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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/gyp/pylib/gyp/generator/msvs.py | python | _GetUniquePlatforms | (spec) | return platforms | Returns the list of unique platforms for this spec, e.g ['win32', ...].
Arguments:
spec: The target dictionary containing the properties of the target.
Returns:
The MSVSUserFile object created. | Returns the list of unique platforms for this spec, e.g ['win32', ...]. | [
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Arguments:
spec: The target dictionary containing the properties of the target.
Returns:
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"""
# Gather list of unique platforms.
platforms = OrderedSet()
for configuration in spec['configurations']:
platforms.add(_ConfigPlatform(spec['configurations'][configuration]))
platforms = list(platforms)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | TextAttr.HasFontUnderlined | (*args, **kwargs) | return _controls_.TextAttr_HasFontUnderlined(*args, **kwargs) | HasFontUnderlined(self) -> bool | HasFontUnderlined(self) -> bool | [
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PlatformLab/Arachne | e67391471007174dd4002dc2c160628e19c284e8 | scripts/cpplint.py | python | FindStartOfExpressionInLine | (line, endpos, stack) | return (-1, stack) | Find position at the matching start of current expression.
This is almost the reverse of FindEndOfExpressionInLine, but note
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Args:
line: a CleansedLines line.
endpos: start searching at this position.
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"""Find position at the matching start of current expression.
This is almost the reverse of FindEndOfExpressionInLine, but note
that the input position and returned position differs by 1.
Args:
line: a CleansedLines line.
endpos: start searching at this position.
stack: nesting stack at endpos.
Returns:
On finding matching start: (index at matching start, None)
On finding an unclosed expression: (-1, None)
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"""
i = endpos
while i >= 0:
char = line[i]
if char in ')]}':
# Found end of expression, push to expression stack
stack.append(char)
elif char == '>':
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# Ignore it if it's a "->" or ">=" or "operator>"
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if i > 0 and line[i - 1] == '<':
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i -= 1
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if stack and stack[-1] == '>':
stack.pop()
if not stack:
return (i, None)
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# Found start of expression.
#
# If there are any unmatched '>' on the stack, they must be
# operators. Remove those.
while stack and stack[-1] == '>':
stack.pop()
if not stack:
return (-1, None)
if ((char == '(' and stack[-1] == ')') or
(char == '[' and stack[-1] == ']') or
(char == '{' and stack[-1] == '}')):
stack.pop()
if not stack:
return (i, None)
else:
# Mismatched parentheses
return (-1, None)
elif char == ';':
# Found something that look like end of statements. If we are currently
# expecting a '<', the matching '>' must have been an operator, since
# template argument list should not contain statements.
while stack and stack[-1] == '>':
stack.pop()
if not stack:
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i -= 1
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/pydoc.py | python | HTMLDoc.markup | (self, text, escape=None, funcs={}, classes={}, methods={}) | return ''.join(results) | Mark up some plain text, given a context of symbols to look for.
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escape = escape or self.escape
results = []
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results.append('<a href="%s">%s</a>' % (url, escape(all)))
elif pep:
url = 'https://www.python.org/dev/peps/pep-%04d/' % int(pep)
results.append('<a href="%s">%s</a>' % (url, escape(all)))
elif selfdot:
# Create a link for methods like 'self.method(...)'
# and use <strong> for attributes like 'self.attr'
if text[end:end+1] == '(':
results.append('self.' + self.namelink(name, methods))
else:
results.append('self.<strong>%s</strong>' % name)
elif text[end:end+1] == '(':
results.append(self.namelink(name, methods, funcs, classes))
else:
results.append(self.namelink(name, classes))
here = end
results.append(escape(text[here:]))
return ''.join(results) | [
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sailing-pmls/bosen | 06cb58902d011fbea5f9428f10ce30e621492204 | style_script/cpplint.py | python | FindNextMultiLineCommentEnd | (lines, lineix) | return len(lines) | We are inside a comment, find the end marker. | We are inside a comment, find the end marker. | [
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lineix += 1
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/distutils/mingw32ccompiler.py | python | _check_for_import_lib | () | return (False, candidates[0]) | Check if an import library for the Python runtime already exists. | Check if an import library for the Python runtime already exists. | [
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"""Check if an import library for the Python runtime already exists."""
major_version, minor_version = tuple(sys.version_info[:2])
# patterns for the file name of the library itself
patterns = ['libpython%d%d.a',
'libpython%d%d.dll.a',
'libpython%d.%d.dll.a']
# directory trees that may contain the library
stems = [sys.prefix]
if hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix:
stems.append(sys.base_prefix)
elif hasattr(sys, 'real_prefix') and sys.real_prefix != sys.prefix:
stems.append(sys.real_prefix)
# possible subdirectories within those trees where it is placed
sub_dirs = ['libs', 'lib']
# generate a list of candidate locations
candidates = []
for pat in patterns:
filename = pat % (major_version, minor_version)
for stem_dir in stems:
for folder in sub_dirs:
candidates.append(os.path.join(stem_dir, folder, filename))
# test the filesystem to see if we can find any of these
for fullname in candidates:
if os.path.isfile(fullname):
# already exists, in location given
return (True, fullname)
# needs to be built, preferred location given first
return (False, candidates[0]) | [
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OkCupid/okws | 1c337392c676ccb4e9a4c92d11d5d2fada6427d2 | contrib/pub3-upgrade.py | python | Pub1Parser.p_pub | (self, p) | pub : switch
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p[0] = p[1] | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/cloudsearch2/search.py | python | SearchResults.next_page | (self) | Call Cloudsearch to get the next page of search results
:rtype: :class:`boto.cloudsearch2.search.SearchResults`
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"""Call Cloudsearch to get the next page of search results
:rtype: :class:`boto.cloudsearch2.search.SearchResults`
:return: the following page of search results
"""
if self.query.page <= self.num_pages_needed:
self.query.start += self.query.real_size
self.query.page += 1
return self.search_service(self.query)
else:
raise StopIteration | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | Font.Scale | (*args, **kwargs) | return _gdi_.Font_Scale(*args, **kwargs) | Scale(self, float x) -> Font | Scale(self, float x) -> Font | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/ogl/_diagram.py | python | Diagram.GetCount | (self) | return len(self._shapeList) | Return the number of shapes in the diagram. | Return the number of shapes in the diagram. | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/training/queue_runner.py | python | QueueRunner.name | (self) | return self._queue.name | The string name of the underlying Queue. | The string name of the underlying Queue. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/tree.py | python | TreeItem._GetSubList | (self) | return sublist | Do not override! Called by TreeNode. | Do not override! Called by TreeNode. | [
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"""Do not override! Called by TreeNode."""
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return []
sublist = self.GetSubList()
if not sublist:
self.expandable = 0
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dolphin-emu/dolphin | b4c7f2b1e834ce5ea4b2301f9d4fb07c11afeabb | Externals/fmt/support/rst2md.py | python | convert | (rst_path) | return core.publish_file(source_path=rst_path, writer=MDWriter()) | Converts RST file to Markdown. | Converts RST file to Markdown. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/psutil/__init__.py | python | Process.cpu_times | (self) | return self._proc.cpu_times() | Return a (user, system, children_user, children_system)
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/models/rnn/translate/seq2seq_model.py | python | Seq2SeqModel.__init__ | (self, source_vocab_size, target_vocab_size, buckets, size,
num_layers, max_gradient_norm, batch_size, learning_rate,
learning_rate_decay_factor, use_lstm=False,
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source_vocab_size: size of the source vocabulary.
target_vocab_size: size of the target vocabulary.
buckets: a list of pairs (I, O), where I specifies maximum input length
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We assume that the list is sorted, e.g., [(2, 4), (8, 16)].
size: number of units in each layer of the model.
num_layers: number of layers in the model.
max_gradient_norm: gradients will be clipped to maximally this norm.
batch_size: the size of the batches used during training;
the model construction is independent of batch_size, so it can be
changed after initialization if this is convenient, e.g., for decoding.
learning_rate: learning rate to start with.
learning_rate_decay_factor: decay learning rate by this much when needed.
use_lstm: if true, we use LSTM cells instead of GRU cells.
num_samples: number of samples for sampled softmax.
forward_only: if set, we do not construct the backward pass in the model. | Create the model. | [
"Create",
"the",
"model",
"."
] | def __init__(self, source_vocab_size, target_vocab_size, buckets, size,
num_layers, max_gradient_norm, batch_size, learning_rate,
learning_rate_decay_factor, use_lstm=False,
num_samples=512, forward_only=False):
"""Create the model.
Args:
source_vocab_size: size of the source vocabulary.
target_vocab_size: size of the target vocabulary.
buckets: a list of pairs (I, O), where I specifies maximum input length
that will be processed in that bucket, and O specifies maximum output
length. Training instances that have inputs longer than I or outputs
longer than O will be pushed to the next bucket and padded accordingly.
We assume that the list is sorted, e.g., [(2, 4), (8, 16)].
size: number of units in each layer of the model.
num_layers: number of layers in the model.
max_gradient_norm: gradients will be clipped to maximally this norm.
batch_size: the size of the batches used during training;
the model construction is independent of batch_size, so it can be
changed after initialization if this is convenient, e.g., for decoding.
learning_rate: learning rate to start with.
learning_rate_decay_factor: decay learning rate by this much when needed.
use_lstm: if true, we use LSTM cells instead of GRU cells.
num_samples: number of samples for sampled softmax.
forward_only: if set, we do not construct the backward pass in the model.
"""
self.source_vocab_size = source_vocab_size
self.target_vocab_size = target_vocab_size
self.buckets = buckets
self.batch_size = batch_size
self.learning_rate = tf.Variable(float(learning_rate), trainable=False)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False)
# If we use sampled softmax, we need an output projection.
output_projection = None
softmax_loss_function = None
# Sampled softmax only makes sense if we sample less than vocabulary size.
if num_samples > 0 and num_samples < self.target_vocab_size:
w = tf.get_variable("proj_w", [size, self.target_vocab_size])
w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [self.target_vocab_size])
output_projection = (w, b)
def sampled_loss(inputs, labels):
labels = tf.reshape(labels, [-1, 1])
return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, num_samples,
self.target_vocab_size)
softmax_loss_function = sampled_loss
# Create the internal multi-layer cell for our RNN.
single_cell = tf.nn.rnn_cell.GRUCell(size)
if use_lstm:
single_cell = tf.nn.rnn_cell.BasicLSTMCell(size)
cell = single_cell
if num_layers > 1:
cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers)
# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return tf.nn.seq2seq.embedding_attention_seq2seq(
encoder_inputs, decoder_inputs, cell,
num_encoder_symbols=source_vocab_size,
num_decoder_symbols=target_vocab_size,
embedding_size=size,
output_projection=output_projection,
feed_previous=do_decode)
# Feeds for inputs.
self.encoder_inputs = []
self.decoder_inputs = []
self.target_weights = []
for i in xrange(buckets[-1][0]): # Last bucket is the biggest one.
self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
name="encoder{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(tf.placeholder(tf.float32, shape=[None],
name="weight{0}".format(i)))
# Our targets are decoder inputs shifted by one.
targets = [self.decoder_inputs[i + 1]
for i in xrange(len(self.decoder_inputs) - 1)]
# Training outputs and losses.
if forward_only:
self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(
self.encoder_inputs, self.decoder_inputs, targets,
self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True),
softmax_loss_function=softmax_loss_function)
# If we use output projection, we need to project outputs for decoding.
if output_projection is not None:
for b in xrange(len(buckets)):
self.outputs[b] = [
tf.matmul(output, output_projection[0]) + output_projection[1]
for output in self.outputs[b]
]
else:
self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(
self.encoder_inputs, self.decoder_inputs, targets,
self.target_weights, buckets,
lambda x, y: seq2seq_f(x, y, False),
softmax_loss_function=softmax_loss_function)
# Gradients and SGD update operation for training the model.
params = tf.trainable_variables()
if not forward_only:
self.gradient_norms = []
self.updates = []
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
for b in xrange(len(buckets)):
gradients = tf.gradients(self.losses[b], params)
clipped_gradients, norm = tf.clip_by_global_norm(gradients,
max_gradient_norm)
self.gradient_norms.append(norm)
self.updates.append(opt.apply_gradients(
zip(clipped_gradients, params), global_step=self.global_step))
self.saver = tf.train.Saver(tf.all_variables()) | [
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mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Node/FS.py | python | Dir.__clearRepositoryCache | (self, duplicate=None) | Called when we change the repository(ies) for a directory.
This clears any cached information that is invalidated by changing
the repository. | Called when we change the repository(ies) for a directory.
This clears any cached information that is invalidated by changing
the repository. | [
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"""Called when we change the repository(ies) for a directory.
This clears any cached information that is invalidated by changing
the repository."""
for node in self.entries.values():
if node != self.dir:
if node != self and isinstance(node, Dir):
node.__clearRepositoryCache(duplicate)
else:
node.clear()
try:
del node._srcreps
except AttributeError:
pass
if duplicate is not None:
node.duplicate = duplicate | [
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glotzerlab/hoomd-blue | f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a | hoomd/filter/all_.py | python | All.__reduce__ | (self) | return (type(self), tuple()) | Enable (deep)copying and pickling of `All` particle filters. | Enable (deep)copying and pickling of `All` particle filters. | [
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] | def __reduce__(self):
"""Enable (deep)copying and pickling of `All` particle filters."""
return (type(self), tuple()) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | LogBuffer.__init__ | (self, *args, **kwargs) | __init__(self) -> LogBuffer | __init__(self) -> LogBuffer | [
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"(",
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"-",
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"""__init__(self) -> LogBuffer"""
_misc_.LogBuffer_swiginit(self,_misc_.new_LogBuffer(*args, **kwargs)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/grid.py | python | GridCellAttr.SetBackgroundColour | (*args, **kwargs) | return _grid.GridCellAttr_SetBackgroundColour(*args, **kwargs) | SetBackgroundColour(self, Colour colBack) | SetBackgroundColour(self, Colour colBack) | [
"SetBackgroundColour",
"(",
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")"
] | def SetBackgroundColour(*args, **kwargs):
"""SetBackgroundColour(self, Colour colBack)"""
return _grid.GridCellAttr_SetBackgroundColour(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/indexes/base.py | python | Index.isna | (self) | return self._isnan | Detect missing values.
Return a boolean same-sized object indicating if the values are NA.
NA values, such as ``None``, :attr:`numpy.NaN` or :attr:`pd.NaT`, get
mapped to ``True`` values.
Everything else get mapped to ``False`` values. Characters such as
empty strings `''` or :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
Returns
-------
numpy.ndarray
A boolean array of whether my values are NA.
See Also
--------
Index.notna : Boolean inverse of isna.
Index.dropna : Omit entries with missing values.
isna : Top-level isna.
Series.isna : Detect missing values in Series object.
Examples
--------
Show which entries in a pandas.Index are NA. The result is an
array.
>>> idx = pd.Index([5.2, 6.0, np.NaN])
>>> idx
Float64Index([5.2, 6.0, nan], dtype='float64')
>>> idx.isna()
array([False, False, True], dtype=bool)
Empty strings are not considered NA values. None is considered an NA
value.
>>> idx = pd.Index(['black', '', 'red', None])
>>> idx
Index(['black', '', 'red', None], dtype='object')
>>> idx.isna()
array([False, False, False, True], dtype=bool)
For datetimes, `NaT` (Not a Time) is considered as an NA value.
>>> idx = pd.DatetimeIndex([pd.Timestamp('1940-04-25'),
... pd.Timestamp(''), None, pd.NaT])
>>> idx
DatetimeIndex(['1940-04-25', 'NaT', 'NaT', 'NaT'],
dtype='datetime64[ns]', freq=None)
>>> idx.isna()
array([False, True, True, True], dtype=bool) | Detect missing values. | [
"Detect",
"missing",
"values",
"."
] | def isna(self):
"""
Detect missing values.
Return a boolean same-sized object indicating if the values are NA.
NA values, such as ``None``, :attr:`numpy.NaN` or :attr:`pd.NaT`, get
mapped to ``True`` values.
Everything else get mapped to ``False`` values. Characters such as
empty strings `''` or :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
Returns
-------
numpy.ndarray
A boolean array of whether my values are NA.
See Also
--------
Index.notna : Boolean inverse of isna.
Index.dropna : Omit entries with missing values.
isna : Top-level isna.
Series.isna : Detect missing values in Series object.
Examples
--------
Show which entries in a pandas.Index are NA. The result is an
array.
>>> idx = pd.Index([5.2, 6.0, np.NaN])
>>> idx
Float64Index([5.2, 6.0, nan], dtype='float64')
>>> idx.isna()
array([False, False, True], dtype=bool)
Empty strings are not considered NA values. None is considered an NA
value.
>>> idx = pd.Index(['black', '', 'red', None])
>>> idx
Index(['black', '', 'red', None], dtype='object')
>>> idx.isna()
array([False, False, False, True], dtype=bool)
For datetimes, `NaT` (Not a Time) is considered as an NA value.
>>> idx = pd.DatetimeIndex([pd.Timestamp('1940-04-25'),
... pd.Timestamp(''), None, pd.NaT])
>>> idx
DatetimeIndex(['1940-04-25', 'NaT', 'NaT', 'NaT'],
dtype='datetime64[ns]', freq=None)
>>> idx.isna()
array([False, True, True, True], dtype=bool)
"""
return self._isnan | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/msvs_emulation.py | python | _ExtractImportantEnvironment | (output_of_set) | return env | Extracts environment variables required for the toolchain to run from
a textual dump output by the cmd.exe 'set' command. | Extracts environment variables required for the toolchain to run from
a textual dump output by the cmd.exe 'set' command. | [
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"dump",
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"by",
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"cmd",
".",
"exe",
"set",
"command",
"."
] | def _ExtractImportantEnvironment(output_of_set):
"""Extracts environment variables required for the toolchain to run from
a textual dump output by the cmd.exe 'set' command."""
envvars_to_save = (
"goma_.*", # TODO(scottmg): This is ugly, but needed for goma.
"include",
"lib",
"libpath",
"path",
"pathext",
"systemroot",
"temp",
"tmp",
)
env = {}
# This occasionally happens and leads to misleading SYSTEMROOT error messages
# if not caught here.
if output_of_set.count("=") == 0:
raise Exception("Invalid output_of_set. Value is:\n%s" % output_of_set)
for line in output_of_set.splitlines():
for envvar in envvars_to_save:
if re.match(envvar + "=", line.lower()):
var, setting = line.split("=", 1)
if envvar == "path":
# Our own rules (for running gyp-win-tool) and other actions in
# Chromium rely on python being in the path. Add the path to this
# python here so that if it's not in the path when ninja is run
# later, python will still be found.
setting = os.path.dirname(sys.executable) + os.pathsep + setting
env[var.upper()] = setting
break
for required in ("SYSTEMROOT", "TEMP", "TMP"):
if required not in env:
raise Exception(
'Environment variable "%s" '
"required to be set to valid path" % required
)
return env | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/legacy_tf_layers/variable_scope_shim.py | python | _EagerVariableStore._get_single_variable | (
self,
name,
shape=None,
dtype=dtypes.float32,
initializer=None,
regularizer=None,
partition_info=None,
reuse=None,
trainable=None,
caching_device=None,
validate_shape=True,
constraint=None,
synchronization=vs.VariableSynchronization.AUTO,
aggregation=vs.VariableAggregation.NONE) | return v | Get or create a single Variable (e.g.
a shard or entire variable).
See the documentation of get_variable above (ignore partitioning components)
for details.
Args:
name: see get_variable.
shape: see get_variable.
dtype: see get_variable.
initializer: see get_variable.
regularizer: see get_variable.
partition_info: _PartitionInfo object.
reuse: see get_variable.
trainable: see get_variable.
caching_device: see get_variable.
validate_shape: see get_variable.
constraint: see get_variable.
synchronization: see get_variable.
aggregation: see get_variable.
Returns:
A Variable. See documentation of get_variable above.
Raises:
ValueError: See documentation of get_variable above. | Get or create a single Variable (e.g. | [
"Get",
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self,
name,
shape=None,
dtype=dtypes.float32,
initializer=None,
regularizer=None,
partition_info=None,
reuse=None,
trainable=None,
caching_device=None,
validate_shape=True,
constraint=None,
synchronization=vs.VariableSynchronization.AUTO,
aggregation=vs.VariableAggregation.NONE):
"""Get or create a single Variable (e.g.
a shard or entire variable).
See the documentation of get_variable above (ignore partitioning components)
for details.
Args:
name: see get_variable.
shape: see get_variable.
dtype: see get_variable.
initializer: see get_variable.
regularizer: see get_variable.
partition_info: _PartitionInfo object.
reuse: see get_variable.
trainable: see get_variable.
caching_device: see get_variable.
validate_shape: see get_variable.
constraint: see get_variable.
synchronization: see get_variable.
aggregation: see get_variable.
Returns:
A Variable. See documentation of get_variable above.
Raises:
ValueError: See documentation of get_variable above.
"""
# Set to true if initializer is a constant.
initializing_from_value = False
if initializer is not None and not callable(initializer):
initializing_from_value = True
if shape is not None and initializing_from_value:
raise ValueError("If initializer is a constant, do not specify shape.")
dtype = dtypes.as_dtype(dtype)
shape = as_shape(shape)
if name in self._vars:
# Here we handle the case when returning an existing variable.
if reuse is False: # pylint: disable=g-bool-id-comparison
err_msg = ("Variable %s already exists, disallowed."
" Did you mean to set reuse=True or "
"reuse=tf.AUTO_REUSE in VarScope?" % name)
# ResourceVariables don't have an op associated with so no traceback
raise ValueError(err_msg)
found_var = self._vars[name]
if not shape.is_compatible_with(found_var.get_shape()):
raise ValueError("Trying to share variable %s, but specified shape %s"
" and found shape %s." %
(name, shape, found_var.get_shape()))
if not dtype.is_compatible_with(found_var.dtype):
dtype_str = dtype.name
found_type_str = found_var.dtype.name
raise ValueError("Trying to share variable %s, but specified dtype %s"
" and found dtype %s." %
(name, dtype_str, found_type_str))
return found_var
# The code below handles only the case of creating a new variable.
if reuse is True: # pylint: disable=g-bool-id-comparison
raise ValueError("Variable %s does not exist, or was not created with "
"tf.get_variable(). Did you mean to set "
"reuse=tf.AUTO_REUSE in VarScope?" % name)
# Create the tensor to initialize the variable with default value.
if initializer is None:
initializer, initializing_from_value = self._get_default_initializer(
name=name, shape=shape, dtype=dtype)
# Enter an init scope when creating the initializer.
with ops.init_scope():
if initializing_from_value:
init_val = initializer
variable_dtype = None
else:
# Instantiate initializer if provided initializer is a type object.
if tf_inspect.isclass(initializer):
initializer = initializer()
if shape.is_fully_defined():
if "partition_info" in tf_inspect.getargspec(initializer).args:
init_val = functools.partial(initializer,
shape.as_list(),
dtype=dtype,
partition_info=partition_info)
else:
init_val = functools.partial(initializer,
shape.as_list(), dtype=dtype)
variable_dtype = dtype.base_dtype
else:
init_val = initializer
variable_dtype = None
# Create the variable (Always eagerly as a workaround for a strange
# tpu / funcgraph / keras functional model interaction )
with ops.init_scope():
v = variables.Variable(
initial_value=init_val,
name=name,
trainable=trainable,
caching_device=caching_device,
dtype=variable_dtype,
validate_shape=validate_shape,
constraint=constraint,
synchronization=synchronization,
aggregation=aggregation)
self._vars[name] = v
logging.vlog(1, "Created variable %s with shape %s and init %s", v.name,
format(shape), initializer)
# Run the regularizer if requested and save the resulting loss.
if regularizer:
self.add_regularizer(v, regularizer)
return v | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/xml/sax/xmlreader.py | python | Locator.getPublicId | (self) | return None | Return the public identifier for the current event. | Return the public identifier for the current event. | [
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tangzhenyu/Scene-Text-Understanding | 0f7ffc7aea5971a50cdc03d33d0a41075285948b | ctpn_crnn_ocr/CTPN/caffe/python/summarize.py | python | print_table | (table, max_width) | Print a simple nicely-aligned table.
table must be a list of (equal-length) lists. Columns are space-separated,
and as narrow as possible, but no wider than max_width. Text may overflow
columns; note that unlike string.format, this will not affect subsequent
columns, if possible. | Print a simple nicely-aligned table. | [
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] | def print_table(table, max_width):
"""Print a simple nicely-aligned table.
table must be a list of (equal-length) lists. Columns are space-separated,
and as narrow as possible, but no wider than max_width. Text may overflow
columns; note that unlike string.format, this will not affect subsequent
columns, if possible."""
max_widths = [max_width] * len(table[0])
column_widths = [max(printed_len(row[j]) + 1 for row in table)
for j in range(len(table[0]))]
column_widths = [min(w, max_w) for w, max_w in zip(column_widths, max_widths)]
for row in table:
row_str = ''
right_col = 0
for cell, width in zip(row, column_widths):
right_col += width
row_str += cell + ' '
row_str += ' ' * max(right_col - printed_len(row_str), 0)
print row_str | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/platform.py | python | python_version | () | return _sys_version()[1] | Returns the Python version as string 'major.minor.patchlevel'
Note that unlike the Python sys.version, the returned value
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""" Returns the Python version as string 'major.minor.patchlevel'
Note that unlike the Python sys.version, the returned value
will always include the patchlevel (it defaults to 0).
"""
return _sys_version()[1] | [
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epiqc/ScaffCC | 66a79944ee4cd116b27bc1a69137276885461db8 | clang/utils/check_cfc/check_cfc.py | python | main_is_frozen | () | return (hasattr(sys, "frozen") or # new py2exe
hasattr(sys, "importers") or # old py2exe
imp.is_frozen("__main__")) | Returns True when running as a py2exe executable. | Returns True when running as a py2exe executable. | [
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"""Returns True when running as a py2exe executable."""
return (hasattr(sys, "frozen") or # new py2exe
hasattr(sys, "importers") or # old py2exe
imp.is_frozen("__main__")) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/framework/op_def_registry.py | python | get_registered_ops | () | return _registered_ops | Returns a dictionary mapping names to OpDefs. | Returns a dictionary mapping names to OpDefs. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/grid.py | python | GridCellFloatRenderer.GetFormat | (*args, **kwargs) | return _grid.GridCellFloatRenderer_GetFormat(*args, **kwargs) | GetFormat(self) -> int | GetFormat(self) -> int | [
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"""GetFormat(self) -> int"""
return _grid.GridCellFloatRenderer_GetFormat(*args, **kwargs) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/android/appstats.py | python | DeviceSnapshot.GetTimestamp | (self) | return self.timestamp | Returns the time since program start that this snapshot was taken. | Returns the time since program start that this snapshot was taken. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | ListEvent.GetLabel | (*args, **kwargs) | return _controls_.ListEvent_GetLabel(*args, **kwargs) | GetLabel(self) -> String | GetLabel(self) -> String | [
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"""GetLabel(self) -> String"""
return _controls_.ListEvent_GetLabel(*args, **kwargs) | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/gluon/contrib/data/vision/transforms/bbox/utils.py | python | bbox_xyxy_to_xywh | (xyxy) | Convert bounding boxes from format (xmin, ymin, xmax, ymax) to (x, y, w, h).
Parameters
----------
xyxy : list, tuple or numpy.ndarray
The bbox in format (xmin, ymin, xmax, ymax).
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shape `(N, 4)`.
Returns
-------
tuple or numpy.ndarray
The converted bboxes in format (x, y, w, h).
If input is numpy.ndarray, return is numpy.ndarray correspondingly. | Convert bounding boxes from format (xmin, ymin, xmax, ymax) to (x, y, w, h). | [
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"""Convert bounding boxes from format (xmin, ymin, xmax, ymax) to (x, y, w, h).
Parameters
----------
xyxy : list, tuple or numpy.ndarray
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"""
if isinstance(xyxy, (tuple, list)):
if not len(xyxy) == 4:
raise IndexError(
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x1, y1 = xyxy[0], xyxy[1]
w, h = xyxy[2] - x1 + 1, xyxy[3] - y1 + 1
return x1, y1, w, h
elif isinstance(xyxy, np.ndarray):
if not xyxy.size % 4 == 0:
raise IndexError(
"Bounding boxes must have n * 4 elements, given {}".format(xyxy.shape))
return np.hstack((xyxy[:, :2], xyxy[:, 2:4] - xyxy[:, :2] + 1))
else:
raise TypeError(
'Expect input xywh a list, tuple or numpy.ndarray, given {}'.format(type(xyxy))) | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/palindrome-permutation-ii.py | python | Solution2.generatePalindromes | (self, s) | return [''.join(half_palindrome + mid + half_palindrome[::-1]) \
for half_palindrome in set(itertools.permutations(chars))] if len(mid) < 2 else [] | :type s: str
:rtype: List[str] | :type s: str
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"""
:type s: str
:rtype: List[str]
"""
cnt = collections.Counter(s)
mid = tuple(k for k, v in cnt.iteritems() if v % 2)
chars = ''.join(k * (v / 2) for k, v in cnt.iteritems())
return [''.join(half_palindrome + mid + half_palindrome[::-1]) \
for half_palindrome in set(itertools.permutations(chars))] if len(mid) < 2 else [] | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/fitting_widgets/tf_asymmetry_fitting/tf_asymmetry_fitting_model.py | python | TFAsymmetryFittingModel._update_tf_asymmetry_simultaneous_fit_function_after_sequential | (self, workspaces: list, functions: list) | Updates the single fit functions after a sequential fit has been run on the Sequential fitting tab. | Updates the single fit functions after a sequential fit has been run on the Sequential fitting tab. | [
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"""Updates the single fit functions after a sequential fit has been run on the Sequential fitting tab."""
for fit_index, workspace_names in enumerate(workspaces):
if self._are_same_workspaces_as_the_datasets(workspace_names):
self.update_tf_asymmetry_simultaneous_fit_function(functions[fit_index])
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metashell/metashell | f4177e4854ea00c8dbc722cadab26ef413d798ea | 3rd/templight/clang/bindings/python/clang/cindex.py | python | CursorKind.is_invalid | (self) | return conf.lib.clang_isInvalid(self) | Test if this is an invalid kind. | Test if this is an invalid kind. | [
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"""Test if this is an invalid kind."""
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/coverage/coverage/files.py | python | TreeMatcher.info | (self) | return self.dirs | A list of strings for displaying when dumping state. | A list of strings for displaying when dumping state. | [
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"""A list of strings for displaying when dumping state."""
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/multiprocessing/managers.py | python | BaseProxy.__str__ | (self) | Return representation of the referent (or a fall-back if that fails) | Return representation of the referent (or a fall-back if that fails) | [
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try:
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | CalculateLayoutEvent.SetRect | (*args, **kwargs) | return _windows_.CalculateLayoutEvent_SetRect(*args, **kwargs) | SetRect(self, Rect rect) | SetRect(self, Rect rect) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/symtable.py | python | Symbol.is_namespace | (self) | return bool(self.__namespaces) | Returns true if name binding introduces new namespace.
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/debug/lib/stepper.py | python | NodeStepper.last_updated | (self) | return self._last_updated | Get the names of the variables updated in the last cont() call.
Returns:
A set of the variable names updated in the previous cont() call.
If no cont() call has occurred before, returns None. | Get the names of the variables updated in the last cont() call. | [
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"""Get the names of the variables updated in the last cont() call.
Returns:
A set of the variable names updated in the previous cont() call.
If no cont() call has occurred before, returns None.
"""
return self._last_updated | [
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apache/singa | 93fd9da72694e68bfe3fb29d0183a65263d238a1 | setup.py | python | generate_singa_config | (with_cuda, with_nccl) | Generate singa_config.h file to define some macros for the cpp code.
Args:
with_cuda(bool): indicator for cudnn and cuda lib
with_nccl(bool): indicator for nccl lib | Generate singa_config.h file to define some macros for the cpp code. | [
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'''Generate singa_config.h file to define some macros for the cpp code.
Args:
with_cuda(bool): indicator for cudnn and cuda lib
with_nccl(bool): indicator for nccl lib
'''
config = ['#define USE_CBLAS', '#define USE_GLOG', '#define USE_DNNL']
if not with_cuda:
config.append('#define CPU_ONLY')
else:
config.append('#define USE_CUDA')
config.append('#define USE_CUDNN')
if with_nccl:
config.append('#define ENABLE_DIST')
config.append('#define USE_DIST')
# singa_config.h to be included by cpp code
cpp_conf_path = SINGA_HDR / 'singa/singa_config.h'
print('Writing configs to {}'.format(cpp_conf_path))
with cpp_conf_path.open('w') as fd:
for line in config:
fd.write(line + '\n')
versions = [int(x) for x in VERSION.split('+')[0].split('.')[:3]]
fd.write('#define SINGA_MAJOR_VERSION {}\n'.format(versions[0]))
fd.write('#define SINGA_MINOR_VERSION {}\n'.format(versions[1]))
fd.write('#define SINGA_PATCH_VERSION {}\n'.format(versions[2]))
fd.write('#define SINGA_VERSION "{}"\n'.format(VERSION))
# config.i to be included by swig files
swig_conf_path = SINGA_SRC / 'api/config.i'
with swig_conf_path.open('w') as fd:
for line in config:
fd.write(line + ' 1 \n')
fd.write('#define USE_PYTHON 1\n')
if not with_nccl:
fd.write('#define USE_DIST 0\n')
if not with_cuda:
fd.write('#define USE_CUDA 0\n')
fd.write('#define USE_CUDNN 0\n')
else:
fd.write('#define CUDNN_VERSION "{}"\n'.format(
os.environ.get('CUDNN_VERSION')))
versions = [int(x) for x in VERSION.split('+')[0].split('.')[:3]]
fd.write('#define SINGA_MAJOR_VERSION {}\n'.format(versions[0]))
fd.write('#define SINGA_MINOR_VERSION {}\n'.format(versions[1]))
fd.write('#define SINGA_PATCH_VERSION {}\n'.format(versions[2]))
fd.write('#define SINGA_VERSION "{}"\n'.format(VERSION)) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/tools/docs/parser.py | python | is_free_function | (py_object, full_name, index) | return True | Check if input is a free function (and not a class- or static method).
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py_object: The the object in question.
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index: The {full_name:py_object} dictionary for the public API.
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py_object: The the object in question.
full_name: The full name of the object, like `tf.module.symbol`.
index: The {full_name:py_object} dictionary for the public API.
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nvdla/sw | 79538ba1b52b040a4a4645f630e457fa01839e90 | umd/external/protobuf-2.6/python/google/protobuf/message.py | python | Message.__getstate__ | (self) | return dict(serialized=self.SerializePartialToString()) | Support the pickle protocol. | Support the pickle protocol. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_internal/operations/check.py | python | create_package_set_from_installed | (**kwargs) | return package_set, problems | Converts a list of distributions into a PackageSet. | Converts a list of distributions into a PackageSet. | [
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# type: (**Any) -> Tuple[PackageSet, bool]
"""Converts a list of distributions into a PackageSet.
"""
# Default to using all packages installed on the system
if kwargs == {}:
kwargs = {"local_only": False, "skip": ()}
package_set = {}
problems = False
for dist in get_installed_distributions(**kwargs):
name = canonicalize_name(dist.project_name)
try:
package_set[name] = PackageDetails(dist.version, dist.requires())
except (OSError, RequirementParseError) as e:
# Don't crash on unreadable or broken metadata
logger.warning("Error parsing requirements for %s: %s", name, e)
problems = True
return package_set, problems | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/boringssl/src/util/bot/extract.py | python | CheckedJoin | (output, path) | return os.path.join(output, path) | CheckedJoin returns os.path.join(output, path). It does sanity checks to
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path = os.path.normpath(path)
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ZhouWeikuan/DouDiZhu | 0d84ff6c0bc54dba6ae37955de9ae9307513dc99 | code/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py | python | Config.set_library_file | (filename) | Set the exact location of libclang | Set the exact location of libclang | [
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"""Set the exact location of libclang"""
if Config.loaded:
raise Exception("library file must be set before before using " \
"any other functionalities in libclang.")
Config.library_file = filename | [
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/fuchsia/device_target.py | python | DeviceTarget._GetSdkHash | (self) | return (version_info[product_key], version_info[version_key]) | Read version of hash in pre-installed package directory.
Returns:
Tuple of (product, version) of image to be installed.
Raises:
VersionNotFoundError: if contents of buildargs.gn cannot be found or the
version number cannot be extracted. | Read version of hash in pre-installed package directory.
Returns:
Tuple of (product, version) of image to be installed.
Raises:
VersionNotFoundError: if contents of buildargs.gn cannot be found or the
version number cannot be extracted. | [
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Raises:
VersionNotFoundError: if contents of buildargs.gn cannot be found or the
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"""
# TODO(crbug.com/1261961): Stop processing buildargs.gn directly.
with open(os.path.join(self._system_image_dir, _BUILD_ARGS)) as f:
contents = f.readlines()
if not contents:
raise VersionNotFoundError('Could not retrieve %s' % _BUILD_ARGS)
version_key = 'build_info_version'
product_key = 'build_info_product'
info_keys = [product_key, version_key]
version_info = {}
for line in contents:
for k in info_keys:
match = re.match(r'%s = "(.*)"' % k, line)
if match:
version_info[k] = match.group(1)
if not (version_key in version_info and product_key in version_info):
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return (version_info[product_key], version_info[version_key]) | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/integer-break.py | python | Solution2.integerBreak | (self, n) | return res[n % 4] | :type n: int
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"""
:type n: int
:rtype: int
"""
if n < 4:
return n - 1
# integerBreak(n) = max(integerBreak(n - 2) * 2, integerBreak(n - 3) * 3)
res = [0, 1, 2, 3]
for i in xrange(4, n + 1):
res[i % 4] = max(res[(i - 2) % 4] * 2, res[(i - 3) % 4] * 3)
return res[n % 4] | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/imaplib.py | python | IMAP4.deleteacl | (self, mailbox, who) | return self._simple_command('DELETEACL', mailbox, who) | Delete the ACLs (remove any rights) set for who on mailbox.
(typ, [data]) = <instance>.deleteacl(mailbox, who) | Delete the ACLs (remove any rights) set for who on mailbox. | [
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"""Delete the ACLs (remove any rights) set for who on mailbox.
(typ, [data]) = <instance>.deleteacl(mailbox, who)
"""
return self._simple_command('DELETEACL', mailbox, who) | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | src/icebox/icebox_py/__init__.py | python | Vm.resume | (self) | Resume vm. | Resume vm. | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings.GetAsmflags | (self, config) | return asmflags | Returns the flags that need to be added to ml invocations. | Returns the flags that need to be added to ml invocations. | [
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config = self._TargetConfig(config)
asmflags = []
safeseh = self._Setting(('MASM', 'UseSafeExceptionHandlers'), config)
if safeseh == 'true':
asmflags.append('/safeseh')
return asmflags | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/_vendor/ordered_set.py | python | OrderedSet.__iter__ | (self) | return iter(self.items) | Example:
>>> list(iter(OrderedSet([1, 2, 3])))
[1, 2, 3] | Example:
>>> list(iter(OrderedSet([1, 2, 3])))
[1, 2, 3] | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/auto_bisect/bisect_perf_regression.py | python | BisectPerformanceMetrics._FindNextDepotToBisect | (
self, current_depot, min_revision_state, max_revision_state) | return external_depot | Decides which depot the script should dive into next (if any).
Args:
current_depot: Current depot being bisected.
min_revision_state: State of the earliest revision in the bisect range.
max_revision_state: State of the latest revision in the bisect range.
Returns:
Name of the depot to bisect next, or None. | Decides which depot the script should dive into next (if any). | [
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"""Decides which depot the script should dive into next (if any).
Args:
current_depot: Current depot being bisected.
min_revision_state: State of the earliest revision in the bisect range.
max_revision_state: State of the latest revision in the bisect range.
Returns:
Name of the depot to bisect next, or None.
"""
external_depot = None
for next_depot in bisect_utils.DEPOT_NAMES:
if ('platform' in bisect_utils.DEPOT_DEPS_NAME[next_depot] and
bisect_utils.DEPOT_DEPS_NAME[next_depot]['platform'] != os.name):
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if not (bisect_utils.DEPOT_DEPS_NAME[next_depot]['recurse']
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if current_depot == 'v8':
# We grab the bleeding_edge info here rather than earlier because we
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self._FillInV8BleedingEdgeInfo(min_revision_state, max_revision_state)
if (min_revision_state.external.get(next_depot) ==
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if (min_revision_state.external.get(next_depot) and
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external_depot = next_depot
break
return external_depot | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/setuptools/package_index.py | python | parse_bdist_wininst | (name) | return base, py_ver, plat | Return (base,pyversion) or (None,None) for possible .exe name | Return (base,pyversion) or (None,None) for possible .exe name | [
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"""Return (base,pyversion) or (None,None) for possible .exe name"""
lower = name.lower()
base, py_ver, plat = None, None, None
if lower.endswith('.exe'):
if lower.endswith('.win32.exe'):
base = name[:-10]
plat = 'win32'
elif lower.startswith('.win32-py', -16):
py_ver = name[-7:-4]
base = name[:-16]
plat = 'win32'
elif lower.endswith('.win-amd64.exe'):
base = name[:-14]
plat = 'win-amd64'
elif lower.startswith('.win-amd64-py', -20):
py_ver = name[-7:-4]
base = name[:-20]
plat = 'win-amd64'
return base, py_ver, plat | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Arch/ArchProfile.py | python | makeProfile | (profile=[0,'REC','REC100x100','R',100,100]) | return obj | makeProfile(profile): returns a shape with the face defined by the profile data | makeProfile(profile): returns a shape with the face defined by the profile data | [
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] | def makeProfile(profile=[0,'REC','REC100x100','R',100,100]):
'''makeProfile(profile): returns a shape with the face defined by the profile data'''
if not FreeCAD.ActiveDocument:
FreeCAD.Console.PrintError("No active document. Aborting\n")
return
obj = FreeCAD.ActiveDocument.addObject("Part::Part2DObjectPython",profile[2])
obj.Label = profile[2]
if profile[3]=="C":
_ProfileC(obj, profile)
elif profile[3]=="H":
_ProfileH(obj, profile)
elif profile[3]=="R":
_ProfileR(obj, profile)
elif profile[3]=="RH":
_ProfileRH(obj, profile)
elif profile[3]=="U":
_ProfileU(obj, profile)
else :
print("Profile not supported")
if FreeCAD.GuiUp:
ViewProviderProfile(obj.ViewObject)
return obj | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/inspect.py | python | Parameter.replace | (self, *, name=_void, kind=_void,
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if name is _void:
name = self._name
if kind is _void:
kind = self._kind
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annotation = self._annotation
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default = self._default
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/py/py/_io/capture.py | python | FDCapture.__init__ | (self, targetfd, tmpfile=None, now=True, patchsys=False) | save targetfd descriptor, and open a new
temporary file there. If no tmpfile is
specified a tempfile.Tempfile() will be opened
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] | def __init__(self, targetfd, tmpfile=None, now=True, patchsys=False):
""" save targetfd descriptor, and open a new
temporary file there. If no tmpfile is
specified a tempfile.Tempfile() will be opened
in text mode.
"""
self.targetfd = targetfd
if tmpfile is None and targetfd != 0:
f = tempfile.TemporaryFile('wb+')
tmpfile = dupfile(f, encoding="UTF-8")
f.close()
self.tmpfile = tmpfile
self._savefd = os.dup(self.targetfd)
if patchsys:
self._oldsys = getattr(sys, patchsysdict[targetfd])
if now:
self.start() | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/tensor_forest/python/ops/training_ops.py | python | _ScatterAddNdimShape | (unused_op) | return [] | Shape function for ScatterAddNdim Op. | Shape function for ScatterAddNdim Op. | [
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] | def _ScatterAddNdimShape(unused_op):
"""Shape function for ScatterAddNdim Op."""
return [] | [
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bairdzhang/smallhardface | 76fa1d87a9602d9b13d7a7fe693fc7aec91cab80 | external/marcopede-face-eval-f2870fd85d48/util.py | python | load | (filename) | return aux | load any python object | load any python object | [
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"object"
] | def load(filename):
"""
load any python object
"""
fd = open(filename, "r")
aux = cPickle.load(fd)
fd.close()
return aux | [
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gaoxiang12/ORBSLAM2_with_pointcloud_map | cf94d66a9e4253abd66398c7ef461a7a903efffe | ORB_SLAM2_modified/Examples/ROS/ORB_SLAM2/build/devel/_setup_util.py | python | rollback_env_variables | (environ, env_var_subfolders) | return lines | Generate shell code to reset environment variables
by unrolling modifications based on all workspaces in CMAKE_PREFIX_PATH.
This does not cover modifications performed by environment hooks. | Generate shell code to reset environment variables
by unrolling modifications based on all workspaces in CMAKE_PREFIX_PATH.
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'''
Generate shell code to reset environment variables
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This does not cover modifications performed by environment hooks.
'''
lines = []
unmodified_environ = copy.copy(environ)
for key in sorted(env_var_subfolders.keys()):
subfolders = env_var_subfolders[key]
if not isinstance(subfolders, list):
subfolders = [subfolders]
value = _rollback_env_variable(unmodified_environ, key, subfolders)
if value is not None:
environ[key] = value
lines.append(assignment(key, value))
if lines:
lines.insert(0, comment('reset environment variables by unrolling modifications based on all workspaces in CMAKE_PREFIX_PATH'))
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/Tools/c_config.py | python | cxx_load_tools | (conf) | Loads the Waf c++ extensions | Loads the Waf c++ extensions | [
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] | def cxx_load_tools(conf):
"""
Loads the Waf c++ extensions
"""
if not conf.env.DEST_OS:
conf.env.DEST_OS = Utils.unversioned_sys_platform()
conf.load('cxx') | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_internal/index/package_finder.py | python | CandidateEvaluator._sort_key | (self, candidate) | return (
has_allowed_hash, yank_value, binary_preference, candidate.version,
build_tag, pri,
) | Function to pass as the `key` argument to a call to sorted() to sort
InstallationCandidates by preference.
Returns a tuple such that tuples sorting as greater using Python's
default comparison operator are more preferred.
The preference is as follows:
First and foremost, candidates with allowed (matching) hashes are
always preferred over candidates without matching hashes. This is
because e.g. if the only candidate with an allowed hash is yanked,
we still want to use that candidate.
Second, excepting hash considerations, candidates that have been
yanked (in the sense of PEP 592) are always less preferred than
candidates that haven't been yanked. Then:
If not finding wheels, they are sorted by version only.
If finding wheels, then the sort order is by version, then:
1. existing installs
2. wheels ordered via Wheel.support_index_min(self._supported_tags)
3. source archives
If prefer_binary was set, then all wheels are sorted above sources.
Note: it was considered to embed this logic into the Link
comparison operators, but then different sdist links
with the same version, would have to be considered equal | Function to pass as the `key` argument to a call to sorted() to sort
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] | def _sort_key(self, candidate):
# type: (InstallationCandidate) -> CandidateSortingKey
"""
Function to pass as the `key` argument to a call to sorted() to sort
InstallationCandidates by preference.
Returns a tuple such that tuples sorting as greater using Python's
default comparison operator are more preferred.
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3. source archives
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Note: it was considered to embed this logic into the Link
comparison operators, but then different sdist links
with the same version, would have to be considered equal
"""
valid_tags = self._supported_tags
support_num = len(valid_tags)
build_tag = () # type: BuildTag
binary_preference = 0
link = candidate.link
if link.is_wheel:
# can raise InvalidWheelFilename
wheel = Wheel(link.filename)
if not wheel.supported(valid_tags):
raise UnsupportedWheel(
"{} is not a supported wheel for this platform. It "
"can't be sorted.".format(wheel.filename)
)
if self._prefer_binary:
binary_preference = 1
pri = -(wheel.support_index_min(valid_tags))
if wheel.build_tag is not None:
match = re.match(r'^(\d+)(.*)$', wheel.build_tag)
build_tag_groups = match.groups()
build_tag = (int(build_tag_groups[0]), build_tag_groups[1])
else: # sdist
pri = -(support_num)
has_allowed_hash = int(link.is_hash_allowed(self._hashes))
yank_value = -1 * int(link.is_yanked) # -1 for yanked.
return (
has_allowed_hash, yank_value, binary_preference, candidate.version,
build_tag, pri,
) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/cuda/api.py | python | device_array | (shape, dtype=np.float, strides=None, order='C', stream=0) | return devicearray.DeviceNDArray(shape=shape, strides=strides, dtype=dtype,
stream=stream) | device_array(shape, dtype=np.float, strides=None, order='C', stream=0)
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"""device_array(shape, dtype=np.float, strides=None, order='C', stream=0)
Allocate an empty device ndarray. Similar to :meth:`numpy.empty`.
"""
shape, strides, dtype = _prepare_shape_strides_dtype(shape, strides, dtype,
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return devicearray.DeviceNDArray(shape=shape, strides=strides, dtype=dtype,
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mysql/mysql-router | cc0179f982bb9739a834eb6fd205a56224616133 | ext/gmock/scripts/gmock_doctor.py | python | _OverloadedFunctionActionDiagnoser | (msg) | return _GenericDiagnoser('OFA', 'Overloaded Function Action',
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] | def _OverloadedFunctionActionDiagnoser(msg):
"""Diagnoses the OFA disease, given the error messages by the compiler."""
gcc_regex = (_GCC_FILE_LINE_RE + r'error: no matching function for call to '
r'\'Invoke\(<unresolved overloaded function type>')
clang_regex = (_CLANG_FILE_LINE_RE + r'error: no matching '
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r'.*\bgmock-\w+-actions\.h:\d+:\d+:\s+'
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diagnosis = """
Function you are passing to Invoke is overloaded. Please tell your compiler
which overloaded version you want to use.
For example, if you want to use the version whose signature is
bool MyFunction(int n, double x);
you should write something like
Invoke(static_cast<bool (*)(int n, double x)>(MyFunction))"""
return _GenericDiagnoser('OFA', 'Overloaded Function Action',
[(gcc_regex, diagnosis),
(clang_regex, diagnosis)],
msg) | [
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vusec/vuzzer64 | 2b1b0ed757a3dca114db0192fa4ab1add92348bc | fuzzer-code/operators.py | python | GAoperator.double_full_mutate | (self,original,fl) | return self.change_random_full(result,fl) | This is called to do heavy mutation when no progress is made in previous generations. | This is called to do heavy mutation when no progress is made in previous generations. | [
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''' This is called to do heavy mutation when no progress is made in previous generations. '''
result = self.change_random_full(original,fl)
return self.change_random_full(result,fl) | [
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rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/Chem/GraphDescriptors.py | python | _pyChi0v | (mol) | return res | From equations (5),(9) and (10) of Rev. Comp. Chem. vol 2, 367-422, (1991) | From equations (5),(9) and (10) of Rev. Comp. Chem. vol 2, 367-422, (1991) | [
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""" From equations (5),(9) and (10) of Rev. Comp. Chem. vol 2, 367-422, (1991)
"""
deltas = _hkDeltas(mol)
while 0 in deltas:
deltas.remove(0)
mol._hkDeltas = None
res = sum(numpy.sqrt(1. / numpy.array(deltas)))
return res | [
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nnrg/opennero | 43e12a1bcba6e228639db3886fec1dc47ddc24cb | mods/Maze/environment.py | python | MazeEnvironment.mark_maze_agent | (self, agent, r1, c1, r2, c2) | mark a maze cell with an agent moving from r1, c1 to r2, c2 | mark a maze cell with an agent moving from r1, c1 to r2, c2 | [
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""" mark a maze cell with an agent moving from r1, c1 to r2, c2 """
# remove the previous object, if necessary
self.unmark_maze_agent(r2,c2)
# add a new marker object
position = Vector3f( (r1+1) * GRID_DX, (c1+1) * GRID_DY, 0)
rotation = self.get_next_rotation( (r2-r1, c2-c1) )
agent_id = addObject(agent, position = position, rotation = rotation)
self.marker_states[agent_id] = ((r1, c1), (r2, c2))
self.agent_map[(r2,c2)] = agent_id | [
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DaFuCoding/MTCNN_Caffe | 09c30c3ff391bd9cb6b249c1910afaf147767ab3 | python/caffe/coord_map.py | python | crop | (top_from, top_to) | return L.Crop(top_from, top_to,
crop_param=dict(axis=ax + 1, # +1 for first cropping dim.
offset=list(-np.round(b).astype(int)))) | Define a Crop layer to crop a top (from) to another top (to) by
determining the coordinate mapping between the two and net spec'ing
the axis and shift parameters of the crop. | Define a Crop layer to crop a top (from) to another top (to) by
determining the coordinate mapping between the two and net spec'ing
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"""
Define a Crop layer to crop a top (from) to another top (to) by
determining the coordinate mapping between the two and net spec'ing
the axis and shift parameters of the crop.
"""
ax, a, b = coord_map_from_to(top_from, top_to)
assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a)
assert (b <= 0).all(), 'cannot crop negative offset (b = {})'.format(b)
assert (np.round(b) == b).all(), 'cannot crop noninteger offset ' \
'(b = {})'.format(b)
return L.Crop(top_from, top_to,
crop_param=dict(axis=ax + 1, # +1 for first cropping dim.
offset=list(-np.round(b).astype(int)))) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/src/motionplanning.py | python | CSpaceInterface.setSampler | (self, pySamp) | return _motionplanning.CSpaceInterface_setSampler(self, pySamp) | setSampler(CSpaceInterface self, PyObject * pySamp) | setSampler(CSpaceInterface self, PyObject * pySamp) | [
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"""
setSampler(CSpaceInterface self, PyObject * pySamp)
"""
return _motionplanning.CSpaceInterface_setSampler(self, pySamp) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_misc.py | python | ConfigBase.GetNextEntry | (*args, **kwargs) | return _misc_.ConfigBase_GetNextEntry(*args, **kwargs) | GetNextEntry(long index) -> (more, value, index)
Allows enumerating the entries in the current group in a config
object. Returns a tuple containing a flag indicating there are more
items, the name of the current item, and an index to pass to
GetNextGroup to fetch the next item. | GetNextEntry(long index) -> (more, value, index) | [
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"""
GetNextEntry(long index) -> (more, value, index)
Allows enumerating the entries in the current group in a config
object. Returns a tuple containing a flag indicating there are more
items, the name of the current item, and an index to pass to
GetNextGroup to fetch the next item.
"""
return _misc_.ConfigBase_GetNextEntry(*args, **kwargs) | [
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wyrover/book-code | 7f4883d9030d553bc6bcfa3da685e34789839900 | 3rdparty/protobuf/python/google/protobuf/internal/well_known_types.py | python | FieldMask.AllFieldsFromDescriptor | (self, message_descriptor) | Gets all direct fields of Message Descriptor to FieldMask. | Gets all direct fields of Message Descriptor to FieldMask. | [
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] | def AllFieldsFromDescriptor(self, message_descriptor):
"""Gets all direct fields of Message Descriptor to FieldMask."""
self.Clear()
for field in message_descriptor.fields:
self.paths.append(field.name) | [
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nnrg/opennero | 43e12a1bcba6e228639db3886fec1dc47ddc24cb | mods/Roomba/module.py | python | furniture | (x,y) | return (constants.XDIM * x, constants.YDIM * (1-y)) | convert from furniture space to roomba space | convert from furniture space to roomba space | [
"convert",
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"furniture",
"space",
"to",
"roomba",
"space"
] | def furniture(x,y):
"""
convert from furniture space to roomba space
"""
return (constants.XDIM * x, constants.YDIM * (1-y)) | [
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rodeofx/OpenWalter | 6116fbe3f04f1146c854afbfbdbe944feaee647e | walter/katana/WalterIn/walterUSDExtern.py | python | WalterUSDExtern.getPrimsCount | (self) | return WALTER_IN_SO.WalterUSDExtern_getPrimsCount(self.obj) | Gets the number of USD primitives. | Gets the number of USD primitives. | [
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] | def getPrimsCount(self):
WALTER_IN_SO.WalterUSDExtern_getPrimsCount.restype = c_int
WALTER_IN_SO.WalterUSDExtern_getPrimsCount.argtypes = [c_void_p]
"""Gets the number of USD primitives."""
return WALTER_IN_SO.WalterUSDExtern_getPrimsCount(self.obj) | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/symbol/symbol.py | python | Symbol._compose | (self, *args, **kwargs) | Composes symbol using inputs.
x._compose(y, z) <=> x(y,z)
This function mutates the current symbol.
Example
-------
>>> data = mx.symbol.Variable('data')
>>> net1 = mx.symbol.FullyConnected(data=data, name='fc1', num_hidden=10)
>>> net2 = mx.symbol.FullyConnected(name='fc3', num_hidden=10)
>>> net2
<Symbol fc3>
>>> net2._compose(fc3_data=net1, name='composed')
>>> net2
<Symbol composed>
Parameters
----------
args:
Positional arguments.
kwargs:
Keyword arguments.
Returns
-------
The resulting symbol. | Composes symbol using inputs. | [
"Composes",
"symbol",
"using",
"inputs",
"."
] | def _compose(self, *args, **kwargs):
"""Composes symbol using inputs.
x._compose(y, z) <=> x(y,z)
This function mutates the current symbol.
Example
-------
>>> data = mx.symbol.Variable('data')
>>> net1 = mx.symbol.FullyConnected(data=data, name='fc1', num_hidden=10)
>>> net2 = mx.symbol.FullyConnected(name='fc3', num_hidden=10)
>>> net2
<Symbol fc3>
>>> net2._compose(fc3_data=net1, name='composed')
>>> net2
<Symbol composed>
Parameters
----------
args:
Positional arguments.
kwargs:
Keyword arguments.
Returns
-------
The resulting symbol.
"""
name = kwargs.pop('name', None)
if name:
name = c_str(name)
if len(args) != 0 and len(kwargs) != 0:
raise TypeError('compose only accept input Symbols \
either as positional or keyword arguments, not both')
for arg in args:
if not isinstance(arg, Symbol):
raise TypeError('Compose expect `Symbol` as arguments')
for val in kwargs.values():
if not isinstance(val, Symbol):
raise TypeError('Compose expect `Symbol` as arguments')
num_args = len(args) + len(kwargs)
if len(kwargs) != 0:
keys = c_str_array(kwargs.keys())
args = c_handle_array(kwargs.values())
else:
keys = None
args = c_handle_array(args)
check_call(_LIB.MXSymbolCompose(
self.handle, name, num_args, keys, args)) | [
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Tencent/TNN | 7acca99f54c55747b415a4c57677403eebc7b706 | third_party/flatbuffers/python/flatbuffers/flexbuffers.py | python | Builder._WriteScalarVector | (self, element_type, byte_width, elements, fixed) | return loc | Writes scalar vector elements to the underlying buffer. | Writes scalar vector elements to the underlying buffer. | [
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"""Writes scalar vector elements to the underlying buffer."""
bit_width = BitWidth.B(byte_width)
# If you get this exception, you're trying to write a vector with a size
# field that is bigger than the scalars you're trying to write (e.g. a
# byte vector > 255 elements). For such types, write a "blob" instead.
if BitWidth.U(len(elements)) > bit_width:
raise ValueError('too many elements for the given byte_width')
self._Align(bit_width)
if not fixed:
self._Write(U, len(elements), byte_width)
loc = len(self._buf)
fmt = {Type.INT: I, Type.UINT: U, Type.FLOAT: F}.get(element_type)
if not fmt:
raise TypeError('unsupported element_type')
self._WriteVector(fmt, elements, byte_width)
type_ = Type.ToTypedVector(element_type, len(elements) if fixed else 0)
self._stack.append(Value(loc, type_, bit_width))
return loc | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/pkg_resources/_vendor/six.py | python | add_metaclass | (metaclass) | return wrapper | Class decorator for creating a class with a metaclass. | Class decorator for creating a class with a metaclass. | [
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] | def add_metaclass(metaclass):
"""Class decorator for creating a class with a metaclass."""
def wrapper(cls):
orig_vars = cls.__dict__.copy()
slots = orig_vars.get('__slots__')
if slots is not None:
if isinstance(slots, str):
slots = [slots]
for slots_var in slots:
orig_vars.pop(slots_var)
orig_vars.pop('__dict__', None)
orig_vars.pop('__weakref__', None)
return metaclass(cls.__name__, cls.__bases__, orig_vars)
return wrapper | [
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H-uru/Plasma | c2140ea046e82e9c199e257a7f2e7edb42602871 | Scripts/Python/plasma/PlasmaTypes.py | python | PtFindAvatar | (events) | return None | Find the avatar in one of the event records | Find the avatar in one of the event records | [
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] | def PtFindAvatar(events):
"Find the avatar in one of the event records"
for event in events:
if event[0]==kCollisionEvent or event[0]==kPickedEvent or event[0]==kFacingEvent or event[0]==kContainedEvent or event[0]==kSpawnedEvent:
return event[2]
if event[0] == kMultiStageEvent:
return event[3]
# didn't find one
return None | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/backend.py | python | _preprocess_conv3d_input | (x, data_format) | return x, tf_data_format | Transpose and cast the input before the conv3d.
Arguments:
x: input tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
Returns:
A tensor. | Transpose and cast the input before the conv3d. | [
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] | def _preprocess_conv3d_input(x, data_format):
"""Transpose and cast the input before the conv3d.
Arguments:
x: input tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
Returns:
A tensor.
"""
tf_data_format = 'NDHWC'
if data_format == 'channels_first':
if not _has_nchw_support():
x = array_ops.transpose(x, (0, 2, 3, 4, 1))
else:
tf_data_format = 'NCDHW'
return x, tf_data_format | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/imaplib.py | python | IMAP4.sort | (self, sort_criteria, charset, *search_criteria) | return self._untagged_response(typ, dat, name) | IMAP4rev1 extension SORT command.
(typ, [data]) = <instance>.sort(sort_criteria, charset, search_criteria, ...) | IMAP4rev1 extension SORT command. | [
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] | def sort(self, sort_criteria, charset, *search_criteria):
"""IMAP4rev1 extension SORT command.
(typ, [data]) = <instance>.sort(sort_criteria, charset, search_criteria, ...)
"""
name = 'SORT'
#if not name in self.capabilities: # Let the server decide!
# raise self.error('unimplemented extension command: %s' % name)
if (sort_criteria[0],sort_criteria[-1]) != ('(',')'):
sort_criteria = '(%s)' % sort_criteria
typ, dat = self._simple_command(name, sort_criteria, charset, *search_criteria)
return self._untagged_response(typ, dat, name) | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/polynomial/chebyshev.py | python | _zseries_mul | (z1, z2) | return np.convolve(z1, z2) | Multiply two z-series.
Multiply two z-series to produce a z-series.
Parameters
----------
z1, z2 : 1-D ndarray
The arrays must be 1-D but this is not checked.
Returns
-------
product : 1-D ndarray
The product z-series.
Notes
-----
This is simply convolution. If symmetric/anti-symmetric z-series are
denoted by S/A then the following rules apply:
S*S, A*A -> S
S*A, A*S -> A | Multiply two z-series. | [
"Multiply",
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"z",
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"."
] | def _zseries_mul(z1, z2) :
"""Multiply two z-series.
Multiply two z-series to produce a z-series.
Parameters
----------
z1, z2 : 1-D ndarray
The arrays must be 1-D but this is not checked.
Returns
-------
product : 1-D ndarray
The product z-series.
Notes
-----
This is simply convolution. If symmetric/anti-symmetric z-series are
denoted by S/A then the following rules apply:
S*S, A*A -> S
S*A, A*S -> A
"""
return np.convolve(z1, z2) | [
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PX4/PX4-Autopilot | 0b9f60a0370be53d683352c63fd92db3d6586e18 | Tools/mavlink_px4.py | python | MAVLink.hil_rc_inputs_raw_send | (self, time_usec, chan1_raw, chan2_raw, chan3_raw, chan4_raw, chan5_raw, chan6_raw, chan7_raw, chan8_raw, chan9_raw, chan10_raw, chan11_raw, chan12_raw, rssi) | return self.send(self.hil_rc_inputs_raw_encode(time_usec, chan1_raw, chan2_raw, chan3_raw, chan4_raw, chan5_raw, chan6_raw, chan7_raw, chan8_raw, chan9_raw, chan10_raw, chan11_raw, chan12_raw, rssi)) | Sent from simulation to autopilot. The RAW values of the RC channels
received. The standard PPM modulation is as follows:
1000 microseconds: 0%, 2000 microseconds: 100%.
Individual receivers/transmitters might violate this
specification.
time_usec : Timestamp (microseconds since UNIX epoch or microseconds since system boot) (uint64_t)
chan1_raw : RC channel 1 value, in microseconds (uint16_t)
chan2_raw : RC channel 2 value, in microseconds (uint16_t)
chan3_raw : RC channel 3 value, in microseconds (uint16_t)
chan4_raw : RC channel 4 value, in microseconds (uint16_t)
chan5_raw : RC channel 5 value, in microseconds (uint16_t)
chan6_raw : RC channel 6 value, in microseconds (uint16_t)
chan7_raw : RC channel 7 value, in microseconds (uint16_t)
chan8_raw : RC channel 8 value, in microseconds (uint16_t)
chan9_raw : RC channel 9 value, in microseconds (uint16_t)
chan10_raw : RC channel 10 value, in microseconds (uint16_t)
chan11_raw : RC channel 11 value, in microseconds (uint16_t)
chan12_raw : RC channel 12 value, in microseconds (uint16_t)
rssi : Receive signal strength indicator, 0: 0%, 255: 100% (uint8_t) | Sent from simulation to autopilot. The RAW values of the RC channels
received. The standard PPM modulation is as follows:
1000 microseconds: 0%, 2000 microseconds: 100%.
Individual receivers/transmitters might violate this
specification. | [
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Individual receivers/transmitters might violate this
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chan4_raw : RC channel 4 value, in microseconds (uint16_t)
chan5_raw : RC channel 5 value, in microseconds (uint16_t)
chan6_raw : RC channel 6 value, in microseconds (uint16_t)
chan7_raw : RC channel 7 value, in microseconds (uint16_t)
chan8_raw : RC channel 8 value, in microseconds (uint16_t)
chan9_raw : RC channel 9 value, in microseconds (uint16_t)
chan10_raw : RC channel 10 value, in microseconds (uint16_t)
chan11_raw : RC channel 11 value, in microseconds (uint16_t)
chan12_raw : RC channel 12 value, in microseconds (uint16_t)
rssi : Receive signal strength indicator, 0: 0%, 255: 100% (uint8_t)
'''
return self.send(self.hil_rc_inputs_raw_encode(time_usec, chan1_raw, chan2_raw, chan3_raw, chan4_raw, chan5_raw, chan6_raw, chan7_raw, chan8_raw, chan9_raw, chan10_raw, chan11_raw, chan12_raw, rssi)) | [
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nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/generators/visitors/AbstractVisitor.py | python | AbstractVisitor.DictStartVisit | (self, obj) | Defined to generate start of command Python class. | Defined to generate start of command Python class. | [
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SoarGroup/Soar | a1c5e249499137a27da60533c72969eef3b8ab6b | scons/scons-local-4.1.0/SCons/Platform/posix.py | python | escape | (arg) | return '"' + arg + '"' | escape shell special characters | escape shell special characters | [
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] | def escape(arg):
"""escape shell special characters"""
slash = '\\'
special = '"$'
arg = arg.replace(slash, slash+slash)
for c in special:
arg = arg.replace(c, slash+c)
# print("ESCAPE RESULT: %s" % arg)
return '"' + arg + '"' | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_misc.py | python | FileType.GetOpenCommand | (*args, **kwargs) | return _misc_.FileType_GetOpenCommand(*args, **kwargs) | GetOpenCommand(self, String filename, String mimetype=EmptyString) -> PyObject | GetOpenCommand(self, String filename, String mimetype=EmptyString) -> PyObject | [
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"""GetOpenCommand(self, String filename, String mimetype=EmptyString) -> PyObject"""
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/programs/scripts/internetcontent/nv_python_libs/rev3/rev3_api.py | python | Videos.getVideoID | (self, link) | return videoID | Read the Web page to find the video ID number used for fullscreen autoplay
return the video ID number
return None if the number cannot be found | Read the Web page to find the video ID number used for fullscreen autoplay
return the video ID number
return None if the number cannot be found | [
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''' Read the Web page to find the video ID number used for fullscreen autoplay
return the video ID number
return None if the number cannot be found
'''
videoID = None
try:
eTree = etree.parse(link, self.FullScreenParser)
except Exception as errormsg:
sys.stderr.write("! Error: The URL (%s) cause the exception error (%s)\n" % (link, errormsg))
return videoID
if self.config['debug_enabled']:
print("Raw unfiltered URL imput:")
sys.stdout.write(etree.tostring(eTree, encoding='UTF-8', pretty_print=True))
print()
if not eTree:
return videoID
# Filter out the video id
try:
tmpVideoID = self.FullScreenVidIDxPath(eTree)
except AssertionError as e:
sys.stderr.write("No filter results for VideoID from url(%s)\n" % link)
sys.stderr.write("! Error:(%s)\n" % e)
return videoID
if len(tmpVideoID):
if tmpVideoID[0].get('id'):
videoID = tmpVideoID[0].attrib['id'].strip().replace('player-', '')
return videoID | [
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generalized-intelligence/GAAS | 29ab17d3e8a4ba18edef3a57c36d8db6329fac73 | algorithms/src/LocalizationAndMapping/registration_localization/fast_gicp/thirdparty/Sophus/py/sophus/so2.py | python | So2.matrix | (self) | return sympy.Matrix([
[self.z.real, -self.z.imag],
[self.z.imag, self.z.real]]) | returns matrix representation | returns matrix representation | [
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""" returns matrix representation """
return sympy.Matrix([
[self.z.real, -self.z.imag],
[self.z.imag, self.z.real]]) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/grid.py | python | GridTableMessage.SetCommandInt2 | (*args, **kwargs) | return _grid.GridTableMessage_SetCommandInt2(*args, **kwargs) | SetCommandInt2(self, int comInt2) | SetCommandInt2(self, int comInt2) | [
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"""SetCommandInt2(self, int comInt2)"""
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/bindings/python/MythTV/dataheap.py | python | Job.setStatus | (self,status) | Job.setStatus(Status) -> None, updates status | Job.setStatus(Status) -> None, updates status | [
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self.status = status
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priyankchheda/algorithms | c361aa9071573fa9966d5b02d05e524815abcf2b | linked_list/is_palindrome.py | python | Node.__init__ | (self, data) | initializing single node with data | initializing single node with data | [
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""" initializing single node with data """
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/clang/bindings/python/clang/cindex.py | python | Cursor.is_anonymous | (self) | return conf.lib.clang_Cursor_isAnonymous(self) | Check if the record is anonymous. | Check if the record is anonymous. | [
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"""
Check if the record is anonymous.
"""
if self.kind == CursorKind.FIELD_DECL:
return self.type.get_declaration().is_anonymous()
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3drobotics/ardupilot-solo | 05a123b002c11dccc905d4d7703a38e5f36ee723 | mk/PX4/Tools/gencpp/src/gencpp/__init__.py | python | msg_type_to_cpp | (type) | Converts a message type (e.g. uint32, std_msgs/String, etc.) into the C++ declaration
for that type (e.g. uint32_t, std_msgs::String_<ContainerAllocator>)
@param type: The message type
@type type: str
@return: The C++ declaration
@rtype: str | Converts a message type (e.g. uint32, std_msgs/String, etc.) into the C++ declaration
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cpp_type = None
if (genmsg.msgs.is_builtin(base_type)):
cpp_type = MSG_TYPE_TO_CPP[base_type]
elif (len(base_type.split('/')) == 1):
if (genmsg.msgs.is_header_type(base_type)):
cpp_type = ' ::std_msgs::Header_<ContainerAllocator> '
else:
cpp_type = '%s_<ContainerAllocator> '%(base_type)
else:
pkg = base_type.split('/')[0]
msg = base_type.split('/')[1]
cpp_type = ' ::%s::%s_<ContainerAllocator> '%(pkg, msg)
if (is_array):
if (array_len is None):
return 'std::vector<%s, typename ContainerAllocator::template rebind<%s>::other > '%(cpp_type, cpp_type)
else:
return 'boost::array<%s, %s> '%(cpp_type, array_len)
else:
return cpp_type | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_stc.py | python | EditraStc.StopRecord | (self) | Stops the recording and builds the macro script
@postcondition: macro recording is stopped | Stops the recording and builds the macro script
@postcondition: macro recording is stopped | [
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"""Stops the recording and builds the macro script
@postcondition: macro recording is stopped
"""
self.recording = False
super(EditraStc, self).StopRecord()
evt = ed_event.StatusEvent(ed_event.edEVT_STATUS, self.GetId(),
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wx.PostEvent(self.TopLevelParent, evt)
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NVIDIA/DALI | bf16cc86ba8f091b145f91962f21fe1b6aff243d | docs/examples/use_cases/tensorflow/efficientdet/model/backbone/efficientnet_model.py | python | Head.call | (self, inputs, training, pooled_features_only) | return outputs | Call the layer. | Call the layer. | [
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"""Call the layer."""
outputs = self._relu_fn(self._bn(self._conv_head(inputs), training=training))
self.endpoints["head_1x1"] = outputs
if self._global_params.local_pooling:
shape = outputs.get_shape().as_list()
kernel_size = [1, shape[self.h_axis], shape[self.w_axis], 1]
outputs = tf.nn.avg_pool(
outputs, ksize=kernel_size, strides=[1, 1, 1, 1], padding="VALID"
)
self.endpoints["pooled_features"] = outputs
if not pooled_features_only:
if self._dropout:
outputs = self._dropout(outputs, training=training)
self.endpoints["global_pool"] = outputs
if self._fc:
outputs = tf.squeeze(outputs, [self.h_axis, self.w_axis])
outputs = self._fc(outputs)
self.endpoints["head"] = outputs
else:
outputs = self._avg_pooling(outputs)
self.endpoints["pooled_features"] = outputs
if not pooled_features_only:
if self._dropout:
outputs = self._dropout(outputs, training=training)
self.endpoints["global_pool"] = outputs
if self._fc:
outputs = self._fc(outputs)
self.endpoints["head"] = outputs
return outputs | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/sparse/base.py | python | spmatrix.tolil | (self, copy=False) | return self.tocsr(copy=False).tolil(copy=copy) | Convert this matrix to LInked List format.
With copy=False, the data/indices may be shared between this matrix and
the resultant lil_matrix. | Convert this matrix to LInked List format. | [
"Convert",
"this",
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"to",
"LInked",
"List",
"format",
"."
] | def tolil(self, copy=False):
"""Convert this matrix to LInked List format.
With copy=False, the data/indices may be shared between this matrix and
the resultant lil_matrix.
"""
return self.tocsr(copy=False).tolil(copy=copy) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/math_ops.py | python | sign | (x, name=None) | Returns an element-wise indication of the sign of a number.
`y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`.
For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`. | Returns an element-wise indication of the sign of a number. | [
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"-",
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"of",
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"a",
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] | def sign(x, name=None):
"""Returns an element-wise indication of the sign of a number.
`y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`.
For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
"""
with ops.op_scope([x], name, "Sign") as name:
if isinstance(x, ops.SparseTensor):
x_sign = gen_math_ops.sign(x.values, name=name)
return ops.SparseTensor(indices=x.indices, values=x_sign, shape=x.shape)
else:
return gen_math_ops.sign(x, name=name) | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | chrome/common/extensions/PRESUBMIT.py | python | DocsGenerated | (input_api) | return any(IsGeneratedDoc(path, input_api)
for path in input_api.LocalPaths()) | Return True if there are any generated docs in this change.
Generated docs are the files that are the output of build.py. Typically
all docs changes will contain both generated docs and non-generated files. | Return True if there are any generated docs in this change. | [
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] | def DocsGenerated(input_api):
"""Return True if there are any generated docs in this change.
Generated docs are the files that are the output of build.py. Typically
all docs changes will contain both generated docs and non-generated files.
"""
return any(IsGeneratedDoc(path, input_api)
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/SANS/isis_instrument.py | python | BaseInstrument.get_default_beam_center | (self) | return [0, 0] | Returns the default beam center position, or the pixel location
of real-space coordinates (0,0). | Returns the default beam center position, or the pixel location
of real-space coordinates (0,0). | [
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] | def get_default_beam_center(self):
"""
Returns the default beam center position, or the pixel location
of real-space coordinates (0,0).
"""
return [0, 0] | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/generic.py | python | NDFrame.to_excel | (
self,
excel_writer,
sheet_name: str = "Sheet1",
na_rep: str = "",
float_format: str | None = None,
columns=None,
header=True,
index=True,
index_label=None,
startrow=0,
startcol=0,
engine=None,
merge_cells=True,
encoding=None,
inf_rep="inf",
verbose=True,
freeze_panes=None,
storage_options: StorageOptions = None,
) | Write {klass} to an Excel sheet.
To write a single {klass} to an Excel .xlsx file it is only necessary to
specify a target file name. To write to multiple sheets it is necessary to
create an `ExcelWriter` object with a target file name, and specify a sheet
in the file to write to.
Multiple sheets may be written to by specifying unique `sheet_name`.
With all data written to the file it is necessary to save the changes.
Note that creating an `ExcelWriter` object with a file name that already
exists will result in the contents of the existing file being erased.
Parameters
----------
excel_writer : path-like, file-like, or ExcelWriter object
File path or existing ExcelWriter.
sheet_name : str, default 'Sheet1'
Name of sheet which will contain DataFrame.
na_rep : str, default ''
Missing data representation.
float_format : str, optional
Format string for floating point numbers. For example
``float_format="%.2f"`` will format 0.1234 to 0.12.
columns : sequence or list of str, optional
Columns to write.
header : bool or list of str, default True
Write out the column names. If a list of string is given it is
assumed to be aliases for the column names.
index : bool, default True
Write row names (index).
index_label : str or sequence, optional
Column label for index column(s) if desired. If not specified, and
`header` and `index` are True, then the index names are used. A
sequence should be given if the DataFrame uses MultiIndex.
startrow : int, default 0
Upper left cell row to dump data frame.
startcol : int, default 0
Upper left cell column to dump data frame.
engine : str, optional
Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this
via the options ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and
``io.excel.xlsm.writer``.
.. deprecated:: 1.2.0
As the `xlwt <https://pypi.org/project/xlwt/>`__ package is no longer
maintained, the ``xlwt`` engine will be removed in a future version
of pandas.
merge_cells : bool, default True
Write MultiIndex and Hierarchical Rows as merged cells.
encoding : str, optional
Encoding of the resulting excel file. Only necessary for xlwt,
other writers support unicode natively.
inf_rep : str, default 'inf'
Representation for infinity (there is no native representation for
infinity in Excel).
verbose : bool, default True
Display more information in the error logs.
freeze_panes : tuple of int (length 2), optional
Specifies the one-based bottommost row and rightmost column that
is to be frozen.
{storage_options}
.. versionadded:: 1.2.0
See Also
--------
to_csv : Write DataFrame to a comma-separated values (csv) file.
ExcelWriter : Class for writing DataFrame objects into excel sheets.
read_excel : Read an Excel file into a pandas DataFrame.
read_csv : Read a comma-separated values (csv) file into DataFrame.
Notes
-----
For compatibility with :meth:`~DataFrame.to_csv`,
to_excel serializes lists and dicts to strings before writing.
Once a workbook has been saved it is not possible to write further
data without rewriting the whole workbook.
Examples
--------
Create, write to and save a workbook:
>>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
... index=['row 1', 'row 2'],
... columns=['col 1', 'col 2'])
>>> df1.to_excel("output.xlsx") # doctest: +SKIP
To specify the sheet name:
>>> df1.to_excel("output.xlsx",
... sheet_name='Sheet_name_1') # doctest: +SKIP
If you wish to write to more than one sheet in the workbook, it is
necessary to specify an ExcelWriter object:
>>> df2 = df1.copy()
>>> with pd.ExcelWriter('output.xlsx') as writer: # doctest: +SKIP
... df1.to_excel(writer, sheet_name='Sheet_name_1')
... df2.to_excel(writer, sheet_name='Sheet_name_2')
ExcelWriter can also be used to append to an existing Excel file:
>>> with pd.ExcelWriter('output.xlsx',
... mode='a') as writer: # doctest: +SKIP
... df.to_excel(writer, sheet_name='Sheet_name_3')
To set the library that is used to write the Excel file,
you can pass the `engine` keyword (the default engine is
automatically chosen depending on the file extension):
>>> df1.to_excel('output1.xlsx', engine='xlsxwriter') # doctest: +SKIP | Write {klass} to an Excel sheet. | [
"Write",
"{",
"klass",
"}",
"to",
"an",
"Excel",
"sheet",
"."
] | def to_excel(
self,
excel_writer,
sheet_name: str = "Sheet1",
na_rep: str = "",
float_format: str | None = None,
columns=None,
header=True,
index=True,
index_label=None,
startrow=0,
startcol=0,
engine=None,
merge_cells=True,
encoding=None,
inf_rep="inf",
verbose=True,
freeze_panes=None,
storage_options: StorageOptions = None,
) -> None:
"""
Write {klass} to an Excel sheet.
To write a single {klass} to an Excel .xlsx file it is only necessary to
specify a target file name. To write to multiple sheets it is necessary to
create an `ExcelWriter` object with a target file name, and specify a sheet
in the file to write to.
Multiple sheets may be written to by specifying unique `sheet_name`.
With all data written to the file it is necessary to save the changes.
Note that creating an `ExcelWriter` object with a file name that already
exists will result in the contents of the existing file being erased.
Parameters
----------
excel_writer : path-like, file-like, or ExcelWriter object
File path or existing ExcelWriter.
sheet_name : str, default 'Sheet1'
Name of sheet which will contain DataFrame.
na_rep : str, default ''
Missing data representation.
float_format : str, optional
Format string for floating point numbers. For example
``float_format="%.2f"`` will format 0.1234 to 0.12.
columns : sequence or list of str, optional
Columns to write.
header : bool or list of str, default True
Write out the column names. If a list of string is given it is
assumed to be aliases for the column names.
index : bool, default True
Write row names (index).
index_label : str or sequence, optional
Column label for index column(s) if desired. If not specified, and
`header` and `index` are True, then the index names are used. A
sequence should be given if the DataFrame uses MultiIndex.
startrow : int, default 0
Upper left cell row to dump data frame.
startcol : int, default 0
Upper left cell column to dump data frame.
engine : str, optional
Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this
via the options ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and
``io.excel.xlsm.writer``.
.. deprecated:: 1.2.0
As the `xlwt <https://pypi.org/project/xlwt/>`__ package is no longer
maintained, the ``xlwt`` engine will be removed in a future version
of pandas.
merge_cells : bool, default True
Write MultiIndex and Hierarchical Rows as merged cells.
encoding : str, optional
Encoding of the resulting excel file. Only necessary for xlwt,
other writers support unicode natively.
inf_rep : str, default 'inf'
Representation for infinity (there is no native representation for
infinity in Excel).
verbose : bool, default True
Display more information in the error logs.
freeze_panes : tuple of int (length 2), optional
Specifies the one-based bottommost row and rightmost column that
is to be frozen.
{storage_options}
.. versionadded:: 1.2.0
See Also
--------
to_csv : Write DataFrame to a comma-separated values (csv) file.
ExcelWriter : Class for writing DataFrame objects into excel sheets.
read_excel : Read an Excel file into a pandas DataFrame.
read_csv : Read a comma-separated values (csv) file into DataFrame.
Notes
-----
For compatibility with :meth:`~DataFrame.to_csv`,
to_excel serializes lists and dicts to strings before writing.
Once a workbook has been saved it is not possible to write further
data without rewriting the whole workbook.
Examples
--------
Create, write to and save a workbook:
>>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
... index=['row 1', 'row 2'],
... columns=['col 1', 'col 2'])
>>> df1.to_excel("output.xlsx") # doctest: +SKIP
To specify the sheet name:
>>> df1.to_excel("output.xlsx",
... sheet_name='Sheet_name_1') # doctest: +SKIP
If you wish to write to more than one sheet in the workbook, it is
necessary to specify an ExcelWriter object:
>>> df2 = df1.copy()
>>> with pd.ExcelWriter('output.xlsx') as writer: # doctest: +SKIP
... df1.to_excel(writer, sheet_name='Sheet_name_1')
... df2.to_excel(writer, sheet_name='Sheet_name_2')
ExcelWriter can also be used to append to an existing Excel file:
>>> with pd.ExcelWriter('output.xlsx',
... mode='a') as writer: # doctest: +SKIP
... df.to_excel(writer, sheet_name='Sheet_name_3')
To set the library that is used to write the Excel file,
you can pass the `engine` keyword (the default engine is
automatically chosen depending on the file extension):
>>> df1.to_excel('output1.xlsx', engine='xlsxwriter') # doctest: +SKIP
"""
df = self if isinstance(self, ABCDataFrame) else self.to_frame()
from pandas.io.formats.excel import ExcelFormatter
formatter = ExcelFormatter(
df,
na_rep=na_rep,
cols=columns,
header=header,
float_format=float_format,
index=index,
index_label=index_label,
merge_cells=merge_cells,
inf_rep=inf_rep,
)
formatter.write(
excel_writer,
sheet_name=sheet_name,
startrow=startrow,
startcol=startcol,
freeze_panes=freeze_panes,
engine=engine,
storage_options=storage_options,
) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/closure_linter/closure_linter/common/tokenizer.py | python | Tokenizer._CreateToken | (self, string, token_type, line, line_number, values=None) | return tokens.Token(string, token_type, line, line_number, values) | Creates a new Token object (or subclass).
Args:
string: The string of input the token represents.
token_type: The type of token.
line: The text of the line this token is in.
line_number: The line number of the token.
values: A dict of named values within the token. For instance, a
function declaration may have a value called 'name' which captures the
name of the function.
Returns:
The newly created Token object. | Creates a new Token object (or subclass). | [
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"(",
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"subclass",
")",
"."
] | def _CreateToken(self, string, token_type, line, line_number, values=None):
"""Creates a new Token object (or subclass).
Args:
string: The string of input the token represents.
token_type: The type of token.
line: The text of the line this token is in.
line_number: The line number of the token.
values: A dict of named values within the token. For instance, a
function declaration may have a value called 'name' which captures the
name of the function.
Returns:
The newly created Token object.
"""
return tokens.Token(string, token_type, line, line_number, values) | [
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Kitware/VTK | 5b4df4d90a4f31194d97d3c639dd38ea8f81e8b8 | Utilities/Maintenance/vtk_generate_pyi.py | python | isenum | (m) | return (isclass(m) and issubclass(m, int)) | Check for enums (currently derived from int) | Check for enums (currently derived from int) | [
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"""Check for enums (currently derived from int)"""
return (isclass(m) and issubclass(m, int)) | [
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SoarGroup/Soar | a1c5e249499137a27da60533c72969eef3b8ab6b | scons/scons-local-4.1.0/SCons/Tool/docbook/__init__.py | python | __extend_targets_sources | (target, source) | return target, source | Prepare the lists of target and source files. | Prepare the lists of target and source files. | [
"Prepare",
"the",
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"target",
"and",
"source",
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"."
] | def __extend_targets_sources(target, source):
""" Prepare the lists of target and source files. """
if not SCons.Util.is_List(target):
target = [target]
if not source:
source = target[:]
elif not SCons.Util.is_List(source):
source = [source]
if len(target) < len(source):
target.extend(source[len(target):])
return target, source | [
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