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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/position.py | python | Position.open_order_buy_premium | (self) | return self._open_order_buy_premium | Gets the open_order_buy_premium of this Position. # noqa: E501
:return: The open_order_buy_premium of this Position. # noqa: E501
:rtype: float | Gets the open_order_buy_premium of this Position. # noqa: E501 | [
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"""Gets the open_order_buy_premium of this Position. # noqa: E501
:return: The open_order_buy_premium of this Position. # noqa: E501
:rtype: float
"""
return self._open_order_buy_premium | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/aui.py | python | AuiTabContainer.SetActivePage | (*args) | return _aui.AuiTabContainer_SetActivePage(*args) | SetActivePage(self, Window page) -> bool
SetActivePage(self, size_t page) -> bool | SetActivePage(self, Window page) -> bool
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"""
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H-uru/Plasma | c2140ea046e82e9c199e257a7f2e7edb42602871 | Scripts/Python/xAvatarCustomization.py | python | xAvatarCustomization.ISetWhatWearing | (self,avatar) | Gets whats being worn and sets the dialogs to show what we are wearing | Gets whats being worn and sets the dialogs to show what we are wearing | [
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global listboxDict
for id, group in TheCloset.items():
listbox = listboxDict[id]
acessoryListbox = listboxDict[id + kAccessoryLBOffset]
# tell the listboxes to update themselves
listbox.SetWhatWearing()
listbox.UpdateScrollArrows()
listbox.UpdateListbox()
if id == kUpperBodyOptionsLB or id == kLwrBodyOptionsLB:
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targetMesh = listbox.GetSelectedItem()
texGroup = TextureGroup(group.clothingType,targetMesh.name)
acessoryListbox.SetClothingList(texGroup.clothingItems)
acessoryListbox.SetWhatWearing()
acessoryListbox.UpdateScrollArrows()
acessoryListbox.UpdateListbox()
else:
# standard accessory panel
acessoryListbox.SetWhatWearing()
acessoryListbox.UpdateScrollArrows()
acessoryListbox.UpdateListbox() | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | build/android/gyp/util/build_utils.py | python | ExpandFileArgs | (args) | return new_args | Replaces file-arg placeholders in args.
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The value of such a placeholder is calculated by reading 'filename' as json.
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The value of such a placeholder is calculated by reading 'filename' as json.
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"""
new_args = list(args)
file_jsons = dict()
r = re.compile('@FileArg\((.*?)\)')
for i, arg in enumerate(args):
match = r.search(arg)
if not match:
continue
if match.end() != len(arg):
raise Exception('Unexpected characters after FileArg: ' + arg)
lookup_path = match.group(1).split(':')
file_path = lookup_path[0]
if not file_path in file_jsons:
file_jsons[file_path] = ReadJson(file_path)
expansion = file_jsons[file_path]
for k in lookup_path[1:]:
expansion = expansion[k]
new_args[i] = arg[:match.start()] + str(expansion)
return new_args | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/grid.py | python | Grid.IsReadOnly | (*args, **kwargs) | return _grid.Grid_IsReadOnly(*args, **kwargs) | IsReadOnly(self, int row, int col) -> bool | IsReadOnly(self, int row, int col) -> bool | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/numbers.py | python | Integral.__rshift__ | (self, other) | self >> other | self >> other | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/plot_widget/plotting_canvas/plot_color_queue.py | python | ColorQueue._initialise_queue | (self) | Initialize the heap queue with the color_cache | Initialize the heap queue with the color_cache | [
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"""Initialize the heap queue with the color_cache"""
self._queue = []
for color, priority in self._color_cache.items():
heapq.heappush(self._queue, (priority, color)) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/indexes/range.py | python | RangeIndex.is_unique | (self) | return True | return if the index has unique values | return if the index has unique values | [
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""" return if the index has unique values """
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py | python | read_batch_features | (file_pattern, batch_size, features, reader,
randomize_input=True, num_epochs=None,
queue_capacity=10000, reader_num_threads=1,
parser_num_threads=1, name=None) | return features | Adds operations to read, queue, batch and parse `Example` protos.
Given file pattern (or list of files), will setup a queue for file names,
read `Example` proto using provided `reader`, use batch queue to create
batches of examples of size `batch_size` and parse example given `features`
specification.
All queue runners are added to the queue runners collection, and may be
started via `start_queue_runners`.
All ops are added to the default graph.
Args:
file_pattern: List of files or pattern of file paths containing
`Example` records. See `tf.gfile.Glob` for pattern rules.
batch_size: An int or scalar `Tensor` specifying the batch size to use.
features: A `dict` mapping feature keys to `FixedLenFeature` or
`VarLenFeature` values.
reader: A function or class that returns an object with
`read` method, (filename tensor) -> (example tensor).
randomize_input: Whether the input should be randomized.
num_epochs: Integer specifying the number of times to read through the
dataset. If None, cycles through the dataset forever. NOTE - If specified,
creates a variable that must be initialized, so call
tf.initialize_local_variables() as shown in the tests.
queue_capacity: Capacity for input queue.
reader_num_threads: The number of threads to read examples.
parser_num_threads: The number of threads to parse examples.
records to read at once
name: Name of resulting op.
Returns:
A dict of `Tensor` or `SparseTensor` objects for each in `features`.
If `keep_keys` is `True`, returns tuple of string `Tensor` and above dict.
Raises:
ValueError: for invalid inputs. | Adds operations to read, queue, batch and parse `Example` protos. | [
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randomize_input=True, num_epochs=None,
queue_capacity=10000, reader_num_threads=1,
parser_num_threads=1, name=None):
"""Adds operations to read, queue, batch and parse `Example` protos.
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parser_num_threads: The number of threads to parse examples.
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name: Name of resulting op.
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A dict of `Tensor` or `SparseTensor` objects for each in `features`.
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Raises:
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"""
_, features = read_keyed_batch_features(
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randomize_input=randomize_input, num_epochs=num_epochs,
queue_capacity=queue_capacity, reader_num_threads=reader_num_threads,
parser_num_threads=parser_num_threads, name=name)
return features | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/jira/client.py | python | JIRA.search_users | (self, user, startAt=0, maxResults=50, includeActive=True, includeInactive=False) | return self._fetch_pages(User, None, 'user/search', startAt, maxResults, params) | Get a list of user Resources that match the specified search string.
:param user: a string to match usernames, name or email against.
:param startAt: index of the first user to return.
:param maxResults: maximum number of users to return.
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params = {
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return self._fetch_pages(User, None, 'user/search', startAt, maxResults, params) | [
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google/angle | d5df233189cad620b8e0de653fe5e6cb778e209d | third_party/logdog/logdog/stream.py | python | StreamProtocolRegistry.create | (self, uri, **kwargs) | return client_cls._create(value, **kwargs) | Returns (StreamClient): A stream client for the specified URI.
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uri (str): The streamserver URI.
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Args:
uri (str): The streamserver URI.
kwargs: keyword arguments to forward to the stream. See
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uri = uri.split(':', 1)
if len(uri) != 2:
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protocol, value = uri
client_cls = self._registry.get(protocol)
if not client_cls:
raise ValueError('Unknown stream client protocol (%s)' % (protocol,))
return client_cls._create(value, **kwargs) | [
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microsoft/ELL | a1d6bacc37a14879cc025d9be2ba40b1a0632315 | tools/importers/common/converters.py | python | LookupTable.get_port_elements_and_memory_layout_for_input | (self, importer_node: ImporterNode, input_index=0) | return (port_elements, port_memory_layout) | Returns an (ell.nodes.PortElements, ell.nodes.PortMemoryLayout) for the corresponding input of the ImporterNode. | Returns an (ell.nodes.PortElements, ell.nodes.PortMemoryLayout) for the corresponding input of the ImporterNode. | [
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"""
Returns an (ell.nodes.PortElements, ell.nodes.PortMemoryLayout) for the corresponding input of the ImporterNode.
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try:
owning_ell_node = self.get_owning_node_for_output(importer_node.inputs[input_index])
owning_importer_node = self.ell_id_to_owning_importer_node[owning_ell_node.GetId()]
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/io/pytables.py | python | AppendableFrameTable.get_object | (cls, obj, transposed: bool) | return obj | these are written transposed | these are written transposed | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/http/cookiejar.py | python | join_header_words | (lists) | return ", ".join(headers) | Do the inverse (almost) of the conversion done by split_header_words.
Takes a list of lists of (key, value) pairs and produces a single header
value. Attribute values are quoted if needed.
>>> join_header_words([[("text/plain", None), ("charset", "iso-8859-1")]])
'text/plain; charset="iso-8859-1"'
>>> join_header_words([[("text/plain", None)], [("charset", "iso-8859-1")]])
'text/plain, charset="iso-8859-1"' | Do the inverse (almost) of the conversion done by split_header_words. | [
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"""Do the inverse (almost) of the conversion done by split_header_words.
Takes a list of lists of (key, value) pairs and produces a single header
value. Attribute values are quoted if needed.
>>> join_header_words([[("text/plain", None), ("charset", "iso-8859-1")]])
'text/plain; charset="iso-8859-1"'
>>> join_header_words([[("text/plain", None)], [("charset", "iso-8859-1")]])
'text/plain, charset="iso-8859-1"'
"""
headers = []
for pairs in lists:
attr = []
for k, v in pairs:
if v is not None:
if not re.search(r"^\w+$", v):
v = HEADER_JOIN_ESCAPE_RE.sub(r"\\\1", v) # escape " and \
v = '"%s"' % v
k = "%s=%s" % (k, v)
attr.append(k)
if attr: headers.append("; ".join(attr))
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/jinja2/ext.py | python | InternationalizationExtension._make_node | (self, singular, plural, variables, plural_expr,
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"""Generates a useful node from the data provided."""
# no variables referenced? no need to escape for old style
# gettext invocations only if there are vars.
if not vars_referenced and not self.environment.newstyle_gettext:
singular = singular.replace('%%', '%')
if plural:
plural = plural.replace('%%', '%')
# singular only:
if plural_expr is None:
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node = nodes.Call(gettext, [nodes.Const(singular)],
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# singular and plural
else:
ngettext = nodes.Name('ngettext', 'load')
node = nodes.Call(ngettext, [
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# in case newstyle gettext is used, the method is powerful
# enough to handle the variable expansion and autoescape
# handling itself
if self.environment.newstyle_gettext:
for key, value in variables.iteritems():
# the function adds that later anyways in case num was
# called num, so just skip it.
if num_called_num and key == 'num':
continue
node.kwargs.append(nodes.Keyword(key, value))
# otherwise do that here
else:
# mark the return value as safe if we are in an
# environment with autoescaping turned on
node = nodes.MarkSafeIfAutoescape(node)
if variables:
node = nodes.Mod(node, nodes.Dict([
nodes.Pair(nodes.Const(key), value)
for key, value in variables.items()
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/profiler/parser/memory_usage_parser.py | python | MemoryUsageParser._parse_graph_memory | (self, graphs) | Parse memory usage based on subgraphs. | Parse memory usage based on subgraphs. | [
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"""Parse memory usage based on subgraphs."""
for graph_proto in graphs:
graph_id = graph_proto.graph_id
if graph_id is None:
logger.info('Graph id is missing, skipped the graph.')
continue
graph_parser = GraphMemoryParser(graph_proto, self._points, self._framework)
graph = graph_parser.parse_graph()
if graph:
self._graphs_dict[graph_id] = graph
# update global memory usage data
self._peak_mem = max(self._peak_mem, graph_parser.peak_mem)
self._mem_summary['static_mem'] += graph_parser.static_mem
self._mem_summary['allocations'] += graph_parser.allocations
self._mem_summary['deallocations'] += graph_parser.deallocations | [
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scanner-research/scanner | 04a0c4b4196341995985acd729c0788aab823e1c | python/scannerpy/kernel.py | python | Kernel.execute | (self, stream_parameter: bytes) | r"""Runs the kernel on input elements and returns new output elements.
Parameters
----------
stream_parameter
An example stream parameter. Must be annotated with a stream parameter type.
See :ref:`stream-parameters`.
Returns
-------
bytes
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Parameters
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stream_parameter
An example stream parameter. Must be annotated with a stream parameter type.
See :ref:`stream-parameters`.
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raise NotImplementedError | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/check_ops.py | python | assert_rank_in_v2 | (x, ranks, message=None, name=None) | return assert_rank_in(x=x, ranks=ranks, message=message, name=name) | Assert that `x` has a rank in `ranks`.
This Op checks that the rank of `x` is in `ranks`.
If `x` has a different rank, `message`, as well as the shape of `x` are
printed, and `InvalidArgumentError` is raised.
Args:
x: `Tensor`.
ranks: `Iterable` of scalar `Tensor` objects.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_rank_in".
Returns:
Op raising `InvalidArgumentError` unless rank of `x` is in `ranks`.
If static checks determine `x` has matching rank, a `no_op` is returned.
This can be used with `tf.control_dependencies` inside of `tf.function`s
to block followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: `x` does not have rank in `ranks`, but the rank cannot
be statically determined.
ValueError: If static checks determine `x` has mismatched rank. | Assert that `x` has a rank in `ranks`. | [
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] | def assert_rank_in_v2(x, ranks, message=None, name=None):
"""Assert that `x` has a rank in `ranks`.
This Op checks that the rank of `x` is in `ranks`.
If `x` has a different rank, `message`, as well as the shape of `x` are
printed, and `InvalidArgumentError` is raised.
Args:
x: `Tensor`.
ranks: `Iterable` of scalar `Tensor` objects.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_rank_in".
Returns:
Op raising `InvalidArgumentError` unless rank of `x` is in `ranks`.
If static checks determine `x` has matching rank, a `no_op` is returned.
This can be used with `tf.control_dependencies` inside of `tf.function`s
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return assert_rank_in(x=x, ranks=ranks, message=message, name=name) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib-tk/Tkinter.py | python | Text.__init__ | (self, master=None, cnf={}, **kw) | Construct a text widget with the parent MASTER.
STANDARD OPTIONS
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] | def __init__(self, master=None, cnf={}, **kw):
"""Construct a text widget with the parent MASTER.
STANDARD OPTIONS
background, borderwidth, cursor,
exportselection, font, foreground,
highlightbackground, highlightcolor,
highlightthickness, insertbackground,
insertborderwidth, insertofftime,
insertontime, insertwidth, padx, pady,
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WIDGET-SPECIFIC OPTIONS
autoseparators, height, maxundo,
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"""
Widget.__init__(self, master, 'text', cnf, kw) | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/which/setup.py | python | _getBinDir | () | return bindir | Return the current Python's bindir. | Return the current Python's bindir. | [
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] | def _getBinDir():
"""Return the current Python's bindir."""
if sys.platform.startswith("win"):
bindir = sys.prefix
else:
bindir = os.path.join(sys.prefix, "bin")
return bindir | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/special_math_ops.py | python | bessel_i0 | (x, name=None) | Computes the Bessel i0 function of `x` element-wise.
Modified Bessel function of order 0.
It is preferable to use the numerically stabler function `i0e(x)` instead.
>>> tf.math.special.bessel_i0([-1., -0.5, 0.5, 1.]).numpy()
array([1.26606588, 1.06348337, 1.06348337, 1.26606588], dtype=float32)
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
@compatibility(scipy)
Equivalent to scipy.special.i0
@end_compatibility | Computes the Bessel i0 function of `x` element-wise. | [
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] | def bessel_i0(x, name=None):
"""Computes the Bessel i0 function of `x` element-wise.
Modified Bessel function of order 0.
It is preferable to use the numerically stabler function `i0e(x)` instead.
>>> tf.math.special.bessel_i0([-1., -0.5, 0.5, 1.]).numpy()
array([1.26606588, 1.06348337, 1.06348337, 1.26606588], dtype=float32)
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
@compatibility(scipy)
Equivalent to scipy.special.i0
@end_compatibility
"""
with ops.name_scope(name, 'bessel_i0', [x]):
return gen_special_math_ops.bessel_i0(x) | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | grc/core/generator/cpp_hier_block.py | python | get_hier_block_io | (flow_graph, direction, domain=None) | Get a list of io ports for this flow graph.
Returns a list of dicts with: type, label, vlen, size, optional | Get a list of io ports for this flow graph. | [
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"""
Get a list of io ports for this flow graph.
Returns a list of dicts with: type, label, vlen, size, optional
"""
pads = flow_graph.get_pad_sources(
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for pad in pads:
for port in (pad.sources if direction == 'inputs' else pad.sinks):
if domain and port.domain != domain:
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yield port | [
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mapeditor/tiled | b7abf3c9606aa53442bab8fc6a44a1b2797226e0 | src/plugins/python/scripts/mappy.py | python | FMPColormap.pack | (self, cmap) | cmap -- T.qt.QImage.colorTable | cmap -- T.qt.QImage.colorTable | [
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yield fmpchunk(id='CMAP', len=len(list(cmap))).pack()
for c in cmap:
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/lib/io/file_io.py | python | atomic_write_string_to_file | (filename, contents, overwrite=True) | Writes to `filename` atomically.
This means that when `filename` appears in the filesystem, it will contain
all of `contents`. With write_string_to_file, it is possible for the file
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filename: string, pathname for a file
contents: string, contents that need to be written to the file
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an existing file. | Writes to `filename` atomically. | [
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"""Writes to `filename` atomically.
This means that when `filename` appears in the filesystem, it will contain
all of `contents`. With write_string_to_file, it is possible for the file
to appear in the filesystem with `contents` only partially written.
Accomplished by writing to a temp file and then renaming it.
Args:
filename: string, pathname for a file
contents: string, contents that need to be written to the file
overwrite: boolean, if false it's an error for `filename` to be occupied by
an existing file.
"""
if not has_atomic_move(filename):
write_string_to_file(filename, contents)
else:
temp_pathname = filename + ".tmp" + uuid.uuid4().hex
write_string_to_file(temp_pathname, contents)
try:
rename(temp_pathname, filename, overwrite)
except errors.OpError:
delete_file(temp_pathname)
raise | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextCtrl.EndSymbolBullet | (*args, **kwargs) | return _richtext.RichTextCtrl_EndSymbolBullet(*args, **kwargs) | EndSymbolBullet(self) -> bool
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gklz1982/caffe-yolov2 | ebb27029db4ddc0d40e520634633b0fa9cdcc10d | scripts/cpp_lint.py | python | FileInfo.FullName | (self) | return os.path.abspath(self._filename).replace('\\', '/') | Make Windows paths like Unix. | Make Windows paths like Unix. | [
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] | def FullName(self):
"""Make Windows paths like Unix."""
return os.path.abspath(self._filename).replace('\\', '/') | [
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andrivet/ADVi3pp | 31afb95333f2d830a5b762ea030a4a59ec27cb88 | buildroot/share/scripts/createTemperatureLookupMarlin.py | python | Thermistor.voltage | (self, adc) | return adc * VSTEP | Convert ADC reading into a Voltage | Convert ADC reading into a Voltage | [
"Convert",
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] | def voltage(self, adc):
"Convert ADC reading into a Voltage"
return adc * VSTEP | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/richtext.py | python | RichTextObject.AdjustAvailableSpace | (*args, **kwargs) | return _richtext.RichTextObject_AdjustAvailableSpace(*args, **kwargs) | AdjustAvailableSpace(DC dc, RichTextBuffer buffer, RichTextAttr parentAttr,
RichTextAttr childAttr, Rect availableParentSpace,
Rect availableContainerSpace) -> Rect | AdjustAvailableSpace(DC dc, RichTextBuffer buffer, RichTextAttr parentAttr,
RichTextAttr childAttr, Rect availableParentSpace,
Rect availableContainerSpace) -> Rect | [
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")",
"-",
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] | def AdjustAvailableSpace(*args, **kwargs):
"""
AdjustAvailableSpace(DC dc, RichTextBuffer buffer, RichTextAttr parentAttr,
RichTextAttr childAttr, Rect availableParentSpace,
Rect availableContainerSpace) -> Rect
"""
return _richtext.RichTextObject_AdjustAvailableSpace(*args, **kwargs) | [
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/max.py | python | max.is_atom_log_log_convex | (self) | return True | Is the atom log-log convex? | Is the atom log-log convex? | [
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] | def is_atom_log_log_convex(self) -> bool:
"""Is the atom log-log convex?
"""
return True | [
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] | https://github.com/cvxpy/cvxpy/blob/5165b4fb750dfd237de8659383ef24b4b2e33aaf/cvxpy/atoms/max.py#L84-L87 | |
musescore/MuseScore | a817fea23e3c2be30847b7fde5b01746222c252e | thirdparty/freetype/src/tools/glnames.py | python | dump_encoding | ( file, encoding_name, encoding_list ) | dump a given encoding | dump a given encoding | [
"dump",
"a",
"given",
"encoding"
] | def dump_encoding( file, encoding_name, encoding_list ):
"""dump a given encoding"""
write = file.write
write( " /* the following are indices into the SID name table */\n" )
write( " static const unsigned short " + encoding_name +
"[" + repr( len( encoding_list ) ) + "] =\n" )
write( " {\n" )
line = " "
comma = ""
col = 0
for value in encoding_list:
line += comma
line += "%3d" % value
comma = ","
col += 1
if col == 16:
col = 0
comma = ",\n "
write( line + "\n };\n\n\n" ) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/roc/target.py | python | set_hsa_kernel | (fn) | Ensure `fn` is usable as a SPIR kernel.
- Fix calling convention
- Add metadata | Ensure `fn` is usable as a SPIR kernel.
- Fix calling convention
- Add metadata | [
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] | def set_hsa_kernel(fn):
"""
Ensure `fn` is usable as a SPIR kernel.
- Fix calling convention
- Add metadata
"""
mod = fn.module
# Set nounwind
# fn.add_attribute(lc.ATTR_NO_UNWIND)
# Set SPIR kernel calling convention
fn.calling_convention = CC_SPIR_KERNEL
# Mark kernels
ocl_kernels = mod.get_or_insert_named_metadata("opencl.kernels")
ocl_kernels.add(lc.MetaData.get(mod, [fn,
gen_arg_addrspace_md(fn),
gen_arg_access_qual_md(fn),
gen_arg_type(fn),
gen_arg_type_qual(fn),
gen_arg_base_type(fn)]))
# SPIR version 2.0
make_constant = lambda x: lc.Constant.int(lc.Type.int(), x)
spir_version_constant = [make_constant(x) for x in SPIR_VERSION]
spir_version = mod.get_or_insert_named_metadata("opencl.spir.version")
if not spir_version.operands:
spir_version.add(lc.MetaData.get(mod, spir_version_constant))
ocl_version = mod.get_or_insert_named_metadata("opencl.ocl.version")
if not ocl_version.operands:
ocl_version.add(lc.MetaData.get(mod, spir_version_constant)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/operator.py | python | sub | (a, b) | return a - b | Same as a - b. | Same as a - b. | [
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potassco/clingo | e0c91d8f95cc28de1c480a871f9c97c30de83d40 | libpyclingo/clingo/ast.py | python | Transformer._dispatch | (self, ast: Union[None, AST, ASTSequence], *args: Any, **kwargs: Any) | Visit and transform an (optional) AST or a sequence of ASTs. | Visit and transform an (optional) AST or a sequence of ASTs. | [
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] | def _dispatch(self, ast: Union[None, AST, ASTSequence], *args: Any, **kwargs: Any) -> Union[None, AST, MutableSequence[AST]]:
'''
Visit and transform an (optional) AST or a sequence of ASTs.
'''
if ast is None:
return ast
if isinstance(ast, AST):
return self.visit(ast, *args, **kwargs) # type: ignore
if isinstance(ast, abc.Sequence):
return self.visit_sequence(ast, *args, **kwargs)
raise TypeError('unexpected type') | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/io/pytables.py | python | AppendableTable.write_data_chunk | (
self,
rows: np.ndarray,
indexes: list[np.ndarray],
mask: np.ndarray | None,
values: list[np.ndarray],
) | Parameters
----------
rows : an empty memory space where we are putting the chunk
indexes : an array of the indexes
mask : an array of the masks
values : an array of the values | Parameters
----------
rows : an empty memory space where we are putting the chunk
indexes : an array of the indexes
mask : an array of the masks
values : an array of the values | [
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":",
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self,
rows: np.ndarray,
indexes: list[np.ndarray],
mask: np.ndarray | None,
values: list[np.ndarray],
):
"""
Parameters
----------
rows : an empty memory space where we are putting the chunk
indexes : an array of the indexes
mask : an array of the masks
values : an array of the values
"""
# 0 len
for v in values:
if not np.prod(v.shape):
return
nrows = indexes[0].shape[0]
if nrows != len(rows):
rows = np.empty(nrows, dtype=self.dtype)
names = self.dtype.names
nindexes = len(indexes)
# indexes
for i, idx in enumerate(indexes):
rows[names[i]] = idx
# values
for i, v in enumerate(values):
rows[names[i + nindexes]] = v
# mask
if mask is not None:
m = ~mask.ravel().astype(bool, copy=False)
if not m.all():
rows = rows[m]
if len(rows):
self.table.append(rows)
self.table.flush() | [
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isl-org/Open3D | 79aec3ddde6a571ce2f28e4096477e52ec465244 | docs/make_docs.py | python | PyAPIDocsBuilder._try_import_module | (self, full_module_name) | Returns the module object for the given module path | Returns the module object for the given module path | [
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] | def _try_import_module(self, full_module_name):
"""Returns the module object for the given module path"""
import open3d # make sure the root module is loaded
if open3d._build_config['BUILD_TENSORFLOW_OPS']:
import open3d.ml.tf
if open3d._build_config['BUILD_PYTORCH_OPS']:
import open3d.ml.torch
try:
# Try to import directly. This will work for pure python submodules
module = importlib.import_module(full_module_name)
return module
except ImportError:
# Traverse the module hierarchy of the root module.
# This code path is necessary for modules for which we manually
# define a specific module path (e.g. the modules defined with
# pybind).
current_module = open3d
for sub_module_name in full_module_name.split(".")[1:]:
current_module = getattr(current_module, sub_module_name)
return current_module | [
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... | https://github.com/isl-org/Open3D/blob/79aec3ddde6a571ce2f28e4096477e52ec465244/docs/make_docs.py#L116-L136 | ||
krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/src/robotsim.py | python | RobotModel.setVelocity | (self, dq) | return _robotsim.RobotModel_setVelocity(self, dq) | setVelocity(RobotModel self, doubleVector dq)
Sets the current velocity of the robot model. Like the configuration, this is
also essentially a temporary variable. | setVelocity(RobotModel self, doubleVector dq) | [
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"doubleVector",
"dq",
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] | def setVelocity(self, dq):
"""
setVelocity(RobotModel self, doubleVector dq)
Sets the current velocity of the robot model. Like the configuration, this is
also essentially a temporary variable.
"""
return _robotsim.RobotModel_setVelocity(self, dq) | [
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freeorion/freeorion | c266a40eccd3a99a17de8fe57c36ef6ba3771665 | default/python/AI/universe/system_network.py | python | get_shortest_distance | (system_1: SystemId, system_2: SystemId) | return _get_shortest_distance(*_min_max(system_1, system_2)) | Return the distance between the systems where objects are located. | Return the distance between the systems where objects are located. | [
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] | def get_shortest_distance(system_1: SystemId, system_2: SystemId) -> float:
"""
Return the distance between the systems where objects are located.
"""
return _get_shortest_distance(*_min_max(system_1, system_2)) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | build/android/gyp/apkbuilder.py | python | _ExpandPaths | (paths) | return ret | Converts src:dst into tuples and enumerates files within directories.
Args:
paths: Paths in the form "src_path:dest_path"
Returns:
A list of (src_path, dest_path) tuples sorted by dest_path (for stable
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] | def _ExpandPaths(paths):
"""Converts src:dst into tuples and enumerates files within directories.
Args:
paths: Paths in the form "src_path:dest_path"
Returns:
A list of (src_path, dest_path) tuples sorted by dest_path (for stable
ordering within output .apk).
"""
ret = []
for path in paths:
src_path, dest_path = _SplitAssetPath(path)
if os.path.isdir(src_path):
for f in build_utils.FindInDirectory(src_path, '*'):
ret.append((f, os.path.join(dest_path, f[len(src_path) + 1:])))
else:
ret.append((src_path, dest_path))
ret.sort(key=lambda t:t[1])
return ret | [
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msitt/blpapi-python | bebcf43668c9e5f5467b1f685f9baebbfc45bc87 | src/blpapi/session.py | python | Session.__dispatchEvent | (sessionRef, eventHandle) | event dispatcher | event dispatcher | [
"event",
"dispatcher"
] | def __dispatchEvent(sessionRef, eventHandle): # pragma: no cover
""" event dispatcher """
try:
session = sessionRef()
if session is not None:
event = Event(eventHandle, session)
session.__handler(event, session)
except:
print("Exception in event handler:", file=sys.stderr)
traceback.print_exc(file=sys.stderr)
os._exit(1) | [
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gklz1982/caffe-yolov2 | ebb27029db4ddc0d40e520634633b0fa9cdcc10d | scripts/cpp_lint.py | python | GetHeaderGuardCPPVariable | (filename) | return re.sub(r'[-./\s]', '_', file_path_from_root).upper() + '_' | Returns the CPP variable that should be used as a header guard.
Args:
filename: The name of a C++ header file.
Returns:
The CPP variable that should be used as a header guard in the
named file. | Returns the CPP variable that should be used as a header guard. | [
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] | def GetHeaderGuardCPPVariable(filename):
"""Returns the CPP variable that should be used as a header guard.
Args:
filename: The name of a C++ header file.
Returns:
The CPP variable that should be used as a header guard in the
named file.
"""
# Restores original filename in case that cpplint is invoked from Emacs's
# flymake.
filename = re.sub(r'_flymake\.h$', '.h', filename)
filename = re.sub(r'/\.flymake/([^/]*)$', r'/\1', filename)
fileinfo = FileInfo(filename)
file_path_from_root = fileinfo.RepositoryName()
if _root:
file_path_from_root = re.sub('^' + _root + os.sep, '', file_path_from_root)
return re.sub(r'[-./\s]', '_', file_path_from_root).upper() + '_' | [
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | scripts/mk_genfile_common.py | python | mk_z3consts_py_internal | (api_files, output_dir) | return z3consts_output_path | Generate ``z3consts.py`` from the list of API header files
in ``api_files`` and write the output file into
the ``output_dir`` directory
Returns the path to the generated file. | Generate ``z3consts.py`` from the list of API header files
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the ``output_dir`` directory | [
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] | def mk_z3consts_py_internal(api_files, output_dir):
"""
Generate ``z3consts.py`` from the list of API header files
in ``api_files`` and write the output file into
the ``output_dir`` directory
Returns the path to the generated file.
"""
assert os.path.isdir(output_dir)
assert isinstance(api_files, list)
blank_pat = re.compile("^ *\r?$")
comment_pat = re.compile("^ *//.*$")
typedef_pat = re.compile("typedef enum *")
typedef2_pat = re.compile("typedef enum { *")
openbrace_pat = re.compile("{ *")
closebrace_pat = re.compile("}.*;")
z3consts = open(os.path.join(output_dir, 'z3', 'z3consts.py'), 'w')
z3consts_output_path = z3consts.name
z3consts.write('# Automatically generated file\n\n')
for api_file in api_files:
api = open(api_file, 'r')
SEARCHING = 0
FOUND_ENUM = 1
IN_ENUM = 2
mode = SEARCHING
decls = {}
idx = 0
linenum = 1
for line in api:
m1 = blank_pat.match(line)
m2 = comment_pat.match(line)
if m1 or m2:
# skip blank lines and comments
linenum = linenum + 1
elif mode == SEARCHING:
m = typedef_pat.match(line)
if m:
mode = FOUND_ENUM
m = typedef2_pat.match(line)
if m:
mode = IN_ENUM
decls = {}
idx = 0
elif mode == FOUND_ENUM:
m = openbrace_pat.match(line)
if m:
mode = IN_ENUM
decls = {}
idx = 0
else:
assert False, "Invalid %s, line: %s" % (api_file, linenum)
else:
assert mode == IN_ENUM
words = re.split('[^\-a-zA-Z0-9_]+', line)
m = closebrace_pat.match(line)
if m:
name = words[1]
z3consts.write('# enum %s\n' % name)
# Iterate over key-value pairs ordered by value
for k, v in sorted(decls.items(), key=lambda pair: pair[1]):
z3consts.write('%s = %s\n' % (k, v))
z3consts.write('\n')
mode = SEARCHING
elif len(words) <= 2:
assert False, "Invalid %s, line: %s" % (api_file, linenum)
else:
if words[2] != '':
if len(words[2]) > 1 and words[2][1] == 'x':
idx = int(words[2], 16)
else:
idx = int(words[2])
decls[words[1]] = idx
idx = idx + 1
linenum = linenum + 1
api.close()
z3consts.close()
return z3consts_output_path | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/model/subrobot.py | python | SubRobotModel.__init__ | (self, robot : Union[RobotModel,'SubRobotModel'], links : Sequence[Union[int,str]]) | Args:
robot (RobotModel or SubRobotModel): the robot to base this on.
links (list of ints or strs): the links to use in this sub-robot. | Args:
robot (RobotModel or SubRobotModel): the robot to base this on.
links (list of ints or strs): the links to use in this sub-robot. | [
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"""
Args:
robot (RobotModel or SubRobotModel): the robot to base this on.
links (list of ints or strs): the links to use in this sub-robot.
"""
assert isinstance(robot,(RobotModel,SubRobotModel)),"SubRobotModel constructor must be given a RobotModel or SubRobotModel as first argument"
self._robot = robot
self._links = links[:] #type : List[int]
self._drivers = None #type : List[RobotModelDriver]
self.index = robot.index
self.world = robot.world
if isinstance(robot,SubRobotModel):
warnings.warn("Taking sub-robot of sub-robot... not tested yet")
self._robot = robot._robot
for i,l in enumerate(self._links):
if isinstance(l,int):
self._links[i] = robot._links[l]
for i,l in enumerate(self._links):
if isinstance(l,str):
self._links[i] = robot.link(l).getIndex()
self._inv_links = dict((l,i) for (i,l) in enumerate(self._links)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/grid.py | python | Grid.DeselectCell | (*args, **kwargs) | return _grid.Grid_DeselectCell(*args, **kwargs) | DeselectCell(self, int row, int col) | DeselectCell(self, int row, int col) | [
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] | def DeselectCell(*args, **kwargs):
"""DeselectCell(self, int row, int col)"""
return _grid.Grid_DeselectCell(*args, **kwargs) | [
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zeakey/DeepSkeleton | dc70170f8fd2ec8ca1157484ce66129981104486 | python/caffe/pycaffe.py | python | _Net_set_input_arrays | (self, data, labels) | return self._set_input_arrays(data, labels) | Set input arrays of the in-memory MemoryDataLayer.
(Note: this is only for networks declared with the memory data layer.) | Set input arrays of the in-memory MemoryDataLayer.
(Note: this is only for networks declared with the memory data layer.) | [
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] | def _Net_set_input_arrays(self, data, labels):
"""
Set input arrays of the in-memory MemoryDataLayer.
(Note: this is only for networks declared with the memory data layer.)
"""
if labels.ndim == 1:
labels = np.ascontiguousarray(labels[:, np.newaxis, np.newaxis,
np.newaxis])
return self._set_input_arrays(data, labels) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/SocketServer.py | python | ForkingMixIn.collect_children | (self) | Internal routine to wait for children that have exited. | Internal routine to wait for children that have exited. | [
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] | def collect_children(self):
"""Internal routine to wait for children that have exited."""
if self.active_children is None:
return
# If we're above the max number of children, wait and reap them until
# we go back below threshold. Note that we use waitpid(-1) below to be
# able to collect children in size(<defunct children>) syscalls instead
# of size(<children>): the downside is that this might reap children
# which we didn't spawn, which is why we only resort to this when we're
# above max_children.
while len(self.active_children) >= self.max_children:
try:
pid, _ = os.waitpid(-1, 0)
self.active_children.discard(pid)
except OSError as e:
if e.errno == errno.ECHILD:
# we don't have any children, we're done
self.active_children.clear()
elif e.errno != errno.EINTR:
break
# Now reap all defunct children.
for pid in self.active_children.copy():
try:
pid, _ = os.waitpid(pid, os.WNOHANG)
# if the child hasn't exited yet, pid will be 0 and ignored by
# discard() below
self.active_children.discard(pid)
except OSError as e:
if e.errno == errno.ECHILD:
# someone else reaped it
self.active_children.discard(pid) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/s3/resumable_download_handler.py | python | ResumableDownloadHandler._attempt_resumable_download | (self, key, fp, headers, cb, num_cb,
torrent, version_id, hash_algs) | Attempts a resumable download.
Raises ResumableDownloadException if any problems occur. | Attempts a resumable download. | [
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"download",
"."
] | def _attempt_resumable_download(self, key, fp, headers, cb, num_cb,
torrent, version_id, hash_algs):
"""
Attempts a resumable download.
Raises ResumableDownloadException if any problems occur.
"""
cur_file_size = get_cur_file_size(fp, position_to_eof=True)
if (cur_file_size and
self.etag_value_for_current_download and
self.etag_value_for_current_download == key.etag.strip('"\'')):
# Try to resume existing transfer.
if cur_file_size > key.size:
raise ResumableDownloadException(
'%s is larger (%d) than %s (%d).\nDeleting tracker file, so '
'if you re-try this download it will start from scratch' %
(fp.name, cur_file_size, str(storage_uri_for_key(key)),
key.size), ResumableTransferDisposition.ABORT)
elif cur_file_size == key.size:
if key.bucket.connection.debug >= 1:
print('Download complete.')
return
if key.bucket.connection.debug >= 1:
print('Resuming download.')
headers = headers.copy()
headers['Range'] = 'bytes=%d-%d' % (cur_file_size, key.size - 1)
cb = ByteTranslatingCallbackHandler(cb, cur_file_size).call
self.download_start_point = cur_file_size
else:
if key.bucket.connection.debug >= 1:
print('Starting new resumable download.')
self._save_tracker_info(key)
self.download_start_point = 0
# Truncate the file, in case a new resumable download is being
# started atop an existing file.
fp.truncate(0)
# Disable AWSAuthConnection-level retry behavior, since that would
# cause downloads to restart from scratch.
if isinstance(key, GSKey):
key.get_file(fp, headers, cb, num_cb, torrent, version_id,
override_num_retries=0, hash_algs=hash_algs)
else:
key.get_file(fp, headers, cb, num_cb, torrent, version_id,
override_num_retries=0)
fp.flush() | [
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google/filament | d21f092645b8e1e312307cbf89f1484891347c63 | third_party/benchmark/tools/gbench/report.py | python | partition_benchmarks | (json1, json2) | return partitions | While preserving the ordering, find benchmarks with the same names in
both of the inputs, and group them.
(i.e. partition/filter into groups with common name) | While preserving the ordering, find benchmarks with the same names in
both of the inputs, and group them.
(i.e. partition/filter into groups with common name) | [
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"""
While preserving the ordering, find benchmarks with the same names in
both of the inputs, and group them.
(i.e. partition/filter into groups with common name)
"""
json1_unique_names = get_unique_benchmark_names(json1)
json2_unique_names = get_unique_benchmark_names(json2)
names = intersect(json1_unique_names, json2_unique_names)
partitions = []
for name in names:
# Pick the time unit from the first entry of the lhs benchmark.
time_unit = (x['time_unit']
for x in json1['benchmarks'] if x['name'] == name).next()
# Filter by name and time unit.
lhs = [x for x in json1['benchmarks'] if x['name'] == name and
x['time_unit'] == time_unit]
rhs = [x for x in json2['benchmarks'] if x['name'] == name and
x['time_unit'] == time_unit]
partitions.append([lhs, rhs])
return partitions | [
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h2oai/deepwater | 80e345c582e6ef912a31f42707a2f31c01b064da | docs/sphinxext/docscrape.py | python | dedent_lines | (lines) | return textwrap.dedent("\n".join(lines)).split("\n") | Deindent a list of lines maximally | Deindent a list of lines maximally | [
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"""Deindent a list of lines maximally"""
return textwrap.dedent("\n".join(lines)).split("\n") | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/botocore/response.py | python | StreamingBody.set_socket_timeout | (self, timeout) | Set the timeout seconds on the socket. | Set the timeout seconds on the socket. | [
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"socket",
"."
] | def set_socket_timeout(self, timeout):
"""Set the timeout seconds on the socket."""
# The problem we're trying to solve is to prevent .read() calls from
# hanging. This can happen in rare cases. What we'd like to ideally
# do is set a timeout on the .read() call so that callers can retry
# the request.
# Unfortunately, this isn't currently possible in requests.
# See: https://github.com/kennethreitz/requests/issues/1803
# So what we're going to do is reach into the guts of the stream and
# grab the socket object, which we can set the timeout on. We're
# putting in a check here so in case this interface goes away, we'll
# know.
try:
# To further complicate things, the way to grab the
# underlying socket object from an HTTPResponse is different
# in py2 and py3. So this code has been pushed to botocore.compat.
set_socket_timeout(self._raw_stream, timeout)
except AttributeError:
logger.error("Cannot access the socket object of "
"a streaming response. It's possible "
"the interface has changed.", exc_info=True)
raise | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/richtext.py | python | RichTextCtrl.MoveLeft | (*args, **kwargs) | return _richtext.RichTextCtrl_MoveLeft(*args, **kwargs) | MoveLeft(self, int noPositions=1, int flags=0) -> bool
Move left | MoveLeft(self, int noPositions=1, int flags=0) -> bool | [
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] | def MoveLeft(*args, **kwargs):
"""
MoveLeft(self, int noPositions=1, int flags=0) -> bool
Move left
"""
return _richtext.RichTextCtrl_MoveLeft(*args, **kwargs) | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBThread.GetStopReason | (self) | return _lldb.SBThread_GetStopReason(self) | GetStopReason(SBThread self) -> lldb::StopReason | GetStopReason(SBThread self) -> lldb::StopReason | [
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"""GetStopReason(SBThread self) -> lldb::StopReason"""
return _lldb.SBThread_GetStopReason(self) | [
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/cherrypy/wsgiserver/wsgiserver2.py | python | HTTPConnection.close | (self) | Close the socket underlying this connection. | Close the socket underlying this connection. | [
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"."
] | def close(self):
"""Close the socket underlying this connection."""
self.rfile.close()
if not self.linger:
# Python's socket module does NOT call close on the kernel socket
# when you call socket.close(). We do so manually here because we
# want this server to send a FIN TCP segment immediately. Note this
# must be called *before* calling socket.close(), because the latter
# drops its reference to the kernel socket.
if hasattr(self.socket, '_sock'):
self.socket._sock.close()
self.socket.close()
else:
# On the other hand, sometimes we want to hang around for a bit
# to make sure the client has a chance to read our entire
# response. Skipping the close() calls here delays the FIN
# packet until the socket object is garbage-collected later.
# Someday, perhaps, we'll do the full lingering_close that
# Apache does, but not today.
pass | [
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openthread/openthread | 9fcdbed9c526c70f1556d1ed84099c1535c7cd32 | tools/harness-thci/OpenThread.py | python | OpenThreadTHCI._connect | (self) | Connect to the device. | Connect to the device. | [
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] | def _connect(self):
"""
Connect to the device.
""" | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/training/queue_runner.py | python | QueueRunner._init_from_args | (self, queue=None, enqueue_ops=None, close_op=None,
cancel_op=None) | Create a QueueRunner from arguments.
Args:
queue: A `Queue`.
enqueue_ops: List of enqueue ops to run in threads later.
close_op: Op to close the queue. Pending enqueue ops are preserved.
cancel_op: Op to close the queue and cancel pending enqueue ops.
Raises:
ValueError: If `queue` or `enqueue_ops` are not provided when not
restoring from `queue_runner_def`. | Create a QueueRunner from arguments. | [
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] | def _init_from_args(self, queue=None, enqueue_ops=None, close_op=None,
cancel_op=None):
"""Create a QueueRunner from arguments.
Args:
queue: A `Queue`.
enqueue_ops: List of enqueue ops to run in threads later.
close_op: Op to close the queue. Pending enqueue ops are preserved.
cancel_op: Op to close the queue and cancel pending enqueue ops.
Raises:
ValueError: If `queue` or `enqueue_ops` are not provided when not
restoring from `queue_runner_def`.
"""
if not queue or not enqueue_ops:
raise ValueError("Must provide queue and enqueue_ops.")
self._queue = queue
self._enqueue_ops = enqueue_ops
self._close_op = close_op
self._cancel_op = cancel_op
# Close when no more will be produced, but pending enqueues should be
# preserved.
if not self._close_op:
self._close_op = self._queue.close()
# Close and cancel pending enqueues since there was an error and we want
# to unblock everything so we can cleanly exit.
if not self._cancel_op:
self._cancel_op = self._queue.close(cancel_pending_enqueues=True) | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/urllib3/util/timeout.py | python | Timeout.from_float | (cls, timeout) | return Timeout(read=timeout, connect=timeout) | Create a new Timeout from a legacy timeout value.
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The timeout value used by httplib.py sets the same timeout on the
connect(), and recv() socket requests. This creates a :class:`Timeout`
object that sets the individual timeouts to the ``timeout`` value
passed to this function.
:param timeout: The legacy timeout value.
:type timeout: integer, float, sentinel default object, or None
:return: Timeout object
:rtype: :class:`Timeout`
"""
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openthread/openthread | 9fcdbed9c526c70f1556d1ed84099c1535c7cd32 | tools/harness-automation/autothreadharness/pdu_controller.py | python | ApcPduController.close | (self) | Close telnet connection | Close telnet connection | [
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"""Close telnet connection"""
logger.info('closing telnet')
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goldeneye-source/ges-code | 2630cd8ef3d015af53c72ec2e19fc1f7e7fe8d9d | thirdparty/protobuf-2.3.0/python/google/protobuf/text_format.py | python | _Tokenizer.TryConsume | (self, token) | return False | Tries to consume a given piece of text.
Args:
token: Text to consume.
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"""Tries to consume a given piece of text.
Args:
token: Text to consume.
Returns:
True iff the text was consumed.
"""
if self.token == token:
self.NextToken()
return True
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nsnam/ns-3-dev-git | efdb2e21f45c0a87a60b47c547b68fa140a7b686 | src/flow-monitor/examples/flowmon-parse-results.py | python | Simulation.__init__ | (self, simulation_el) | ! The initializer.
@param self The object pointer.
@param simulation_el The element. | ! The initializer. | [
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] | def __init__(self, simulation_el):
'''! The initializer.
@param self The object pointer.
@param simulation_el The element.
'''
self.flows = []
FlowClassifier_el, = simulation_el.findall("Ipv4FlowClassifier")
flow_map = {}
for flow_el in simulation_el.findall("FlowStats/Flow"):
flow = Flow(flow_el)
flow_map[flow.flowId] = flow
self.flows.append(flow)
for flow_cls in FlowClassifier_el.findall("Flow"):
flowId = int(flow_cls.get('flowId'))
flow_map[flowId].fiveTuple = FiveTuple(flow_cls)
for probe_elem in simulation_el.findall("FlowProbes/FlowProbe"):
probeId = int(probe_elem.get('index'))
for stats in probe_elem.findall("FlowStats"):
flowId = int(stats.get('flowId'))
s = ProbeFlowStats()
s.packets = int(stats.get('packets'))
s.bytes = float(stats.get('bytes'))
s.probeId = probeId
if s.packets > 0:
s.delayFromFirstProbe = parse_time_ns(stats.get('delayFromFirstProbeSum')) / float(s.packets)
else:
s.delayFromFirstProbe = 0
flow_map[flowId].probe_stats_unsorted.append(s) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/functions/Examples/ExamplePeakFunction.py | python | ExamplePeakFunction.setFwhm | (self, new_fwhm) | Called by an external entity, probably a GUI, in response to a user guessing
the height. | Called by an external entity, probably a GUI, in response to a user guessing
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] | def setFwhm(self, new_fwhm):
"""
Called by an external entity, probably a GUI, in response to a user guessing
the height.
"""
sigma = new_fwhm/(2.0*math.sqrt(2.0*math.log(2.0)))
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/s3transfer/bandwidth.py | python | BandwidthLimitedStream.read | (self, amount) | return self._fileobj.read(amount) | Read a specified amount
Reads will only be throttled if bandwidth limiting is enabled. | Read a specified amount | [
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] | def read(self, amount):
"""Read a specified amount
Reads will only be throttled if bandwidth limiting is enabled.
"""
if not self._bandwidth_limiting_enabled:
return self._fileobj.read(amount)
# We do not want to be calling consume on every read as the read
# amounts can be small causing the lock of the leaky bucket to
# introduce noticeable overhead. So instead we keep track of
# how many bytes we have seen and only call consume once we pass a
# certain threshold.
self._bytes_seen += amount
if self._bytes_seen < self._bytes_threshold:
return self._fileobj.read(amount)
self._consume_through_leaky_bucket()
return self._fileobj.read(amount) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py | python | BeamSearchDecoder.initialize | (self, name=None) | return (finished, start_inputs, initial_state) | Initialize the decoder.
Args:
name: Name scope for any created operations.
Returns:
`(finished, start_inputs, initial_state)`. | Initialize the decoder. | [
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] | def initialize(self, name=None):
"""Initialize the decoder.
Args:
name: Name scope for any created operations.
Returns:
`(finished, start_inputs, initial_state)`.
"""
finished, start_inputs = self._finished, self._start_inputs
initial_state = BeamSearchDecoderState(
cell_state=self._initial_cell_state,
log_probs=array_ops.zeros(
[self._batch_size, self._beam_width],
dtype=nest.flatten(self._initial_cell_state)[0].dtype),
finished=finished,
lengths=array_ops.zeros(
[self._batch_size, self._beam_width], dtype=dtypes.int64))
return (finished, start_inputs, initial_state) | [
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grpc/grpc | 27bc6fe7797e43298dc931b96dc57322d0852a9f | src/python/grpcio/grpc/framework/interfaces/face/face.py | python | ResponseReceiver.response | (self, response) | Receives a response from the service-side of the RPC.
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"""Receives a response from the service-side of the RPC.
Args:
response: A response object emitted from the service-side of the RPC.
"""
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2class.py | python | isIdeographic | (ch) | return ret | This function is DEPRECATED. Use xmlIsIdeographicQ instead | This function is DEPRECATED. Use xmlIsIdeographicQ instead | [
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] | def isIdeographic(ch):
"""This function is DEPRECATED. Use xmlIsIdeographicQ instead """
ret = libxml2mod.xmlIsIdeographic(ch)
return ret | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/util/nest.py | python | is_nested_or_composite | (seq) | return _is_nested_or_composite(seq) | Returns true if its input is a nested structure or a composite.
Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
for the definition of a nested structure.
Args:
seq: the value to test.
Returns:
True if the input is a nested structure or a composite. | Returns true if its input is a nested structure or a composite. | [
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] | def is_nested_or_composite(seq):
"""Returns true if its input is a nested structure or a composite.
Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
for the definition of a nested structure.
Args:
seq: the value to test.
Returns:
True if the input is a nested structure or a composite.
"""
return _is_nested_or_composite(seq) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/lib/scimath.py | python | _tocomplex | (arr) | Convert its input `arr` to a complex array.
The input is returned as a complex array of the smallest type that will fit
the original data: types like single, byte, short, etc. become csingle,
while others become cdouble.
A copy of the input is always made.
Parameters
----------
arr : array
Returns
-------
array
An array with the same input data as the input but in complex form.
Examples
--------
First, consider an input of type short:
>>> a = np.array([1,2,3],np.short)
>>> ac = np.lib.scimath._tocomplex(a); ac
array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
>>> ac.dtype
dtype('complex64')
If the input is of type double, the output is correspondingly of the
complex double type as well:
>>> b = np.array([1,2,3],np.double)
>>> bc = np.lib.scimath._tocomplex(b); bc
array([ 1.+0.j, 2.+0.j, 3.+0.j])
>>> bc.dtype
dtype('complex128')
Note that even if the input was complex to begin with, a copy is still
made, since the astype() method always copies:
>>> c = np.array([1,2,3],np.csingle)
>>> cc = np.lib.scimath._tocomplex(c); cc
array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
>>> c *= 2; c
array([ 2.+0.j, 4.+0.j, 6.+0.j], dtype=complex64)
>>> cc
array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64) | Convert its input `arr` to a complex array. | [
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] | def _tocomplex(arr):
"""Convert its input `arr` to a complex array.
The input is returned as a complex array of the smallest type that will fit
the original data: types like single, byte, short, etc. become csingle,
while others become cdouble.
A copy of the input is always made.
Parameters
----------
arr : array
Returns
-------
array
An array with the same input data as the input but in complex form.
Examples
--------
First, consider an input of type short:
>>> a = np.array([1,2,3],np.short)
>>> ac = np.lib.scimath._tocomplex(a); ac
array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
>>> ac.dtype
dtype('complex64')
If the input is of type double, the output is correspondingly of the
complex double type as well:
>>> b = np.array([1,2,3],np.double)
>>> bc = np.lib.scimath._tocomplex(b); bc
array([ 1.+0.j, 2.+0.j, 3.+0.j])
>>> bc.dtype
dtype('complex128')
Note that even if the input was complex to begin with, a copy is still
made, since the astype() method always copies:
>>> c = np.array([1,2,3],np.csingle)
>>> cc = np.lib.scimath._tocomplex(c); cc
array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
>>> c *= 2; c
array([ 2.+0.j, 4.+0.j, 6.+0.j], dtype=complex64)
>>> cc
array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
"""
if issubclass(arr.dtype.type, (nt.single, nt.byte, nt.short, nt.ubyte,
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return arr.astype(nt.csingle)
else:
return arr.astype(nt.cdouble) | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | third_party/tlslite/tlslite/utils/RSAKey.py | python | RSAKey.generate | (bits) | Generate a new key with the specified bit length.
@rtype: L{tlslite.utils.RSAKey.RSAKey} | Generate a new key with the specified bit length. | [
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"""Generate a new key with the specified bit length.
@rtype: L{tlslite.utils.RSAKey.RSAKey}
"""
raise NotImplementedError() | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/crywaflib/default_settings.py | python | load_user_settings | (ctx) | return (user_settings, new_options) | Apply all loaded options if they are different that the default value, and no cmd line value is presented | Apply all loaded options if they are different that the default value, and no cmd line value is presented | [
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] | def load_user_settings(ctx):
""" Apply all loaded options if they are different that the default value, and no cmd line value is presented """
global user_settings
_load_default_settings_file(ctx)
write_user_settings = False
user_settings = ConfigParser.ConfigParser()
user_setting_file = ctx.get_user_settings_node().abspath()
new_options = {}
# Load existing user settings
if not os.path.exists( user_setting_file ):
write_user_settings = True # No file, hence we need to write it
else:
user_settings.read( [user_setting_file] )
# Load settings and check for newly set ones
for section_name, settings_list in ctx.default_settings.items():
# Add not already present sections
if not user_settings.has_section(section_name):
user_settings.add_section(section_name)
write_user_settings = True
# Iterate over all options in this group
for settings in settings_list:
option_name = settings['attribute']
default_value = settings.get('default_value', '')
# Load the value from user settings if it is already present
if user_settings.has_option(section_name, option_name):
value = user_settings.get(section_name, settings['attribute'])
LOADED_OPTIONS[ option_name ] = value
else:
# Add info about newly added option
if not new_options.has_key(section_name):
new_options[section_name] = []
new_options[section_name].append(option_name)
# Load value for current option and stringify it
value = settings.get('default_value', '')
if getattr(ctx.options, option_name) != value:
value = getattr(ctx.options, option_name)
if not isinstance(value, str):
value = str(value)
if ATTRIBUTE_CALLBACKS.get(option_name, None):
value = ATTRIBUTE_CALLBACKS[option_name](ctx, section_name, settings['attribute'], value)
(isValid, warning, error) = ctx.verify_settings_option(option_name, value)
# Add option
if isValid:
user_settings.set( section_name, settings['attribute'], str(value) )
LOADED_OPTIONS[ option_name ] = value
write_user_settings = True
# Check for settings provided by the cmd line
long_form = settings['long_form']
short_form = settings.get('short_form', None)
# Settings on cmdline should have priority, do a sub string match to batch both --option=<SomeThing> and --option <Something>
bOptionSetOnCmdLine = False
for arg in sys.argv:
if long_form in arg:
bOptionSetOnCmdLine = True
value = getattr(ctx.options, option_name)
break
for arg in sys.argv:
if short_form and short_form in arg:
bOptionSetOnCmdLine = True
value = getattr(ctx.options, option_name)
break
# Remember option for internal processing
if bOptionSetOnCmdLine:
LOADED_OPTIONS[ option_name ] = value
elif user_settings.has_option(section_name, option_name): # Load all settings not coming form the cmd line from the config file
setattr(ctx.options, option_name, user_settings.get(section_name, option_name))
# Ensure that the user_settings file only contains valid options that are part of the default list
if not write_user_settings:
for section in user_settings.sections():
# Check for "section" in default list else remove
if section not in ctx.default_settings:
Logs.warn("[user_settings.options]: Removing section: \"[%s]\" as it is not part of the supported settings found in default_settings.options" % (section))
user_settings.remove_section(section)
write_user_settings = True
continue
# Check for "option" in default list else remove
for option in user_settings.options(section):
found_match = False
for option_desc in ctx.default_settings[section]: # loop over all option descriptions :(
if option_desc['attribute'] == option:
found_match = True
break
if not found_match:
Logs.warn("[user_settings.options]: Removing entry: \"%s=%s\" from section: \"[%s]\" as it is not part of the supported settings found in default_settings.options" % (option, user_settings.get(section, option), section))
user_settings.remove_option(section, option)
write_user_settings = True
# Write user settings
if write_user_settings:
ctx.save_user_settings(user_settings)
# Verify IB registry settings after loading all options
_validate_incredibuild_registry_settings(ctx)
_validate_auto_detect_compiler(ctx)
return (user_settings, new_options) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/tools/graph_transforms/__init__.py | python | TransformGraph | (input_graph_def, inputs, outputs, transforms) | return output_graph_def | Python wrapper for the Graph Transform Tool.
Gives access to all graph transforms available through the command line tool.
See documentation at https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md
for full details of the options available.
Args:
input_graph_def: GraphDef object containing a model to be transformed.
inputs: List of node names for the model inputs.
outputs: List of node names for the model outputs.
transforms: List of strings containing transform names and parameters.
Returns:
New GraphDef with transforms applied. | Python wrapper for the Graph Transform Tool. | [
"Python",
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] | def TransformGraph(input_graph_def, inputs, outputs, transforms):
"""Python wrapper for the Graph Transform Tool.
Gives access to all graph transforms available through the command line tool.
See documentation at https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md
for full details of the options available.
Args:
input_graph_def: GraphDef object containing a model to be transformed.
inputs: List of node names for the model inputs.
outputs: List of node names for the model outputs.
transforms: List of strings containing transform names and parameters.
Returns:
New GraphDef with transforms applied.
"""
input_graph_def_string = input_graph_def.SerializeToString()
inputs_string = compat.as_bytes(",".join(inputs))
outputs_string = compat.as_bytes(",".join(outputs))
transforms_string = compat.as_bytes(" ".join(transforms))
with errors.raise_exception_on_not_ok_status() as status:
output_graph_def_string = TransformGraphWithStringInputs(
input_graph_def_string, inputs_string, outputs_string,
transforms_string, status)
output_graph_def = graph_pb2.GraphDef()
output_graph_def.ParseFromString(output_graph_def_string)
return output_graph_def | [
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facebookincubator/BOLT | 88c70afe9d388ad430cc150cc158641701397f70 | clang/bindings/python/clang/cindex.py | python | Cursor.get_tokens | (self) | return TokenGroup.get_tokens(self._tu, self.extent) | Obtain Token instances formulating that compose this Cursor.
This is a generator for Token instances. It returns all tokens which
occupy the extent this cursor occupies. | Obtain Token instances formulating that compose this Cursor. | [
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"""Obtain Token instances formulating that compose this Cursor.
This is a generator for Token instances. It returns all tokens which
occupy the extent this cursor occupies.
"""
return TokenGroup.get_tokens(self._tu, self.extent) | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/google/protobuf-py/google/protobuf/service.py | python | RpcController.SetFailed | (self, reason) | Sets a failure reason.
Causes Failed() to return true on the client side. "reason" will be
incorporated into the message returned by ErrorText(). If you find
you need to return machine-readable information about failures, you
should incorporate it into your response protocol buffer and should
NOT call SetFailed(). | Sets a failure reason. | [
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"""Sets a failure reason.
Causes Failed() to return true on the client side. "reason" will be
incorporated into the message returned by ErrorText(). If you find
you need to return machine-readable information about failures, you
should incorporate it into your response protocol buffer and should
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"""
raise NotImplementedError | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py2/prompt_toolkit/layout/dimension.py | python | LayoutDimension.exact | (cls, amount) | return cls(min=amount, max=amount, preferred=amount) | Return a :class:`.LayoutDimension` with an exact size. (min, max and
preferred set to ``amount``). | Return a :class:`.LayoutDimension` with an exact size. (min, max and
preferred set to ``amount``). | [
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] | def exact(cls, amount):
"""
Return a :class:`.LayoutDimension` with an exact size. (min, max and
preferred set to ``amount``).
"""
return cls(min=amount, max=amount, preferred=amount) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/grid.py | python | Grid.SetSelectionMode | (*args, **kwargs) | return _grid.Grid_SetSelectionMode(*args, **kwargs) | SetSelectionMode(self, WXGRIDSELECTIONMODES selmode) | SetSelectionMode(self, WXGRIDSELECTIONMODES selmode) | [
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"(",
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")"
] | def SetSelectionMode(*args, **kwargs):
"""SetSelectionMode(self, WXGRIDSELECTIONMODES selmode)"""
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google/skia | 82d65d0487bd72f5f7332d002429ec2dc61d2463 | infra/bots/recipes.py | python | parse | (repo_root, recipes_cfg_path) | Parse is a lightweight a recipes.cfg file parser.
Args:
repo_root (str) - native path to the root of the repo we're trying to run
recipes for.
recipes_cfg_path (str) - native path to the recipes.cfg file to process.
Returns (as tuple):
engine_dep (EngineDep|None): The recipe_engine dependency, or None, if the
current repo IS the recipe_engine.
recipes_path (str) - native path to where the recipes live inside of the
current repo (i.e. the folder containing `recipes/` and/or
`recipe_modules`) | Parse is a lightweight a recipes.cfg file parser. | [
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"a",
"recipes",
".",
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"file",
"parser",
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] | def parse(repo_root, recipes_cfg_path):
"""Parse is a lightweight a recipes.cfg file parser.
Args:
repo_root (str) - native path to the root of the repo we're trying to run
recipes for.
recipes_cfg_path (str) - native path to the recipes.cfg file to process.
Returns (as tuple):
engine_dep (EngineDep|None): The recipe_engine dependency, or None, if the
current repo IS the recipe_engine.
recipes_path (str) - native path to where the recipes live inside of the
current repo (i.e. the folder containing `recipes/` and/or
`recipe_modules`)
"""
with open(recipes_cfg_path, 'r') as fh:
pb = json.load(fh)
try:
if pb['api_version'] != 2:
raise MalformedRecipesCfg('unknown version %d' % pb['api_version'],
recipes_cfg_path)
# If we're running ./recipes.py from the recipe_engine repo itself, then
# return None to signal that there's no EngineDep.
repo_name = pb.get('repo_name')
if not repo_name:
repo_name = pb['project_id']
if repo_name == 'recipe_engine':
return None, pb.get('recipes_path', '')
engine = pb['deps']['recipe_engine']
if 'url' not in engine:
raise MalformedRecipesCfg(
'Required field "url" in dependency "recipe_engine" not found',
recipes_cfg_path)
engine.setdefault('revision', '')
engine.setdefault('branch', 'refs/heads/main')
recipes_path = pb.get('recipes_path', '')
# TODO(iannucci): only support absolute refs
if not engine['branch'].startswith('refs/'):
engine['branch'] = 'refs/heads/' + engine['branch']
recipes_path = os.path.join(repo_root,
recipes_path.replace('/', os.path.sep))
return EngineDep(**engine), recipes_path
except KeyError as ex:
raise MalformedRecipesCfg(str(ex), recipes_cfg_path) | [
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... | https://github.com/google/skia/blob/82d65d0487bd72f5f7332d002429ec2dc61d2463/infra/bots/recipes.py#L59-L109 | ||
apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/symbol/numpy_extension/random.py | python | uniform_n | (low=0.0, high=1.0, batch_shape=None, dtype=None, ctx=None) | r"""Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval
``[low, high)`` (includes low, but excludes high). In other words,
any value within the given interval is equally likely to be drawn
by `uniform`.
Parameters
----------
low : float, ndarray, optional
Lower boundary of the output interval. All values generated will be
greater than or equal to low. The default value is 0.
high : float, ndarray, optional
Upper boundary of the output interval. All values generated will be
less than high. The default value is 1.0.
shape : int or tuple of ints, optional
Batch shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k * broadcast(low, high).size`` samples are drawn.
If size is ``None`` (default),
a scalar tensor containing a single value is returned if
``low`` and ``high`` are both scalars. Otherwise,
``np.broadcast(low, high).size`` samples are drawn.
dtype : {'float16', 'float32', 'float64'}, optional
Data type of output samples. Default is 'float32'
ctx : Context, optional
Device context of output. Default is current context.
Returns
-------
out : ndarray
Drawn samples from the parameterized uniform distribution.
See Also
--------
randint : Discrete uniform distribution, yielding integers.
rand : Convenience function that accepts dimensions as input, e.g.,
``rand(2,2)`` would generate a 2-by-2 array of floats,
uniformly distributed over ``[0, 1)``.
Notes
-----
The probability density function of the uniform distribution is
.. math:: p(x) = \frac{1}{b - a}
anywhere within the interval ``[a, b)``, and zero elsewhere.
When ``high`` == ``low``, values of ``low`` will be returned.
If ``high`` < ``low``, the results are officially undefined
and may eventually raise an error, i.e. do not rely on this
function to behave when passed arguments satisfying that
inequality condition. | r"""Draw samples from a uniform distribution. | [
"r",
"Draw",
"samples",
"from",
"a",
"uniform",
"distribution",
"."
] | def uniform_n(low=0.0, high=1.0, batch_shape=None, dtype=None, ctx=None):
r"""Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval
``[low, high)`` (includes low, but excludes high). In other words,
any value within the given interval is equally likely to be drawn
by `uniform`.
Parameters
----------
low : float, ndarray, optional
Lower boundary of the output interval. All values generated will be
greater than or equal to low. The default value is 0.
high : float, ndarray, optional
Upper boundary of the output interval. All values generated will be
less than high. The default value is 1.0.
shape : int or tuple of ints, optional
Batch shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k * broadcast(low, high).size`` samples are drawn.
If size is ``None`` (default),
a scalar tensor containing a single value is returned if
``low`` and ``high`` are both scalars. Otherwise,
``np.broadcast(low, high).size`` samples are drawn.
dtype : {'float16', 'float32', 'float64'}, optional
Data type of output samples. Default is 'float32'
ctx : Context, optional
Device context of output. Default is current context.
Returns
-------
out : ndarray
Drawn samples from the parameterized uniform distribution.
See Also
--------
randint : Discrete uniform distribution, yielding integers.
rand : Convenience function that accepts dimensions as input, e.g.,
``rand(2,2)`` would generate a 2-by-2 array of floats,
uniformly distributed over ``[0, 1)``.
Notes
-----
The probability density function of the uniform distribution is
.. math:: p(x) = \frac{1}{b - a}
anywhere within the interval ``[a, b)``, and zero elsewhere.
When ``high`` == ``low``, values of ``low`` will be returned.
If ``high`` < ``low``, the results are officially undefined
and may eventually raise an error, i.e. do not rely on this
function to behave when passed arguments satisfying that
inequality condition.
"""
from ..numpy import _Symbol as np_symbol
input_type = (isinstance(low, np_symbol), isinstance(high, np_symbol))
if dtype is None:
dtype = 'float32'
if ctx is None:
ctx = current_context()
if batch_shape == ():
batch_shape = None
else:
if isinstance(batch_shape, int):
batch_shape = (batch_shape,)
batch_shape = (-2,) + batch_shape
if input_type == (True, True):
return _npi.uniform(low, high, low=None, high=None, size=batch_shape,
ctx=ctx, dtype=dtype)
elif input_type == (False, True):
return _npi.uniform(high, low=low, high=None, size=batch_shape,
ctx=ctx, dtype=dtype)
elif input_type == (True, False):
return _npi.uniform(low, low=None, high=high, size=batch_shape,
ctx=ctx, dtype=dtype)
else:
return _npi.uniform(low=low, high=high, size=batch_shape,
ctx=ctx, dtype=dtype) | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py | python | xmlTextReader.NewFd | (self, fd, URL, encoding, options) | return ret | Setup an xmltextReader to parse an XML from a file
descriptor. NOTE that the file descriptor will not be
closed when the reader is closed or reset. The parsing
flags @options are a combination of xmlParserOption. This
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flags @options are a combination of xmlParserOption. This
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ret = libxml2mod.xmlReaderNewFd(self._o, fd, URL, encoding, options)
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zdevito/ATen | 4aa3e1de29ed58457e530f84217e53db0998476c | aten/src/ATen/nn_parse.py | python | remove_unused_args | (args, thnn_args) | return args | Returns the subset of args whose name appears in thnn_args | Returns the subset of args whose name appears in thnn_args | [
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"""Returns the subset of args whose name appears in thnn_args"""
def clean_name(name):
name = name[:name.index('[')] if '[' in name else name
if name.endswith('_'):
name = name[:-1]
return name
uses = set([clean_name(arg['name']) for arg in thnn_args])
uses.add('output_mask')
args = [arg for arg in args if arg['name'] in uses]
for arg in args:
if 'default' in arg:
del arg['default']
return args | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | gr-blocks/python/blocks/qa_repack_bits_bb.py | python | qa_repack_bits_bb.test_002_three_msb | (self) | 8 -> 3 | 8 -> 3 | [
"8",
"-",
">",
"3"
] | def test_002_three_msb(self):
""" 8 -> 3 """
src_data = [0b11111101, 0b11111111, 0b11111111]
expected_data = [0b111, ] + [0b111, ] + [0b011, ] + [0b111, ] * 5
k = 8
l = 3
src = blocks.vector_source_b(src_data, False, 1)
repack = blocks.repack_bits_bb(k, l, "", False, gr.GR_MSB_FIRST)
sink = blocks.vector_sink_b()
self.tb.connect(src, repack, sink)
self.tb.run()
self.assertEqual(sink.data(), expected_data) | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | example/image-classification/symbols/resnet-v1.py | python | residual_unit | (data, num_filter, stride, dim_match, name, bottle_neck=True, bn_mom=0.9, workspace=256, memonger=False) | Return ResNet Unit symbol for building ResNet
Parameters
----------
data : str
Input data
num_filter : int
Number of output channels
bnf : int
Bottle neck channels factor with regard to num_filter
stride : tuple
Stride used in convolution
dim_match : Boolean
True means channel number between input and output is the same, otherwise means differ
name : str
Base name of the operators
workspace : int
Workspace used in convolution operator | Return ResNet Unit symbol for building ResNet
Parameters
----------
data : str
Input data
num_filter : int
Number of output channels
bnf : int
Bottle neck channels factor with regard to num_filter
stride : tuple
Stride used in convolution
dim_match : Boolean
True means channel number between input and output is the same, otherwise means differ
name : str
Base name of the operators
workspace : int
Workspace used in convolution operator | [
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"""Return ResNet Unit symbol for building ResNet
Parameters
----------
data : str
Input data
num_filter : int
Number of output channels
bnf : int
Bottle neck channels factor with regard to num_filter
stride : tuple
Stride used in convolution
dim_match : Boolean
True means channel number between input and output is the same, otherwise means differ
name : str
Base name of the operators
workspace : int
Workspace used in convolution operator
"""
if bottle_neck:
conv1 = mx.sym.Convolution(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=stride, pad=(0,0),
no_bias=True, workspace=workspace, name=name + '_conv1')
bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv2 = mx.sym.Convolution(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv2')
bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
conv3 = mx.sym.Convolution(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
workspace=workspace, name=name + '_conv3')
bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
if dim_match:
shortcut = data
else:
conv1sc = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
workspace=workspace, name=name+'_conv1sc')
shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')
if memonger:
shortcut._set_attr(mirror_stage='True')
return mx.sym.Activation(data=bn3 + shortcut, act_type='relu', name=name + '_relu3')
else:
conv1 = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv1')
bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv2 = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv2')
bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')
if dim_match:
shortcut = data
else:
conv1sc = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
workspace=workspace, name=name+'_conv1sc')
shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')
if memonger:
shortcut._set_attr(mirror_stage='True')
return mx.sym.Activation(data=bn2 + shortcut, act_type='relu', name=name + '_relu3') | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/stc.py | python | StyledTextCtrl.StyleSetSpec | (*args, **kwargs) | return _stc.StyledTextCtrl_StyleSetSpec(*args, **kwargs) | StyleSetSpec(self, int styleNum, String spec)
Extract style settings from a spec-string which is composed of one or
more of the following comma separated elements::
bold turns on bold
italic turns on italics
fore:[name or #RRGGBB] sets the foreground colour
back:[name or #RRGGBB] sets the background colour
face:[facename] sets the font face name to use
size:[num] sets the font size in points
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"""
StyleSetSpec(self, int styleNum, String spec)
Extract style settings from a spec-string which is composed of one or
more of the following comma separated elements::
bold turns on bold
italic turns on italics
fore:[name or #RRGGBB] sets the foreground colour
back:[name or #RRGGBB] sets the background colour
face:[facename] sets the font face name to use
size:[num] sets the font size in points
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/slim/python/slim/nets/resnet_v2.py | python | resnet_v2_101 | (inputs,
num_classes=None,
global_pool=True,
is_training=None,
output_stride=None,
reuse=None,
scope='resnet_v2_101') | return resnet_v2(
inputs,
blocks,
num_classes,
is_training,
global_pool,
output_stride,
include_root_block=True,
reuse=reuse,
scope=scope) | ResNet-101 model of [1]. See resnet_v2() for arg and return description. | ResNet-101 model of [1]. See resnet_v2() for arg and return description. | [
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num_classes=None,
global_pool=True,
is_training=None,
output_stride=None,
reuse=None,
scope='resnet_v2_101'):
"""ResNet-101 model of [1]. See resnet_v2() for arg and return description."""
blocks = [
resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),
resnet_v2_block('block3', base_depth=256, num_units=23, stride=2),
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return resnet_v2(
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blocks,
num_classes,
is_training,
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include_root_block=True,
reuse=reuse,
scope=scope) | [
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ZhouWeikuan/DouDiZhu | 0d84ff6c0bc54dba6ae37955de9ae9307513dc99 | code/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py | python | Token.location | (self) | return conf.lib.clang_getTokenLocation(self._tu, self) | The SourceLocation this Token occurs at. | The SourceLocation this Token occurs at. | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/mozbuild/mozpack/chrome/flags.py | python | Flags.__str__ | (self) | return ' '.join(str(self[k]) for k in self) | Serialize the set of flags. | Serialize the set of flags. | [
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'''
Serialize the set of flags.
'''
return ' '.join(str(self[k]) for k in self) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/random_ops.py | python | random_uniform | (shape,
minval=0,
maxval=None,
dtype=dtypes.float32,
seed=None,
name=None) | Outputs random values from a uniform distribution.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded.
For floats, the default range is `[0, 1)`. For ints, at least `maxval` must
be specified explicitly.
In the integer case, the random integers are slightly biased unless
`maxval - minval` is an exact power of two. The bias is small for values of
`maxval - minval` significantly smaller than the range of the output (either
`2**32` or `2**64`).
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
range of random values to generate. Defaults to 0.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
the range of random values to generate. Defaults to 1 if `dtype` is
floating point.
dtype: The type of the output: `float32`, `float64`, `int32`, or `int64`.
seed: A Python integer. Used to create a random seed for the distribution.
See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and `maxval` is not specified. | Outputs random values from a uniform distribution. | [
"Outputs",
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"uniform",
"distribution",
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] | def random_uniform(shape,
minval=0,
maxval=None,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a uniform distribution.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded.
For floats, the default range is `[0, 1)`. For ints, at least `maxval` must
be specified explicitly.
In the integer case, the random integers are slightly biased unless
`maxval - minval` is an exact power of two. The bias is small for values of
`maxval - minval` significantly smaller than the range of the output (either
`2**32` or `2**64`).
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
range of random values to generate. Defaults to 0.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
the range of random values to generate. Defaults to 1 if `dtype` is
floating point.
dtype: The type of the output: `float32`, `float64`, `int32`, or `int64`.
seed: A Python integer. Used to create a random seed for the distribution.
See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and `maxval` is not specified.
"""
dtype = dtypes.as_dtype(dtype)
if maxval is None:
if dtype.is_integer:
raise ValueError("Must specify maxval for integer dtype %r" % dtype)
maxval = 1
with ops.op_scope([shape, minval, maxval], name, "random_uniform") as name:
shape = _ShapeTensor(shape)
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
seed1, seed2 = random_seed.get_seed(seed)
if dtype.is_integer:
return gen_random_ops._random_uniform_int(shape,
minval,
maxval,
seed=seed1,
seed2=seed2,
name=name)
else:
rnd = gen_random_ops._random_uniform(shape,
dtype,
seed=seed1,
seed2=seed2)
return math_ops.add(rnd * (maxval - minval), minval, name=name) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | SystemSettings.SetScreenType | (*args, **kwargs) | return _misc_.SystemSettings_SetScreenType(*args, **kwargs) | SetScreenType(int screen) | SetScreenType(int screen) | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/gluon/probability/distributions/utils.py | python | erfinv | () | return compute | Unified erfinv interface for both scalar and tensor | Unified erfinv interface for both scalar and tensor | [
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"""Unified erfinv interface for both scalar and tensor
"""
def compute(value):
if isinstance(value, Number):
if sc is not None:
return sc.erfinv(value)
else:
raise ValueError('Numbers are not supported as input if scipy is not installed')
return npx.erfinv(value)
return compute | [
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livecode/livecode | 4606a10ea10b16d5071d0f9f263ccdd7ede8b31d | gyp/pylib/gyp/common.py | python | InvertRelativePath | (path, toplevel_dir=None) | return RelativePath(toplevel_dir, os.path.join(toplevel_dir, path)) | Given a path like foo/bar that is relative to toplevel_dir, return
the inverse relative path back to the toplevel_dir.
E.g. os.path.normpath(os.path.join(path, InvertRelativePath(path)))
should always produce the empty string, unless the path contains symlinks. | Given a path like foo/bar that is relative to toplevel_dir, return
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"""Given a path like foo/bar that is relative to toplevel_dir, return
the inverse relative path back to the toplevel_dir.
E.g. os.path.normpath(os.path.join(path, InvertRelativePath(path)))
should always produce the empty string, unless the path contains symlinks.
"""
if not path:
return path
toplevel_dir = '.' if toplevel_dir is None else toplevel_dir
return RelativePath(toplevel_dir, os.path.join(toplevel_dir, path)) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/urllib.py | python | quote_plus | (s, safe='') | return quote(s, safe) | Quote the query fragment of a URL; replacing ' ' with '+ | Quote the query fragment of a URL; replacing ' ' with '+ | [
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"""Quote the query fragment of a URL; replacing ' ' with '+'"""
if ' ' in s:
s = quote(s, safe + ' ')
return s.replace(' ', '+')
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/distributions/python/ops/shape.py | python | _DistributionShape.get_shape | (self, x, name="get_shape") | Returns `Tensor`'s shape partitioned into `sample`, `batch`, `event`.
Args:
x: `Tensor`.
name: Python `str`. The name to give this op.
Returns:
sample_shape: `Tensor` (1D, `int32`).
batch_shape: `Tensor` (1D, `int32`).
event_shape: `Tensor` (1D, `int32`). | Returns `Tensor`'s shape partitioned into `sample`, `batch`, `event`. | [
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"""Returns `Tensor`'s shape partitioned into `sample`, `batch`, `event`.
Args:
x: `Tensor`.
name: Python `str`. The name to give this op.
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sample_shape: `Tensor` (1D, `int32`).
batch_shape: `Tensor` (1D, `int32`).
event_shape: `Tensor` (1D, `int32`).
"""
with self._name_scope(name, values=[x]):
x = ops.convert_to_tensor(x, name="x")
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"""Closure to slice out shape."""
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stop = start + tensor_util.constant_value(size)
slice_ = x.get_shape()[start:stop].as_list()
if all(s is not None for s in slice_):
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return array_ops.slice(array_ops.shape(x), [sum(start_sum)], [size])
sample_ndims = self.get_sample_ndims(x, name=name)
return (slice_shape([], sample_ndims,
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/v8/third_party/jinja2/filters.py | python | do_urlize | (eval_ctx, value, trim_url_limit=None, nofollow=False,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/calltip.py | python | get_entity | (expression) | Return the object corresponding to expression evaluated
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/s3transfer/tasks.py | python | Task.__init__ | (self, transfer_coordinator, main_kwargs=None,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/aui.py | python | AuiPaneInfo.IsDestroyOnClose | (*args, **kwargs) | return _aui.AuiPaneInfo_IsDestroyOnClose(*args, **kwargs) | IsDestroyOnClose(self) -> bool | IsDestroyOnClose(self) -> bool | [
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carla-simulator/carla | 8854804f4d7748e14d937ec763a2912823a7e5f5 | PythonAPI/examples/rss/rss_visualization.py | python | RssUnstructuredSceneVisualizer._world_to_sensor | (cords, camera_transform) | return sensor_cords | Transforms world coordinates to sensor. | Transforms world coordinates to sensor. | [
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HKUST-Aerial-Robotics/Fast-Planner | 2ddd7793eecd573dbb5b47e2c985aa06606df3cf | uav_simulator/Utils/multi_map_server/quadrotor_msgs/src/quadrotor_msgs/msg/_AuxCommand.py | python | AuxCommand.__init__ | (self, *args, **kwds) | Constructor. Any message fields that are implicitly/explicitly
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GXYM/DRRG | 9e074fa9052de8d131f55ca1f6ae6673c1bfeca4 | dataset/icdar15/Evaluation_Protocol/rrc_evaluation_funcs.py | python | decode_utf8 | (raw) | Returns a Unicode object on success, or None on failure | Returns a Unicode object on success, or None on failure | [
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crosslife/OpenBird | 9e0198a1a2295f03fa1e8676e216e22c9c7d380b | cocos2d/tools/bindings-generator/backup/clang-llvm-3.3-pybinding/cindex.py | python | Cursor.kind | (self) | return CursorKind.from_id(self._kind_id) | Return the kind of this cursor. | Return the kind of this cursor. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/selectors.py | python | _BaseSelectorImpl._fileobj_lookup | (self, fileobj) | Return a file descriptor from a file object.
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/ops/control_flow_ops.py | python | ZerosLikeOutsideLoop | (op, index) | Create zeros_like for the specified output of an op. | Create zeros_like for the specified output of an op. | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | scripts/authors.py | python | update_count | (c, lang, counts) | Add the counts from to the total count
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBData.SetDataFromUInt64Array | (self, *args) | return _lldb.SBData_SetDataFromUInt64Array(self, *args) | SetDataFromUInt64Array(self, uint64_t array) -> bool | SetDataFromUInt64Array(self, uint64_t array) -> bool | [
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] | def SetDataFromUInt64Array(self, *args):
"""SetDataFromUInt64Array(self, uint64_t array) -> bool"""
return _lldb.SBData_SetDataFromUInt64Array(self, *args) | [
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