])
<FloatingArray>
[1.1, 2.2]
Length: 2, dtype: Float64
>>> pd.array(["a", None, "c"])
<StringArray>
['a', <NA>, 'c']
Length: 3, dtype: string
>>> with pd.option_context("string_storage", "pyarrow"):
... arr = pd.array(["a", None, "c"])
...
>>> arr
<ArrowStringArray>
['a', <NA>, 'c']
Length: 3, dtype: string
>>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
<PeriodArray>
['2000-01-01', '2000-01-01']
Length: 2, dtype: period[D]
You can use the string alias for `dtype`
>>> pd.array(['a', 'b', 'a'], dtype='category')
['a', 'b', 'a']
Categories (2, object): ['a', 'b']
Or specify the actual dtype
>>> pd.array(['a', 'b', 'a'],
... dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True))
['a', 'b', 'a']
Categories (3, object): ['a' < 'b' < 'c']
If pandas does not infer a dedicated extension type a
:class:`arrays.PandasArray` is returned.
>>> pd.array([1 + 1j, 3 + 2j])
<PandasArray>
[(1+1j), (3+2j)]
Length: 2, dtype: complex128
As mentioned in the "Notes" section, new extension types may be added
in the future (by pandas or 3rd party libraries), causing the return
value to no longer be a :class:`arrays.PandasArray`. Specify the `dtype`
as a NumPy dtype if you need to ensure there's no future change in
behavior.
>>> pd.array([1, 2], dtype=np.dtype("int32"))
<PandasArray>
[1, 2]
Length: 2, dtype: int32
`data` must be 1-dimensional. A ValueError is raised when the input
has the wrong dimensionality.
>>> pd.array(1)
Traceback (most recent call last):
...
ValueError: Cannot pass scalar '1' to 'pandas.array'. | Create an array. | [
"Create",
"an",
"array",
"."
] | def array(
data: Sequence[object] | AnyArrayLike,
dtype: Dtype | None = None,
copy: bool = True,
) -> ExtensionArray:
"""
Create an array.
Parameters
----------
data : Sequence of objects
The scalars inside `data` should be instances of the
scalar type for `dtype`. It's expected that `data`
represents a 1-dimensional array of data.
When `data` is an Index or Series, the underlying array
will be extracted from `data`.
dtype : str, np.dtype, or ExtensionDtype, optional
The dtype to use for the array. This may be a NumPy
dtype or an extension type registered with pandas using
:meth:`pandas.api.extensions.register_extension_dtype`.
If not specified, there are two possibilities:
1. When `data` is a :class:`Series`, :class:`Index`, or
:class:`ExtensionArray`, the `dtype` will be taken
from the data.
2. Otherwise, pandas will attempt to infer the `dtype`
from the data.
Note that when `data` is a NumPy array, ``data.dtype`` is
*not* used for inferring the array type. This is because
NumPy cannot represent all the types of data that can be
held in extension arrays.
Currently, pandas will infer an extension dtype for sequences of
============================== =======================================
Scalar Type Array Type
============================== =======================================
:class:`pandas.Interval` :class:`pandas.arrays.IntervalArray`
:class:`pandas.Period` :class:`pandas.arrays.PeriodArray`
:class:`datetime.datetime` :class:`pandas.arrays.DatetimeArray`
:class:`datetime.timedelta` :class:`pandas.arrays.TimedeltaArray`
:class:`int` :class:`pandas.arrays.IntegerArray`
:class:`float` :class:`pandas.arrays.FloatingArray`
:class:`str` :class:`pandas.arrays.StringArray` or
:class:`pandas.arrays.ArrowStringArray`
:class:`bool` :class:`pandas.arrays.BooleanArray`
============================== =======================================
The ExtensionArray created when the scalar type is :class:`str` is determined by
``pd.options.mode.string_storage`` if the dtype is not explicitly given.
For all other cases, NumPy's usual inference rules will be used.
.. versionchanged:: 1.0.0
Pandas infers nullable-integer dtype for integer data,
string dtype for string data, and nullable-boolean dtype
for boolean data.
.. versionchanged:: 1.2.0
Pandas now also infers nullable-floating dtype for float-like
input data
copy : bool, default True
Whether to copy the data, even if not necessary. Depending
on the type of `data`, creating the new array may require
copying data, even if ``copy=False``.
Returns
-------
ExtensionArray
The newly created array.
Raises
------
ValueError
When `data` is not 1-dimensional.
See Also
--------
numpy.array : Construct a NumPy array.
Series : Construct a pandas Series.
Index : Construct a pandas Index.
arrays.PandasArray : ExtensionArray wrapping a NumPy array.
Series.array : Extract the array stored within a Series.
Notes
-----
Omitting the `dtype` argument means pandas will attempt to infer the
best array type from the values in the data. As new array types are
added by pandas and 3rd party libraries, the "best" array type may
change. We recommend specifying `dtype` to ensure that
1. the correct array type for the data is returned
2. the returned array type doesn't change as new extension types
are added by pandas and third-party libraries
Additionally, if the underlying memory representation of the returned
array matters, we recommend specifying the `dtype` as a concrete object
rather than a string alias or allowing it to be inferred. For example,
a future version of pandas or a 3rd-party library may include a
dedicated ExtensionArray for string data. In this event, the following
would no longer return a :class:`arrays.PandasArray` backed by a NumPy
array.
>>> pd.array(['a', 'b'], dtype=str)
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
This would instead return the new ExtensionArray dedicated for string
data. If you really need the new array to be backed by a NumPy array,
specify that in the dtype.
>>> pd.array(['a', 'b'], dtype=np.dtype("<U1"))
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
Finally, Pandas has arrays that mostly overlap with NumPy
* :class:`arrays.DatetimeArray`
* :class:`arrays.TimedeltaArray`
When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
rather than a ``PandasArray``. This is for symmetry with the case of
timezone-aware data, which NumPy does not natively support.
>>> pd.array(['2015', '2016'], dtype='datetime64[ns]')
<DatetimeArray>
['2015-01-01 00:00:00', '2016-01-01 00:00:00']
Length: 2, dtype: datetime64[ns]
>>> pd.array(["1H", "2H"], dtype='timedelta64[ns]')
<TimedeltaArray>
['0 days 01:00:00', '0 days 02:00:00']
Length: 2, dtype: timedelta64[ns]
Examples
--------
If a dtype is not specified, pandas will infer the best dtype from the values.
See the description of `dtype` for the types pandas infers for.
>>> pd.array([1, 2])
<IntegerArray>
[1, 2]
Length: 2, dtype: Int64
>>> pd.array([1, 2, np.nan])
<IntegerArray>
[1, 2, <NA>]
Length: 3, dtype: Int64
>>> pd.array([1.1, 2.2])
<FloatingArray>
[1.1, 2.2]
Length: 2, dtype: Float64
>>> pd.array(["a", None, "c"])
<StringArray>
['a', <NA>, 'c']
Length: 3, dtype: string
>>> with pd.option_context("string_storage", "pyarrow"):
... arr = pd.array(["a", None, "c"])
...
>>> arr
<ArrowStringArray>
['a', <NA>, 'c']
Length: 3, dtype: string
>>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
<PeriodArray>
['2000-01-01', '2000-01-01']
Length: 2, dtype: period[D]
You can use the string alias for `dtype`
>>> pd.array(['a', 'b', 'a'], dtype='category')
['a', 'b', 'a']
Categories (2, object): ['a', 'b']
Or specify the actual dtype
>>> pd.array(['a', 'b', 'a'],
... dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True))
['a', 'b', 'a']
Categories (3, object): ['a' < 'b' < 'c']
If pandas does not infer a dedicated extension type a
:class:`arrays.PandasArray` is returned.
>>> pd.array([1 + 1j, 3 + 2j])
<PandasArray>
[(1+1j), (3+2j)]
Length: 2, dtype: complex128
As mentioned in the "Notes" section, new extension types may be added
in the future (by pandas or 3rd party libraries), causing the return
value to no longer be a :class:`arrays.PandasArray`. Specify the `dtype`
as a NumPy dtype if you need to ensure there's no future change in
behavior.
>>> pd.array([1, 2], dtype=np.dtype("int32"))
<PandasArray>
[1, 2]
Length: 2, dtype: int32
`data` must be 1-dimensional. A ValueError is raised when the input
has the wrong dimensionality.
>>> pd.array(1)
Traceback (most recent call last):
...
ValueError: Cannot pass scalar '1' to 'pandas.array'.
"""
from pandas.core.arrays import (
BooleanArray,
DatetimeArray,
FloatingArray,
IntegerArray,
IntervalArray,
PandasArray,
PeriodArray,
TimedeltaArray,
)
from pandas.core.arrays.string_ import StringDtype
if lib.is_scalar(data):
msg = f"Cannot pass scalar '{data}' to 'pandas.array'."
raise ValueError(msg)
if dtype is None and isinstance(data, (ABCSeries, ABCIndex, ABCExtensionArray)):
# Note: we exclude np.ndarray here, will do type inference on it
dtype = data.dtype
data = extract_array(data, extract_numpy=True)
# this returns None for not-found dtypes.
if isinstance(dtype, str):
dtype = registry.find(dtype) or dtype
if is_extension_array_dtype(dtype):
cls = cast(ExtensionDtype, dtype).construct_array_type()
return cls._from_sequence(data, dtype=dtype, copy=copy)
if dtype is None:
inferred_dtype = lib.infer_dtype(data, skipna=True)
if inferred_dtype == "period":
return PeriodArray._from_sequence(data, copy=copy)
elif inferred_dtype == "interval":
return IntervalArray(data, copy=copy)
elif inferred_dtype.startswith("datetime"):
# datetime, datetime64
try:
return DatetimeArray._from_sequence(data, copy=copy)
except ValueError:
# Mixture of timezones, fall back to PandasArray
pass
elif inferred_dtype.startswith("timedelta"):
# timedelta, timedelta64
return TimedeltaArray._from_sequence(data, copy=copy)
elif inferred_dtype == "string":
# StringArray/ArrowStringArray depending on pd.options.mode.string_storage
return StringDtype().construct_array_type()._from_sequence(data, copy=copy)
elif inferred_dtype == "integer":
return IntegerArray._from_sequence(data, copy=copy)
elif inferred_dtype in ("floating", "mixed-integer-float"):
return FloatingArray._from_sequence(data, copy=copy)
elif inferred_dtype == "boolean":
return BooleanArray._from_sequence(data, copy=copy)
# Pandas overrides NumPy for
# 1. datetime64[ns]
# 2. timedelta64[ns]
# so that a DatetimeArray is returned.
if is_datetime64_ns_dtype(dtype):
return DatetimeArray._from_sequence(data, dtype=dtype, copy=copy)
elif is_timedelta64_ns_dtype(dtype):
return TimedeltaArray._from_sequence(data, dtype=dtype, copy=copy)
return PandasArray._from_sequence(data, dtype=dtype, copy=copy) | [
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martinmoene/lest | f3e9dfe4a66c3e60dfdac7a3d3e4ddc0dcf06b26 | script/create-cov-rpt.py | python | executable_name | ( f ) | return os.path.basename( f ) | Folder where the executable is | Folder where the executable is | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/posixpath.py | python | expandvars | (path) | return path | Expand shell variables of form $var and ${var}. Unknown variables
are left unchanged. | Expand shell variables of form $var and ${var}. Unknown variables
are left unchanged. | [
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"""Expand shell variables of form $var and ${var}. Unknown variables
are left unchanged."""
path = os.fspath(path)
global _varprog, _varprogb
if isinstance(path, bytes):
if b'$' not in path:
return path
if not _varprogb:
import re
_varprogb = re.compile(br'\$(\w+|\{[^}]*\})', re.ASCII)
search = _varprogb.search
start = b'{'
end = b'}'
environ = getattr(os, 'environb', None)
else:
if '$' not in path:
return path
if not _varprog:
import re
_varprog = re.compile(r'\$(\w+|\{[^}]*\})', re.ASCII)
search = _varprog.search
start = '{'
end = '}'
environ = os.environ
i = 0
while True:
m = search(path, i)
if not m:
break
i, j = m.span(0)
name = m.group(1)
if name.startswith(start) and name.endswith(end):
name = name[1:-1]
try:
if environ is None:
value = os.fsencode(os.environ[os.fsdecode(name)])
else:
value = environ[name]
except KeyError:
i = j
else:
tail = path[j:]
path = path[:i] + value
i = len(path)
path += tail
return path | [
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exomerge3.py | python | ExodusModel._get_external_element_faces | (self, element_block_ids='all') | return [
value for value in list(external_faces.values())
if value is not None
] | Return a list of external element faces.
External faces are element faces which are shared by no other elements.
A list is returned with members of the form:
* '(element_block_id, element_index, face_index)' | Return a list of external element faces. | [
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] | def _get_external_element_faces(self, element_block_ids='all'):
"""
Return a list of external element faces.
External faces are element faces which are shared by no other elements.
A list is returned with members of the form:
* '(element_block_id, element_index, face_index)'
"""
element_block_ids = self._format_element_block_id_list(
element_block_ids)
external_faces = dict()
for id_ in element_block_ids:
info = self._get_block_info(id_)
connectivity = self.get_connectivity(id_)
face_mapping = self._get_face_mapping_from_id(id_)
for element_index in range(info[1]):
for face_index, (_, face) in enumerate(face_mapping):
sorted_nodes = tuple(
sorted([
connectivity[element_index * info[2] + x]
for x in face
]))
if sorted_nodes in external_faces:
external_faces[sorted_nodes] = None
else:
this_face = (id_, element_index, face_index)
external_faces[sorted_nodes] = this_face
return [
value for value in list(external_faces.values())
if value is not None
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ApolloAuto/apollo | 463fb82f9e979d02dcb25044e60931293ab2dba0 | third_party/gpus/find_cuda_config.py | python | _find_header | (base_paths, header_name, required_version, get_version) | return _find_versioned_file(base_paths, _header_paths(), header_name,
required_version, get_version) | Returns first valid path to a header that matches the requested version. | Returns first valid path to a header that matches the requested version. | [
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] | def _find_header(base_paths, header_name, required_version, get_version):
"""Returns first valid path to a header that matches the requested version."""
return _find_versioned_file(base_paths, _header_paths(), header_name,
required_version, get_version) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/customtreectrl.py | python | CustomTreeCtrl.SetBorderPen | (self, pen) | Sets the pen used to draw the selected item border.
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Sets the pen used to draw the selected item border.
:param `pen`: an instance of :class:`Pen`.
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"""
self._borderPen = pen
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bairdzhang/smallhardface | 76fa1d87a9602d9b13d7a7fe693fc7aec91cab80 | caffe/scripts/cpp_lint.py | python | ProcessLine | (filename, file_extension, clean_lines, line,
include_state, function_state, nesting_state, error,
extra_check_functions=[]) | Processes a single line in the file.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
clean_lines: An array of strings, each representing a line of the file,
with comments stripped.
line: Number of line being processed.
include_state: An _IncludeState instance in which the headers are inserted.
function_state: A _FunctionState instance which counts function lines, etc.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error | Processes a single line in the file. | [
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] | def ProcessLine(filename, file_extension, clean_lines, line,
include_state, function_state, nesting_state, error,
extra_check_functions=[]):
"""Processes a single line in the file.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
clean_lines: An array of strings, each representing a line of the file,
with comments stripped.
line: Number of line being processed.
include_state: An _IncludeState instance in which the headers are inserted.
function_state: A _FunctionState instance which counts function lines, etc.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
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"""
raw_lines = clean_lines.raw_lines
ParseNolintSuppressions(filename, raw_lines[line], line, error)
nesting_state.Update(filename, clean_lines, line, error)
if nesting_state.stack and nesting_state.stack[-1].inline_asm != _NO_ASM:
return
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CheckForMultilineCommentsAndStrings(filename, clean_lines, line, error)
CheckStyle(filename, clean_lines, line, file_extension, nesting_state, error)
CheckLanguage(filename, clean_lines, line, file_extension, include_state,
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CheckForNonConstReference(filename, clean_lines, line, nesting_state, error)
CheckForNonStandardConstructs(filename, clean_lines, line,
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CheckVlogArguments(filename, clean_lines, line, error)
CheckCaffeAlternatives(filename, clean_lines, line, error)
CheckCaffeDataLayerSetUp(filename, clean_lines, line, error)
CheckCaffeRandom(filename, clean_lines, line, error)
CheckPosixThreading(filename, clean_lines, line, error)
CheckInvalidIncrement(filename, clean_lines, line, error)
CheckMakePairUsesDeduction(filename, clean_lines, line, error)
for check_fn in extra_check_functions:
check_fn(filename, clean_lines, line, error) | [
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tkn-tub/ns3-gym | 19bfe0a583e641142609939a090a09dfc63a095f | src/visualizer/visualizer/core.py | python | Visualizer._create_advanced_controls | (self) | return expander | !
Create advanced controls.
@param self: class object.
@return expander | !
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"""!
Create advanced controls.
@param self: class object.
@return expander
"""
expander = Gtk.Expander.new("Advanced")
expander.show()
main_vbox = GObject.new(Gtk.VBox, border_width=8, visible=True)
expander.add(main_vbox)
main_hbox1 = GObject.new(Gtk.HBox, border_width=8, visible=True)
main_vbox.pack_start(main_hbox1, True, True, 0)
show_transmissions_group = GObject.new(Gtk.HeaderBar,
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main_hbox1.pack_start(show_transmissions_group, False, False, 8)
vbox = Gtk.VBox(homogeneous=True, spacing=4)
vbox.show()
show_transmissions_group.add(vbox)
all_nodes = Gtk.RadioButton.new(None)
all_nodes.set_label("All nodes")
all_nodes.set_active(True)
all_nodes.show()
vbox.add(all_nodes)
selected_node = Gtk.RadioButton.new_from_widget(all_nodes)
selected_node.show()
selected_node.set_label("Selected node")
selected_node.set_active(False)
vbox.add(selected_node)
no_node = Gtk.RadioButton.new_from_widget(all_nodes)
no_node.show()
no_node.set_label("Disabled")
no_node.set_active(False)
vbox.add(no_node)
def toggled(radio):
if radio.get_active():
self.set_show_transmissions_mode(ShowTransmissionsMode.ALL)
all_nodes.connect("toggled", toggled)
def toggled(radio):
if radio.get_active():
self.set_show_transmissions_mode(ShowTransmissionsMode.NONE)
no_node.connect("toggled", toggled)
def toggled(radio):
if radio.get_active():
self.set_show_transmissions_mode(ShowTransmissionsMode.SELECTED)
selected_node.connect("toggled", toggled)
# -- misc settings
misc_settings_group = GObject.new(Gtk.HeaderBar, title="Misc Settings", visible=True)
main_hbox1.pack_start(misc_settings_group, False, False, 8)
settings_hbox = GObject.new(Gtk.HBox, border_width=8, visible=True)
misc_settings_group.add(settings_hbox)
# --> node size
vbox = GObject.new(Gtk.VBox, border_width=0, visible=True)
scale = GObject.new(Gtk.HScale, visible=True, digits=2)
vbox.pack_start(scale, True, True, 0)
vbox.pack_start(GObject.new(Gtk.Label, label="Node Size", visible=True), True, True, 0)
settings_hbox.pack_start(vbox, False, False, 6)
self.node_size_adjustment = scale.get_adjustment()
def node_size_changed(adj):
for node in self.nodes.itervalues():
node.set_size(adj.get_value())
self.node_size_adjustment.connect("value-changed", node_size_changed)
self.node_size_adjustment.set_lower(0.01)
self.node_size_adjustment.set_upper(20)
self.node_size_adjustment.set_step_increment(0.1)
self.node_size_adjustment.set_value(DEFAULT_NODE_SIZE)
# --> transmissions smooth factor
vbox = GObject.new(Gtk.VBox, border_width=0, visible=True)
scale = GObject.new(Gtk.HScale, visible=True, digits=1)
vbox.pack_start(scale, True, True, 0)
vbox.pack_start(GObject.new(Gtk.Label, label="Tx. Smooth Factor (s)", visible=True), True, True, 0)
settings_hbox.pack_start(vbox, False, False, 6)
self.transmissions_smoothing_adjustment = scale.get_adjustment()
adj = self.transmissions_smoothing_adjustment
adj.set_lower(0.1)
adj.set_upper(10)
adj.set_step_increment(0.1)
adj.set_value(DEFAULT_TRANSMISSIONS_MEMORY*0.1)
return expander | [
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"border_wi... | https://github.com/tkn-tub/ns3-gym/blob/19bfe0a583e641142609939a090a09dfc63a095f/src/visualizer/visualizer/core.py#L785-L878 | |
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py3/prompt_toolkit/layout/containers.py | python | Window._fill_bg | (
self, screen: Screen, write_position: WritePosition, erase_bg: bool
) | Erase/fill the background.
(Useful for floats and when a `char` has been given.) | Erase/fill the background.
(Useful for floats and when a `char` has been given.) | [
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"char",
"has",
"been",
"given",
".",
")"
] | def _fill_bg(
self, screen: Screen, write_position: WritePosition, erase_bg: bool
) -> None:
"""
Erase/fill the background.
(Useful for floats and when a `char` has been given.)
"""
char: Optional[str]
if callable(self.char):
char = self.char()
else:
char = self.char
if erase_bg or char:
wp = write_position
char_obj = _CHAR_CACHE[char or " ", ""]
for y in range(wp.ypos, wp.ypos + wp.height):
row = screen.data_buffer[y]
for x in range(wp.xpos, wp.xpos + wp.width):
row[x] = char_obj | [
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Slicer/SlicerGitSVNArchive | 65e92bb16c2b32ea47a1a66bee71f238891ee1ca | Modules/Scripted/EditorLib/EditUtil.py | python | EditUtil.toggleForegroundBackground | () | Swap the foreground and background volumes for all composite nodes in the scene | Swap the foreground and background volumes for all composite nodes in the scene | [
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] | def toggleForegroundBackground():
"""Swap the foreground and background volumes for all composite nodes in the scene"""
for sliceCompositeNode in slicer.util.getNodes('vtkMRMLSliceCompositeNode*').values():
oldForeground = sliceCompositeNode.GetForegroundVolumeID()
sliceCompositeNode.SetForegroundVolumeID(sliceCompositeNode.GetBackgroundVolumeID())
sliceCompositeNode.SetBackgroundVolumeID(oldForeground) | [
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danxuhk/ContinuousCRF-CNN | 2b6dcaf179620f118b225ed12c890414ca828e21 | scripts/cpp_lint.py | python | _ClassifyInclude | (fileinfo, include, is_system) | return _OTHER_HEADER | Figures out what kind of header 'include' is.
Args:
fileinfo: The current file cpplint is running over. A FileInfo instance.
include: The path to a #included file.
is_system: True if the #include used <> rather than "".
Returns:
One of the _XXX_HEADER constants.
For example:
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
_C_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
_CPP_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
_LIKELY_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
... 'bar/foo_other_ext.h', False)
_POSSIBLE_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
_OTHER_HEADER | Figures out what kind of header 'include' is. | [
"Figures",
"out",
"what",
"kind",
"of",
"header",
"include",
"is",
"."
] | def _ClassifyInclude(fileinfo, include, is_system):
"""Figures out what kind of header 'include' is.
Args:
fileinfo: The current file cpplint is running over. A FileInfo instance.
include: The path to a #included file.
is_system: True if the #include used <> rather than "".
Returns:
One of the _XXX_HEADER constants.
For example:
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
_C_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
_CPP_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
_LIKELY_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
... 'bar/foo_other_ext.h', False)
_POSSIBLE_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
_OTHER_HEADER
"""
# This is a list of all standard c++ header files, except
# those already checked for above.
is_cpp_h = include in _CPP_HEADERS
if is_system:
if is_cpp_h:
return _CPP_SYS_HEADER
else:
return _C_SYS_HEADER
# If the target file and the include we're checking share a
# basename when we drop common extensions, and the include
# lives in . , then it's likely to be owned by the target file.
target_dir, target_base = (
os.path.split(_DropCommonSuffixes(fileinfo.RepositoryName())))
include_dir, include_base = os.path.split(_DropCommonSuffixes(include))
if target_base == include_base and (
include_dir == target_dir or
include_dir == os.path.normpath(target_dir + '/../public')):
return _LIKELY_MY_HEADER
# If the target and include share some initial basename
# component, it's possible the target is implementing the
# include, so it's allowed to be first, but we'll never
# complain if it's not there.
target_first_component = _RE_FIRST_COMPONENT.match(target_base)
include_first_component = _RE_FIRST_COMPONENT.match(include_base)
if (target_first_component and include_first_component and
target_first_component.group(0) ==
include_first_component.group(0)):
return _POSSIBLE_MY_HEADER
return _OTHER_HEADER | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/resmokelib/selector.py | python | _Selector.__init__ | (self, test_file_explorer, tests_are_files=True) | Initialize the _Selector.
Args:
test_file_explorer: a TestFileExplorer instance. | Initialize the _Selector. | [
"Initialize",
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"."
] | def __init__(self, test_file_explorer, tests_are_files=True):
"""Initialize the _Selector.
Args:
test_file_explorer: a TestFileExplorer instance.
"""
self._test_file_explorer = test_file_explorer
self._tests_are_files = tests_are_files | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/contrib/imports/mirobridge/mirobridge/mirobridge_interpreter_2_5_2.py | python | MiroInterpreter.do_items | (self, line) | items -- Lists the items in the feed/playlist/tab selected. | items -- Lists the items in the feed/playlist/tab selected. | [
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"--",
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"items",
"in",
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"feed",
"/",
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"/",
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"selected",
"."
] | def do_items(self, line):
"""items -- Lists the items in the feed/playlist/tab selected."""
if self.selection_type is None:
print "Error: No tab/feed/playlist selected"
return
elif self.selection_type == 'feed':
feed = self.tab
view = feed.items
self.printout_item_list(view)
elif self.selection_type == 'playlist':
playlist = self.tab.obj
self.printout_item_list(playlist.getView())
elif self.selection_type == 'downloads':
self.printout_item_list(item.Item.downloading_view(),
item.Item.paused_view())
elif self.selection_type == 'channel-folder':
folder = self.tab.obj
allItems = views.items.filterWithIndex(
indexes.itemsByChannelFolder, folder)
allItemsSorted = allItems.sort(folder.itemSort.sort)
self.printout_item_list(allItemsSorted)
allItemsSorted.unlink()
else:
raise ValueError("Unknown tab type") | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/mailbox.py | python | mbox.__init__ | (self, path, factory=None, create=True) | Initialize an mbox mailbox. | Initialize an mbox mailbox. | [
"Initialize",
"an",
"mbox",
"mailbox",
"."
] | def __init__(self, path, factory=None, create=True):
"""Initialize an mbox mailbox."""
self._message_factory = mboxMessage
_mboxMMDF.__init__(self, path, factory, create) | [
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verilog-to-routing/vtr-verilog-to-routing | d9719cf7374821156c3cee31d66991cb85578562 | vtr_flow/scripts/python_libs/vtr/util.py | python | file_replace | (filename, search_replace_dict) | searches file for specified values and replaces them with specified values. | searches file for specified values and replaces them with specified values. | [
"searches",
"file",
"for",
"specified",
"values",
"and",
"replaces",
"them",
"with",
"specified",
"values",
"."
] | def file_replace(filename, search_replace_dict):
"""
searches file for specified values and replaces them with specified values.
"""
lines = []
with open(filename, "r") as file:
lines = file.readlines()
with open(filename, "w") as file:
for line in lines:
for search, replace in search_replace_dict.items():
line = line.replace(search, str(replace))
print(line, file=file) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/external/bazel_tools/third_party/py/gflags/gflags_validators.py | python | SimpleValidator.__init__ | (self, flag_name, checker, message) | Constructor.
Args:
flag_name: string, name of the flag.
checker: function to verify the validator.
input - value of the corresponding flag (string, boolean, etc).
output - Boolean. Must return True if validator constraint is satisfied.
If constraint is not satisfied, it should either return False or
raise Error.
message: string, error message to be shown to the user if validator's
condition is not satisfied | Constructor. | [
"Constructor",
"."
] | def __init__(self, flag_name, checker, message):
"""Constructor.
Args:
flag_name: string, name of the flag.
checker: function to verify the validator.
input - value of the corresponding flag (string, boolean, etc).
output - Boolean. Must return True if validator constraint is satisfied.
If constraint is not satisfied, it should either return False or
raise Error.
message: string, error message to be shown to the user if validator's
condition is not satisfied
"""
super(SimpleValidator, self).__init__(checker, message)
self.flag_name = flag_name | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/core/arrayprint.py | python | get_printoptions | () | return d | Return the current print options.
Returns
-------
print_opts : dict
Dictionary of current print options with keys
- precision : int
- threshold : int
- edgeitems : int
- linewidth : int
- suppress : bool
- nanstr : str
- infstr : str
For a full description of these options, see `set_printoptions`.
See Also
--------
set_printoptions, set_string_function | Return the current print options. | [
"Return",
"the",
"current",
"print",
"options",
"."
] | def get_printoptions():
"""
Return the current print options.
Returns
-------
print_opts : dict
Dictionary of current print options with keys
- precision : int
- threshold : int
- edgeitems : int
- linewidth : int
- suppress : bool
- nanstr : str
- infstr : str
For a full description of these options, see `set_printoptions`.
See Also
--------
set_printoptions, set_string_function
"""
d = dict(precision=_float_output_precision,
threshold=_summaryThreshold,
edgeitems=_summaryEdgeItems,
linewidth=_line_width,
suppress=_float_output_suppress_small,
nanstr=_nan_str,
infstr=_inf_str)
return d | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/jedi/jedi/evaluate/docstrings.py | python | _search_return_in_numpydocstr | (docstr) | Search `docstr` (in numpydoc format) for type(-s) of function returns. | Search `docstr` (in numpydoc format) for type(-s) of function returns. | [
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"-",
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"returns",
"."
] | def _search_return_in_numpydocstr(docstr):
"""
Search `docstr` (in numpydoc format) for type(-s) of function returns.
"""
try:
doc = _get_numpy_doc_string_cls()(docstr)
except ImportError:
return
try:
# This is a non-public API. If it ever changes we should be
# prepared and return gracefully.
returns = doc._parsed_data['Returns']
returns += doc._parsed_data['Yields']
except (KeyError, AttributeError):
return
for r_name, r_type, r_descr in returns:
# Return names are optional and if so the type is in the name
if not r_type:
r_type = r_name
for type_ in _expand_typestr(r_type):
yield type_ | [
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vigsterkr/libjingle | 92dcbb1aac08c35d1c002c4dd7e978528b8173d2 | talk/site_scons/site_tools/talk_linux.py | python | _GetPkgConfigCommand | () | return os.environ.get('PKG_CONFIG') or 'pkg-config' | Return the pkg-config command line to use.
Returns:
A string specifying the pkg-config command line to use. | Return the pkg-config command line to use. | [
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"to",
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] | def _GetPkgConfigCommand():
"""Return the pkg-config command line to use.
Returns:
A string specifying the pkg-config command line to use.
"""
return os.environ.get('PKG_CONFIG') or 'pkg-config' | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | build/android/pylib/forwarder.py | python | Forwarder.__init__ | (self, tool) | Constructs a new instance of Forwarder.
Note that Forwarder is a singleton therefore this constructor should be
called only once.
Args:
tool: Tool class to use to get wrapper, if necessary, for executing the
forwarder (see valgrind_tools.py). | Constructs a new instance of Forwarder. | [
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"of",
"Forwarder",
"."
] | def __init__(self, tool):
"""Constructs a new instance of Forwarder.
Note that Forwarder is a singleton therefore this constructor should be
called only once.
Args:
tool: Tool class to use to get wrapper, if necessary, for executing the
forwarder (see valgrind_tools.py).
"""
assert not Forwarder._instance
self._tool = tool
self._initialized_devices = set()
self._device_to_host_port_map = dict()
self._host_to_device_port_map = dict()
self._host_forwarder_path = os.path.join(
constants.GetOutDirectory(), 'host_forwarder')
assert os.path.exists(self._host_forwarder_path), 'Please build forwarder2'
self._device_forwarder_path_on_host = os.path.join(
constants.GetOutDirectory(), 'forwarder_dist')
self._InitHostLocked() | [
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goldeneye-source/ges-code | 2630cd8ef3d015af53c72ec2e19fc1f7e7fe8d9d | thirdparty/protobuf-2.3.0/python/google/protobuf/message.py | python | Message.ParseFromString | (self, serialized) | Like MergeFromString(), except we clear the object first. | Like MergeFromString(), except we clear the object first. | [
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] | def ParseFromString(self, serialized):
"""Like MergeFromString(), except we clear the object first."""
self.Clear()
self.MergeFromString(serialized) | [
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mousebird-consulting-inc/WhirlyGlobe | 436478011c847c729b485009ed4db0645e304763 | common/local_libs/nanopb/generator/nanopb_generator.py | python | get_nanopb_suboptions | (subdesc, options, name) | return new_options | Get copy of options, and merge information from subdesc. | Get copy of options, and merge information from subdesc. | [
"Get",
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"of",
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"and",
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"information",
"from",
"subdesc",
"."
] | def get_nanopb_suboptions(subdesc, options, name):
'''Get copy of options, and merge information from subdesc.'''
new_options = nanopb_pb2.NanoPBOptions()
new_options.CopyFrom(options)
if hasattr(subdesc, 'syntax') and subdesc.syntax == "proto3":
new_options.proto3 = True
# Handle options defined in a separate file
dotname = '.'.join(name.parts)
for namemask, options in Globals.separate_options:
if fnmatchcase(dotname, namemask):
Globals.matched_namemasks.add(namemask)
new_options.MergeFrom(options)
# Handle options defined in .proto
if isinstance(subdesc.options, descriptor.FieldOptions):
ext_type = nanopb_pb2.nanopb
elif isinstance(subdesc.options, descriptor.FileOptions):
ext_type = nanopb_pb2.nanopb_fileopt
elif isinstance(subdesc.options, descriptor.MessageOptions):
ext_type = nanopb_pb2.nanopb_msgopt
elif isinstance(subdesc.options, descriptor.EnumOptions):
ext_type = nanopb_pb2.nanopb_enumopt
else:
raise Exception("Unknown options type")
if subdesc.options.HasExtension(ext_type):
ext = subdesc.options.Extensions[ext_type]
new_options.MergeFrom(ext)
if Globals.verbose_options:
sys.stderr.write("Options for " + dotname + ": ")
sys.stderr.write(text_format.MessageToString(new_options) + "\n")
return new_options | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/urllib3/connectionpool.py | python | _normalize_host | (host, scheme) | return host | Normalize hosts for comparisons and use with sockets. | [] | def _normalize_host(host, scheme):
"""
Normalize hosts for comparisons and use with sockets.
"""
host = normalize_host(host, scheme)
# httplib doesn't like it when we include brackets in IPv6 addresses
# Specifically, if we include brackets but also pass the port then
# httplib crazily doubles up the square brackets on the Host header.
# Instead, we need to make sure we never pass ``None`` as the port.
# However, for backward compatibility reasons we can't actually
# *assert* that. See http://bugs.python.org/issue28539
if host.startswith("[") and host.endswith("]"):
host = host[1:-1]
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/pathlib.py | python | Path.expanduser | (self) | return self | Return a new path with expanded ~ and ~user constructs
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""" Return a new path with expanded ~ and ~user constructs
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if (not (self._drv or self._root) and
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/keras/python/keras/backend.py | python | softplus | (x) | return nn.softplus(x) | Softplus of a tensor.
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x: A tensor or variable.
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"""Softplus of a tensor.
Arguments:
x: A tensor or variable.
Returns:
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return nn.softplus(x) | [
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Lavender105/DFF | 152397cec4a3dac2aa86e92a65cc27e6c8016ab9 | pytorch-encoding/encoding/models/plain.py | python | get_plain | (dataset='pascal_voc', backbone='resnet50', pretrained=False,
root='./pretrain_models', **kwargs) | return model | r"""PlainNet model from the paper `"Dual Attention Network for Scene Segmentation"
<https://arxiv.org/abs/1809.02983.pdf>` | r"""PlainNet model from the paper `"Dual Attention Network for Scene Segmentation"
<https://arxiv.org/abs/1809.02983.pdf>` | [
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root='./pretrain_models', **kwargs):
r"""PlainNet model from the paper `"Dual Attention Network for Scene Segmentation"
<https://arxiv.org/abs/1809.02983.pdf>`
"""
acronyms = {
'pascal_voc': 'voc',
'pascal_aug': 'voc',
'pcontext': 'pcontext',
'ade20k': 'ade',
'cityscapes': 'cityscapes',
}
# infer number of classes
from ..datasets import datasets, VOCSegmentation, VOCAugSegmentation, ADE20KSegmentation
model = PlainNet(datasets[dataset.lower()].NUM_CLASS, backbone=backbone, root=root, **kwargs)
if pretrained:
from .model_store import get_model_file
model.load_state_dict(torch.load(
get_model_file('fcn_%s_%s'%(backbone, acronyms[dataset]), root=root)),
strict=False)
return model | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/math_grad.py | python | _RsqrtGradGrad | (op, grad) | Returns backprop gradient for f(a,b) = -0.5 * b * conj(a)^3. | Returns backprop gradient for f(a,b) = -0.5 * b * conj(a)^3. | [
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"""Returns backprop gradient for f(a,b) = -0.5 * b * conj(a)^3."""
a = op.inputs[0] # a = x^{-1/2}
b = op.inputs[1] # backprop gradient for a
with ops.control_dependencies([grad]):
ca = math_ops.conj(a)
cg = math_ops.conj(grad)
grad_a = -1.5 * cg * b * math_ops.square(ca)
# pylint: disable=protected-access
grad_b = gen_math_ops._rsqrt_grad(ca, grad)
return grad_a, grad_b | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/traceback.py | python | print_last | (limit=None, file=None, chain=True) | This is a shorthand for 'print_exception(sys.last_type,
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"""This is a shorthand for 'print_exception(sys.last_type,
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if not hasattr(sys, "last_type"):
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | GraphicsRenderer.CreatePen | (*args, **kwargs) | return _gdi_.GraphicsRenderer_CreatePen(*args, **kwargs) | CreatePen(self, Pen pen) -> GraphicsPen | CreatePen(self, Pen pen) -> GraphicsPen | [
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"""CreatePen(self, Pen pen) -> GraphicsPen"""
return _gdi_.GraphicsRenderer_CreatePen(*args, **kwargs) | [
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priyankchheda/algorithms | c361aa9071573fa9966d5b02d05e524815abcf2b | avl_tree/avl_tree.py | python | AVLTree.get_height | (node) | return node.height | returns height of node | returns height of node | [
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""" returns height of node """
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/layers/merge.py | python | multiply | (inputs, **kwargs) | return Multiply(**kwargs)(inputs) | Functional interface to the `Multiply` layer.
Arguments:
inputs: A list of input tensors (at least 2).
**kwargs: Standard layer keyword arguments.
Returns:
A tensor, the element-wise product of the inputs. | Functional interface to the `Multiply` layer. | [
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"""Functional interface to the `Multiply` layer.
Arguments:
inputs: A list of input tensors (at least 2).
**kwargs: Standard layer keyword arguments.
Returns:
A tensor, the element-wise product of the inputs.
"""
return Multiply(**kwargs)(inputs) | [
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gv22ga/dlib-face-recognition-android | 42d6305cbd85833f2b85bb79b70ab9ab004153c9 | tools/lint/cpplint.py | python | _BlockInfo.CheckEnd | (self, filename, clean_lines, linenum, error) | Run checks that applies to text after the closing brace.
This is mostly used for checking end of namespace comments.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Run checks that applies to text after the closing brace.
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Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
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clean_lines: A CleansedLines instance containing the file.
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nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/generators/writers/AbstractWriter.py | python | AbstractWriter.DictHeaderWrite | (self, obj) | Defined to generate header for Python command class. | Defined to generate header for Python command class. | [
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Defined to generate header for Python command class.
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raise Exception(
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zhaoweicai/hwgq | ebc706bee3e2d145de1da4be446ce8de8740738f | python/caffe/net_spec.py | python | Top.to_proto | (self) | return to_proto(self) | Generate a NetParameter that contains all layers needed to compute
this top. | Generate a NetParameter that contains all layers needed to compute
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infinit/memo | 3a8394d0f647efe03ccb8bfe885a7279cb8be8a6 | elle/drake/src/drake/go/__init__.py | python | Config.add_ldflags | (self, flags) | return self | Add ldflags at the end of current ldflags.
:param flags: A list of flags.
:type flags: list of str
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:param flags: A list of flags.
:type flags: list of str
:return: self.
"""
collections.deque(map(self.__ldflags.add, flags))
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martinmoene/optional-lite | a006f229a77b3b2dacf927e4029b8c1c60c86b52 | script/create-vcpkg.py | python | control_path | ( args ) | return tpl_path_vcpkg_control.format( vcpkg=args.vcpkg_root, prj=args.project ) | Create path like vcpks/ports/_project_/CONTROL | Create path like vcpks/ports/_project_/CONTROL | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/internals/blocks.py | python | Block.internal_values | (self, dtype=None) | return self.values | return an internal format, currently just the ndarray
this should be the pure internal API format | return an internal format, currently just the ndarray
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | FileCtrlEvent.GetFilterIndex | (*args, **kwargs) | return _controls_.FileCtrlEvent_GetFilterIndex(*args, **kwargs) | GetFilterIndex(self) -> int | GetFilterIndex(self) -> int | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/hypertreelist.py | python | TreeListMainWindow.SetMainColumn | (self, column) | Sets the :class:`HyperTreeList` main column (i.e. the position of the underlying
:class:`~lib.agw.customtreectrl.CustomTreeCtrl`.
:param `column`: if not ``None``, an integer specifying the column index.
If it is ``None``, the main column index is used. | Sets the :class:`HyperTreeList` main column (i.e. the position of the underlying
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hakuna-m/wubiuefi | caec1af0a09c78fd5a345180ada1fe45e0c63493 | src/openpgp/sap/list.py | python | find_compressed_msg | (pkts) | Find a compressed OpenPGP message in a list of packets.
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"""
if PKT_COMPRESSED == pkts[0].tag.type:
msg = CompressedMsg()
msg._seq = [pkts[0]]
msg.compressed = pkts[0]
return msg, pkts[1:]
else:
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | python/psutil/psutil/_psbsd.py | python | Process.get_cpu_times | (self) | return nt_cputimes(user, system) | return a tuple containing process user/kernel time. | return a tuple containing process user/kernel time. | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TIntIntVV.GetSecHashCd | (self) | return _snap.TIntIntVV_GetSecHashCd(self) | GetSecHashCd(TIntIntVV self) -> int
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perilouswithadollarsign/cstrike15_src | f82112a2388b841d72cb62ca48ab1846dfcc11c8 | thirdparty/protobuf-2.5.0/python/google/protobuf/internal/cpp_message.py | python | _IsMessageSetExtension | (field) | return (field.is_extension and
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/nodes.py | python | Element.copy_attr_coerce | (self, attr, value, replace) | If attr is an attribute of self and either self[attr] or value is a
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/genericpath.py | python | commonprefix | (m) | return s1 | Given a list of pathnames, returns the longest common leading component | Given a list of pathnames, returns the longest common leading component | [
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s1 = min(m)
s2 = max(m)
for i, c in enumerate(s1):
if c != s2[i]:
return s1[:i]
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hcdth011/ROS-Hydro-SLAM | 629448eecd2c9a3511158115fa53ea9e4ae41359 | rpg_svo/svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_rpe.py | python | compute_distance | (transform) | return numpy.linalg.norm(transform[0:3,3]) | Compute the distance of the translational component of a 4x4 homogeneous matrix. | Compute the distance of the translational component of a 4x4 homogeneous matrix. | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | media/webrtc/trunk/tools/gyp/pylib/gyp/generator/msvs.py | python | _AddConditionalProperty | (properties, condition, name, value) | Adds a property / conditional value pair to a dictionary.
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properties: The dictionary to be modified. The key is the name of the
property. The value is itself a dictionary; its key is the value and
the value a list of condition for which this value is true.
condition: The condition under which the named property has the value.
name: The name of the property.
value: The value of the property.
"""
if name not in properties:
properties[name] = {}
values = properties[name]
if value not in values:
values[value] = []
conditions = values[value]
conditions.append(condition) | [
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anestisb/oatdump_plus | ba858c1596598f0d9ae79c14d08c708cecc50af3 | tools/common/common.py | python | DeviceTestEnv.PushClasspath | (self, classpath) | return ':'.join(device_paths) | Push classpath to on-device test directory.
Classpath can contain multiple colon separated file paths, each file is
pushed. Returns analogous classpath with paths valid on device.
Args:
classpath: string, classpath in format 'a/b/c:d/e/f'.
Returns:
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Args:
classpath: string, classpath in format 'a/b/c:d/e/f'.
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"""
paths = classpath.split(':')
device_paths = []
for path in paths:
device_paths.append('{0}/{1}'.format(
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self._AdbPush(path, self._device_env_path)
return ':'.join(device_paths) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/randomimpl.py | python | get_np_state_ptr | (context, builder) | return get_state_ptr(context, builder, 'np') | Get a pointer to the thread-local Numpy random state. | Get a pointer to the thread-local Numpy random state. | [
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15172658790/Blog | 46e5036f5fbcad535af2255dc0e095cebcd8d710 | 计算机与信息类/算法基础/lab-徐云/2018/lab3/lcs.py | python | lcs2 | (a,b) | return board[n] | time: O(mn); space: O(min(m,n)) | time: O(mn); space: O(min(m,n)) | [
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if len(b)>len(a):
a,b= b,a
m,n = len(a),len(b)
board = [[] for i in range(n+1)]
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apache/incubator-weex | 5c25f0b59f7ac90703c363e7261f60bd06356dbe | weex_core/Source/base/android/jniprebuild/jni_generator.py | python | GetEnvCall | (is_constructor, is_static, return_type) | return 'Call' + call + 'Method' | Maps the types availabe via env->Call__Method. | Maps the types availabe via env->Call__Method. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/optimize/linesearch.py | python | _zoom | (a_lo, a_hi, phi_lo, phi_hi, derphi_lo,
phi, derphi, phi0, derphi0, c1, c2, extra_condition) | return a_star, val_star, valprime_star | Part of the optimization algorithm in `scalar_search_wolfe2`. | Part of the optimization algorithm in `scalar_search_wolfe2`. | [
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"""
Part of the optimization algorithm in `scalar_search_wolfe2`.
"""
maxiter = 10
i = 0
delta1 = 0.2 # cubic interpolant check
delta2 = 0.1 # quadratic interpolant check
phi_rec = phi0
a_rec = 0
while True:
# interpolate to find a trial step length between a_lo and
# a_hi Need to choose interpolation here. Use cubic
# interpolation and then if the result is within delta *
# dalpha or outside of the interval bounded by a_lo or a_hi
# then use quadratic interpolation, if the result is still too
# close, then use bisection
dalpha = a_hi - a_lo
if dalpha < 0:
a, b = a_hi, a_lo
else:
a, b = a_lo, a_hi
# minimizer of cubic interpolant
# (uses phi_lo, derphi_lo, phi_hi, and the most recent value of phi)
#
# if the result is too close to the end points (or out of the
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# derphi_lo and phi_hi if the result is still too close to the
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if (i > 0):
cchk = delta1 * dalpha
a_j = _cubicmin(a_lo, phi_lo, derphi_lo, a_hi, phi_hi,
a_rec, phi_rec)
if (i == 0) or (a_j is None) or (a_j > b - cchk) or (a_j < a + cchk):
qchk = delta2 * dalpha
a_j = _quadmin(a_lo, phi_lo, derphi_lo, a_hi, phi_hi)
if (a_j is None) or (a_j > b-qchk) or (a_j < a+qchk):
a_j = a_lo + 0.5*dalpha
# Check new value of a_j
phi_aj = phi(a_j)
if (phi_aj > phi0 + c1*a_j*derphi0) or (phi_aj >= phi_lo):
phi_rec = phi_hi
a_rec = a_hi
a_hi = a_j
phi_hi = phi_aj
else:
derphi_aj = derphi(a_j)
if abs(derphi_aj) <= -c2*derphi0 and extra_condition(a_j, phi_aj):
a_star = a_j
val_star = phi_aj
valprime_star = derphi_aj
break
if derphi_aj*(a_hi - a_lo) >= 0:
phi_rec = phi_hi
a_rec = a_hi
a_hi = a_lo
phi_hi = phi_lo
else:
phi_rec = phi_lo
a_rec = a_lo
a_lo = a_j
phi_lo = phi_aj
derphi_lo = derphi_aj
i += 1
if (i > maxiter):
# Failed to find a conforming step size
a_star = None
val_star = None
valprime_star = None
break
return a_star, val_star, valprime_star | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/distributions/transformed_distribution.py | python | TransformedDistribution._finish_log_prob_for_one_fiber | (self, y, x, ildj) | return log_prob | Finish computation of log_prob on one element of the inverse image. | Finish computation of log_prob on one element of the inverse image. | [
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] | def _finish_log_prob_for_one_fiber(self, y, x, ildj):
"""Finish computation of log_prob on one element of the inverse image."""
x = self._maybe_rotate_dims(x, rotate_right=True)
log_prob = self.distribution.log_prob(x)
if self._is_maybe_event_override:
log_prob = math_ops.reduce_sum(log_prob, self._reduce_event_indices)
log_prob = ildj + log_prob
if self._is_maybe_event_override:
log_prob.set_shape(array_ops.broadcast_static_shape(
y.get_shape().with_rank_at_least(1)[:-1], self.batch_shape))
return log_prob | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/arrays/categorical.py | python | Categorical.__getitem__ | (self, key) | return result | Return an item. | Return an item. | [
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] | def __getitem__(self, key):
"""
Return an item.
"""
result = super().__getitem__(key)
if getattr(result, "ndim", 0) > 1:
result = result._ndarray
deprecate_ndim_indexing(result)
return result | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/plat-mac/findertools.py | python | openwindow | (object) | Open a Finder window for object, Specify object by name or fsspec. | Open a Finder window for object, Specify object by name or fsspec. | [
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] | def openwindow(object):
"""Open a Finder window for object, Specify object by name or fsspec."""
finder = _getfinder()
object = Carbon.File.FSRef(object)
object_alias = object.FSNewAliasMinimal()
args = {}
attrs = {}
_code = 'aevt'
_subcode = 'odoc'
aeobj_0 = aetypes.ObjectSpecifier(want=aetypes.Type('cfol'), form="alis", seld=object_alias, fr=None)
args['----'] = aeobj_0
_reply, args, attrs = finder.send(_code, _subcode, args, attrs)
if 'errn' in args:
raise Error, aetools.decodeerror(args) | [
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RoboJackets/robocup-software | bce13ce53ddb2ecb9696266d980722c34617dc15 | rj_gameplay/stp/rc.py | python | Field.floor_width_m | (self) | return self.__floor_width_m | :return: check on this one | :return: check on this one | [
":",
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] | def floor_width_m(self) -> float:
"""
:return: check on this one
"""
return self.__floor_width_m | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/reduce4circleControl.py | python | CWSCDReductionControl.set_detector_center | (self, exp_number, center_row, center_col, default=False) | return | Set detector center
:param exp_number:
:param center_row:
:param center_col:
:param default:
:return: | Set detector center
:param exp_number:
:param center_row:
:param center_col:
:param default:
:return: | [
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":",
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":",
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] | def set_detector_center(self, exp_number, center_row, center_col, default=False):
"""
Set detector center
:param exp_number:
:param center_row:
:param center_col:
:param default:
:return:
"""
# check
assert isinstance(exp_number, int) and exp_number > 0, 'Experiment number must be integer'
assert center_row is None or (isinstance(center_row, int) and center_row >= 0), \
'Center row number {0} of type {1} must either None or non-negative integer.' \
''.format(center_row, type(center_row))
assert center_col is None or (isinstance(center_col, int) and center_col >= 0), \
'Center column number {0} of type {1} must be either None or non-negative integer.' \
''.format(center_col, type(center_col))
if default:
self._defaultDetectorCenter = center_row, center_col
else:
self._detCenterDict[exp_number] = center_row, center_col
return | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py | python | FormulaConstant.__unicode__ | (self) | return 'Formula constant: ' + self.string | Return a printable representation. | Return a printable representation. | [
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"."
] | def __unicode__(self):
"Return a printable representation."
return 'Formula constant: ' + self.string | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/arrays/categorical.py | python | Categorical.to_dense | (self) | return np.asarray(self) | Return my 'dense' representation
For internal compatibility with numpy arrays.
Returns
-------
dense : array | Return my 'dense' representation | [
"Return",
"my",
"dense",
"representation"
] | def to_dense(self):
"""
Return my 'dense' representation
For internal compatibility with numpy arrays.
Returns
-------
dense : array
"""
return np.asarray(self) | [
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8_inspector/third_party/jinja2/jinja2/ext.py | python | InternationalizationExtension._make_node | (self, singular, plural, variables, plural_expr,
vars_referenced, num_called_num) | return nodes.Output([node]) | Generates a useful node from the data provided. | Generates a useful node from the data provided. | [
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"useful",
"node",
"from",
"the",
"data",
"provided",
"."
] | def _make_node(self, singular, plural, variables, plural_expr,
vars_referenced, num_called_num):
"""Generates a useful node from the data provided."""
# no variables referenced? no need to escape for old style
# gettext invocations only if there are vars.
if not vars_referenced and not self.environment.newstyle_gettext:
singular = singular.replace('%%', '%')
if plural:
plural = plural.replace('%%', '%')
# singular only:
if plural_expr is None:
gettext = nodes.Name('gettext', 'load')
node = nodes.Call(gettext, [nodes.Const(singular)],
[], None, None)
# singular and plural
else:
ngettext = nodes.Name('ngettext', 'load')
node = nodes.Call(ngettext, [
nodes.Const(singular),
nodes.Const(plural),
plural_expr
], [], None, None)
# in case newstyle gettext is used, the method is powerful
# enough to handle the variable expansion and autoescape
# handling itself
if self.environment.newstyle_gettext:
for key, value in iteritems(variables):
# the function adds that later anyways in case num was
# called num, so just skip it.
if num_called_num and key == 'num':
continue
node.kwargs.append(nodes.Keyword(key, value))
# otherwise do that here
else:
# mark the return value as safe if we are in an
# environment with autoescaping turned on
node = nodes.MarkSafeIfAutoescape(node)
if variables:
node = nodes.Mod(node, nodes.Dict([
nodes.Pair(nodes.Const(key), value)
for key, value in variables.items()
]))
return nodes.Output([node]) | [
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Pay20Y/FOTS_TF | c42ea59a20c28d506fee35cfb4c553b0cb20eee8 | nets/resnet_v1.py | python | bottleneck | (inputs, depth, depth_bottleneck, stride, rate=1,
outputs_collections=None, scope=None) | Bottleneck residual unit variant with BN after convolutions.
This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
its definition. Note that we use here the bottleneck variant which has an
extra bottleneck layer.
When putting together two consecutive ResNet blocks that use this unit, one
should use stride = 2 in the last unit of the first block.
Args:
inputs: A tensor of size [batch, height, width, channels].
depth: The depth of the ResNet unit output.
depth_bottleneck: The depth of the bottleneck layers.
stride: The ResNet unit's stride. Determines the amount of downsampling of
the units output compared to its input.
rate: An integer, rate for atrous convolution.
outputs_collections: Collection to add the ResNet unit output.
scope: Optional variable_scope.
Returns:
The ResNet unit's output. | Bottleneck residual unit variant with BN after convolutions. | [
"Bottleneck",
"residual",
"unit",
"variant",
"with",
"BN",
"after",
"convolutions",
"."
] | def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
outputs_collections=None, scope=None):
"""Bottleneck residual unit variant with BN after convolutions.
This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
its definition. Note that we use here the bottleneck variant which has an
extra bottleneck layer.
When putting together two consecutive ResNet blocks that use this unit, one
should use stride = 2 in the last unit of the first block.
Args:
inputs: A tensor of size [batch, height, width, channels].
depth: The depth of the ResNet unit output.
depth_bottleneck: The depth of the bottleneck layers.
stride: The ResNet unit's stride. Determines the amount of downsampling of
the units output compared to its input.
rate: An integer, rate for atrous convolution.
outputs_collections: Collection to add the ResNet unit output.
scope: Optional variable_scope.
Returns:
The ResNet unit's output.
"""
with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
if depth == depth_in:
shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
else:
shortcut = slim.conv2d(inputs, depth, [1, 1], stride=stride,
activation_fn=None, scope='shortcut')
residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
scope='conv1')
residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
rate=rate, scope='conv2')
residual = slim.conv2d(residual, depth, [1, 1], stride=1,
activation_fn=None, scope='conv3')
output = tf.nn.relu(shortcut + residual)
return slim.utils.collect_named_outputs(outputs_collections,
sc.original_name_scope,
output) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/ccompiler.py | python | get_default_compiler | (osname=None, platform=None) | return 'unix' | Determine the default compiler to use for the given platform.
osname should be one of the standard Python OS names (i.e. the
ones returned by os.name) and platform the common value
returned by sys.platform for the platform in question.
The default values are os.name and sys.platform in case the
parameters are not given. | Determine the default compiler to use for the given platform. | [
"Determine",
"the",
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"compiler",
"to",
"use",
"for",
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] | def get_default_compiler(osname=None, platform=None):
"""Determine the default compiler to use for the given platform.
osname should be one of the standard Python OS names (i.e. the
ones returned by os.name) and platform the common value
returned by sys.platform for the platform in question.
The default values are os.name and sys.platform in case the
parameters are not given.
"""
if osname is None:
osname = os.name
if platform is None:
platform = sys.platform
for pattern, compiler in _default_compilers:
if re.match(pattern, platform) is not None or \
re.match(pattern, osname) is not None:
return compiler
# Default to Unix compiler
return 'unix' | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | src/third_party/scons-3.1.2/scons-local-3.1.2/SCons/Tool/MSCommon/sdk.py | python | get_default_sdk | () | return InstalledSDKList[0] | Set up the default Platform/Windows SDK. | Set up the default Platform/Windows SDK. | [
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] | def get_default_sdk():
"""Set up the default Platform/Windows SDK."""
get_installed_sdks()
if not InstalledSDKList:
return None
return InstalledSDKList[0] | [
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google/sling | f408a148a06bc2d62e853a292a8ba7266c642839 | python/task/workflow.py | python | length_of | (l) | return len(l) if isinstance(l, list) else None | Get number of elements in list or None for singletons. | Get number of elements in list or None for singletons. | [
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] | def length_of(l):
"""Get number of elements in list or None for singletons."""
return len(l) if isinstance(l, list) else None | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/setuptools/msvc.py | python | SystemInfo.WindowsSdkDir | (self) | return sdkdir | Microsoft Windows SDK directory.
Return
------
str
path | Microsoft Windows SDK directory. | [
"Microsoft",
"Windows",
"SDK",
"directory",
"."
] | def WindowsSdkDir(self):
"""
Microsoft Windows SDK directory.
Return
------
str
path
"""
sdkdir = ''
for ver in self.WindowsSdkVersion:
# Try to get it from registry
loc = join(self.ri.windows_sdk, 'v%s' % ver)
sdkdir = self.ri.lookup(loc, 'installationfolder')
if sdkdir:
break
if not sdkdir or not isdir(sdkdir):
# Try to get "VC++ for Python" version from registry
path = join(self.ri.vc_for_python, '%0.1f' % self.vc_ver)
install_base = self.ri.lookup(path, 'installdir')
if install_base:
sdkdir = join(install_base, 'WinSDK')
if not sdkdir or not isdir(sdkdir):
# If fail, use default new path
for ver in self.WindowsSdkVersion:
intver = ver[:ver.rfind('.')]
path = r'Microsoft SDKs\Windows Kits\%s' % intver
d = join(self.ProgramFiles, path)
if isdir(d):
sdkdir = d
if not sdkdir or not isdir(sdkdir):
# If fail, use default old path
for ver in self.WindowsSdkVersion:
path = r'Microsoft SDKs\Windows\v%s' % ver
d = join(self.ProgramFiles, path)
if isdir(d):
sdkdir = d
if not sdkdir:
# If fail, use Platform SDK
sdkdir = join(self.VCInstallDir, 'PlatformSDK')
return sdkdir | [
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... | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/setuptools/py2/setuptools/msvc.py#L781-L821 | |
rsummers11/CADLab | 976ed959a0b5208bb4173127a7ef732ac73a9b6f | LymphNodeRFCNNPipeline/pyconvnet/ordereddict.py | python | OrderedDict.fromkeys | (cls, iterable, value=None) | return d | OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S
and values equal to v (which defaults to None). | OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S
and values equal to v (which defaults to None). | [
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'''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S
and values equal to v (which defaults to None).
'''
d = cls()
for key in iterable:
d[key] = value
return d | [
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