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dmtcp/dmtcp | 48a23686e1ce6784829b783ced9c62a14d620507 | util/cpplint.py | python | FileInfo.IsSource | (self) | return _IsSourceExtension(self.Extension()[1:]) | File has a source file extension. | File has a source file extension. | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/mod_pywebsocket/dispatch.py | python | Dispatcher.source_warnings | (self) | return self._source_warnings | Return warnings in sourcing handlers. | Return warnings in sourcing handlers. | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | gr-utils/modtool/core/base.py | python | validate_name | (kind, name) | Checks that block, module etc names are alphanumeric. | Checks that block, module etc names are alphanumeric. | [
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if not re.fullmatch('[a-zA-Z0-9_]+', name):
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microsoft/ivy | 9f3c7ecc0b2383129fdd0953e10890d98d09a82d | ivy/ivy_parser.py | python | p_moduleend | (p) | moduleend : | moduleend : | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | IconLocation.__init__ | (self, *args, **kwargs) | __init__(self, String filename=&wxPyEmptyString, int num=0) -> IconLocation | __init__(self, String filename=&wxPyEmptyString, int num=0) -> IconLocation | [
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apache/arrow | af33dd1157eb8d7d9bfac25ebf61445b793b7943 | dev/archery/archery/utils/maven.py | python | MavenDefinition.build_arguments | (self) | return arguments | Return the arguments to maven invocation for build. | Return the arguments to maven invocation for build. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/dummyarray.py | python | iter_strides_c_contig | (arr, shape=None) | yields the c-contiguous strides | yields the c-contiguous strides | [
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itemsize = arr.itemsize
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | example/gluon/style_transfer/utils.py | python | CenterCrop.__call__ | (self, img) | return img.crop((x1, y1, x1 + tw, y1 + th)) | Args:
img (PIL.Image): Image to be cropped.
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PIL.Image: Cropped image. | Args:
img (PIL.Image): Image to be cropped.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/grid.py | python | GridRangeSelectEvent.GetTopLeftCoords | (*args, **kwargs) | return _grid.GridRangeSelectEvent_GetTopLeftCoords(*args, **kwargs) | GetTopLeftCoords(self) -> GridCellCoords | GetTopLeftCoords(self) -> GridCellCoords | [
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MegEngine/MegEngine | ce9ad07a27ec909fb8db4dd67943d24ba98fb93a | imperative/python/megengine/functional/tensor.py | python | arange | (
start: Union[int, float, Tensor] = 0,
stop: Optional[Union[int, float, Tensor]] = None,
step: Union[int, float, Tensor] = 1,
dtype="float32",
device: Optional[CompNode] = None,
) | return result | r"""Returns evenly spaced values within the half-open interval ``[start, stop)`` as a one-dimensional tensor.
Note:
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stop: the end of the interval. Default: ``None``.
step: the distance between two adjacent elements ( ``out[i+1] - out[i]`` ). Must not be 0 ;
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Keyword args:
dtype( :attr:`.Tensor.dtype` ): output tensor data type. Default: ``float32``.
device( :attr:`.Tensor.device` ): device on which to place the created tensor. Default: ``None``.
Returns:
A one-dimensional tensor containing evenly spaced values.
The length of the output tensor must be ``ceil((stop-start)/step)``
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Examples:
>>> F.arange(5)
Tensor([0. 1. 2. 3. 4.], device=xpux:0)
>>> F.arange(1, 4)
Tensor([1. 2. 3.], device=xpux:0) | r"""Returns evenly spaced values within the half-open interval ``[start, stop)`` as a one-dimensional tensor. | [
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>>> F.arange(1, 4)
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"""
if stop is None:
start, stop = 0, start
start = Tensor(start, dtype="float32")
stop = Tensor(stop, dtype="float32")
step = Tensor(step, dtype="float32")
num = ceil((stop - start) / step)
stop = start + step * (num - 1)
result = linspace(start, stop, num, device=device)
if np.dtype(dtype) != np.float32:
return result.astype(dtype)
return result | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2class.py | python | uCSIsCatCo | (code) | return ret | Check whether the character is part of Co UCS Category | Check whether the character is part of Co UCS Category | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/indexes/multi.py | python | MultiIndex.is_monotonic_increasing | (self) | return if the index is monotonic increasing (only equal or
increasing) values. | return if the index is monotonic increasing (only equal or
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return if the index is monotonic increasing (only equal or
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"""
# reversed() because lexsort() wants the most significant key last.
values = [self._get_level_values(i).values
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try:
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return Index(sort_order).is_monotonic
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# we have mixed types and np.lexsort is not happy
return Index(self.values).is_monotonic | [
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emscripten-core/emscripten | 0d413d3c5af8b28349682496edc14656f5700c2f | tools/filelock.py | python | BaseFileLock.release | (self, force = False) | return None | Releases the file lock.
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self._lock_counter = 0
logger().debug('Lock %s released on %s', lock_id, lock_filename)
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mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Tool/msvc.py | python | object_emitter | (target, source, env, parent_emitter) | return (target, source) | Sets up the PCH dependencies for an object file. | Sets up the PCH dependencies for an object file. | [
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# Add a dependency, but only if the target (e.g. 'Source1.obj')
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#
# See issue #2505 for a discussion of what to do if it turns
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/_pydecimal.py | python | Context.is_finite | (self, a) | return a.is_finite() | Return True if the operand is finite; otherwise return False.
A Decimal instance is considered finite if it is neither
infinite nor a NaN.
>>> ExtendedContext.is_finite(Decimal('2.50'))
True
>>> ExtendedContext.is_finite(Decimal('-0.3'))
True
>>> ExtendedContext.is_finite(Decimal('0'))
True
>>> ExtendedContext.is_finite(Decimal('Inf'))
False
>>> ExtendedContext.is_finite(Decimal('NaN'))
False
>>> ExtendedContext.is_finite(1)
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"""Return True if the operand is finite; otherwise return False.
A Decimal instance is considered finite if it is neither
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>>> ExtendedContext.is_finite(Decimal('2.50'))
True
>>> ExtendedContext.is_finite(Decimal('-0.3'))
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>>> ExtendedContext.is_finite(Decimal('0'))
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>>> ExtendedContext.is_finite(Decimal('Inf'))
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>>> ExtendedContext.is_finite(Decimal('NaN'))
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>>> ExtendedContext.is_finite(1)
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"""
a = _convert_other(a, raiseit=True)
return a.is_finite() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/setuptools/command/build_py.py | python | build_py.exclude_data_files | (self, package, src_dir, files) | return list(_unique_everseen(keepers)) | Filter filenames for package's data files in 'src_dir | Filter filenames for package's data files in 'src_dir | [
"Filter",
"filenames",
"for",
"package",
"s",
"data",
"files",
"in",
"src_dir"
] | def exclude_data_files(self, package, src_dir, files):
"""Filter filenames for package's data files in 'src_dir'"""
files = list(files)
patterns = self._get_platform_patterns(
self.exclude_package_data,
package,
src_dir,
)
match_groups = (
fnmatch.filter(files, pattern)
for pattern in patterns
)
# flatten the groups of matches into an iterable of matches
matches = itertools.chain.from_iterable(match_groups)
bad = set(matches)
keepers = (
fn
for fn in files
if fn not in bad
)
# ditch dupes
return list(_unique_everseen(keepers)) | [
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psnonis/FinBERT | c0c555d833a14e2316a3701e59c0b5156f804b4e | bert-gpu/tensorrt-inference-server/src/clients/python/__init__.py | python | InferContext.run | (self, inputs, outputs, batch_size=1, flags=0) | return self._get_results(outputs, batch_size) | Run inference using the supplied 'inputs' to calculate the outputs
specified by 'outputs'.
Parameters
----------
inputs : dict
Dictionary from input name to the value(s) for that
input. An input value is specified as a numpy array. Each
input in the dictionary maps to a list of values (i.e. a
list of numpy array objects), where the length of the list
must equal the 'batch_size'.
outputs : dict
Dictionary from output name to a value indicating the
ResultFormat that should be used for that output. For RAW
the value should be ResultFormat.RAW. For CLASS the value
should be a tuple (ResultFormat.CLASS, k), where 'k'
indicates how many classification results should be
returned for the output.
batch_size : int
The batch size of the inference. Each input must provide
an appropriately sized batch of inputs.
flags : int
The flags to use for the inference. The bitwise-or of
InferRequestHeader.Flag values.
Returns
-------
dict
A dictionary from output name to the list of values for
that output (one list element for each entry of the
batch). The format of a value returned for an output
depends on the output format specified in 'outputs'. For
format RAW a value is a numpy array of the appropriate
type and shape for the output. For format CLASS a value is
the top 'k' output values returned as an array of (class
index, class value, class label) tuples.
Raises
------
InferenceServerException
If all inputs are not specified, if the size of input data
does not match expectations, if unknown output names are
specified or if server fails to perform inference. | Run inference using the supplied 'inputs' to calculate the outputs
specified by 'outputs'. | [
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"calculate",
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"."
] | def run(self, inputs, outputs, batch_size=1, flags=0):
"""Run inference using the supplied 'inputs' to calculate the outputs
specified by 'outputs'.
Parameters
----------
inputs : dict
Dictionary from input name to the value(s) for that
input. An input value is specified as a numpy array. Each
input in the dictionary maps to a list of values (i.e. a
list of numpy array objects), where the length of the list
must equal the 'batch_size'.
outputs : dict
Dictionary from output name to a value indicating the
ResultFormat that should be used for that output. For RAW
the value should be ResultFormat.RAW. For CLASS the value
should be a tuple (ResultFormat.CLASS, k), where 'k'
indicates how many classification results should be
returned for the output.
batch_size : int
The batch size of the inference. Each input must provide
an appropriately sized batch of inputs.
flags : int
The flags to use for the inference. The bitwise-or of
InferRequestHeader.Flag values.
Returns
-------
dict
A dictionary from output name to the list of values for
that output (one list element for each entry of the
batch). The format of a value returned for an output
depends on the output format specified in 'outputs'. For
format RAW a value is a numpy array of the appropriate
type and shape for the output. For format CLASS a value is
the top 'k' output values returned as an array of (class
index, class value, class label) tuples.
Raises
------
InferenceServerException
If all inputs are not specified, if the size of input data
does not match expectations, if unknown output names are
specified or if server fails to perform inference.
"""
self._last_request_id = None
self._last_request_model_name = None
self._last_request_model_version = None
# The input values must be contiguous and the lifetime of those
# contiguous copies must span until the inference completes
# so grab a reference to them at this scope.
contiguous_input = list()
# Set run option and input values
self._prepare_request(inputs, outputs, flags, batch_size, contiguous_input)
# Run inference...
self._last_request_id = _raise_if_error(c_void_p(_crequest_infer_ctx_run(self._ctx)))
return self._get_results(outputs, batch_size) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/bisect-builds.py | python | Bisect | (base_url,
platform,
official_builds,
is_aura,
good_rev=0,
bad_rev=0,
num_runs=1,
command="%p %a",
try_args=(),
profile=None,
flash_path=None,
pdf_path=None,
interactive=True,
evaluate=AskIsGoodBuild) | return (revlist[minrev], revlist[maxrev]) | Given known good and known bad revisions, run a binary search on all
archived revisions to determine the last known good revision.
@param platform Which build to download/run ('mac', 'win', 'linux64', etc.).
@param official_builds Specify build type (Chromium or Official build).
@param good_rev Number/tag of the known good revision.
@param bad_rev Number/tag of the known bad revision.
@param num_runs Number of times to run each build for asking good/bad.
@param try_args A tuple of arguments to pass to the test application.
@param profile The name of the user profile to run with.
@param interactive If it is false, use command exit code for good or bad
judgment of the argument build.
@param evaluate A function which returns 'g' if the argument build is good,
'b' if it's bad or 'u' if unknown.
Threading is used to fetch Chromium revisions in the background, speeding up
the user's experience. For example, suppose the bounds of the search are
good_rev=0, bad_rev=100. The first revision to be checked is 50. Depending on
whether revision 50 is good or bad, the next revision to check will be either
25 or 75. So, while revision 50 is being checked, the script will download
revisions 25 and 75 in the background. Once the good/bad verdict on rev 50 is
known:
- If rev 50 is good, the download of rev 25 is cancelled, and the next test
is run on rev 75.
- If rev 50 is bad, the download of rev 75 is cancelled, and the next test
is run on rev 25. | Given known good and known bad revisions, run a binary search on all
archived revisions to determine the last known good revision. | [
"Given",
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] | def Bisect(base_url,
platform,
official_builds,
is_aura,
good_rev=0,
bad_rev=0,
num_runs=1,
command="%p %a",
try_args=(),
profile=None,
flash_path=None,
pdf_path=None,
interactive=True,
evaluate=AskIsGoodBuild):
"""Given known good and known bad revisions, run a binary search on all
archived revisions to determine the last known good revision.
@param platform Which build to download/run ('mac', 'win', 'linux64', etc.).
@param official_builds Specify build type (Chromium or Official build).
@param good_rev Number/tag of the known good revision.
@param bad_rev Number/tag of the known bad revision.
@param num_runs Number of times to run each build for asking good/bad.
@param try_args A tuple of arguments to pass to the test application.
@param profile The name of the user profile to run with.
@param interactive If it is false, use command exit code for good or bad
judgment of the argument build.
@param evaluate A function which returns 'g' if the argument build is good,
'b' if it's bad or 'u' if unknown.
Threading is used to fetch Chromium revisions in the background, speeding up
the user's experience. For example, suppose the bounds of the search are
good_rev=0, bad_rev=100. The first revision to be checked is 50. Depending on
whether revision 50 is good or bad, the next revision to check will be either
25 or 75. So, while revision 50 is being checked, the script will download
revisions 25 and 75 in the background. Once the good/bad verdict on rev 50 is
known:
- If rev 50 is good, the download of rev 25 is cancelled, and the next test
is run on rev 75.
- If rev 50 is bad, the download of rev 75 is cancelled, and the next test
is run on rev 25.
"""
if not profile:
profile = 'profile'
context = PathContext(base_url, platform, good_rev, bad_rev,
official_builds, is_aura, flash_path, pdf_path)
cwd = os.getcwd()
print "Downloading list of known revisions..."
_GetDownloadPath = lambda rev: os.path.join(cwd,
'%s-%s' % (str(rev), context.archive_name))
if official_builds:
revlist = context.GetOfficialBuildsList()
else:
revlist = context.GetRevList()
# Get a list of revisions to bisect across.
if len(revlist) < 2: # Don't have enough builds to bisect.
msg = 'We don\'t have enough builds to bisect. revlist: %s' % revlist
raise RuntimeError(msg)
# Figure out our bookends and first pivot point; fetch the pivot revision.
minrev = 0
maxrev = len(revlist) - 1
pivot = maxrev / 2
rev = revlist[pivot]
zipfile = _GetDownloadPath(rev)
fetch = DownloadJob(context, 'initial_fetch', rev, zipfile)
fetch.Start()
fetch.WaitFor()
# Binary search time!
while fetch and fetch.zipfile and maxrev - minrev > 1:
if bad_rev < good_rev:
min_str, max_str = "bad", "good"
else:
min_str, max_str = "good", "bad"
print 'Bisecting range [%s (%s), %s (%s)].' % (revlist[minrev], min_str, \
revlist[maxrev], max_str)
# Pre-fetch next two possible pivots
# - down_pivot is the next revision to check if the current revision turns
# out to be bad.
# - up_pivot is the next revision to check if the current revision turns
# out to be good.
down_pivot = int((pivot - minrev) / 2) + minrev
down_fetch = None
if down_pivot != pivot and down_pivot != minrev:
down_rev = revlist[down_pivot]
down_fetch = DownloadJob(context, 'down_fetch', down_rev,
_GetDownloadPath(down_rev))
down_fetch.Start()
up_pivot = int((maxrev - pivot) / 2) + pivot
up_fetch = None
if up_pivot != pivot and up_pivot != maxrev:
up_rev = revlist[up_pivot]
up_fetch = DownloadJob(context, 'up_fetch', up_rev,
_GetDownloadPath(up_rev))
up_fetch.Start()
# Run test on the pivot revision.
status = None
stdout = None
stderr = None
try:
(status, stdout, stderr) = RunRevision(context,
rev,
fetch.zipfile,
profile,
num_runs,
command,
try_args)
except Exception, e:
print >> sys.stderr, e
# Call the evaluate function to see if the current revision is good or bad.
# On that basis, kill one of the background downloads and complete the
# other, as described in the comments above.
try:
if not interactive:
if status:
answer = 'b'
print 'Bad revision: %s' % rev
else:
answer = 'g'
print 'Good revision: %s' % rev
else:
answer = evaluate(rev, official_builds, status, stdout, stderr)
if answer == 'g' and good_rev < bad_rev or \
answer == 'b' and bad_rev < good_rev:
fetch.Stop()
minrev = pivot
if down_fetch:
down_fetch.Stop() # Kill the download of the older revision.
fetch = None
if up_fetch:
up_fetch.WaitFor()
pivot = up_pivot
fetch = up_fetch
elif answer == 'b' and good_rev < bad_rev or \
answer == 'g' and bad_rev < good_rev:
fetch.Stop()
maxrev = pivot
if up_fetch:
up_fetch.Stop() # Kill the download of the newer revision.
fetch = None
if down_fetch:
down_fetch.WaitFor()
pivot = down_pivot
fetch = down_fetch
elif answer == 'r':
pass # Retry requires no changes.
elif answer == 'u':
# Nuke the revision from the revlist and choose a new pivot.
fetch.Stop()
revlist.pop(pivot)
maxrev -= 1 # Assumes maxrev >= pivot.
if maxrev - minrev > 1:
# Alternate between using down_pivot or up_pivot for the new pivot
# point, without affecting the range. Do this instead of setting the
# pivot to the midpoint of the new range because adjacent revisions
# are likely affected by the same issue that caused the (u)nknown
# response.
if up_fetch and down_fetch:
fetch = [up_fetch, down_fetch][len(revlist) % 2]
elif up_fetch:
fetch = up_fetch
else:
fetch = down_fetch
fetch.WaitFor()
if fetch == up_fetch:
pivot = up_pivot - 1 # Subtracts 1 because revlist was resized.
else:
pivot = down_pivot
zipfile = fetch.zipfile
if down_fetch and fetch != down_fetch:
down_fetch.Stop()
if up_fetch and fetch != up_fetch:
up_fetch.Stop()
else:
assert False, "Unexpected return value from evaluate(): " + answer
except SystemExit:
print "Cleaning up..."
for f in [_GetDownloadPath(revlist[down_pivot]),
_GetDownloadPath(revlist[up_pivot])]:
try:
os.unlink(f)
except OSError:
pass
sys.exit(0)
rev = revlist[pivot]
return (revlist[minrev], revlist[maxrev]) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/PathThreadMilling.py | python | _InternalThread.overshoots | (self, z) | return z + self.hPitch > self.zFinal | overshoots(z) ... returns true if adding another half helix goes beyond the thread bounds | overshoots(z) ... returns true if adding another half helix goes beyond the thread bounds | [
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] | def overshoots(self, z):
"""overshoots(z) ... returns true if adding another half helix goes beyond the thread bounds"""
if self.pitch < 0:
return z + self.hPitch < self.zFinal
return z + self.hPitch > self.zFinal | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/beta.py | python | Beta.log_prob | (self, x, name="log_prob") | `Log(P[counts])`, computed for every batch member.
Args:
x: Non-negative floating point tensor whose shape can
be broadcast with `self.a` and `self.b`. For fixed leading
dimensions, the last dimension represents counts for the corresponding
Beta distribution in `self.a` and `self.b`. `x` is only legal if
0 < x < 1.
name: Name to give this Op, defaults to "log_prob".
Returns:
Log probabilities for each record, shape `[N1,...,Nm]`. | `Log(P[counts])`, computed for every batch member. | [
"Log",
"(",
"P",
"[",
"counts",
"]",
")",
"computed",
"for",
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"member",
"."
] | def log_prob(self, x, name="log_prob"):
"""`Log(P[counts])`, computed for every batch member.
Args:
x: Non-negative floating point tensor whose shape can
be broadcast with `self.a` and `self.b`. For fixed leading
dimensions, the last dimension represents counts for the corresponding
Beta distribution in `self.a` and `self.b`. `x` is only legal if
0 < x < 1.
name: Name to give this Op, defaults to "log_prob".
Returns:
Log probabilities for each record, shape `[N1,...,Nm]`.
"""
a = self._a
b = self._b
with ops.name_scope(self.name):
with ops.op_scope([a, x], name):
x = self._check_x(x)
unnorm_pdf = (a - 1) * math_ops.log(x) + (
b - 1) * math_ops.log(1 - x)
normalization_factor = -(math_ops.lgamma(a) + math_ops.lgamma(b)
- math_ops.lgamma(a + b))
log_prob = unnorm_pdf + normalization_factor
return log_prob | [
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CaoWGG/TensorRT-YOLOv4 | 4d7c2edce99e8794a4cb4ea3540d51ce91158a36 | onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/clang/cindex.py | python | SourceLocation.file | (self) | return self._get_instantiation()[0] | Get the file represented by this source location. | Get the file represented by this source location. | [
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"file",
"represented",
"by",
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] | def file(self):
"""Get the file represented by this source location."""
return self._get_instantiation()[0] | [
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lmb-freiburg/ogn | 974f72ef4bf840d6f6693d22d1843a79223e77ce | python/caffe/detector.py | python | Detector.detect_windows | (self, images_windows) | return detections | Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
Parameters
----------
images_windows: (image filename, window list) iterable.
context_crop: size of context border to crop in pixels.
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts. | Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net. | [
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"dimensions",
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"net",
"."
] | def detect_windows(self, images_windows):
"""
Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
Parameters
----------
images_windows: (image filename, window list) iterable.
context_crop: size of context border to crop in pixels.
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.
"""
# Extract windows.
window_inputs = []
for image_fname, windows in images_windows:
image = caffe.io.load_image(image_fname).astype(np.float32)
for window in windows:
window_inputs.append(self.crop(image, window))
# Run through the net (warping windows to input dimensions).
in_ = self.inputs[0]
caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2])
+ self.blobs[in_].data.shape[2:],
dtype=np.float32)
for ix, window_in in enumerate(window_inputs):
caffe_in[ix] = self.transformer.preprocess(in_, window_in)
out = self.forward_all(**{in_: caffe_in})
predictions = out[self.outputs[0]]
# Package predictions with images and windows.
detections = []
ix = 0
for image_fname, windows in images_windows:
for window in windows:
detections.append({
'window': window,
'prediction': predictions[ix],
'filename': image_fname
})
ix += 1
return detections | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/model_selection/_split.py | python | BaseCrossValidator.split | (self, X, y=None, groups=None) | Generate indices to split data into training and test set.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, of length n_samples
The target variable for supervised learning problems.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into
train/test set.
Returns
-------
train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split. | Generate indices to split data into training and test set. | [
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"""Generate indices to split data into training and test set.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, of length n_samples
The target variable for supervised learning problems.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into
train/test set.
Returns
-------
train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
"""
X, y, groups = indexable(X, y, groups)
indices = np.arange(_num_samples(X))
for test_index in self._iter_test_masks(X, y, groups):
train_index = indices[np.logical_not(test_index)]
test_index = indices[test_index]
yield train_index, test_index | [
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Illumina/manta | 75b5c38d4fcd2f6961197b28a41eb61856f2d976 | src/python/lib/workflowUtil.py | python | isLocalSmtp | () | return True | return true if a local smtp server is available | return true if a local smtp server is available | [
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"""
return true if a local smtp server is available
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/widgets/sliceviewer/lineplots.py | python | RectangleSelectionLinePlot.handle_key | (self, key) | Called if a keypress was accepted to export a region
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/deps/v8/tools/release/common_includes.py | python | Step.Retry | (self, cb, retry_on=None, wait_plan=None) | Retry a function.
Params:
cb: The function to retry.
retry_on: A callback that takes the result of the function and returns
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wait_plan: A list of waiting delays between retries in seconds. The
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wait_plan: A list of waiting delays between retries in seconds. The
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wait_plan: A list of waiting delays between retries in seconds. The
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retry_on = retry_on or (lambda x: False)
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wait_plan.reverse()
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/buttonpanel.py | python | ButtonPanel.SetAlignment | (self, alignment) | Sets the buttons alignment.
:param integer `alignment`: can be one of the following bits:
====================== ======= ==========================
Alignment Flag Value Description
====================== ======= ==========================
``BP_ALIGN_RIGHT`` 1 Buttons are aligned on the right
``BP_ALIGN_LEFT`` 2 Buttons are aligned on the left
``BP_ALIGN_TOP`` 4 Buttons are aligned at the top
``BP_ALIGN_BOTTOM`` 8 Buttons are aligned at the bottom
====================== ======= ========================== | Sets the buttons alignment. | [
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"""
Sets the buttons alignment.
:param integer `alignment`: can be one of the following bits:
====================== ======= ==========================
Alignment Flag Value Description
====================== ======= ==========================
``BP_ALIGN_RIGHT`` 1 Buttons are aligned on the right
``BP_ALIGN_LEFT`` 2 Buttons are aligned on the left
``BP_ALIGN_TOP`` 4 Buttons are aligned at the top
``BP_ALIGN_BOTTOM`` 8 Buttons are aligned at the bottom
====================== ======= ==========================
"""
if alignment == self._alignment:
return
self.Freeze()
text = self.GetBarText()
# Remove the text in any case
self.RemoveText()
# Remove the first and last spacers
self._mainsizer.Remove(0, -1)
self._mainsizer.Remove(len(self._mainsizer.GetChildren())-1, -1)
self._alignment = alignment
# Recreate the sizer accordingly to the new alignment
self.ReCreateSizer(text) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/pkg_resources/__init__.py | python | IResourceProvider.resource_isdir | (resource_name) | Is the named resource a directory? (like ``os.path.isdir()``) | Is the named resource a directory? (like ``os.path.isdir()``) | [
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GeometryCollective/boundary-first-flattening | 8250e5a0e85980ec50b5e8aa8f49dd6519f915cd | deps/nanogui/ext/pybind11/tools/clang/cindex.py | python | SourceLocation.offset | (self) | return self._get_instantiation()[3] | Get the file offset represented by this source location. | Get the file offset represented by this source location. | [
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return self._get_instantiation()[3] | [
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facebook/ThreatExchange | 31914a51820c73c8a0daffe62ccca29a6e3d359e | api-reference-examples/python/pytx/pytx/common.py | python | Common.__init__ | (self, **kwargs) | Initialize the object. Set any attributes that were provided. | Initialize the object. Set any attributes that were provided. | [
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"""
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"""
for name, value in kwargs.items():
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/ma/core.py | python | _MaskedBinaryOperation.__init__ | (self, mbfunc, fillx=0, filly=0) | abfunc(fillx, filly) must be defined.
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"""
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"""
super(_MaskedBinaryOperation, self).__init__(mbfunc)
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asLody/whale | 6a661b27cc4cf83b7b5a3b02451597ee1ac7f264 | whale/cpplint.py | python | _BlockInfo.CheckEnd | (self, filename, clean_lines, linenum, error) | Run checks that applies to text after the closing brace.
This is mostly used for checking end of namespace comments.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
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"""Run checks that applies to text after the closing brace.
This is mostly used for checking end of namespace comments.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
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pass | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/optimizer/optimizer.py | python | Optimizer.fused_step | (self, indices, weights, grads, states) | Perform a fused optimization step using gradients and states.
New operators that fuses optimizer's update should be put in this function.
Parameters
----------
indices : list of int
List of unique indices of the parameters into the individual learning rates
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weights : list of NDArray
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List of gradients of the objective with respect to this parameter.
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List of unique indices of the parameters into the individual learning rates
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/logging/__init__.py | python | Logger.debug | (self, msg, *args, **kwargs) | Log 'msg % args' with severity 'DEBUG'.
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logger.debug("Houston, we have a %s", "thorny problem", exc_info=1)
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smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/build/lib/google/protobuf/message.py | python | Message.IsInitialized | (self) | Checks if the message is initialized.
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The method returns True if the message is initialized (i.e. all of its
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"""Checks if the message is initialized.
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alexgkendall/caffe-segnet | 344c113bf1832886f1cbe9f33ffe28a3beeaf412 | scripts/cpp_lint.py | python | CheckCaffeDataLayerSetUp | (filename, clean_lines, linenum, error) | Except the base classes, Caffe DataLayer should define DataLayerSetUp
instead of LayerSetUp.
The base DataLayers define common SetUp steps, the subclasses should
not override them.
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. | Except the base classes, Caffe DataLayer should define DataLayerSetUp
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The base DataLayers define common SetUp steps, the subclasses should
not override them.
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. | [
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"""Except the base classes, Caffe DataLayer should define DataLayerSetUp
instead of LayerSetUp.
The base DataLayers define common SetUp steps, the subclasses should
not override them.
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.
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line = clean_lines.elided[linenum]
ix = line.find('DataLayer<Dtype>::LayerSetUp')
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line.find('void WindowDataLayer<Dtype>::LayerSetUp') != -1):
error(filename, linenum, 'caffe/data_layer_setup', 2,
'Except the base classes, Caffe DataLayer should define'
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if ix >= 0 and (
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line.find('void WindowDataLayer<Dtype>::DataLayerSetUp') == -1):
error(filename, linenum, 'caffe/data_layer_setup', 2,
'Except the base classes, Caffe DataLayer should define'
+ ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+ ' define common SetUp steps, the subclasses should'
+ ' not override them.') | [
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... | https://github.com/alexgkendall/caffe-segnet/blob/344c113bf1832886f1cbe9f33ffe28a3beeaf412/scripts/cpp_lint.py#L1595-L1631 | ||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | RadioButton.GetValue | (*args, **kwargs) | return _controls_.RadioButton_GetValue(*args, **kwargs) | GetValue(self) -> bool | GetValue(self) -> bool | [
"GetValue",
"(",
"self",
")",
"-",
">",
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] | def GetValue(*args, **kwargs):
"""GetValue(self) -> bool"""
return _controls_.RadioButton_GetValue(*args, **kwargs) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/distribute/experimental/rpc/rpc_ops.py | python | GrpcClient._add_method | (self, method_name, output_specs, input_specs, client_handle,
doc_string) | Method to add RPC methods to the client object. | Method to add RPC methods to the client object. | [
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] | def _add_method(self, method_name, output_specs, input_specs, client_handle,
doc_string):
"""Method to add RPC methods to the client object."""
def validate_and_get_flat_inputs(*args):
if args is None:
args = []
if input_specs:
nest.assert_same_structure(args, input_specs)
flat_inputs = nest.flatten(args)
return flat_inputs
def call_wrapper(*args, timeout_in_ms=0):
status_or, deleter = gen_rpc_ops.rpc_call(
client_handle,
args=validate_and_get_flat_inputs(*args),
method_name=method_name,
timeout_in_ms=timeout_in_ms)
return StatusOrResult(status_or, deleter, output_specs)
def call_blocking_wrapper(*args, timeout_in_ms=0):
status_or, deleter = gen_rpc_ops.rpc_call(
client_handle,
args=validate_and_get_flat_inputs(*args),
method_name=method_name,
timeout_in_ms=timeout_in_ms)
status_or = StatusOrResult(status_or, deleter, output_specs)
if status_or.is_ok():
return status_or.get_value()
else:
error_code, error_msg = status_or.get_error()
raise errors.exception_type_from_error_code(error_code.numpy())(
None, None, error_msg.numpy())
setattr(self, method_name, call_wrapper)
call_wrapper.__doc__ = doc_string
blocking_method_name = method_name + "_blocking"
setattr(self, blocking_method_name, call_blocking_wrapper)
call_blocking_wrapper.__doc__ = doc_string | [
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llvm-dcpu16/llvm-dcpu16 | ae6b01fecd03219677e391d4421df5d966d80dcf | bindings/python/llvm/object.py | python | Relocation.expire | (self) | Expire this instance, making future API accesses fail. | Expire this instance, making future API accesses fail. | [
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"""Expire this instance, making future API accesses fail."""
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dartsim/dart | 495c82120c836005f2d136d4a50c8cc997fb879b | tools/cpplint.py | python | _FunctionState.Count | (self) | Count line in current function body. | Count line in current function body. | [
"Count",
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"body",
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] | def Count(self):
"""Count line in current function body."""
if self.in_a_function:
self.lines_in_function += 1 | [
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lammps/lammps | b75c3065430a75b1b5543a10e10f46d9b4c91913 | tools/i-pi/ipi/engine/normalmodes.py | python | NormalModes.get_kins | (self) | return kmd | Gets the MD kinetic energy for all the normal modes.
Returns:
A list of the kinetic energy for each NM. | Gets the MD kinetic energy for all the normal modes. | [
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] | def get_kins(self):
"""Gets the MD kinetic energy for all the normal modes.
Returns:
A list of the kinetic energy for each NM.
"""
kmd = np.zeros(self.nbeads,float)
sm = depstrip(self.beads.sm3[0])
pnm = depstrip(self.pnm)
nmf = depstrip(self.nm_factor)
# computes the MD ke in the normal modes representation, to properly account for CMD mass scaling
for b in range(self.nbeads):
sp = pnm[b]/sm # mass-scaled momentum of b-th NM
kmd[b] = np.dot(sp,sp)*0.5/nmf[b] # include the partially adiabatic CMD mass scaling
return kmd | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | modules/stochastic_tools/python/stochastic/histogram.py | python | command_line_options | () | return parser.parse_args() | Command-line options for histogram tool. | Command-line options for histogram tool. | [
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"-",
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"for",
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"."
] | def command_line_options():
"""
Command-line options for histogram tool.
"""
parser = argparse.ArgumentParser(description="Command-line utility for creating histograms from VectorPostprocessor data.")
parser.add_argument('filename', type=str, help="The VectorPostprocessor data file pattern to open, for sample 'foo_x_*.csv'.")
parser.add_argument('-t', '--timesteps', default=[-1], nargs='+', type=int, help="List of timesteps to consider, by default only the final timestep is shown.")
parser.add_argument('-v', '--vectors', default=[], nargs='+', type=str, help="List of vector names to consider, by default all vectors are shown.")
parser.add_argument('--bins', default=None, type=int, help="Number of bins to consider.")
parser.add_argument('--alpha', default=0.5, type=float, help="Set the bar chart opacity alpha setting.")
parser.add_argument('--xlabel', default='Value', type=str, help="The X-axis label.")
parser.add_argument('--ylabel', default='Probability', type=str, help="The X-axis label.")
parser.add_argument('--uniform', default=None, type=float, nargs=2, help="Show a uniform distribution between a and b (e.g., --uniform 8 10).")
parser.add_argument('--weibull', default=None, type=float, nargs=2, help="Show a Weibull distribution with given shape and scale parameters (e.g., --uniform 1 5).")
return parser.parse_args() | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/waflib/extras/codelite.py | python | codelite_generator.collect_dirs | (self) | Create the folder structure in the CodeLite project view | Create the folder structure in the CodeLite project view | [
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] | def collect_dirs(self):
"""
Create the folder structure in the CodeLite project view
"""
seen = {}
def make_parents(proj):
# look at a project, try to make a parent
if getattr(proj, 'parent', None):
# aliases already have parents
return
x = proj.iter_path
if x in seen:
proj.parent = seen[x]
return
# There is not vsnode_vsdir for x.
# So create a project representing the folder "x"
n = proj.parent = seen[x] = self.vsnode_vsdir(self, make_uuid(x.abspath()), x.name)
n.iter_path = x.parent
self.all_projects.append(n)
# recurse up to the project directory
if x.height() > self.srcnode.height() + 1:
make_parents(n)
for p in self.all_projects[:]: # iterate over a copy of all projects
if not getattr(p, 'tg', None):
# but only projects that have a task generator
continue
# make a folder for each task generator
p.iter_path = p.tg.path
make_parents(p) | [
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facebookincubator/katran | 192eb988c398afc673620254097defb7035d669e | build/fbcode_builder/getdeps/copytree.py | python | prefetch_dir_if_eden | (dirpath) | After an amend/rebase, Eden may need to fetch a large number
of trees from the servers. The simplistic single threaded walk
performed by copytree makes this more expensive than is desirable
so we help accelerate things by performing a prefetch on the
source directory | After an amend/rebase, Eden may need to fetch a large number
of trees from the servers. The simplistic single threaded walk
performed by copytree makes this more expensive than is desirable
so we help accelerate things by performing a prefetch on the
source directory | [
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"""After an amend/rebase, Eden may need to fetch a large number
of trees from the servers. The simplistic single threaded walk
performed by copytree makes this more expensive than is desirable
so we help accelerate things by performing a prefetch on the
source directory"""
global PREFETCHED_DIRS
if dirpath in PREFETCHED_DIRS:
return
root = find_eden_root(dirpath)
if root is None:
return
glob = f"{os.path.relpath(dirpath, root).replace(os.sep, '/')}/**"
print(f"Prefetching {glob}")
subprocess.call(
["edenfsctl", "prefetch", "--repo", root, "--silent", glob, "--background"]
)
PREFETCHED_DIRS.add(dirpath) | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/distributed/fleet/data_generator/data_generator.py | python | MultiSlotDataGenerator._gen_str | (self, line) | return output + "\n" | Further processing the output of the process() function rewritten by
user, outputting data that can be directly read by the MultiSlotDataFeed,
and updating proto_info information.
The input line will be in this format:
>>> [(name, [feasign, ...]), ...]
>>> or ((name, [feasign, ...]), ...)
The output will be in this format:
>>> [ids_num id1 id2 ...] ...
The proto_info will be in this format:
>>> [(name, type), ...]
For example, if the input is like this:
>>> [("words", [1926, 08, 17]), ("label", [1])]
>>> or (("words", [1926, 08, 17]), ("label", [1]))
the output will be:
>>> 3 1234 2345 3456 1 1
the proto_info will be:
>>> [("words", "uint64"), ("label", "uint64")]
Args:
line(str): the output of the process() function rewritten by user.
Returns:
Return a string data that can be read directly by the MultiSlotDataFeed. | Further processing the output of the process() function rewritten by
user, outputting data that can be directly read by the MultiSlotDataFeed,
and updating proto_info information. | [
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] | def _gen_str(self, line):
'''
Further processing the output of the process() function rewritten by
user, outputting data that can be directly read by the MultiSlotDataFeed,
and updating proto_info information.
The input line will be in this format:
>>> [(name, [feasign, ...]), ...]
>>> or ((name, [feasign, ...]), ...)
The output will be in this format:
>>> [ids_num id1 id2 ...] ...
The proto_info will be in this format:
>>> [(name, type), ...]
For example, if the input is like this:
>>> [("words", [1926, 08, 17]), ("label", [1])]
>>> or (("words", [1926, 08, 17]), ("label", [1]))
the output will be:
>>> 3 1234 2345 3456 1 1
the proto_info will be:
>>> [("words", "uint64"), ("label", "uint64")]
Args:
line(str): the output of the process() function rewritten by user.
Returns:
Return a string data that can be read directly by the MultiSlotDataFeed.
'''
if sys.version > '3' and isinstance(line, zip):
line = list(line)
if not isinstance(line, list) and not isinstance(line, tuple):
raise ValueError(
"the output of process() must be in list or tuple type"
"Example: [('words', [1926, 08, 17]), ('label', [1])]")
output = ""
if self._proto_info is None:
self._proto_info = []
for item in line:
name, elements = item
if not isinstance(name, str):
raise ValueError("name%s must be in str type" % type(name))
if not isinstance(elements, list):
raise ValueError("elements%s must be in list type" %
type(elements))
if not elements:
raise ValueError(
"the elements of each field can not be empty, you need padding it in process()."
)
self._proto_info.append((name, "uint64"))
if output:
output += " "
output += str(len(elements))
for elem in elements:
if isinstance(elem, float):
self._proto_info[-1] = (name, "float")
elif not isinstance(elem, int) and not isinstance(elem,
long):
raise ValueError(
"the type of element%s must be in int or float" %
type(elem))
output += " " + str(elem)
else:
if len(line) != len(self._proto_info):
raise ValueError(
"the complete field set of two given line are inconsistent.")
for index, item in enumerate(line):
name, elements = item
if not isinstance(name, str):
raise ValueError("name%s must be in str type" % type(name))
if not isinstance(elements, list):
raise ValueError("elements%s must be in list type" %
type(elements))
if not elements:
raise ValueError(
"the elements of each field can not be empty, you need padding it in process()."
)
if name != self._proto_info[index][0]:
raise ValueError(
"the field name of two given line are not match: require<%s>, get<%s>."
% (self._proto_info[index][0], name))
if output:
output += " "
output += str(len(elements))
for elem in elements:
if self._proto_info[index][1] != "float":
if isinstance(elem, float):
self._proto_info[index] = (name, "float")
elif not isinstance(elem, int) and not isinstance(elem,
long):
raise ValueError(
"the type of element%s must be in int or float"
% type(elem))
output += " " + str(elem)
return output + "\n" | [
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jackaudio/jack2 | 21b293dbc37d42446141a08922cdec0d2550c6a0 | waflib/Tools/ar.py | python | find_ar | (conf) | Configuration helper used by C/C++ tools to enable the support for static libraries | Configuration helper used by C/C++ tools to enable the support for static libraries | [
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] | def find_ar(conf):
"""Configuration helper used by C/C++ tools to enable the support for static libraries"""
conf.load('ar') | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/linalg/_interpolative_backend.py | python | iddr_asvd | (A, k) | return U, V, S | Compute SVD of a real matrix to a specified rank using random sampling.
:param A:
Matrix.
:type A: :class:`numpy.ndarray`
:param k:
Rank of SVD.
:type k: int
:return:
Left singular vectors.
:rtype: :class:`numpy.ndarray`
:return:
Right singular vectors.
:rtype: :class:`numpy.ndarray`
:return:
Singular values.
:rtype: :class:`numpy.ndarray` | Compute SVD of a real matrix to a specified rank using random sampling. | [
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] | def iddr_asvd(A, k):
"""
Compute SVD of a real matrix to a specified rank using random sampling.
:param A:
Matrix.
:type A: :class:`numpy.ndarray`
:param k:
Rank of SVD.
:type k: int
:return:
Left singular vectors.
:rtype: :class:`numpy.ndarray`
:return:
Right singular vectors.
:rtype: :class:`numpy.ndarray`
:return:
Singular values.
:rtype: :class:`numpy.ndarray`
"""
A = np.asfortranarray(A)
m, n = A.shape
w = np.empty((2*k + 28)*m + (6*k + 21)*n + 25*k**2 + 100, order='F')
w_ = iddr_aidi(m, n, k)
w[:w_.size] = w_
U, V, S, ier = _id.iddr_asvd(A, k, w)
if ier != 0:
raise _RETCODE_ERROR
return U, V, S | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/mturk/connection.py | python | MTurkConnection.set_rest_notification | (self, hit_type, url, event_types=None) | return self._set_notification(hit_type, 'REST', url,
'SetHITTypeNotification', event_types) | Performs a SetHITTypeNotification operation to set REST notification
for a specified HIT type | Performs a SetHITTypeNotification operation to set REST notification
for a specified HIT type | [
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] | def set_rest_notification(self, hit_type, url, event_types=None):
"""
Performs a SetHITTypeNotification operation to set REST notification
for a specified HIT type
"""
return self._set_notification(hit_type, 'REST', url,
'SetHITTypeNotification', event_types) | [
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GeometryCollective/boundary-first-flattening | 8250e5a0e85980ec50b5e8aa8f49dd6519f915cd | deps/nanogui/docs/exhale.py | python | ExhaleRoot.parse | (self) | The first method that should be called after creating an ExhaleRoot object. The
Breathe graph is parsed first, followed by the Doxygen xml documents. By the
end of this method, all of the ``self.<breathe_kind>``, ``self.all_compounds``,
and ``self.all_nodes`` lists as well as the ``self.node_by_refid`` dictionary
will be populated. Lastly, this method sorts all of the internal lists. The
order of execution is exactly
1. :func:`exhale.ExhaleRoot.discoverAllNodes`
2. :func:`exhale.ExhaleRoot.reparentAll`
3. Populate ``self.node_by_refid`` using ``self.all_nodes``.
4. :func:`exhale.ExhaleRoot.fileRefDiscovery`
5. :func:`exhale.ExhaleRoot.filePostProcess`
6. :func:`exhale.ExhaleRoot.sortInternals` | The first method that should be called after creating an ExhaleRoot object. The
Breathe graph is parsed first, followed by the Doxygen xml documents. By the
end of this method, all of the ``self.<breathe_kind>``, ``self.all_compounds``,
and ``self.all_nodes`` lists as well as the ``self.node_by_refid`` dictionary
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vslavik/poedit | f7a9daa0a10037e090aa0a86f5ce0f24ececdf6a | deps/boost/tools/build/src/build/type.py | python | is_derived | (type, base) | Returns true if 'type' is 'base' or has 'base' as its direct or indirect base. | Returns true if 'type' is 'base' or has 'base' as its direct or indirect base. | [
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assert isinstance(type, basestring)
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SonarOpenCommunity/sonar-cxx | 6e1d456fdcd45d35bcdc61c980e34d85fe88971e | cxx-squid/dox/tools/grammar_parser/grammar_parser.py | python | GrammarParser.use | (self, search) | Search all rules using an expression and remove all other rules. | Search all rules using an expression and remove all other rules. | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/image_ops_impl.py | python | adjust_gamma | (image, gamma=1, gain=1) | Performs Gamma Correction on the input image.
Also known as Power Law Transform. This function converts the
input images at first to float representation, then transforms them
pixelwise according to the equation `Out = gain * In**gamma`,
and then converts the back to the original data type.
Args:
image : RGB image or images to adjust.
gamma : A scalar or tensor. Non negative real number.
gain : A scalar or tensor. The constant multiplier.
Returns:
A Tensor. A Gamma-adjusted tensor of the same shape and type as `image`.
Usage Example:
```python
>> import tensorflow as tf
>> x = tf.random.normal(shape=(256, 256, 3))
>> tf.image.adjust_gamma(x, 0.2)
```
Raises:
ValueError: If gamma is negative.
Notes:
For gamma greater than 1, the histogram will shift towards left and
the output image will be darker than the input image.
For gamma less than 1, the histogram will shift towards right and
the output image will be brighter than the input image.
References:
[1] http://en.wikipedia.org/wiki/Gamma_correction | Performs Gamma Correction on the input image. | [
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"""Performs Gamma Correction on the input image.
Also known as Power Law Transform. This function converts the
input images at first to float representation, then transforms them
pixelwise according to the equation `Out = gain * In**gamma`,
and then converts the back to the original data type.
Args:
image : RGB image or images to adjust.
gamma : A scalar or tensor. Non negative real number.
gain : A scalar or tensor. The constant multiplier.
Returns:
A Tensor. A Gamma-adjusted tensor of the same shape and type as `image`.
Usage Example:
```python
>> import tensorflow as tf
>> x = tf.random.normal(shape=(256, 256, 3))
>> tf.image.adjust_gamma(x, 0.2)
```
Raises:
ValueError: If gamma is negative.
Notes:
For gamma greater than 1, the histogram will shift towards left and
the output image will be darker than the input image.
For gamma less than 1, the histogram will shift towards right and
the output image will be brighter than the input image.
References:
[1] http://en.wikipedia.org/wiki/Gamma_correction
"""
with ops.name_scope(None, 'adjust_gamma', [image, gamma, gain]) as name:
image = ops.convert_to_tensor(image, name='image')
# Remember original dtype to so we can convert back if needed
orig_dtype = image.dtype
if orig_dtype in [dtypes.float16, dtypes.float32]:
flt_image = image
else:
flt_image = convert_image_dtype(image, dtypes.float32)
assert_op = _assert(gamma >= 0, ValueError,
'Gamma should be a non-negative real number.')
if assert_op:
gamma = control_flow_ops.with_dependencies(assert_op, gamma)
# According to the definition of gamma correction.
adjusted_img = gain * flt_image**gamma
return convert_image_dtype(adjusted_img, orig_dtype, saturate=True) | [
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apache/singa | 93fd9da72694e68bfe3fb29d0183a65263d238a1 | python/singa/autograd.py | python | pooling_2d | (handle, x, odd_padding=(0, 0, 0, 0)) | return _Pooling2d(handle, odd_padding)(x)[0] | Pooling 2d operator
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/affine/promote.py | python | Promote.is_symmetric | (self) | return self.ndim == 2 and self.shape[0] == self.shape[1] | Is the expression symmetric? | Is the expression symmetric? | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | third_party/python_gflags/gflags.py | python | DEFINE_enum | (name, default, enum_values, help, flag_values=FLAGS,
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/fuchsia/boot_data.py | python | _GetAuthorizedKeysPath | () | return os.path.join(_SSH_DIR, 'fuchsia_authorized_keys') | Returns a path to the authorized keys which get copied to your Fuchsia
device during paving | Returns a path to the authorized keys which get copied to your Fuchsia
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/image/image.py | python | scale_down | (src_size, size) | return int(w), int(h) | Scales down crop size if it's larger than image size.
If width/height of the crop is larger than the width/height of the image,
sets the width/height to the width/height of the image.
Parameters
----------
src_size : tuple of int
Size of the image in (width, height) format.
size : tuple of int
Size of the crop in (width, height) format.
Returns
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Example
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>>> src_size = (640,480)
>>> size = (720,120)
>>> new_size = mx.img.scale_down(src_size, size)
>>> new_size
(640,106) | Scales down crop size if it's larger than image size. | [
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"""Scales down crop size if it's larger than image size.
If width/height of the crop is larger than the width/height of the image,
sets the width/height to the width/height of the image.
Parameters
----------
src_size : tuple of int
Size of the image in (width, height) format.
size : tuple of int
Size of the crop in (width, height) format.
Returns
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tuple of int
A tuple containing the scaled crop size in (width, height) format.
Example
--------
>>> src_size = (640,480)
>>> size = (720,120)
>>> new_size = mx.img.scale_down(src_size, size)
>>> new_size
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w, h = size
sw, sh = src_size
if sh < h:
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_SelfTest_REQUEST.toTpm | (self, buf) | TpmMarshaller method | TpmMarshaller method | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py2/IPython/utils/py3compat.py | python | safe_unicode | (e) | return u'Unrecoverably corrupt evalue' | unicode(e) with various fallbacks. Used for exceptions, which may not be
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pass
try:
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/combo.py | python | ComboCtrl.ShowPopup | (*args, **kwargs) | return _combo.ComboCtrl_ShowPopup(*args, **kwargs) | ShowPopup(self)
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/learn/python/learn/estimators/estimator.py | python | BaseEstimator._get_train_ops | (self, features, labels) | Method that builds model graph and returns trainer ops.
Expected to be overridden by sub-classes that require custom support.
Args:
features: `Tensor` or `dict` of `Tensor` objects.
labels: `Tensor` or `dict` of `Tensor` objects.
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features: `Tensor` or `dict` of `Tensor` objects.
labels: `Tensor` or `dict` of `Tensor` objects.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/email/__init__.py | python | message_from_file | (fp, *args, **kws) | return Parser(*args, **kws).parse(fp) | Read a file and parse its contents into a Message object model.
Optional _class and strict are passed to the Parser constructor. | Read a file and parse its contents into a Message object model. | [
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] | def message_from_file(fp, *args, **kws):
"""Read a file and parse its contents into a Message object model.
Optional _class and strict are passed to the Parser constructor.
"""
from email.parser import Parser
return Parser(*args, **kws).parse(fp) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/distlib/index.py | python | PackageIndex.upload_documentation | (self, metadata, doc_dir) | return self.send_request(request) | Upload documentation to the index.
:param metadata: A :class:`Metadata` instance defining at least a name
and version number for the documentation to be
uploaded.
:param doc_dir: The pathname of the directory which contains the
documentation. This should be the directory that
contains the ``index.html`` for the documentation.
:return: The HTTP response received from PyPI upon submission of the
request. | [] | def upload_documentation(self, metadata, doc_dir):
"""
Upload documentation to the index.
:param metadata: A :class:`Metadata` instance defining at least a name
and version number for the documentation to be
uploaded.
:param doc_dir: The pathname of the directory which contains the
documentation. This should be the directory that
contains the ``index.html`` for the documentation.
:return: The HTTP response received from PyPI upon submission of the
request.
"""
self.check_credentials()
if not os.path.isdir(doc_dir):
raise DistlibException('not a directory: %r' % doc_dir)
fn = os.path.join(doc_dir, 'index.html')
if not os.path.exists(fn):
raise DistlibException('not found: %r' % fn)
metadata.validate()
name, version = metadata.name, metadata.version
zip_data = zip_dir(doc_dir).getvalue()
fields = [(':action', 'doc_upload'),
('name', name), ('version', version)]
files = [('content', name, zip_data)]
request = self.encode_request(fields, files)
return self.send_request(request) | [
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/pybind/mgr/volumes/fs/operations/versions/auth_metadata.py | python | AuthMetadataManager.subvol_metadata_lock | (self, group_name, subvol_name) | return self._lock(self._subvolume_metadata_path(group_name, subvol_name)) | Return a ContextManager which locks the authorization metadata for
a particular subvolume, and persists a flag to the metadata indicating
that it is currently locked, so that we can detect dirty situations
during recovery.
This lock isn't just to make access to the metadata safe: it's also
designed to be used over the two-step process of checking the
metadata and then responding to an authorization request, to
ensure that at the point we respond the metadata hasn't changed
in the background. It's key to how we avoid security holes
resulting from races during that problem , | Return a ContextManager which locks the authorization metadata for
a particular subvolume, and persists a flag to the metadata indicating
that it is currently locked, so that we can detect dirty situations
during recovery. | [
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"""
Return a ContextManager which locks the authorization metadata for
a particular subvolume, and persists a flag to the metadata indicating
that it is currently locked, so that we can detect dirty situations
during recovery.
This lock isn't just to make access to the metadata safe: it's also
designed to be used over the two-step process of checking the
metadata and then responding to an authorization request, to
ensure that at the point we respond the metadata hasn't changed
in the background. It's key to how we avoid security holes
resulting from races during that problem ,
"""
return self._lock(self._subvolume_metadata_path(group_name, subvol_name)) | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/last-day-where-you-can-still-cross.py | python | Solution.latestDayToCross | (self, row, col, cells) | return -1 | :type row: int
:type col: int
:type cells: List[List[int]]
:rtype: int | :type row: int
:type col: int
:type cells: List[List[int]]
:rtype: int | [
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] | def latestDayToCross(self, row, col, cells):
"""
:type row: int
:type col: int
:type cells: List[List[int]]
:rtype: int
"""
directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]
def index(n, i, j):
return i*n+j
start, end = row*col, row*col+1
uf = UnionFind(row*col+2)
lookup = [[False]*col for _ in xrange(row)]
for i in reversed(xrange(len(cells))):
r, c = cells[i]
r, c = r-1, c-1
for dr, dc in directions:
nr, nc = r+dr, c+dc
if not (0 <= nr < row and 0 <= nc < col and lookup[nr][nc]):
continue
uf.union_set(index(col, r, c), index(col, nr, nc))
if r == 0:
uf.union_set(start, index(col, r, c))
if r == row-1:
uf.union_set(end, index(col, r, c))
if uf.find_set(start) == uf.find_set(end):
return i
lookup[r][c] = True
return -1 | [
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/tools/gyp/pylib/gyp/mac_tool.py | python | MacTool._InstallResourceRules | (self, resource_rules) | return target_path | Installs ResourceRules.plist from user or SDK into the bundle.
Args:
resource_rules: string, optional, path to the ResourceRules.plist file
to use, default to "${SDKROOT}/ResourceRules.plist"
Returns:
Path to the copy of ResourceRules.plist into the bundle. | Installs ResourceRules.plist from user or SDK into the bundle. | [
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] | def _InstallResourceRules(self, resource_rules):
"""Installs ResourceRules.plist from user or SDK into the bundle.
Args:
resource_rules: string, optional, path to the ResourceRules.plist file
to use, default to "${SDKROOT}/ResourceRules.plist"
Returns:
Path to the copy of ResourceRules.plist into the bundle.
"""
source_path = resource_rules
target_path = os.path.join(
os.environ['BUILT_PRODUCTS_DIR'],
os.environ['CONTENTS_FOLDER_PATH'],
'ResourceRules.plist')
if not source_path:
source_path = os.path.join(
os.environ['SDKROOT'], 'ResourceRules.plist')
shutil.copy2(source_path, target_path)
return target_path | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | samples/pySketch/pySketch.py | python | TextObjectValidator.Validate | (self, win) | Validate the contents of the given text control. | Validate the contents of the given text control. | [
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] | def Validate(self, win):
""" Validate the contents of the given text control.
"""
textCtrl = self.GetWindow()
text = textCtrl.GetValue()
if len(text) == 0:
wx.MessageBox("A text object must contain some text!", "Error")
return False
else:
return True | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/feature_column/serialization.py | python | deserialize_feature_columns | (configs, custom_objects=None) | return [
deserialize_feature_column(c, custom_objects, columns_by_name)
for c in configs
] | Deserializes a list of FeatureColumns configs.
Returns a list of FeatureColumns given a list of config dicts acquired by
`serialize_feature_columns`.
Args:
configs: A list of Dicts with the serialization of feature columns acquired
by `serialize_feature_columns`.
custom_objects: A Dict from custom_object name to the associated keras
serializable objects (FeatureColumns, classes or functions).
Returns:
FeatureColumn objects corresponding to the input configs.
Raises:
ValueError if called with input that is not a list of FeatureColumns. | Deserializes a list of FeatureColumns configs. | [
"Deserializes",
"a",
"list",
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"FeatureColumns",
"configs",
"."
] | def deserialize_feature_columns(configs, custom_objects=None):
"""Deserializes a list of FeatureColumns configs.
Returns a list of FeatureColumns given a list of config dicts acquired by
`serialize_feature_columns`.
Args:
configs: A list of Dicts with the serialization of feature columns acquired
by `serialize_feature_columns`.
custom_objects: A Dict from custom_object name to the associated keras
serializable objects (FeatureColumns, classes or functions).
Returns:
FeatureColumn objects corresponding to the input configs.
Raises:
ValueError if called with input that is not a list of FeatureColumns.
"""
columns_by_name = {}
return [
deserialize_feature_column(c, custom_objects, columns_by_name)
for c in configs
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NREL/EnergyPlus | fadc5973b85c70e8cc923efb69c144e808a26078 | third_party/ssc/jsoncpp/amalgamate.py | python | amalgamate_source | (source_top_dir=None,
target_source_path=None,
header_include_path=None) | Produces amalgamated source.
Parameters:
source_top_dir: top-directory
target_source_path: output .cpp path
header_include_path: generated header path relative to target_source_path. | Produces amalgamated source.
Parameters:
source_top_dir: top-directory
target_source_path: output .cpp path
header_include_path: generated header path relative to target_source_path. | [
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target_source_path=None,
header_include_path=None):
"""Produces amalgamated source.
Parameters:
source_top_dir: top-directory
target_source_path: output .cpp path
header_include_path: generated header path relative to target_source_path.
"""
print("Amalgamating header...")
header = AmalgamationFile(source_top_dir)
header.add_text("/// Json-cpp amalgamated header (http://jsoncpp.sourceforge.net/).")
header.add_text('/// It is intended to be used with #include "%s"' % header_include_path)
header.add_file("LICENSE", wrap_in_comment=True)
header.add_text("#ifndef JSON_AMALGAMATED_H_INCLUDED")
header.add_text("# define JSON_AMALGAMATED_H_INCLUDED")
header.add_text("/// If defined, indicates that the source file is amalgamated")
header.add_text("/// to prevent private header inclusion.")
header.add_text("#define JSON_IS_AMALGAMATION")
header.add_file(os.path.join(INCLUDE_PATH, "version.h"))
header.add_file(os.path.join(INCLUDE_PATH, "allocator.h"))
header.add_file(os.path.join(INCLUDE_PATH, "config.h"))
header.add_file(os.path.join(INCLUDE_PATH, "forwards.h"))
header.add_file(os.path.join(INCLUDE_PATH, "json_features.h"))
header.add_file(os.path.join(INCLUDE_PATH, "value.h"))
header.add_file(os.path.join(INCLUDE_PATH, "reader.h"))
header.add_file(os.path.join(INCLUDE_PATH, "writer.h"))
header.add_file(os.path.join(INCLUDE_PATH, "assertions.h"))
header.add_text("#endif //ifndef JSON_AMALGAMATED_H_INCLUDED")
target_header_path = os.path.join(os.path.dirname(target_source_path), header_include_path)
print("Writing amalgamated header to %r" % target_header_path)
header.write_to(target_header_path)
base, ext = os.path.splitext(header_include_path)
forward_header_include_path = base + "-forwards" + ext
print("Amalgamating forward header...")
header = AmalgamationFile(source_top_dir)
header.add_text("/// Json-cpp amalgamated forward header (http://jsoncpp.sourceforge.net/).")
header.add_text('/// It is intended to be used with #include "%s"' % forward_header_include_path)
header.add_text("/// This header provides forward declaration for all JsonCpp types.")
header.add_file("LICENSE", wrap_in_comment=True)
header.add_text("#ifndef JSON_FORWARD_AMALGAMATED_H_INCLUDED")
header.add_text("# define JSON_FORWARD_AMALGAMATED_H_INCLUDED")
header.add_text("/// If defined, indicates that the source file is amalgamated")
header.add_text("/// to prevent private header inclusion.")
header.add_text("#define JSON_IS_AMALGAMATION")
header.add_file(os.path.join(INCLUDE_PATH, "config.h"))
header.add_file(os.path.join(INCLUDE_PATH, "forwards.h"))
header.add_text("#endif //ifndef JSON_FORWARD_AMALGAMATED_H_INCLUDED")
target_forward_header_path = os.path.join(os.path.dirname(target_source_path),
forward_header_include_path)
print("Writing amalgamated forward header to %r" % target_forward_header_path)
header.write_to(target_forward_header_path)
print("Amalgamating source...")
source = AmalgamationFile(source_top_dir)
source.add_text("/// Json-cpp amalgamated source (http://jsoncpp.sourceforge.net/).")
source.add_text('/// It is intended to be used with #include "%s"' % header_include_path)
source.add_file("LICENSE", wrap_in_comment=True)
source.add_text("")
source.add_text('#include "%s"' % header_include_path)
source.add_text("""
#ifndef JSON_IS_AMALGAMATION
#error "Compile with -I PATH_TO_JSON_DIRECTORY"
#endif
""")
source.add_text("")
source.add_file(os.path.join(SRC_PATH, "json_tool.h"))
source.add_file(os.path.join(SRC_PATH, "json_reader.cpp"))
source.add_file(os.path.join(SRC_PATH, "json_valueiterator.inl"))
source.add_file(os.path.join(SRC_PATH, "json_value.cpp"))
source.add_file(os.path.join(SRC_PATH, "json_writer.cpp"))
print("Writing amalgamated source to %r" % target_source_path)
source.write_to(target_source_path) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | Image_RGBtoHSV | (*args, **kwargs) | return _core_.Image_RGBtoHSV(*args, **kwargs) | Image_RGBtoHSV(Image_RGBValue rgb) -> Image_HSVValue
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"""
Image_RGBtoHSV(Image_RGBValue rgb) -> Image_HSVValue
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"""
return _core_.Image_RGBtoHSV(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | Brush.IsHatch | (*args, **kwargs) | return _gdi_.Brush_IsHatch(*args, **kwargs) | IsHatch(self) -> bool
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"""
IsHatch(self) -> bool
Is the current style a hatch type?
"""
return _gdi_.Brush_IsHatch(*args, **kwargs) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/learn/python/learn/datasets/synthetic.py | python | _archimedes_spiral | (theta, theta_offset=0., *args, **kwargs) | return x, y | Return Archimedes spiral
Args:
theta: array-like, angles from polar coordinates to be converted
theta_offset: float, angle offset in radians (2*pi = 0) | Return Archimedes spiral | [
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] | def _archimedes_spiral(theta, theta_offset=0., *args, **kwargs):
"""Return Archimedes spiral
Args:
theta: array-like, angles from polar coordinates to be converted
theta_offset: float, angle offset in radians (2*pi = 0)
"""
x, y = theta*np.cos(theta + theta_offset), theta*np.sin(theta + theta_offset)
x_norm = np.max(np.abs(x))
y_norm = np.max(np.abs(y))
x, y = x / x_norm, y / y_norm
return x, y | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/turtle.py | python | TNavigator.home | (self) | Move turtle to the origin - coordinates (0,0).
No arguments.
Move turtle to the origin - coordinates (0,0) and set its
heading to its start-orientation (which depends on mode).
Example (for a Turtle instance named turtle):
>>> turtle.home() | Move turtle to the origin - coordinates (0,0). | [
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"""Move turtle to the origin - coordinates (0,0).
No arguments.
Move turtle to the origin - coordinates (0,0) and set its
heading to its start-orientation (which depends on mode).
Example (for a Turtle instance named turtle):
>>> turtle.home()
"""
self.goto(0, 0)
self.setheading(0) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | FontEnumerator_GetEncodings | (*args) | return _gdi_.FontEnumerator_GetEncodings(*args) | FontEnumerator_GetEncodings() -> PyObject | FontEnumerator_GetEncodings() -> PyObject | [
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"""FontEnumerator_GetEncodings() -> PyObject"""
return _gdi_.FontEnumerator_GetEncodings(*args) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/framework/ops.py | python | Graph.graph_def_versions | (self) | return self._graph_def_versions | The GraphDef version information of this graph.
For details on the meaning of each version, see
[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto).
Returns:
A `VersionDef`. | The GraphDef version information of this graph. | [
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] | def graph_def_versions(self):
# pylint: disable=line-too-long
"""The GraphDef version information of this graph.
For details on the meaning of each version, see
[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto).
Returns:
A `VersionDef`.
"""
# pylint: enable=line-too-long
return self._graph_def_versions | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/sparse/scipy_sparse.py | python | _to_ijv | (ss, row_levels=(0, ), column_levels=(1, ), sort_labels=False) | return values, i_coord, j_coord, i_labels, j_labels | For arbitrary (MultiIndexed) SparseSeries return
(v, i, j, ilabels, jlabels) where (v, (i, j)) is suitable for
passing to scipy.sparse.coo constructor. | For arbitrary (MultiIndexed) SparseSeries return
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""" For arbitrary (MultiIndexed) SparseSeries return
(v, i, j, ilabels, jlabels) where (v, (i, j)) is suitable for
passing to scipy.sparse.coo constructor. """
# index and column levels must be a partition of the index
_check_is_partition([row_levels, column_levels], range(ss.index.nlevels))
# from the SparseSeries: get the labels and data for non-null entries
values = ss._data.internal_values()._valid_sp_values
nonnull_labels = ss.dropna()
def get_indexers(levels):
""" Return sparse coords and dense labels for subset levels """
# TODO: how to do this better? cleanly slice nonnull_labels given the
# coord
values_ilabels = [tuple(x[i] for i in levels)
for x in nonnull_labels.index]
if len(levels) == 1:
values_ilabels = [x[0] for x in values_ilabels]
# # performance issues with groupby ###################################
# TODO: these two lines can rejplace the code below but
# groupby is too slow (in some cases at least)
# labels_to_i = ss.groupby(level=levels, sort=sort_labels).first()
# labels_to_i[:] = np.arange(labels_to_i.shape[0])
def _get_label_to_i_dict(labels, sort_labels=False):
""" Return OrderedDict of unique labels to number.
Optionally sort by label.
"""
labels = Index(lmap(tuple, labels)).unique().tolist() # squish
if sort_labels:
labels = sorted(list(labels))
d = OrderedDict((k, i) for i, k in enumerate(labels))
return (d)
def _get_index_subset_to_coord_dict(index, subset, sort_labels=False):
ilabels = list(zip(*[index._get_level_values(i) for i in subset]))
labels_to_i = _get_label_to_i_dict(ilabels,
sort_labels=sort_labels)
labels_to_i = Series(labels_to_i)
if len(subset) > 1:
labels_to_i.index = MultiIndex.from_tuples(labels_to_i.index)
labels_to_i.index.names = [index.names[i] for i in subset]
else:
labels_to_i.index = Index(x[0] for x in labels_to_i.index)
labels_to_i.index.name = index.names[subset[0]]
labels_to_i.name = 'value'
return (labels_to_i)
labels_to_i = _get_index_subset_to_coord_dict(ss.index, levels,
sort_labels=sort_labels)
# #####################################################################
# #####################################################################
i_coord = labels_to_i[values_ilabels].tolist()
i_labels = labels_to_i.index.tolist()
return i_coord, i_labels
i_coord, i_labels = get_indexers(row_levels)
j_coord, j_labels = get_indexers(column_levels)
return values, i_coord, j_coord, i_labels, j_labels | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | TreeCtrl.GetPrevVisible | (*args, **kwargs) | return _controls_.TreeCtrl_GetPrevVisible(*args, **kwargs) | GetPrevVisible(self, TreeItemId item) -> TreeItemId | GetPrevVisible(self, TreeItemId item) -> TreeItemId | [
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baidu/AnyQ | d94d450d2aaa5f7ed73424b10aa4539835b97527 | tools/simnet/train/paddle/optimizers/paddle_optimizers.py | python | AdamOptimizer.ops | (self, loss) | Adam optimizer operation | Adam optimizer operation | [
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] | def ops(self, loss):
"""
Adam optimizer operation
"""
adam = fluid.optimizer.AdamOptimizer(
self.learning_rate, beta1=self.beta1, beta2=self.beta2, epsilon=self.epsilon)
adam.minimize(loss) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/requests/cookies.py | python | RequestsCookieJar._find | (self, name, domain=None, path=None) | Requests uses this method internally to get cookie values.
If there are conflicting cookies, _find arbitrarily chooses one.
See _find_no_duplicates if you want an exception thrown if there are
conflicting cookies.
:param name: a string containing name of cookie
:param domain: (optional) string containing domain of cookie
:param path: (optional) string containing path of cookie
:return: cookie.value | Requests uses this method internally to get cookie values. | [
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] | def _find(self, name, domain=None, path=None):
"""Requests uses this method internally to get cookie values.
If there are conflicting cookies, _find arbitrarily chooses one.
See _find_no_duplicates if you want an exception thrown if there are
conflicting cookies.
:param name: a string containing name of cookie
:param domain: (optional) string containing domain of cookie
:param path: (optional) string containing path of cookie
:return: cookie.value
"""
for cookie in iter(self):
if cookie.name == name:
if domain is None or cookie.domain == domain:
if path is None or cookie.path == path:
return cookie.value
raise KeyError('name=%r, domain=%r, path=%r' % (name, domain, path)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | ListBox_GetClassDefaultAttributes | (*args, **kwargs) | return _controls_.ListBox_GetClassDefaultAttributes(*args, **kwargs) | ListBox_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
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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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this. | ListBox_GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes | [
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"""
ListBox_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
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The variant parameter is only relevant under Mac currently and is
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return _controls_.ListBox_GetClassDefaultAttributes(*args, **kwargs) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/ndlstm/python/lstm1d.py | python | ndlstm_base_unrolled | (inputs, noutput, scope=None, reverse=False) | Run an LSTM, either forward or backward.
This is a 1D LSTM implementation using unrolling and the TensorFlow
LSTM op.
Args:
inputs: input sequence (length, batch_size, ninput)
noutput: depth of output
scope: optional scope name
reverse: run LSTM in reverse
Returns:
Output sequence (length, batch_size, noutput) | Run an LSTM, either forward or backward. | [
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] | def ndlstm_base_unrolled(inputs, noutput, scope=None, reverse=False):
"""Run an LSTM, either forward or backward.
This is a 1D LSTM implementation using unrolling and the TensorFlow
LSTM op.
Args:
inputs: input sequence (length, batch_size, ninput)
noutput: depth of output
scope: optional scope name
reverse: run LSTM in reverse
Returns:
Output sequence (length, batch_size, noutput)
"""
with variable_scope.variable_scope(scope, "SeqLstmUnrolled", [inputs]):
length, batch_size, _ = _shape(inputs)
lstm_cell = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False)
state = array_ops.zeros([batch_size, lstm_cell.state_size])
output_u = []
inputs_u = array_ops.unstack(inputs)
if reverse:
inputs_u = list(reversed(inputs_u))
for i in xrange(length):
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
output, state = lstm_cell(inputs_u[i], state)
output_u += [output]
if reverse:
output_u = list(reversed(output_u))
outputs = array_ops.stack(output_u)
return outputs | [
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google-ar/WebARonTango | e86965d2cbc652156b480e0fcf77c716745578cd | chromium/src/gpu/command_buffer/build_gles2_cmd_buffer.py | python | Function.WriteCmdFlag | (self, f) | Writes the cmd cmd_flags constant. | Writes the cmd cmd_flags constant. | [
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"""Writes the cmd cmd_flags constant."""
flags = []
# By default trace only at the highest level 3.
trace_level = int(self.GetInfo('trace_level', default = 3))
if trace_level not in xrange(0, 4):
raise KeyError("Unhandled trace_level: %d" % trace_level)
flags.append('CMD_FLAG_SET_TRACE_LEVEL(%d)' % trace_level)
if len(flags) > 0:
cmd_flags = ' | '.join(flags)
else:
cmd_flags = 0
f.write(" static const uint8_t cmd_flags = %s;\n" % cmd_flags) | [
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ApolloAuto/apollo | 463fb82f9e979d02dcb25044e60931293ab2dba0 | modules/tools/routing/debug_passage_region.py | python | plot_junction | (junction) | Plot junction | Plot junction | [
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] | def plot_junction(junction):
"""Plot junction"""
plot_region(junction.passage_region, 'red') | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/protobuf/python/google/protobuf/internal/encoder.py | python | MapEncoder | (field_descriptor) | return EncodeField | Encoder for extensions of MessageSet.
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message MapEntry {
key_type key = 1;
value_type value = 2;
}
repeated MapEntry map = N; | Encoder for extensions of MessageSet. | [
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"""Encoder for extensions of MessageSet.
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message MapEntry {
key_type key = 1;
value_type value = 2;
}
repeated MapEntry map = N;
"""
# Can't look at field_descriptor.message_type._concrete_class because it may
# not have been initialized yet.
message_type = field_descriptor.message_type
encode_message = MessageEncoder(field_descriptor.number, False, False)
def EncodeField(write, value):
for key in value:
entry_msg = message_type._concrete_class(key=key, value=value[key])
encode_message(write, entry_msg)
return EncodeField | [
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OpenLightingProject/ola | d1433a1bed73276fbe55ce18c03b1c208237decc | scripts/enforce_licence.py | python | GetDirectoryLicences | (root_dir) | return licences | Walk the directory tree and determine the licence for each directory. | Walk the directory tree and determine the licence for each directory. | [
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"""Walk the directory tree and determine the licence for each directory."""
LICENCE_FILE = 'LICENCE'
licences = {}
for dir_name, subdirs, files in os.walk(root_dir):
# skip the root_dir since the licence file is different there
if dir_name == root_dir:
continue
# skip ignored dirs since we don't check them anyways
skip = False
for ignored_dir in IGNORED_DIRECTORIES:
if dir_name.rfind(ignored_dir) != -1:
skip = True
if skip:
continue
# don't descend into hidden dirs like .libs and .deps
subdirs[:] = [d for d in subdirs if not d.startswith('.')]
if LICENCE_FILE in files:
f = open(os.path.join(dir_name, LICENCE_FILE))
lines = f.readlines()
f.close()
licences[dir_name] = TransformLicence(lines)
print('Found LICENCE for directory %s' % dir_name)
# use this licence for all subdirs
licence = licences.get(dir_name)
if licence is not None:
for sub_dir in subdirs:
licences[os.path.join(dir_name, sub_dir)] = licence
return licences | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/__init__.py | python | RegeneratableOptionParser.add_argument | (self, *args, **kw) | Add an option to the parser.
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following:
regenerate: can be set to False to prevent this option from being included
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env_name: name of environment variable that additional values for this
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type: adds type='path', to tell the regenerator that the values of
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"""Add an option to the parser.
This accepts the same arguments as ArgumentParser.add_argument, plus the
following:
regenerate: can be set to False to prevent this option from being included
in regeneration.
env_name: name of environment variable that additional values for this
option come from.
type: adds type='path', to tell the regenerator that the values of
this option need to be made relative to options.depth
"""
env_name = kw.pop("env_name", None)
if "dest" in kw and kw.pop("regenerate", True):
dest = kw["dest"]
# The path type is needed for regenerating, for optparse we can just treat
# it as a string.
type = kw.get("type")
if type == "path":
kw["type"] = str
self.__regeneratable_options[dest] = {
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/robotsim.py | python | SpherePoser.get | (self) | return _robotsim.SpherePoser_get(self) | r""" | r""" | [
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physercoe/starquant | c00cad64d1de2da05081b3dc320ef264c6295e08 | cppsrc/fmt-5.3.0/support/docopt.py | python | parse_long | (tokens, options) | return [o] | long ::= '--' chars [ ( ' ' | '=' ) chars ] ; | long ::= '--' chars [ ( ' ' | '=' ) chars ] ; | [
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"""long ::= '--' chars [ ( ' ' | '=' ) chars ] ;"""
long, eq, value = tokens.move().partition('=')
assert long.startswith('--')
value = None if eq == value == '' else value
similar = [o for o in options if o.long == long]
if tokens.error is DocoptExit and similar == []: # if no exact match
similar = [o for o in options if o.long and o.long.startswith(long)]
if len(similar) > 1: # might be simply specified ambiguously 2+ times?
raise tokens.error('%s is not a unique prefix: %s?' %
(long, ', '.join(o.long for o in similar)))
elif len(similar) < 1:
argcount = 1 if eq == '=' else 0
o = Option(None, long, argcount)
options.append(o)
if tokens.error is DocoptExit:
o = Option(None, long, argcount, value if argcount else True)
else:
o = Option(similar[0].short, similar[0].long,
similar[0].argcount, similar[0].value)
if o.argcount == 0:
if value is not None:
raise tokens.error('%s must not have an argument' % o.long)
else:
if value is None:
if tokens.current() in [None, '--']:
raise tokens.error('%s requires argument' % o.long)
value = tokens.move()
if tokens.error is DocoptExit:
o.value = value if value is not None else True
return [o] | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py | python | registerHTTPPostCallbacks | () | By default, libxml submits HTTP output requests using the
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"""By default, libxml submits HTTP output requests using the
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/training/monitored_session.py | python | _HookedSession._call_hook_before_run | (self, run_context, fetch_dict, user_feed_dict,
options) | return hook_feeds | Calls hooks.before_run and handles requests from hooks. | Calls hooks.before_run and handles requests from hooks. | [
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] | def _call_hook_before_run(self, run_context, fetch_dict, user_feed_dict,
options):
"""Calls hooks.before_run and handles requests from hooks."""
hook_feeds = {}
for hook in self._hooks:
request = hook.before_run(run_context)
if request is not None:
if request.fetches is not None:
fetch_dict[hook] = request.fetches
if request.feed_dict:
self._raise_if_feeds_intersects(
hook_feeds, request.feed_dict,
'Same tensor is fed by two hooks.')
hook_feeds.update(request.feed_dict)
if request.options:
self._merge_run_options(options, request.options)
if not hook_feeds:
return user_feed_dict
if not user_feed_dict:
return hook_feeds
self._raise_if_feeds_intersects(
user_feed_dict, hook_feeds,
'Same tensor is fed by a SessionRunHook and user.')
hook_feeds.update(user_feed_dict)
return hook_feeds | [
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apache/qpid-proton | 6bcdfebb55ea3554bc29b1901422532db331a591 | python/proton/_events.py | python | EventBase.handler | (self) | return None | The handler for this event type. Not implemented, always returns ``None``.
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The handler for this event type. Not implemented, always returns ``None``.
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xenia-project/xenia | 9b1fdac98665ac091b9660a5d0fbb259ed79e578 | third_party/google-styleguide/cpplint/cpplint.py | python | FileInfo.Split | (self) | return (project,) + os.path.splitext(rest) | Splits the file into the directory, basename, and extension.
For 'chrome/browser/browser.cc', Split() would
return ('chrome/browser', 'browser', '.cc')
Returns:
A tuple of (directory, basename, extension). | Splits the file into the directory, basename, and extension. | [
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"""Splits the file into the directory, basename, and extension.
For 'chrome/browser/browser.cc', Split() would
return ('chrome/browser', 'browser', '.cc')
Returns:
A tuple of (directory, basename, extension).
"""
googlename = self.RepositoryName()
project, rest = os.path.split(googlename)
return (project,) + os.path.splitext(rest) | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/peacock/Input/InputFileEditorPlugin.py | python | InputFileEditorPlugin.onCurrentChanged | (self, index) | This is called when the tab is changed.
If the block editor window is open we want to raise it
to the front so it doesn't get lost. | This is called when the tab is changed.
If the block editor window is open we want to raise it
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"""
This is called when the tab is changed.
If the block editor window is open we want to raise it
to the front so it doesn't get lost.
"""
if index == self._index:
if self.block_editor:
self.block_editor.raise_() | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/tkMessageBox.py | python | askretrycancel | (title=None, message=None, **options) | return s == RETRY | Ask if operation should be retried; return true if the answer is yes | Ask if operation should be retried; return true if the answer is yes | [
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"Ask if operation should be retried; return true if the answer is yes"
s = _show(title, message, WARNING, RETRYCANCEL, **options)
return s == RETRY | [
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thunil/tempoGAN | a9b92181e28a82c2029049791f875a6c1341d62b | tensorflow/tools/fluiddataloader.py | python | FluidDataLoader.mogrifyFilenameIndex | (self, fn, idxOffset) | return fn | Parse, determine index, and change | Parse, determine index, and change | [
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""" Parse, determine index, and change
"""
match = re.search("(.*_)([\d]+)\.([\w]+)", fn) # split into groups: path/name_ , %04d , ext
if match:
if len(match.groups())!=3:
raise FluidDataLoaderError("FluidDataLoader error: got filename %s, but could not fully split up into name,4-digit and extension " % (fn))
#print "A " + format(match.groups())
idx = int(match.group(2))
idx = max(self.filename_index_min, min(self.filename_index_max-1, idx+idxOffset) )
#print "A " + format(match.group(2))
fn = "%s%04d.%s" % (match.group(1), idx, match.group(3))
#print "fn " + fn
else:
raise FluidDataLoaderError("FluidDataLoader error: got filename %s, but could not split up into name,4-digit and extension " % (fn))
#exit()
# density1_([\w]+).npz
return fn | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/fancy-sequence.py | python | Fancy.addAll | (self, inc) | :type inc: int
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"""
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"""
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baidu/lac | 3e10dbed9bfd87bea927c84a6627a167c17b5617 | python/LAC/models.py | python | Model.train | (self, model_save_dir, train_data, test_data, iter_num, thread_num) | 执行模型增量训练
Args:
model_save_dir: 训练结束后模型保存的路径
train_data: 训练数据路径
test_data: 测试数据路径，若为None则不进行测试
iter_num: 训练数据的迭代次数
thread_num: 执行训练的线程数 | 执行模型增量训练
Args:
model_save_dir: 训练结束后模型保存的路径
train_data: 训练数据路径
test_data: 测试数据路径，若为None则不进行测试
iter_num: 训练数据的迭代次数
thread_num: 执行训练的线程数 | [
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"""执行模型增量训练
Args:
model_save_dir: 训练结束后模型保存的路径
train_data: 训练数据路径
test_data: 测试数据路径，若为None则不进行测试
iter_num: 训练数据的迭代次数
thread_num: 执行训练的线程数
"""
self.args.model = self.mode
self.args.train_data = train_data
self.args.test_data = test_data
self.args.epoch = iter_num
self.args.cpu_num = thread_num
logging.info("Start Training!")
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
test_program, fetch_list = nets.do_train(self.args, self.dataset, self.segment_tool)
fluid.io.save_inference_model(os.path.join(model_save_dir, 'model'),
['words'],
fetch_list,
self.exe,
main_program=test_program,
)
# 拷贝配置文件
if os.path.exists(os.path.join(model_save_dir, 'conf')):
shutil.rmtree(os.path.join(model_save_dir, 'conf'))
shutil.copytree(os.path.join(self.model_path, 'conf'),
os.path.join(model_save_dir, 'conf'))
self.load_model(model_save_dir)
logging.info("Finish Training!") | [
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evpo/EncryptPad | 156904860aaba8e7e8729b44e269b2992f9fe9f4 | deps/libencryptmsg/configure.py | python | lex_me_harder | (infofile, allowed_groups, allowed_maps, name_val_pairs) | return out | Generic lexer function for info.txt and src/build-data files | Generic lexer function for info.txt and src/build-data files | [
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"""
Generic lexer function for info.txt and src/build-data files
"""
out = LexResult()
# Format as a nameable Python variable
def py_var(group):
return group.replace(':', '_')
lexer = shlex.shlex(open(infofile), infofile, posix=True)
lexer.wordchars += ':.<>/,-!?+*' # handle various funky chars in info.txt
groups = allowed_groups + allowed_maps
for group in groups:
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# Check for a grouping
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group = match.group(1)
if group not in groups:
raise LexerError('Unknown group "%s"' % (group),
infofile, lexer.lineno)
end_marker = '</' + group + '>'
token = lexer.get_token()
while token != end_marker:
out.__dict__[py_var(group)].append(token)
token = lexer.get_token()
if token is None:
raise LexerError('Group "%s" not terminated' % (group),
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elif token in name_val_pairs.keys():
if isinstance(out.__dict__[token], list):
out.__dict__[token].append(lexer.get_token())
else:
out.__dict__[token] = lexer.get_token()
else: # No match -> error
raise LexerError('Bad token "%s"' % (token), infofile, lexer.lineno)
for group in allowed_maps:
out.__dict__[group] = parse_lex_dict(out.__dict__[group])
return out | [
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | lldb/examples/python/file_extract.py | python | FileExtract.get_n_uint8 | (self, n, fail_value=0) | Extract "n" uint8_t integers from the binary file at the current file position, returns a list of integers | Extract "n" uint8_t integers from the binary file at the current file position, returns a list of integers | [
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] | def get_n_uint8(self, n, fail_value=0):
'''Extract "n" uint8_t integers from the binary file at the current file position, returns a list of integers'''
s = self.read_size(n)
if s:
return struct.unpack(self.byte_order + ("%u" % n) + 'B', s)
else:
return (fail_value,) * n | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | TextBoxAttr.GetClearMode | (*args, **kwargs) | return _richtext.TextBoxAttr_GetClearMode(*args, **kwargs) | GetClearMode(self) -> int | GetClearMode(self) -> int | [
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] | def GetClearMode(*args, **kwargs):
"""GetClearMode(self) -> int"""
return _richtext.TextBoxAttr_GetClearMode(*args, **kwargs) | [
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