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CaoWGG/TensorRT-YOLOv4 | 4d7c2edce99e8794a4cb4ea3540d51ce91158a36 | onnx-tensorrt/third_party/onnx/onnx/__init__.py | python | _serialize | (proto) | Serialize a in-memory proto to bytes
@params
proto is a in-memory proto, such as a ModelProto, TensorProto, etc
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Serialize a in-memory proto to bytes
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proto is a in-memory proto, such as a ModelProto, TensorProto, etc
@return
Serialized proto in bytes
'''
if isinstance(proto, bytes):
return proto
elif hasattr(proto, 'SerializeToString') and callable(proto.SerializeToString):
result = proto.SerializeToString()
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BSVino/DoubleAction | c550b168a3e919926c198c30240f506538b92e75 | mp/src/thirdparty/protobuf-2.3.0/python/google/protobuf/internal/containers.py | python | BaseContainer.__getitem__ | (self, key) | return self._values[key] | Retrieves item by the specified key. | Retrieves item by the specified key. | [
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plaidml/plaidml | f3c6681db21460e5fdc11ae651d6d7b6c27f8262 | mlperf/pycoco.py | python | COCO.info | (self) | Print information about the annotation file.
:return: | Print information about the annotation file.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/targets/arrayobj.py | python | get_sort_func | (kind, is_float, is_argsort=False) | Get a sort implementation of the given kind. | Get a sort implementation of the given kind. | [
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Get a sort implementation of the given kind.
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key = kind, is_float, is_argsort
try:
return _sorts[key]
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func = sort.run_quicksort
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Tencent/TNN | 7acca99f54c55747b415a4c57677403eebc7b706 | third_party/flatbuffers/python/flatbuffers/table.py | python | Table.Union | (self, t2, off) | Union initializes any Table-derived type to point to the union at
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/stat.py | python | S_ISWHT | (mode) | return False | Return True if mode is from a whiteout. | Return True if mode is from a whiteout. | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/waflib/extras/codelite.py | python | vsnode.get_waf | (self) | return '%s/%s' % (self.ctx.srcnode.abspath(), getattr(self.ctx, 'waf_command', 'waf')) | Override in subclasses... | Override in subclasses... | [
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apache/singa | 93fd9da72694e68bfe3fb29d0183a65263d238a1 | python/singa/autograd.py | python | add | (a, b) | return Add()(a, b)[0] | Return `a+b`, where a and b are Tensor. | Return `a+b`, where a and b are Tensor. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/swagger_spec_validator/validator12.py | python | validate_resource_listing | (resource_listing) | Validate a Resource Listing (§5.1).
:param resource_listing: a dictionary respresentation of a Resource Listing.
Note that you will have to invoke `validate_api_declaration` on each
linked API Declaration.
:returns: `None` in case of success, otherwise raises an exception.
:raises: :py:class:`swagger_spec_validator.SwaggerValidationError`
:raises: :py:class:`jsonschema.exceptions.ValidationError` | Validate a Resource Listing (§5.1). | [
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] | def validate_resource_listing(resource_listing):
"""Validate a Resource Listing (§5.1).
:param resource_listing: a dictionary respresentation of a Resource Listing.
Note that you will have to invoke `validate_api_declaration` on each
linked API Declaration.
:returns: `None` in case of success, otherwise raises an exception.
:raises: :py:class:`swagger_spec_validator.SwaggerValidationError`
:raises: :py:class:`jsonschema.exceptions.ValidationError`
"""
validate_json(resource_listing, 'schemas/v1.2/resourceListing.json') | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/abins/input/textparser.py | python | TextParser.file_end | (self, file_obj=None) | Checks end of the text file.
:param file_obj: file object which was open in "r" mode
:returns: True if end of file, otherwise False | Checks end of the text file.
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Checks end of the text file.
:param file_obj: file object which was open in "r" mode
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"""
pos = file_obj.tell()
potential_end = file_obj.read(ONE_CHARACTER)
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/nn_grad.py | python | _SparseSoftmaxCrossEntropyWithLogitsGrad | (op, grad_0, _) | return _BroadcastMul(grad_0, sparse_softmax_grad_without_gradient), None | Gradient function for SparseSoftmaxCrossEntropyWithLogits. | Gradient function for SparseSoftmaxCrossEntropyWithLogits. | [
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# grad_0 is the backprop for cost, and we multiply it with the gradients
# (which is output[1])
# There is no gradient for the labels
#
# Currently there is no way to take the second derivative of this op
# due to the fused implementation's interaction with tf.gradients(),
# so we make sure we prevent silently incorrect results by raising
# an error if the second derivative is requested via prevent_gradient.
sparse_softmax_grad_without_gradient = array_ops.prevent_gradient(
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return _BroadcastMul(grad_0, sparse_softmax_grad_without_gradient), None | [
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DanielSWolf/rhubarb-lip-sync | 5cface0af3b6e4e58c0b829c51561d784fb9f52f | rhubarb/lib/pocketsphinx-rev13216/src/gst-plugin/livedemo.py | python | DemoApp.init_gui | (self) | Initialize the GUI components | Initialize the GUI components | [
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"""Initialize the GUI components"""
self.window = gtk.Window()
self.window.connect("delete-event", gtk.main_quit)
self.window.set_default_size(400,200)
self.window.set_border_width(10)
vbox = gtk.VBox()
self.textbuf = gtk.TextBuffer()
self.text = gtk.TextView(buffer=self.textbuf)
self.text.set_wrap_mode(gtk.WRAP_WORD)
vbox.pack_start(self.text)
self.button = gtk.ToggleButton("Speak")
self.button.connect('clicked', self.button_clicked)
vbox.pack_start(self.button, False, False, 5)
self.window.add(vbox)
self.window.show_all() | [
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wy1iu/LargeMargin_Softmax_Loss | c3e9f20e4f16e2b4daf7d358a614366b9b39a6ec | scripts/cpp_lint.py | python | _SetOutputFormat | (output_format) | Sets the module's output format. | Sets the module's output format. | [
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1989Ryan/Semantic_SLAM | 0284b3f832ca431c494f9c134fe46c40ec86ee38 | Third_Part/PSPNet_Keras_tensorflow/caffe-tensorflow/kaffe/tensorflow/network.py | python | Network.make_var | (self, name, shape) | return tf.get_variable(name, shape, trainable=self.trainable) | Creates a new TensorFlow variable. | Creates a new TensorFlow variable. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/ipaddress.py | python | _BaseV6._compress_hextets | (cls, hextets) | return hextets | Compresses a list of hextets.
Compresses a list of strings, replacing the longest continuous
sequence of "0" in the list with "" and adding empty strings at
the beginning or at the end of the string such that subsequently
calling ":".join(hextets) will produce the compressed version of
the IPv6 address.
Args:
hextets: A list of strings, the hextets to compress.
Returns:
A list of strings. | Compresses a list of hextets. | [
"Compresses",
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] | def _compress_hextets(cls, hextets):
"""Compresses a list of hextets.
Compresses a list of strings, replacing the longest continuous
sequence of "0" in the list with "" and adding empty strings at
the beginning or at the end of the string such that subsequently
calling ":".join(hextets) will produce the compressed version of
the IPv6 address.
Args:
hextets: A list of strings, the hextets to compress.
Returns:
A list of strings.
"""
best_doublecolon_start = -1
best_doublecolon_len = 0
doublecolon_start = -1
doublecolon_len = 0
for index, hextet in enumerate(hextets):
if hextet == '0':
doublecolon_len += 1
if doublecolon_start == -1:
# Start of a sequence of zeros.
doublecolon_start = index
if doublecolon_len > best_doublecolon_len:
# This is the longest sequence of zeros so far.
best_doublecolon_len = doublecolon_len
best_doublecolon_start = doublecolon_start
else:
doublecolon_len = 0
doublecolon_start = -1
if best_doublecolon_len > 1:
best_doublecolon_end = (best_doublecolon_start +
best_doublecolon_len)
# For zeros at the end of the address.
if best_doublecolon_end == len(hextets):
hextets += ['']
hextets[best_doublecolon_start:best_doublecolon_end] = ['']
# For zeros at the beginning of the address.
if best_doublecolon_start == 0:
hextets = [''] + hextets
return hextets | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | xmlNode.listGetRawString | (self, doc, inLine) | return ret | Builds the string equivalent to the text contained in the
Node list made of TEXTs and ENTITY_REFs, contrary to
xmlNodeListGetString() this function doesn't do any
character encoding handling. | Builds the string equivalent to the text contained in the
Node list made of TEXTs and ENTITY_REFs, contrary to
xmlNodeListGetString() this function doesn't do any
character encoding handling. | [
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"""Builds the string equivalent to the text contained in the
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xmlNodeListGetString() this function doesn't do any
character encoding handling. """
if doc is None: doc__o = None
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | PrintData.__init__ | (self, *args) | __init__(self) -> PrintData
__init__(self, PrintData data) -> PrintData | __init__(self) -> PrintData
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/analyzer.py | python | _GenerateTargets | (data, target_list, target_dicts, toplevel_dir, files,
build_files) | return name_to_target, matching_targets, roots & build_file_targets | Returns a tuple of the following:
. A dictionary mapping from fully qualified name to Target.
. A list of the targets that have a source file in |files|.
. Targets that constitute the 'all' target. See description at top of file
for details on the 'all' target.
This sets the |match_status| of the targets that contain any of the source
files in |files| to MATCH_STATUS_MATCHES.
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for details on the 'all' target.
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"""Returns a tuple of the following:
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. A list of the targets that have a source file in |files|.
. Targets that constitute the 'all' target. See description at top of file
for details on the 'all' target.
This sets the |match_status| of the targets that contain any of the source
files in |files| to MATCH_STATUS_MATCHES.
|toplevel_dir| is the root of the source tree."""
# Maps from target name to Target.
name_to_target = {}
# Targets that matched.
matching_targets = []
# Queue of targets to visit.
targets_to_visit = target_list[:]
# Maps from build file to a boolean indicating whether the build file is in
# |files|.
build_file_in_files = {}
# Root targets across all files.
roots = set()
# Set of Targets in |build_files|.
build_file_targets = set()
while len(targets_to_visit) > 0:
target_name = targets_to_visit.pop()
created_target, target = _GetOrCreateTargetByName(name_to_target,
target_name)
if created_target:
roots.add(target)
elif target.visited:
continue
target.visited = True
target.requires_build = _DoesTargetTypeRequireBuild(
target_dicts[target_name])
target_type = target_dicts[target_name]['type']
target.is_executable = target_type == 'executable'
target.is_static_library = target_type == 'static_library'
target.is_or_has_linked_ancestor = (target_type == 'executable' or
target_type == 'shared_library')
build_file = gyp.common.ParseQualifiedTarget(target_name)[0]
if not build_file in build_file_in_files:
build_file_in_files[build_file] = \
_WasBuildFileModified(build_file, data, files, toplevel_dir)
if build_file in build_files:
build_file_targets.add(target)
# If a build file (or any of its included files) is modified we assume all
# targets in the file are modified.
if build_file_in_files[build_file]:
print 'matching target from modified build file', target_name
target.match_status = MATCH_STATUS_MATCHES
matching_targets.append(target)
else:
sources = _ExtractSources(target_name, target_dicts[target_name],
toplevel_dir)
for source in sources:
if _ToGypPath(os.path.normpath(source)) in files:
print 'target', target_name, 'matches', source
target.match_status = MATCH_STATUS_MATCHES
matching_targets.append(target)
break
# Add dependencies to visit as well as updating back pointers for deps.
for dep in target_dicts[target_name].get('dependencies', []):
targets_to_visit.append(dep)
created_dep_target, dep_target = _GetOrCreateTargetByName(name_to_target,
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if not created_dep_target:
roots.discard(dep_target)
target.deps.add(dep_target)
dep_target.back_deps.add(target)
return name_to_target, matching_targets, roots & build_file_targets | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/ipaddress.py | python | _BaseV6._explode_shorthand_ip_string | (self) | return ':'.join(parts) | Expand a shortened IPv6 address.
Args:
ip_str: A string, the IPv6 address.
Returns:
A string, the expanded IPv6 address. | Expand a shortened IPv6 address. | [
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] | def _explode_shorthand_ip_string(self):
"""Expand a shortened IPv6 address.
Args:
ip_str: A string, the IPv6 address.
Returns:
A string, the expanded IPv6 address.
"""
if isinstance(self, IPv6Network):
ip_str = _compat_str(self.network_address)
elif isinstance(self, IPv6Interface):
ip_str = _compat_str(self.ip)
else:
ip_str = _compat_str(self)
ip_int = self._ip_int_from_string(ip_str)
hex_str = '%032x' % ip_int
parts = [hex_str[x:x + 4] for x in range(0, 32, 4)]
if isinstance(self, (_BaseNetwork, IPv6Interface)):
return '%s/%d' % (':'.join(parts), self._prefixlen)
return ':'.join(parts) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pkg_resources/_vendor/pyparsing.py | python | ParserElement.__sub__ | (self, other) | return self + And._ErrorStop() + other | Implementation of - operator, returns C{L{And}} with error stop | Implementation of - operator, returns C{L{And}} with error stop | [
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if not isinstance( other, ParserElement ):
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return None
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/rnn.py | python | _reverse_seq | (input_seq, lengths) | return results | Reverse a list of Tensors up to specified lengths.
Args:
input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)
or nested tuples of tensors.
lengths: A `Tensor` of dimension batch_size, containing lengths for each
sequence in the batch. If "None" is specified, simply reverses the list.
Returns:
time-reversed sequence | Reverse a list of Tensors up to specified lengths. | [
"Reverse",
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"specified",
"lengths",
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"""Reverse a list of Tensors up to specified lengths.
Args:
input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)
or nested tuples of tensors.
lengths: A `Tensor` of dimension batch_size, containing lengths for each
sequence in the batch. If "None" is specified, simply reverses the list.
Returns:
time-reversed sequence
"""
if lengths is None:
return list(reversed(input_seq))
flat_input_seq = tuple(nest.flatten(input_) for input_ in input_seq)
flat_results = [[] for _ in range(len(input_seq))]
for sequence in zip(*flat_input_seq):
input_shape = tensor_shape.unknown_shape(rank=sequence[0].get_shape().rank)
for input_ in sequence:
input_shape.assert_is_compatible_with(input_.get_shape())
input_.set_shape(input_shape)
# Join into (time, batch_size, depth)
s_joined = array_ops.stack(sequence)
# Reverse along dimension 0
s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
# Split again into list
result = array_ops.unstack(s_reversed)
for r, flat_result in zip(result, flat_results):
r.set_shape(input_shape)
flat_result.append(r)
results = [
nest.pack_sequence_as(structure=input_, flat_sequence=flat_result)
for input_, flat_result in zip(input_seq, flat_results)
]
return results | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | StatusBar.GetClassDefaultAttributes | (*args, **kwargs) | return _windows_.StatusBar_GetClassDefaultAttributes(*args, **kwargs) | GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
control -- which is a much better idea than hard coding specific
colours or fonts which might look completely out of place on the
user's system, especially if it uses themes.
The variant parameter is only relevant under Mac currently and is
ignore under other platforms. Under Mac, it will change the size of
the returned font. See `wx.Window.SetWindowVariant` for more about
this. | GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes | [
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"""
GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
control -- which is a much better idea than hard coding specific
colours or fonts which might look completely out of place on the
user's system, especially if it uses themes.
The variant parameter is only relevant under Mac currently and is
ignore under other platforms. Under Mac, it will change the size of
the returned font. See `wx.Window.SetWindowVariant` for more about
this.
"""
return _windows_.StatusBar_GetClassDefaultAttributes(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/arrays/datetimelike.py | python | DatetimeLikeArrayMixin._sub_nat | (self) | return result.view("timedelta64[ns]") | Subtract pd.NaT from self | Subtract pd.NaT from self | [
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"""
Subtract pd.NaT from self
"""
# GH#19124 Timedelta - datetime is not in general well-defined.
# We make an exception for pd.NaT, which in this case quacks
# like a timedelta.
# For datetime64 dtypes by convention we treat NaT as a datetime, so
# this subtraction returns a timedelta64 dtype.
# For period dtype, timedelta64 is a close-enough return dtype.
result = np.empty(self.shape, dtype=np.int64)
result.fill(iNaT)
return result.view("timedelta64[ns]") | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/framework/common_shapes.py | python | depthwise_conv2d_native_shape | (op) | return [tensor_shape.TensorShape([batch_size, out_rows, out_cols, depth_out])] | Shape function for a DepthwiseConv2D op.
This op has two inputs:
* input, a 4D tensor with shape = [batch_size, rows, cols, depth_in]
* filter, a 4D tensor with shape = [filter_rows, filter_cols,
depth_in, depthwise_multiplier]
The output is a 4D tensor with shape = [batch_size, out_rows,
out_cols, depth_in*depthwise_multiplier], where out_rows and out_cols depend
on the value of the op's "padding" and "strides" attrs.
Args:
op: A DepthwiseConv2dNative Operation.
Returns:
A list containing the Shape of the DepthwiseConv2DNative output.
Raises:
ValueError: If the shapes of the input or filter are incompatible. | Shape function for a DepthwiseConv2D op. | [
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"""Shape function for a DepthwiseConv2D op.
This op has two inputs:
* input, a 4D tensor with shape = [batch_size, rows, cols, depth_in]
* filter, a 4D tensor with shape = [filter_rows, filter_cols,
depth_in, depthwise_multiplier]
The output is a 4D tensor with shape = [batch_size, out_rows,
out_cols, depth_in*depthwise_multiplier], where out_rows and out_cols depend
on the value of the op's "padding" and "strides" attrs.
Args:
op: A DepthwiseConv2dNative Operation.
Returns:
A list containing the Shape of the DepthwiseConv2DNative output.
Raises:
ValueError: If the shapes of the input or filter are incompatible.
"""
input_shape = op.inputs[0].get_shape().with_rank(4)
filter_shape = op.inputs[1].get_shape().with_rank(4)
batch_size = input_shape[0]
in_rows = input_shape[1]
in_cols = input_shape[2]
filter_rows = filter_shape[0]
filter_cols = filter_shape[1]
depth_out = filter_shape[3] * filter_shape[2]
# Check that the input depths are compatible.
input_shape[3].assert_is_compatible_with(filter_shape[2])
stride_b, stride_r, stride_c, stride_d = op.get_attr("strides")
if stride_b != 1 or stride_d != 1:
raise ValueError("Current implementation does not yet support "
"strides in the batch and depth dimensions.")
if stride_r != stride_c:
# TODO(shlens): Add support for this.
raise ValueError("Current implementation only supports equal length "
"strides in the row and column dimensions.")
# TODO(mrry,shlens): Raise an error if the stride would cause
# information in the input to be ignored. This will require a change
# in the kernel implementation.
stride = stride_r
padding = op.get_attr("padding")
out_rows, out_cols = get2d_conv_output_size(in_rows, in_cols, filter_rows,
filter_cols, stride, stride,
padding)
return [tensor_shape.TensorShape([batch_size, out_rows, out_cols, depth_out])] | [
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baidu/bigflow | 449245016c0df7d1252e85581e588bfc60cefad3 | bigflow_python/python/bigflow/base.py | python | Transformer.begin_process | (self, *side_inputs) | return [] | 此方法在开始处理数据之前被调用，以通知用户要开始处理数据了。
用户必须返回一个可迭代的对象，其中值将会被放入结果的PCollection中。 | 此方法在开始处理数据之前被调用，以通知用户要开始处理数据了。 | [
"此方法在开始处理数据之前被调用，以通知用户要开始处理数据了。"
] | def begin_process(self, *side_inputs):
"""
此方法在开始处理数据之前被调用，以通知用户要开始处理数据了。
用户必须返回一个可迭代的对象，其中值将会被放入结果的PCollection中。
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return [] | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/platform/network_controller_backend.py | python | NetworkControllerBackend.StopReplay | (self) | Stop web page replay.
Stops both the replay server and the forwarder if currently active. | Stop web page replay. | [
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"""Stop web page replay.
Stops both the replay server and the forwarder if currently active.
"""
if self._forwarder:
self._forwarder.Close()
self._forwarder = None
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_internal/cli/cmdoptions.py | python | _handle_python_version | (option, opt_str, value, parser) | Handle a provided --python-version value. | Handle a provided --python-version value. | [
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# type: (Option, str, str, OptionParser) -> None
"""
Handle a provided --python-version value.
"""
version_info, error_msg = _convert_python_version(value)
if error_msg is not None:
msg = (
'invalid --python-version value: {!r}: {}'.format(
value, error_msg,
)
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raise_option_error(parser, option=option, msg=msg)
parser.values.python_version = version_info | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/text_format.py | python | Parse | (text,
message,
allow_unknown_extension=False,
allow_field_number=False,
descriptor_pool=None) | return ParseLines(text.split('\n'),
message,
allow_unknown_extension,
allow_field_number,
descriptor_pool=descriptor_pool) | Parses a text representation of a protocol message into a message.
Args:
text: Message text representation.
message: A protocol buffer message to merge into.
allow_unknown_extension: if True, skip over missing extensions and keep
parsing
allow_field_number: if True, both field number and field name are allowed.
descriptor_pool: A DescriptorPool used to resolve Any types.
Returns:
The same message passed as argument.
Raises:
ParseError: On text parsing problems. | Parses a text representation of a protocol message into a message. | [
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message,
allow_unknown_extension=False,
allow_field_number=False,
descriptor_pool=None):
"""Parses a text representation of a protocol message into a message.
Args:
text: Message text representation.
message: A protocol buffer message to merge into.
allow_unknown_extension: if True, skip over missing extensions and keep
parsing
allow_field_number: if True, both field number and field name are allowed.
descriptor_pool: A DescriptorPool used to resolve Any types.
Returns:
The same message passed as argument.
Raises:
ParseError: On text parsing problems.
"""
if not isinstance(text, str):
text = text.decode('utf-8')
return ParseLines(text.split('\n'),
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bumptop/BumpTop | 466d23597a07ae738f4265262fa01087fc6e257c | trunk/win/Source/bin/jinja2/debug.py | python | ProcessedTraceback.standard_exc_info | (self) | return self.exc_type, self.exc_value, self.frames[0].tb | Standard python exc_info for re-raising | Standard python exc_info for re-raising | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/plat-mac/lib-scriptpackages/Finder/Standard_Suite.py | python | Standard_Suite_Events.make | (self, _no_object=None, _attributes={}, **_arguments) | make: Make a new element
Keyword argument new: the class of the new element
Keyword argument at: the location at which to insert the element
Keyword argument to: when creating an alias file, the original item to create an alias to or when creating a file viewer window, the target of the window
Keyword argument with_properties: the initial values for the properties of the element
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: to the new object(s) | make: Make a new element
Keyword argument new: the class of the new element
Keyword argument at: the location at which to insert the element
Keyword argument to: when creating an alias file, the original item to create an alias to or when creating a file viewer window, the target of the window
Keyword argument with_properties: the initial values for the properties of the element
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: to the new object(s) | [
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Returns: to the new object(s)
"""
_code = 'core'
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aetools.keysubst(_arguments, self._argmap_make)
if _no_object is not None: raise TypeError, 'No direct arg expected'
_reply, _arguments, _attributes = self.send(_code, _subcode,
_arguments, _attributes)
if _arguments.get('errn', 0):
raise aetools.Error, aetools.decodeerror(_arguments)
# XXXX Optionally decode result
if _arguments.has_key('----'):
return _arguments['----'] | [
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linyouhappy/kongkongxiyou | 7a69b2913eb29f4be77f9a62fb90cdd72c4160f1 | cocosjs/frameworks/cocos2d-x/plugin/tools/android-build.py | python | select_toolchain_version | () | Because ndk-r8e uses gcc4.6 as default. gcc4.6 doesn't support c++11. So we should select gcc4.7 when
using ndk-r8e. But gcc4.7 is removed in ndk-r9, so we should determine whether gcc4.7 exist.
Conclution:
ndk-r8e -> use gcc4.7
ndk-r9 -> use gcc4.8 | Because ndk-r8e uses gcc4.6 as default. gcc4.6 doesn't support c++11. So we should select gcc4.7 when
using ndk-r8e. But gcc4.7 is removed in ndk-r9, so we should determine whether gcc4.7 exist.
Conclution:
ndk-r8e -> use gcc4.7
ndk-r9 -> use gcc4.8 | [
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'''Because ndk-r8e uses gcc4.6 as default. gcc4.6 doesn't support c++11. So we should select gcc4.7 when
using ndk-r8e. But gcc4.7 is removed in ndk-r9, so we should determine whether gcc4.7 exist.
Conclution:
ndk-r8e -> use gcc4.7
ndk-r9 -> use gcc4.8
'''
ndk_root = check_environment_variables()
if os.path.isdir(os.path.join(ndk_root,"toolchains/arm-linux-androideabi-4.8")):
os.environ['NDK_TOOLCHAIN_VERSION'] = '4.8'
print "The Selected NDK toolchain version was 4.8 !"
elif os.path.isdir(os.path.join(ndk_root,"toolchains/arm-linux-androideabi-4.7")):
os.environ['NDK_TOOLCHAIN_VERSION'] = '4.7'
print "The Selected NDK toolchain version was 4.7 !"
else:
print "Couldn't find the gcc toolchain."
exit(1) | [
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grpc/grpc | 27bc6fe7797e43298dc931b96dc57322d0852a9f | src/python/grpcio/grpc/_channel.py | python | _MultiThreadedRendezvous.trailing_metadata | (self) | See grpc.Call.trailing_metadata | See grpc.Call.trailing_metadata | [
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"""See grpc.Call.trailing_metadata"""
with self._state.condition:
def _done():
return self._state.trailing_metadata is not None
_common.wait(self._state.condition.wait, _done)
return self._state.trailing_metadata | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/keras/python/keras/backend.py | python | repeat_elements | (x, rep, axis) | return concatenate(x_rep, axis) | Repeats the elements of a tensor along an axis, like `np.repeat`.
If `x` has shape `(s1, s2, s3)` and `axis` is `1`, the output
will have shape `(s1, s2 * rep, s3)`.
Arguments:
x: Tensor or variable.
rep: Python integer, number of times to repeat.
axis: Axis along which to repeat.
Raises:
ValueError: In case `x.shape[axis]` is undefined.
Returns:
A tensor. | Repeats the elements of a tensor along an axis, like `np.repeat`. | [
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"""Repeats the elements of a tensor along an axis, like `np.repeat`.
If `x` has shape `(s1, s2, s3)` and `axis` is `1`, the output
will have shape `(s1, s2 * rep, s3)`.
Arguments:
x: Tensor or variable.
rep: Python integer, number of times to repeat.
axis: Axis along which to repeat.
Raises:
ValueError: In case `x.shape[axis]` is undefined.
Returns:
A tensor.
"""
x_shape = x.get_shape().as_list()
if x_shape[axis] is None:
raise ValueError('Axis ' + str(axis) + ' of input tensor '
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# slices along the repeat axis
splits = array_ops.split(value=x, num_or_size_splits=x_shape[axis], axis=axis)
# repeat each slice the given number of reps
x_rep = [s for s in splits for _ in range(rep)]
return concatenate(x_rep, axis) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/lite/tutorials/dataset.py | python | download | (directory, filename) | return filepath | Download (and unzip) a file from the MNIST dataset if not already done. | Download (and unzip) a file from the MNIST dataset if not already done. | [
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"""Download (and unzip) a file from the MNIST dataset if not already done."""
filepath = os.path.join(directory, filename)
if tf.gfile.Exists(filepath):
return filepath
if not tf.gfile.Exists(directory):
tf.gfile.MakeDirs(directory)
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
url = 'https://storage.googleapis.com/cvdf-datasets/mnist/' + filename + '.gz'
_, zipped_filepath = tempfile.mkstemp(suffix='.gz')
print('Downloading %s to %s' % (url, zipped_filepath))
urllib.request.urlretrieve(url, zipped_filepath)
with gzip.open(zipped_filepath, 'rb') as f_in, \
tf.gfile.Open(filepath, 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
os.remove(zipped_filepath)
return filepath | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBTarget.BreakpointCreateByAddress | (self, *args) | return _lldb.SBTarget_BreakpointCreateByAddress(self, *args) | BreakpointCreateByAddress(self, addr_t address) -> SBBreakpoint | BreakpointCreateByAddress(self, addr_t address) -> SBBreakpoint | [
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">",
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] | def BreakpointCreateByAddress(self, *args):
"""BreakpointCreateByAddress(self, addr_t address) -> SBBreakpoint"""
return _lldb.SBTarget_BreakpointCreateByAddress(self, *args) | [
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smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/mox.py | python | And.__init__ | (self, *args) | Initialize.
Args:
*args: One or more Comparator | Initialize. | [
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"""Initialize.
Args:
*args: One or more Comparator
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self._comparators = args | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/_vendor/pyparsing.py | python | ParserElement.validate | ( self, validateTrace=[] ) | Check defined expressions for valid structure, check for infinite recursive definitions. | Check defined expressions for valid structure, check for infinite recursive definitions. | [
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"""
Check defined expressions for valid structure, check for infinite recursive definitions.
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/timeit.py | python | Timer.__init__ | (self, stmt="pass", setup="pass", timer=default_timer) | Constructor. See class doc string. | Constructor. See class doc string. | [
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self.timer = timer
ns = {}
if isinstance(stmt, basestring):
stmt = reindent(stmt, 8)
if isinstance(setup, basestring):
setup = reindent(setup, 4)
src = template % {'stmt': stmt, 'setup': setup}
elif hasattr(setup, '__call__'):
src = template % {'stmt': stmt, 'setup': '_setup()'}
ns['_setup'] = setup
else:
raise ValueError("setup is neither a string nor callable")
self.src = src # Save for traceback display
code = compile(src, dummy_src_name, "exec")
exec code in globals(), ns
self.inner = ns["inner"]
elif hasattr(stmt, '__call__'):
self.src = None
if isinstance(setup, basestring):
_setup = setup
def setup():
exec _setup in globals(), ns
elif not hasattr(setup, '__call__'):
raise ValueError("setup is neither a string nor callable")
self.inner = _template_func(setup, stmt)
else:
raise ValueError("stmt is neither a string nor callable") | [
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google/mediapipe | e6c19885c6d3c6f410c730952aeed2852790d306 | mediapipe/python/solutions/selfie_segmentation.py | python | SelfieSegmentation.process | (self, image: np.ndarray) | return super().process(input_data={'image': image}) | Processes an RGB image and returns a segmentation mask.
Args:
image: An RGB image represented as a numpy ndarray.
Raises:
RuntimeError: If the underlying graph throws any error.
ValueError: If the input image is not three channel RGB.
Returns:
A NamedTuple object with a "segmentation_mask" field that contains a float
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image: An RGB image represented as a numpy ndarray.
Raises:
RuntimeError: If the underlying graph throws any error.
ValueError: If the input image is not three channel RGB.
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root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | bindings/pyroot/pythonizations/python/ROOT/_pythonization/_rdataframe.py | python | AsNumpyResult.__init__ | (self, result_ptrs, columns) | Constructs an AsNumpyResult object.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/llvmlite/ir/builder.py | python | IRBuilder.and_ | (self, lhs, rhs, name='') | Bitwise integer AND:
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devsisters/libquic | 8954789a056d8e7d5fcb6452fd1572ca57eb5c4e | src/third_party/protobuf/python/google/protobuf/internal/well_known_types.py | python | Duration.ToJsonString | (self) | return result + '.%09ds' % nanos | Converts Duration to string format.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/command/install.py | python | install.has_scripts | (self) | return self.distribution.has_scripts() | Returns true if the current distribution has any scripts to.
install. | Returns true if the current distribution has any scripts to.
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/extras/sphinx.py | python | configure | (cnf) | Check if sphinx-build program is available and loads gnu_dirs tool. | Check if sphinx-build program is available and loads gnu_dirs tool. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | DC.DrawRoundedRectangle | (*args, **kwargs) | return _gdi_.DC_DrawRoundedRectangle(*args, **kwargs) | DrawRoundedRectangle(self, int x, int y, int width, int height, double radius)
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros_comm/roslaunch/src/roslaunch/config.py | python | ROSLaunchConfig.add_machine | (self, m, verbose=True) | Declare a machine and associated parameters so that it can be used for
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@type m: L{Machine}
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Ewenwan/MVision | 97b394dfa48cb21c82cd003b1a952745e413a17f | CNN/ShuffleNet/layers.py | python | conv2d | (name, x, w=None, num_filters=16, kernel_size=(3, 3), padding='SAME', stride=(1, 1),
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is_training=True) | return conv_o | This block is responsible for a convolution 2D layer followed by optional (non-linearity, dropout, max-pooling).
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initializer=tf.contrib.layers.xavier_initializer(), l2_strength=0.0, bias=0.0,
activation=None, batchnorm_enabled=False, max_pool_enabled=False, dropout_keep_prob=-1,
is_training=True):
"""
This block is responsible for a convolution 2D layer followed by optional (non-linearity, dropout, max-pooling).
Note that: "is_training" should be passed by a correct value based on being in either training or testing.
:param name: (string) The name scope provided by the upper tf.name_scope('name') as scope.
:param x: (tf.tensor) The input to the layer (N, H, W, C).
:param num_filters: (integer) No. of filters (This is the output depth)
:param kernel_size: (integer tuple) The size of the convolving kernel.
:param padding: (string) The amount of padding required.
:param stride: (integer tuple) The stride required.
:param initializer: (tf.contrib initializer) The initialization scheme, He et al. normal or Xavier normal are recommended.
:param l2_strength:(weight decay) (float) L2 regularization parameter.
:param bias: (float) Amount of bias.
:param activation: (tf.graph operator) The activation function applied after the convolution operation. If None, linear is applied.
:param batchnorm_enabled: (boolean) for enabling batch normalization.
:param max_pool_enabled: (boolean) for enabling max-pooling 2x2 to decrease width and height by a factor of 2.
:param dropout_keep_prob: (float) for the probability of keeping neurons. If equals -1, it means no dropout
:param is_training: (boolean) to diff. between training and testing (important for batch normalization and dropout)
:return: The output tensor of the layer (N, H', W', C').
"""
with tf.variable_scope(name) as scope:
# 2d卷积
conv_o_b = __conv2d_p('conv', x=x, w=w, num_filters=num_filters, kernel_size=kernel_size, stride=stride,
padding=padding,
initializer=initializer, l2_strength=l2_strength, bias=bias)
# BN + 激活
if batchnorm_enabled:
conv_o_bn = tf.layers.batch_normalization(conv_o_b, training=is_training, epsilon=1e-5)
if not activation:
conv_a = conv_o_bn
else:
conv_a = activation(conv_o_bn)
else:
if not activation:
conv_a = conv_o_b
else:
conv_a = activation(conv_o_b)
# droupout 随机失活
def dropout_with_keep():
return tf.nn.dropout(conv_a, dropout_keep_prob)
# 全部 激活
def dropout_no_keep():
return tf.nn.dropout(conv_a, 1.0)
if dropout_keep_prob != -1:
conv_o_dr = tf.cond(is_training, dropout_with_keep, dropout_no_keep)
else:
conv_o_dr = conv_a
conv_o = conv_o_dr
# 最大值池化
if max_pool_enabled:
conv_o = max_pool_2d(conv_o_dr)
return conv_o | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBBlock.GetInlinedCallSiteLine | (self) | return _lldb.SBBlock_GetInlinedCallSiteLine(self) | GetInlinedCallSiteLine(self) -> uint32_t
Get the call site line if this block represents an inlined function;
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GetInlinedCallSiteLine(self) -> uint32_t
Get the call site line if this block represents an inlined function;
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/xml/sax/handler.py | python | ContentHandler.startDocument | (self) | Receive notification of the beginning of a document.
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | bolt/utils/llvm-bolt-wrapper.py | python | parse_cmp_offset | (cmp_out) | return int(re.search(r'byte (\d+),', cmp_out).groups()[0]) | Extracts byte number from cmp output:
file1 file2 differ: byte X, line Y | Extracts byte number from cmp output:
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'''
Extracts byte number from cmp output:
file1 file2 differ: byte X, line Y
'''
return int(re.search(r'byte (\d+),', cmp_out).groups()[0]) | [
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hcdth011/ROS-Hydro-SLAM | 629448eecd2c9a3511158115fa53ea9e4ae41359 | rpg_svo/svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_ate.py | python | plot_traj | (ax,stamps,traj,style,color,label) | Plot a trajectory using matplotlib.
Input:
ax -- the plot
stamps -- time stamps (1xn)
traj -- trajectory (3xn)
style -- line style
color -- line color
label -- plot legend | Plot a trajectory using matplotlib.
Input:
ax -- the plot
stamps -- time stamps (1xn)
traj -- trajectory (3xn)
style -- line style
color -- line color
label -- plot legend | [
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Plot a trajectory using matplotlib.
Input:
ax -- the plot
stamps -- time stamps (1xn)
traj -- trajectory (3xn)
style -- line style
color -- line color
label -- plot legend
"""
stamps.sort()
interval = numpy.median([s-t for s,t in zip(stamps[1:],stamps[:-1])])
x = []
y = []
last = stamps[0]
for i in range(len(stamps)):
if stamps[i]-last < 2*interval:
x.append(traj[i][0])
y.append(traj[i][1])
elif len(x)>0:
ax.plot(x,y,style,color=color,label=label)
label=""
x=[]
y=[]
last= stamps[i]
if len(x)>0:
ax.plot(x,y,style,color=color,label=label) | [
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mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | win32/Lib/win32timezone.py | python | TimeZoneInfo.getWinInfo | (self, targetYear) | return self.dynamicInfo.get(targetYear, self.dynamicInfo[RangeMap.last_item]) | Return the most relevant "info" for this time zone
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/maximum-number-of-events-that-can-be-attended-ii.py | python | Solution.maxValue | (self, events, k) | return dp[-1][-1] | :type events: List[List[int]]
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prev_i_m_1 = bisect.bisect_left(sorted_ends, events[i-1][0])-1
for j in xrange(1, k+1):
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return dp[-1][-1] | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/psutil/_common.py | python | _WrapNumbers.run | (self, input_dict, name) | return new_dict | Cache dict and sum numbers which overflow and wrap.
Return an updated copy of `input_dict` | Cache dict and sum numbers which overflow and wrap.
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# This was the first call.
self._add_dict(input_dict, name)
return input_dict
self._remove_dead_reminders(input_dict, name)
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# The input dict has a new key (e.g. a new disk or NIC)
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continue
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# it wrapped!
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self.reminder_keys[name][key].add(remkey)
bits.append(input_value + self.reminders[name][remkey])
new_dict[key] = tuple(bits)
self.cache[name] = input_dict
return new_dict | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/mailbox.py | python | _singlefileMailbox._post_message_hook | (self, f) | return | Called after writing each message to file f. | Called after writing each message to file f. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/generic.py | python | NDFrame.describe | (self, percentiles=None, include=None, exclude=None) | return d | Generate descriptive statistics that summarize the central tendency,
dispersion and shape of a dataset's distribution, excluding
``NaN`` values.
Analyzes both numeric and object series, as well
as ``DataFrame`` column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
Parameters
----------
percentiles : list-like of numbers, optional
The percentiles to include in the output. All should
fall between 0 and 1. The default is
``[.25, .5, .75]``, which returns the 25th, 50th, and
75th percentiles.
include : 'all', list-like of dtypes or None (default), optional
A white list of data types to include in the result. Ignored
for ``Series``. Here are the options:
- 'all' : All columns of the input will be included in the output.
- A list-like of dtypes : Limits the results to the
provided data types.
To limit the result to numeric types submit
``numpy.number``. To limit it instead to object columns submit
the ``numpy.object`` data type. Strings
can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
select pandas categorical columns, use ``'category'``
- None (default) : The result will include all numeric columns.
exclude : list-like of dtypes or None (default), optional,
A black list of data types to omit from the result. Ignored
for ``Series``. Here are the options:
- A list-like of dtypes : Excludes the provided data types
from the result. To exclude numeric types submit
``numpy.number``. To exclude object columns submit the data
type ``numpy.object``. Strings can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
exclude pandas categorical columns, use ``'category'``
- None (default) : The result will exclude nothing.
Returns
-------
Series or DataFrame
Summary statistics of the Series or Dataframe provided.
See Also
--------
DataFrame.count: Count number of non-NA/null observations.
DataFrame.max: Maximum of the values in the object.
DataFrame.min: Minimum of the values in the object.
DataFrame.mean: Mean of the values.
DataFrame.std: Standard deviation of the obersvations.
DataFrame.select_dtypes: Subset of a DataFrame including/excluding
columns based on their dtype.
Notes
-----
For numeric data, the result's index will include ``count``,
``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
upper percentiles. By default the lower percentile is ``25`` and the
upper percentile is ``75``. The ``50`` percentile is the
same as the median.
For object data (e.g. strings or timestamps), the result's index
will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
is the most common value. The ``freq`` is the most common value's
frequency. Timestamps also include the ``first`` and ``last`` items.
If multiple object values have the highest count, then the
``count`` and ``top`` results will be arbitrarily chosen from
among those with the highest count.
For mixed data types provided via a ``DataFrame``, the default is to
return only an analysis of numeric columns. If the dataframe consists
only of object and categorical data without any numeric columns, the
default is to return an analysis of both the object and categorical
columns. If ``include='all'`` is provided as an option, the result
will include a union of attributes of each type.
The `include` and `exclude` parameters can be used to limit
which columns in a ``DataFrame`` are analyzed for the output.
The parameters are ignored when analyzing a ``Series``.
Examples
--------
Describing a numeric ``Series``.
>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
dtype: float64
Describing a categorical ``Series``.
>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count 4
unique 3
top a
freq 2
dtype: object
Describing a timestamp ``Series``.
>>> s = pd.Series([
... np.datetime64("2000-01-01"),
... np.datetime64("2010-01-01"),
... np.datetime64("2010-01-01")
... ])
>>> s.describe()
count 3
unique 2
top 2010-01-01 00:00:00
freq 2
first 2000-01-01 00:00:00
last 2010-01-01 00:00:00
dtype: object
Describing a ``DataFrame``. By default only numeric fields
are returned.
>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
... 'numeric': [1, 2, 3],
... 'object': ['a', 'b', 'c']
... })
>>> df.describe()
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Describing all columns of a ``DataFrame`` regardless of data type.
>>> df.describe(include='all')
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN c
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
Describing a column from a ``DataFrame`` by accessing it as
an attribute.
>>> df.numeric.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Name: numeric, dtype: float64
Including only numeric columns in a ``DataFrame`` description.
>>> df.describe(include=[np.number])
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Including only string columns in a ``DataFrame`` description.
>>> df.describe(include=[np.object])
object
count 3
unique 3
top c
freq 1
Including only categorical columns from a ``DataFrame`` description.
>>> df.describe(include=['category'])
categorical
count 3
unique 3
top f
freq 1
Excluding numeric columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.number])
categorical object
count 3 3
unique 3 3
top f c
freq 1 1
Excluding object columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.object])
categorical numeric
count 3 3.0
unique 3 NaN
top f NaN
freq 1 NaN
mean NaN 2.0
std NaN 1.0
min NaN 1.0
25% NaN 1.5
50% NaN 2.0
75% NaN 2.5
max NaN 3.0 | Generate descriptive statistics that summarize the central tendency,
dispersion and shape of a dataset's distribution, excluding
``NaN`` values. | [
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] | def describe(self, percentiles=None, include=None, exclude=None):
"""
Generate descriptive statistics that summarize the central tendency,
dispersion and shape of a dataset's distribution, excluding
``NaN`` values.
Analyzes both numeric and object series, as well
as ``DataFrame`` column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
Parameters
----------
percentiles : list-like of numbers, optional
The percentiles to include in the output. All should
fall between 0 and 1. The default is
``[.25, .5, .75]``, which returns the 25th, 50th, and
75th percentiles.
include : 'all', list-like of dtypes or None (default), optional
A white list of data types to include in the result. Ignored
for ``Series``. Here are the options:
- 'all' : All columns of the input will be included in the output.
- A list-like of dtypes : Limits the results to the
provided data types.
To limit the result to numeric types submit
``numpy.number``. To limit it instead to object columns submit
the ``numpy.object`` data type. Strings
can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
select pandas categorical columns, use ``'category'``
- None (default) : The result will include all numeric columns.
exclude : list-like of dtypes or None (default), optional,
A black list of data types to omit from the result. Ignored
for ``Series``. Here are the options:
- A list-like of dtypes : Excludes the provided data types
from the result. To exclude numeric types submit
``numpy.number``. To exclude object columns submit the data
type ``numpy.object``. Strings can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
exclude pandas categorical columns, use ``'category'``
- None (default) : The result will exclude nothing.
Returns
-------
Series or DataFrame
Summary statistics of the Series or Dataframe provided.
See Also
--------
DataFrame.count: Count number of non-NA/null observations.
DataFrame.max: Maximum of the values in the object.
DataFrame.min: Minimum of the values in the object.
DataFrame.mean: Mean of the values.
DataFrame.std: Standard deviation of the obersvations.
DataFrame.select_dtypes: Subset of a DataFrame including/excluding
columns based on their dtype.
Notes
-----
For numeric data, the result's index will include ``count``,
``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
upper percentiles. By default the lower percentile is ``25`` and the
upper percentile is ``75``. The ``50`` percentile is the
same as the median.
For object data (e.g. strings or timestamps), the result's index
will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
is the most common value. The ``freq`` is the most common value's
frequency. Timestamps also include the ``first`` and ``last`` items.
If multiple object values have the highest count, then the
``count`` and ``top`` results will be arbitrarily chosen from
among those with the highest count.
For mixed data types provided via a ``DataFrame``, the default is to
return only an analysis of numeric columns. If the dataframe consists
only of object and categorical data without any numeric columns, the
default is to return an analysis of both the object and categorical
columns. If ``include='all'`` is provided as an option, the result
will include a union of attributes of each type.
The `include` and `exclude` parameters can be used to limit
which columns in a ``DataFrame`` are analyzed for the output.
The parameters are ignored when analyzing a ``Series``.
Examples
--------
Describing a numeric ``Series``.
>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
dtype: float64
Describing a categorical ``Series``.
>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count 4
unique 3
top a
freq 2
dtype: object
Describing a timestamp ``Series``.
>>> s = pd.Series([
... np.datetime64("2000-01-01"),
... np.datetime64("2010-01-01"),
... np.datetime64("2010-01-01")
... ])
>>> s.describe()
count 3
unique 2
top 2010-01-01 00:00:00
freq 2
first 2000-01-01 00:00:00
last 2010-01-01 00:00:00
dtype: object
Describing a ``DataFrame``. By default only numeric fields
are returned.
>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
... 'numeric': [1, 2, 3],
... 'object': ['a', 'b', 'c']
... })
>>> df.describe()
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Describing all columns of a ``DataFrame`` regardless of data type.
>>> df.describe(include='all')
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN c
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
Describing a column from a ``DataFrame`` by accessing it as
an attribute.
>>> df.numeric.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Name: numeric, dtype: float64
Including only numeric columns in a ``DataFrame`` description.
>>> df.describe(include=[np.number])
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Including only string columns in a ``DataFrame`` description.
>>> df.describe(include=[np.object])
object
count 3
unique 3
top c
freq 1
Including only categorical columns from a ``DataFrame`` description.
>>> df.describe(include=['category'])
categorical
count 3
unique 3
top f
freq 1
Excluding numeric columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.number])
categorical object
count 3 3
unique 3 3
top f c
freq 1 1
Excluding object columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.object])
categorical numeric
count 3 3.0
unique 3 NaN
top f NaN
freq 1 NaN
mean NaN 2.0
std NaN 1.0
min NaN 1.0
25% NaN 1.5
50% NaN 2.0
75% NaN 2.5
max NaN 3.0
"""
if self.ndim >= 3:
msg = "describe is not implemented on Panel objects."
raise NotImplementedError(msg)
elif self.ndim == 2 and self.columns.size == 0:
raise ValueError("Cannot describe a DataFrame without columns")
if percentiles is not None:
# explicit conversion of `percentiles` to list
percentiles = list(percentiles)
# get them all to be in [0, 1]
self._check_percentile(percentiles)
# median should always be included
if 0.5 not in percentiles:
percentiles.append(0.5)
percentiles = np.asarray(percentiles)
else:
percentiles = np.array([0.25, 0.5, 0.75])
# sort and check for duplicates
unique_pcts = np.unique(percentiles)
if len(unique_pcts) < len(percentiles):
raise ValueError("percentiles cannot contain duplicates")
percentiles = unique_pcts
formatted_percentiles = format_percentiles(percentiles)
def describe_numeric_1d(series):
stat_index = (['count', 'mean', 'std', 'min'] +
formatted_percentiles + ['max'])
d = ([series.count(), series.mean(), series.std(), series.min()] +
series.quantile(percentiles).tolist() + [series.max()])
return pd.Series(d, index=stat_index, name=series.name)
def describe_categorical_1d(data):
names = ['count', 'unique']
objcounts = data.value_counts()
count_unique = len(objcounts[objcounts != 0])
result = [data.count(), count_unique]
if result[1] > 0:
top, freq = objcounts.index[0], objcounts.iloc[0]
if is_datetime64_any_dtype(data):
tz = data.dt.tz
asint = data.dropna().values.view('i8')
top = Timestamp(top)
if top.tzinfo is not None and tz is not None:
# Don't tz_localize(None) if key is already tz-aware
top = top.tz_convert(tz)
else:
top = top.tz_localize(tz)
names += ['top', 'freq', 'first', 'last']
result += [top, freq,
Timestamp(asint.min(), tz=tz),
Timestamp(asint.max(), tz=tz)]
else:
names += ['top', 'freq']
result += [top, freq]
return pd.Series(result, index=names, name=data.name)
def describe_1d(data):
if is_bool_dtype(data):
return describe_categorical_1d(data)
elif is_numeric_dtype(data):
return describe_numeric_1d(data)
elif is_timedelta64_dtype(data):
return describe_numeric_1d(data)
else:
return describe_categorical_1d(data)
if self.ndim == 1:
return describe_1d(self)
elif (include is None) and (exclude is None):
# when some numerics are found, keep only numerics
data = self.select_dtypes(include=[np.number])
if len(data.columns) == 0:
data = self
elif include == 'all':
if exclude is not None:
msg = "exclude must be None when include is 'all'"
raise ValueError(msg)
data = self
else:
data = self.select_dtypes(include=include, exclude=exclude)
ldesc = [describe_1d(s) for _, s in data.iteritems()]
# set a convenient order for rows
names = []
ldesc_indexes = sorted((x.index for x in ldesc), key=len)
for idxnames in ldesc_indexes:
for name in idxnames:
if name not in names:
names.append(name)
d = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)
d.columns = data.columns.copy()
return d | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/cgi.py | python | parse_qsl | (qs, keep_blank_values=0, strict_parsing=0) | return urlparse.parse_qsl(qs, keep_blank_values, strict_parsing) | Parse a query given as a string argument. | Parse a query given as a string argument. | [
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] | def parse_qsl(qs, keep_blank_values=0, strict_parsing=0):
"""Parse a query given as a string argument."""
warn("cgi.parse_qsl is deprecated, use urlparse.parse_qsl instead",
PendingDeprecationWarning, 2)
return urlparse.parse_qsl(qs, keep_blank_values, strict_parsing) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/variables.py | python | initialize_variables | (var_list, name="init") | return variables_initializer(var_list, name=name) | See `tf.variables_initializer`. | See `tf.variables_initializer`. | [
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] | def initialize_variables(var_list, name="init"):
"""See `tf.variables_initializer`."""
return variables_initializer(var_list, name=name) | [
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ucb-bar/esp-llvm | 8aec2ae754fd66d4e73b9b777a9f20c4583a0f03 | utils/lit/lit/ShUtil.py | python | ShLexer.maybe_eat | (self, c) | return False | maybe_eat(c) - Consume the character c if it is the next character,
returning True if a character was consumed. | maybe_eat(c) - Consume the character c if it is the next character,
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | example/neural-style/nstyle.py | python | get_tv_grad_executor | (img, ctx, tv_weight) | return out.bind(ctx, args={"img": img,
"kernel": kernel}) | create TV gradient executor with input binded on img | create TV gradient executor with input binded on img | [
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"""create TV gradient executor with input binded on img
"""
if tv_weight <= 0.0:
return None
nchannel = img.shape[1]
simg = mx.sym.Variable("img")
skernel = mx.sym.Variable("kernel")
channels = mx.sym.SliceChannel(simg, num_outputs=nchannel)
out = mx.sym.Concat(*[
mx.sym.Convolution(data=channels[i], weight=skernel,
num_filter=1,
kernel=(3, 3), pad=(1,1),
no_bias=True, stride=(1,1))
for i in range(nchannel)])
kernel = mx.nd.array(np.array([[0, -1, 0],
[-1, 4, -1],
[0, -1, 0]])
.reshape((1, 1, 3, 3)),
ctx) / 8.0
out = out * tv_weight
return out.bind(ctx, args={"img": img,
"kernel": kernel}) | [
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Constellation/iv | 64c3a9c7c517063f29d90d449180ea8f6f4d946f | tools/cpplint.py | python | CloseExpression | (clean_lines, linenum, pos) | return (line, clean_lines.NumLines(), -1) | If input points to ( or { or [ or <, finds the position that closes it.
If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
linenum/pos that correspond to the closing of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *past* the closing brace, or
(line, len(lines), -1) if we never find a close. Note we ignore
strings and comments when matching; and the line we return is the
'cleansed' line at linenum. | If input points to ( or { or [ or <, finds the position that closes it. | [
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] | def CloseExpression(clean_lines, linenum, pos):
"""If input points to ( or { or [ or <, finds the position that closes it.
If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
linenum/pos that correspond to the closing of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *past* the closing brace, or
(line, len(lines), -1) if we never find a close. Note we ignore
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"""
line = clean_lines.elided[linenum]
startchar = line[pos]
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if startchar == '(': endchar = ')'
if startchar == '[': endchar = ']'
if startchar == '{': endchar = '}'
if startchar == '<': endchar = '>'
# Check first line
(end_pos, num_open) = FindEndOfExpressionInLine(
line, pos, 0, startchar, endchar)
if end_pos > -1:
return (line, linenum, end_pos)
# Continue scanning forward
while linenum < clean_lines.NumLines() - 1:
linenum += 1
line = clean_lines.elided[linenum]
(end_pos, num_open) = FindEndOfExpressionInLine(
line, 0, num_open, startchar, endchar)
if end_pos > -1:
return (line, linenum, end_pos)
# Did not find endchar before end of file, give up
return (line, clean_lines.NumLines(), -1) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/pythonwin/pywin/framework/scriptutils.py | python | GetActiveView | () | Gets the edit control (eg, EditView) with the focus, or None | Gets the edit control (eg, EditView) with the focus, or None | [
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"""Gets the edit control (eg, EditView) with the focus, or None
"""
try:
childFrame, bIsMaximised = win32ui.GetMainFrame().MDIGetActive()
return childFrame.GetActiveView()
except win32ui.error:
return None | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/crf/python/ops/crf.py | python | crf_log_likelihood | (inputs,
tag_indices,
sequence_lengths,
transition_params=None) | return log_likelihood, transition_params | Computes the log-likelihood of tag sequences in a CRF.
Args:
inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
to use as input to the CRF layer.
tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we
compute the log-likelihood.
sequence_lengths: A [batch_size] vector of true sequence lengths.
transition_params: A [num_tags, num_tags] transition matrix, if available.
Returns:
log_likelihood: A scalar containing the log-likelihood of the given sequence
of tag indices.
transition_params: A [num_tags, num_tags] transition matrix. This is either
provided by the caller or created in this function. | Computes the log-likelihood of tag sequences in a CRF. | [
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] | def crf_log_likelihood(inputs,
tag_indices,
sequence_lengths,
transition_params=None):
"""Computes the log-likelihood of tag sequences in a CRF.
Args:
inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
to use as input to the CRF layer.
tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we
compute the log-likelihood.
sequence_lengths: A [batch_size] vector of true sequence lengths.
transition_params: A [num_tags, num_tags] transition matrix, if available.
Returns:
log_likelihood: A scalar containing the log-likelihood of the given sequence
of tag indices.
transition_params: A [num_tags, num_tags] transition matrix. This is either
provided by the caller or created in this function.
"""
# Get shape information.
num_tags = inputs.get_shape()[2].value
# Get the transition matrix if not provided.
if transition_params is None:
transition_params = vs.get_variable("transitions", [num_tags, num_tags])
sequence_scores = crf_sequence_score(inputs, tag_indices, sequence_lengths,
transition_params)
log_norm = crf_log_norm(inputs, sequence_lengths, transition_params)
# Normalize the scores to get the log-likelihood.
log_likelihood = sequence_scores - log_norm
return log_likelihood, transition_params | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/paint-house.py | python | Solution.minCost | (self, costs) | return min(min_cost[(n - 1) % 2]) | :type costs: List[List[int]]
:rtype: int | :type costs: List[List[int]]
:rtype: int | [
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"""
:type costs: List[List[int]]
:rtype: int
"""
if not costs:
return 0
min_cost = [costs[0], [0, 0, 0]]
n = len(costs)
for i in xrange(1, n):
min_cost[i % 2][0] = costs[i][0] + \
min(min_cost[(i - 1) % 2][1], min_cost[(i - 1) % 2][2])
min_cost[i % 2][1] = costs[i][1] + \
min(min_cost[(i - 1) % 2][0], min_cost[(i - 1) % 2][2])
min_cost[i % 2][2] = costs[i][2] + \
min(min_cost[(i - 1) % 2][0], min_cost[(i - 1) % 2][1])
return min(min_cost[(n - 1) % 2]) | [
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bristolcrypto/SPDZ-2 | 721abfae849625a02ea49aabc534f9cf41ca643f | Compiler/comparison.py | python | Trunc | (d, a, k, m, kappa, signed) | d = a >> m
k: bit length of a
m: compile-time integer
signed: True/False, describes a | d = a >> m | [
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] | def Trunc(d, a, k, m, kappa, signed):
"""
d = a >> m
k: bit length of a
m: compile-time integer
signed: True/False, describes a
"""
a_prime = program.curr_block.new_reg('s')
t = program.curr_block.new_reg('s')
c = [program.curr_block.new_reg('c') for i in range(3)]
c2m = program.curr_block.new_reg('c')
if m == 0:
movs(d, a)
return
elif m == 1:
Mod2(a_prime, a, k, kappa, signed)
else:
Mod2m(a_prime, a, k, m, kappa, signed)
subs(t, a, a_prime)
ldi(c[1], 1)
ld2i(c2m, m)
divc(c[2], c[1], c2m)
mulm(d, t, c[2]) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/labeled_tensor/python/ops/core.py | python | Axis.index | (self, value) | return self._index[value] | Returns the integer position of the given tick label. | Returns the integer position of the given tick label. | [
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"integer",
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"given",
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] | def index(self, value):
"""Returns the integer position of the given tick label."""
if self._index is None:
raise ValueError('Axis does not have tick labels')
return self._index[value] | [
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google/ion | ef47f3b824050499ce5c6f774b366f6c4dbce0af | ion/build.py | python | _SmartDeleteDirectory | (path) | Delete a directory, or a symlink to a directory.
Deleting a symlink to a directory requires extra effort than usual.
Args:
path: The directory to delete, or a symlink to a directory to delete. In the
latter case, the target of the symlink AND the symlink will be
deleted. | Delete a directory, or a symlink to a directory. | [
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"directory",
"or",
"a",
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"to",
"a",
"directory",
"."
] | def _SmartDeleteDirectory(path):
"""Delete a directory, or a symlink to a directory.
Deleting a symlink to a directory requires extra effort than usual.
Args:
path: The directory to delete, or a symlink to a directory to delete. In the
latter case, the target of the symlink AND the symlink will be
deleted.
"""
if os.path.isdir(path) and not os.path.islink(path):
shutil.rmtree(path)
elif os.path.islink(path):
target = os.readlink(path)
shutil.rmtree(target)
os.unlink(path) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/learn/python/learn/monitors.py | python | ValidationMonitor.best_value | (self) | return self._best_value | Returns the best early stopping metric value found so far. | Returns the best early stopping metric value found so far. | [
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"""Returns the best early stopping metric value found so far."""
return self._best_value | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/computation/expr.py | python | _node_not_implemented | (node_name, cls) | return f | Return a function that raises a NotImplementedError with a passed node
name. | Return a function that raises a NotImplementedError with a passed node
name. | [
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"""Return a function that raises a NotImplementedError with a passed node
name.
"""
def f(self, *args, **kwargs):
raise NotImplementedError(f"{repr(node_name)} nodes are not implemented")
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/tbe/apply_centered_rms_prop_ds.py | python | _apply_centered_rms_prop_ds_tbe | () | return | ApplyCenteredRMSPropD TBE register | ApplyCenteredRMSPropD TBE register | [
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"TBE",
"register"
] | def _apply_centered_rms_prop_ds_tbe():
"""ApplyCenteredRMSPropD TBE register"""
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/compileall.py | python | compile_dir | (dir, maxlevels=None, ddir=None, force=False,
rx=None, quiet=0, legacy=False, optimize=-1, workers=1,
invalidation_mode=None, *, stripdir=None,
prependdir=None, limit_sl_dest=None, hardlink_dupes=False) | return success | Byte-compile all modules in the given directory tree.
Arguments (only dir is required):
dir: the directory to byte-compile
maxlevels: maximum recursion level (default `sys.getrecursionlimit()`)
ddir: the directory that will be prepended to the path to the
file as it is compiled into each byte-code file.
force: if True, force compilation, even if timestamps are up-to-date
quiet: full output with False or 0, errors only with 1,
no output with 2
legacy: if True, produce legacy pyc paths instead of PEP 3147 paths
optimize: int or list of optimization levels or -1 for level of
the interpreter. Multiple levels leads to multiple compiled
files each with one optimization level.
workers: maximum number of parallel workers
invalidation_mode: how the up-to-dateness of the pyc will be checked
stripdir: part of path to left-strip from source file path
prependdir: path to prepend to beginning of original file path, applied
after stripdir
limit_sl_dest: ignore symlinks if they are pointing outside of
the defined path
hardlink_dupes: hardlink duplicated pyc files | Byte-compile all modules in the given directory tree. | [
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] | def compile_dir(dir, maxlevels=None, ddir=None, force=False,
rx=None, quiet=0, legacy=False, optimize=-1, workers=1,
invalidation_mode=None, *, stripdir=None,
prependdir=None, limit_sl_dest=None, hardlink_dupes=False):
"""Byte-compile all modules in the given directory tree.
Arguments (only dir is required):
dir: the directory to byte-compile
maxlevels: maximum recursion level (default `sys.getrecursionlimit()`)
ddir: the directory that will be prepended to the path to the
file as it is compiled into each byte-code file.
force: if True, force compilation, even if timestamps are up-to-date
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prependdir: path to prepend to beginning of original file path, applied
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limit_sl_dest: ignore symlinks if they are pointing outside of
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hardlink_dupes: hardlink duplicated pyc files
"""
ProcessPoolExecutor = None
if ddir is not None and (stripdir is not None or prependdir is not None):
raise ValueError(("Destination dir (ddir) cannot be used "
"in combination with stripdir or prependdir"))
if ddir is not None:
stripdir = dir
prependdir = ddir
ddir = None
if workers < 0:
raise ValueError('workers must be greater or equal to 0')
if workers != 1:
try:
# Only import when needed, as low resource platforms may
# fail to import it
from concurrent.futures import ProcessPoolExecutor
except ImportError:
workers = 1
if maxlevels is None:
maxlevels = sys.getrecursionlimit()
files = _walk_dir(dir, quiet=quiet, maxlevels=maxlevels)
success = True
if workers != 1 and ProcessPoolExecutor is not None:
# If workers == 0, let ProcessPoolExecutor choose
workers = workers or None
with ProcessPoolExecutor(max_workers=workers) as executor:
results = executor.map(partial(compile_file,
ddir=ddir, force=force,
rx=rx, quiet=quiet,
legacy=legacy,
optimize=optimize,
invalidation_mode=invalidation_mode,
stripdir=stripdir,
prependdir=prependdir,
limit_sl_dest=limit_sl_dest,
hardlink_dupes=hardlink_dupes),
files)
success = min(results, default=True)
else:
for file in files:
if not compile_file(file, ddir, force, rx, quiet,
legacy, optimize, invalidation_mode,
stripdir=stripdir, prependdir=prependdir,
limit_sl_dest=limit_sl_dest,
hardlink_dupes=hardlink_dupes):
success = False
return success | [
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commaai/openpilot | 4416c21b1e738ab7d04147c5ae52b5135e0cdb40 | tools/lib/kbhit.py | python | KBHit.kbhit | () | return select([sys.stdin], [], [], 0)[0] != [] | Returns True if keyboard character was hit, False otherwise. | Returns True if keyboard character was hit, False otherwise. | [
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''' Returns True if keyboard character was hit, False otherwise.
'''
return select([sys.stdin], [], [], 0)[0] != [] | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/flatmenu.py | python | FlatMenuBar.ResetToolbarItems | (self) | Used internally. | Used internally. | [
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""" Used internally. """
for but in self._tbButtons:
but._state = ControlNormal | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcodeproj_file.py | python | XCObject.Children | (self) | return children | Returns a list of all of this object's owned (strong) children. | Returns a list of all of this object's owned (strong) children. | [
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"""Returns a list of all of this object's owned (strong) children."""
children = []
for property, attributes in self._schema.iteritems():
(is_list, property_type, is_strong) = attributes[0:3]
if is_strong and property in self._properties:
if not is_list:
children.append(self._properties[property])
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children.extend(self._properties[property])
return children | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/support/parse_khhttpd_access_log.py | python | KML.closeFolder | (self) | return kml | Closes folder element. | Closes folder element. | [
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'''Closes folder element.'''
kml = '</Folder>\n'
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carla-simulator/carla | 8854804f4d7748e14d937ec763a2912823a7e5f5 | PythonAPI/examples/rss/rss_visualization.py | python | RssBoundingBoxVisualizer._create_bb_points | (vehicle) | return cords | Returns 3D bounding box for a vehicle. | Returns 3D bounding box for a vehicle. | [
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"""
Returns 3D bounding box for a vehicle.
"""
cords = np.zeros((8, 4))
extent = vehicle.bounding_box.extent
cords[0, :] = np.array([extent.x, extent.y, -extent.z, 1])
cords[1, :] = np.array([-extent.x, extent.y, -extent.z, 1])
cords[2, :] = np.array([-extent.x, -extent.y, -extent.z, 1])
cords[3, :] = np.array([extent.x, -extent.y, -extent.z, 1])
cords[4, :] = np.array([extent.x, extent.y, extent.z, 1])
cords[5, :] = np.array([-extent.x, extent.y, extent.z, 1])
cords[6, :] = np.array([-extent.x, -extent.y, extent.z, 1])
cords[7, :] = np.array([extent.x, -extent.y, extent.z, 1])
return cords | [
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SoarGroup/Soar | a1c5e249499137a27da60533c72969eef3b8ab6b | scons/scons-local-4.1.0/SCons/SConf.py | python | CheckProg | (context, prog_name) | return res | Simple check if a program exists in the path. Returns the path
for the application, or None if not found. | Simple check if a program exists in the path. Returns the path
for the application, or None if not found. | [
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] | def CheckProg(context, prog_name):
"""Simple check if a program exists in the path. Returns the path
for the application, or None if not found.
"""
res = SCons.Conftest.CheckProg(context, prog_name)
context.did_show_result = 1
return res | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/base.py | python | IndexOpsMixin._memory_usage | (self, deep: bool = False) | return v | Memory usage of the values.
Parameters
----------
deep : bool, default False
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption.
Returns
-------
bytes used
See Also
--------
numpy.ndarray.nbytes : Total bytes consumed by the elements of the
array.
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False or if used on PyPy | Memory usage of the values. | [
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] | def _memory_usage(self, deep: bool = False) -> int:
"""
Memory usage of the values.
Parameters
----------
deep : bool, default False
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption.
Returns
-------
bytes used
See Also
--------
numpy.ndarray.nbytes : Total bytes consumed by the elements of the
array.
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False or if used on PyPy
"""
if hasattr(self.array, "memory_usage"):
# error: "ExtensionArray" has no attribute "memory_usage"
return self.array.memory_usage(deep=deep) # type: ignore[attr-defined]
v = self.array.nbytes
if deep and is_object_dtype(self) and not PYPY:
values = cast(np.ndarray, self._values)
v += lib.memory_usage_of_objects(values)
return v | [
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/gui/OnscreenText.py | python | OnscreenText.__init__ | (self, text = '',
style = Plain,
pos = (0, 0),
roll = 0,
scale = None,
fg = None,
bg = None,
shadow = None,
shadowOffset = (0.04, 0.04),
frame = None,
align = None,
wordwrap = None,
drawOrder = None,
decal = 0,
font = None,
parent = None,
sort = 0,
mayChange = True,
direction = None) | Make a text node from string, put it into the 2d sg and set it
up with all the indicated parameters.
Parameters:
text: the actual text to display. This may be omitted and
specified later via setText() if you don't have it
available, but it is better to specify it up front.
style: one of the pre-canned style parameters defined at the
head of this file. This sets up the default values for
many of the remaining parameters if they are
unspecified; however, a parameter may still be specified
to explicitly set it, overriding the pre-canned style.
pos: the x, y position of the text on the screen.
scale: the size of the text. This may either be a single
float (and it will usually be a small number like 0.07)
or it may be a 2-tuple of floats, specifying a different
x, y scale.
fg: the (r, g, b, a) foreground color of the text. This is
normally a 4-tuple of floats or ints.
bg: the (r, g, b, a) background color of the text. If the
fourth value, a, is nonzero, a card is created to place
behind the text and set to the given color.
shadow: the (r, g, b, a) color of the shadow behind the text.
If the fourth value, a, is nonzero, a little drop shadow
is created and placed behind the text.
frame: the (r, g, b, a) color of the frame drawn around the
text. If the fourth value, a, is nonzero, a frame is
created around the text.
align: one of TextNode.ALeft, TextNode.ARight, or TextNode.ACenter.
wordwrap: either the width to wordwrap the text at, or None
to specify no automatic word wrapping.
drawOrder: the drawing order of this text with respect to
all other things in the 'fixed' bin within render2d.
The text will actually use drawOrder through drawOrder +
2.
decal: if this is True, the text is decalled onto its
background card. Useful when the text will be parented
into the 3-D scene graph.
font: the font to use for the text.
parent: the NodePath to parent the text to initially.
mayChange: pass true if the text or its properties may need
to be changed at runtime, false if it is static once
created (which leads to better memory optimization).
direction: this can be set to 'ltr' or 'rtl' to override the
direction of the text. | Make a text node from string, put it into the 2d sg and set it
up with all the indicated parameters. | [
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style = Plain,
pos = (0, 0),
roll = 0,
scale = None,
fg = None,
bg = None,
shadow = None,
shadowOffset = (0.04, 0.04),
frame = None,
align = None,
wordwrap = None,
drawOrder = None,
decal = 0,
font = None,
parent = None,
sort = 0,
mayChange = True,
direction = None):
"""
Make a text node from string, put it into the 2d sg and set it
up with all the indicated parameters.
Parameters:
text: the actual text to display. This may be omitted and
specified later via setText() if you don't have it
available, but it is better to specify it up front.
style: one of the pre-canned style parameters defined at the
head of this file. This sets up the default values for
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unspecified; however, a parameter may still be specified
to explicitly set it, overriding the pre-canned style.
pos: the x, y position of the text on the screen.
scale: the size of the text. This may either be a single
float (and it will usually be a small number like 0.07)
or it may be a 2-tuple of floats, specifying a different
x, y scale.
fg: the (r, g, b, a) foreground color of the text. This is
normally a 4-tuple of floats or ints.
bg: the (r, g, b, a) background color of the text. If the
fourth value, a, is nonzero, a card is created to place
behind the text and set to the given color.
shadow: the (r, g, b, a) color of the shadow behind the text.
If the fourth value, a, is nonzero, a little drop shadow
is created and placed behind the text.
frame: the (r, g, b, a) color of the frame drawn around the
text. If the fourth value, a, is nonzero, a frame is
created around the text.
align: one of TextNode.ALeft, TextNode.ARight, or TextNode.ACenter.
wordwrap: either the width to wordwrap the text at, or None
to specify no automatic word wrapping.
drawOrder: the drawing order of this text with respect to
all other things in the 'fixed' bin within render2d.
The text will actually use drawOrder through drawOrder +
2.
decal: if this is True, the text is decalled onto its
background card. Useful when the text will be parented
into the 3-D scene graph.
font: the font to use for the text.
parent: the NodePath to parent the text to initially.
mayChange: pass true if the text or its properties may need
to be changed at runtime, false if it is static once
created (which leads to better memory optimization).
direction: this can be set to 'ltr' or 'rtl' to override the
direction of the text.
"""
if parent is None:
from direct.showbase import ShowBaseGlobal
parent = ShowBaseGlobal.aspect2d
# make a text node
textNode = TextNode('')
self.textNode = textNode
# We ARE a node path. Initially, we're an empty node path.
NodePath.__init__(self)
# Choose the default parameters according to the selected
# style.
if style == Plain:
scale = scale or 0.07
fg = fg or (0, 0, 0, 1)
bg = bg or (0, 0, 0, 0)
shadow = shadow or (0, 0, 0, 0)
frame = frame or (0, 0, 0, 0)
if align is None:
align = TextNode.ACenter
elif style == ScreenTitle:
scale = scale or 0.15
fg = fg or (1, 0.2, 0.2, 1)
bg = bg or (0, 0, 0, 0)
shadow = shadow or (0, 0, 0, 1)
frame = frame or (0, 0, 0, 0)
if align is None:
align = TextNode.ACenter
elif style == ScreenPrompt:
scale = scale or 0.1
fg = fg or (1, 1, 0, 1)
bg = bg or (0, 0, 0, 0)
shadow = shadow or (0, 0, 0, 1)
frame = frame or (0, 0, 0, 0)
if align is None:
align = TextNode.ACenter
elif style == NameConfirm:
scale = scale or 0.1
fg = fg or (0, 1, 0, 1)
bg = bg or (0, 0, 0, 0)
shadow = shadow or (0, 0, 0, 0)
frame = frame or (0, 0, 0, 0)
if align is None:
align = TextNode.ACenter
elif style == BlackOnWhite:
scale = scale or 0.1
fg = fg or (0, 0, 0, 1)
bg = bg or (1, 1, 1, 1)
shadow = shadow or (0, 0, 0, 0)
frame = frame or (0, 0, 0, 0)
if align is None:
align = TextNode.ACenter
else:
raise ValueError
if not isinstance(scale, tuple):
# If the scale is already a tuple, it's a 2-d (x, y) scale.
# Otherwise, it's a uniform scale--make it a tuple.
scale = (scale, scale)
# Save some of the parameters for posterity.
self.__scale = scale
self.__pos = pos
self.__roll = roll
self.__wordwrap = wordwrap
if decal:
textNode.setCardDecal(1)
if font is None:
font = DGG.getDefaultFont()
textNode.setFont(font)
textNode.setTextColor(fg[0], fg[1], fg[2], fg[3])
textNode.setAlign(align)
if wordwrap:
textNode.setWordwrap(wordwrap)
if bg[3] != 0:
# If we have a background color, create a card.
textNode.setCardColor(bg[0], bg[1], bg[2], bg[3])
textNode.setCardAsMargin(0.1, 0.1, 0.1, 0.1)
if shadow[3] != 0:
# If we have a shadow color, create a shadow.
# Can't use the *shadow interface because it might be a VBase4.
#textNode.setShadowColor(*shadow)
textNode.setShadowColor(shadow[0], shadow[1], shadow[2], shadow[3])
textNode.setShadow(*shadowOffset)
if frame[3] != 0:
# If we have a frame color, create a frame.
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/eager/function.py | python | _ForwardBackwardCall.record | (self, flat_outputs) | Given outputs from the execution of `forward`, records the operation. | Given outputs from the execution of `forward`, records the operation. | [
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if (self._tape_watching
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# We only record function calls which have outputs, and then only when a
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self._functions.record(
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metashell/metashell | f4177e4854ea00c8dbc722cadab26ef413d798ea | 3rd/templight/clang/bindings/python/clang/cindex.py | python | TokenGroup.get_tokens | (tu, extent) | Helper method to return all tokens in an extent.
This functionality is needed multiple places in this module. We define
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"""
tokens_memory = POINTER(Token)()
tokens_count = c_uint()
conf.lib.clang_tokenize(tu, extent, byref(tokens_memory),
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count = int(tokens_count.value)
# If we get no tokens, no memory was allocated. Be sure not to return
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if count < 1:
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token_group = TokenGroup(tu, tokens_memory, tokens_count)
for i in range(0, count):
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/statistics.py | python | fmean | (data) | Convert data to floats and compute the arithmetic mean.
This runs faster than the mean() function and it always returns a float.
If the input dataset is empty, it raises a StatisticsError.
>>> fmean([3.5, 4.0, 5.25])
4.25 | Convert data to floats and compute the arithmetic mean. | [
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try:
n = len(data)
except TypeError:
# Handle iterators that do not define __len__().
n = 0
def count(iterable):
nonlocal n
for n, x in enumerate(iterable, start=1):
yield x
total = fsum(count(data))
else:
total = fsum(data)
try:
return total / n
except ZeroDivisionError:
raise StatisticsError('fmean requires at least one data point') from None | [
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cloudendpoints/esp | 2c4f1df4b9cc82e0682ce4713d470b61b8f970de | start_esp/fetch_service_config.py | python | fetch_service_name | (metadata) | return name | Fetch service name from metadata URL. | Fetch service name from metadata URL. | [
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"""Fetch service name from metadata URL."""
name = fetch_metadata(
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logging.info("Service name: " + name)
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synfig/synfig | a5ec91db5b751dc12e4400ccfb5c063fd6d2d928 | synfig-studio/plugins/lottie-exporter/export_without_variable_width.py | python | init_logs | () | Initializes the logger, sets the level of the logging(DEBUG | INFO : depending on what is
specified) | Initializes the logger, sets the level of the logging(DEBUG | INFO : depending on what is
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"""
Initializes the logger, sets the level of the logging(DEBUG | INFO : depending on what is
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"""
logging.basicConfig(stream=sys.stdout, format='%(name)s - %(levelname)s - %(message)s')
logging.getLogger().setLevel(logging.DEBUG) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/cluster/_optics.py | python | compute_optics_graph | (X, min_samples, max_eps, metric, p, metric_params,
algorithm, leaf_size, n_jobs) | return ordering, core_distances_, reachability_, predecessor_ | Computes the OPTICS reachability graph.
Read more in the :ref:`User Guide <optics>`.
Parameters
----------
X : array, shape (n_samples, n_features), or (n_samples, n_samples) \
if metric=’precomputed’.
A feature array, or array of distances between samples if
metric='precomputed'
min_samples : int > 1 or float between 0 and 1
The number of samples in a neighborhood for a point to be considered
as a core point. Expressed as an absolute number or a fraction of the
number of samples (rounded to be at least 2).
max_eps : float, optional (default=np.inf)
The maximum distance between two samples for one to be considered as
in the neighborhood of the other. Default value of ``np.inf`` will
identify clusters across all scales; reducing ``max_eps`` will result
in shorter run times.
metric : string or callable, optional (default='minkowski')
Metric to use for distance computation. Any metric from scikit-learn
or scipy.spatial.distance can be used.
If metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays as input and return one value indicating the
distance between them. This works for Scipy's metrics, but is less
efficient than passing the metric name as a string. If metric is
"precomputed", X is assumed to be a distance matrix and must be square.
Valid values for metric are:
- from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
'manhattan']
- from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
'yule']
See the documentation for scipy.spatial.distance for details on these
metrics.
p : integer, optional (default=2)
Parameter for the Minkowski metric from
:class:`sklearn.metrics.pairwise_distances`. When p = 1, this is
equivalent to using manhattan_distance (l1), and euclidean_distance
(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
metric_params : dict, optional (default=None)
Additional keyword arguments for the metric function.
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
Algorithm used to compute the nearest neighbors:
- 'ball_tree' will use :class:`BallTree`
- 'kd_tree' will use :class:`KDTree`
- 'brute' will use a brute-force search.
- 'auto' will attempt to decide the most appropriate algorithm
based on the values passed to :meth:`fit` method. (default)
Note: fitting on sparse input will override the setting of
this parameter, using brute force.
leaf_size : int, optional (default=30)
Leaf size passed to :class:`BallTree` or :class:`KDTree`. This can
affect the speed of the construction and query, as well as the memory
required to store the tree. The optimal value depends on the
nature of the problem.
n_jobs : int or None, optional (default=None)
The number of parallel jobs to run for neighbors search.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
Returns
-------
ordering_ : array, shape (n_samples,)
The cluster ordered list of sample indices.
core_distances_ : array, shape (n_samples,)
Distance at which each sample becomes a core point, indexed by object
order. Points which will never be core have a distance of inf. Use
``clust.core_distances_[clust.ordering_]`` to access in cluster order.
reachability_ : array, shape (n_samples,)
Reachability distances per sample, indexed by object order. Use
``clust.reachability_[clust.ordering_]`` to access in cluster order.
predecessor_ : array, shape (n_samples,)
Point that a sample was reached from, indexed by object order.
Seed points have a predecessor of -1.
References
----------
.. [1] Ankerst, Mihael, Markus M. Breunig, Hans-Peter Kriegel,
and Jörg Sander. "OPTICS: ordering points to identify the clustering
structure." ACM SIGMOD Record 28, no. 2 (1999): 49-60. | Computes the OPTICS reachability graph. | [
"Computes",
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"reachability",
"graph",
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] | def compute_optics_graph(X, min_samples, max_eps, metric, p, metric_params,
algorithm, leaf_size, n_jobs):
"""Computes the OPTICS reachability graph.
Read more in the :ref:`User Guide <optics>`.
Parameters
----------
X : array, shape (n_samples, n_features), or (n_samples, n_samples) \
if metric=’precomputed’.
A feature array, or array of distances between samples if
metric='precomputed'
min_samples : int > 1 or float between 0 and 1
The number of samples in a neighborhood for a point to be considered
as a core point. Expressed as an absolute number or a fraction of the
number of samples (rounded to be at least 2).
max_eps : float, optional (default=np.inf)
The maximum distance between two samples for one to be considered as
in the neighborhood of the other. Default value of ``np.inf`` will
identify clusters across all scales; reducing ``max_eps`` will result
in shorter run times.
metric : string or callable, optional (default='minkowski')
Metric to use for distance computation. Any metric from scikit-learn
or scipy.spatial.distance can be used.
If metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays as input and return one value indicating the
distance between them. This works for Scipy's metrics, but is less
efficient than passing the metric name as a string. If metric is
"precomputed", X is assumed to be a distance matrix and must be square.
Valid values for metric are:
- from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
'manhattan']
- from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
'yule']
See the documentation for scipy.spatial.distance for details on these
metrics.
p : integer, optional (default=2)
Parameter for the Minkowski metric from
:class:`sklearn.metrics.pairwise_distances`. When p = 1, this is
equivalent to using manhattan_distance (l1), and euclidean_distance
(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
metric_params : dict, optional (default=None)
Additional keyword arguments for the metric function.
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
Algorithm used to compute the nearest neighbors:
- 'ball_tree' will use :class:`BallTree`
- 'kd_tree' will use :class:`KDTree`
- 'brute' will use a brute-force search.
- 'auto' will attempt to decide the most appropriate algorithm
based on the values passed to :meth:`fit` method. (default)
Note: fitting on sparse input will override the setting of
this parameter, using brute force.
leaf_size : int, optional (default=30)
Leaf size passed to :class:`BallTree` or :class:`KDTree`. This can
affect the speed of the construction and query, as well as the memory
required to store the tree. The optimal value depends on the
nature of the problem.
n_jobs : int or None, optional (default=None)
The number of parallel jobs to run for neighbors search.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
Returns
-------
ordering_ : array, shape (n_samples,)
The cluster ordered list of sample indices.
core_distances_ : array, shape (n_samples,)
Distance at which each sample becomes a core point, indexed by object
order. Points which will never be core have a distance of inf. Use
``clust.core_distances_[clust.ordering_]`` to access in cluster order.
reachability_ : array, shape (n_samples,)
Reachability distances per sample, indexed by object order. Use
``clust.reachability_[clust.ordering_]`` to access in cluster order.
predecessor_ : array, shape (n_samples,)
Point that a sample was reached from, indexed by object order.
Seed points have a predecessor of -1.
References
----------
.. [1] Ankerst, Mihael, Markus M. Breunig, Hans-Peter Kriegel,
and Jörg Sander. "OPTICS: ordering points to identify the clustering
structure." ACM SIGMOD Record 28, no. 2 (1999): 49-60.
"""
n_samples = X.shape[0]
_validate_size(min_samples, n_samples, 'min_samples')
if min_samples <= 1:
min_samples = max(2, int(min_samples * n_samples))
# Start all points as 'unprocessed' ##
reachability_ = np.empty(n_samples)
reachability_.fill(np.inf)
predecessor_ = np.empty(n_samples, dtype=int)
predecessor_.fill(-1)
nbrs = NearestNeighbors(n_neighbors=min_samples,
algorithm=algorithm,
leaf_size=leaf_size,
metric=metric,
metric_params=metric_params,
p=p,
n_jobs=n_jobs)
nbrs.fit(X)
# Here we first do a kNN query for each point, this differs from
# the original OPTICS that only used epsilon range queries.
# TODO: handle working_memory somehow?
core_distances_ = _compute_core_distances_(X=X, neighbors=nbrs,
min_samples=min_samples,
working_memory=None)
# OPTICS puts an upper limit on these, use inf for undefined.
core_distances_[core_distances_ > max_eps] = np.inf
# Main OPTICS loop. Not parallelizable. The order that entries are
# written to the 'ordering_' list is important!
# Note that this implementation is O(n^2) theoretically, but
# supposedly with very low constant factors.
processed = np.zeros(X.shape[0], dtype=bool)
ordering = np.zeros(X.shape[0], dtype=int)
for ordering_idx in range(X.shape[0]):
# Choose next based on smallest reachability distance
# (And prefer smaller ids on ties, possibly np.inf!)
index = np.where(processed == 0)[0]
point = index[np.argmin(reachability_[index])]
processed[point] = True
ordering[ordering_idx] = point
if core_distances_[point] != np.inf:
_set_reach_dist(core_distances_=core_distances_,
reachability_=reachability_,
predecessor_=predecessor_,
point_index=point,
processed=processed, X=X, nbrs=nbrs,
metric=metric, metric_params=metric_params,
p=p, max_eps=max_eps)
if np.all(np.isinf(reachability_)):
warnings.warn("All reachability values are inf. Set a larger"
" max_eps or all data will be considered outliers.",
UserWarning)
return ordering, core_distances_, reachability_, predecessor_ | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | build/android/pylib/utils/emulator.py | python | LaunchTempEmulators | (emulator_count, abi, api_level, enable_kvm=False,
kill_and_launch=True, sdcard_size=DEFAULT_SDCARD_SIZE,
storage_size=DEFAULT_STORAGE_SIZE, wait_for_boot=True,
headless=False) | return emulators | Create and launch temporary emulators and wait for them to boot.
Args:
emulator_count: number of emulators to launch.
abi: the emulator target platform
api_level: the api level (e.g., 19 for Android v4.4 - KitKat release)
wait_for_boot: whether or not to wait for emulators to boot up
headless: running emulator with no ui
Returns:
List of emulators. | Create and launch temporary emulators and wait for them to boot. | [
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"boot",
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] | def LaunchTempEmulators(emulator_count, abi, api_level, enable_kvm=False,
kill_and_launch=True, sdcard_size=DEFAULT_SDCARD_SIZE,
storage_size=DEFAULT_STORAGE_SIZE, wait_for_boot=True,
headless=False):
"""Create and launch temporary emulators and wait for them to boot.
Args:
emulator_count: number of emulators to launch.
abi: the emulator target platform
api_level: the api level (e.g., 19 for Android v4.4 - KitKat release)
wait_for_boot: whether or not to wait for emulators to boot up
headless: running emulator with no ui
Returns:
List of emulators.
"""
emulators = []
for n in xrange(emulator_count):
t = time_profile.TimeProfile('Emulator launch %d' % n)
# Creates a temporary AVD.
avd_name = 'run_tests_avd_%d' % n
logging.info('Emulator launch %d with avd_name=%s and api=%d',
n, avd_name, api_level)
emulator = Emulator(avd_name, abi, enable_kvm=enable_kvm,
sdcard_size=sdcard_size, storage_size=storage_size,
headless=headless)
emulator.CreateAVD(api_level)
emulator.Launch(kill_all_emulators=(n == 0 and kill_and_launch))
t.Stop()
emulators.append(emulator)
# Wait for all emulators to boot completed.
if wait_for_boot:
for emulator in emulators:
emulator.ConfirmLaunch(True)
logging.info('All emulators are fully booted')
return emulators | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | build/config/win/get_msvc_config_real.py | python | VisualStudioVersion.ToolPath | (self, tool) | return os.path.normpath(os.path.join(self.path, "VC/bin", tool)) | Returns the path to a given compiler tool. | Returns the path to a given compiler tool. | [
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] | def ToolPath(self, tool):
"""Returns the path to a given compiler tool. """
return os.path.normpath(os.path.join(self.path, "VC/bin", tool)) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/_constraints.py | python | new_constraint_to_old | (con, x0) | return old_constraints | Converts new-style constraint objects to old-style constraint dictionaries. | Converts new-style constraint objects to old-style constraint dictionaries. | [
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] | def new_constraint_to_old(con, x0):
"""
Converts new-style constraint objects to old-style constraint dictionaries.
"""
if isinstance(con, NonlinearConstraint):
if (con.finite_diff_jac_sparsity is not None or
con.finite_diff_rel_step is not None or
not isinstance(con.hess, BFGS) or # misses user specified BFGS
con.keep_feasible):
warn("Constraint options `finite_diff_jac_sparsity`, "
"`finite_diff_rel_step`, `keep_feasible`, and `hess`"
"are ignored by this method.", OptimizeWarning)
fun = con.fun
if callable(con.jac):
jac = con.jac
else:
jac = None
else: # LinearConstraint
if con.keep_feasible:
warn("Constraint option `keep_feasible` is ignored by this "
"method.", OptimizeWarning)
A = con.A
if issparse(A):
A = A.todense()
fun = lambda x: np.dot(A, x)
jac = lambda x: A
# FIXME: when bugs in VectorFunction/LinearVectorFunction are worked out,
# use pcon.fun.fun and pcon.fun.jac. Until then, get fun/jac above.
pcon = PreparedConstraint(con, x0)
lb, ub = pcon.bounds
i_eq = lb == ub
i_bound_below = np.logical_xor(lb != -np.inf, i_eq)
i_bound_above = np.logical_xor(ub != np.inf, i_eq)
i_unbounded = np.logical_and(lb == -np.inf, ub == np.inf)
if np.any(i_unbounded):
warn("At least one constraint is unbounded above and below. Such "
"constraints are ignored.", OptimizeWarning)
ceq = []
if np.any(i_eq):
def f_eq(x):
y = np.array(fun(x)).flatten()
return y[i_eq] - lb[i_eq]
ceq = [{"type": "eq", "fun": f_eq}]
if jac is not None:
def j_eq(x):
dy = jac(x)
if issparse(dy):
dy = dy.todense()
dy = np.atleast_2d(dy)
return dy[i_eq, :]
ceq[0]["jac"] = j_eq
cineq = []
n_bound_below = np.sum(i_bound_below)
n_bound_above = np.sum(i_bound_above)
if n_bound_below + n_bound_above:
def f_ineq(x):
y = np.zeros(n_bound_below + n_bound_above)
y_all = np.array(fun(x)).flatten()
y[:n_bound_below] = y_all[i_bound_below] - lb[i_bound_below]
y[n_bound_below:] = -(y_all[i_bound_above] - ub[i_bound_above])
return y
cineq = [{"type": "ineq", "fun": f_ineq}]
if jac is not None:
def j_ineq(x):
dy = np.zeros((n_bound_below + n_bound_above, len(x0)))
dy_all = jac(x)
if issparse(dy_all):
dy_all = dy_all.todense()
dy_all = np.atleast_2d(dy_all)
dy[:n_bound_below, :] = dy_all[i_bound_below]
dy[n_bound_below:, :] = -dy_all[i_bound_above]
return dy
cineq[0]["jac"] = j_ineq
old_constraints = ceq + cineq
if len(old_constraints) > 1:
warn("Equality and inequality constraints are specified in the same "
"element of the constraint list. For efficient use with this "
"method, equality and inequality constraints should be specified "
"in separate elements of the constraint list. ", OptimizeWarning)
return old_constraints | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/train/callback/_landscape.py | python | _PCA._random_svd | (self, m, n_components, n_oversamples=10, random_state="warn") | return u[:, :n_components], s[:n_components], vt[:n_components, :] | Compute a truncated randomized SVD. | Compute a truncated randomized SVD. | [
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] | def _random_svd(self, m, n_components, n_oversamples=10, random_state="warn"):
"""Compute a truncated randomized SVD."""
n_random = n_components + n_oversamples
n_samples, n_features = m.shape
# Adjust 7 or 4 was found a good compromise for randomized SVD.
n_iter = 7 if n_components < 0.1 * min(m.shape) else 4
transpose = n_samples < n_features
if transpose:
m = m.T
q = self._random_range_finder(m, size=n_random, n_iter=n_iter, random_state=random_state)
# Project m to the low dimensional space using the basis vectors (q vector).
b = self._safe_dot(q.T, m)
# Compute the svd on this matrix (b matrix)
uhat, s, vt = linalg.svd(b, full_matrices=False)
del b
u = np.dot(q, uhat)
if not transpose:
u, vt = self._svd_turn(u, vt)
else:
u, vt = self._svd_turn(u, vt, u_decision=False)
if transpose:
return vt[:n_components, :].T, s[:n_components], u[:, :n_components].T
return u[:, :n_components], s[:n_components], vt[:n_components, :] | [
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papyrussolution/OpenPapyrus | bbfb5ec2ea2109b8e2f125edd838e12eaf7b8b91 | Src/OSF/protobuf-3.19.1/python/google/protobuf/internal/decoder.py | python | _SkipGroup | (buffer, pos, end) | Skip sub-group. Returns the new position. | Skip sub-group. Returns the new position. | [
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"""Skip sub-group. Returns the new position."""
while 1:
(tag_bytes, pos) = ReadTag(buffer, pos)
new_pos = SkipField(buffer, pos, end, tag_bytes)
if new_pos == -1:
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pos = new_pos | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/floatspin.py | python | FloatSpin.IsFinite | (self, value) | return finite, snap_value | Tries to determine if a value is finite or infinite/NaN.
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qt/qt | 0a2f2382541424726168804be2c90b91381608c6 | src/3rdparty/webkit/Source/ThirdParty/gyp/pylib/gyp/MSVSSettings.py | python | _Renamed | (tool, msvs_name, msbuild_name, type) | Defines a setting for which the name has changed.
Args:
tool: a dictionary that gives the names of the tool for MSVS and MSBuild.
msvs_name: the name of the MSVS setting.
msbuild_name: the name of the MSBuild setting.
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Args:
tool: a dictionary that gives the names of the tool for MSVS and MSBuild.
msvs_name: the name of the MSVS setting.
msbuild_name: the name of the MSBuild setting.
type: the type of this setting.
"""
def _Translate(value, msbuild_settings):
msbuild_tool_settings = _GetMsBuildToolSettings(msbuild_settings, tool)
msbuild_tool_settings[msbuild_name] = type.ConvertToMSBuild(value)
msvs_tool_name = tool['msvs']
msbuild_tool_name = tool['msbuild']
_msvs_validators[msvs_tool_name][msvs_name] = type.ValidateMSVS
_msbuild_validators[msbuild_tool_name][msbuild_name] = type.ValidateMSBuild
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tum-vision/fusenet | a1451be2971b348a01b0f525c2a3a7a0e215a591 | python/caffe/net_spec.py | python | assign_proto | (proto, name, val) | Assign a Python object to a protobuf message, based on the Python
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"""Assign a Python object to a protobuf message, based on the Python
type (in recursive fashion). Lists become repeated fields/messages, dicts
become messages, and other types are assigned directly. For convenience,
repeated fields whose values are not lists are converted to single-element
lists; e.g., `my_repeated_int_field=3` is converted to
`my_repeated_int_field=[3]`."""
is_repeated_field = hasattr(getattr(proto, name), 'extend')
if is_repeated_field and not isinstance(val, list):
val = [val]
if isinstance(val, list):
if isinstance(val[0], dict):
for item in val:
proto_item = getattr(proto, name).add()
for k, v in six.iteritems(item):
assign_proto(proto_item, k, v)
else:
getattr(proto, name).extend(val)
elif isinstance(val, dict):
for k, v in six.iteritems(val):
assign_proto(getattr(proto, name), k, v)
else:
setattr(proto, name, val) | [
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] | https://github.com/tum-vision/fusenet/blob/a1451be2971b348a01b0f525c2a3a7a0e215a591/python/caffe/net_spec.py#L56-L79 | ||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/sysconfig.py | python | _init_non_posix | (vars) | Initialize the module as appropriate for NT | Initialize the module as appropriate for NT | [
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"for",
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] | def _init_non_posix(vars):
"""Initialize the module as appropriate for NT"""
# set basic install directories
vars['LIBDEST'] = get_path('stdlib')
vars['BINLIBDEST'] = get_path('platstdlib')
vars['INCLUDEPY'] = get_path('include')
vars['SO'] = '.pyd'
vars['EXE'] = '.exe'
vars['VERSION'] = _PY_VERSION_SHORT_NO_DOT
vars['BINDIR'] = os.path.dirname(_safe_realpath(sys.executable)) | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/graph_editor/subgraph.py | python | SubGraphView._remap_outputs_to_consumers | (self) | Remap the outputs in place to match the number of consumers. | Remap the outputs in place to match the number of consumers. | [
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] | def _remap_outputs_to_consumers(self):
"""Remap the outputs in place to match the number of consumers."""
self._remap_outputs_make_unique()
output_ts = list(self._output_ts)
self._output_ts = []
for t in output_ts:
self._output_ts += [t]*len(t.consumers()) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/robotsim.py | python | IKSolver.lastSolveIters | (self) | return _robotsim.IKSolver_lastSolveIters(self) | lastSolveIters(IKSolver self) -> int
Returns the number of Newton-Raphson iterations used in the last solve() call. | lastSolveIters(IKSolver self) -> int | [
"lastSolveIters",
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] | def lastSolveIters(self):
"""
lastSolveIters(IKSolver self) -> int
Returns the number of Newton-Raphson iterations used in the last solve() call.
"""
return _robotsim.IKSolver_lastSolveIters(self) | [
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naver/sling | 5671cd445a2caae0b4dd0332299e4cfede05062c | webkit/Tools/Scripts/webkitpy/port/base.py | python | Port.test_exists | (self, test_name) | return self.test_isfile(test_name) or self.test_isdir(test_name) | Return True if the test name refers to an existing test or baseline. | Return True if the test name refers to an existing test or baseline. | [
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] | def test_exists(self, test_name):
"""Return True if the test name refers to an existing test or baseline."""
# Used by test_expectations.py to determine if an entry refers to a
# valid test and by printing.py to determine if baselines exist.
return self.test_isfile(test_name) or self.test_isdir(test_name) | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | grc/core/FlowGraph.py | python | _update_old_message_port_keys | (source_key, sink_key, source_block, sink_block) | return source_key, sink_key | Backward compatibility for message port keys
Message ports use their names as key (like in the 'connect' method).
Flowgraph files from former versions still have numeric keys stored for
message connections. These have to be replaced by the name of the
respective port. The correct message port is deduced from the integer
value of the key (assuming the order has not changed).
The connection ends are updated only if both ends translate into a
message port. | Backward compatibility for message port keys | [
"Backward",
"compatibility",
"for",
"message",
"port",
"keys"
] | def _update_old_message_port_keys(source_key, sink_key, source_block, sink_block):
"""
Backward compatibility for message port keys
Message ports use their names as key (like in the 'connect' method).
Flowgraph files from former versions still have numeric keys stored for
message connections. These have to be replaced by the name of the
respective port. The correct message port is deduced from the integer
value of the key (assuming the order has not changed).
The connection ends are updated only if both ends translate into a
message port.
"""
try:
# get ports using the "old way" (assuming linear indexed keys)
source_port = source_block.sources[int(source_key)]
sink_port = sink_block.sinks[int(sink_key)]
if source_port.dtype == "message" and sink_port.dtype == "message":
source_key, sink_key = source_port.key, sink_port.key
except (ValueError, IndexError):
pass
return source_key, sink_key | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_aarch64/python2.7/dist-packages/catkin_pkg/packages.py | python | find_packages_allowing_duplicates | (basepath, exclude_paths=None, exclude_subspaces=False, warnings=None) | return packages | Crawls the filesystem to find package manifest files and parses them.
:param basepath: The path to search in, ``str``
:param exclude_paths: A list of paths which should not be searched, ``list``
:param exclude_subspaces: The flag is subfolders containing a .catkin file should not be
searched, ``bool``
:param warnings: Print warnings if None or return them in the given list
:returns: A dict mapping relative paths to ``Package`` objects ``dict`` | Crawls the filesystem to find package manifest files and parses them. | [
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"filesystem",
"to",
"find",
"package",
"manifest",
"files",
"and",
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"."
] | def find_packages_allowing_duplicates(basepath, exclude_paths=None, exclude_subspaces=False, warnings=None):
"""
Crawls the filesystem to find package manifest files and parses them.
:param basepath: The path to search in, ``str``
:param exclude_paths: A list of paths which should not be searched, ``list``
:param exclude_subspaces: The flag is subfolders containing a .catkin file should not be
searched, ``bool``
:param warnings: Print warnings if None or return them in the given list
:returns: A dict mapping relative paths to ``Package`` objects ``dict``
"""
packages = {}
package_paths = find_package_paths(basepath, exclude_paths=exclude_paths, exclude_subspaces=exclude_subspaces)
for path in package_paths:
packages[path] = parse_package(os.path.join(basepath, path), warnings=warnings)
return packages | [
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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | official-ws/python/bitmex_websocket.py | python | BitMEXWebsocket.__get_auth | (self) | Return auth headers. Will use API Keys if present in settings. | Return auth headers. Will use API Keys if present in settings. | [
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] | def __get_auth(self):
'''Return auth headers. Will use API Keys if present in settings.'''
if self.api_key:
self.logger.info("Authenticating with API Key.")
# To auth to the WS using an API key, we generate a signature of a nonce and
# the WS API endpoint.
expires = generate_nonce()
return [
"api-expires: " + str(expires),
"api-signature: " + generate_signature(self.api_secret, 'GET', '/realtime', expires, ''),
"api-key:" + self.api_key
]
else:
self.logger.info("Not authenticating.")
return [] | [
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