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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/ops/math_ops.py | python | _as_indexed_slices_list | (inputs) | return casted_outputs | Convert all elements of 'inputs' to IndexedSlices.
Additionally, homogenize the types of all the indices to
either int32 or int64.
Args:
inputs: List containing either Tensor or IndexedSlices objects.
Returns:
A list of IndexedSlices objects.
Raises:
TypeError: If 'inputs' is not a list or a tuple. | Convert all elements of 'inputs' to IndexedSlices. | [
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"of",
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"IndexedSlices",
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] | def _as_indexed_slices_list(inputs):
"""Convert all elements of 'inputs' to IndexedSlices.
Additionally, homogenize the types of all the indices to
either int32 or int64.
Args:
inputs: List containing either Tensor or IndexedSlices objects.
Returns:
A list of IndexedSlices objects.
Raises:
TypeError: If 'inputs' is not a list or a tuple.
"""
if not isinstance(inputs, (list, tuple)):
raise TypeError("Expected a list or tuple, not a %s" % type(inputs))
outputs = [_as_indexed_slices(i) for i in inputs]
with_int32_index = [o.indices for o in outputs
if o.indices.dtype == dtypes.int32]
if not with_int32_index or len(with_int32_index) == len(outputs):
return outputs
casted_outputs = []
for o in outputs:
if o.indices.dtype == dtypes.int32:
casted_outputs.append(
ops.IndexedSlices(o.values, cast(o.indices, dtypes.int64),
o.dense_shape))
else:
casted_outputs.append(o)
return casted_outputs | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/pkg_resources/_vendor/pyparsing.py | python | pyparsing_common.convertToDatetime | (fmt="%Y-%m-%dT%H:%M:%S.%f") | return cvt_fn | Helper to create a parse action for converting parsed datetime string to Python datetime.datetime
Params -
- fmt - format to be passed to datetime.strptime (default=C{"%Y-%m-%dT%H:%M:%S.%f"})
Example::
dt_expr = pyparsing_common.iso8601_datetime.copy()
dt_expr.setParseAction(pyparsing_common.convertToDatetime())
print(dt_expr.parseString("1999-12-31T23:59:59.999"))
prints::
[datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)] | Helper to create a parse action for converting parsed datetime string to Python datetime.datetime | [
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] | def convertToDatetime(fmt="%Y-%m-%dT%H:%M:%S.%f"):
"""
Helper to create a parse action for converting parsed datetime string to Python datetime.datetime
Params -
- fmt - format to be passed to datetime.strptime (default=C{"%Y-%m-%dT%H:%M:%S.%f"})
Example::
dt_expr = pyparsing_common.iso8601_datetime.copy()
dt_expr.setParseAction(pyparsing_common.convertToDatetime())
print(dt_expr.parseString("1999-12-31T23:59:59.999"))
prints::
[datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)]
"""
def cvt_fn(s,l,t):
try:
return datetime.strptime(t[0], fmt)
except ValueError as ve:
raise ParseException(s, l, str(ve))
return cvt_fn | [
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jolibrain/deepdetect | 9bc840f0b1055426670d64b5285701d6faceabb9 | clients/python/dd_client/__init__.py | python | DD.post_train | (
self,
sname,
data,
parameters_input,
parameters_mllib,
parameters_output,
jasync=True,
) | return self.post(self.__urls["train"], json=data) | Creates a training job
Parameters:
sname -- service name as a resource
jasync -- whether to run the job as non-blocking
data -- array of input data / dataset for training
parameters_input -- dict of input parameters
parameters_mllib -- dict ML library parameters
parameters_output -- dict of output parameters | Creates a training job
Parameters:
sname -- service name as a resource
jasync -- whether to run the job as non-blocking
data -- array of input data / dataset for training
parameters_input -- dict of input parameters
parameters_mllib -- dict ML library parameters
parameters_output -- dict of output parameters | [
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self,
sname,
data,
parameters_input,
parameters_mllib,
parameters_output,
jasync=True,
):
"""
Creates a training job
Parameters:
sname -- service name as a resource
jasync -- whether to run the job as non-blocking
data -- array of input data / dataset for training
parameters_input -- dict of input parameters
parameters_mllib -- dict ML library parameters
parameters_output -- dict of output parameters
"""
data = {
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"async": jasync,
"parameters": {
"input": parameters_input,
"mllib": parameters_mllib,
"output": parameters_output,
},
"data": data,
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return self.post(self.__urls["train"], json=data) | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/learn/python/learn/experiment.py | python | Experiment.local_run | (self) | return self.evaluate(delay_secs=0) | Run when called on local machine.
Returns:
The result of the `evaluate` call to the `Estimator`. | Run when called on local machine. | [
"Run",
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"machine",
"."
] | def local_run(self):
"""Run when called on local machine.
Returns:
The result of the `evaluate` call to the `Estimator`.
"""
self._train_monitors = self._train_monitors or []
if self._local_eval_frequency:
self._train_monitors += [monitors.ValidationMonitor(
input_fn=self._eval_input_fn, eval_steps=self._eval_steps,
metrics=self._eval_metrics, every_n_steps=self._local_eval_frequency
)]
self.train(delay_secs=0)
return self.evaluate(delay_secs=0) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/fourcircle_utility.py | python | get_spice_file_name | (instrument_name, exp_number, scan_number) | return file_name | Get standard HB3A SPICE file name from experiment number and scan number
:param instrument_name
:param exp_number:
:param scan_number:
:return: | Get standard HB3A SPICE file name from experiment number and scan number
:param instrument_name
:param exp_number:
:param scan_number:
:return: | [
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"""
Get standard HB3A SPICE file name from experiment number and scan number
:param instrument_name
:param exp_number:
:param scan_number:
:return:
"""
assert isinstance(instrument_name, str)
assert isinstance(exp_number, int) and isinstance(scan_number, int)
file_name = '%s_exp%04d_scan%04d.dat' % (instrument_name, exp_number, scan_number)
return file_name | [
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OPAE/opae-sdk | 221124343c8275243a249eb72d69e0ea2d568d1b | python/opae.admin/opae/admin/sysfs.py | python | pci_node.device | (self) | return self._pci_address['device'] | device get the pci device of the node | device get the pci device of the node | [
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] | def device(self):
"""device get the pci device of the node"""
return self._pci_address['device'] | [
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rbgirshick/caffe-fast-rcnn | 28a579eaf0668850705598b3075b8969f22226d9 | python/caffe/draw.py | python | choose_color_by_layertype | (layertype) | return color | Define colors for nodes based on the layer type. | Define colors for nodes based on the layer type. | [
"Define",
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"for",
"nodes",
"based",
"on",
"the",
"layer",
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] | def choose_color_by_layertype(layertype):
"""Define colors for nodes based on the layer type.
"""
color = '#6495ED' # Default
if layertype == 'Convolution':
color = '#FF5050'
elif layertype == 'Pooling':
color = '#FF9900'
elif layertype == 'InnerProduct':
color = '#CC33FF'
return color | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/numbers.py | python | Integral.__and__ | (self, other) | self & other | self & other | [
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"""self & other"""
raise NotImplementedError | [
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akai-katto/dandere2x | bf1a46d5c9f9ffc8145a412c3571b283345b8512 | src/dandere2x/dandere2xlib/wrappers/ffmpeg/ffprobe.py | python | get_video_info | (ffprobe_dir, input_video) | return json.loads(json_str.decode('utf-8')) | Gets input video information
This method reads input video information
using ffprobe in dictionary.
Arguments:
input_video {string} -- input video file path
Returns:
dictionary -- JSON text of input video information | Gets input video information
This method reads input video information
using ffprobe in dictionary.
Arguments:
input_video {string} -- input video file path
Returns:
dictionary -- JSON text of input video information | [
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""" Gets input video information
This method reads input video information
using ffprobe in dictionary.
Arguments:
input_video {string} -- input video file path
Returns:
dictionary -- JSON text of input video information
"""
assert get_operating_system() != "win32" or os.path.exists(ffprobe_dir), "%s does not exist!" % ffprobe_dir
# this execution command needs to be hard-coded
# since video2x only strictly recignizes this one format
execute = [
ffprobe_dir,
'-v',
'panic',
'-print_format',
'json',
'-show_format',
'-show_streams',
'-i',
input_video
]
log = logging.getLogger()
log.info("Loading video meta-data with ffprobe.. this might take a while.")
log.info("Command: %s" % str(execute))
json_str = subprocess.run(execute, check=True, stdout=subprocess.PIPE).stdout
return json.loads(json_str.decode('utf-8')) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/robotsim.py | python | PointCloud.join | (self, pc) | return _robotsim.PointCloud_join(self, pc) | join(PointCloud self, PointCloud pc)
Adds the given point cloud to this one. They must share the same properties or
else an exception is raised. | join(PointCloud self, PointCloud pc) | [
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] | def join(self, pc):
"""
join(PointCloud self, PointCloud pc)
Adds the given point cloud to this one. They must share the same properties or
else an exception is raised.
"""
return _robotsim.PointCloud_join(self, pc) | [
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vslavik/poedit | f7a9daa0a10037e090aa0a86f5ce0f24ececdf6a | deps/boost/tools/build/src/build/targets.py | python | ProjectTarget.targets_to_build | (self) | return result | Computes and returns a list of AbstractTarget instances which
must be built when this project is built. | Computes and returns a list of AbstractTarget instances which
must be built when this project is built. | [
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] | def targets_to_build (self):
""" Computes and returns a list of AbstractTarget instances which
must be built when this project is built.
"""
result = []
if not self.built_main_targets_:
self.build_main_targets ()
# Collect all main targets here, except for "explicit" ones.
for n, t in self.main_target_.iteritems ():
if not t.name () in self.explicit_targets_:
result.append (t)
# Collect all projects referenced via "projects-to-build" attribute.
self_location = self.get ('location')
for pn in self.get ('projects-to-build'):
result.append (self.find(pn + "/"))
return result | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py3/google/protobuf/descriptor.py | python | Descriptor.__init__ | (self, name, full_name, filename, containing_type, fields,
nested_types, enum_types, extensions, options=None,
serialized_options=None,
is_extendable=True, extension_ranges=None, oneofs=None,
file=None, serialized_start=None, serialized_end=None, # pylint: disable=redefined-builtin
syntax=None, create_key=None) | Arguments to __init__() are as described in the description
of Descriptor fields above.
Note that filename is an obsolete argument, that is not used anymore.
Please use file.name to access this as an attribute. | Arguments to __init__() are as described in the description
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nested_types, enum_types, extensions, options=None,
serialized_options=None,
is_extendable=True, extension_ranges=None, oneofs=None,
file=None, serialized_start=None, serialized_end=None, # pylint: disable=redefined-builtin
syntax=None, create_key=None):
"""Arguments to __init__() are as described in the description
of Descriptor fields above.
Note that filename is an obsolete argument, that is not used anymore.
Please use file.name to access this as an attribute.
"""
if create_key is not _internal_create_key:
_Deprecated('Descriptor')
super(Descriptor, self).__init__(
options, 'MessageOptions', name, full_name, file,
containing_type, serialized_start=serialized_start,
serialized_end=serialized_end, serialized_options=serialized_options)
# We have fields in addition to fields_by_name and fields_by_number,
# so that:
# 1. Clients can index fields by "order in which they're listed."
# 2. Clients can easily iterate over all fields with the terse
# syntax: for f in descriptor.fields: ...
self.fields = fields
for field in self.fields:
field.containing_type = self
self.fields_by_number = dict((f.number, f) for f in fields)
self.fields_by_name = dict((f.name, f) for f in fields)
self._fields_by_camelcase_name = None
self.nested_types = nested_types
for nested_type in nested_types:
nested_type.containing_type = self
self.nested_types_by_name = dict((t.name, t) for t in nested_types)
self.enum_types = enum_types
for enum_type in self.enum_types:
enum_type.containing_type = self
self.enum_types_by_name = dict((t.name, t) for t in enum_types)
self.enum_values_by_name = dict(
(v.name, v) for t in enum_types for v in t.values)
self.extensions = extensions
for extension in self.extensions:
extension.extension_scope = self
self.extensions_by_name = dict((f.name, f) for f in extensions)
self.is_extendable = is_extendable
self.extension_ranges = extension_ranges
self.oneofs = oneofs if oneofs is not None else []
self.oneofs_by_name = dict((o.name, o) for o in self.oneofs)
for oneof in self.oneofs:
oneof.containing_type = self
self.syntax = syntax or "proto2" | [
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/Task.py | python | compile_fun_shell | (line) | return (funex(c), dvars) | Creates a compiled function to execute a process through a sub-shell | Creates a compiled function to execute a process through a sub-shell | [
"Creates",
"a",
"compiled",
"function",
"to",
"execute",
"a",
"process",
"through",
"a",
"sub",
"-",
"shell"
] | def compile_fun_shell(line):
"""
Creates a compiled function to execute a process through a sub-shell
"""
extr = []
def repl(match):
g = match.group
if g('dollar'):
return "$"
elif g('backslash'):
return '\\\\'
elif g('subst'):
extr.append((g('var'), g('code')))
return "%s"
return None
line = reg_act.sub(repl, line) or line
dvars = []
def add_dvar(x):
if x not in dvars:
dvars.append(x)
def replc(m):
# performs substitutions and populates dvars
if m.group('and'):
return ' and '
elif m.group('or'):
return ' or '
else:
x = m.group('var')
add_dvar(x)
return 'env[%r]' % x
parm = []
app = parm.append
for (var, meth) in extr:
if var == 'SRC':
if meth:
app('tsk.inputs%s' % meth)
else:
app('" ".join([a.path_from(cwdx) for a in tsk.inputs])')
elif var == 'TGT':
if meth:
app('tsk.outputs%s' % meth)
else:
app('" ".join([a.path_from(cwdx) for a in tsk.outputs])')
elif meth:
if meth.startswith(':'):
add_dvar(var)
m = meth[1:]
if m == 'SRC':
m = '[a.path_from(cwdx) for a in tsk.inputs]'
elif m == 'TGT':
m = '[a.path_from(cwdx) for a in tsk.outputs]'
elif re_novar.match(m):
m = '[tsk.inputs%s]' % m[3:]
elif re_novar.match(m):
m = '[tsk.outputs%s]' % m[3:]
else:
add_dvar(m)
if m[:3] not in ('tsk', 'gen', 'bld'):
m = '%r' % m
app('" ".join(tsk.colon(%r, %s))' % (var, m))
elif meth.startswith('?'):
# In A?B|C output env.A if one of env.B or env.C is non-empty
expr = re_cond.sub(replc, meth[1:])
app('p(%r) if (%s) else ""' % (var, expr))
else:
call = '%s%s' % (var, meth)
add_dvar(call)
app(call)
else:
add_dvar(var)
app("p('%s')" % var)
if parm:
parm = "%% (%s) " % (',\n\t\t'.join(parm))
else:
parm = ''
c = COMPILE_TEMPLATE_SHELL % (line, parm)
Logs.debug('action: %s', c.strip().splitlines())
return (funex(c), dvars) | [
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... | https://github.com/kushview/Element/blob/1cc16380caa2ab79461246ba758b9de1f46db2a5/waflib/Task.py#L1056-L1136 | |
eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/coremodules/demand/virtualpop.py | python | Virtualpopulation.select_plans_random | (self, fraction=0.1, **kwargs) | return True | A fraction of the population changes a plan.
The new plans are chosen randomly. | A fraction of the population changes a plan.
The new plans are chosen randomly. | [
"A",
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"population",
"changes",
"a",
"plan",
".",
"The",
"new",
"plans",
"are",
"chosen",
"randomly",
"."
] | def select_plans_random(self, fraction=0.1, **kwargs):
"""
A fraction of the population changes a plan.
The new plans are chosen randomly.
"""
ids_pers_all = self.get_ids()
print 'select_plans_random', len(ids_pers_all), fraction
times_est = self.get_plans().times_est
# self.ids_plan.reset()
# ids_mode[random_choice(n,shares/np.sum(shares))]
ids_pers = ids_pers_all[np.random.random(len(ids_pers_all)) > (1.0-fraction)]
print ' ids_pers', ids_pers
for id_pers, ids_plan in zip(ids_pers, self.lists_ids_plan[ids_pers]):
if len(ids_plan) > 0:
# print ' id_pers,ids_plan',id_pers,ids_plan
self.ids_plan[id_pers] = ids_plan[np.random.randint(len(ids_plan))]
return True | [
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llvm-mirror/libcxx | 78d6a7767ed57b50122a161b91f59f19c9bd0d19 | utils/google-benchmark/tools/gbench/util.py | python | run_benchmark | (exe_name, benchmark_flags) | return json_res | Run a benchmark specified by 'exe_name' with the specified
'benchmark_flags'. The benchmark is run directly as a subprocess to preserve
real time console output.
RETURNS: A JSON object representing the benchmark output | Run a benchmark specified by 'exe_name' with the specified
'benchmark_flags'. The benchmark is run directly as a subprocess to preserve
real time console output.
RETURNS: A JSON object representing the benchmark output | [
"Run",
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"to",
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"time",
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"output",
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"RETURNS",
":",
"A",
... | def run_benchmark(exe_name, benchmark_flags):
"""
Run a benchmark specified by 'exe_name' with the specified
'benchmark_flags'. The benchmark is run directly as a subprocess to preserve
real time console output.
RETURNS: A JSON object representing the benchmark output
"""
output_name = find_benchmark_flag('--benchmark_out=',
benchmark_flags)
is_temp_output = False
if output_name is None:
is_temp_output = True
thandle, output_name = tempfile.mkstemp()
os.close(thandle)
benchmark_flags = list(benchmark_flags) + \
['--benchmark_out=%s' % output_name]
cmd = [exe_name] + benchmark_flags
print("RUNNING: %s" % ' '.join(cmd))
exitCode = subprocess.call(cmd)
if exitCode != 0:
print('TEST FAILED...')
sys.exit(exitCode)
json_res = load_benchmark_results(output_name)
if is_temp_output:
os.unlink(output_name)
return json_res | [
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eldar/deepcut-cnn | 928bf2f224fce132f6e4404b4c95fb017297a5e0 | scripts/cpp_lint.py | python | _IncludeState.IsInAlphabeticalOrder | (self, clean_lines, linenum, header_path) | return True | Check if a header is in alphabetical order with the previous header.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
header_path: Canonicalized header to be checked.
Returns:
Returns true if the header is in alphabetical order. | Check if a header is in alphabetical order with the previous header. | [
"Check",
"if",
"a",
"header",
"is",
"in",
"alphabetical",
"order",
"with",
"the",
"previous",
"header",
"."
] | def IsInAlphabeticalOrder(self, clean_lines, linenum, header_path):
"""Check if a header is in alphabetical order with the previous header.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
header_path: Canonicalized header to be checked.
Returns:
Returns true if the header is in alphabetical order.
"""
# If previous section is different from current section, _last_header will
# be reset to empty string, so it's always less than current header.
#
# If previous line was a blank line, assume that the headers are
# intentionally sorted the way they are.
if (self._last_header > header_path and
not Match(r'^\s*$', clean_lines.elided[linenum - 1])):
return False
return True | [
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"# If previous section is different from current section, _last_header will",
"# be reset to empty string, so it's always less than current header.",
"#",
"# If previous line ... | https://github.com/eldar/deepcut-cnn/blob/928bf2f224fce132f6e4404b4c95fb017297a5e0/scripts/cpp_lint.py#L612-L631 | |
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/platform/android_platform_backend.py | python | AndroidPlatformBackend.PurgeUnpinnedMemory | (self) | Purges the unpinned ashmem memory for the whole system.
This can be used to make memory measurements more stable. Requires root. | Purges the unpinned ashmem memory for the whole system. | [
"Purges",
"the",
"unpinned",
"ashmem",
"memory",
"for",
"the",
"whole",
"system",
"."
] | def PurgeUnpinnedMemory(self):
"""Purges the unpinned ashmem memory for the whole system.
This can be used to make memory measurements more stable. Requires root.
"""
if not self._can_elevate_privilege:
logging.warning('Cannot run purge_ashmem. Requires a rooted device.')
return
if not android_prebuilt_profiler_helper.InstallOnDevice(
self._device, 'purge_ashmem'):
raise Exception('Error installing purge_ashmem.')
output = self._device.RunShellCommand([
android_prebuilt_profiler_helper.GetDevicePath('purge_ashmem')])
for l in output:
logging.info(l) | [
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"In... | https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/telemetry/telemetry/internal/platform/android_platform_backend.py#L240-L255 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/urllib3/connectionpool.py | python | HTTPSConnectionPool._new_conn | (self) | return self._prepare_conn(conn) | Return a fresh :class:`httplib.HTTPSConnection`. | Return a fresh :class:`httplib.HTTPSConnection`. | [
"Return",
"a",
"fresh",
":",
"class",
":",
"httplib",
".",
"HTTPSConnection",
"."
] | def _new_conn(self):
"""
Return a fresh :class:`httplib.HTTPSConnection`.
"""
self.num_connections += 1
log.debug(
"Starting new HTTPS connection (%d): %s:%s",
self.num_connections,
self.host,
self.port or "443",
)
if not self.ConnectionCls or self.ConnectionCls is DummyConnection:
raise SSLError(
"Can't connect to HTTPS URL because the SSL module is not available."
)
actual_host = self.host
actual_port = self.port
if self.proxy is not None:
actual_host = self.proxy.host
actual_port = self.proxy.port
conn = self.ConnectionCls(
host=actual_host,
port=actual_port,
timeout=self.timeout.connect_timeout,
strict=self.strict,
cert_file=self.cert_file,
key_file=self.key_file,
key_password=self.key_password,
**self.conn_kw
)
return self._prepare_conn(conn) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Arch/Dice3DS/util.py | python | calculate_normals_by_angle_subtended | (pointarray,facearray,smarray) | return points, numpy.asarray(fnorms,numpy.float32) | Calculate normals by smoothing, weighting by angle subtended.
points,norms = calculate_normals_by_angle_subtended(
pointarray,facearray,smarray)
Takes an array of points, faces, and a smoothing group in exactly
the same form in which they appear in the 3DS DOM.
Returns a numpy.array of points, one per row, and a numpy.array of
the corresponding normals. The points are returned as a list of
consecutive triangles; the first three rows make up the first
triangle, the second three rows make up the second triangle, and
so on.
To calculate the normal of a given vertex on a given face, this
function averages the normal vector for all faces which have share
that vertex, and a smoothing group.
The normals being averaged are weighted by the angle subtended. | Calculate normals by smoothing, weighting by angle subtended. | [
"Calculate",
"normals",
"by",
"smoothing",
"weighting",
"by",
"angle",
"subtended",
"."
] | def calculate_normals_by_angle_subtended(pointarray,facearray,smarray):
"""Calculate normals by smoothing, weighting by angle subtended.
points,norms = calculate_normals_by_angle_subtended(
pointarray,facearray,smarray)
Takes an array of points, faces, and a smoothing group in exactly
the same form in which they appear in the 3DS DOM.
Returns a numpy.array of points, one per row, and a numpy.array of
the corresponding normals. The points are returned as a list of
consecutive triangles; the first three rows make up the first
triangle, the second three rows make up the second triangle, and
so on.
To calculate the normal of a given vertex on a given face, this
function averages the normal vector for all faces which have share
that vertex, and a smoothing group.
The normals being averaged are weighted by the angle subtended.
"""
# prepare to calculate normals. define some arrays
m = len(facearray)
rnorms = numpy.zeros((m*3,3),_calc_precision_type)
fnorms = numpy.zeros((m*3,3),_calc_precision_type)
points = pointarray[facearray.ravel()]
exarray = numpy.zeros(3*m,numpy.uint32)
if smarray is not None:
exarray[0::3] = exarray[1::3] = exarray[2::3] = smarray
# weed out degenerate triangles first
# unlike cross-product, angle subtended blows up on degeneracy
A = numpy.asarray(pointarray[facearray[:,0]],_calc_precision_type)
B = numpy.asarray(pointarray[facearray[:,1]],_calc_precision_type)
C = numpy.asarray(pointarray[facearray[:,2]],_calc_precision_type)
a = C - B
b = A - C
c = B - A
p = numpy.zeros((len(facearray),3),_calc_precision_type)
p[:,0] = c[:,2]*b[:,1]-c[:,1]*b[:,2]
p[:,1] = c[:,0]*b[:,2]-c[:,2]*b[:,0]
p[:,2] = c[:,1]*b[:,0]-c[:,0]*b[:,1]
aa = numpy.sum(a*a,axis=1)
bb = numpy.sum(b*b,axis=1)
cc = numpy.sum(c*c,axis=1)
pp = numpy.sum(p*p,axis=1)
ndg = numpy.nonzero(numpy.logical_and.reduce((aa,bb,cc,pp)))[0]
# calculate scaled normals (according to angle subtended)
p = p[ndg]
la = numpy.sqrt(aa[ndg])
lb = numpy.sqrt(bb[ndg])
lc = numpy.sqrt(cc[ndg])
lp = numpy.sqrt(pp[ndg])
sinA = numpy.clip(lp/lb/lc,-1.0,1.0)
sinB = numpy.clip(lp/la/lc,-1.0,1.0)
sinC = numpy.clip(lp/la/lb,-1.0,1.0)
sinA2 = sinA*sinA
sinB2 = sinB*sinB
sinC2 = sinC*sinC
angA = numpy.arcsin(sinA)
angB = numpy.arcsin(sinB)
angC = numpy.arcsin(sinC)
angA = numpy.where(sinA2 > sinB2 + sinC2, numpy.pi - angA, angA)
angB = numpy.where(sinB2 > sinA2 + sinC2, numpy.pi - angB, angB)
angC = numpy.where(sinC2 > sinA2 + sinB2, numpy.pi - angC, angC)
rnorms[0::3][ndg] = p*(angA/lp)[:,numpy.newaxis]
rnorms[1::3][ndg] = p*(angB/lp)[:,numpy.newaxis]
rnorms[2::3][ndg] = p*(angC/lp)[:,numpy.newaxis]
# normalize vectors according to passed in smoothing group
lex = numpy.lexsort(numpy.transpose(points))
brs = numpy.nonzero(
numpy.any(points[lex[1:],:]-points[lex[:-1],:],axis=1))[0]+1
lslice = numpy.empty((len(brs)+1,),numpy.int)
lslice[0] = 0
lslice[1:] = brs
rslice = numpy.empty((len(brs)+1,),numpy.int)
rslice[:-1] = brs
rslice[-1] = 3*m
for i in xrange(len(brs)+1):
rgroup = lex[lslice[i]:rslice[i]]
xgroup = exarray[rgroup]
normpat = numpy.logical_or(
numpy.bitwise_and.outer(xgroup,xgroup),
numpy.eye(len(xgroup)))
fnorms[rgroup,:] = numpy.dot(normpat,rnorms[rgroup,:])
q = numpy.sum(fnorms*fnorms,axis=1)
qnz = numpy.nonzero(q)[0]
lq = 1.0 / numpy.sqrt(q[qnz])
fnt = numpy.transpose(fnorms)
fnt[:,qnz] *= lq
# we're done
return points, numpy.asarray(fnorms,numpy.float32) | [
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"3... | https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/Arch/Dice3DS/util.py#L175-L276 | |
FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Arch/ArchStructure.py | python | makeStructure | (baseobj=None,length=None,width=None,height=None,name="Structure") | return obj | makeStructure([obj],[length],[width],[height],[swap]): creates a
structure element based on the given profile object and the given
extrusion height. If no base object is given, you can also specify
length and width for a cubic object. | makeStructure([obj],[length],[width],[height],[swap]): creates a
structure element based on the given profile object and the given
extrusion height. If no base object is given, you can also specify
length and width for a cubic object. | [
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"extrusion"... | def makeStructure(baseobj=None,length=None,width=None,height=None,name="Structure"):
'''makeStructure([obj],[length],[width],[height],[swap]): creates a
structure element based on the given profile object and the given
extrusion height. If no base object is given, you can also specify
length and width for a cubic object.'''
if not FreeCAD.ActiveDocument:
FreeCAD.Console.PrintError("No active document. Aborting\n")
return
p = FreeCAD.ParamGet("User parameter:BaseApp/Preferences/Mod/Arch")
obj = FreeCAD.ActiveDocument.addObject("Part::FeaturePython","Structure")
obj.Label = translate("Arch","Structure")
_Structure(obj)
if FreeCAD.GuiUp:
_ViewProviderStructure(obj.ViewObject)
if baseobj:
obj.Base = baseobj
if FreeCAD.GuiUp:
obj.Base.ViewObject.hide()
if width:
obj.Width = width
else:
obj.Width = p.GetFloat("StructureWidth",100)
if height:
obj.Height = height
else:
if not length:
obj.Height = p.GetFloat("StructureHeight",1000)
if length:
obj.Length = length
else:
if not baseobj:
# don't set the length if we have a base object, otherwise the length X height calc
# gets wrong
obj.Length = p.GetFloat("StructureLength",100)
if baseobj:
w = 0
h = 0
if hasattr(baseobj,"Width") and hasattr(baseobj,"Height"):
w = baseobj.Width.Value
h = baseobj.Height.Value
elif hasattr(baseobj,"Length") and hasattr(baseobj,"Width"):
w = baseobj.Length.Value
h = baseobj.Width.Value
elif hasattr(baseobj,"Length") and hasattr(baseobj,"Height"):
w = baseobj.Length.Value
h = baseobj.Height.Value
if w and h:
if length and not height:
obj.Width = w
obj.Height = h
elif height and not length:
obj.Width = w
obj.Length = h
if not height and not length:
obj.IfcType = "Undefined"
elif obj.Length > obj.Height:
obj.IfcType = "Beam"
obj.Label = translate("Arch","Beam")
elif obj.Height > obj.Length:
obj.IfcType = "Column"
obj.Label = translate("Arch","Column")
return obj | [
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RamadhanAmizudin/malware | 2c6c53c8b0d556f5d8078d6ca0fc4448f4697cf1 | Fuzzbunch/fuzzbunch/pyreadline/modes/basemode.py | python | BaseMode.cut_selection_to_clipboard | (self, e) | Copy the text in the region to the windows clipboard. | Copy the text in the region to the windows clipboard. | [
"Copy",
"the",
"text",
"in",
"the",
"region",
"to",
"the",
"windows",
"clipboard",
"."
] | def cut_selection_to_clipboard(self, e): # ()
'''Copy the text in the region to the windows clipboard.'''
self.l_buffer.cut_selection_to_clipboard() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/urllib3/packages/six.py | python | add_move | (move) | Add an item to six.moves. | Add an item to six.moves. | [
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"""Add an item to six.moves."""
setattr(_MovedItems, move.name, move) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/plat-mac/applesingle.py | python | decode | (infile, outpath, resonly=False, verbose=False) | decode(infile, outpath [, resonly=False, verbose=False])
Creates a decoded file from an AppleSingle encoded file.
If resonly is True, then it will create a regular file at
outpath containing only the resource fork from infile.
Otherwise it will create an AppleDouble file at outpath
with the data and resource forks from infile. On platforms
without the MacOS module, it will create inpath and inpath+'.rsrc'
with the data and resource forks respectively. | decode(infile, outpath [, resonly=False, verbose=False]) | [
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] | def decode(infile, outpath, resonly=False, verbose=False):
"""decode(infile, outpath [, resonly=False, verbose=False])
Creates a decoded file from an AppleSingle encoded file.
If resonly is True, then it will create a regular file at
outpath containing only the resource fork from infile.
Otherwise it will create an AppleDouble file at outpath
with the data and resource forks from infile. On platforms
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"""
if not hasattr(infile, 'read'):
if isinstance(infile, Carbon.File.Alias):
infile = infile.ResolveAlias()[0]
if hasattr(Carbon.File, "FSSpec"):
if isinstance(infile, (Carbon.File.FSSpec, Carbon.File.FSRef)):
infile = infile.as_pathname()
else:
if isinstance(infile, Carbon.File.FSRef):
infile = infile.as_pathname()
infile = open(infile, 'rb')
asfile = AppleSingle(infile, verbose=verbose)
asfile.tofile(outpath, resonly=resonly) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/matplotlib-inline/matplotlib_inline/config.py | python | pil_available | () | return out | Test if PIL/Pillow is available | Test if PIL/Pillow is available | [
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"""Test if PIL/Pillow is available"""
out = False
try:
from PIL import Image # noqa
out = True
except ImportError:
pass
return out | [
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apache/kudu | 90895ce76590f10730ad7aac3613b69d89ff5422 | python/kudu/util.py | python | from_unixtime_micros | (unixtime_micros) | Convert the input unixtime_micros value to a datetime in UTC.
Parameters
----------
unixtime_micros : int
Number of microseconds since the unix epoch.
Returns
-------
timestamp : datetime.datetime in UTC | Convert the input unixtime_micros value to a datetime in UTC. | [
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] | def from_unixtime_micros(unixtime_micros):
"""
Convert the input unixtime_micros value to a datetime in UTC.
Parameters
----------
unixtime_micros : int
Number of microseconds since the unix epoch.
Returns
-------
timestamp : datetime.datetime in UTC
"""
if isinstance(unixtime_micros, int):
return _epoch() + datetime.timedelta(microseconds=unixtime_micros)
else:
raise ValueError("Invalid unixtime_micros value." +
"You must provide an integer value.") | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/plugin.py | python | PluginConfigObject.GetBitmap | (self) | return wx.NullBitmap | Get the 32x32 bitmap to show in the config dialog
@return: wx.Bitmap
@note: Optional if not implemented default icon will be used | Get the 32x32 bitmap to show in the config dialog
@return: wx.Bitmap
@note: Optional if not implemented default icon will be used | [
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"""Get the 32x32 bitmap to show in the config dialog
@return: wx.Bitmap
@note: Optional if not implemented default icon will be used
"""
return wx.NullBitmap | [
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wujixiu/helmet-detection | 8eff5c59ddfba5a29e0b76aeb48babcb49246178 | hardhat-wearing-detection/SSD-RPA/scripts/cpp_lint.py | python | FindPreviousMatchingAngleBracket | (clean_lines, linenum, init_prefix) | return False | Find the corresponding < that started a template.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Current line number.
init_prefix: Part of the current line before the initial >.
Returns:
True if a matching bracket exists. | Find the corresponding < that started a template. | [
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] | def FindPreviousMatchingAngleBracket(clean_lines, linenum, init_prefix):
"""Find the corresponding < that started a template.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Current line number.
init_prefix: Part of the current line before the initial >.
Returns:
True if a matching bracket exists.
"""
line = init_prefix
nesting_stack = ['>']
while True:
# Find the previous operator
match = Search(r'^(.*)([<>(),;\[\]])[^<>(),;\[\]]*$', line)
if match:
# Found an operator, update nesting stack
operator = match.group(2)
line = match.group(1)
if nesting_stack[-1] == '>':
# Expecting opening angle bracket
if operator in ('>', ')', ']'):
nesting_stack.append(operator)
elif operator == '<':
nesting_stack.pop()
if not nesting_stack:
# Found matching angle bracket
return True
elif operator == ',':
# Got a comma before a bracket, this is most likely a
# template argument. The opening angle bracket is probably
# there if we look for it, so just return early here.
return True
else:
# Got some other operator.
return False
else:
# Expecting opening parenthesis or opening bracket
if operator in ('>', ')', ']'):
nesting_stack.append(operator)
elif operator in ('(', '['):
nesting_stack.pop()
else:
# Scan the previous line
linenum -= 1
if linenum < 0:
break
line = clean_lines.elided[linenum]
# Exhausted all earlier lines and still no matching angle bracket.
return False | [
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xlgames-inc/XLE | cdd8682367d9e9fdbdda9f79d72bb5b1499cec46 | Foreign/FreeType/src/tools/docmaker/content.py | python | ContentProcessor.add_markup | ( self ) | Add a new markup section. | Add a new markup section. | [
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"""Add a new markup section."""
if self.markup and self.markup_lines:
# get rid of last line of markup if it's empty
marks = self.markup_lines
if len( marks ) > 0 and not string.strip( marks[-1] ):
self.markup_lines = marks[:-1]
m = DocMarkup( self.markup, self.markup_lines )
self.markups.append( m )
self.markup = None
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBThreadPlan.SetPlanComplete | (self, success) | return _lldb.SBThreadPlan_SetPlanComplete(self, success) | SetPlanComplete(SBThreadPlan self, bool success) | SetPlanComplete(SBThreadPlan self, bool success) | [
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"""SetPlanComplete(SBThreadPlan self, bool success)"""
return _lldb.SBThreadPlan_SetPlanComplete(self, success) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/mantid/simpleapi.py | python | _get_function_spec | (func) | return calltip | Get the python function signature for the given function object
:param func: A Python function object | Get the python function signature for the given function object | [
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] | def _get_function_spec(func):
"""Get the python function signature for the given function object
:param func: A Python function object
"""
import inspect
try:
argspec = inspect.getfullargspec(func)
except TypeError:
return ''
# Algorithm functions have varargs set not args
args = argspec[0]
if args:
# For methods strip the self argument
if hasattr(func, 'im_func'):
args = args[1:]
defs = argspec[3]
elif argspec[1] is not None:
# Get from varargs/keywords
arg_str = argspec[1].strip().lstrip('\b')
defs = []
# Keyword args
kwargs = argspec[2]
if kwargs is not None:
kwargs = kwargs.strip().lstrip('\b\b')
if kwargs == 'kwargs':
kwargs = '**' + kwargs + '=None'
arg_str += ',%s' % kwargs
# Any default argument appears in the string
# on the rhs of an equal
for arg in arg_str.split(','):
arg = arg.strip()
if '=' in arg:
arg_token = arg.split('=')
args.append(arg_token[0])
defs.append(arg_token[1])
else:
args.append(arg)
if len(defs) == 0:
defs = None
else:
return ''
if defs is None:
calltip = ','.join(args)
calltip = '(' + calltip + ')'
else:
# The defaults list contains the default values for the last n arguments
diff = len(args) - len(defs)
calltip = ''
for index in range(len(args) - 1, -1, -1):
def_index = index - diff
if def_index >= 0:
calltip = '[' + args[index] + '],' + calltip
else:
calltip = args[index] + "," + calltip
calltip = '(' + calltip.rstrip(',') + ')'
return calltip | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/lite/python/op_hint.py | python | _find_all_hints_in_nodes | (nodes) | return func_calls | Look at the all the input nodes and return a list of LiteFuncCall objs.
Args:
nodes: A TensorFlow graph_def to look for LiteFuncCalls.
Returns:
a list of `LifeFuncCall` objects in the form | Look at the all the input nodes and return a list of LiteFuncCall objs. | [
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] | def _find_all_hints_in_nodes(nodes):
"""Look at the all the input nodes and return a list of LiteFuncCall objs.
Args:
nodes: A TensorFlow graph_def to look for LiteFuncCalls.
Returns:
a list of `LifeFuncCall` objects in the form
"""
func_calls = _collections.defaultdict(_LiteFuncCall)
for node in nodes:
attr = node.attr
# This is an op hint if it has a FUNCTION_UUID_ATTR, otherwise skip
if (OpHint.FUNCTION_UUID_ATTR not in attr
or not attr[OpHint.FUNCTION_UUID_ATTR].s):
continue
uuid = attr[OpHint.FUNCTION_UUID_ATTR].s
# Start building function
call_def = func_calls[uuid]
call_def.uuid = uuid
call_def.function_name = attr[OpHint.FUNCTION_NAME_ATTR].s
call_def.level = attr[OpHint.FUNCTION_LEVEL_ATTR].i
# Get sorting and aggregation information
sort = (attr[OpHint.FUNCTION_SORT_INDEX_ATTR].i
if OpHint.FUNCTION_SORT_INDEX_ATTR in attr else None)
if sort == -1: sort = None
aggregation = None
if OpHint.FUNCTION_AGGREGATE_ATTR in attr:
aggregation = _compat.as_text(attr[OpHint.FUNCTION_AGGREGATE_ATTR].s)
if OpHint.CHILDREN_INPUTS_MAPPINGS in attr:
call_def.children_inputs_mappings = _json.loads(
_compat.as_text(attr[OpHint.CHILDREN_INPUTS_MAPPINGS].s))
# Add the input or output
def put_operand(stuff, index, sort, operand, aggregation):
"""Add a given index into the function structure."""
if sort is None:
stuff[index] = _LiteSingleOperand(operand)
else:
if index not in stuff:
stuff[index] = _LiteAggregateOperand(aggregation)
stuff[index].add(sort, operand)
if OpHint.FUNCTION_INPUT_INDEX_ATTR in attr:
put_operand(call_def.inputs, attr[OpHint.FUNCTION_INPUT_INDEX_ATTR].i,
sort, node, aggregation)
if OpHint.FUNCTION_OUTPUT_INDEX_ATTR in attr:
put_operand(call_def.outputs, attr[OpHint.FUNCTION_OUTPUT_INDEX_ATTR].i,
sort, node, aggregation)
# Remember attributes
for a in attr:
if a.startswith("_tflite_attr_"):
call_def.params[a.replace("_tflite_attr_,", "")] = attr[a].tensor
return func_calls | [
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mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | com/win32com/server/register.py | python | _set_string | (path, value, base=win32con.HKEY_CLASSES_ROOT) | Set a string value in the registry. | Set a string value in the registry. | [
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] | def _set_string(path, value, base=win32con.HKEY_CLASSES_ROOT):
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/grappler/item.py | python | Item.__init__ | (self,
metagraph,
ignore_colocation=True,
ignore_user_placement=False) | Creates an Item.
Args:
metagraph: a TensorFlow metagraph.
ignore_colocation: if set, the tool will ignore all the colocation
constraints generated by TensorFlow.
ignore_user_placement: if set, all the placement annotations annotated in
the metagraph will be ignored.
Raises:
ValueError: the metagraph is incomplete or invalid. | Creates an Item. | [
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] | def __init__(self,
metagraph,
ignore_colocation=True,
ignore_user_placement=False):
"""Creates an Item.
Args:
metagraph: a TensorFlow metagraph.
ignore_colocation: if set, the tool will ignore all the colocation
constraints generated by TensorFlow.
ignore_user_placement: if set, all the placement annotations annotated in
the metagraph will be ignored.
Raises:
ValueError: the metagraph is incomplete or invalid.
"""
self._metagraph = metagraph
self._item_graph = meta_graph_pb2.MetaGraphDef()
self._item_graph.CopyFrom(metagraph)
self._ignore_colocation = ignore_colocation
self._ignore_user_placement = ignore_user_placement
self._tf_item = None
self._BuildTFItem() | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/distributions/beta.py | python | Beta.concentration1 | (self) | return self._concentration1 | Concentration parameter associated with a `1` outcome. | Concentration parameter associated with a `1` outcome. | [
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"""Concentration parameter associated with a `1` outcome."""
return self._concentration1 | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/linalg/_interpolative_backend.py | python | idz_frm | (n, w, x) | return _id.idz_frm(n, w, x) | Transform complex vector via a composition of Rokhlin's random transform,
random subselection, and an FFT.
In contrast to :func:`idz_sfrm`, this routine works best when the length of
the transformed vector is the power-of-two integer output by
:func:`idz_frmi`, or when the length is not specified but instead
determined a posteriori from the output. The returned transformed vector is
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:param n:
Greatest power-of-two integer satisfying `n <= x.size` as obtained from
:func:`idz_frmi`; `n` is also the length of the output vector.
:type n: int
:param w:
Initialization array constructed by :func:`idz_frmi`.
:type w: :class:`numpy.ndarray`
:param x:
Vector to be transformed.
:type x: :class:`numpy.ndarray`
:return:
Transformed vector.
:rtype: :class:`numpy.ndarray` | Transform complex vector via a composition of Rokhlin's random transform,
random subselection, and an FFT. | [
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"""
Transform complex vector via a composition of Rokhlin's random transform,
random subselection, and an FFT.
In contrast to :func:`idz_sfrm`, this routine works best when the length of
the transformed vector is the power-of-two integer output by
:func:`idz_frmi`, or when the length is not specified but instead
determined a posteriori from the output. The returned transformed vector is
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return _id.idz_frm(n, w, x) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/interpolate/interpolate.py | python | spleval | (xck, xnew, deriv=0) | return res | Evaluate a fixed spline represented by the given tuple at the new x-values
The `xj` values are the interior knot points. The approximation
region is `xj[0]` to `xj[-1]`. If N+1 is the length of `xj`, then `cvals`
should have length N+k where `k` is the order of the spline.
Parameters
----------
(xj, cvals, k) : tuple
Parameters that define the fixed spline
xj : array_like
Interior knot points
cvals : array_like
Curvature
k : int
Order of the spline
xnew : array_like
Locations to calculate spline
deriv : int
Deriv
Returns
-------
spleval : ndarray
If `cvals` represents more than one curve (`cvals.ndim` > 1) and/or
`xnew` is N-d, then the result is `xnew.shape` + `cvals.shape[1:]`
providing the interpolation of multiple curves.
Notes
-----
Internally, an additional `k`-1 knot points are added on either side of
the spline. | Evaluate a fixed spline represented by the given tuple at the new x-values | [
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"""
Evaluate a fixed spline represented by the given tuple at the new x-values
The `xj` values are the interior knot points. The approximation
region is `xj[0]` to `xj[-1]`. If N+1 is the length of `xj`, then `cvals`
should have length N+k where `k` is the order of the spline.
Parameters
----------
(xj, cvals, k) : tuple
Parameters that define the fixed spline
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Interior knot points
cvals : array_like
Curvature
k : int
Order of the spline
xnew : array_like
Locations to calculate spline
deriv : int
Deriv
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spleval : ndarray
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"""
(xj,cvals,k) = xck
oldshape = np.shape(xnew)
xx = np.ravel(xnew)
sh = cvals.shape[1:]
res = np.empty(xx.shape + sh, dtype=cvals.dtype)
for index in np.ndindex(*sh):
sl = (slice(None),)+index
if issubclass(cvals.dtype.type, np.complexfloating):
res[sl].real = _fitpack._bspleval(xx,xj,cvals.real[sl],k,deriv)
res[sl].imag = _fitpack._bspleval(xx,xj,cvals.imag[sl],k,deriv)
else:
res[sl] = _fitpack._bspleval(xx,xj,cvals[sl],k,deriv)
res.shape = oldshape + sh
return res | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/server/wsgi/serve/publish/publish_manager_helper.py | python | PublishManagerHelper.GetTargetDetails | (self, target_path) | return target_details | gets target details by target path.
Args:
target_path: target path.
Raises:
PublishServeException
Returns:
target details of a target. | gets target details by target path. | [
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"""gets target details by target path.
Args:
target_path: target path.
Raises:
PublishServeException
Returns:
target details of a target.
"""
target_details = {}
target_db_details = {}
publish_context = {}
target_db_details = self._QueryTargetDbDetailsByPath(target_path)
if not target_db_details:
error_msg = (
"GetTargetDetails: No target details found for target path %s. "
"Make sure target path exists." % target_path)
raise exceptions.PublishServeException(error_msg)
target_details.update({
"targetpath": target_path,
"servewms": target_db_details["servewms"],
"fusion_host": target_db_details["fusion_host"],
"dbname": target_db_details["dbname"],
"vhname": target_db_details["vhname"],
})
publish_context = self._QueryPublishContextByTargetPath(target_path)
if publish_context:
target_details.update({"publishcontext": publish_context,})
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ablab/spades | 3a754192b88540524ce6fb69eef5ea9273a38465 | webvis/pydot.py | python | graph_from_adjacency_matrix | (matrix, node_prefix= u'', directed=False) | return graph | Creates a basic graph out of an adjacency matrix.
The matrix has to be a list of rows of values
representing an adjacency matrix.
The values can be anything: bool, int, float, as long
as they can evaluate to True or False. | Creates a basic graph out of an adjacency matrix.
The matrix has to be a list of rows of values
representing an adjacency matrix.
The values can be anything: bool, int, float, as long
as they can evaluate to True or False. | [
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"""Creates a basic graph out of an adjacency matrix.
The matrix has to be a list of rows of values
representing an adjacency matrix.
The values can be anything: bool, int, float, as long
as they can evaluate to True or False.
"""
node_orig = 1
if directed:
graph = Dot(graph_type='digraph')
else:
graph = Dot(graph_type='graph')
for row in matrix:
if not directed:
skip = matrix.index(row)
r = row[skip:]
else:
skip = 0
r = row
node_dest = skip+1
for e in r:
if e:
graph.add_edge(
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/jinja2/ext.py | python | InternationalizationExtension._make_node | (self, singular, plural, variables, plural_expr,
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"""Generates a useful node from the data provided."""
# no variables referenced? no need to escape for old style
# gettext invocations only if there are vars.
if not vars_referenced and not self.environment.newstyle_gettext:
singular = singular.replace('%%', '%')
if plural:
plural = plural.replace('%%', '%')
# singular only:
if plural_expr is None:
gettext = nodes.Name('gettext', 'load')
node = nodes.Call(gettext, [nodes.Const(singular)],
[], None, None)
# singular and plural
else:
ngettext = nodes.Name('ngettext', 'load')
node = nodes.Call(ngettext, [
nodes.Const(singular),
nodes.Const(plural),
plural_expr
], [], None, None)
# in case newstyle gettext is used, the method is powerful
# enough to handle the variable expansion and autoescape
# handling itself
if self.environment.newstyle_gettext:
for key, value in iteritems(variables):
# the function adds that later anyways in case num was
# called num, so just skip it.
if num_called_num and key == 'num':
continue
node.kwargs.append(nodes.Keyword(key, value))
# otherwise do that here
else:
# mark the return value as safe if we are in an
# environment with autoescaping turned on
node = nodes.MarkSafeIfAutoescape(node)
if variables:
node = nodes.Mod(node, nodes.Dict([
nodes.Pair(nodes.Const(key), value)
for key, value in variables.items()
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return nodes.Output([node]) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/roc/dispatch.py | python | HsaUFuncDispatcher.__call__ | (self, *args, **kws) | return HsaUFuncMechanism.call(self.functions, args, kws) | *args: numpy arrays
**kws:
stream -- hsa stream; when defined, asynchronous mode is used.
out -- output array. Can be a numpy array or DeviceArrayBase
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the input arguments. | *args: numpy arrays
**kws:
stream -- hsa stream; when defined, asynchronous mode is used.
out -- output array. Can be a numpy array or DeviceArrayBase
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the input arguments. | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | cd/utils/artifact_repository.py | python | write_libmxnet_meta | (args: argparse.Namespace, destination: str) | Writes a file called libmxnet.meta in the 'destination' folder that contains
the libmxnet library information (commit id, type, etc.).
:param args: A Namespace object containing the library
:param destination: The folder in which to place the libmxnet.meta | Writes a file called libmxnet.meta in the 'destination' folder that contains
the libmxnet library information (commit id, type, etc.).
:param args: A Namespace object containing the library
:param destination: The folder in which to place the libmxnet.meta | [
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Writes a file called libmxnet.meta in the 'destination' folder that contains
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:param args: A Namespace object containing the library
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with open(os.path.join(destination, 'libmxnet.meta'), 'w') as fp:
fp.write(yaml.dump({
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root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | interpreter/llvm/src/tools/clang/docs/tools/dump_ast_matchers.py | python | unify_arguments | (args) | return args | Gets rid of anything the user doesn't care about in the argument list. | Gets rid of anything the user doesn't care about in the argument list. | [
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"""Gets rid of anything the user doesn't care about in the argument list."""
args = re.sub(r'internal::', r'', args)
args = re.sub(r'extern const\s+(.*)&', r'\1 ', args)
args = re.sub(r'&', r' ', args)
args = re.sub(r'(^|\s)M\d?(\s)', r'\1Matcher<*>\2', args)
return args | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/grit/grit/format/policy_templates/writers/plist_writer.py | python | PListWriter._AddTargets | (self, parent, policy) | Adds the following XML snippet to an XML element:
<key>pfm_targets</key>
<array>
<string>user-managed</string>
</array>
Args:
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'''Adds the following XML snippet to an XML element:
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<string>user-managed</string>
</array>
Args:
parent: The parent XML element where the snippet will be added.
'''
array = self._AddKeyValuePair(parent, 'pfm_targets', 'array')
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SoarGroup/Soar | a1c5e249499137a27da60533c72969eef3b8ab6b | scons/scons-time.py | python | SConsTimer.get_object_counts | (self, file, object_name, index=None) | return result | Returns the counts of the specified object_name. | Returns the counts of the specified object_name. | [
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object_string = ' ' + object_name + '\n'
with open(file) as f:
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result = [int(field) for field in line.split()[:4]]
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/setuptools/command/easy_install.py | python | update_dist_caches | (dist_path, fix_zipimporter_caches) | Fix any globally cached `dist_path` related data
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original distribution, are left behind, they can fail to load modules from
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is used to load data from a new zipped egg archive, it may cause the
operation to attempt to locate the requested data in the wrong location -
one indicated by the original distribution's zip archive directory
information. Such an operation may then fail outright, e.g. report having
read a 'bad local file header', or even worse, it may fail silently &
return invalid data.
zipimport._zip_directory_cache contains cached zip archive directory
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information.
If asked, and the underlying Python implementation allows it, we can fix
all existing zipimport.zipimporter instances instead of having to track
them down and remove them one by one, by updating their shared cached zip
archive directory information. This, of course, assumes that the
replacement distribution is packaged as a zipped egg.
If not asked to fix existing zipimport.zipimporter instances, we still do
our best to clear any remaining zipimport.zipimporter related cached data
that might somehow later get used when attempting to load data from the new
distribution and thus cause such load operations to fail. Note that when
tracking down such remaining stale data, we can not catch every conceivable
usage from here, and we clear only those that we know of and have found to
cause problems if left alive. Any remaining caches should be updated by
whomever is in charge of maintaining them, i.e. they should be ready to
handle us replacing their zip archives with new distributions at runtime. | Fix any globally cached `dist_path` related data | [
"Fix",
"any",
"globally",
"cached",
"dist_path",
"related",
"data"
] | def update_dist_caches(dist_path, fix_zipimporter_caches):
"""
Fix any globally cached `dist_path` related data
`dist_path` should be a path of a newly installed egg distribution (zipped
or unzipped).
sys.path_importer_cache contains finder objects that have been cached when
importing data from the original distribution. Any such finders need to be
cleared since the replacement distribution might be packaged differently,
e.g. a zipped egg distribution might get replaced with an unzipped egg
folder or vice versa. Having the old finders cached may then cause Python
to attempt loading modules from the replacement distribution using an
incorrect loader.
zipimport.zipimporter objects are Python loaders charged with importing
data packaged inside zip archives. If stale loaders referencing the
original distribution, are left behind, they can fail to load modules from
the replacement distribution. E.g. if an old zipimport.zipimporter instance
is used to load data from a new zipped egg archive, it may cause the
operation to attempt to locate the requested data in the wrong location -
one indicated by the original distribution's zip archive directory
information. Such an operation may then fail outright, e.g. report having
read a 'bad local file header', or even worse, it may fail silently &
return invalid data.
zipimport._zip_directory_cache contains cached zip archive directory
information for all existing zipimport.zipimporter instances and all such
instances connected to the same archive share the same cached directory
information.
If asked, and the underlying Python implementation allows it, we can fix
all existing zipimport.zipimporter instances instead of having to track
them down and remove them one by one, by updating their shared cached zip
archive directory information. This, of course, assumes that the
replacement distribution is packaged as a zipped egg.
If not asked to fix existing zipimport.zipimporter instances, we still do
our best to clear any remaining zipimport.zipimporter related cached data
that might somehow later get used when attempting to load data from the new
distribution and thus cause such load operations to fail. Note that when
tracking down such remaining stale data, we can not catch every conceivable
usage from here, and we clear only those that we know of and have found to
cause problems if left alive. Any remaining caches should be updated by
whomever is in charge of maintaining them, i.e. they should be ready to
handle us replacing their zip archives with new distributions at runtime.
"""
# There are several other known sources of stale zipimport.zipimporter
# instances that we do not clear here, but might if ever given a reason to
# do so:
# * Global setuptools pkg_resources.working_set (a.k.a. 'master working
# set') may contain distributions which may in turn contain their
# zipimport.zipimporter loaders.
# * Several zipimport.zipimporter loaders held by local variables further
# up the function call stack when running the setuptools installation.
# * Already loaded modules may have their __loader__ attribute set to the
# exact loader instance used when importing them. Python 3.4 docs state
# that this information is intended mostly for introspection and so is
# not expected to cause us problems.
normalized_path = normalize_path(dist_path)
_uncache(normalized_path, sys.path_importer_cache)
if fix_zipimporter_caches:
_replace_zip_directory_cache_data(normalized_path)
else:
# Here, even though we do not want to fix existing and now stale
# zipimporter cache information, we still want to remove it. Related to
# Python's zip archive directory information cache, we clear each of
# its stale entries in two phases:
# 1. Clear the entry so attempting to access zip archive information
# via any existing stale zipimport.zipimporter instances fails.
# 2. Remove the entry from the cache so any newly constructed
# zipimport.zipimporter instances do not end up using old stale
# zip archive directory information.
# This whole stale data removal step does not seem strictly necessary,
# but has been left in because it was done before we started replacing
# the zip archive directory information cache content if possible, and
# there are no relevant unit tests that we can depend on to tell us if
# this is really needed.
_remove_and_clear_zip_directory_cache_data(normalized_path) | [
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flexflow/FlexFlow | 581fad8ba8d10a16a3102ee2b406b0319586df24 | python/flexflow/keras/utils/data_utils.py | python | OrderedEnqueuer._run | (self) | Submits request to the executor and queue the `Future` objects. | Submits request to the executor and queue the `Future` objects. | [
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"""Submits request to the executor and queue the `Future` objects."""
while True:
sequence = list(range(len(self.sequence)))
self._send_sequence() # Share the initial sequence
if self.shuffle:
random.shuffle(sequence)
with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:
for i in sequence:
if self.stop_signal.is_set():
return
future = executor.apply_async(get_index, (self.uid, i))
future.idx = i
self.queue.put(future, block=True)
# Done with the current epoch, waiting for the final batches
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if self.stop_signal.is_set():
# We're done
return
# Call the internal on epoch end.
self.sequence.on_epoch_end()
# communicate on_epoch_end to the main thread
self.end_of_epoch_signal.set() | [
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Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | Wrapping/Python/paraview/selection.py | python | SelectIDs | (IDs=[], FieldType='POINT', ContainingCells=False, Source=None, Modifier=None) | Select attributes by attribute IDs.
- IDs - list of IDs of attribute types to select. Defined as (process number, attribute ID) pairs
interleaved in a single list. For multiblock datasets, this will select attributes on all
blocks of the provided (processor number, attribute ID) pairs
- FieldType - type of attribute to select, e.g., 'POINT', 'CELL'
- ContainingCells - if True and FieldType is 'POINT', select the cells containing the
points corresponding to the given IDs.
- Source - If not None, specifies the sources whose elements should be selected by ID.
- Modifier - 'ADD', 'SUBTRACT', 'TOGGLE', or None to define whether and how the selection
should modify the existing selection. | Select attributes by attribute IDs. | [
"Select",
"attributes",
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"attribute",
"IDs",
"."
] | def SelectIDs(IDs=[], FieldType='POINT', ContainingCells=False, Source=None, Modifier=None):
"""Select attributes by attribute IDs.
- IDs - list of IDs of attribute types to select. Defined as (process number, attribute ID) pairs
interleaved in a single list. For multiblock datasets, this will select attributes on all
blocks of the provided (processor number, attribute ID) pairs
- FieldType - type of attribute to select, e.g., 'POINT', 'CELL'
- ContainingCells - if True and FieldType is 'POINT', select the cells containing the
points corresponding to the given IDs.
- Source - If not None, specifies the sources whose elements should be selected by ID.
- Modifier - 'ADD', 'SUBTRACT', 'TOGGLE', or None to define whether and how the selection
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"""
_selectIDsHelper('IDSelectionSource', **locals()) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/backends/chrome/tab_list_backend.py | python | TabListBackend.CloseTab | (self, tab_id, timeout=300) | Closes the tab with the given debugger_url.
Raises:
devtools_http.DevToolsClientConnectionError
devtools_client_backend.TabNotFoundError
TabUnexpectedResponseException
exceptions.TimeoutException | Closes the tab with the given debugger_url. | [
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"tab",
"with",
"the",
"given",
"debugger_url",
"."
] | def CloseTab(self, tab_id, timeout=300):
"""Closes the tab with the given debugger_url.
Raises:
devtools_http.DevToolsClientConnectionError
devtools_client_backend.TabNotFoundError
TabUnexpectedResponseException
exceptions.TimeoutException
"""
assert self._browser_backend.supports_tab_control
# TODO(dtu): crbug.com/160946, allow closing the last tab on some platforms.
# For now, just create a new tab before closing the last tab.
if len(self) <= 1:
self.New(timeout)
response = self._browser_backend.devtools_client.CloseTab(tab_id, timeout)
if response != 'Target is closing':
raise TabUnexpectedResponseException(
app=self._browser_backend.browser,
msg='Received response: %s' % response)
util.WaitFor(lambda: tab_id not in self.IterContextIds(), timeout=5) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/util/tf_inspect.py | python | isfunction | (object) | return _inspect.isfunction(tf_decorator.unwrap(object)[1]) | TFDecorator-aware replacement for inspect.isfunction. | TFDecorator-aware replacement for inspect.isfunction. | [
"TFDecorator",
"-",
"aware",
"replacement",
"for",
"inspect",
".",
"isfunction",
"."
] | def isfunction(object): # pylint: disable=redefined-builtin
"""TFDecorator-aware replacement for inspect.isfunction."""
return _inspect.isfunction(tf_decorator.unwrap(object)[1]) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/RNN/rnn_quantizer/pytorch_binding/pytorch_nndct/nn/qat/modules/conv.py | python | QuantizedConv2d.from_float | (cls, mod, qconfig) | return conv | Create a qat module from a float module.
Args:
mod: A float module of type torch.nn.Conv2d.
qconfig (pytorch_nndct.quantization.quant_aware_training.QConfig):
A qconfig object that saves the quantizers for the module. | Create a qat module from a float module. | [
"Create",
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"qat",
"module",
"from",
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"float",
"module",
"."
] | def from_float(cls, mod, qconfig):
"""Create a qat module from a float module.
Args:
mod: A float module of type torch.nn.Conv2d.
qconfig (pytorch_nndct.quantization.quant_aware_training.QConfig):
A qconfig object that saves the quantizers for the module.
"""
assert qconfig, 'qconfig must be provided for quantized module'
assert type(mod) == cls._FLOAT_MODULE, ' qat.' + cls.__name__ + \
'.from_float only works for ' + cls._FLOAT_MODULE.__name__
conv = cls(
mod.in_channels,
mod.out_channels,
mod.kernel_size,
stride=mod.stride,
padding=mod.padding,
dilation=mod.dilation,
groups=mod.groups,
bias=mod.bias is not None,
padding_mode=mod.padding_mode,
qconfig=qconfig)
conv.weight = mod.weight
conv.bias = mod.bias
return conv | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/core/fromnumeric.py | python | put | (a, ind, v, mode='raise') | return put(ind, v, mode=mode) | Replaces specified elements of an array with given values.
The indexing works on the flattened target array. `put` is roughly
equivalent to:
::
a.flat[ind] = v
Parameters
----------
a : ndarray
Target array.
ind : array_like
Target indices, interpreted as integers.
v : array_like
Values to place in `a` at target indices. If `v` is shorter than
`ind` it will be repeated as necessary.
mode : {'raise', 'wrap', 'clip'}, optional
Specifies how out-of-bounds indices will behave.
* 'raise' -- raise an error (default)
* 'wrap' -- wrap around
* 'clip' -- clip to the range
'clip' mode means that all indices that are too large are replaced
by the index that addresses the last element along that axis. Note
that this disables indexing with negative numbers. In 'raise' mode,
if an exception occurs the target array may still be modified.
See Also
--------
putmask, place
put_along_axis : Put elements by matching the array and the index arrays
Examples
--------
>>> a = np.arange(5)
>>> np.put(a, [0, 2], [-44, -55])
>>> a
array([-44, 1, -55, 3, 4])
>>> a = np.arange(5)
>>> np.put(a, 22, -5, mode='clip')
>>> a
array([ 0, 1, 2, 3, -5]) | Replaces specified elements of an array with given values. | [
"Replaces",
"specified",
"elements",
"of",
"an",
"array",
"with",
"given",
"values",
"."
] | def put(a, ind, v, mode='raise'):
"""
Replaces specified elements of an array with given values.
The indexing works on the flattened target array. `put` is roughly
equivalent to:
::
a.flat[ind] = v
Parameters
----------
a : ndarray
Target array.
ind : array_like
Target indices, interpreted as integers.
v : array_like
Values to place in `a` at target indices. If `v` is shorter than
`ind` it will be repeated as necessary.
mode : {'raise', 'wrap', 'clip'}, optional
Specifies how out-of-bounds indices will behave.
* 'raise' -- raise an error (default)
* 'wrap' -- wrap around
* 'clip' -- clip to the range
'clip' mode means that all indices that are too large are replaced
by the index that addresses the last element along that axis. Note
that this disables indexing with negative numbers. In 'raise' mode,
if an exception occurs the target array may still be modified.
See Also
--------
putmask, place
put_along_axis : Put elements by matching the array and the index arrays
Examples
--------
>>> a = np.arange(5)
>>> np.put(a, [0, 2], [-44, -55])
>>> a
array([-44, 1, -55, 3, 4])
>>> a = np.arange(5)
>>> np.put(a, 22, -5, mode='clip')
>>> a
array([ 0, 1, 2, 3, -5])
"""
try:
put = a.put
except AttributeError:
raise TypeError("argument 1 must be numpy.ndarray, "
"not {name}".format(name=type(a).__name__))
return put(ind, v, mode=mode) | [
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etotheipi/BitcoinArmory | 2a6fc5355bb0c6fe26e387ccba30a5baafe8cd98 | armorycolors.py | python | tweakColor | (qcolor, op, tweaks) | return QColor(r,g,b) | We want to be able to take existing colors (from the palette)
and tweak them. This may involved "inverting" them, or
multiplying or adding scalars to the various channels. | We want to be able to take existing colors (from the palette)
and tweak them. This may involved "inverting" them, or
multiplying or adding scalars to the various channels. | [
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"""
We want to be able to take existing colors (from the palette)
and tweak them. This may involved "inverting" them, or
multiplying or adding scalars to the various channels.
"""
if len(tweaks) != 3:
raise InvalidColor, 'Must supply list or tuple of RGB tweaks'
# Determine what the "tweaks" list/tuple means
tweakChannel = lambda x,mod: x # identity
if op.lower() in ('times', '*'):
def tweakChannel(color, mod):
if mod < 0:
color = (255 - color)
mod *= -1
returnColor = color * mod
returnColor = min(returnColor, 255)
return int(max(returnColor, 0))
elif op.lower() in ('plus', '+'):
def tweakChannel(color, mod):
returnColor = color + mod
returnColor = min(returnColor, 255)
return int(max(returnColor, 0))
else:
raise InvalidColor, 'Invalid color operation: "%s"' % op
r,g,b = qcolor.red(), qcolor.green(), qcolor.blue()
r = tweakChannel(r, tweaks[0])
g = tweakChannel(g, tweaks[1])
b = tweakChannel(b, tweaks[2])
return QColor(r,g,b) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/xml/sax/handler.py | python | ContentHandler.ignorableWhitespace | (self, whitespace) | Receive notification of ignorable whitespace in element content.
Validating Parsers must use this method to report each chunk
of ignorable whitespace (see the W3C XML 1.0 recommendation,
section 2.10): non-validating parsers may also use this method
if they are capable of parsing and using content models.
SAX parsers may return all contiguous whitespace in a single
chunk, or they may split it into several chunks; however, all
of the characters in any single event must come from the same
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] | def ignorableWhitespace(self, whitespace):
"""Receive notification of ignorable whitespace in element content.
Validating Parsers must use this method to report each chunk
of ignorable whitespace (see the W3C XML 1.0 recommendation,
section 2.10): non-validating parsers may also use this method
if they are capable of parsing and using content models.
SAX parsers may return all contiguous whitespace in a single
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of the characters in any single event must come from the same
external entity, so that the Locator provides useful
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exodus2.in.py | python | exodus.get_side_set | (self, id) | return (side_set_elem_list, side_set_side_list) | ss_elems, ss_sides = exo.get_side_set(side_set_id)
-> get the lists of element and side indices in a side set; the
two lists correspond: together, ss_elems[i] and ss_sides[i]
define the face of an element
input value(s):
<int> side_set_id side set *ID* (not *INDEX*)
return value(s):
if array_type == 'ctype':
<list<int>> ss_elems
<list<int>> ss_sides
if array_type == 'numpy':
<np_array<int>> ss_elems
<np_array<int>> ss_sides | ss_elems, ss_sides = exo.get_side_set(side_set_id) | [
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] | def get_side_set(self, id):
"""
ss_elems, ss_sides = exo.get_side_set(side_set_id)
-> get the lists of element and side indices in a side set; the
two lists correspond: together, ss_elems[i] and ss_sides[i]
define the face of an element
input value(s):
<int> side_set_id side set *ID* (not *INDEX*)
return value(s):
if array_type == 'ctype':
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<list<int>> ss_sides
if array_type == 'numpy':
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<np_array<int>> ss_sides
"""
(side_set_elem_list, side_set_side_list) = self.__ex_get_side_set(id)
if self.use_numpy:
side_set_elem_list = ctype_to_numpy(self, side_set_elem_list)
side_set_side_list = ctype_to_numpy(self, side_set_side_list)
return (side_set_elem_list, side_set_side_list) | [
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daijifeng001/caffe-rfcn | 543f8f6a4b7c88256ea1445ae951a12d1ad9cffd | scripts/cpp_lint.py | python | CloseExpression | (clean_lines, linenum, pos) | return (line, clean_lines.NumLines(), -1) | If input points to ( or { or [ or <, finds the position that closes it.
If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
linenum/pos that correspond to the closing of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *past* the closing brace, or
(line, len(lines), -1) if we never find a close. Note we ignore
strings and comments when matching; and the line we return is the
'cleansed' line at linenum. | If input points to ( or { or [ or <, finds the position that closes it. | [
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] | def CloseExpression(clean_lines, linenum, pos):
"""If input points to ( or { or [ or <, finds the position that closes it.
If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
linenum/pos that correspond to the closing of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *past* the closing brace, or
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'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
startchar = line[pos]
if startchar not in '({[<':
return (line, clean_lines.NumLines(), -1)
if startchar == '(': endchar = ')'
if startchar == '[': endchar = ']'
if startchar == '{': endchar = '}'
if startchar == '<': endchar = '>'
# Check first line
(end_pos, num_open) = FindEndOfExpressionInLine(
line, pos, 0, startchar, endchar)
if end_pos > -1:
return (line, linenum, end_pos)
# Continue scanning forward
while linenum < clean_lines.NumLines() - 1:
linenum += 1
line = clean_lines.elided[linenum]
(end_pos, num_open) = FindEndOfExpressionInLine(
line, 0, num_open, startchar, endchar)
if end_pos > -1:
return (line, linenum, end_pos)
# Did not find endchar before end of file, give up
return (line, clean_lines.NumLines(), -1) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/debug/lib/common.py | python | get_graph_element_name | (elem) | return elem.name if hasattr(elem, "name") else str(elem) | Obtain the name or string representation of a graph element.
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"""Obtain the name or string representation of a graph element.
If the graph element has the attribute "name", return name. Otherwise, return
a __str__ representation of the graph element. Certain graph elements, such as
`SparseTensor`s, do not have the attribute "name".
Args:
elem: The graph element in question.
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_Rewrap_REQUEST.fromBytes | (buffer) | return TpmBuffer(buffer).createObj(TPM2_Rewrap_REQUEST) | Returns new TPM2_Rewrap_REQUEST object constructed from its
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turi-code/SFrame | 796b9bdfb2fa1b881d82080754643c7e68629cd2 | oss_src/unity/python/sframe/toolkits/_internal_utils.py | python | _summarize_coefficients | (top_coefs, bottom_coefs) | return ([top_coefs_list, bottom_coefs_list], \
[ 'Highest Positive Coefficients', 'Lowest Negative Coefficients'] ) | Return a tuple of sections and section titles.
Sections are pretty print of model coefficients
Parameters
----------
top_coefs : SFrame of top k coefficients
bottom_coefs : SFrame of bottom k coefficients
Returns
-------
(sections, section_titles) : tuple
sections : list
summary sections for top/bottom k coefficients
section_titles : list
summary section titles | Return a tuple of sections and section titles.
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"""
Return a tuple of sections and section titles.
Sections are pretty print of model coefficients
Parameters
----------
top_coefs : SFrame of top k coefficients
bottom_coefs : SFrame of bottom k coefficients
Returns
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sections : list
summary sections for top/bottom k coefficients
section_titles : list
summary section titles
"""
def get_row_name(row):
if row['index'] == None:
return row['name']
else:
return "%s[%s]" % (row['name'], row['index'])
if len(top_coefs) == 0:
top_coefs_list = [('No Positive Coefficients', _precomputed_field('') )]
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top_coefs_list = [ (get_row_name(row),
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bottom_coefs_list = [ (get_row_name(row),
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return ([top_coefs_list, bottom_coefs_list], \
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/agilepy/lib_wx/mainframe.py | python | AgileMainframe.on_open | (self, event) | Open a document | Open a document | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/propgrid.py | python | PropertyGridInterface.SetPropertyAttribute | (*args, **kwargs) | return _propgrid.PropertyGridInterface_SetPropertyAttribute(*args, **kwargs) | SetPropertyAttribute(self, PGPropArg id, String attrName, wxVariant value, long argFlags=0) | SetPropertyAttribute(self, PGPropArg id, String attrName, wxVariant value, long argFlags=0) | [
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"""SetPropertyAttribute(self, PGPropArg id, String attrName, wxVariant value, long argFlags=0)"""
return _propgrid.PropertyGridInterface_SetPropertyAttribute(*args, **kwargs) | [
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microsoft/clang | 86d4513d3e0daa4d5a29b0b1de7c854ca15f9fe5 | tools/scan-build-py/libscanbuild/analyze.py | python | analyze_compiler_wrapper | () | return compiler_wrapper(analyze_compiler_wrapper_impl) | Entry point for `analyze-cc` and `analyze-c++` compiler wrappers. | Entry point for `analyze-cc` and `analyze-c++` compiler wrappers. | [
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""" Entry point for `analyze-cc` and `analyze-c++` compiler wrappers. """
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | qa/tasks/cephfs/kernel_mount.py | python | KernelMount.get_osd_epoch | (self) | return epoch, barrier | Return 2-tuple of osd_epoch, osd_epoch_barrier | Return 2-tuple of osd_epoch, osd_epoch_barrier | [
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"""
Return 2-tuple of osd_epoch, osd_epoch_barrier
"""
osd_map = self.read_debug_file("osdmap")
assert osd_map
lines = osd_map.split("\n")
first_line_tokens = lines[0].split()
epoch, barrier = int(first_line_tokens[1]), int(first_line_tokens[3])
return epoch, barrier | [
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPMS_CREATION_INFO.fromTpm | (buf) | return buf.createObj(TPMS_CREATION_INFO) | Returns new TPMS_CREATION_INFO object constructed from its marshaled
representation in the given TpmBuffer buffer | Returns new TPMS_CREATION_INFO object constructed from its marshaled
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""" Returns new TPMS_CREATION_INFO object constructed from its marshaled
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wy1iu/LargeMargin_Softmax_Loss | c3e9f20e4f16e2b4daf7d358a614366b9b39a6ec | python/caffe/pycaffe.py | python | _Net_forward_backward_all | (self, blobs=None, diffs=None, **kwargs) | return all_outs, all_diffs | Run net forward + backward in batches.
Parameters
----------
blobs: list of blobs to extract as in forward()
diffs: list of diffs to extract as in backward()
kwargs: Keys are input (for forward) and output (for backward) blob names
and values are ndarrays. Refer to forward() and backward().
Prefilled variants are called for lack of input or output blobs.
Returns
-------
all_blobs: {blob name: blob ndarray} dict.
all_diffs: {blob name: diff ndarray} dict. | Run net forward + backward in batches. | [
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"""
Run net forward + backward in batches.
Parameters
----------
blobs: list of blobs to extract as in forward()
diffs: list of diffs to extract as in backward()
kwargs: Keys are input (for forward) and output (for backward) blob names
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Prefilled variants are called for lack of input or output blobs.
Returns
-------
all_blobs: {blob name: blob ndarray} dict.
all_diffs: {blob name: diff ndarray} dict.
"""
# Batch blobs and diffs.
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
all_diffs = {diff: [] for diff in set(self.inputs + (diffs or []))}
forward_batches = self._batch({in_: kwargs[in_]
for in_ in self.inputs if in_ in kwargs})
backward_batches = self._batch({out: kwargs[out]
for out in self.outputs if out in kwargs})
# Collect outputs from batches (and heed lack of forward/backward batches).
for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
batch_blobs = self.forward(blobs=blobs, **fb)
batch_diffs = self.backward(diffs=diffs, **bb)
for out, out_blobs in six.iteritems(batch_blobs):
all_outs[out].extend(out_blobs.copy())
for diff, out_diffs in six.iteritems(batch_diffs):
all_diffs[diff].extend(out_diffs.copy())
# Package in ndarray.
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = np.asarray(all_outs[out])
all_diffs[diff] = np.asarray(all_diffs[diff])
# Discard padding at the end and package in ndarray.
pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
if pad:
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = all_outs[out][:-pad]
all_diffs[diff] = all_diffs[diff][:-pad]
return all_outs, all_diffs | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | Region.UnionRegion | (*args, **kwargs) | return _gdi_.Region_UnionRegion(*args, **kwargs) | UnionRegion(self, Region region) -> bool | UnionRegion(self, Region region) -> bool | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/quantize/python/graph_matcher.py | python | GraphMatcher.__init__ | (self, pattern) | Initializes a GraphMatcher.
Args:
pattern: The `Pattern` against which `GraphMatcher` matches
subgraphs. | Initializes a GraphMatcher. | [
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"""Initializes a GraphMatcher.
Args:
pattern: The `Pattern` against which `GraphMatcher` matches
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self._pattern = pattern | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | DateTime.__init__ | (self, *args, **kwargs) | __init__(self) -> DateTime | __init__(self) -> DateTime | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Rect.SetTopLeft | (*args, **kwargs) | return _core_.Rect_SetTopLeft(*args, **kwargs) | SetTopLeft(self, Point p) | SetTopLeft(self, Point p) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/site.py | python | enablerlcompleter | () | Enable default readline configuration on interactive prompts, by
registering a sys.__interactivehook__.
If the readline module can be imported, the hook will set the Tab key
as completion key and register ~/.python_history as history file.
This can be overridden in the sitecustomize or usercustomize module,
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registering a sys.__interactivehook__.
If the readline module can be imported, the hook will set the Tab key
as completion key and register ~/.python_history as history file.
This can be overridden in the sitecustomize or usercustomize module,
or in a PYTHONSTARTUP file.
"""
def register_readline():
import atexit
try:
import readline
import rlcompleter
except ImportError:
return
# Reading the initialization (config) file may not be enough to set a
# completion key, so we set one first and then read the file.
readline_doc = getattr(readline, '__doc__', '')
if readline_doc is not None and 'libedit' in readline_doc:
readline.parse_and_bind('bind ^I rl_complete')
else:
readline.parse_and_bind('tab: complete')
try:
readline.read_init_file()
except OSError:
# An OSError here could have many causes, but the most likely one
# is that there's no .inputrc file (or .editrc file in the case of
# Mac OS X + libedit) in the expected location. In that case, we
# want to ignore the exception.
pass
if readline.get_current_history_length() == 0:
# If no history was loaded, default to .python_history.
# The guard is necessary to avoid doubling history size at
# each interpreter exit when readline was already configured
# through a PYTHONSTARTUP hook, see:
# http://bugs.python.org/issue5845#msg198636
history = os.path.join(os.path.expanduser('~'),
'.python_history')
try:
readline.read_history_file(history)
except OSError:
pass
def write_history():
try:
readline.write_history_file(history)
except OSError:
# bpo-19891, bpo-41193: Home directory does not exist
# or is not writable, or the filesystem is read-only.
pass
atexit.register(write_history)
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/tools/grit/grit/format/chrome_messages_json.py | python | Format | (root, lang='en', output_dir='.') | Format the messages as JSON. | Format the messages as JSON. | [
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placeholder_format = '"%i":{"content":"$%i"}'
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first = False
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yield format % (id, loc_message, placeholders)
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros_comm/roscpp/rosbuild/scripts/msg_gen.py | python | write_end | (s, spec) | Writes the end of the header file: the ending of the include guards
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/optparse.py | python | HelpFormatter.format_option_strings | (self, option) | return ", ".join(opts) | Return a comma-separated list of option strings & metavariables. | Return a comma-separated list of option strings & metavariables. | [
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metavar = option.metavar or option.dest.upper()
short_opts = [self._short_opt_fmt % (sopt, metavar)
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long_opts = [self._long_opt_fmt % (lopt, metavar)
for lopt in option._long_opts]
else:
short_opts = option._short_opts
long_opts = option._long_opts
if self.short_first:
opts = short_opts + long_opts
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/tools/gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings._GetAdditionalLibraryDirectories | (self, root, config, gyp_to_build_path) | return ['/LIBPATH:"' + p + '"' for p in libpaths] | Get and normalize the list of paths in AdditionalLibraryDirectories
setting. | Get and normalize the list of paths in AdditionalLibraryDirectories
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config = self._TargetConfig(config)
libpaths = self._Setting(
(root, "AdditionalLibraryDirectories"), config, default=[]
)
libpaths = [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/model/coordinates.py | python | Frame.worldOrigin | (self) | return self._worldCoordinates[1] | Returns an element of R^3 denoting the translation of the origin
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aui/tabart.py | python | VC8TabArt.Clone | (self) | return art | Clones the art object. | Clones the art object. | [
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""" Clones the art object. """
art = type(self)()
art.SetNormalFont(self.GetNormalFont())
art.SetSelectedFont(self.GetSelectedFont())
art.SetMeasuringFont(self.GetMeasuringFont())
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/tools/gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings._GetLdManifestFlags | (self, config, name, gyp_to_build_path,
allow_isolation, build_dir) | return flags, output_name, manifest_files | Returns a 3-tuple:
- the set of flags that need to be added to the link to generate
a default manifest
- the intermediate manifest that the linker will generate that should be
used to assert it doesn't add anything to the merged one.
- the list of all the manifest files to be merged by the manifest tool and
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- the set of flags that need to be added to the link to generate
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- the intermediate manifest that the linker will generate that should be
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a default manifest
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flags.append('/MANIFESTUAC:NO')
config = self._TargetConfig(config)
enable_uac = self._Setting(('VCLinkerTool', 'EnableUAC'), config,
default='true')
manifest_files = []
generated_manifest_outer = \
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"<assembly xmlns='urn:schemas-microsoft-com:asm.v1' manifestVersion='1.0'>%s" \
"</assembly>"
if enable_uac == 'true':
execution_level = self._Setting(('VCLinkerTool', 'UACExecutionLevel'),
config, default='0')
execution_level_map = {
'0': 'asInvoker',
'1': 'highestAvailable',
'2': 'requireAdministrator'
}
ui_access = self._Setting(('VCLinkerTool', 'UACUIAccess'), config,
default='false')
inner = '''
<trustInfo xmlns="urn:schemas-microsoft-com:asm.v3">
<security>
<requestedPrivileges>
<requestedExecutionLevel level='%s' uiAccess='%s' />
</requestedPrivileges>
</security>
</trustInfo>''' % (execution_level_map[execution_level], ui_access)
else:
inner = ''
generated_manifest_contents = generated_manifest_outer % inner
generated_name = name + '.generated.manifest'
# Need to join with the build_dir here as we're writing it during
# generation time, but we return the un-joined version because the build
# will occur in that directory. We only write the file if the contents
# have changed so that simply regenerating the project files doesn't
# cause a relink.
build_dir_generated_name = os.path.join(build_dir, generated_name)
gyp.common.EnsureDirExists(build_dir_generated_name)
f = gyp.common.WriteOnDiff(build_dir_generated_name)
f.write(generated_manifest_contents)
f.close()
manifest_files = [generated_name]
if allow_isolation:
flags.append('/ALLOWISOLATION')
manifest_files += self._GetAdditionalManifestFiles(config,
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return flags, output_name, manifest_files | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBTypeSynthetic.CreateWithScriptCode | (*args) | return _lldb.SBTypeSynthetic_CreateWithScriptCode(*args) | CreateWithScriptCode(str data, uint32_t options = 0) -> SBTypeSynthetic
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llvm-dcpu16/llvm-dcpu16 | ae6b01fecd03219677e391d4421df5d966d80dcf | utils/lit/lit/LitConfig.py | python | LitConfig.getBashPath | (self) | return self.bashPath | getBashPath - Get the path to 'bash | getBashPath - Get the path to 'bash | [
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if self.bashPath is not None:
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self.bashPath = Util.which('bash', os.pathsep.join(self.path))
if self.bashPath is None:
# Check some known paths.
for path in ('/bin/bash', '/usr/bin/bash', '/usr/local/bin/bash'):
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self.bashPath = path
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/urllib3/packages/six.py | python | with_metaclass | (meta, *bases) | return type.__new__(metaclass, "temporary_class", (), {}) | Create a base class with a metaclass. | Create a base class with a metaclass. | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/oldnumeric/random_array.py | python | random_integers | (maximum, minimum=1, shape=[]) | return randint(minimum, maximum+1, shape) | random_integers(max, min=1, shape=[]) = random integers in range min-max inclusive | random_integers(max, min=1, shape=[]) = random integers in range min-max inclusive | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/PathGeom.py | python | isHorizontal | (obj) | return None | isHorizontal(obj) ... answer True if obj points into X or Y | isHorizontal(obj) ... answer True if obj points into X or Y | [
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] | def isHorizontal(obj):
"""isHorizontal(obj) ... answer True if obj points into X or Y"""
if type(obj) == FreeCAD.Vector:
return isRoughly(obj.z, 0)
if obj.ShapeType == "Face":
if type(obj.Surface) == Part.Plane:
return isVertical(obj.Surface.Axis)
if type(obj.Surface) == Part.Cylinder or type(obj.Surface) == Part.Cone:
return isHorizontal(obj.Surface.Axis)
if type(obj.Surface) == Part.Sphere:
return True
if type(obj.Surface) == Part.SurfaceOfExtrusion:
return isHorizontal(obj.Surface.Direction)
if type(obj.Surface) == Part.SurfaceOfRevolution:
return isVertical(obj.Surface.Direction)
return isRoughly(obj.BoundBox.ZLength, 0.0)
if obj.ShapeType == "Edge":
if type(obj.Curve) == Part.Line or type(obj.Curve) == Part.LineSegment:
return isHorizontal(obj.Vertexes[1].Point - obj.Vertexes[0].Point)
if (
type(obj.Curve) == Part.Circle or type(obj.Curve) == Part.Ellipse
): # or type(obj.Curve) == Part.BSplineCurve:
return isVertical(obj.Curve.Axis)
return isRoughly(obj.BoundBox.ZLength, 0.0)
PathLog.error(translate("PathGeom", "isHorizontal(%s) not supported") % obj)
return None | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/robotsim.py | python | WorldModel.enableGeometryLoading | (self, enabled) | return _robotsim.WorldModel_enableGeometryLoading(self, enabled) | enableGeometryLoading(WorldModel self, bool enabled)
If geometry loading is set to false, then only the kinematics are loaded from
disk, and no geometry / visualization / collision detection structures will be
loaded. Useful for quick scripts that just use kinematics / dynamics of a robot. | enableGeometryLoading(WorldModel self, bool enabled) | [
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"""
enableGeometryLoading(WorldModel self, bool enabled)
If geometry loading is set to false, then only the kinematics are loaded from
disk, and no geometry / visualization / collision detection structures will be
loaded. Useful for quick scripts that just use kinematics / dynamics of a robot.
"""
return _robotsim.WorldModel_enableGeometryLoading(self, enabled) | [
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spring/spring | 553a21526b144568b608a0507674b076ec80d9f9 | buildbot/stacktrace_translator/stacktrace_translator.py | python | translate_module_addresses | (module, debugarchive, addresses, debugfile) | return [fixup(addr, *line.split(':')) for addr, line in zip(addresses, stdout.splitlines())] | \
Translate addresses in a module to (module, address, filename, lineno) tuples
by invoking addr2line exactly once on the debugging symbols for that module.
>>> translate_module_addresses( 'spring.dbg', TESTFILE, ['0x0'], 'spring.dbg')
[('spring.dbg', '0x0', '??', 0)] | \
Translate addresses in a module to (module, address, filename, lineno) tuples
by invoking addr2line exactly once on the debugging symbols for that module.
>>> translate_module_addresses( 'spring.dbg', TESTFILE, ['0x0'], 'spring.dbg')
[('spring.dbg', '0x0', '??', 0)] | [
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'''\
Translate addresses in a module to (module, address, filename, lineno) tuples
by invoking addr2line exactly once on the debugging symbols for that module.
>>> translate_module_addresses( 'spring.dbg', TESTFILE, ['0x0'], 'spring.dbg')
[('spring.dbg', '0x0', '??', 0)]
'''
with NamedTemporaryFile() as tempfile:
log.info('\tExtracting debug symbols for module %s from archive %s...' % (module, os.path.basename(debugfile)))
# e = extract without path, -so = write output to stdout, -y = yes to all questions
sevenzip = Popen([SEVENZIP, 'e', '-so', '-y', debugfile, debugarchive], stdout = tempfile, stderr = PIPE, universal_newlines=True)
stdout, stderr = sevenzip.communicate()
if stderr:
log.debug('%s stderr: %s' % (SEVENZIP, stderr))
if sevenzip.returncode != 0:
fatal('%s exited with status %s' % (SEVENZIP, sevenzip.returncode))
log.info('\t\t[OK]')
log.info('\tTranslating addresses for module %s...' % module)
if module.endswith('.dll'):
cmd = [ADDR2LINE, '-j', '.text', '-e', tempfile.name]
else:
cmd = [ADDR2LINE, '-e', tempfile.name]
log.debug('\tCommand line: ' + ' '.join(cmd))
addr2line = Popen(cmd, stdin = PIPE, stdout = PIPE, stderr = PIPE, universal_newlines=True)
if addr2line.poll() == None:
stdout, stderr = addr2line.communicate('\n'.join(addresses))
else:
stdout, stderr = addr2line.communicate()
if stderr:
log.debug('%s stderr: %s' % (ADDR2LINE, stderr))
if addr2line.returncode != 0:
fatal('%s exited with status %s' % (ADDR2LINE, addr2line.returncode))
log.info('\t\t[OK]')
def fixup(addr, file, line):
for psu in PATH_STRIP_UNTIL:
if psu in file:
file = file[file.index(psu)+len(psu):]
break
return module, addr, file, line
return [fixup(addr, *line.split(':')) for addr, line in zip(addresses, stdout.splitlines())] | [
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root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | bindings/pyroot/pythonizations/python/ROOT/_pythonization/_tfile.py | python | pythonize_tfile | (klass) | TFile inherits from
- TDirectory the pythonized attr syntax (__getattr__) and WriteObject method.
- TDirectoryFile the pythonized Get method (pythonized only in Python) | TFile inherits from
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"""
TFile inherits from
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- TDirectoryFile the pythonized Get method (pythonized only in Python)
"""
# Pythonizations for TFile::Open
AddFileOpenPyz(klass)
klass._OriginalOpen = klass.Open
klass.Open = classmethod(_TFileOpen)
# Pythonization for TFile constructor
klass._OriginalConstructor = klass.__init__
klass.__init__ = _TFileConstructor | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/applications/workbench/workbench/widgets/about/presenter.py | python | AboutPresenter.should_show_on_startup | () | return current_version != lastVersion | Determines if the first time dialog should be shown
:return: True if the dialog should be shown | Determines if the first time dialog should be shown
:return: True if the dialog should be shown | [
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""" Determines if the first time dialog should be shown
:return: True if the dialog should be shown
"""
# first check the facility and instrument
facility = ConfigService.getString(AboutPresenter.FACILITY)
instrument = ConfigService.getString(AboutPresenter.INSTRUMENT)
if not facility:
return True
else:
# check we can get the facility and instrument
try:
facilityInfo = ConfigService.getFacility(facility)
instrumentInfo = ConfigService.getInstrument(instrument)
logger.information("Default facility '{0}', instrument '{1}'\n".format(facilityInfo.name(),
instrumentInfo.name()))
except RuntimeError:
# failed to find the facility or instrument
logger.error("Could not find your default facility '{0}' or instrument '{1}' in facilities.xml, "
+ "showing please select again.\n".format(facility, instrument))
return True
settings = QSettings()
settings.beginGroup(AboutPresenter.DO_NOT_SHOW_GROUP)
doNotShowUntilNextRelease = int(settings.value(AboutPresenter.DO_NOT_SHOW, '0'))
lastVersion = settings.value(AboutPresenter.PREVIOUS_VERSION, "")
current_version = version().major + "." + version().minor
settings.endGroup()
if not doNotShowUntilNextRelease:
return True
# Now check if the version has changed since last time
return current_version != lastVersion | [
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alibaba/MNN | c4d9566171d589c3ded23aa18ffb197016995a12 | pymnn/pip_package/MNN/expr/__init__.py | python | pow | (x, y) | return _F.pow(x, y) | pow(x, y)
Return the ``x ** y``, element-wise.
alias name: `power`.
Parameters
----------
x : var_like, input value.
y : var_like, input value.
Returns
-------
z : Var. The ``x ** y`` of `x` and `y`.
Example:
-------
>>> expr.pow([9., 0.5], [1.2, -3.0])
var([13.966612, 8.]) | pow(x, y)
Return the ``x ** y``, element-wise.
alias name: `power`. | [
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] | def pow(x, y):
'''
pow(x, y)
Return the ``x ** y``, element-wise.
alias name: `power`.
Parameters
----------
x : var_like, input value.
y : var_like, input value.
Returns
-------
z : Var. The ``x ** y`` of `x` and `y`.
Example:
-------
>>> expr.pow([9., 0.5], [1.2, -3.0])
var([13.966612, 8.])
'''
x = _to_var(x)
y = _to_var(y)
x, y = _match_dtype(x, y)
return _F.pow(x, y) | [
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microsoft/EdgeML | ef9f8a77f096acbdeb941014791f8eda1c1bc35b | tf/edgeml_tf/trainer/fastTrainer.py | python | FastTrainer.classifier | (self, feats) | return tf.matmul(feats, self.FC) + self.FCbias | Can be raplaced by any classifier
TODO: Make this a separate class if needed | Can be raplaced by any classifier
TODO: Make this a separate class if needed | [
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'''
Can be raplaced by any classifier
TODO: Make this a separate class if needed
'''
self.FC = tf.Variable(tf.random_normal(
[self.FastObj.output_size, self.numClasses]), name='FC')
self.FCbias = tf.Variable(tf.random_normal(
[self.numClasses]), name='FCbias')
return tf.matmul(feats, self.FC) + self.FCbias | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py | python | uCSIsYiRadicals | (code) | return ret | Check whether the character is part of YiRadicals UCS Block | Check whether the character is part of YiRadicals UCS Block | [
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"""Check whether the character is part of YiRadicals UCS Block """
ret = libxml2mod.xmlUCSIsYiRadicals(code)
return ret | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/urllib.py | python | FancyURLopener.http_error_301 | (self, url, fp, errcode, errmsg, headers, data=None) | return self.http_error_302(url, fp, errcode, errmsg, headers, data) | Error 301 -- also relocated (permanently). | Error 301 -- also relocated (permanently). | [
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"""Error 301 -- also relocated (permanently)."""
return self.http_error_302(url, fp, errcode, errmsg, headers, data) | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/contrib/decoder/beam_search_decoder.py | python | StateCell.get_state | (self, state_name) | return self._cur_states[state_name] | The getter of state object. Find the state variable by its name.
Args:
state_name (str): A string of the state's name.
Returns:
The associated state object. | The getter of state object. Find the state variable by its name. | [
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"""
The getter of state object. Find the state variable by its name.
Args:
state_name (str): A string of the state's name.
Returns:
The associated state object.
"""
if self._in_decoder and not self._switched_decoder:
self._switch_decoder()
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return self._cur_states[state_name] | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/wizard.py | python | Wizard.SetPageSize | (*args, **kwargs) | return _wizard.Wizard_SetPageSize(*args, **kwargs) | SetPageSize(self, Size size) | SetPageSize(self, Size size) | [
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alibaba/MNN | c4d9566171d589c3ded23aa18ffb197016995a12 | pymnn/pip_package/MNN/expr/__init__.py | python | logical_or | (x, y) | return _F.logical_or(x, y) | logical_or(x, y)
The dtype of x, y must be same.
Parameters
----------
x : var_like, input value, dtype just support int32.
y : var_like, input value, dtype just support int32.
Returns
-------
z : Var. The ``x or y`` of `x` and `y`, dtype is int32.
Example:
-------
>>> expr.logical_or([2, 1], [4, 2])
var([1, 1]) | logical_or(x, y)
The dtype of x, y must be same. | [
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'''
logical_or(x, y)
The dtype of x, y must be same.
Parameters
----------
x : var_like, input value, dtype just support int32.
y : var_like, input value, dtype just support int32.
Returns
-------
z : Var. The ``x or y`` of `x` and `y`, dtype is int32.
Example:
-------
>>> expr.logical_or([2, 1], [4, 2])
var([1, 1])
'''
x = _to_var(x)
y = _to_var(y)
x, y = _match_dtype(x, y)
if x.dtype != _F.int or y.dtype != _F.int:
raise ValueError('MNN.expr.logical_or just support int32')
return _F.logical_or(x, y) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/image_ops_impl.py | python | rgb_to_yuv | (images) | return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) | Converts one or more images from RGB to YUV.
Outputs a tensor of the same shape as the `images` tensor, containing the YUV
value of the pixels.
The output is only well defined if the value in images are in [0, 1].
There are two ways of representing an image: [0, 255] pixel values range or
[0, 1] (as float) pixel values range. Users need to convert the input image
into a float [0, 1] range.
Args:
images: 2-D or higher rank. Image data to convert. Last dimension must be
size 3.
Returns:
images: tensor with the same shape as `images`. | Converts one or more images from RGB to YUV. | [
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"YUV",
"."
] | def rgb_to_yuv(images):
"""Converts one or more images from RGB to YUV.
Outputs a tensor of the same shape as the `images` tensor, containing the YUV
value of the pixels.
The output is only well defined if the value in images are in [0, 1].
There are two ways of representing an image: [0, 255] pixel values range or
[0, 1] (as float) pixel values range. Users need to convert the input image
into a float [0, 1] range.
Args:
images: 2-D or higher rank. Image data to convert. Last dimension must be
size 3.
Returns:
images: tensor with the same shape as `images`.
"""
images = ops.convert_to_tensor(images, name='images')
kernel = ops.convert_to_tensor(
_rgb_to_yuv_kernel, dtype=images.dtype, name='kernel')
ndims = images.get_shape().ndims
return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/idl/idl/generator.py | python | _CppFileWriterBase._predicate | (self, check_str, use_else_if=False, constexpr=False) | return writer.IndentedScopedBlock(self._writer, '%s (%s) {' % (conditional, check_str), '}') | Generate an if block if the condition is not-empty.
Generate 'else if' instead of use_else_if is True. | Generate an if block if the condition is not-empty. | [
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"-",
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] | def _predicate(self, check_str, use_else_if=False, constexpr=False):
# type: (str, bool, bool) -> Union[writer.IndentedScopedBlock,writer.EmptyBlock]
"""
Generate an if block if the condition is not-empty.
Generate 'else if' instead of use_else_if is True.
"""
if not check_str:
return writer.EmptyBlock()
conditional = 'if'
if use_else_if:
conditional = 'else if'
if constexpr:
conditional = conditional + ' constexpr'
return writer.IndentedScopedBlock(self._writer, '%s (%s) {' % (conditional, check_str), '}') | [
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... | https://github.com/mongodb/mongo/blob/d8ff665343ad29cf286ee2cf4a1960d29371937b/buildscripts/idl/idl/generator.py#L390-L407 | |
miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/math_ops.py | python | accumulate_n | (inputs, shape=None, tensor_dtype=None, name=None) | Returns the element-wise sum of a list of tensors.
Optionally, pass `shape` and `tensor_dtype` for shape and type checking,
otherwise, these are inferred.
For example:
```python
# tensor 'a' is [[1, 2], [3, 4]]
# tensor `b` is [[5, 0], [0, 6]]
tf.accumulate_n([a, b, a]) ==> [[7, 4], [6, 14]]
# Explicitly pass shape and type
tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
==> [[7, 4], [6, 14]]
```
Args:
inputs: A list of `Tensor` objects, each with same shape and type.
shape: Shape of elements of `inputs`.
tensor_dtype: The type of `inputs`.
name: A name for the operation (optional).
Returns:
A `Tensor` of same shape and type as the elements of `inputs`.
Raises:
ValueError: If `inputs` don't all have same shape and dtype or the shape
cannot be inferred. | Returns the element-wise sum of a list of tensors. | [
"Returns",
"the",
"element",
"-",
"wise",
"sum",
"of",
"a",
"list",
"of",
"tensors",
"."
] | def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
"""Returns the element-wise sum of a list of tensors.
Optionally, pass `shape` and `tensor_dtype` for shape and type checking,
otherwise, these are inferred.
For example:
```python
# tensor 'a' is [[1, 2], [3, 4]]
# tensor `b` is [[5, 0], [0, 6]]
tf.accumulate_n([a, b, a]) ==> [[7, 4], [6, 14]]
# Explicitly pass shape and type
tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
==> [[7, 4], [6, 14]]
```
Args:
inputs: A list of `Tensor` objects, each with same shape and type.
shape: Shape of elements of `inputs`.
tensor_dtype: The type of `inputs`.
name: A name for the operation (optional).
Returns:
A `Tensor` of same shape and type as the elements of `inputs`.
Raises:
ValueError: If `inputs` don't all have same shape and dtype or the shape
cannot be inferred.
"""
if tensor_dtype is None:
if not inputs or not isinstance(inputs, (list, tuple)):
raise ValueError("inputs must be a list of at least one Tensor with the "
"same dtype and shape")
inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs)
if not all(isinstance(x, ops.Tensor) for x in inputs):
raise ValueError("inputs must be a list of at least one Tensor with the "
"same dtype and shape")
if not all(x.dtype == inputs[0].dtype for x in inputs):
raise ValueError("inputs must be a list of at least one Tensor with the "
"same dtype and shape")
tensor_dtype = inputs[0].dtype
if shape is not None:
shape = tensor_shape.as_shape(shape)
else:
shape = tensor_shape.unknown_shape()
for input_tensor in inputs:
if isinstance(input_tensor, ops.Tensor):
shape = shape.merge_with(input_tensor.get_shape())
if not shape.is_fully_defined():
# TODO(pbar): Make a version of assign_add that accepts an uninitialized
# lvalue, and takes its shape from that? This would allow accumulate_n to
# work in all situations that add_n currently works.
raise ValueError("Cannot infer the shape of the accumulator for "
"accumulate_n. Pass the shape argument, or set the shape "
"of at least one of the inputs.")
with ops.op_scope(inputs, name, "AccumulateN") as name:
if len(inputs) == 1:
return inputs[0]
var = gen_state_ops._temporary_variable(shape=shape, dtype=tensor_dtype)
var_name = var.op.name
var = state_ops.assign(var, array_ops.zeros_like(inputs[0]))
update_ops = []
for input_tensor in inputs:
op = state_ops.assign_add(var, input_tensor, use_locking=True)
update_ops.append(op)
with ops.control_dependencies(update_ops):
return gen_state_ops._destroy_temporary_variable(var,
var_name=var_name,
name=name) | [
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RamadhanAmizudin/malware | 2c6c53c8b0d556f5d8078d6ca0fc4448f4697cf1 | Fuzzbunch/fuzzbunch/pyreadline/modes/emacs.py | python | EmacsMode.transpose_chars | (self, e) | Drag the character before the cursor forward over the character
at the cursor, moving the cursor forward as well. If the insertion
point is at the end of the line, then this transposes the last two
characters of the line. Negative arguments have no effect. | Drag the character before the cursor forward over the character
at the cursor, moving the cursor forward as well. If the insertion
point is at the end of the line, then this transposes the last two
characters of the line. Negative arguments have no effect. | [
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... | def transpose_chars(self, e): # (C-t)
'''Drag the character before the cursor forward over the character
at the cursor, moving the cursor forward as well. If the insertion
point is at the end of the line, then this transposes the last two
characters of the line. Negative arguments have no effect.'''
self.l_buffer.transpose_chars() | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/fusion/portableglobe/servers/local_server.py | python | LocalServer.LocalJsonSearchHandler | (self, handler) | Handle GET request for json search results. | Handle GET request for json search results. | [
"Handle",
"GET",
"request",
"for",
"json",
"search",
"results",
"."
] | def LocalJsonSearchHandler(self, handler):
"""Handle GET request for json search results."""
if not handler.IsValidRequest():
raise tornado.web.HTTPError(404)
cb = handler.request.arguments["cb"][0]
service = handler.request.arguments["service"][0]
try:
self.search_services_[service].JsonSearch(handler, cb)
return
except KeyError:
pass # Search service has not been defined; use default.
if "displayKeys" in handler.request.arguments.keys():
key = handler.request.arguments["displayKeys"][0]
if key in handler.request.arguments.keys():
search_term = handler.request.arguments[key][0].lower()
handler.write(tornado.web.globe_.search_db_.JsonSearch(search_term, cb)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/msw/gizmos.py | python | TreeListCtrl.GetLineSpacing | (*args, **kwargs) | return _gizmos.TreeListCtrl_GetLineSpacing(*args, **kwargs) | GetLineSpacing(self) -> unsigned int | GetLineSpacing(self) -> unsigned int | [
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"(",
"self",
")",
"-",
">",
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] | def GetLineSpacing(*args, **kwargs):
"""GetLineSpacing(self) -> unsigned int"""
return _gizmos.TreeListCtrl_GetLineSpacing(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/ma/core.py | python | is_masked | (x) | return False | Determine whether input has masked values.
Accepts any object as input, but always returns False unless the
input is a MaskedArray containing masked values.
Parameters
----------
x : array_like
Array to check for masked values.
Returns
-------
result : bool
True if `x` is a MaskedArray with masked values, False otherwise.
Examples
--------
>>> import numpy.ma as ma
>>> x = ma.masked_equal([0, 1, 0, 2, 3], 0)
>>> x
masked_array(data = [-- 1 -- 2 3],
mask = [ True False True False False],
fill_value=999999)
>>> ma.is_masked(x)
True
>>> x = ma.masked_equal([0, 1, 0, 2, 3], 42)
>>> x
masked_array(data = [0 1 0 2 3],
mask = False,
fill_value=999999)
>>> ma.is_masked(x)
False
Always returns False if `x` isn't a MaskedArray.
>>> x = [False, True, False]
>>> ma.is_masked(x)
False
>>> x = 'a string'
>>> ma.is_masked(x)
False | Determine whether input has masked values. | [
"Determine",
"whether",
"input",
"has",
"masked",
"values",
"."
] | def is_masked(x):
"""
Determine whether input has masked values.
Accepts any object as input, but always returns False unless the
input is a MaskedArray containing masked values.
Parameters
----------
x : array_like
Array to check for masked values.
Returns
-------
result : bool
True if `x` is a MaskedArray with masked values, False otherwise.
Examples
--------
>>> import numpy.ma as ma
>>> x = ma.masked_equal([0, 1, 0, 2, 3], 0)
>>> x
masked_array(data = [-- 1 -- 2 3],
mask = [ True False True False False],
fill_value=999999)
>>> ma.is_masked(x)
True
>>> x = ma.masked_equal([0, 1, 0, 2, 3], 42)
>>> x
masked_array(data = [0 1 0 2 3],
mask = False,
fill_value=999999)
>>> ma.is_masked(x)
False
Always returns False if `x` isn't a MaskedArray.
>>> x = [False, True, False]
>>> ma.is_masked(x)
False
>>> x = 'a string'
>>> ma.is_masked(x)
False
"""
m = getmask(x)
if m is nomask:
return False
elif m.any():
return True
return False | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TIntHI.IsEmpty | (self) | return _snap.TIntHI_IsEmpty(self) | IsEmpty(TIntHI self) -> bool
Parameters:
self: THashKeyDatI< TInt,TInt > const * | IsEmpty(TIntHI self) -> bool | [
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"TIntHI",
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")",
"-",
">",
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] | def IsEmpty(self):
"""
IsEmpty(TIntHI self) -> bool
Parameters:
self: THashKeyDatI< TInt,TInt > const *
"""
return _snap.TIntHI_IsEmpty(self) | [
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] | https://github.com/snap-stanford/snap-python/blob/d53c51b0a26aa7e3e7400b014cdf728948fde80a/setup/snap.py#L19037-L19045 |
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