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koth/kcws | 88efbd36a7022de4e6e90f5a1fb880cf87cfae9f | third_party/setuptools/pkg_resources.py | python | ZipProvider._is_current | (self, file_path, zip_path) | return zip_contents == file_contents | Return True if the file_path is current for this zip_path | Return True if the file_path is current for this zip_path | [
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"""
Return True if the file_path is current for this zip_path
"""
timestamp, size = self._get_date_and_size(self.zipinfo[zip_path])
if not os.path.isfile(file_path):
return False
stat = os.stat(file_path)
if stat.st_size!=size or stat.st_mtime!=timestamp:
return False
# check that the contents match
zip_contents = self.loader.get_data(zip_path)
with open(file_path, 'rb') as f:
file_contents = f.read()
return zip_contents == file_contents | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/pot/openvino/tools/pot/configs/config.py | python | Config._configure_ac_params | (self) | Converts engine config into accuracy checker config | Converts engine config into accuracy checker config | [
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""" Converts engine config into accuracy checker config
"""
filtering_params = {'target_devices': ['CPU'], 'target_framework': 'openvino', 'use_new_api': True}
if 'config' in self.engine:
ac_conf, mode = ConfigReader.merge(Dict({**self.engine, **filtering_params}))
ac_conf = Dict(ac_conf)
else:
mode = 'evaluations' if self.engine.module else 'models'
ac_conf = Dict({mode: [self.engine]})
ac_conf[mode][0].name = self.model.model_name
datasets_config = ac_conf[mode][0].datasets if mode == 'models' \
else ac_conf[mode][0].module_config.datasets
if isinstance(datasets_config, dict) and 'preprocessing' in datasets_config:
logger.debug('Global dataset preprocessing configuration found')
preprocessing_config = datasets_config.pop('preprocessing')
for dataset_name, dataset in datasets_config.items():
if not dataset.preprocessing:
dataset['preprocessing'] = preprocessing_config
else:
logger.debug('Local preprocessing configuration is used for {} dataset'.format(dataset_name))
ConfigReader.check_local_config(ac_conf)
ac_conf = ConfigReader.convert_paths(ac_conf)
ConfigReader._filter_launchers(
ac_conf, filtering_params, mode=mode
)
for req_num in ['stat_requests_number', 'eval_requests_number']:
ac_conf[req_num] = self.engine[req_num] if req_num in self.engine else None
self['engine'] = ac_conf | [
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ZhouWeikuan/DouDiZhu | 0d84ff6c0bc54dba6ae37955de9ae9307513dc99 | code/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py | python | TranslationUnit.__init__ | (self, ptr, index) | Create a TranslationUnit instance.
TranslationUnits should be created using one of the from_* @classmethod
functions above. __init__ is only called internally. | Create a TranslationUnit instance. | [
"Create",
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"TranslationUnit",
"instance",
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] | def __init__(self, ptr, index):
"""Create a TranslationUnit instance.
TranslationUnits should be created using one of the from_* @classmethod
functions above. __init__ is only called internally.
"""
assert isinstance(index, Index)
ClangObject.__init__(self, ptr) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/urllib/request.py | python | URLopener.open_unknown | (self, fullurl, data=None) | Overridable interface to open unknown URL type. | Overridable interface to open unknown URL type. | [
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] | def open_unknown(self, fullurl, data=None):
"""Overridable interface to open unknown URL type."""
type, url = _splittype(fullurl)
raise OSError('url error', 'unknown url type', type) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/polynomial/hermite_e.py | python | hermediv | (c1, c2) | return pu._div(hermemul, c1, c2) | Divide one Hermite series by another.
Returns the quotient-with-remainder of two Hermite series
`c1` / `c2`. The arguments are sequences of coefficients from lowest
order "term" to highest, e.g., [1,2,3] represents the series
``P_0 + 2*P_1 + 3*P_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Hermite series coefficients ordered from low to
high.
Returns
-------
[quo, rem] : ndarrays
Of Hermite series coefficients representing the quotient and
remainder.
See Also
--------
hermeadd, hermesub, hermemulx, hermemul, hermepow
Notes
-----
In general, the (polynomial) division of one Hermite series by another
results in quotient and remainder terms that are not in the Hermite
polynomial basis set. Thus, to express these results as a Hermite
series, it is necessary to "reproject" the results onto the Hermite
basis set, which may produce "unintuitive" (but correct) results; see
Examples section below.
Examples
--------
>>> from numpy.polynomial.hermite_e import hermediv
>>> hermediv([ 14., 15., 28., 7., 6.], [0, 1, 2])
(array([1., 2., 3.]), array([0.]))
>>> hermediv([ 15., 17., 28., 7., 6.], [0, 1, 2])
(array([1., 2., 3.]), array([1., 2.])) | Divide one Hermite series by another. | [
"Divide",
"one",
"Hermite",
"series",
"by",
"another",
"."
] | def hermediv(c1, c2):
"""
Divide one Hermite series by another.
Returns the quotient-with-remainder of two Hermite series
`c1` / `c2`. The arguments are sequences of coefficients from lowest
order "term" to highest, e.g., [1,2,3] represents the series
``P_0 + 2*P_1 + 3*P_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Hermite series coefficients ordered from low to
high.
Returns
-------
[quo, rem] : ndarrays
Of Hermite series coefficients representing the quotient and
remainder.
See Also
--------
hermeadd, hermesub, hermemulx, hermemul, hermepow
Notes
-----
In general, the (polynomial) division of one Hermite series by another
results in quotient and remainder terms that are not in the Hermite
polynomial basis set. Thus, to express these results as a Hermite
series, it is necessary to "reproject" the results onto the Hermite
basis set, which may produce "unintuitive" (but correct) results; see
Examples section below.
Examples
--------
>>> from numpy.polynomial.hermite_e import hermediv
>>> hermediv([ 14., 15., 28., 7., 6.], [0, 1, 2])
(array([1., 2., 3.]), array([0.]))
>>> hermediv([ 15., 17., 28., 7., 6.], [0, 1, 2])
(array([1., 2., 3.]), array([1., 2.]))
"""
return pu._div(hermemul, c1, c2) | [
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seqan/seqan | f5f658343c366c9c3d44ba358ffc9317e78a09ed | util/py_lib/seqan/auto_build.py | python | work | (options) | Run the steps. | Run the steps. | [
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"""Run the steps."""
if options.src_tar:
return workSrcTar(options)
elif not options.build_trunk_as:
return workTags(options)
else:
return workTrunk(options) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/pkg_resources.py | python | find_distributions | (path_item, only=False) | return finder(importer, path_item, only) | Yield distributions accessible via `path_item` | Yield distributions accessible via `path_item` | [
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] | def find_distributions(path_item, only=False):
"""Yield distributions accessible via `path_item`"""
importer = get_importer(path_item)
finder = _find_adapter(_distribution_finders, importer)
return finder(importer, path_item, only) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/coverage/coverage/cmdline.py | python | unglob_args | (args) | return args | Interpret shell wildcards for platforms that need it. | Interpret shell wildcards for platforms that need it. | [
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"""Interpret shell wildcards for platforms that need it."""
if env.WINDOWS:
globbed = []
for arg in args:
if '?' in arg or '*' in arg:
globbed.extend(glob.glob(arg))
else:
globbed.append(arg)
args = globbed
return args | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/ctypes/__init__.py | python | create_string_buffer | (init, size=None) | create_string_buffer(aBytes) -> character array
create_string_buffer(anInteger) -> character array
create_string_buffer(aBytes, anInteger) -> character array | create_string_buffer(aBytes) -> character array
create_string_buffer(anInteger) -> character array
create_string_buffer(aBytes, anInteger) -> character array | [
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"""create_string_buffer(aBytes) -> character array
create_string_buffer(anInteger) -> character array
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"""
if isinstance(init, bytes):
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apache/impala | 8ddac48f3428c86f2cbd037ced89cfb903298b12 | bin/jenkins/critique-gerrit-review.py | python | add_misc_comments_for_line | (comments, line, curr_file, curr_line_num) | Helper for get_misc_comments to generate comments for 'line' at 'curr_line_num' in
'curr_file' and append them to 'comments'. | Helper for get_misc_comments to generate comments for 'line' at 'curr_line_num' in
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"""Helper for get_misc_comments to generate comments for 'line' at 'curr_line_num' in
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/wsgiref/handlers.py | python | BaseHandler.add_cgi_vars | (self) | Override in subclass to insert CGI variables in 'self.environ | Override in subclass to insert CGI variables in 'self.environ | [
"Override",
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] | def add_cgi_vars(self):
"""Override in subclass to insert CGI variables in 'self.environ'"""
raise NotImplementedError | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py | python | SequenceQueueingStateSaver.close | (self, cancel_pending_enqueues=False, name=None) | Closes the barrier and the FIFOQueue.
This operation signals that no more segments of new sequences will be
enqueued. New segments of already inserted sequences may still be enqueued
and dequeued if there is a sufficient number filling a batch or
allow_small_batch is true. Otherwise dequeue operations will fail
immediately.
Args:
cancel_pending_enqueues: (Optional.) A boolean, defaulting to
`False`. If `True`, all pending enqueues to the underlying queues will
be cancelled, and completing already started sequences is not possible.
name: Optional name for the op.
Returns:
The operation that closes the barrier and the FIFOQueue. | Closes the barrier and the FIFOQueue. | [
"Closes",
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"barrier",
"and",
"the",
"FIFOQueue",
"."
] | def close(self, cancel_pending_enqueues=False, name=None):
"""Closes the barrier and the FIFOQueue.
This operation signals that no more segments of new sequences will be
enqueued. New segments of already inserted sequences may still be enqueued
and dequeued if there is a sufficient number filling a batch or
allow_small_batch is true. Otherwise dequeue operations will fail
immediately.
Args:
cancel_pending_enqueues: (Optional.) A boolean, defaulting to
`False`. If `True`, all pending enqueues to the underlying queues will
be cancelled, and completing already started sequences is not possible.
name: Optional name for the op.
Returns:
The operation that closes the barrier and the FIFOQueue.
"""
with ops.name_scope(name, "SQSSClose", [self._prefetch_op]) as name:
barrier_close = self.barrier.close(cancel_pending_enqueues,
"BarrierClose")
fifo_queue_close = self._capacity_queue.close(cancel_pending_enqueues,
"FIFOClose")
return control_flow_ops.group(barrier_close, fifo_queue_close, name=name) | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/abc.py | python | ABCMeta.register | (cls, subclass) | Register a virtual subclass of an ABC. | Register a virtual subclass of an ABC. | [
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"""Register a virtual subclass of an ABC."""
if not isinstance(cls, type):
raise TypeError("Can only register classes")
if issubclass(subclass, cls):
return # Already a subclass
# Subtle: test for cycles *after* testing for "already a subclass";
# this means we allow X.register(X) and interpret it as a no-op.
if issubclass(cls, subclass):
# This would create a cycle, which is bad for the algorithm below
raise RuntimeError("Refusing to create an inheritance cycle")
cls._abc_registry.add(subclass)
ABCMeta._abc_invalidation_counter += 1 | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/callbacks.py | python | CallbackList._call_begin_hook | (self, mode) | Helper function for on_{train|test|predict}_begin methods. | Helper function for on_{train|test|predict}_begin methods. | [
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"""Helper function for on_{train|test|predict}_begin methods."""
if mode == ModeKeys.TRAIN:
self.on_train_begin()
elif mode == ModeKeys.TEST:
self.on_test_begin()
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self.on_predict_begin() | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/apitools/apitools/gen/message_registry.py | python | MessageRegistry.__AddAdditionalProperties | (self, message, schema, properties) | Add an additionalProperties field to message. | Add an additionalProperties field to message. | [
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"""Add an additionalProperties field to message."""
additional_properties_info = schema['additionalProperties']
entries_type_name = self.__AddAdditionalPropertyType(
message.name, additional_properties_info)
description = util.CleanDescription(
additional_properties_info.get('description'))
if description is None:
description = 'Additional properties of type %s' % message.name
attrs = {
'items': {
'$ref': entries_type_name,
},
'description': description,
'type': 'array',
}
field_name = 'additionalProperties'
message.fields.append(self.__FieldDescriptorFromProperties(
field_name, len(properties) + 1, attrs))
self.__AddImport('from %s import encoding' % self.__base_files_package)
message.decorators.append(
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lammps/lammps | b75c3065430a75b1b5543a10e10f46d9b4c91913 | tools/i-pi/ipi/engine/thermostats.py | python | ThermoPILE_L.bind | (self, nm=None, prng=None, bindcentroid=True, fixdof=None) | Binds the appropriate degrees of freedom to the thermostat.
This takes a beads object with degrees of freedom, and makes its momentum
and mass vectors members of the thermostat. It also then creates the
objects that will hold the data needed in the thermostat algorithms
and the dependency network.
Gives the interface for both the PILE_L and PILE_G thermostats, which
only differ in their treatment of the centroid coordinate momenta.
Args:
nm: An optional normal mode object to take the mass and momentum
vectors from.
prng: An optional pseudo random number generator object. Defaults to
Random().
bindcentroid: An optional boolean which decides whether a Langevin
thermostat is attached to the centroid mode of each atom
separately, or the total kinetic energy. Defaults to True, which
gives a thermostat bound to each centroid momentum.
fixdof: An optional integer which can specify the number of constraints
applied to the system. Defaults to zero.
Raises:
TypeError: Raised if no appropriate degree of freedom or object
containing a momentum vector is specified for
the thermostat to couple to. | Binds the appropriate degrees of freedom to the thermostat. | [
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] | def bind(self, nm=None, prng=None, bindcentroid=True, fixdof=None):
"""Binds the appropriate degrees of freedom to the thermostat.
This takes a beads object with degrees of freedom, and makes its momentum
and mass vectors members of the thermostat. It also then creates the
objects that will hold the data needed in the thermostat algorithms
and the dependency network.
Gives the interface for both the PILE_L and PILE_G thermostats, which
only differ in their treatment of the centroid coordinate momenta.
Args:
nm: An optional normal mode object to take the mass and momentum
vectors from.
prng: An optional pseudo random number generator object. Defaults to
Random().
bindcentroid: An optional boolean which decides whether a Langevin
thermostat is attached to the centroid mode of each atom
separately, or the total kinetic energy. Defaults to True, which
gives a thermostat bound to each centroid momentum.
fixdof: An optional integer which can specify the number of constraints
applied to the system. Defaults to zero.
Raises:
TypeError: Raised if no appropriate degree of freedom or object
containing a momentum vector is specified for
the thermostat to couple to.
"""
if nm is None or not type(nm) is NormalModes:
raise TypeError("ThermoPILE_L.bind expects a NormalModes argument to bind to")
if prng is None:
self.prng = Random()
else:
self.prng = prng
prev_ethermo = self.ethermo
# creates a set of thermostats to be applied to individual normal modes
self._thermos = [ ThermoLangevin(temp=1, dt=1, tau=1) for b in range(nm.nbeads) ]
# optionally does not bind the centroid, so we can re-use all of this
# in the PILE_G case
if not bindcentroid:
self._thermos[0] = None
self.nm = nm
dset(self,"tauk",
depend_array(name="tauk", value=np.zeros(nm.nbeads-1,float),
func=self.get_tauk, dependencies=[dget(self,"pilescale"), dget(nm,"dynomegak")] ) )
# must pipe all the dependencies in such a way that values for the nm thermostats
# are automatically updated based on the "master" thermostat
def make_taugetter(k):
return lambda: self.tauk[k-1]
it = 0
for t in self._thermos:
if t is None:
it += 1
continue
if it > 0:
fixdof = None # only the centroid thermostat may have constraints
# bind thermostat t to the it-th bead
t.bind(pm=(nm.pnm[it,:],nm.dynm3[it,:]),prng=self.prng, fixdof=fixdof)
# pipes temp and dt
deppipe(self,"temp", t, "temp")
deppipe(self,"dt", t, "dt")
# for tau it is slightly more complex
if it == 0:
deppipe(self,"tau", t, "tau")
else:
# Here we manually connect _thermos[i].tau to tauk[i].
# Simple and clear.
dget(t,"tau").add_dependency(dget(self,"tauk"))
dget(t,"tau")._func = make_taugetter(it)
dget(self,"ethermo").add_dependency(dget(t,"ethermo"))
it += 1
# since the ethermo will be "delegated" to the normal modes thermostats,
# one has to split
# any previously-stored value between the sub-thermostats
if bindcentroid:
for t in self._thermos:
t.ethermo = prev_ethermo/nm.nbeads
dget(self,"ethermo")._func = self.get_ethermo; | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/grid.py | python | Grid.AutoSize | (*args, **kwargs) | return _grid.Grid_AutoSize(*args, **kwargs) | AutoSize(self) | AutoSize(self) | [
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return _grid.Grid_AutoSize(*args, **kwargs) | [
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gem5/gem5 | 141cc37c2d4b93959d4c249b8f7e6a8b2ef75338 | ext/ply/example/BASIC/basparse.py | python | p_expr_unary | (p) | expr : MINUS expr %prec UMINUS | expr : MINUS expr %prec UMINUS | [
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swift/swift | 12d031cf8177fdec0137f9aa7e2912fa23c4416b | 3rdParty/SCons/scons-3.0.1/engine/SCons/Tool/MSCommon/vc.py | python | find_vc_pdir_vswhere | (msvc_version) | Find the MSVC product directory using vswhere.exe .
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Run it asking for specified version and get MSVS install location
:param msvc_version:
:return: MSVC install dir
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vsdir = vsdir.rstrip()
vc_pdir = os.path.join(vsdir, 'VC')
return vc_pdir
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# No vswhere on system, no install info available
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/summary/writer/writer.py | python | SummaryToEventTransformer.__init__ | (self, event_writer, graph=None, graph_def=None) | Creates a `SummaryWriter` and an event file.
On construction the summary writer creates a new event file in `logdir`.
This event file will contain `Event` protocol buffers constructed when you
call one of the following functions: `add_summary()`, `add_session_log()`,
`add_event()`, or `add_graph()`.
If you pass a `Graph` to the constructor it is added to
the event file. (This is equivalent to calling `add_graph()` later).
TensorBoard will pick the graph from the file and display it graphically so
you can interactively explore the graph you built. You will usually pass
the graph from the session in which you launched it:
```python
...create a graph...
# Launch the graph in a session.
sess = tf.compat.v1.Session()
# Create a summary writer, add the 'graph' to the event file.
writer = tf.compat.v1.summary.FileWriter(<some-directory>, sess.graph)
```
Args:
event_writer: An EventWriter. Implements add_event and get_logdir.
graph: A `Graph` object, such as `sess.graph`.
graph_def: DEPRECATED: Use the `graph` argument instead. | Creates a `SummaryWriter` and an event file. | [
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"""Creates a `SummaryWriter` and an event file.
On construction the summary writer creates a new event file in `logdir`.
This event file will contain `Event` protocol buffers constructed when you
call one of the following functions: `add_summary()`, `add_session_log()`,
`add_event()`, or `add_graph()`.
If you pass a `Graph` to the constructor it is added to
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TensorBoard will pick the graph from the file and display it graphically so
you can interactively explore the graph you built. You will usually pass
the graph from the session in which you launched it:
```python
...create a graph...
# Launch the graph in a session.
sess = tf.compat.v1.Session()
# Create a summary writer, add the 'graph' to the event file.
writer = tf.compat.v1.summary.FileWriter(<some-directory>, sess.graph)
```
Args:
event_writer: An EventWriter. Implements add_event and get_logdir.
graph: A `Graph` object, such as `sess.graph`.
graph_def: DEPRECATED: Use the `graph` argument instead.
"""
self.event_writer = event_writer
# For storing used tags for session.run() outputs.
self._session_run_tags = {}
if graph is not None or graph_def is not None:
# Calling it with both graph and graph_def for backward compatibility.
self.add_graph(graph=graph, graph_def=graph_def)
# Also export the meta_graph_def in this case.
# graph may itself be a graph_def due to positional arguments
maybe_graph_as_def = (graph.as_graph_def(add_shapes=True)
if isinstance(graph, ops.Graph) else graph)
self.add_meta_graph(
meta_graph.create_meta_graph_def(graph_def=graph_def or
maybe_graph_as_def))
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self._seen_summary_tags = set() | [
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GoSSIP-SJTU/Armariris | ad5d868482956b2194a77b39c8d543c7c2318200 | bindings/python/llvm/common.py | python | LLVMObject.from_param | (self) | return self._as_parameter_ | ctypes function that converts this object to a function parameter. | ctypes function that converts this object to a function parameter. | [
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Kitware/VTK | 5b4df4d90a4f31194d97d3c639dd38ea8f81e8b8 | Wrapping/Python/vtkmodules/gtk/GtkVTKRenderWindowInteractor.py | python | GtkVTKRenderWindowInteractor.OnButtonUp | (self, wid, event) | return gtk.FALSE | Mouse button released. | Mouse button released. | [
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"""Mouse button released."""
m = self.get_pointer()
ctrl, shift = self._GetCtrlShift(event)
self._Iren.SetEventInformationFlipY(m[0], m[1], ctrl, shift,
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button = event.button
if button == 3:
self._Iren.RightButtonReleaseEvent()
return gtk.TRUE
elif button == 1:
self._Iren.LeftButtonReleaseEvent()
return gtk.TRUE
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self._Iren.MiddleButtonReleaseEvent()
return gtk.TRUE
return gtk.FALSE | [
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bulletphysics/bullet3 | f0f2a952e146f016096db6f85cf0c44ed75b0b9a | examples/pybullet/gym/pybullet_envs/bullet/minitaur.py | python | Minitaur.ConvertFromLegModel | (self, actions) | return motor_angle | Convert the actions that use leg model to the real motor actions.
Args:
actions: The theta, phi of the leg model.
Returns:
The eight desired motor angles that can be used in ApplyActions(). | Convert the actions that use leg model to the real motor actions. | [
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"""Convert the actions that use leg model to the real motor actions.
Args:
actions: The theta, phi of the leg model.
Returns:
The eight desired motor angles that can be used in ApplyActions().
"""
motor_angle = copy.deepcopy(actions)
scale_for_singularity = 1
offset_for_singularity = 1.5
half_num_motors = int(self.num_motors / 2)
quater_pi = math.pi / 4
for i in range(self.num_motors):
action_idx = i // 2
forward_backward_component = (
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extension_component = (-1)**i * quater_pi * actions[action_idx]
if i >= half_num_motors:
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motor_angle[i] = (math.pi + forward_backward_component + extension_component)
return motor_angle | [
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NeoGeographyToolkit/StereoPipeline | eedf54a919fb5cce1ab0e280bb0df4050763aa11 | src/asp/IceBridge/full_processing_script.py | python | solveIntrinsics_Part1 | (options, jpegFolder, cameraFolder, navCameraFolder, processedFolder,
logger) | return (options, cameraFolder, navCameraFolder, processedFolder) | Some preliminary work before solving for intrinsics. Here we
look up the default calibration file, and generate an RPC
approximation of its distortion model with polynomials of degree
4. We will then create cameras and stereo DEMs using this initial
camera file with RPC distortion. | Some preliminary work before solving for intrinsics. Here we
look up the default calibration file, and generate an RPC
approximation of its distortion model with polynomials of degree
4. We will then create cameras and stereo DEMs using this initial
camera file with RPC distortion. | [
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logger):
'''Some preliminary work before solving for intrinsics. Here we
look up the default calibration file, and generate an RPC
approximation of its distortion model with polynomials of degree
4. We will then create cameras and stereo DEMs using this initial
camera file with RPC distortion.'''
# Sanity checks
if options.startFrame == icebridge_common.getSmallestFrame() or \
options.stopFrame == icebridge_common.getLargestFrame():
raise Exception("When solving for intrinsics, must specify a frame range.")
if options.bundleLength != 2:
raise Exception("When solving for intrinsics, we assume bundle length of 2.")
if (options.stopFrame - options.startFrame) % 2 == 0:
raise Exception("When solving for intrinsics, must have an even number of frames, " +
" so stopFrame - startFrame must be odd.")
if options.processingSubfolder:
raise Exception("Processing subfolder not supported when solving for intrinsics.")
# Generate extra data we will use later to float intrinsics
options.stereoArgs += " --num-matches-from-disp-triplets 10000 --unalign-disparity " # --enable-fill-holes "
# Create separate directories for cameras and processed data,
# as these will be distinct than what we will finally be
# using to do the full run.
suff = "_camgen"
cameraFolder += suff
navCameraFolder += suff
processedFolder += suff
# Get the input calibration file
defaultCalibFile = ""
for frame in range(options.startFrame, options.stopFrame+1):
currCalibFile = input_conversions.getCalibrationFileForFrame(options.cameraLookupFile,
options.inputCalFolder,
frame, options.yyyymmdd,
options.site, logger)
if defaultCalibFile == "":
defaultCalibFile = currCalibFile
if defaultCalibFile != currCalibFile:
# This is important, the calibration file must be unique
raise Exception("Found two distinct calibration files: " + defaultCalibFile + \
" and " + currCalibFile)
logger.info("Default calibration file: " + defaultCalibFile)
if options.inputCalCamera != "":
defaultCalibFile = options.inputCalCamera
logger.info("Using instead the user-provided: " + defaultCalibFile)
# Find the first image in the range
jpegIndex = icebridge_common.csvIndexFile(jpegFolder)
(jpegFrameDict, jpegUrlDict) = icebridge_common.readIndexFile(jpegIndex,
prependFolder = True)
if options.startFrame not in jpegFrameDict.keys():
raise Exception("Could not find jpeg image for frame: " + options.startFrame)
firstImage = jpegFrameDict[options.startFrame]
# Create the RPC file before optimization
rpcCalibFile = os.path.join(processedFolder, os.path.basename(defaultCalibFile))
rpcCalibFile = rpcCalibFile.replace(".tsai", "_INIT_RPC.tsai")
logger.info("Will approximate camera model " + defaultCalibFile + " with " + \
options.outputModelType + " model " + rpcCalibFile)
if not os.path.exists(defaultCalibFile):
raise Exception('Cannot find file: ' + defaultCalibFile)
os.system("mkdir -p " + os.path.dirname(rpcCalibFile))
cmd = "convert_pinhole_model --input-file " + firstImage + ' --camera-file ' + \
defaultCalibFile + ' --output-type ' + options.outputModelType + \
' --sample-spacing 50 -o ' + rpcCalibFile
logger.info(cmd)
os.system(cmd)
# Use this one from now on
options.inputCalCamera = rpcCalibFile
# Return the modified values
return (options, cameraFolder, navCameraFolder, processedFolder) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_misc.py | python | DateTime.IsLeapYear | (*args, **kwargs) | return _misc_.DateTime_IsLeapYear(*args, **kwargs) | IsLeapYear(int year=Inv_Year, int cal=Gregorian) -> bool | IsLeapYear(int year=Inv_Year, int cal=Gregorian) -> bool | [
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] | def IsLeapYear(*args, **kwargs):
"""IsLeapYear(int year=Inv_Year, int cal=Gregorian) -> bool"""
return _misc_.DateTime_IsLeapYear(*args, **kwargs) | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | tools/diagnose.py | python | test_connection | (name, url, timeout=10) | Simple connection test | Simple connection test | [
"Simple",
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"test"
] | def test_connection(name, url, timeout=10):
"""Simple connection test"""
urlinfo = urlparse(url)
start = time.time()
try:
ip = socket.gethostbyname(urlinfo.netloc)
except Exception as e:
print('Error resolving DNS for {}: {}, {}'.format(name, url, e))
return
dns_elapsed = time.time() - start
start = time.time()
try:
_ = urlopen(url, timeout=timeout)
except Exception as e:
print("Error open {}: {}, {}, DNS finished in {} sec.".format(name, url, e, dns_elapsed))
return
load_elapsed = time.time() - start
print("Timing for {}: {}, DNS: {:.4f} sec, LOAD: {:.4f} sec.".format(name, url, dns_elapsed, load_elapsed)) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/training/proximal_adagrad.py | python | ProximalAdagradOptimizer.__init__ | (self, learning_rate, initial_accumulator_value=0.1,
l1_regularization_strength=0.0, l2_regularization_strength=0.0,
use_locking=False, name="ProximalAdagrad") | Construct a new ProximalAdagrad optimizer.
Args:
learning_rate: A `Tensor` or a floating point value. The learning rate.
initial_accumulator_value: A floating point value.
Starting value for the accumulators, must be positive.
l1_regularization_strength: A float value, must be greater than or
equal to zero.
l2_regularization_strength: A float value, must be greater than or
equal to zero.
use_locking: If `True` use locks for update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "Adagrad".
Raises:
ValueError: If the `initial_accumulator_value` is invalid. | Construct a new ProximalAdagrad optimizer. | [
"Construct",
"a",
"new",
"ProximalAdagrad",
"optimizer",
"."
] | def __init__(self, learning_rate, initial_accumulator_value=0.1,
l1_regularization_strength=0.0, l2_regularization_strength=0.0,
use_locking=False, name="ProximalAdagrad"):
"""Construct a new ProximalAdagrad optimizer.
Args:
learning_rate: A `Tensor` or a floating point value. The learning rate.
initial_accumulator_value: A floating point value.
Starting value for the accumulators, must be positive.
l1_regularization_strength: A float value, must be greater than or
equal to zero.
l2_regularization_strength: A float value, must be greater than or
equal to zero.
use_locking: If `True` use locks for update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "Adagrad".
Raises:
ValueError: If the `initial_accumulator_value` is invalid.
"""
if initial_accumulator_value <= 0.0:
raise ValueError("initial_accumulator_value must be positive: %s" %
initial_accumulator_value)
super(ProximalAdagradOptimizer, self).__init__(use_locking, name)
self._learning_rate = learning_rate
self._initial_accumulator_value = initial_accumulator_value
self._l1_regularization_strength = l1_regularization_strength
self._l2_regularization_strength = l2_regularization_strength
# Created in Initialize.
self._l1_regularization_strength_tensor = None
self._l2_regularization_strength_tensor = None
self._learning_rate_tensor = None | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythplugins/mytharchive/mythburn/scripts/mythburn.py | python | fatalError | (msg) | Display an error message and exit app | Display an error message and exit app | [
"Display",
"an",
"error",
"message",
"and",
"exit",
"app"
] | def fatalError(msg):
"""Display an error message and exit app"""
write("*"*60)
write("ERROR: " + msg)
write("See mythburn.log for more information.")
write("*"*60)
write("")
saveSetting("MythArchiveLastRunResult", "Failed: " + quoteString(msg));
saveSetting("MythArchiveLastRunEnd", time.strftime("%Y-%m-%d %H:%M:%S "))
sys.exit(0) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Fem/ObjectsFem.py | python | makeMeshBoundaryLayer | (
doc,
base_mesh,
name="MeshBoundaryLayer"
) | return obj | makeMeshBoundaryLayer(document, base_mesh, [name]):
creates a FEM mesh BoundaryLayer object to define boundary layer properties | makeMeshBoundaryLayer(document, base_mesh, [name]):
creates a FEM mesh BoundaryLayer object to define boundary layer properties | [
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] | def makeMeshBoundaryLayer(
doc,
base_mesh,
name="MeshBoundaryLayer"
):
"""makeMeshBoundaryLayer(document, base_mesh, [name]):
creates a FEM mesh BoundaryLayer object to define boundary layer properties"""
obj = doc.addObject("Fem::FeaturePython", name)
from femobjects import mesh_boundarylayer
mesh_boundarylayer.MeshBoundaryLayer(obj)
# obj.BaseMesh = base_mesh
# App::PropertyLinkList does not support append
# we will use a temporary list to append the mesh BoundaryLayer obj. to the list
tmplist = base_mesh.MeshBoundaryLayerList
tmplist.append(obj)
base_mesh.MeshBoundaryLayerList = tmplist
if FreeCAD.GuiUp:
from femviewprovider import view_mesh_boundarylayer
view_mesh_boundarylayer.VPMeshBoundaryLayer(obj.ViewObject)
return obj | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib2to3/fixer_util.py | python | Name | (name, prefix=None) | return Leaf(token.NAME, name, prefix=prefix) | Return a NAME leaf | Return a NAME leaf | [
"Return",
"a",
"NAME",
"leaf"
] | def Name(name, prefix=None):
"""Return a NAME leaf"""
return Leaf(token.NAME, name, prefix=prefix) | [
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isl-org/Open3D | 79aec3ddde6a571ce2f28e4096477e52ec465244 | python/open3d/visualization/tensorboard_plugin/util.py | python | RenderUpdate.apply | (self, o3dvis, geometry_name, geometry, inference_data_proto=None) | Apply the RenderUpdate to a geometry.
Args:
o3dvis (O3DVisualizer): Window containing the geometry.
geometry_name (str): Geometry name in the window.
geometry (o3d.t.geometry): Geometry whose rendering is to be
updated.
inference_data_proto : BoundingBox labels and confidences. | Apply the RenderUpdate to a geometry. | [
"Apply",
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"RenderUpdate",
"to",
"a",
"geometry",
"."
] | def apply(self, o3dvis, geometry_name, geometry, inference_data_proto=None):
"""Apply the RenderUpdate to a geometry.
Args:
o3dvis (O3DVisualizer): Window containing the geometry.
geometry_name (str): Geometry name in the window.
geometry (o3d.t.geometry): Geometry whose rendering is to be
updated.
inference_data_proto : BoundingBox labels and confidences.
"""
if (len(self._updated) == 0 or geometry.is_empty()):
_log.debug("No updates, or empty geometry.")
return
def get_labelLUT():
"""Create {label: color} mapping from list of colors."""
return {
label: self.LABELLUT_COLORS[k]
for k, label in enumerate(self._label_to_names)
}
geometry_vertex = (geometry.point
if hasattr(geometry, 'point') else geometry.vertex)
have_colors = ("colors" in geometry.line if hasattr(
geometry, 'line') else ("colors" in geometry.triangle if hasattr(
geometry, 'triangle') else "colors" in geometry_vertex))
if inference_data_proto is not None:
inference_result = inference_data_proto.inference_result
else:
inference_result = []
label_props, custom_props = _classify_properties(geometry_vertex)
self._set_render_defaults(geometry, inference_result, label_props,
custom_props)
if o3dvis.scene.has_geometry(geometry_name):
updated = self._updated
# update_geometry() only accepts tPointCloud
if not isinstance(geometry, o3d.t.geometry.PointCloud):
o3dvis.remove_geometry(geometry_name)
else:
updated = deepcopy(self.ALL_UPDATED)
geometry_update_flag = 0
material_update_flag = 0
swap = RenderUpdate.BackupRestore()
material = o3dvis.get_geometry_material(geometry_name)
# Visualize scalar / 3-vector property with color map
if "property" in updated or "shader" in updated:
# Float Scalar with colormap
if ((self._property in custom_props or
self._property in label_props) and
self._shader.startswith("unlitGradient") and
geometry_vertex[self._property].shape[1] > self._index):
geometry_vertex["__visualization_scalar"] = geometry_vertex[
self._property][:, self._index].to(
o3d.core.float32).contiguous()
geometry_update_flag |= rendering.Scene.UPDATE_UV0_FLAG
# 3-vector as RGB
elif (self._property in custom_props and
self._shader == "defaultUnlit" and
geometry_vertex[self._property].shape[1] >= 3):
swap.backup(geometry_vertex, "colors")
geometry_vertex["colors"], min_val, max_val = _normalize(
geometry_vertex[self._property][:, :3])
self._update_range(min_val, max_val)
geometry_update_flag |= rendering.Scene.UPDATE_COLORS_FLAG
# Bounding boxes / LineSet
if isinstance(geometry, o3d.t.geometry.LineSet):
if self._property == "" and self._shader == "unlitSolidColor":
swap.backup(geometry.line,
"colors",
shape=(len(geometry.line["indices"]), 3),
dtype=o3d.core.uint8)
geometry.line["colors"][:] = next(iter(
self._colormap.values()))[:3]
elif self._property != "" and self._shader == "unlitGradient.LUT":
if self._colormap is None:
self._colormap = get_labelLUT()
if "colormap" in updated:
swap.backup(geometry.line, "indices", clone=True)
swap.backup(geometry.line,
"colors",
shape=(len(geometry.line["indices"]), 3),
dtype=o3d.core.uint8)
t_lines = geometry.line["indices"]
t_colors = geometry.line["colors"]
idx = 0
for bbir in inference_result:
col = self._colormap.setdefault(bbir.label,
(128, 128, 128, 255))
t_colors[idx:idx + self._LINES_PER_BBOX] = col[:3]
if col[3] == 0: # alpha
t_lines[idx:idx + self._LINES_PER_BBOX] = 0
idx += self._LINES_PER_BBOX
# PointCloud, Mesh, LineSet with colors
elif (("shader" in updated or "colormap" in updated) and
not geometry.has_valid_material()):
material_update_flag = 1
material.base_color = ((1.0, 1.0, 1.0, 1.0) if have_colors else
(0.5, 0.5, 0.5, 1.0))
if self._shader == "defaultLit":
material.shader = "defaultLit"
elif self._shader == "defaultUnlit":
material.shader = "defaultUnlit"
elif self._shader == "unlitSolidColor":
material.shader = "unlitSolidColor"
if self._colormap is None:
self._colormap = {0.0: [128, 128, 128, 255]}
material.base_color = _u8_to_float(
next(iter(self._colormap.values())))
elif self._shader == "unlitGradient.LUT": # Label Colormap
if self._colormap is None:
self._colormap = get_labelLUT()
material.shader = "unlitGradient"
material.gradient = rendering.Gradient()
material.gradient.mode = rendering.Gradient.LUT
lmin = min(self._label_to_names.keys())
lmax = max(self._label_to_names.keys())
material.scalar_min, material.scalar_max = lmin, lmax
if len(self._colormap) > 1:
norm_cmap = list((float(label - lmin) / (lmax - lmin),
_u8_to_float(color))
for label, color in self._colormap.items())
material.gradient.points = list(
rendering.Gradient.Point(*lc) for lc in norm_cmap)
else:
material.gradient.points = [
rendering.Gradient.Point(0.0, [1.0, 0.0, 1.0, 1.0])
]
# Colormap (RAINBOW / GREYSCALE): continuous data
elif self._shader.startswith("unlitGradient.GRADIENT."):
if self._colormap is None:
self._colormap = deepcopy(
self.DICT_COLORMAPS[self._shader[23:]])
material.shader = "unlitGradient"
material.gradient = rendering.Gradient()
material.gradient.points = list(
rendering.Gradient.Point(value,
_u8_to_float(color[:3]) + (1.,))
for value, color in self._colormap.items())
material.gradient.mode = rendering.Gradient.GRADIENT
self._set_vis_minmax(geometry_vertex, material)
if o3dvis.scene.has_geometry(geometry_name):
if material_update_flag > 0:
self._gui.run_sync(
o3dvis.modify_geometry_material, geometry_name, material
) # does not do force_redraw(), so also need update_geometry()
self._gui.run_sync(o3dvis.update_geometry, geometry_name, geometry,
geometry_update_flag)
_log.debug(
f"Geometry {geometry_name} updated with flags "
f"Geo:{geometry_update_flag:b}, Mat:{material_update_flag:b}")
else:
self._gui.run_sync(o3dvis.add_geometry, geometry_name, geometry,
material if material_update_flag else None)
self._gui.run_sync(o3dvis.post_redraw)
swap.restore()
_log.debug(f"apply complete: {geometry_name} with "
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/python/turicreate/toolkits/image_analysis/image_analysis.py | python | get_deep_features | (images, model_name, batch_size=64, verbose=True) | return feature_extractor.extract_features(images_sf, "image", verbose=verbose,
batch_size=batch_size) | Extracts features from images from a specific model.
Parameters
----------
images : SArray
Input data.
model_name : string
string optional
Uses a pretrained model to bootstrap an image classifier:
- "resnet-50" : Uses a pretrained resnet model.
Exported Core ML model will be ~90M.
- "squeezenet_v1.1" : Uses a pretrained squeezenet model.
Exported Core ML model will be ~4.7M.
- "VisionFeaturePrint_Scene": Uses an OS internal feature extractor.
Only on available on iOS 12.0+,
macOS 10.14+ and tvOS 12.0+.
Exported Core ML model will be ~41K.
Models are downloaded from the internet if not available locally. Once
downloaded, the models are cached for future use.
Returns
-------
out : SArray
Returns an SArray with all the extracted features.
See Also
--------
turicreate.image_classifier.create
turicreate.image_similarity.create
Examples
--------
>>> url = 'https://static.turi.com/datasets/images/nested'
>>> image_sframe = turicreate.load_images(url)
>>> image_sarray = image_sframe["image"]
>>> deep_features_sframe = turicreate.image_analysis.get_deep_features(image_sarray, model_name="resnet-50") | Extracts features from images from a specific model. | [
"Extracts",
"features",
"from",
"images",
"from",
"a",
"specific",
"model",
"."
] | def get_deep_features(images, model_name, batch_size=64, verbose=True):
"""
Extracts features from images from a specific model.
Parameters
----------
images : SArray
Input data.
model_name : string
string optional
Uses a pretrained model to bootstrap an image classifier:
- "resnet-50" : Uses a pretrained resnet model.
Exported Core ML model will be ~90M.
- "squeezenet_v1.1" : Uses a pretrained squeezenet model.
Exported Core ML model will be ~4.7M.
- "VisionFeaturePrint_Scene": Uses an OS internal feature extractor.
Only on available on iOS 12.0+,
macOS 10.14+ and tvOS 12.0+.
Exported Core ML model will be ~41K.
Models are downloaded from the internet if not available locally. Once
downloaded, the models are cached for future use.
Returns
-------
out : SArray
Returns an SArray with all the extracted features.
See Also
--------
turicreate.image_classifier.create
turicreate.image_similarity.create
Examples
--------
>>> url = 'https://static.turi.com/datasets/images/nested'
>>> image_sframe = turicreate.load_images(url)
>>> image_sarray = image_sframe["image"]
>>> deep_features_sframe = turicreate.image_analysis.get_deep_features(image_sarray, model_name="resnet-50")
"""
# Check model parameter
allowed_models = list(_pre_trained_models.IMAGE_MODELS.keys())
if _mac_ver() >= (10, 14):
allowed_models.append("VisionFeaturePrint_Scene")
_tkutl._check_categorical_option_type("model", model_name, allowed_models)
# Check images parameter
if not isinstance(images, _tc.SArray):
raise TypeError("Unrecognized type for 'images'. An SArray is expected.")
if len(images) == 0:
raise _ToolkitError("Unable to extract features on an empty SArray object")
if batch_size < 1:
raise ValueError("'batch_size' must be greater than or equal to 1")
# Extract features
feature_extractor = _image_feature_extractor._create_feature_extractor(model_name)
images_sf = _tc.SFrame({"image":images})
return feature_extractor.extract_features(images_sf, "image", verbose=verbose,
batch_size=batch_size) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | google_apis/google_api_keys.py | python | GetAPIKey | () | return _GetToken('GOOGLE_API_KEY') | Returns the simple API key. | Returns the simple API key. | [
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"""Returns the simple API key."""
return _GetToken('GOOGLE_API_KEY') | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/engine/training_v2.py | python | run_one_epoch | (model,
iterator,
execution_function,
dataset_size=None,
batch_size=None,
strategy=None,
steps_per_epoch=None,
num_samples=None,
mode=ModeKeys.TRAIN,
training_context=None,
total_epochs=None) | return aggregator.results | Run the execution function with the data from iterator.
Given the dataset iterator and execution function, get the data from iterator
and call it with the execution function to get the result (metric/loss).
It will run for steps_per_epoch or until to the iterator is fully consumed.
Args:
model: The keras model to run.
iterator: the dataset iterator to fetch the data.
execution_function: a tf.function that can be called with data.
dataset_size: the size of iterator, None when unknown.
batch_size: The size of the current batch.
strategy: the distribution strategy instance from the model.
steps_per_epoch: the number of steps to run for the epoch.
num_samples: the number of samples for the whole epoch if known. This can be
used to calculate the final partial batch, and scale the loss.
mode: the mode for the current epoch.
training_context: the context that contains callbacks and progress bar.
total_epochs: the total number of epochs that will be run.
Used when throw error when the iterator unexpectedly
reaches its end.
Returns:
The loss and metric value from the model. | Run the execution function with the data from iterator. | [
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iterator,
execution_function,
dataset_size=None,
batch_size=None,
strategy=None,
steps_per_epoch=None,
num_samples=None,
mode=ModeKeys.TRAIN,
training_context=None,
total_epochs=None):
"""Run the execution function with the data from iterator.
Given the dataset iterator and execution function, get the data from iterator
and call it with the execution function to get the result (metric/loss).
It will run for steps_per_epoch or until to the iterator is fully consumed.
Args:
model: The keras model to run.
iterator: the dataset iterator to fetch the data.
execution_function: a tf.function that can be called with data.
dataset_size: the size of iterator, None when unknown.
batch_size: The size of the current batch.
strategy: the distribution strategy instance from the model.
steps_per_epoch: the number of steps to run for the epoch.
num_samples: the number of samples for the whole epoch if known. This can be
used to calculate the final partial batch, and scale the loss.
mode: the mode for the current epoch.
training_context: the context that contains callbacks and progress bar.
total_epochs: the total number of epochs that will be run.
Used when throw error when the iterator unexpectedly
reaches its end.
Returns:
The loss and metric value from the model.
"""
# Only use the sample to count if there is a partial batch at the end.
use_steps = num_samples is None
if mode == ModeKeys.PREDICT:
aggregator = training_utils.OutputsAggregator(
use_steps=use_steps,
steps=steps_per_epoch,
num_samples=num_samples,
batch_size=batch_size)
else:
aggregator = training_utils.MetricsAggregator(
use_steps=use_steps, steps=steps_per_epoch, num_samples=num_samples)
callbacks = training_context.callbacks
progbar = training_context.progbar
if callbacks.model.stop_training:
return
target_steps = steps_per_epoch or np.inf
step = 0
while step < target_steps:
if use_steps:
current_batch_size = 1
elif step < target_steps - 1:
current_batch_size = batch_size
else:
current_batch_size = num_samples - step * batch_size
with training_context.on_batch(
step=step, mode=mode, size=current_batch_size) as batch_logs:
try:
batch_outs = execution_function(iterator)
except (StopIteration, errors.OutOfRangeError):
# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
# Are there any other C++ errors tf function should recapture?
# The only acceptable case here is that the input has a unknown
# length, and configured to fully consume it.
if (dataset_size is None
and steps_per_epoch is None
and step > 0):
# The input passed by the user ran out of batches.
# Now we know the cardinality of the input(dataset or generator).
steps_per_epoch = step
aggregator.steps = steps_per_epoch
progbar.params['steps'] = steps_per_epoch
progbar.progbar.target = steps_per_epoch
else:
callbacks.model.stop_training = True
logging.warning(
'Your input ran out of data; interrupting training. '
'Make sure that your dataset or generator can generate at '
'least `steps_per_epoch * epochs` batches (in this case, '
'{} batches). You may need to use the repeat() function '
'when building your dataset.'.format(
total_epochs * steps_per_epoch))
# In either case, break out the loop for training batch.
# Also note the training_context that data inputs are exhausted, so all
# the post batch hooks can be skipped.
batch_logs['data_exhausted'] = True
break
if mode != ModeKeys.PREDICT:
data_batch_size = batch_outs['batch_size']
batch_outs = (batch_outs['total_loss'] + batch_outs['output_losses']
+ batch_outs['metrics'])
if current_batch_size != data_batch_size:
batch_logs['size'] = data_batch_size
current_batch_size = data_batch_size
else:
batch_outs = _aggregate_predict_results(strategy, batch_outs, model)
if step == 0:
aggregator.create(batch_outs)
if use_steps:
aggregator.aggregate(batch_outs)
else:
aggregator.aggregate(
batch_outs,
batch_start=step * batch_size,
batch_end=step * batch_size + current_batch_size)
cbks.make_logs(model, batch_logs, batch_outs, mode)
step += 1
if callbacks.model.stop_training:
break
# End of an epoch.
aggregator.finalize()
return aggregator.results | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | MenuBar.FindItemById | (*args, **kwargs) | return _core_.MenuBar_FindItemById(*args, **kwargs) | FindItemById(self, int id) -> MenuItem | FindItemById(self, int id) -> MenuItem | [
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"""FindItemById(self, int id) -> MenuItem"""
return _core_.MenuBar_FindItemById(*args, **kwargs) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/framework/op_callbacks.py | python | remove_op_callback | (op_callback) | Remove an already-added op callback.
Args:
op_callback: The op callback to be removed.
Raises:
KeyError: If `op_callback` has not been registered using `add_op_callback()`
before. | Remove an already-added op callback. | [
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"""Remove an already-added op callback.
Args:
op_callback: The op callback to be removed.
Raises:
KeyError: If `op_callback` has not been registered using `add_op_callback()`
before.
"""
ctx = context.context()
ctx.remove_op_callback(op_callback)
if ctx.executing_eagerly() and not ctx.op_callbacks:
# Undo monkey-patch of execute.execute if there are no more callbacks.
execute.execute = execute.quick_execute | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | Gauge.Pulse | (*args, **kwargs) | return _controls_.Gauge_Pulse(*args, **kwargs) | Pulse(self) | Pulse(self) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | caffe2/python/helpers/algebra.py | python | mat_mul | (model, blob_in, blob_out, **kwargs) | return model.net.MatMul(blob_in, blob_out, **kwargs) | Matrix multiplication | Matrix multiplication | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/dashboard/dashboard/start_try_job.py | python | _GetTryServerBucket | (bisect_job) | return master_bucket_map.get(bisect_job.master_name, default) | Returns the bucket name to be used by buildbucket. | Returns the bucket name to be used by buildbucket. | [
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master_bucket_map = namespaced_stored_object.Get(_MASTER_BUILDBUCKET_MAP_KEY)
default = 'master.tryserver.chromium.perf'
if not master_bucket_map:
logging.warning(
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return default
return master_bucket_map.get(bisect_job.master_name, default) | [
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Illumina/manta | 75b5c38d4fcd2f6961197b28a41eb61856f2d976 | src/srcqc/run_cppcheck.py | python | compareVersions | (version1, version2) | return 0 | Compare two version strings.
Return
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> 0 if version2 is less than version1
0 if version1 == version2 | Compare two version strings. | [
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"""
Compare two version strings.
Return
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> 0 if version2 is less than version1
0 if version1 == version2
"""
def versionToIntArray(version) :
return [int(i) for i in version.split('.')]
# parse version components
version1Nums = versionToIntArray(version1)
version2Nums = versionToIntArray(version2)
# pad versions to be the same length:
maxlen = max(len(version1Nums), len(version2Nums))
while len(version1Nums) < maxlen : version1Nums.append(0)
while len(version2Nums) < maxlen : version2Nums.append(0)
# compare
for v1,v2 in zip(version1Nums, version2Nums) :
if v1 < v2 : return -1
if v1 > v2 : return 1
return 0 | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/markdown/extensions/footnotes.py | python | FootnoteExtension.makeFootnoteId | (self, id) | Return footnote link id. | Return footnote link id. | [
"Return",
"footnote",
"link",
"id",
"."
] | def makeFootnoteId(self, id):
""" Return footnote link id. """
if self.getConfig("UNIQUE_IDS"):
return 'fn%s%d-%s' % (self.sep, self.unique_prefix, id)
else:
return 'fn%s%s' % (self.sep, id) | [
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mingchen/protobuf-ios | 0958df34558cd54cb7b6e6ca5c8855bf3d475046 | compiler/python/google/protobuf/internal/decoder.py | python | Decoder.ReadMessageInto | (self, msg) | Calls msg.MergeFromString() to merge
length-delimited serialized message data into |msg|.
REQUIRES: The decoder must be positioned at the serialized "length"
prefix to a length-delmiited serialized message.
POSTCONDITION: The decoder is positioned just after the
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"""Calls msg.MergeFromString() to merge
length-delimited serialized message data into |msg|.
REQUIRES: The decoder must be positioned at the serialized "length"
prefix to a length-delmiited serialized message.
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"""
length = self._stream.ReadVarUInt32()
sub_buffer = self._stream.GetSubBuffer(length)
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if num_bytes_used != length:
raise message.DecodeError(
'Submessage told to deserialize from %d-byte encoding, '
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root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | interpreter/llvm/src/utils/benchmark/tools/gbench/report.py | python | calculate_change | (old_val, new_val) | return float(new_val - old_val) / abs(old_val) | Return a float representing the decimal change between old_val and new_val. | Return a float representing the decimal change between old_val and new_val. | [
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"""
Return a float representing the decimal change between old_val and new_val.
"""
if old_val == 0 and new_val == 0:
return 0.0
if old_val == 0:
return float(new_val - old_val) / (float(old_val + new_val) / 2)
return float(new_val - old_val) / abs(old_val) | [
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Ifsttar/I-Simpa | 2283385f4cac769a92e265edabb9c79cb6c42d03 | currentRelease/ExperimentalCore/md_octave/kdtree.py | python | KDNode.search_nn_dist | (self, point, distance, best=None) | return results | Search the n nearest nodes of the given point which are within given
distance
point must be a location, not a node. A list containing the n nearest
nodes to the point within the distance will be returned. | Search the n nearest nodes of the given point which are within given
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"""
Search the n nearest nodes of the given point which are within given
distance
point must be a location, not a node. A list containing the n nearest
nodes to the point within the distance will be returned.
"""
results = []
get_dist = lambda n: n.dist(point)
self._search_nn_dist(point, distance, results, get_dist)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/stc.py | python | StyledTextCtrl.PositionAfter | (*args, **kwargs) | return _stc.StyledTextCtrl_PositionAfter(*args, **kwargs) | PositionAfter(self, int pos) -> int
Given a valid document position, return the next position taking code
page into account. Maximum value returned is the last position in the document. | PositionAfter(self, int pos) -> int | [
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"""
PositionAfter(self, int pos) -> int
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page into account. Maximum value returned is the last position in the document.
"""
return _stc.StyledTextCtrl_PositionAfter(*args, **kwargs) | [
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JoseExposito/touchegg | 1f3fda214358d071c05da4bf17c070c33d67b5eb | cmake/cpplint.py | python | CheckSpacingForFunctionCall | (filename, clean_lines, linenum, error) | Checks for the correctness of various spacing around function calls.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Checks for the correctness of various spacing around function calls. | [
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] | def CheckSpacingForFunctionCall(filename, clean_lines, linenum, error):
"""Checks for the correctness of various spacing around function calls.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Since function calls often occur inside if/for/while/switch
# expressions - which have their own, more liberal conventions - we
# first see if we should be looking inside such an expression for a
# function call, to which we can apply more strict standards.
fncall = line # if there's no control flow construct, look at whole line
for pattern in (r'\bif\s*\((.*)\)\s*{',
r'\bfor\s*\((.*)\)\s*{',
r'\bwhile\s*\((.*)\)\s*[{;]',
r'\bswitch\s*\((.*)\)\s*{'):
match = Search(pattern, line)
if match:
fncall = match.group(1) # look inside the parens for function calls
break
# Except in if/for/while/switch, there should never be space
# immediately inside parens (eg "f( 3, 4 )"). We make an exception
# for nested parens ( (a+b) + c ). Likewise, there should never be
# a space before a ( when it's a function argument. I assume it's a
# function argument when the char before the whitespace is legal in
# a function name (alnum + _) and we're not starting a macro. Also ignore
# pointers and references to arrays and functions coz they're too tricky:
# we use a very simple way to recognize these:
# " (something)(maybe-something)" or
# " (something)(maybe-something," or
# " (something)[something]"
# Note that we assume the contents of [] to be short enough that
# they'll never need to wrap.
if ( # Ignore control structures.
not Search(r'\b(if|for|while|switch|return|new|delete|catch|sizeof)\b',
fncall) and
# Ignore pointers/references to functions.
not Search(r' \([^)]+\)\([^)]*(\)|,$)', fncall) and
# Ignore pointers/references to arrays.
not Search(r' \([^)]+\)\[[^\]]+\]', fncall)):
if Search(r'\w\s*\(\s(?!\s*\\$)', fncall): # a ( used for a fn call
error(filename, linenum, 'whitespace/parens', 4,
'Extra space after ( in function call')
elif Search(r'\(\s+(?!(\s*\\)|\()', fncall):
error(filename, linenum, 'whitespace/parens', 2,
'Extra space after (')
if (Search(r'\w\s+\(', fncall) and
not Search(r'_{0,2}asm_{0,2}\s+_{0,2}volatile_{0,2}\s+\(', fncall) and
not Search(r'#\s*define|typedef|using\s+\w+\s*=', fncall) and
not Search(r'\w\s+\((\w+::)*\*\w+\)\(', fncall) and
not Search(r'\bcase\s+\(', fncall)):
# TODO(unknown): Space after an operator function seem to be a common
# error, silence those for now by restricting them to highest verbosity.
if Search(r'\boperator_*\b', line):
error(filename, linenum, 'whitespace/parens', 0,
'Extra space before ( in function call')
else:
error(filename, linenum, 'whitespace/parens', 4,
'Extra space before ( in function call')
# If the ) is followed only by a newline or a { + newline, assume it's
# part of a control statement (if/while/etc), and don't complain
if Search(r'[^)]\s+\)\s*[^{\s]', fncall):
# If the closing parenthesis is preceded by only whitespaces,
# try to give a more descriptive error message.
if Search(r'^\s+\)', fncall):
error(filename, linenum, 'whitespace/parens', 2,
'Closing ) should be moved to the previous line')
else:
error(filename, linenum, 'whitespace/parens', 2,
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omnisci/omniscidb | b9c95f1bd602b4ffc8b0edf18bfad61031e08d86 | ThirdParty/googlebenchmark/mingw.py | python | root | (location = None, arch = None, version = None, threading = None,
exceptions = None, revision = None, log = EmptyLogger()) | return root_dir | Returns the root folder of a specific version of the mingw-builds variant
of gcc. Will download the compiler if needed | Returns the root folder of a specific version of the mingw-builds variant
of gcc. Will download the compiler if needed | [
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exceptions = None, revision = None, log = EmptyLogger()):
'''
Returns the root folder of a specific version of the mingw-builds variant
of gcc. Will download the compiler if needed
'''
# Get the repository if we don't have all the information
if not (arch and version and threading and exceptions and revision):
versions = repository(log = log)
# Determine some defaults
version = version or max(versions.keys())
if not arch:
arch = platform.machine().lower()
if arch == 'x86':
arch = 'i686'
elif arch == 'amd64':
arch = 'x86_64'
if not threading:
keys = versions[version][arch].keys()
if 'posix' in keys:
threading = 'posix'
elif 'win32' in keys:
threading = 'win32'
else:
threading = keys[0]
if not exceptions:
keys = versions[version][arch][threading].keys()
if 'seh' in keys:
exceptions = 'seh'
elif 'sjlj' in keys:
exceptions = 'sjlj'
else:
exceptions = keys[0]
if revision == None:
revision = max(versions[version][arch][threading][exceptions].keys())
if not location:
location = os.path.join(tempfile.gettempdir(), 'mingw-builds')
# Get the download url
url = versions[version][arch][threading][exceptions][revision]
# Tell the user whatzzup
log.info('finding MinGW %s', '.'.join(str(v) for v in version))
log.debug(' - arch: %s', arch)
log.debug(' - threading: %s', threading)
log.debug(' - exceptions: %s', exceptions)
log.debug(' - revision: %s', revision)
log.debug(' - url: %s', url)
# Store each specific revision differently
slug = '{version}-{arch}-{threading}-{exceptions}-rev{revision}'
slug = slug.format(
version = '.'.join(str(v) for v in version),
arch = arch,
threading = threading,
exceptions = exceptions,
revision = revision
)
if arch == 'x86_64':
root_dir = os.path.join(location, slug, 'mingw64')
elif arch == 'i686':
root_dir = os.path.join(location, slug, 'mingw32')
else:
raise ValueError('Unknown MinGW arch: ' + arch)
# Download if needed
if not os.path.exists(root_dir):
downloaded = download(url, os.path.join(location, slug), log = log)
if downloaded != root_dir:
raise ValueError('The location of mingw did not match\n%s\n%s'
% (downloaded, root_dir))
return root_dir | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/jinja2/utils.py | python | evalcontextfunction | (f) | return f | This decorator can be used to mark a function or method as an eval
context callable. This is similar to the :func:`contextfunction`
but instead of passing the context, an evaluation context object is
passed. For more information about the eval context, see
:ref:`eval-context`.
.. versionadded:: 2.4 | This decorator can be used to mark a function or method as an eval
context callable. This is similar to the :func:`contextfunction`
but instead of passing the context, an evaluation context object is
passed. For more information about the eval context, see
:ref:`eval-context`. | [
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"""This decorator can be used to mark a function or method as an eval
context callable. This is similar to the :func:`contextfunction`
but instead of passing the context, an evaluation context object is
passed. For more information about the eval context, see
:ref:`eval-context`.
.. versionadded:: 2.4
"""
f.evalcontextfunction = True
return f | [
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randombit/botan | e068d80953469fc8a3ec1715d0f64756d972daba | src/scripts/ci_build.py | python | determine_flags | (target, target_os, target_cpu, target_cc, cc_bin,
ccache, root_dir, pkcs11_lib, use_gdb, disable_werror, extra_cxxflags,
disabled_tests) | return flags, run_test_command, make_prefix | Return the configure.py flags as well as make/test running prefixes | Return the configure.py flags as well as make/test running prefixes | [
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ccache, root_dir, pkcs11_lib, use_gdb, disable_werror, extra_cxxflags,
disabled_tests):
# pylint: disable=too-many-branches,too-many-statements,too-many-arguments,too-many-locals
"""
Return the configure.py flags as well as make/test running prefixes
"""
is_cross_target = target.startswith('cross-')
if target_os not in ['linux', 'osx', 'windows', 'freebsd']:
print('Error unknown OS %s' % (target_os))
return (None, None, None)
if is_cross_target:
if target_os == 'osx':
target_os = 'ios'
elif target == 'cross-win64':
target_os = 'mingw'
elif target in ['cross-android-arm32', 'cross-android-arm64']:
target_os = 'android'
if target_os == 'windows' and target_cc == 'gcc':
target_os = 'mingw'
if target == 'baremetal':
target_os = 'none'
if target == 'emscripten':
target_os = 'emscripten'
make_prefix = []
test_prefix = []
test_cmd = [os.path.join(root_dir, 'botan-test')]
install_prefix = tempfile.mkdtemp(prefix='botan-install-')
flags = ['--prefix=%s' % (install_prefix),
'--cc=%s' % (target_cc),
'--os=%s' % (target_os),
'--build-targets=%s' % ','.join(build_targets(target, target_os))]
if ccache is not None:
flags += ['--no-store-vc-rev', '--compiler-cache=%s' % (ccache)]
if not disable_werror:
flags += ['--werror-mode']
if target_cpu is not None:
flags += ['--cpu=%s' % (target_cpu)]
for flag in extra_cxxflags:
flags += ['--extra-cxxflags=%s' % (flag)]
if target in ['minimized']:
flags += ['--minimized-build', '--enable-modules=system_rng,sha2_32,sha2_64,aes']
if target in ['amalgamation']:
flags += ['--amalgamation']
if target in ['bsi', 'nist']:
# tls is optional for bsi/nist but add it so verify tests work with these minimized configs
flags += ['--module-policy=%s' % (target), '--enable-modules=tls']
if target == 'docs':
flags += ['--with-doxygen', '--with-sphinx', '--with-rst2man']
test_cmd = None
if target == 'cross-win64':
# this test compiles under MinGW but fails when run under Wine
disabled_tests.append('certstor_system')
if target == 'coverage':
flags += ['--with-coverage-info', '--with-debug-info', '--test-mode']
if target == 'valgrind':
flags += ['--with-valgrind']
test_prefix = ['valgrind', '--error-exitcode=9', '-v', '--leak-check=full', '--show-reachable=yes']
# valgrind is single threaded anyway
test_cmd += ['--test-threads=1']
# valgrind is slow
slow_tests = [
'cryptobox', 'dh_invalid', 'dh_kat', 'dh_keygen',
'dl_group_gen', 'dlies', 'dsa_param', 'ecc_basemul',
'ecdsa_verify_wycheproof', 'mce_keygen', 'passhash9',
'rsa_encrypt', 'rsa_pss', 'rsa_pss_raw', 'scrypt',
'srp6_kat', 'x509_path_bsi', 'xmss_keygen', 'xmss_sign',
'pbkdf', 'argon2', 'bcrypt', 'bcrypt_pbkdf', 'compression',
'ed25519_sign', 'elgamal_keygen', 'x509_path_rsa_pss']
disabled_tests += slow_tests
if target == 'fuzzers':
flags += ['--unsafe-fuzzer-mode']
if target in ['fuzzers', 'coverage']:
flags += ['--build-fuzzers=test']
if target in ['fuzzers', 'sanitizer']:
flags += ['--with-debug-asserts']
if target_cc in ['clang', 'gcc']:
flags += ['--enable-sanitizers=address,undefined']
else:
flags += ['--with-sanitizers']
if target in ['valgrind', 'sanitizer', 'fuzzers']:
flags += ['--disable-modules=locking_allocator']
if target == 'baremetal':
cc_bin = 'arm-none-eabi-c++'
flags += ['--cpu=arm32', '--disable-neon', '--without-stack-protector', '--ldflags=-specs=nosys.specs']
test_cmd = None
if target == 'emscripten':
flags += ['--cpu=wasm']
# need to find a way to run the wasm-compiled tests w/o a browser
test_cmd = None
if is_cross_target:
if target_os == 'ios':
make_prefix = ['xcrun', '--sdk', 'iphoneos']
test_cmd = None
if target == 'cross-ios-arm64':
flags += ['--cpu=arm64', '--cc-abi-flags=-arch arm64 -stdlib=libc++']
else:
raise Exception("Unknown cross target '%s' for iOS" % (target))
elif target_os == 'android':
ndk = os.getenv('ANDROID_NDK')
if ndk is None:
raise Exception('Android CI build requires ANDROID_NDK env variable be set')
api_lvl = int(os.getenv('ANDROID_API_LEVEL', '0'))
if api_lvl == 0:
# If not set arbitrarily choose API 16 (Android 4.1) for ARMv7 and 28 (Android 9) for AArch64
api_lvl = 16 if target == 'cross-android-arm32' else 28
toolchain_dir = os.path.join(ndk, 'toolchains/llvm/prebuilt/linux-x86_64/bin')
test_cmd = None
if target == 'cross-android-arm32':
cc_bin = os.path.join(toolchain_dir, 'armv7a-linux-androideabi%d-clang++' % (api_lvl))
flags += ['--cpu=armv7',
'--ar-command=%s' % (os.path.join(toolchain_dir, 'arm-linux-androideabi-ar'))]
elif target == 'cross-android-arm64':
cc_bin = os.path.join(toolchain_dir, 'aarch64-linux-android%d-clang++' % (api_lvl))
flags += ['--cpu=arm64',
'--ar-command=%s' % (os.path.join(toolchain_dir, 'aarch64-linux-android-ar'))]
if api_lvl < 18:
flags += ['--without-os-features=getauxval']
if api_lvl >= 28:
flags += ['--with-os-features=getentropy']
elif target == 'cross-i386':
flags += ['--cpu=x86_32']
elif target == 'cross-win64':
# MinGW in 16.04 is lacking std::mutex for unknown reason
cc_bin = 'x86_64-w64-mingw32-g++'
flags += ['--cpu=x86_64', '--cc-abi-flags=-static',
'--ar-command=x86_64-w64-mingw32-ar', '--without-os-feature=threads']
test_cmd = [os.path.join(root_dir, 'botan-test.exe')] + test_cmd[1:]
test_prefix = ['wine']
else:
if target == 'cross-arm32':
flags += ['--cpu=armv7']
cc_bin = 'arm-linux-gnueabihf-g++'
# Currently arm32 CI only runs on native AArch64
#test_prefix = ['qemu-arm', '-L', '/usr/arm-linux-gnueabihf/']
elif target == 'cross-arm64':
flags += ['--cpu=aarch64']
cc_bin = 'aarch64-linux-gnu-g++'
test_prefix = ['qemu-aarch64', '-L', '/usr/aarch64-linux-gnu/']
elif target == 'cross-ppc32':
flags += ['--cpu=ppc32']
cc_bin = 'powerpc-linux-gnu-g++'
test_prefix = ['qemu-ppc', '-L', '/usr/powerpc-linux-gnu/']
elif target == 'cross-ppc64':
flags += ['--cpu=ppc64', '--with-endian=little']
cc_bin = 'powerpc64le-linux-gnu-g++'
test_prefix = ['qemu-ppc64le', '-cpu', 'POWER8', '-L', '/usr/powerpc64le-linux-gnu/']
elif target == 'cross-mips64':
flags += ['--cpu=mips64', '--with-endian=big']
cc_bin = 'mips64-linux-gnuabi64-g++'
test_prefix = ['qemu-mips64', '-L', '/usr/mips64-linux-gnuabi64/']
test_cmd.remove('simd_32') # no SIMD on MIPS
else:
raise Exception("Unknown cross target '%s' for Linux" % (target))
else:
# Flags specific to native targets
if target_os in ['osx', 'linux']:
flags += ['--with-bzip2', '--with-sqlite', '--with-zlib']
if target_os in ['osx', 'ios']:
flags += ['--with-commoncrypto']
if target == 'coverage':
flags += ['--with-boost']
if target_os == 'windows' and target in ['shared', 'static']:
# ./configure.py needs extra hand-holding for boost on windows
boost_root = os.environ.get('BOOST_ROOT')
boost_libs = os.environ.get('BOOST_LIBRARYDIR')
boost_system = os.environ.get('BOOST_SYSTEM_LIBRARY')
if boost_root and boost_libs and boost_system:
flags += ['--with-boost',
'--with-external-includedir', boost_root,
'--with-external-libdir', boost_libs,
'--boost-library-name', boost_system]
if target_os == 'linux':
flags += ['--with-lzma']
if target in ['coverage']:
flags += ['--with-tpm']
test_cmd += ['--run-online-tests']
if pkcs11_lib and os.access(pkcs11_lib, os.R_OK):
test_cmd += ['--pkcs11-lib=%s' % (pkcs11_lib)]
if target in ['coverage', 'sanitizer']:
test_cmd += ['--run-long-tests']
flags += ['--cc-bin=%s' % (cc_bin)]
if test_cmd is None:
run_test_command = None
else:
if use_gdb:
disabled_tests.append("os_utils")
# render 'disabled_tests' array into test_cmd
if disabled_tests:
test_cmd += ['--skip-tests=%s' % (','.join(disabled_tests))]
if use_gdb:
(cmd, args) = test_cmd[0], test_cmd[1:]
run_test_command = test_prefix + ['gdb', cmd,
'-ex', 'run %s' % (' '.join(args)),
'-ex', 'bt',
'-ex', 'quit']
else:
run_test_command = test_prefix + test_cmd
return flags, run_test_command, make_prefix | [
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infinit/memo | 3a8394d0f647efe03ccb8bfe885a7279cb8be8a6 | elle/drake/src/drake/__init__.py | python | Path.without_suffix | (self, rhs) | return drake.Path(path,
absolute = self.__absolute,
virtual = self.__virtual,
volume = self.__volume) | Remove rhs suffix from self.
rhs -- the suffix to strip, as a Path or a string.
>>> p = Path('foo/bar/baz/quux')
>>> p
Path("foo/bar/baz/quux")
>>> p.without_suffix("baz/quux")
Path("foo/bar")
Throws if rhs is not a prefix of self.
>>> p.without_suffix("baz")
Traceback (most recent call last):
...
Exception: baz is not a suffix of foo/bar/baz/quux | Remove rhs suffix from self. | [
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>>> p
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>>> p.without_suffix("baz/quux")
Path("foo/bar")
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>>> p.without_suffix("baz")
Traceback (most recent call last):
...
Exception: baz is not a suffix of foo/bar/baz/quux
"""
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/inspect.py | python | getsourcelines | (object) | Return a list of source lines and starting line number for an object.
The argument may be a module, class, method, function, traceback, frame,
or code object. The source code is returned as a list of the lines
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/tornado/tornado-6/tornado/util.py | python | _websocket_mask_python | (mask: bytes, data: bytes) | return unmasked_arr.tobytes() | Websocket masking function.
`mask` is a `bytes` object of length 4; `data` is a `bytes` object of any length.
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Returns a `bytes` object of the same length as `data` with the mask applied
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mask_arr = array.array("B", mask)
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Netflix/NfWebCrypto | 499faf4eb9f9ccf0b21dc728e974970f54bd6c52 | plugin/ppapi/ppapi/generators/idl_gen_wrapper.py | python | WrapperGen.OwnHeaderFile | (self) | Return the header file that specifies the API of this wrapper.
We do not generate the header files. | Return the header file that specifies the API of this wrapper.
We do not generate the header files. | [
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apache/qpid-proton | 6bcdfebb55ea3554bc29b1901422532db331a591 | python/proton/_data.py | python | Data.put_uint | (self, ui: Union[uint, int]) | Puts an unsigned int value.
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Puts an unsigned int value.
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:raise: * ``AssertionError`` if parameter is out of the range :math:`0` to :math:`2^{32} - 1` inclusive.
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"""
self._check(pn_data_put_uint(self._data, ui)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | PyApp_SetMacPreferencesMenuItemId | (*args, **kwargs) | return _core_.PyApp_SetMacPreferencesMenuItemId(*args, **kwargs) | PyApp_SetMacPreferencesMenuItemId(long val) | PyApp_SetMacPreferencesMenuItemId(long val) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/check_ops.py | python | _assert_rank_condition | (x, rank, static_condition, dynamic_condition, data,
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rank: Scalar `Tensor`.
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Defaults to "assert_rank_at_least".
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"""Assert `x` has a rank that satisfies a given condition.
Args:
x: Numeric `Tensor`.
rank: Scalar `Tensor`.
static_condition: A python function that takes `[actual_rank, given_rank]`
and returns `True` if the condition is satisfied, `False` otherwise.
dynamic_condition: An `op` that takes [actual_rank, given_rank]
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data: The tensors to print out if the condition is false. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional).
Defaults to "assert_rank_at_least".
Returns:
Op raising `InvalidArgumentError` if `x` fails dynamic_condition.
Raises:
ValueError: If static checks determine `x` fails static_condition.
"""
with ops.op_scope([x], name, 'assert_rank'):
x = ops.convert_to_tensor(x, name='x')
rank = ops.convert_to_tensor(rank, name='rank')
# Attempt to statically defined rank.
x_rank_static = x.get_shape().ndims
rank_static = tensor_util.constant_value(rank)
assert_type(rank, dtypes.int32)
if rank_static is not None:
if rank_static.ndim != 0:
raise ValueError('Rank must be a scalar')
if x_rank_static is not None:
if not static_condition(x_rank_static, rank_static):
raise ValueError(
'Static rank condition failed', x_rank_static, rank_static)
return control_flow_ops.no_op(name='static_checks_determined_all_ok')
condition = dynamic_condition(array_ops.rank(x), rank)
# Add the condition that `rank` must have rank zero. Prevents the bug where
# someone does assert_rank(x, [n]), rather than assert_rank(x, n).
if rank_static is None:
this_data = ['Rank must be a scalar. Received rank: ', rank]
rank_check = assert_rank(rank, 0, data=this_data)
condition = control_flow_ops.with_dependencies([rank_check], condition)
return logging_ops.Assert(condition, data, summarize=summarize) | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/graph_editor/util.py | python | ControlOutputs.__init__ | (self, graph) | Create a dictionary of control-output dependencies.
Args:
graph: a tf.Graph.
Returns:
A dictionary where a key is a tf.Operation instance and the corresponding
value is a list of all the ops which have the key as one of their
control-input dependencies.
Raises:
TypeError: graph is not a tf.Graph. | Create a dictionary of control-output dependencies. | [
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Args:
graph: a tf.Graph.
Returns:
A dictionary where a key is a tf.Operation instance and the corresponding
value is a list of all the ops which have the key as one of their
control-input dependencies.
Raises:
TypeError: graph is not a tf.Graph.
"""
if not isinstance(graph, tf_ops.Graph):
raise TypeError("Expected a tf.Graph, got: {}".format(type(graph)))
self._control_outputs = {}
self._graph = graph
self._version = None
self._build() | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site.py | python | setquit | () | Define new builtins 'quit' and 'exit'.
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"""Define new builtins 'quit' and 'exit'.
These are objects which make the interpreter exit when called.
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"""
if os.sep == ':':
eof = 'Cmd-Q'
elif os.sep == '\\':
eof = 'Ctrl-Z plus Return'
else:
eof = 'Ctrl-D (i.e. EOF)'
class Quitter(object):
def __init__(self, name):
self.name = name
def __repr__(self):
return 'Use %s() or %s to exit' % (self.name, eof)
def __call__(self, code=None):
# Shells like IDLE catch the SystemExit, but listen when their
# stdin wrapper is closed.
try:
sys.stdin.close()
except:
pass
raise SystemExit(code)
__builtin__.quit = Quitter('quit')
__builtin__.exit = Quitter('exit') | [
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llvm-mirror/libcxx | 78d6a7767ed57b50122a161b91f59f19c9bd0d19 | utils/google-benchmark/tools/gbench/util.py | python | load_benchmark_results | (fname) | Read benchmark output from a file and return the JSON object.
REQUIRES: 'fname' names a file containing JSON benchmark output. | Read benchmark output from a file and return the JSON object.
REQUIRES: 'fname' names a file containing JSON benchmark output. | [
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Read benchmark output from a file and return the JSON object.
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with open(fname, 'r') as f:
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | media/webrtc/trunk/tools/gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings.GetLibFlags | (self, config, gyp_to_build_path) | return libflags | Returns the flags that need to be added to lib commands. | Returns the flags that need to be added to lib commands. | [
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config = self._RealConfig(config)
libflags = []
lib = self._GetWrapper(self, self.msvs_settings[config],
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libflags.extend(self._GetAdditionalLibraryDirectories(
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lib('AdditionalOptions')
return libflags | [
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MicBosi/VisualizationLibrary | d2a0e321288152008957e29a0bc270ad192f75be | src/external/freetype/src/tools/docmaker/content.py | python | ContentProcessor.add_markup | ( self ) | add a new markup section | add a new markup section | [
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if self.markup and self.markup_lines:
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marks = self.markup_lines
if len( marks ) > 0 and not string.strip( marks[-1] ):
self.markup_lines = marks[:-1]
m = DocMarkup( self.markup, self.markup_lines )
self.markups.append( m )
self.markup = None
self.markup_lines = [] | [
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/showbase/PythonUtil.py | python | listToItem2index | (L) | return d | converts list to dict of list item->list index
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"""converts list to dict of list item->list index
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for i, item in enumerate(L):
d[item] = i
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/logging/__init__.py | python | StreamHandler.flush | (self) | Flushes the stream. | Flushes the stream. | [
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google/mysql-protobuf | 467cda676afaa49e762c5c9164a43f6ad31a1fbf | protobuf/python/mox.py | python | MockAnything._Replay | (self) | Start replaying expected method calls. | Start replaying expected method calls. | [
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root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | interpreter/llvm/src/tools/clang/tools/scan-build-py/libear/__init__.py | python | Toolset.add_definitions | (self, defines) | part of public interface | part of public interface | [
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""" part of public interface """
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dmlc/dmlc-core | 20ec5bee3a655e8d66e50e3abf7ab5c35ea028fe | tracker/dmlc_tracker/opts.py | python | get_cache_file_set | (args) | return fset, cmds | Get the list of files to be cached.
Parameters
----------
args: ArgumentParser.Argument
The arguments returned by the parser.
Returns
-------
cache_file_set: set of str
The set of files to be cached to local execution environment.
command: list of str
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Parameters
----------
args: ArgumentParser.Argument
The arguments returned by the parser.
Returns
-------
cache_file_set: set of str
The set of files to be cached to local execution environment.
command: list of str
The commands that get rewritten after the file cache is used.
"""
fset = set()
cmds = []
if args.auto_file_cache:
for i in range(len(args.command)):
fname = args.command[i]
if os.path.exists(fname):
fset.add(fname)
cmds.append('./' + fname.split('/')[-1])
else:
cmds.append(fname)
for fname in args.files:
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/Inelastic/Direct/RunDescriptor.py | python | RunList.get_run_list2sum | (self,num_to_sum=None) | return self._run_numbers[:num_to_sum] | Get run numbers of the files to be summed together
from the list of defined run numbers | Get run numbers of the files to be summed together
from the list of defined run numbers | [
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"""Get run numbers of the files to be summed together
from the list of defined run numbers
"""
n_runs = len(self._run_numbers)
if num_to_sum:
if num_to_sum <= 0:
num_to_sum = 1
if num_to_sum > n_runs:
num_to_sum = n_runs
else:
num_to_sum = n_runs
if self._last_ind2sum >= 0 and self._last_ind2sum < num_to_sum:
num_to_sum = self._last_ind2sum + 1
return self._run_numbers[:num_to_sum] | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/distlib/_backport/tarfile.py | python | TarFile.utime | (self, tarinfo, targetpath) | Set modification time of targetpath according to tarinfo. | Set modification time of targetpath according to tarinfo. | [
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"""Set modification time of targetpath according to tarinfo.
"""
if not hasattr(os, 'utime'):
return
try:
os.utime(targetpath, (tarinfo.mtime, tarinfo.mtime))
except EnvironmentError as e:
raise ExtractError("could not change modification time") | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/python-gflags/gflags.py | python | FlagValues._AssertValidators | (self, validators) | Assert if all validators in the list are satisfied.
Asserts validators in the order they were created.
Args:
validators: Iterable(gflags_validators.Validator), validators to be
verified
Raises:
AttributeError: if validators work with a non-existing flag.
IllegalFlagValue: if validation fails for at least one validator | Assert if all validators in the list are satisfied. | [
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"""Assert if all validators in the list are satisfied.
Asserts validators in the order they were created.
Args:
validators: Iterable(gflags_validators.Validator), validators to be
verified
Raises:
AttributeError: if validators work with a non-existing flag.
IllegalFlagValue: if validation fails for at least one validator
"""
for validator in sorted(
validators, key=lambda validator: validator.insertion_index):
try:
validator.Verify(self)
except gflags_validators.Error, e:
message = validator.PrintFlagsWithValues(self)
raise IllegalFlagValue('%s: %s' % (message, str(e))) | [
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ARM-software/armnn | 5e9965cae1cc6162649910f423ebd86001fc1931 | python/pyarmnn/examples/image_classification/example_utils.py | python | unzip_file | (filename: str) | Unzips a file.
Args:
filename(str): Name of the file
Returns:
None | Unzips a file. | [
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] | def unzip_file(filename: str):
"""Unzips a file.
Args:
filename(str): Name of the file
Returns:
None
"""
with ZipFile(filename, 'r') as zip_obj:
zip_obj.extractall() | [
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facebookincubator/fizz | bd0ba1b80f72023cb7ede671a4caa85f6664d3f6 | build/fbcode_builder/getdeps/cargo.py | python | CargoBuilder._patchup_workspace | (self) | This method makes some assumptions about the state of the project and
its cargo dependendies:
1. Crates from cargo dependencies can be extracted from Cargo.toml files
using _extract_crates function. It is using a heuristic so check its
code to understand how it is done.
2. The extracted cargo dependencies crates can be found in the
dependency's install dir using _resolve_crate_to_path function
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my-rename-of-crate = { package = "crate", git = "..." }
they can count themselves lucky because the code will raise an
Exception. There migh be more cases where the code will silently pass
producing bad results. | This method makes some assumptions about the state of the project and
its cargo dependendies:
1. Crates from cargo dependencies can be extracted from Cargo.toml files
using _extract_crates function. It is using a heuristic so check its
code to understand how it is done.
2. The extracted cargo dependencies crates can be found in the
dependency's install dir using _resolve_crate_to_path function
which again is using a heuristic. | [
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"""
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1. Crates from cargo dependencies can be extracted from Cargo.toml files
using _extract_crates function. It is using a heuristic so check its
code to understand how it is done.
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my-rename-of-crate = { package = "crate", git = "..." }
they can count themselves lucky because the code will raise an
Exception. There migh be more cases where the code will silently pass
producing bad results.
"""
workspace_dir = self.workspace_dir()
config = self._resolve_config()
if config:
with open(os.path.join(workspace_dir, "Cargo.toml"), "r+") as f:
manifest_content = f.read()
if "[package]" not in manifest_content:
# A fake manifest has to be crated to change the virtual
# manifest into a non-virtual. The virtual manifests are limited
# in many ways and the inability to define patches on them is
# one. Check https://github.com/rust-lang/cargo/issues/4934 to
# see if it is resolved.
f.write(
"""
[package]
name = "fake_manifest_of_{}"
version = "0.0.0"
[lib]
path = "/dev/null"
""".format(
self.manifest.name
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else:
f.write("\n")
f.write(config) | [
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | 3rdparty/protobuf-3.0.0/python/google/protobuf/message.py | python | Message.ListFields | (self) | Returns a list of (FieldDescriptor, value) tuples for all
fields in the message which are not empty. A singular field is non-empty
if HasField() would return true, and a repeated field is non-empty if
it contains at least one element. The fields are ordered by field
number | Returns a list of (FieldDescriptor, value) tuples for all
fields in the message which are not empty. A singular field is non-empty
if HasField() would return true, and a repeated field is non-empty if
it contains at least one element. The fields are ordered by field
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"""Returns a list of (FieldDescriptor, value) tuples for all
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it contains at least one element. The fields are ordered by field
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/keras/_impl/keras/preprocessing/image.py | python | array_to_img | (x, data_format=None, scale=True) | Converts a 3D Numpy array to a PIL Image instance.
Arguments:
x: Input Numpy array.
data_format: Image data format.
scale: Whether to rescale image values
to be within [0, 255].
Returns:
A PIL Image instance.
Raises:
ImportError: if PIL is not available.
ValueError: if invalid `x` or `data_format` is passed. | Converts a 3D Numpy array to a PIL Image instance. | [
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] | def array_to_img(x, data_format=None, scale=True):
"""Converts a 3D Numpy array to a PIL Image instance.
Arguments:
x: Input Numpy array.
data_format: Image data format.
scale: Whether to rescale image values
to be within [0, 255].
Returns:
A PIL Image instance.
Raises:
ImportError: if PIL is not available.
ValueError: if invalid `x` or `data_format` is passed.
"""
if pil_image is None:
raise ImportError('Could not import PIL.Image. '
'The use of `array_to_img` requires PIL.')
x = np.asarray(x, dtype=K.floatx())
if x.ndim != 3:
raise ValueError('Expected image array to have rank 3 (single image). '
'Got array with shape:', x.shape)
if data_format is None:
data_format = K.image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Invalid data_format:', data_format)
# Original Numpy array x has format (height, width, channel)
# or (channel, height, width)
# but target PIL image has format (width, height, channel)
if data_format == 'channels_first':
x = x.transpose(1, 2, 0)
if scale:
x = x + max(-np.min(x), 0) # pylint: disable=g-no-augmented-assignment
x_max = np.max(x)
if x_max != 0:
x /= x_max
x *= 255
if x.shape[2] == 3:
# RGB
return pil_image.fromarray(x.astype('uint8'), 'RGB')
elif x.shape[2] == 1:
# grayscale
return pil_image.fromarray(x[:, :, 0].astype('uint8'), 'L')
else:
raise ValueError('Unsupported channel number: ', x.shape[2]) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | caffe2/python/schema.py | python | Field.__init__ | (self, children) | Derived classes must call this after their initialization. | Derived classes must call this after their initialization. | [
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/environments/cliff_walking.py | python | Environment._is_goal | (self, pos) | return pos[0] == self._height - 1 and pos[1] == self._width - 1 | Check if position is bottom right corner of grid. | Check if position is bottom right corner of grid. | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/distutils/ccompiler.py | python | CCompiler._fix_object_args | (self, objects, output_dir) | return (objects, output_dir) | Typecheck and fix up some arguments supplied to various methods.
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib2to3/pytree.py | python | Leaf.clone | (self) | return Leaf(self.type, self.value,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | TextAttr.IsParagraphStyle | (*args, **kwargs) | return _controls_.TextAttr_IsParagraphStyle(*args, **kwargs) | IsParagraphStyle(self) -> bool | IsParagraphStyle(self) -> bool | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/count-vowel-substrings-of-a-string.py | python | Solution.countVowelSubstrings | (self, word) | return atLeastK(word, k) | :type word: str
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k = 5
def atLeastK(word, k):
cnt = collections.Counter()
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cnt[c] += 1
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if not cnt[word[right]]:
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return result
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niwinz/phantompy | ae25ddb6791e13cb7c35126971c410030ee5dfda | phantompy/context.py | python | Context.process_events | (self, timeout=200) | Method like a `time.sleep` but while waiths a timeout
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Method like a `time.sleep` but while waiths a timeout
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bingwin/MicroChat | 81d9a71a212c1cbca5bba497ec42659a7d25dccf | mars/lint/cpplint.py | python | IsCppString | (line) | return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1 | Does line terminate so, that the next symbol is in string constant.
This function does not consider single-line nor multi-line comments.
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/controls/InputState.py | python | InputState.debugPrint | (self, message) | return self.notify.debug(
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/msvccompiler.py | python | get_build_architecture | () | return sys.version[i+len(prefix):j] | Return the processor architecture.
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prefix = " bit ("
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py | python | SinhArcsinh.tailweight | (self) | return self._tailweight | The `tailweight` in: `Y = Sinh((Arcsinh(X) + skewness) * tailweight)`. | The `tailweight` in: `Y = Sinh((Arcsinh(X) + skewness) * tailweight)`. | [
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BSVino/DoubleAction | c550b168a3e919926c198c30240f506538b92e75 | mp/src/thirdparty/protobuf-2.3.0/python/google/protobuf/internal/encoder.py | python | _SignedVarintEncoder | () | return EncodeSignedVarint | Return an encoder for a basic signed varint value (does not include
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def EncodeSignedVarint(write, value):
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value += (1 << 64)
bits = value & 0x7f
value >>= 7
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bits = value & 0x7f
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chromiumembedded/cef | 80caf947f3fe2210e5344713c5281d8af9bdc295 | tools/yapf/yapf/yapflib/pytree_visitor.py | python | DumpPyTree | (tree, target_stream=sys.stdout) | Convenience function for dumping a given pytree.
This function presents a very minimal interface. For more configurability (for
example, controlling how specific node types are displayed), use PyTreeDumper
directly.
Arguments:
tree: the tree to dump.
target_stream: the stream to dump the tree to. A file-like object. By
default will dump into stdout. | Convenience function for dumping a given pytree. | [
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"""Convenience function for dumping a given pytree.
This function presents a very minimal interface. For more configurability (for
example, controlling how specific node types are displayed), use PyTreeDumper
directly.
Arguments:
tree: the tree to dump.
target_stream: the stream to dump the tree to. A file-like object. By
default will dump into stdout.
"""
dumper = PyTreeDumper(target_stream)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | Point2D.GetDotProduct | (*args, **kwargs) | return _core_.Point2D_GetDotProduct(*args, **kwargs) | GetDotProduct(self, Point2D vec) -> double | GetDotProduct(self, Point2D vec) -> double | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | GIFHandler.__init__ | (self, *args, **kwargs) | __init__(self) -> GIFHandler
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"""
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/idl/idl/binder.py | python | _bind_expression | (expr, allow_literal_string=True) | return None | Bind an expression. | Bind an expression. | [
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# type: (syntax.Expression, bool) -> ast.Expression
"""Bind an expression."""
node = ast.Expression(expr.file_name, expr.line, expr.column)
if expr.literal is None:
node.expr = expr.expr
node.validate_constexpr = expr.is_constexpr
node.export = expr.is_constexpr
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return node
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intval = int(expr.literal)
if intval >= -0x80000000 and intval <= 0x7FFFFFFF: # pylint: disable=chained-comparison
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | StaticBitmap_GetClassDefaultAttributes | (*args, **kwargs) | return _controls_.StaticBitmap_GetClassDefaultAttributes(*args, **kwargs) | StaticBitmap_GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
control -- which is a much better idea than hard coding specific
colours or fonts which might look completely out of place on the
user's system, especially if it uses themes.
The variant parameter is only relevant under Mac currently and is
ignore under other platforms. Under Mac, it will change the size of
the returned font. See `wx.Window.SetWindowVariant` for more about
this. | StaticBitmap_GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes | [
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"""
StaticBitmap_GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
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user's system, especially if it uses themes.
The variant parameter is only relevant under Mac currently and is
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exomerge2.py | python | ExodusModel._get_local_index | (self, this_id, id_list, entity='entity') | return id_list.index(this_id) | Return the local index corresponding to the given id.
If an entity is not present, throw an error.
Example:
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>>> model._get_local_index('first', [10, 20, 30])
10 | Return the local index corresponding to the given id. | [
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"""
Return the local index corresponding to the given id.
If an entity is not present, throw an error.
Example:
>>> model._get_local_index(10, [10, 20, 30])
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>>> model._get_local_index('first', [10, 20, 30])
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"""
if this_id == 'first':
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self._error('Undefined %s reference.' % entity,
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return 0
if this_id == 'last':
if not id_list:
self._error('Undefined %s reference.' % entity,
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return len(id_list) - 1
if this_id not in id_list:
entity_list = ', '.join([str(x) for x in id_list])
self._error('Reference to undefined %s.' % entity,
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% (entity, str(this_id), len(id_list), entity,
entity_list))
return id_list.index(this_id) | [
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ApolloAuto/apollo | 463fb82f9e979d02dcb25044e60931293ab2dba0 | third_party/gpus/find_cuda_config.py | python | _library_paths | () | return [
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"""Returns hard-coded set of relative paths to look for library files."""
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vslavik/poedit | f7a9daa0a10037e090aa0a86f5ce0f24ececdf6a | deps/boost/tools/build/src/build/type.py | python | base | (type) | return __types[type]['base'] | Returns a base type for the given type or nothing in case the given type is
not derived. | Returns a base type for the given type or nothing in case the given type is
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/dataview.py | python | PyDataViewCustomRenderer.Activate | (*args, **kwargs) | return _dataview.PyDataViewCustomRenderer_Activate(*args, **kwargs) | Activate(self, Rect cell, DataViewModel model, DataViewItem item,
unsigned int col) -> bool
Override this to react to double clicks or <ENTER>. | Activate(self, Rect cell, DataViewModel model, DataViewItem item,
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"""
Activate(self, Rect cell, DataViewModel model, DataViewItem item,
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return _dataview.PyDataViewCustomRenderer_Activate(*args, **kwargs) | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/gluon/probability/distributions/negative_binomial.py | python | NegativeBinomial.logit | (self) | return prob2logit(self.prob, True) | Get the log-odds of sampling `1`.
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Parameter tensor.
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google/fhir | d77f57706c1a168529b0b87ca7ccb1c0113e83c2 | py/google/fhir/utils/proto_utils.py | python | are_same_message_type | (descriptor_a: descriptor.Descriptor,
descriptor_b: descriptor.Descriptor) | return (descriptor_a == descriptor_b or
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trailofbits/llvm-sanitizer-tutorial | d29dfeec7f51fbf234fd0080f28f2b30cd0b6e99 | llvm/tools/clang/utils/check_cfc/check_cfc.py | python | replace_output_file | (args, new_name) | return args | Replaces the specified name of an output file with the specified name.
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apiaryio/drafter | 4634ebd07f6c6f257cc656598ccd535492fdfb55 | tools/gyp/pylib/gyp/generator/analyzer.py | python | _ToGypPath | (path) | return path | Converts a path to the format used by gyp. | Converts a path to the format used by gyp. | [
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"to",
"the",
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] | def _ToGypPath(path):
"""Converts a path to the format used by gyp."""
if os.sep == '\\' and os.altsep == '/':
return path.replace('\\', '/')
return path | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/pickle.py | python | Pickler.__init__ | (self, file, protocol=None) | This takes a file-like object for writing a pickle data stream.
The optional protocol argument tells the pickler to use the
given protocol; supported protocols are 0, 1, 2. The default
protocol is 0, to be backwards compatible. (Protocol 0 is the
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mode and read back successfully. When using a protocol higher
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Protocol 1 is more efficient than protocol 0; protocol 2 is
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Specifying a negative protocol version selects the highest
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more recent the version of Python needed to read the pickle
produced.
The file parameter must have a write() method that accepts a single
string argument. It can thus be an open file object, a StringIO
object, or any other custom object that meets this interface. | This takes a file-like object for writing a pickle data stream. | [
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if protocol is None:
protocol = 0
if protocol < 0:
protocol = HIGHEST_PROTOCOL
elif not 0 <= protocol <= HIGHEST_PROTOCOL:
raise ValueError("pickle protocol must be <= %d" % HIGHEST_PROTOCOL)
self.write = file.write
self.memo = {}
self.proto = int(protocol)
self.bin = protocol >= 1
self.fast = 0 | [
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LLNL/lbann | 26083e6c86050302ce33148aea70f62e61cacb92 | python/lbann/modules/rnn.py | python | GRU.forward | (self, x, prev_state) | return ht, ht | Apply GRU step.
Args:
x (Layer): Input.
prev_state: State from previous GRU step.
Returns:
(Layer, Layer): The output (out) and state (hn).
The state can be passed directly into
the next GRU step. | Apply GRU step. | [
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] | def forward(self, x, prev_state):
"""Apply GRU step.
Args:
x (Layer): Input.
prev_state: State from previous GRU step.
Returns:
(Layer, Layer): The output (out) and state (hn).
The state can be passed directly into
the next GRU step.
"""
self.step += 1
name = '{0}_step{1}'.format(self.name, self.step)
fc1 = self.ih_fc(x) #input_fc
fc2 = self.hh_fc(prev_state) #hidden_fc
# Get gates and cell update
fc1_slice = lbann.Slice(fc1,
slice_points=str_list([0, self.size, 2*self.size, 3*self.size]),
name=name + '_fc1_slice',
data_layout=self.data_layout)
Wir_x = lbann.Identity(fc1_slice, name=name + '_Wrx',
data_layout=self.data_layout)
Wiz_x = lbann.Identity(fc1_slice, name=name + '_Wzx',
data_layout=self.data_layout)
Win_x = lbann.Identity(fc1_slice, name=name + '_Wnx',
data_layout=self.data_layout)
fc2_slice = lbann.Slice(fc2,
slice_points=str_list([0, self.size, 2*self.size, 3*self.size]),
name=name + '_fc2_slice',
data_layout=self.data_layout)
Whr_prev = lbann.Identity(fc2_slice, name=name + '_Wrh',
data_layout=self.data_layout)
Whz_prev = lbann.Identity(fc2_slice, name=name + '_Wzh',
data_layout=self.data_layout)
Whn_prev = lbann.Identity(fc2_slice, name=name + '_Wnh',
data_layout=self.data_layout)
rt = \
lbann.Sigmoid(
lbann.Add(Wir_x, Whr_prev, data_layout=self.data_layout),
name=name + '_reset_gate',
data_layout=self.data_layout
)
zt = \
lbann.Sigmoid(
lbann.Add(Wiz_x, Whz_prev, data_layout=self.data_layout),
name=name + '_update_gate',
data_layout=self.data_layout,
)
nt = \
lbann.Tanh(
lbann.Add(
Win_x,
lbann.Multiply(rt, Whn_prev, data_layout=self.data_layout),
data_layout=self.data_layout,
),
name=name + '_new_gate', data_layout=self.data_layout,
)
ht = \
lbann.Add(
lbann.Multiply(
lbann.WeightedSum(
self.ones,
zt,
scaling_factors='1 -1', data_layout=self.data_layout
),
nt,
data_layout=self.data_layout
),
lbann.Multiply(zt, prev_state, data_layout=self.data_layout),
name=name+ '_output', data_layout=self.data_layout,
)
# Return output
return ht, ht | [
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] | https://github.com/LLNL/lbann/blob/26083e6c86050302ce33148aea70f62e61cacb92/python/lbann/modules/rnn.py#L223-L307 |
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