nwo stringlengths 5 86 | sha stringlengths 40 40 | path stringlengths 4 189 | language stringclasses 1 value | identifier stringlengths 1 94 | parameters stringlengths 2 4.03k | argument_list stringclasses 1 value | return_statement stringlengths 0 11.5k | docstring stringlengths 1 33.2k | docstring_summary stringlengths 0 5.15k | docstring_tokens list | function stringlengths 34 151k | function_tokens list | url stringlengths 90 278 |
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AcademySoftwareFoundation/OpenShadingLanguage | b816c21b1901beba3dec53cc229d381f80882171 | doc/build_install/windows/build_osl.py | python | CopyFiles | (context, src, dest) | Copy files like shutil.copy, but src may be a glob pattern. | Copy files like shutil.copy, but src may be a glob pattern. | [
"Copy",
"files",
"like",
"shutil",
".",
"copy",
"but",
"src",
"may",
"be",
"a",
"glob",
"pattern",
"."
] | def CopyFiles(context, src, dest):
"""Copy files like shutil.copy, but src may be a glob pattern."""
filesToCopy = glob.glob(src)
if not filesToCopy:
raise RuntimeError("File(s) to copy {src} not found".format(src=src))
instDestDir = os.path.join(context.instDir, dest)
for f in filesToCopy:
PrintCommandOutput(
"Copying {file} to {destDir}\n".format(file=f, destDir=instDestDir)
)
shutil.copy(f, instDestDir) | [
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/algorithms/exploitability_descent.py | python | Solver.step | (self, session, learning_rate) | return nash_conv | Takes a single exploitability descent step. | Takes a single exploitability descent step. | [
"Takes",
"a",
"single",
"exploitability",
"descent",
"step",
"."
] | def step(self, session, learning_rate):
"""Takes a single exploitability descent step."""
_, nash_conv = session.run([self._optimizer_step, self._nash_conv],
feed_dict={self._learning_rate: learning_rate})
return nash_conv | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/setuptools/_vendor/pyparsing.py | python | ParseResults.asDict | ( self ) | return dict((k,toItem(v)) for k,v in item_fn()) | Returns the named parse results as a nested dictionary.
Example::
integer = Word(nums)
date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
result = date_str.parseString('12/31/1999')
print(type(result), repr(result)) # -> <class 'pyparsing.ParseResults'> (['12', '/', '31', '/', '1999'], {'day': [('1999', 4)], 'year': [('12', 0)], 'month': [('31', 2)]})
result_dict = result.asDict()
print(type(result_dict), repr(result_dict)) # -> <class 'dict'> {'day': '1999', 'year': '12', 'month': '31'}
# even though a ParseResults supports dict-like access, sometime you just need to have a dict
import json
print(json.dumps(result)) # -> Exception: TypeError: ... is not JSON serializable
print(json.dumps(result.asDict())) # -> {"month": "31", "day": "1999", "year": "12"} | Returns the named parse results as a nested dictionary. | [
"Returns",
"the",
"named",
"parse",
"results",
"as",
"a",
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] | def asDict( self ):
"""
Returns the named parse results as a nested dictionary.
Example::
integer = Word(nums)
date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
result = date_str.parseString('12/31/1999')
print(type(result), repr(result)) # -> <class 'pyparsing.ParseResults'> (['12', '/', '31', '/', '1999'], {'day': [('1999', 4)], 'year': [('12', 0)], 'month': [('31', 2)]})
result_dict = result.asDict()
print(type(result_dict), repr(result_dict)) # -> <class 'dict'> {'day': '1999', 'year': '12', 'month': '31'}
# even though a ParseResults supports dict-like access, sometime you just need to have a dict
import json
print(json.dumps(result)) # -> Exception: TypeError: ... is not JSON serializable
print(json.dumps(result.asDict())) # -> {"month": "31", "day": "1999", "year": "12"}
"""
if PY_3:
item_fn = self.items
else:
item_fn = self.iteritems
def toItem(obj):
if isinstance(obj, ParseResults):
if obj.haskeys():
return obj.asDict()
else:
return [toItem(v) for v in obj]
else:
return obj
return dict((k,toItem(v)) for k,v in item_fn()) | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBProcess.Kill | (self) | return _lldb.SBProcess_Kill(self) | Kill(SBProcess self) -> SBError | Kill(SBProcess self) -> SBError | [
"Kill",
"(",
"SBProcess",
"self",
")",
"-",
">",
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] | def Kill(self):
"""Kill(SBProcess self) -> SBError"""
return _lldb.SBProcess_Kill(self) | [
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")",
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py2/google/protobuf/json_format.py | python | _ConvertFloat | (value, field) | Convert an floating point number. | Convert an floating point number. | [
"Convert",
"an",
"floating",
"point",
"number",
"."
] | def _ConvertFloat(value, field):
"""Convert an floating point number."""
if isinstance(value, float):
if math.isnan(value):
raise ParseError('Couldn\'t parse NaN, use quoted "NaN" instead.')
if math.isinf(value):
if value > 0:
raise ParseError('Couldn\'t parse Infinity or value too large, '
'use quoted "Infinity" instead.')
else:
raise ParseError('Couldn\'t parse -Infinity or value too small, '
'use quoted "-Infinity" instead.')
if field.cpp_type == descriptor.FieldDescriptor.CPPTYPE_FLOAT:
# pylint: disable=protected-access
if value > type_checkers._FLOAT_MAX:
raise ParseError('Float value too large')
# pylint: disable=protected-access
if value < type_checkers._FLOAT_MIN:
raise ParseError('Float value too small')
if value == 'nan':
raise ParseError('Couldn\'t parse float "nan", use "NaN" instead.')
try:
# Assume Python compatible syntax.
return float(value)
except ValueError:
# Check alternative spellings.
if value == _NEG_INFINITY:
return float('-inf')
elif value == _INFINITY:
return float('inf')
elif value == _NAN:
return float('nan')
else:
raise ParseError('Couldn\'t parse float: {0}.'.format(value)) | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | gr-digital/python/digital/qa_header_payload_demux.py | python | qa_header_payload_demux.test_003_t | (self) | Like test 1, but twice, plus one fail | Like test 1, but twice, plus one fail | [
"Like",
"test",
"1",
"but",
"twice",
"plus",
"one",
"fail"
] | def test_003_t(self):
"""
Like test 1, but twice, plus one fail
"""
# Tx Data
n_zeros = 5
header = [1, 2, 3]
header_fail = [-1, -2, -4] # Contents don't really matter
payload1 = list(range(5, 20))
payload2 = [42, ]
sampling_rate = 2
data_signal = [0, ] * n_zeros + header + payload1
trigger_signal = [0, ] * len(data_signal) * 2
trigger_signal[n_zeros] = 1
trigger_signal[len(data_signal)] = 1
trigger_signal[len(data_signal) + len(header_fail) + n_zeros] = 1
print("Triggers at: {0} {1} {2}".format(
n_zeros,
len(data_signal),
len(data_signal) + len(header_fail) + n_zeros))
tx_signal = data_signal + \
header_fail + [0, ] * n_zeros + \
header + payload2 + [0, ] * 1000
# Timing tag: This is preserved and updated:
timing_tag = make_tag('rx_time', (0, 0), 0)
# Rx freq tags:
rx_freq_tag1 = make_tag('rx_freq', 1.0, 0)
rx_freq_tag2 = make_tag('rx_freq', 1.5, 29)
rx_freq_tag3 = make_tag('rx_freq', 2.0, 30)
# Flow graph
data_src = blocks.vector_source_f(
tx_signal, False,
tags=(timing_tag, rx_freq_tag1, rx_freq_tag2, rx_freq_tag3)
)
trigger_src = blocks.vector_source_b(trigger_signal, False)
hpd = digital.header_payload_demux(
header_len=len(header),
items_per_symbol=1,
guard_interval=0,
length_tag_key="frame_len",
trigger_tag_key="detect",
output_symbols=False,
itemsize=gr.sizeof_float,
timing_tag_key='rx_time',
samp_rate=sampling_rate,
special_tags=('rx_freq',),
)
# extra system port defined for you
self.assertEqual(pmt.length(hpd.message_ports_in()), 2)
header_sink = blocks.vector_sink_f()
payload_sink = blocks.vector_sink_f()
self.tb.connect(data_src, (hpd, 0))
self.tb.connect(trigger_src, (hpd, 1))
self.tb.connect((hpd, 0), header_sink)
self.tb.connect((hpd, 1), payload_sink)
self.tb.start()
time.sleep(.2) # Need this, otherwise, the next message is ignored
hpd.to_basic_block()._post(
pmt.intern('header_data'),
pmt.from_long(len(payload1))
)
while len(payload_sink.data()) < len(payload1):
time.sleep(.2)
hpd.to_basic_block()._post(
pmt.intern('header_data'),
pmt.PMT_F
)
# This next command is a bit of a showstopper, but there's no condition to check upon
# to see if the previous msg handling is finished
time.sleep(.7)
hpd.to_basic_block()._post(
pmt.intern('header_data'),
pmt.from_long(len(payload2))
)
while len(payload_sink.data()) < len(payload1) + len(payload2):
time.sleep(.2)
self.tb.stop()
self.tb.wait()
# Signal description:
# 0: 5 zeros
# 5: header 1
# 8: payload 1 (length: 15)
# 23: header 2 (fail)
# 26: 5 zeros
# 31: header 3
# 34: payload 2 (length 1)
# 35: 1000 zeros
self.assertEqual(
header_sink.data(), list(
header + header_fail + header))
self.assertEqual(payload_sink.data(), payload1 + payload2)
tags_payload = [gr.tag_to_python(x) for x in payload_sink.tags()]
tags_payload = sorted([(x.offset, x.key, x.value)
for x in tags_payload])
tags_expected_payload = [
(0, 'frame_len', len(payload1)),
(len(payload1), 'frame_len', len(payload2)),
]
tags_header = [gr.tag_to_python(x) for x in header_sink.tags()]
tags_header = sorted([(x.offset, x.key, x.value) for x in tags_header])
tags_expected_header = [
(0, 'rx_freq', 1.0),
# Hard coded time value :( Is n_zeros/sampling_rate
(0, 'rx_time', (2, 0.5)),
(len(header), 'rx_freq', 1.0),
# Hard coded time value :(. See above.
(len(header), 'rx_time', (11, .5)),
(2 * len(header), 'rx_freq', 2.0),
# Hard coded time value :(. See above.
(2 * len(header), 'rx_time', (15, .5)),
]
self.assertEqual(tags_header, tags_expected_header)
self.assertEqual(tags_payload, tags_expected_payload) | [
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... | https://github.com/gnuradio/gnuradio/blob/09c3c4fa4bfb1a02caac74cb5334dfe065391e3b/gr-digital/python/digital/qa_header_payload_demux.py#L454-L566 | ||
kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/binary-watch.py | python | Solution.readBinaryWatch | (self, num) | return ['%d:%02d' % (h, m) for h in xrange(12) for m in xrange(60)
if bit_count(h) + bit_count(m) == num] | :type num: int
:rtype: List[str] | :type num: int
:rtype: List[str] | [
":",
"type",
"num",
":",
"int",
":",
"rtype",
":",
"List",
"[",
"str",
"]"
] | def readBinaryWatch(self, num):
"""
:type num: int
:rtype: List[str]
"""
def bit_count(bits):
count = 0
while bits:
bits &= bits-1
count += 1
return count
return ['%d:%02d' % (h, m) for h in xrange(12) for m in xrange(60)
if bit_count(h) + bit_count(m) == num] | [
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... | https://github.com/kamyu104/LeetCode-Solutions/blob/77605708a927ea3b85aee5a479db733938c7c211/Python/binary-watch.py#L6-L19 | |
vtraag/louvain-igraph | 124ea1be49ee74eec2eaca8006599d7fc5560db6 | src/louvain/VertexPartition.py | python | RBConfigurationVertexPartition.__init__ | (self, graph, initial_membership=None, weights=None, resolution_parameter=1.0) | Parameters
----------
graph : :class:`ig.Graph`
Graph to define the partition on.
initial_membership : list of int
Initial membership for the partition. If :obj:`None` then defaults to a
singleton partition.
weights : list of double, or edge attribute
Weights of edges. Can be either an iterable or an edge attribute.
resolution_parameter : double
Resolution parameter. | Parameters
----------
graph : :class:`ig.Graph`
Graph to define the partition on. | [
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] | def __init__(self, graph, initial_membership=None, weights=None, resolution_parameter=1.0):
"""
Parameters
----------
graph : :class:`ig.Graph`
Graph to define the partition on.
initial_membership : list of int
Initial membership for the partition. If :obj:`None` then defaults to a
singleton partition.
weights : list of double, or edge attribute
Weights of edges. Can be either an iterable or an edge attribute.
resolution_parameter : double
Resolution parameter.
"""
if initial_membership is not None:
initial_membership = list(initial_membership)
super(RBConfigurationVertexPartition, self).__init__(graph, initial_membership)
pygraph_t = _get_py_capsule(graph)
if weights is not None:
if isinstance(weights, str):
weights = graph.es[weights]
else:
# Make sure it is a list
weights = list(weights)
self._partition = _c_louvain._new_RBConfigurationVertexPartition(pygraph_t,
initial_membership, weights, resolution_parameter)
self._update_internal_membership() | [
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Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/ar.py | python | generate | (env) | Add Builders and construction variables for ar to an Environment. | Add Builders and construction variables for ar to an Environment. | [
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"construction",
"variables",
"for",
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"to",
"an",
"Environment",
"."
] | def generate(env):
"""Add Builders and construction variables for ar to an Environment."""
SCons.Tool.createStaticLibBuilder(env)
env['AR'] = 'ar'
env['ARFLAGS'] = SCons.Util.CLVar('rc')
env['ARCOM'] = '$AR $ARFLAGS $TARGET $SOURCES'
env['LIBPREFIX'] = 'lib'
env['LIBSUFFIX'] = '.a'
if env.Detect('ranlib'):
env['RANLIB'] = 'ranlib'
env['RANLIBFLAGS'] = SCons.Util.CLVar('')
env['RANLIBCOM'] = '$RANLIB $RANLIBFLAGS $TARGET' | [
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Manu343726/siplasplas | 9fae7559f87087cf8ef34f04bd1e774b84b2ea9c | reference/cindex.py | python | Cursor.kind | (self) | return CursorKind.from_id(self._kind_id) | Return the kind of this cursor. | Return the kind of this cursor. | [
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] | def kind(self):
"""Return the kind of this cursor."""
return CursorKind.from_id(self._kind_id) | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/nn/functional/common.py | python | class_center_sample | (label, num_classes, num_samples, group=None) | return remapped_label, sampled_class_center | Class center sample method is proposed from the paper PartialFC that only sample a subset of the class centers.
The process of sampling subset class centers is straightforward:
1. First select the positive class centers;
2. Then randomly sample negative class centers.
Specifically, given a label tensor, shape [batch_size], select all the positive class centers and randomly
sample negative class centers, then remap the input label tensor using the sampled class centers.
For more information, Partial FC: Training 10 Million Identities on a Single Machine
arxiv: https://arxiv.org/abs/2010.05222
.. hint::
If the number of the positive class centers is greater than the input num_samples, it keeps all the positive
class centers and the shape of sampled_class_center will be [num_positive_class_centers].
The API supports CPU, single GPU and multi GPU.
Args:
label (Tensor): 1-D tensor with shape [N], each label in [0, num_classes)
num_classes (int): A positive integer to specify the number of classes at local rank.
Note that num_classes of each GPU can be different.
num_samples (int): A positive integer to specify the number of class center to sample.
group (Group, optional): The abstract representation of group.
See paddle.distributed.collective.Group. Default is ``None``.
Returns:
Tuple of two ``Tensor`` : (remapped_label, sampled_class_center), remapped label using sampled class center,
sampled class center from [0, num_classes).
Examples:
.. code-block:: python
:name: code-example1
# CPU or single GPU
import paddle
num_classes = 20
batch_size = 10
num_samples = 6
label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64')
remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes, num_samples)
print(label)
print(remapped_label)
print(sampled_class_index)
# the output is
#Tensor(shape=[10], dtype=int64, place=CPUPlace, stop_gradient=True,
# [11, 5 , 1 , 3 , 12, 2 , 15, 19, 18, 19])
#Tensor(shape=[10], dtype=int64, place=CPUPlace, stop_gradient=True,
# [4, 3, 0, 2, 5, 1, 6, 8, 7, 8])
#Tensor(shape=[9], dtype=int64, place=CPUPlace, stop_gradient=True,
# [1 , 2 , 3 , 5 , 11, 12, 15, 18, 19])
.. code-block:: python
:name: code-example2
# required: distributed
# Multi GPU, test_class_center_sample.py
import paddle
import paddle.distributed as dist
strategy = dist.fleet.DistributedStrategy()
dist.fleet.init(is_collective=True, strategy=strategy)
batch_size = 10
num_samples = 6
rank_id = dist.get_rank()
# num_classes of each GPU can be different, e.g num_classes_list = [10, 8]
num_classes_list = [10, 10]
num_classes = paddle.sum(paddle.to_tensor(num_classes_list))
label = paddle.randint(low=0, high=num_classes.item(), shape=[batch_size], dtype='int64')
label_list = []
dist.all_gather(label_list, label)
label = paddle.concat(label_list, axis=0)
remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes_list[rank_id], num_samples)
print(label)
print(remapped_label)
print(sampled_class_index)
#python -m paddle.distributed.launch --gpus=0,1 test_class_center_sample.py
# rank 0 output:
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ])
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ])
#Tensor(shape=[6], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [0, 2, 4, 8, 9, 3])
# rank 1 output:
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ])
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ])
#Tensor(shape=[7], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [0, 1, 2, 3, 5, 7, 8]) | Class center sample method is proposed from the paper PartialFC that only sample a subset of the class centers.
The process of sampling subset class centers is straightforward: | [
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"""
Class center sample method is proposed from the paper PartialFC that only sample a subset of the class centers.
The process of sampling subset class centers is straightforward:
1. First select the positive class centers;
2. Then randomly sample negative class centers.
Specifically, given a label tensor, shape [batch_size], select all the positive class centers and randomly
sample negative class centers, then remap the input label tensor using the sampled class centers.
For more information, Partial FC: Training 10 Million Identities on a Single Machine
arxiv: https://arxiv.org/abs/2010.05222
.. hint::
If the number of the positive class centers is greater than the input num_samples, it keeps all the positive
class centers and the shape of sampled_class_center will be [num_positive_class_centers].
The API supports CPU, single GPU and multi GPU.
Args:
label (Tensor): 1-D tensor with shape [N], each label in [0, num_classes)
num_classes (int): A positive integer to specify the number of classes at local rank.
Note that num_classes of each GPU can be different.
num_samples (int): A positive integer to specify the number of class center to sample.
group (Group, optional): The abstract representation of group.
See paddle.distributed.collective.Group. Default is ``None``.
Returns:
Tuple of two ``Tensor`` : (remapped_label, sampled_class_center), remapped label using sampled class center,
sampled class center from [0, num_classes).
Examples:
.. code-block:: python
:name: code-example1
# CPU or single GPU
import paddle
num_classes = 20
batch_size = 10
num_samples = 6
label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64')
remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes, num_samples)
print(label)
print(remapped_label)
print(sampled_class_index)
# the output is
#Tensor(shape=[10], dtype=int64, place=CPUPlace, stop_gradient=True,
# [11, 5 , 1 , 3 , 12, 2 , 15, 19, 18, 19])
#Tensor(shape=[10], dtype=int64, place=CPUPlace, stop_gradient=True,
# [4, 3, 0, 2, 5, 1, 6, 8, 7, 8])
#Tensor(shape=[9], dtype=int64, place=CPUPlace, stop_gradient=True,
# [1 , 2 , 3 , 5 , 11, 12, 15, 18, 19])
.. code-block:: python
:name: code-example2
# required: distributed
# Multi GPU, test_class_center_sample.py
import paddle
import paddle.distributed as dist
strategy = dist.fleet.DistributedStrategy()
dist.fleet.init(is_collective=True, strategy=strategy)
batch_size = 10
num_samples = 6
rank_id = dist.get_rank()
# num_classes of each GPU can be different, e.g num_classes_list = [10, 8]
num_classes_list = [10, 10]
num_classes = paddle.sum(paddle.to_tensor(num_classes_list))
label = paddle.randint(low=0, high=num_classes.item(), shape=[batch_size], dtype='int64')
label_list = []
dist.all_gather(label_list, label)
label = paddle.concat(label_list, axis=0)
remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes_list[rank_id], num_samples)
print(label)
print(remapped_label)
print(sampled_class_index)
#python -m paddle.distributed.launch --gpus=0,1 test_class_center_sample.py
# rank 0 output:
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ])
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ])
#Tensor(shape=[6], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [0, 2, 4, 8, 9, 3])
# rank 1 output:
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ])
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ])
#Tensor(shape=[7], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [0, 1, 2, 3, 5, 7, 8])
"""
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
rank = 0
nranks = 1
if core.is_compiled_with_dist():
parallel_env = paddle.distributed.ParallelEnv()
global_rank = parallel_env.rank
rank = global_rank if group is None else group.get_group_rank(
global_rank)
nranks = parallel_env.world_size if group is None else group.nranks
if num_samples > num_classes:
raise ValueError(
'Expected num_samples less than or equal to {}, got num_samples {}'.
format(num_classes, num_samples))
label_size = 1
for dim in list(label.shape):
label_size *= dim
if label_size != -1 and label_size < 1:
raise ValueError('Expected label_size > 0 \
(got label_size: {})'.format(label_size))
label_dims = len(list(label.shape))
if label_dims != 1:
raise ValueError('Expected label_dims == 1 \
(got label_dims: {})'.format(label_dims))
seed = None
if (seed is None or seed == 0) and default_main_program().random_seed != 0:
seed = default_main_program().random_seed
if in_dygraph_mode():
remapped_label, sampled_class_center = _C_ops.class_center_sample(
label, 'num_classes', num_classes, 'num_samples', num_samples,
'ring_id', ring_id, 'nranks', nranks, 'rank', rank, 'fix_seed',
seed is not None, 'seed', seed if seed is not None else 0)
return remapped_label, sampled_class_center
check_variable_and_dtype(label, 'label', ['int64', 'int32'],
'class_center_sample')
op_type = 'class_center_sample'
helper = LayerHelper(op_type, **locals())
remapped_label = helper.create_variable_for_type_inference(
dtype=label.dtype)
sampled_class_center = helper.create_variable_for_type_inference(
dtype=label.dtype)
helper.append_op(
type=op_type,
inputs={'Label': label},
outputs={
'RemappedLabel': remapped_label,
'SampledLocalClassCenter': sampled_class_center
},
attrs={
'num_classes': num_classes,
'num_samples': num_samples,
'ring_id': ring_id,
'nranks': nranks,
'rank': rank,
'fix_seed': seed is not None,
'seed': seed if seed is not None else 0
})
return remapped_label, sampled_class_center | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/io/formats/info.py | python | _initialize_memory_usage | (
memory_usage: bool | str | None = None,
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memory_usage: bool | str | None = None,
) -> bool | str:
"""Get memory usage based on inputs and display options."""
if memory_usage is None:
memory_usage = get_option("display.memory_usage")
return memory_usage | [
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | llvm/utils/benchmark/tools/gbench/report.py | python | filter_benchmark | (json_orig, family, replacement="") | return filtered | Apply a filter to the json, and only leave the 'family' of benchmarks. | Apply a filter to the json, and only leave the 'family' of benchmarks. | [
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"""
Apply a filter to the json, and only leave the 'family' of benchmarks.
"""
regex = re.compile(family)
filtered = {}
filtered['benchmarks'] = []
for be in json_orig['benchmarks']:
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continue
filteredbench = copy.deepcopy(be) # Do NOT modify the old name!
filteredbench['name'] = regex.sub(replacement, filteredbench['name'])
filtered['benchmarks'].append(filteredbench)
return filtered | [
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devsisters/libquic | 8954789a056d8e7d5fcb6452fd1572ca57eb5c4e | src/third_party/protobuf/python/google/protobuf/internal/decoder.py | python | _FloatDecoder | () | return _SimpleDecoder(wire_format.WIRETYPE_FIXED32, InnerDecode) | Returns a decoder for a float field.
This code works around a bug in struct.unpack for non-finite 32-bit
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"""Returns a decoder for a float field.
This code works around a bug in struct.unpack for non-finite 32-bit
floating-point values.
"""
local_unpack = struct.unpack
def InnerDecode(buffer, pos):
# We expect a 32-bit value in little-endian byte order. Bit 1 is the sign
# bit, bits 2-9 represent the exponent, and bits 10-32 are the significand.
new_pos = pos + 4
float_bytes = buffer[pos:new_pos]
# If this value has all its exponent bits set, then it's non-finite.
# In Python 2.4, struct.unpack will convert it to a finite 64-bit value.
# To avoid that, we parse it specially.
if (float_bytes[3:4] in b'\x7F\xFF' and float_bytes[2:3] >= b'\x80'):
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# If sign bit is set...
if float_bytes[3:4] == b'\xFF':
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return (_POS_INF, new_pos)
# Note that we expect someone up-stack to catch struct.error and convert
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result = local_unpack('<f', float_bytes)[0]
return (result, new_pos)
return _SimpleDecoder(wire_format.WIRETYPE_FIXED32, InnerDecode) | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/MimeWriter.py | python | MimeWriter.startbody | (self, ctype, plist=[], prefix=1) | return self._fp | Returns a file-like object for writing the body of the message.
The content-type is set to the provided ctype, and the optional
parameter, plist, provides additional parameters for the
content-type declaration. The optional argument prefix determines
where the header is inserted; 0 means append at the end, 1 means
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"""Returns a file-like object for writing the body of the message.
The content-type is set to the provided ctype, and the optional
parameter, plist, provides additional parameters for the
content-type declaration. The optional argument prefix determines
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"""
for name, value in plist:
ctype = ctype + ';\n %s=\"%s\"' % (name, value)
self.addheader("Content-Type", ctype, prefix=prefix)
self.flushheaders()
self._fp.write("\n")
return self._fp | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/optimizer/adamw.py | python | AdamW._append_decoupled_weight_decay | (self, block, param_and_grad) | Add decoupled weight decay op.
parameter = parameter - parameter * coeff * lr
Args:
block: block in which variable is to be created
param_and_grad: (parameters, gradients) pairs,
the parameters need to decay.
Raises:
Exception: The type of coeff and parameter is not consistent. | Add decoupled weight decay op.
parameter = parameter - parameter * coeff * lr
Args:
block: block in which variable is to be created
param_and_grad: (parameters, gradients) pairs,
the parameters need to decay.
Raises:
Exception: The type of coeff and parameter is not consistent. | [
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"""
Add decoupled weight decay op.
parameter = parameter - parameter * coeff * lr
Args:
block: block in which variable is to be created
param_and_grad: (parameters, gradients) pairs,
the parameters need to decay.
Raises:
Exception: The type of coeff and parameter is not consistent.
"""
if isinstance(param_and_grad, dict):
param_and_grad = self._update_param_group(param_and_grad)
param, grad = param_and_grad
if self._apply_decay_param_fun is not None \
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return
if isinstance(self._learning_rate, float):
learning_rate = self._learning_rate
else:
# NOTE. We add this function to the _append_optimize_op(),
# for we must make sure _create_param_lr() be called after
# optimizer._create_global_learning_rate().
learning_rate = self._create_param_lr(param_and_grad)
with block.program._optimized_guard(
[param, grad]), framework.name_scope('weight decay'):
self._params_name.add(param.name)
# If it has been calculated, the result will be reused.
# NOTE(wangxi): In dygraph mode, apply_gradient will be executed
# every step, so need clear _lr_to_coeff every step,
# we do this in _create_optimization_pass
decay_coeff = self._lr_to_coeff.get(learning_rate, None)
if decay_coeff is None:
# NOTE(wangxi): for pipeline to set device:all
with paddle.static.device_guard(None):
decay_coeff = 1.0 - learning_rate * self._coeff
self._lr_to_coeff[learning_rate] = decay_coeff
find_master = (self._multi_precision and
param.dtype == core.VarDesc.VarType.FP16)
if find_master:
master_weight = self._master_weights[param.name]
scaled_param = master_weight * decay_coeff
paddle.fluid.layers.assign(
input=scaled_param, output=master_weight)
else:
scaled_param = param * decay_coeff
paddle.fluid.layers.assign(input=scaled_param, output=param) | [
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9miao/CrossApp | 1f5375e061bf69841eb19728598f5ae3f508d620 | tools/bindings-generator/clang/cindex.py | python | Cursor.result_type | (self) | return self._result_type | Retrieve the Type of the result for this Cursor. | Retrieve the Type of the result for this Cursor. | [
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self._result_type = conf.lib.clang_getResultType(self.type)
return self._result_type | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | PseudoDC.EndDrawing | (*args, **kwargs) | return _gdi_.PseudoDC_EndDrawing(*args, **kwargs) | EndDrawing(self)
Ends the group of drawing primitives started with `BeginDrawing`, and
invokes whatever optimization is available for this DC type on the
current platform. | EndDrawing(self) | [
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"""
EndDrawing(self)
Ends the group of drawing primitives started with `BeginDrawing`, and
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"""
return _gdi_.PseudoDC_EndDrawing(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/collections.py | python | Counter.__init__ | (*args, **kwds) | Create a new, empty Counter object. And if given, count elements
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>>> c = Counter() # a new, empty counter
>>> c = Counter('gallahad') # a new counter from an iterable
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>>> c = Counter(a=4, b=2) # a new counter from keyword args | Create a new, empty Counter object. And if given, count elements
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] | def __init__(*args, **kwds):
'''Create a new, empty Counter object. And if given, count elements
from an input iterable. Or, initialize the count from another mapping
of elements to their counts.
>>> c = Counter() # a new, empty counter
>>> c = Counter('gallahad') # a new counter from an iterable
>>> c = Counter({'a': 4, 'b': 2}) # a new counter from a mapping
>>> c = Counter(a=4, b=2) # a new counter from keyword args
'''
if not args:
raise TypeError("descriptor '__init__' of 'Counter' object "
"needs an argument")
self = args[0]
args = args[1:]
if len(args) > 1:
raise TypeError('expected at most 1 arguments, got %d' % len(args))
super(Counter, self).__init__()
self.update(*args, **kwds) | [
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psnonis/FinBERT | c0c555d833a14e2316a3701e59c0b5156f804b4e | bert-gpu/modeling.py | python | embedding_postprocessor | (input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=16,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1,
use_one_hot_embeddings=False) | return output | Performs various post-processing on a word embedding tensor.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length,
embedding_size].
use_token_type: bool. Whether to add embeddings for `token_type_ids`.
token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
Must be specified if `use_token_type` is True.
token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
token_type_embedding_name: string. The name of the embedding table variable
for token type ids.
use_position_embeddings: bool. Whether to add position embeddings for the
position of each token in the sequence.
position_embedding_name: string. The name of the embedding table variable
for positional embeddings.
initializer_range: float. Range of the weight initialization.
max_position_embeddings: int. Maximum sequence length that might ever be
used with this model. This can be longer than the sequence length of
input_tensor, but cannot be shorter.
dropout_prob: float. Dropout probability applied to the final output tensor.
use_one_hot_embeddings: (optional) bool. Whether to use one-hot word
embeddings or tf.embedding_lookup() for the word embeddings.
Returns:
float tensor with same shape as `input_tensor`.
Raises:
ValueError: One of the tensor shapes or input values is invalid. | Performs various post-processing on a word embedding tensor. | [
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"-",
"processing",
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"tensor",
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] | def embedding_postprocessor(input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=16,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1,
use_one_hot_embeddings=False):
"""Performs various post-processing on a word embedding tensor.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length,
embedding_size].
use_token_type: bool. Whether to add embeddings for `token_type_ids`.
token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
Must be specified if `use_token_type` is True.
token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
token_type_embedding_name: string. The name of the embedding table variable
for token type ids.
use_position_embeddings: bool. Whether to add position embeddings for the
position of each token in the sequence.
position_embedding_name: string. The name of the embedding table variable
for positional embeddings.
initializer_range: float. Range of the weight initialization.
max_position_embeddings: int. Maximum sequence length that might ever be
used with this model. This can be longer than the sequence length of
input_tensor, but cannot be shorter.
dropout_prob: float. Dropout probability applied to the final output tensor.
use_one_hot_embeddings: (optional) bool. Whether to use one-hot word
embeddings or tf.embedding_lookup() for the word embeddings.
Returns:
float tensor with same shape as `input_tensor`.
Raises:
ValueError: One of the tensor shapes or input values is invalid.
"""
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor
if use_token_type:
if token_type_ids is None:
raise ValueError("`token_type_ids` must be specified if"
"`use_token_type` is True.")
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
flat_token_type_ids = tf.reshape(token_type_ids, [-1])
if use_one_hot_embeddings:
# This vocab will be small so we always do one-hot here, since it is
# always faster for a small vocabulary.
one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
else:
token_type_embeddings = tf.gather(token_type_table, flat_token_type_ids)
token_type_embeddings = tf.reshape(token_type_embeddings,
[batch_size, seq_length, width])
output += token_type_embeddings
if use_position_embeddings:
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range))
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length `max_position_embeddings`. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So `full_position_embeddings` is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])
num_dims = len(output.shape.as_list())
# Only the last two dimensions are relevant (`seq_length` and `width`), so
# we broadcast among the first dimensions, which is typically just
# the batch size.
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend([seq_length, width])
position_embeddings = tf.reshape(position_embeddings,
position_broadcast_shape)
output += position_embeddings
output = layer_norm_and_dropout(output, dropout_prob)
return output | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | DC.GetBackgroundMode | (*args, **kwargs) | return _gdi_.DC_GetBackgroundMode(*args, **kwargs) | GetBackgroundMode(self) -> int
Returns the current background mode, either ``wx.SOLID`` or
``wx.TRANSPARENT``. | GetBackgroundMode(self) -> int | [
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"(",
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")",
"-",
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] | def GetBackgroundMode(*args, **kwargs):
"""
GetBackgroundMode(self) -> int
Returns the current background mode, either ``wx.SOLID`` or
``wx.TRANSPARENT``.
"""
return _gdi_.DC_GetBackgroundMode(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/requests/sessions.py | python | SessionRedirectMixin.rebuild_method | (self, prepared_request, response) | When being redirected we may want to change the method of the request
based on certain specs or browser behavior. | When being redirected we may want to change the method of the request
based on certain specs or browser behavior. | [
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] | def rebuild_method(self, prepared_request, response):
"""When being redirected we may want to change the method of the request
based on certain specs or browser behavior.
"""
method = prepared_request.method
# https://tools.ietf.org/html/rfc7231#section-6.4.4
if response.status_code == codes.see_other and method != 'HEAD':
method = 'GET'
# Do what the browsers do, despite standards...
# First, turn 302s into GETs.
if response.status_code == codes.found and method != 'HEAD':
method = 'GET'
# Second, if a POST is responded to with a 301, turn it into a GET.
# This bizarre behaviour is explained in Issue 1704.
if response.status_code == codes.moved and method == 'POST':
method = 'GET'
prepared_request.method = method | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/pdb.py | python | Pdb.bp_commands | (self,frame) | return 1 | Call every command that was set for the current active breakpoint
(if there is one).
Returns True if the normal interaction function must be called,
False otherwise. | Call every command that was set for the current active breakpoint
(if there is one). | [
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] | def bp_commands(self,frame):
"""Call every command that was set for the current active breakpoint
(if there is one).
Returns True if the normal interaction function must be called,
False otherwise."""
# self.currentbp is set in bdb in Bdb.break_here if a breakpoint was hit
if getattr(self, "currentbp", False) and \
self.currentbp in self.commands:
currentbp = self.currentbp
self.currentbp = 0
lastcmd_back = self.lastcmd
self.setup(frame, None)
for line in self.commands[currentbp]:
self.onecmd(line)
self.lastcmd = lastcmd_back
if not self.commands_silent[currentbp]:
self.print_stack_entry(self.stack[self.curindex])
if self.commands_doprompt[currentbp]:
self.cmdloop()
self.forget()
return
return 1 | [
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rsummers11/CADLab | 976ed959a0b5208bb4173127a7ef732ac73a9b6f | lesion_detector_3DCE/rcnn/processing/generate_anchor.py | python | generate_anchors | (base_size=16, ratios=[0.5, 1, 2],
scales=2 ** np.arange(3, 6)) | return anchors | Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, 15, 15) window. | Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, 15, 15) window. | [
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"15",
")",
"window",
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] | def generate_anchors(base_size=16, ratios=[0.5, 1, 2],
scales=2 ** np.arange(3, 6)):
"""
Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, 15, 15) window.
"""
base_anchor = np.array([1, 1, base_size, base_size]) - 1
ratio_anchors = _ratio_enum(base_anchor, ratios)
anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales)
for i in xrange(ratio_anchors.shape[0])])
return anchors | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/tools/quantization/quantize_graph.py | python | GraphRewriter.__init__ | (self,
input_graph,
mode,
quantized_input_range,
fallback_quantization_range=None) | Sets up the class to rewrite a float graph.
Args:
input_graph: A float graph to transform.
mode: A string controlling how quantization is performed -
round, quantize, eightbit, or weights.
quantized_input_range: if set, assume the input is
quantized and represents the range
[quantized_input_range[0], quantized_input_range[1]]
fallback_quantization_range: if set, then for nodes where the quantization
range can't be inferred from the graph, use the range
[fallback_quantization_range[0], fallback_quantization_range[1]) instead
of using a RequantizationRange node in the graph.
Raises:
ValueError: Two nodes with the same name were found in the graph. | Sets up the class to rewrite a float graph. | [
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] | def __init__(self,
input_graph,
mode,
quantized_input_range,
fallback_quantization_range=None):
"""Sets up the class to rewrite a float graph.
Args:
input_graph: A float graph to transform.
mode: A string controlling how quantization is performed -
round, quantize, eightbit, or weights.
quantized_input_range: if set, assume the input is
quantized and represents the range
[quantized_input_range[0], quantized_input_range[1]]
fallback_quantization_range: if set, then for nodes where the quantization
range can't be inferred from the graph, use the range
[fallback_quantization_range[0], fallback_quantization_range[1]) instead
of using a RequantizationRange node in the graph.
Raises:
ValueError: Two nodes with the same name were found in the graph.
"""
self.input_graph = input_graph
self.nodes_map = self.create_nodes_map(input_graph)
self.output_graph = None
self.mode = mode
self.final_node_renames = {}
if quantized_input_range:
self.input_range = (quantized_input_range[0], quantized_input_range[1])
if self.input_range[0] >= self.input_range[1]:
raise ValueError("Invalid quantized_input_range: [%s,%s]" %
self.input_range)
if self.mode != "eightbit":
raise ValueError(
"quantized_input_range can only be specified in eightbit mode")
else:
self.input_range = None
if fallback_quantization_range:
self.fallback_quantization_range = [
fallback_quantization_range[0], fallback_quantization_range[1]
]
if (self.fallback_quantization_range[0] >=
self.fallback_quantization_range[1]):
raise ValueError("Invalid fallback_quantization_range: [%s,%s]" %
self.fallback_quantization_range)
if self.mode != "eightbit":
raise ValueError("fallback_quantization_range can only be "
"specified in eightbit mode")
else:
self.fallback_quantization_range = None
# Data that is valid only during the recursive call to rewrite the graph.
self.state = None | [
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neo-ai/neo-ai-dlr | bf397aa0367a5207654c00d2985f900d94ad1543 | python/dlr/counter/phone_home.py | python | PhoneHome.get_model_hash | (self, model) | return name | Get hashsed model name
Args:
model str: name of model
Returns:
str: hased str of model | Get hashsed model name | [
"Get",
"hashsed",
"model",
"name"
] | def get_model_hash(self, model):
""" Get hashsed model name
Args:
model str: name of model
Returns:
str: hased str of model
"""
hashed = get_hash_string(model.encode())
name = str(hashed.hexdigest())
return name | [
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giuspen/cherrytree | 84712f206478fcf9acf30174009ad28c648c6344 | pygtk2/modules/core.py | python | CherryTree.bookmarks_handle | (self, *args) | Handle the Bookmarks List | Handle the Bookmarks List | [
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] | def bookmarks_handle(self, *args):
"""Handle the Bookmarks List"""
if support.bookmarks_handle(self):
self.update_window_save_needed("book") | [
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NicknineTheEagle/TF2-Base | 20459c5a7fbc995b6bf54fa85c2f62a101e9fb64 | src/thirdparty/protobuf-2.3.0/python/google/protobuf/internal/encoder.py | python | _SignedVarintEncoder | () | return EncodeSignedVarint | Return an encoder for a basic signed varint value (does not include
tag). | Return an encoder for a basic signed varint value (does not include
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] | def _SignedVarintEncoder():
"""Return an encoder for a basic signed varint value (does not include
tag)."""
local_chr = chr
def EncodeSignedVarint(write, value):
if value < 0:
value += (1 << 64)
bits = value & 0x7f
value >>= 7
while value:
write(local_chr(0x80|bits))
bits = value & 0x7f
value >>= 7
return write(local_chr(bits))
return EncodeSignedVarint | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | tools/sampcd_processor.py | python | get_incrementapi | () | this function will get the apis that difference between API_DEV.spec and API_PR.spec. | this function will get the apis that difference between API_DEV.spec and API_PR.spec. | [
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] | def get_incrementapi():
'''
this function will get the apis that difference between API_DEV.spec and API_PR.spec.
'''
global API_DEV_SPEC_FN, API_PR_SPEC_FN, API_DIFF_SPEC_FN ## readonly
dev_api = get_api_md5(API_DEV_SPEC_FN)
pr_api = get_api_md5(API_PR_SPEC_FN)
with open(API_DIFF_SPEC_FN, 'w') as f:
for key in pr_api:
if key in dev_api:
if dev_api[key] != pr_api[key]:
logger.debug("%s in dev is %s, different from pr's %s", key,
dev_api[key], pr_api[key])
f.write(key)
f.write('\n')
else:
logger.debug("%s is not in dev", key)
f.write(key)
f.write('\n') | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/traci/_vehicle.py | python | VehicleDomain.getEmergencyDecel | (self, vehID) | return self._getUniversal(tc.VAR_EMERGENCY_DECEL, vehID) | getEmergencyDecel(string) -> double
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"""
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pristineio/webrtc-mirror | 7a5bcdffaab90a05bc1146b2b1ea71c004e54d71 | tools_webrtc/presubmit_checks_lib/check_orphan_headers.py | python | IsHeaderInBuildGn | (header_path, build_gn_path) | return header_path in headers_in_build_gn | Returns True if the header is listed in the BUILD.gn file.
Args:
header_path: the absolute path to the header to check.
build_gn_path: the absolute path to the header to check.
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header_path: the absolute path to the header to check.
build_gn_path: the absolute path to the header to check.
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naver/sling | 5671cd445a2caae0b4dd0332299e4cfede05062c | webkit/Tools/Scripts/webkitpy/thirdparty/irc/irclib.py | python | ServerConnection.motd | (self, server="") | Send an MOTD command. | Send an MOTD command. | [
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isl-org/Open3D | 79aec3ddde6a571ce2f28e4096477e52ec465244 | python/open3d/visualization/tensorboard_plugin/summary.py | python | _to_integer | (tensor) | Test converting a tensor (TF, PyTorch, Open3D, Numpy array or a scalar)
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try:
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9miao/CrossApp | 1f5375e061bf69841eb19728598f5ae3f508d620 | tools/bindings-generator/clang/cindex.py | python | Type.is_volatile_qualified | (self) | return conf.lib.clang_isVolatileQualifiedType(self) | Determine whether a Type has the "volatile" qualifier set.
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This does not look through typedefs that may have added "volatile"
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"""
return conf.lib.clang_isVolatileQualifiedType(self) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | VarVScrollHelper.RefreshRows | (*args, **kwargs) | return _windows_.VarVScrollHelper_RefreshRows(*args, **kwargs) | RefreshRows(self, size_t from, size_t to) | RefreshRows(self, size_t from, size_t to) | [
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return _windows_.VarVScrollHelper_RefreshRows(*args, **kwargs) | [
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_PCR_Event_REQUEST.fromBytes | (buffer) | return TpmBuffer(buffer).createObj(TPM2_PCR_Event_REQUEST) | Returns new TPM2_PCR_Event_REQUEST object constructed from its
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""" Returns new TPM2_PCR_Event_REQUEST object constructed from its
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/nn_ops.py | python | _non_atrous_convolution | (
input, # pylint: disable=redefined-builtin
filter, # pylint: disable=redefined-builtin
padding,
data_format=None, # pylint: disable=redefined-builtin
strides=None,
name=None) | Computes sums of N-D convolutions (actually cross correlation).
It is required that 1 <= N <= 3.
This is used to implement the more generic `convolution` function, which
extends the interface of this function with a `dilation_rate` parameter.
Args:
input: Rank N+2 tensor of type T of shape
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does not start with `"NC"`, or
`[batch_size, in_channels] + input_spatial_shape` if `data_format` starts
with `"NC"`.
filter: Rank N+2 tensor of type T of shape
`filter_spatial_shape + [in_channels, out_channels]`. Rank of either
`input` or `filter` must be known.
padding: Padding method to use, must be either "VALID" or "SAME".
data_format: A string or None. Specifies whether the channel dimension of
the `input` and output is the last dimension (default, or if `data_format`
does not start with "NC"), or the second dimension (if `data_format`
starts with "NC"). For N=1, the valid values are "NWC" (default) and
"NCW". For N=2, the valid values are "NHWC" (default) and "NCHW".
For N=3, the valid values are "NDHWC" (default) and "NCDHW".
strides: Sequence of N positive integers, defaults to `[1] * N`.
name: Name prefix to use.
Returns:
Rank N+2 tensor of type T of shape
`[batch_size] + output_spatial_shape + [out_channels]`, where
if padding == "SAME":
output_spatial_shape = input_spatial_shape
if padding == "VALID":
output_spatial_shape = input_spatial_shape - filter_spatial_shape + 1.
Raises:
ValueError: if ranks are incompatible. | Computes sums of N-D convolutions (actually cross correlation). | [
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input, # pylint: disable=redefined-builtin
filter, # pylint: disable=redefined-builtin
padding,
data_format=None, # pylint: disable=redefined-builtin
strides=None,
name=None):
"""Computes sums of N-D convolutions (actually cross correlation).
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This is used to implement the more generic `convolution` function, which
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Args:
input: Rank N+2 tensor of type T of shape
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filter: Rank N+2 tensor of type T of shape
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padding: Padding method to use, must be either "VALID" or "SAME".
data_format: A string or None. Specifies whether the channel dimension of
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does not start with "NC"), or the second dimension (if `data_format`
starts with "NC"). For N=1, the valid values are "NWC" (default) and
"NCW". For N=2, the valid values are "NHWC" (default) and "NCHW".
For N=3, the valid values are "NDHWC" (default) and "NCDHW".
strides: Sequence of N positive integers, defaults to `[1] * N`.
name: Name prefix to use.
Returns:
Rank N+2 tensor of type T of shape
`[batch_size] + output_spatial_shape + [out_channels]`, where
if padding == "SAME":
output_spatial_shape = input_spatial_shape
if padding == "VALID":
output_spatial_shape = input_spatial_shape - filter_spatial_shape + 1.
Raises:
ValueError: if ranks are incompatible.
"""
with ops.name_scope(name, "non_atrous_convolution", [input, filter]) as scope:
input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin
input_shape = input.shape
filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin
filter_shape = filter.shape
op = _NonAtrousConvolution(
input_shape,
filter_shape=filter_shape,
padding=padding,
data_format=data_format,
strides=strides,
name=scope)
return op(input, filter) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/xrc.py | python | XmlResource.LoadMenu | (*args, **kwargs) | return _xrc.XmlResource_LoadMenu(*args, **kwargs) | LoadMenu(self, String name) -> Menu | LoadMenu(self, String name) -> Menu | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | Text.replace | (self, index1, index2, chars, *args) | Replaces the range of characters between index1 and index2 with
the given characters and tags specified by args.
See the method insert for some more information about args, and the
method delete for information about the indices. | Replaces the range of characters between index1 and index2 with
the given characters and tags specified by args. | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/protobuf/python/google/protobuf/descriptor_pool.py | python | DescriptorPool._ConvertEnumDescriptor | (self, enum_proto, package=None, file_desc=None,
containing_type=None, scope=None) | return desc | Make a protobuf EnumDescriptor given an EnumDescriptorProto protobuf.
Args:
enum_proto: The descriptor_pb2.EnumDescriptorProto protobuf message.
package: Optional package name for the new message EnumDescriptor.
file_desc: The file containing the enum descriptor.
containing_type: The type containing this enum.
scope: Scope containing available types.
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"""Make a protobuf EnumDescriptor given an EnumDescriptorProto protobuf.
Args:
enum_proto: The descriptor_pb2.EnumDescriptorProto protobuf message.
package: Optional package name for the new message EnumDescriptor.
file_desc: The file containing the enum descriptor.
containing_type: The type containing this enum.
scope: Scope containing available types.
Returns:
The added descriptor
"""
if package:
enum_name = '.'.join((package, enum_proto.name))
else:
enum_name = enum_proto.name
if file_desc is None:
file_name = None
else:
file_name = file_desc.name
values = [self._MakeEnumValueDescriptor(value, index)
for index, value in enumerate(enum_proto.value)]
desc = descriptor.EnumDescriptor(name=enum_proto.name,
full_name=enum_name,
filename=file_name,
file=file_desc,
values=values,
containing_type=containing_type,
options=enum_proto.options)
scope[enum_proto.name] = desc
scope['.%s' % enum_name] = desc
self._enum_descriptors[enum_name] = desc
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | native_client_sdk/src/tools/oshelpers.py | python | MakeZipPath | (os_path, isdir, iswindows) | return zip_path | Changes a path into zipfile format.
# doctest doesn't seem to honor r'' strings, so the backslashes need to be
# escaped.
>>> MakeZipPath(r'C:\\users\\foobar\\blah', False, True)
'users/foobar/blah'
>>> MakeZipPath('/tmp/tmpfoobar/something', False, False)
'tmp/tmpfoobar/something'
>>> MakeZipPath('./somefile.txt', False, False)
'somefile.txt'
>>> MakeZipPath('somedir', True, False)
'somedir/'
>>> MakeZipPath('../dir/filename.txt', False, False)
'../dir/filename.txt'
>>> MakeZipPath('dir/../filename.txt', False, False)
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"""Changes a path into zipfile format.
# doctest doesn't seem to honor r'' strings, so the backslashes need to be
# escaped.
>>> MakeZipPath(r'C:\\users\\foobar\\blah', False, True)
'users/foobar/blah'
>>> MakeZipPath('/tmp/tmpfoobar/something', False, False)
'tmp/tmpfoobar/something'
>>> MakeZipPath('./somefile.txt', False, False)
'somefile.txt'
>>> MakeZipPath('somedir', True, False)
'somedir/'
>>> MakeZipPath('../dir/filename.txt', False, False)
'../dir/filename.txt'
>>> MakeZipPath('dir/../filename.txt', False, False)
'filename.txt'
"""
zip_path = os_path
if iswindows:
import ntpath
# zipfile paths are always posix-style. They also have the drive
# letter and leading slashes removed.
zip_path = ntpath.splitdrive(os_path)[1].replace('\\', '/')
if zip_path.startswith('/'):
zip_path = zip_path[1:]
zip_path = posixpath.normpath(zip_path)
# zipfile also always appends a slash to a directory name.
if isdir:
zip_path += '/'
return zip_path | [
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dmlc/xgboost | 2775c2a1abd4b5b759ff517617434c8b9aeb4cc0 | python-package/xgboost/core.py | python | Booster.copy | (self) | return self.__copy__() | Copy the booster object.
Returns
-------
booster: `Booster`
a copied booster model | Copy the booster object. | [
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] | def copy(self) -> "Booster":
"""Copy the booster object.
Returns
-------
booster: `Booster`
a copied booster model
"""
return self.__copy__() | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/distributed/distributed_c10d.py | python | monitored_barrier | (group=GroupMember.WORLD, timeout=None, wait_all_ranks=False) | return group_to_use.monitored_barrier(timeout, wait_all_ranks=wait_all_ranks) | Synchronizes all processes similar to ``torch.distributed.barrier``, but takes
a configurable timeout and is able to report ranks that did not pass this
barrier within that timeout. Specifically, for non-zero ranks, will block
until a send/recv is processed from rank 0. Rank 0 will block until all send
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This collective will block all processes/ranks in the group, until the
whole group exits the function successfully, making it useful for debugging
and synchronizing. However, it can have a performance impact and should only
be used for debugging or scenarios that require full synchronization points
on the host-side. For debugging purposees, this barrier can be inserted
before the application's collective calls to check if any ranks are
desynchronized.
.. note:: Note that this collective is only supported with the GLOO backend.
Args:
group (ProcessGroup, optional): The process group to work on. If
``None``, the default process group will be used.
timeout (datetime.timedelta, optional): Timeout for monitored_barrier.
If ``None``, the default process group timeout will be used.
wait_all_ranks (bool, optional): Whether to collect all failed ranks or
not. By default, this is ``False`` and ``monitored_barrier`` on rank 0
will throw on the first failed rank it encounters in order to fail
fast. By setting ``wait_all_ranks=True`` ``monitored_barrier`` will
collect all failed ranks and throw an error containing information
about all failed ranks.
Returns:
``None``.
Example::
>>> # Note: Process group initialization omitted on each rank.
>>> import torch.distributed as dist
>>> if dist.get_rank() != 1:
>>> dist.monitored_barrier() # Raises exception indicating that
>>> # rank 1 did not call into monitored_barrier.
>>> # Example with wait_all_ranks=True
>>> if dist.get_rank() == 0:
>>> dist.monitored_barrier(wait_all_ranks=True) # Raises exception
>>> # indicating that ranks 1, 2, ... world_size - 1 did not call into
>>> # monitored_barrier. | Synchronizes all processes similar to ``torch.distributed.barrier``, but takes
a configurable timeout and is able to report ranks that did not pass this
barrier within that timeout. Specifically, for non-zero ranks, will block
until a send/recv is processed from rank 0. Rank 0 will block until all send
/recv from other ranks are processed, and will report failures for ranks
that failed to respond in time. Note that if one rank does not reach the
monitored_barrier (for example due to a hang), all other ranks would fail
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"""
Synchronizes all processes similar to ``torch.distributed.barrier``, but takes
a configurable timeout and is able to report ranks that did not pass this
barrier within that timeout. Specifically, for non-zero ranks, will block
until a send/recv is processed from rank 0. Rank 0 will block until all send
/recv from other ranks are processed, and will report failures for ranks
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and synchronizing. However, it can have a performance impact and should only
be used for debugging or scenarios that require full synchronization points
on the host-side. For debugging purposees, this barrier can be inserted
before the application's collective calls to check if any ranks are
desynchronized.
.. note:: Note that this collective is only supported with the GLOO backend.
Args:
group (ProcessGroup, optional): The process group to work on. If
``None``, the default process group will be used.
timeout (datetime.timedelta, optional): Timeout for monitored_barrier.
If ``None``, the default process group timeout will be used.
wait_all_ranks (bool, optional): Whether to collect all failed ranks or
not. By default, this is ``False`` and ``monitored_barrier`` on rank 0
will throw on the first failed rank it encounters in order to fail
fast. By setting ``wait_all_ranks=True`` ``monitored_barrier`` will
collect all failed ranks and throw an error containing information
about all failed ranks.
Returns:
``None``.
Example::
>>> # Note: Process group initialization omitted on each rank.
>>> import torch.distributed as dist
>>> if dist.get_rank() != 1:
>>> dist.monitored_barrier() # Raises exception indicating that
>>> # rank 1 did not call into monitored_barrier.
>>> # Example with wait_all_ranks=True
>>> if dist.get_rank() == 0:
>>> dist.monitored_barrier(wait_all_ranks=True) # Raises exception
>>> # indicating that ranks 1, 2, ... world_size - 1 did not call into
>>> # monitored_barrier.
"""
# Need to call rank not in group before using the group, otherwise
# "Invalid process group" error is raised.
if _rank_not_in_group(group):
_warn_not_in_group("monitored_barrier")
return
if get_backend(group) != Backend.GLOO:
raise RuntimeError("monitored_barrier is only implemented for GLOO backend.")
if timeout is None:
timeout = default_pg_timeout
group_to_use = _get_default_group() if group is None else group
return group_to_use.monitored_barrier(timeout, wait_all_ranks=wait_all_ranks) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/osx_carbon/gizmos.py | python | TreeListCtrl.IsSelected | (*args, **kwargs) | return _gizmos.TreeListCtrl_IsSelected(*args, **kwargs) | IsSelected(self, TreeItemId item) -> bool | IsSelected(self, TreeItemId item) -> bool | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | TimerEvent.GetTimer | (*args, **kwargs) | return _misc_.TimerEvent_GetTimer(*args, **kwargs) | GetTimer(self) -> wxTimer | GetTimer(self) -> wxTimer | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_misc.py | python | BitmapDataObject.GetBitmap | (*args, **kwargs) | return _misc_.BitmapDataObject_GetBitmap(*args, **kwargs) | GetBitmap(self) -> Bitmap
Returns the bitmap associated with the data object. You may wish to
override this method (by deriving from `wx.PyBitmapDataObject`) when
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"""
GetBitmap(self) -> Bitmap
Returns the bitmap associated with the data object. You may wish to
override this method (by deriving from `wx.PyBitmapDataObject`) when
offering data on-demand, but this is not required by wxWidgets'
internals. Use this method to get data in bitmap form from the
`wx.Clipboard`.
"""
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/io/formats/format.py | python | _get_format_timedelta64 | (
values: Union[np.ndarray, TimedeltaIndex, TimedeltaArray],
nat_rep: str = "NaT",
box: bool = False,
) | return _formatter | Return a formatter function for a range of timedeltas.
These will all have the same format argument
If box, then show the return in quotes | Return a formatter function for a range of timedeltas.
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values: Union[np.ndarray, TimedeltaIndex, TimedeltaArray],
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box: bool = False,
) -> Callable:
"""
Return a formatter function for a range of timedeltas.
These will all have the same format argument
If box, then show the return in quotes
"""
values_int = values.astype(np.int64)
consider_values = values_int != iNaT
one_day_nanos = 86400 * 1e9
even_days = (
np.logical_and(consider_values, values_int % one_day_nanos != 0).sum() == 0
)
all_sub_day = (
np.logical_and(consider_values, np.abs(values_int) >= one_day_nanos).sum() == 0
)
if even_days:
format = None
elif all_sub_day:
format = "sub_day"
else:
format = "long"
def _formatter(x):
if x is None or (is_scalar(x) and isna(x)):
return nat_rep
if not isinstance(x, Timedelta):
x = Timedelta(x)
result = x._repr_base(format=format)
if box:
result = "'{res}'".format(res=result)
return result
return _formatter | [
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perilouswithadollarsign/cstrike15_src | f82112a2388b841d72cb62ca48ab1846dfcc11c8 | thirdparty/protobuf-2.5.0/python/google/protobuf/descriptor.py | python | _NestedDescriptorBase.__init__ | (self, options, options_class_name, name, full_name,
file, containing_type, serialized_start=None,
serialized_end=None) | Constructor.
Args:
options: Protocol message options or None
to use default message options.
options_class_name: (str) The class name of the above options.
name: (str) Name of this protocol message type.
full_name: (str) Fully-qualified name of this protocol message type,
which will include protocol "package" name and the name of any
enclosing types.
file: (FileDescriptor) Reference to file info.
containing_type: if provided, this is a nested descriptor, with this
descriptor as parent, otherwise None.
serialized_start: The start index (inclusive) in block in the
file.serialized_pb that describes this descriptor.
serialized_end: The end index (exclusive) in block in the
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] | def __init__(self, options, options_class_name, name, full_name,
file, containing_type, serialized_start=None,
serialized_end=None):
"""Constructor.
Args:
options: Protocol message options or None
to use default message options.
options_class_name: (str) The class name of the above options.
name: (str) Name of this protocol message type.
full_name: (str) Fully-qualified name of this protocol message type,
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file: (FileDescriptor) Reference to file info.
containing_type: if provided, this is a nested descriptor, with this
descriptor as parent, otherwise None.
serialized_start: The start index (inclusive) in block in the
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serialized_end: The end index (exclusive) in block in the
file.serialized_pb that describes this descriptor.
"""
super(_NestedDescriptorBase, self).__init__(
options, options_class_name)
self.name = name
# TODO(falk): Add function to calculate full_name instead of having it in
# memory?
self.full_name = full_name
self.file = file
self.containing_type = containing_type
self._serialized_start = serialized_start
self._serialized_end = serialized_end | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py | python | StateSpaceModel.initialize_graph | (self, input_statistics=None) | Define variables and ops relevant to the top-level model in an ensemble.
For generic model parameters, _define_parameters() is called recursively on
all members of an ensemble.
Args:
input_statistics: A math_utils.InputStatistics object containing input
statistics. If None, data-independent defaults are used, which may
result in longer or unstable training. | Define variables and ops relevant to the top-level model in an ensemble. | [
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"""Define variables and ops relevant to the top-level model in an ensemble.
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all members of an ensemble.
Args:
input_statistics: A math_utils.InputStatistics object containing input
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"""
self._set_input_statistics(input_statistics=input_statistics)
self._define_parameters()
with variable_scope.variable_scope(self._variable_scope):
self._observation_noise_covariance = ops.convert_to_tensor(
self.get_observation_noise_covariance(), dtype=self.dtype)
self._kalman_filter = kalman_filter.KalmanFilter(dtype=self.dtype)
(self.prior_state_mean,
self.prior_state_var) = self._make_priors() | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/telemetry/telemetry/core/bitmap.py | python | Bitmap.Diff | (self, other) | return diff | Returns a new Bitmap that represents the difference between this image
and another Bitmap. | Returns a new Bitmap that represents the difference between this image
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"""Returns a new Bitmap that represents the difference between this image
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# Output dimensions will be the maximum of the two input dimensions
out_width = max(self.width, other.width)
out_height = max(self.height, other.height)
diff = [[0 for x in xrange(out_width * 3)] for x in xrange(out_height)]
# Loop over each pixel and write out the difference
for y in range(out_height):
for x in range(out_width):
if x < self.width and y < self.height:
c0 = self.GetPixelColor(x, y)
else:
c0 = RgbaColor(0, 0, 0, 0)
if x < other.width and y < other.height:
c1 = other.GetPixelColor(x, y)
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c1 = RgbaColor(0, 0, 0, 0)
offset = x * 3
diff[y][offset] = abs(c0.r - c1.r)
diff[y][offset+1] = abs(c0.g - c1.g)
diff[y][offset+2] = abs(c0.b - c1.b)
# This particular method can only save to a file, so the result will be
# written into an in-memory buffer and read back into a Bitmap
diff_img = png.from_array(diff, mode='RGB')
output = cStringIO.StringIO()
try:
diff_img.save(output)
diff = Bitmap.FromPng(output.getvalue())
finally:
output.close()
return diff | [
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wy1iu/LargeMargin_Softmax_Loss | c3e9f20e4f16e2b4daf7d358a614366b9b39a6ec | scripts/cpp_lint.py | python | IsCppString | (line) | return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1 | Does line terminate so, that the next symbol is in string constant.
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Returns:
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line = line.replace(r'\\', 'XX') # after this, \\" does not match to \"
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/Jinja2/py3/jinja2/sandbox.py | python | safe_range | (*args: int) | return rng | A range that can't generate ranges with a length of more than
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"""A range that can't generate ranges with a length of more than
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"""
rng = range(*args)
if len(rng) > MAX_RANGE:
raise OverflowError(
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f" MAX_RANGE ({MAX_RANGE})."
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return rng | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/Jinja2/py2/jinja2/filters.py | python | do_mark_safe | (value) | return Markup(value) | Mark the value as safe which means that in an environment with automatic
escaping enabled this variable will not be escaped. | Mark the value as safe which means that in an environment with automatic
escaping enabled this variable will not be escaped. | [
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] | def do_mark_safe(value):
"""Mark the value as safe which means that in an environment with automatic
escaping enabled this variable will not be escaped.
"""
return Markup(value) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/client/timeline.py | python | Timeline._allocate_pids | (self) | Allocate fake process ids for each device in the StepStats. | Allocate fake process ids for each device in the StepStats. | [
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"."
] | def _allocate_pids(self):
"""Allocate fake process ids for each device in the StepStats."""
self._allocators_pid = self._alloc_pid()
self._chrome_trace.emit_pid('Allocators', self._allocators_pid)
# Add processes in the Chrome trace to show compute and data activity.
for dev_stats in self._step_stats.dev_stats:
device_pid = self._alloc_pid()
self._device_pids[dev_stats.device] = device_pid
tensors_pid = self._alloc_pid()
self._tensor_pids[dev_stats.device] = tensors_pid
self._chrome_trace.emit_pid(dev_stats.device + ' Compute', device_pid)
self._chrome_trace.emit_pid(dev_stats.device + ' Tensors', tensors_pid) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/framework/ops.py | python | get_collection_ref | (key) | return get_default_graph().get_collection_ref(key) | Wrapper for `Graph.get_collection_ref()` using the default graph.
See `tf.Graph.get_collection_ref`
for more details.
Args:
key: The key for the collection. For example, the `GraphKeys` class contains
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@compatibility(eager)
Collections are not supported when eager execution is enabled.
@end_compatibility | Wrapper for `Graph.get_collection_ref()` using the default graph. | [
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] | def get_collection_ref(key):
"""Wrapper for `Graph.get_collection_ref()` using the default graph.
See `tf.Graph.get_collection_ref`
for more details.
Args:
key: The key for the collection. For example, the `GraphKeys` class contains
many standard names for collections.
Returns:
The list of values in the collection with the given `name`, or an empty
list if no value has been added to that collection. Note that this returns
the collection list itself, which can be modified in place to change the
collection.
@compatibility(eager)
Collections are not supported when eager execution is enabled.
@end_compatibility
"""
return get_default_graph().get_collection_ref(key) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/engine/data_adapter.py | python | DataAdapter.on_epoch_end | (self) | A hook called after each epoch. | A hook called after each epoch. | [
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"""A hook called after each epoch."""
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pyparsing/py3/pyparsing/common.py | python | pyparsing_common.convert_to_date | (fmt: str = "%Y-%m-%d") | return cvt_fn | Helper to create a parse action for converting parsed date string to Python datetime.date
Params -
- fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%d"``)
Example::
date_expr = pyparsing_common.iso8601_date.copy()
date_expr.setParseAction(pyparsing_common.convertToDate())
print(date_expr.parseString("1999-12-31"))
prints::
[datetime.date(1999, 12, 31)] | Helper to create a parse action for converting parsed date string to Python datetime.date | [
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"datetime",
".",
"date"
] | def convert_to_date(fmt: str = "%Y-%m-%d"):
"""
Helper to create a parse action for converting parsed date string to Python datetime.date
Params -
- fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%d"``)
Example::
date_expr = pyparsing_common.iso8601_date.copy()
date_expr.setParseAction(pyparsing_common.convertToDate())
print(date_expr.parseString("1999-12-31"))
prints::
[datetime.date(1999, 12, 31)]
"""
def cvt_fn(ss, ll, tt):
try:
return datetime.strptime(tt[0], fmt).date()
except ValueError as ve:
raise ParseException(ss, ll, str(ve))
return cvt_fn | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/generic.py | python | NDFrame.asfreq | (
self: FrameOrSeries,
freq,
method=None,
how: str | None = None,
normalize: bool_t = False,
fill_value=None,
) | return asfreq(
self,
freq,
method=method,
how=how,
normalize=normalize,
fill_value=fill_value,
) | Convert time series to specified frequency.
Returns the original data conformed to a new index with the specified
frequency.
If the index of this {klass} is a :class:`~pandas.PeriodIndex`, the new index
is the result of transforming the original index with
:meth:`PeriodIndex.asfreq <pandas.PeriodIndex.asfreq>` (so the original index
will map one-to-one to the new index).
Otherwise, the new index will be equivalent to ``pd.date_range(start, end,
freq=freq)`` where ``start`` and ``end`` are, respectively, the first and
last entries in the original index (see :func:`pandas.date_range`). The
values corresponding to any timesteps in the new index which were not present
in the original index will be null (``NaN``), unless a method for filling
such unknowns is provided (see the ``method`` parameter below).
The :meth:`resample` method is more appropriate if an operation on each group of
timesteps (such as an aggregate) is necessary to represent the data at the new
frequency.
Parameters
----------
freq : DateOffset or str
Frequency DateOffset or string.
method : {{'backfill'/'bfill', 'pad'/'ffill'}}, default None
Method to use for filling holes in reindexed Series (note this
does not fill NaNs that already were present):
* 'pad' / 'ffill': propagate last valid observation forward to next
valid
* 'backfill' / 'bfill': use NEXT valid observation to fill.
how : {{'start', 'end'}}, default end
For PeriodIndex only (see PeriodIndex.asfreq).
normalize : bool, default False
Whether to reset output index to midnight.
fill_value : scalar, optional
Value to use for missing values, applied during upsampling (note
this does not fill NaNs that already were present).
Returns
-------
{klass}
{klass} object reindexed to the specified frequency.
See Also
--------
reindex : Conform DataFrame to new index with optional filling logic.
Notes
-----
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Start by creating a series with 4 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({{'s': series}})
>>> df
s
2000-01-01 00:00:00 0.0
2000-01-01 00:01:00 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:03:00 3.0
Upsample the series into 30 second bins.
>>> df.asfreq(freq='30S')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 NaN
2000-01-01 00:03:00 3.0
Upsample again, providing a ``fill value``.
>>> df.asfreq(freq='30S', fill_value=9.0)
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 9.0
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 9.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 9.0
2000-01-01 00:03:00 3.0
Upsample again, providing a ``method``.
>>> df.asfreq(freq='30S', method='bfill')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 2.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 3.0
2000-01-01 00:03:00 3.0 | Convert time series to specified frequency. | [
"Convert",
"time",
"series",
"to",
"specified",
"frequency",
"."
] | def asfreq(
self: FrameOrSeries,
freq,
method=None,
how: str | None = None,
normalize: bool_t = False,
fill_value=None,
) -> FrameOrSeries:
"""
Convert time series to specified frequency.
Returns the original data conformed to a new index with the specified
frequency.
If the index of this {klass} is a :class:`~pandas.PeriodIndex`, the new index
is the result of transforming the original index with
:meth:`PeriodIndex.asfreq <pandas.PeriodIndex.asfreq>` (so the original index
will map one-to-one to the new index).
Otherwise, the new index will be equivalent to ``pd.date_range(start, end,
freq=freq)`` where ``start`` and ``end`` are, respectively, the first and
last entries in the original index (see :func:`pandas.date_range`). The
values corresponding to any timesteps in the new index which were not present
in the original index will be null (``NaN``), unless a method for filling
such unknowns is provided (see the ``method`` parameter below).
The :meth:`resample` method is more appropriate if an operation on each group of
timesteps (such as an aggregate) is necessary to represent the data at the new
frequency.
Parameters
----------
freq : DateOffset or str
Frequency DateOffset or string.
method : {{'backfill'/'bfill', 'pad'/'ffill'}}, default None
Method to use for filling holes in reindexed Series (note this
does not fill NaNs that already were present):
* 'pad' / 'ffill': propagate last valid observation forward to next
valid
* 'backfill' / 'bfill': use NEXT valid observation to fill.
how : {{'start', 'end'}}, default end
For PeriodIndex only (see PeriodIndex.asfreq).
normalize : bool, default False
Whether to reset output index to midnight.
fill_value : scalar, optional
Value to use for missing values, applied during upsampling (note
this does not fill NaNs that already were present).
Returns
-------
{klass}
{klass} object reindexed to the specified frequency.
See Also
--------
reindex : Conform DataFrame to new index with optional filling logic.
Notes
-----
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Start by creating a series with 4 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({{'s': series}})
>>> df
s
2000-01-01 00:00:00 0.0
2000-01-01 00:01:00 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:03:00 3.0
Upsample the series into 30 second bins.
>>> df.asfreq(freq='30S')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 NaN
2000-01-01 00:03:00 3.0
Upsample again, providing a ``fill value``.
>>> df.asfreq(freq='30S', fill_value=9.0)
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 9.0
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 9.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 9.0
2000-01-01 00:03:00 3.0
Upsample again, providing a ``method``.
>>> df.asfreq(freq='30S', method='bfill')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 2.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 3.0
2000-01-01 00:03:00 3.0
"""
from pandas.core.resample import asfreq
return asfreq(
self,
freq,
method=method,
how=how,
normalize=normalize,
fill_value=fill_value,
) | [
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google/or-tools | 2cb85b4eead4c38e1c54b48044f92087cf165bce | ortools/sat/python/cp_model.py | python | CpSolver.BestObjectiveBound | (self) | return self.__solution.best_objective_bound | Returns the best lower (upper) bound found when min(max)imizing. | Returns the best lower (upper) bound found when min(max)imizing. | [
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] | def BestObjectiveBound(self):
"""Returns the best lower (upper) bound found when min(max)imizing."""
return self.__solution.best_objective_bound | [
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citizenfx/fivem | 88276d40cc7baf8285d02754cc5ae42ec7a8563f | vendor/chromium/mojo/public/tools/bindings/pylib/mojom/parse/parser.py | python | Parser.p_identifier | (self, p) | identifier : NAME
| NAME DOT identifier | identifier : NAME
| NAME DOT identifier | [
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"""identifier : NAME
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/serial/urlhandler/protocol_loop.py | python | Serial._reconfigure_port | (self) | \
Set communication parameters on opened port. For the loop://
protocol all settings are ignored! | \
Set communication parameters on opened port. For the loop://
protocol all settings are ignored! | [
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"""\
Set communication parameters on opened port. For the loop://
protocol all settings are ignored!
"""
# not that's it of any real use, but it helps in the unit tests
if not isinstance(self._baudrate, numbers.Integral) or not 0 < self._baudrate < 2 ** 32:
raise ValueError("invalid baudrate: {!r}".format(self._baudrate))
if self.logger:
self.logger.info('_reconfigure_port()') | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/SimpleXMLRPCServer.py | python | remove_duplicates | (lst) | return u.keys() | remove_duplicates([2,2,2,1,3,3]) => [3,1,2]
Returns a copy of a list without duplicates. Every list
item must be hashable and the order of the items in the
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"""remove_duplicates([2,2,2,1,3,3]) => [3,1,2]
Returns a copy of a list without duplicates. Every list
item must be hashable and the order of the items in the
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u = {}
for x in lst:
u[x] = 1
return u.keys() | [
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Jittor/jittor | e9aca0444c2bdc8e2389d99122954cd0903eec46 | python/jittor/transform/__init__.py | python | resize | (img, size, interpolation=Image.BILINEAR) | Function for resizing image.
Args::
[in] img(Image.Image): Input image.
[in] size: resize size. [h, w]
[in] interpolation(int): type of resize. default: PIL.Image.BILINEAR
Example::
img = Image.open(...)
img_ = transform.resize(img, (100, 100)) | Function for resizing image. | [
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"image",
"."
] | def resize(img, size, interpolation=Image.BILINEAR):
'''
Function for resizing image.
Args::
[in] img(Image.Image): Input image.
[in] size: resize size. [h, w]
[in] interpolation(int): type of resize. default: PIL.Image.BILINEAR
Example::
img = Image.open(...)
img_ = transform.resize(img, (100, 100))
'''
if isinstance(size, Sequence):
return img.resize(size[::-1], interpolation)
else:
w, h = img.size
if (h > w):
return img.resize((size, int(round(size * h / w))), interpolation)
else:
return img.resize((int(round(size * w / h)), size), interpolation) | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBBreakpoint.MatchesName | (self, *args) | return _lldb.SBBreakpoint_MatchesName(self, *args) | MatchesName(self, str name) -> bool | MatchesName(self, str name) -> bool | [
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"self",
"str",
"name",
")",
"-",
">",
"bool"
] | def MatchesName(self, *args):
"""MatchesName(self, str name) -> bool"""
return _lldb.SBBreakpoint_MatchesName(self, *args) | [
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qboticslabs/mastering_ros | d83e78f30acc45b0f18522c1d5fae3a7f52974b9 | chapter_3_codes/seven_dof_arm_gazebo/scripts/pick_and_place_pick_working.py | python | CokeCanPickAndPlace._publish_places | (self, places) | Publish places as poses, using a PoseArray message | Publish places as poses, using a PoseArray message | [
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"as",
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"using",
"a",
"PoseArray",
"message"
] | def _publish_places(self, places):
"""
Publish places as poses, using a PoseArray message
"""
if self._places_pub.get_num_connections() > 0:
msg = PoseArray()
msg.header.frame_id = self._robot.get_planning_frame()
msg.header.stamp = rospy.Time.now()
for place in places:
msg.poses.append(place.place_pose.pose)
self._places_pub.publish(msg) | [
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/p4util/solvers.py | python | hamiltonian_solver | (engine, guess: List, *, nroot: int, r_convergence: float = 1.0E-4, max_ss_size: int = 100, maxiter: int = 60, verbose: int = 1) | return {"eigvals": best_vals, "eigvecs": list(zip(best_R, best_L)), "stats": stats} | Finds the smallest eigenvalues and associated right and left hand
eigenvectors of a large real Hamiltonian eigenvalue problem emulated
through an engine.
A Hamiltonian eigenvalue problem (EVP) has the following structure:
[A B][X] = [1 0](w)[X]
[B A][Y] [0 -1](w)[Y]
with A, B of some large dimension N, the problem is of dimension 2Nx2N.
The real, Hamiltonian EVP can be rewritten as the NxN, non-hermitian EVP:
(A+B)(A-B)(X+Y) = w^2(X+Y)
With left-hand eigenvectors:
(X-Y)(A-B)(A+B) = w^2(X-Y)
if (A-B) is positive definite, we can transform the problem to arrive at the hermitian NxN EVP:
(A-B)^1/2(A+B)(A-B)^1/2 = w^2 T
Where T = (A-B)^-1/2(X+Y).
We use a Davidson like iteration where we transform (A+B) (H1) and (A-B)
(H2) in to the subspace defined by the trial vectors.
The subspace analog of the NxN hermitian EVP is diagonalized and left (X-Y)
and right (X+Y) eigenvectors of the NxN non-hermitian EVP are approximated.
Residual vectors are formed for both and the guess space is augmented with
two correction vectors per iteration. The advantages and properties of this
algorithm are described in the literature [stratmann:1998]_ .
Parameters
-----------
engine : object (subclass of :class:`SolverEngine`)
The engine drive all operations involving data structures that have at
least one "large" dimension. See :class:`SolverEngine` for requirements
guess
list {engine dependent}
At least `nroot` initial expansion vectors
nroot
Number of roots desired
r_convergence
Convergence tolerance for residual vectors
max_ss_size:
The maximum number of trial vectors in the iterative subspace that will
be stored before a collapse is done.
maxiter
The maximum number of iterations
verbose
The amount of logging info to print (0 -> none, 1 -> some, >1 -> everything)
Returns
-------
best_values : numpy.ndarray (nroots, )
The best approximation of the eigenvalues of `w`, computed on the last iteration of the solver
best_R: list of `vector` (nroots)
The best approximation of the right hand eigenvectors, `X+Y`, computed on the last iteration of the solver.
best_L: list of `vector` (nroots)
The best approximation of the left hand eigenvectors, `X-Y`, computed on the last iteration of the solver.
stats : list of `dict`
Statistics collected on each iteration
count : int, iteration number
res_norm : np.ndarray (nroots, ), the norm of residual vector for each roots
val : np.ndarray (nroots, ), the eigenvalue corresponding to each root
delta_val : np.ndarray (nroots, ), the change in eigenvalue from the last iteration to this ones
collapse : bool, if a subspace collapse was performed
product_count : int, the running total of product evaluations that was performed
done : bool, if all roots were converged
Notes
-----
The solution vector is normalized to 1/2
The solver will return even when ``maxiter`` iterations are performed without convergence.
The caller **must check** `stats[-1]['done']` for failure and handle each case accordingly.
References
----------
R. Eric Stratmann, G. E. Scuseria, and M. J. Frisch, "An efficient
implementation of time-dependent density-functional theory for the
calculation of excitation energies of large molecules." J. Chem. Phys.,
109, 8218 (1998) | Finds the smallest eigenvalues and associated right and left hand
eigenvectors of a large real Hamiltonian eigenvalue problem emulated
through an engine. | [
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"eigenvalue",
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"engine",
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] | def hamiltonian_solver(engine, guess: List, *, nroot: int, r_convergence: float = 1.0E-4, max_ss_size: int = 100, maxiter: int = 60, verbose: int = 1):
"""Finds the smallest eigenvalues and associated right and left hand
eigenvectors of a large real Hamiltonian eigenvalue problem emulated
through an engine.
A Hamiltonian eigenvalue problem (EVP) has the following structure:
[A B][X] = [1 0](w)[X]
[B A][Y] [0 -1](w)[Y]
with A, B of some large dimension N, the problem is of dimension 2Nx2N.
The real, Hamiltonian EVP can be rewritten as the NxN, non-hermitian EVP:
(A+B)(A-B)(X+Y) = w^2(X+Y)
With left-hand eigenvectors:
(X-Y)(A-B)(A+B) = w^2(X-Y)
if (A-B) is positive definite, we can transform the problem to arrive at the hermitian NxN EVP:
(A-B)^1/2(A+B)(A-B)^1/2 = w^2 T
Where T = (A-B)^-1/2(X+Y).
We use a Davidson like iteration where we transform (A+B) (H1) and (A-B)
(H2) in to the subspace defined by the trial vectors.
The subspace analog of the NxN hermitian EVP is diagonalized and left (X-Y)
and right (X+Y) eigenvectors of the NxN non-hermitian EVP are approximated.
Residual vectors are formed for both and the guess space is augmented with
two correction vectors per iteration. The advantages and properties of this
algorithm are described in the literature [stratmann:1998]_ .
Parameters
-----------
engine : object (subclass of :class:`SolverEngine`)
The engine drive all operations involving data structures that have at
least one "large" dimension. See :class:`SolverEngine` for requirements
guess
list {engine dependent}
At least `nroot` initial expansion vectors
nroot
Number of roots desired
r_convergence
Convergence tolerance for residual vectors
max_ss_size:
The maximum number of trial vectors in the iterative subspace that will
be stored before a collapse is done.
maxiter
The maximum number of iterations
verbose
The amount of logging info to print (0 -> none, 1 -> some, >1 -> everything)
Returns
-------
best_values : numpy.ndarray (nroots, )
The best approximation of the eigenvalues of `w`, computed on the last iteration of the solver
best_R: list of `vector` (nroots)
The best approximation of the right hand eigenvectors, `X+Y`, computed on the last iteration of the solver.
best_L: list of `vector` (nroots)
The best approximation of the left hand eigenvectors, `X-Y`, computed on the last iteration of the solver.
stats : list of `dict`
Statistics collected on each iteration
count : int, iteration number
res_norm : np.ndarray (nroots, ), the norm of residual vector for each roots
val : np.ndarray (nroots, ), the eigenvalue corresponding to each root
delta_val : np.ndarray (nroots, ), the change in eigenvalue from the last iteration to this ones
collapse : bool, if a subspace collapse was performed
product_count : int, the running total of product evaluations that was performed
done : bool, if all roots were converged
Notes
-----
The solution vector is normalized to 1/2
The solver will return even when ``maxiter`` iterations are performed without convergence.
The caller **must check** `stats[-1]['done']` for failure and handle each case accordingly.
References
----------
R. Eric Stratmann, G. E. Scuseria, and M. J. Frisch, "An efficient
implementation of time-dependent density-functional theory for the
calculation of excitation energies of large molecules." J. Chem. Phys.,
109, 8218 (1998)
"""
nk = nroot
iter_info = {
"count": 0,
"res_norm": np.zeros((nk)),
"val": np.zeros((nk)),
"delta_val": np.zeros((nk)),
# conv defaults to true, and will be flipped when a non-conv root is hit
"conv": True,
"nvec": 0,
"product_count": 0,
}
print_name = "HamiltonianSolver"
title_lines = ["Generalized Hamiltonian Solver", "By Andrew M. James"]
_diag_print_heading(title_lines, print_name, max_ss_size, nroot, r_convergence, maxiter, verbose)
vecs = guess
best_L = []
best_R = []
best_vals = []
stats = []
while iter_info['count'] < maxiter:
# increment iteration/ save old vals
iter_info['count'] += 1
old_w = iter_info['val'].copy()
# reset flags
iter_info['collapse'] = False
iter_info['done'] = True
# get subspace dimension
l = len(vecs)
iter_info['nvec'] = l
# check if subspace dimension has exceeded limits
if l >= max_ss_size:
iter_info['collapse'] = True
# compute [A+B]*v (H1x) and [A-B]*v (H2x)
H1x, H2x, nprod = engine.compute_products(vecs)
iter_info['product_count'] += nprod
# form x*H1x (H1_ss) and x*H2x (H2_ss)
H1_ss = np.zeros((l, l))
H2_ss = np.zeros((l, l))
for i in range(l):
for j in range(l):
H1_ss[i, j] = engine.vector_dot(vecs[i], H1x[j])
H2_ss[i, j] = engine.vector_dot(vecs[i], H2x[j])
_print_array("Subspace Transformed (A+B)", H1_ss, verbose)
_print_array("Subspace Transformed (A-B)", H2_ss, verbose)
# Diagonalize H2 in the subspace (eigen-decomposition to compute H2^(1/2))
H2_ss_val, H2_ss_vec = np.linalg.eigh(H2_ss)
_print_array("eigenvalues H2_ss", H2_ss_val, verbose)
_print_array("eigenvectors H2_ss", H2_ss_vec, verbose)
# Check H2 is PD
# NOTE: If this triggers failure the SCF solution is not stable. A few ways to handle this
# 1. Use davidson solver where product function evaluates (H2 * (H1 * X))
# - Poor convergence
# 2. Switch to CIS/TDA
# - User would probably not expect this
# 3. Perform Stability update and restart with new reference
if np.any(H2_ss_val < 0.0):
msg = ("The H2 matrix is not Positive Definite. " "This means the reference state is not stable.")
raise RuntimeError(msg)
# Build H2^(1/2)
H2_ss_half = np.einsum("ik,k,jk->ij", H2_ss_vec, np.sqrt(H2_ss_val), H2_ss_vec, optimize=True)
_print_array("SS Transformed (A-B)^(1/2)", H2_ss_half, verbose)
# Build Hermitian SS product (H2)^(1/2)(H1)(H2)^(1/2)
Hss = np.einsum('ij,jk,km->im', H2_ss_half, H1_ss, H2_ss_half, optimize=True)
_print_array("(H2)^(1/2)(H1)(H2)^(1/2)", Hss, verbose)
#diagonalize Hss -> w^2, Tss
w2, Tss = np.linalg.eigh(Hss)
_print_array("Eigenvalues (A-B)^(1/2)(A+B)(A-B)^(1/2)", w2, verbose)
_print_array("Eigvectors (A-B)^(1/2)(A+B)(A-B)^(1/2)", Tss, verbose)
# pick positive roots
Tss = Tss[:, w2 > 1.0e-10]
w2 = w2[w2 > 1.0e-10]
# check for invalid eigvals
with np.errstate(invalid='raise'):
w = np.sqrt(w2)
# sort roots
idx = w.argsort()[:nk]
Tss = Tss[:, idx]
w = w[idx]
# Extract Rss = H2^{1/2}Tss
Rss = np.dot(H2_ss_half, Tss)
# Extract Lss = (H1 R)/ w
Lss = np.dot(H1_ss, Rss).dot(np.diag(1.0 / w))
# Biorthonormalize R/L solution vectors
inners = np.einsum("ix,ix->x", Rss, Lss, optimize=True)
Rss = np.einsum("x,ix->ix", 1. / np.sqrt(inners), Rss, optimize=True)
Lss = np.einsum("x,ix->ix", 1. / np.sqrt(inners), Lss, optimize=True)
# Save best R/L vectors and eigenvalues
best_R = _best_vectors(engine, Rss[:, :nk], vecs)
best_L = _best_vectors(engine, Lss[:, :nk], vecs)
best_vals = w[:nk]
# check convergence of each solution
new_vecs = []
for k in range(nk):
# residual vectors for right and left eigenvectors
WR_k = engine.new_vector()
WL_k = engine.new_vector()
wk = w[k]
for i in range(l):
H1x_i = H1x[i]
H2x_i = H2x[i]
WL_k = engine.vector_axpy(Rss[i, k], H1x_i, WL_k)
WR_k = engine.vector_axpy(Lss[i, k], H2x_i, WR_k)
WL_k = engine.vector_axpy(-1.0 * wk, best_L[k], WL_k)
WR_k = engine.vector_axpy(-1.0 * wk, best_R[k], WR_k)
norm_R = np.sqrt(engine.vector_dot(WR_k, WR_k))
norm_L = np.sqrt(engine.vector_dot(WL_k, WL_k))
norm = norm_R + norm_L
iter_info['res_norm'][k] = norm
iter_info['delta_val'][k] = np.abs(old_w[k] - w[k])
iter_info['val'][k] = w[k]
# augment the guess space for non-converged roots
if (iter_info['res_norm'][k] > r_convergence):
iter_info['done'] = False
new_vecs.append(engine.precondition(WR_k, w[k]))
new_vecs.append(engine.precondition(WL_k, w[k]))
# print iteration info to output
_diag_print_info(print_name, iter_info, verbose)
# save stats for this iteration
stats.append(iter_info.copy())
if iter_info['done']:
# Finished
_diag_print_converged(print_name, stats, w[:nk], rvec=best_R, lvec=best_L, verbose=verbose)
break
elif iter_info['collapse']:
# need to orthonormalize union of the Left/Right solutions on restart
vecs = _gs_orth(engine, [], best_R + best_L)
else:
# Regular subspace update, orthonormalize preconditioned residuals and add to the trial set
vecs = _gs_orth(engine, vecs, new_vecs)
# always return, the caller should check ret["stats"][-1]['done'] == True for convergence
return {"eigvals": best_vals, "eigvecs": list(zip(best_R, best_L)), "stats": stats} | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/inspect.py | python | getabsfile | (object, _filename=None) | return os.path.normcase(os.path.abspath(_filename)) | Return an absolute path to the source or compiled file for an object.
The idea is for each object to have a unique origin, so this routine
normalizes the result as much as possible. | Return an absolute path to the source or compiled file for an object. | [
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"""Return an absolute path to the source or compiled file for an object.
The idea is for each object to have a unique origin, so this routine
normalizes the result as much as possible."""
if _filename is None:
_filename = getsourcefile(object) or getfile(object)
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/math/symbolic.py | python | Context.makePyFunction | (self,expr,varorder=None) | Converts an Expression or Function to a Python function ``f(x)``
that takes ``x`` as a list of scalar values, maps those to Variable
values, and returns the result of evaluating the expression / function.
Args:
expr (:class:`Function` or :class:`Expression`): the function or
expression to evaluate
varorder (list, optional): If given, the list of Variables that
should appear in the flattened argument list ``x``.
If this isn't provided, then the Variables in ``expr`` are
ordered by the order in which were added to this ``Context``.
Returns:
tuple: A pair ``(f,varorder)``, where:
* ``f(x)`` is a 1-argument Python function equivalent to ``expr``
but where ``x`` is a list of variable values.
* ``varorder`` gives the order of variables that should be
sent in ``x`` | Converts an Expression or Function to a Python function ``f(x)``
that takes ``x`` as a list of scalar values, maps those to Variable
values, and returns the result of evaluating the expression / function. | [
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"""Converts an Expression or Function to a Python function ``f(x)``
that takes ``x`` as a list of scalar values, maps those to Variable
values, and returns the result of evaluating the expression / function.
Args:
expr (:class:`Function` or :class:`Expression`): the function or
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varorder (list, optional): If given, the list of Variables that
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If this isn't provided, then the Variables in ``expr`` are
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Returns:
tuple: A pair ``(f,varorder)``, where:
* ``f(x)`` is a 1-argument Python function equivalent to ``expr``
but where ``x`` is a list of variable values.
* ``varorder`` gives the order of variables that should be
sent in ``x``
"""
if isinstance(expr,Function):
if varorder is None:
varnames = expr.argNames
varorder = [self.variableDict[v] for v in varnames]
iorder = list(range(len(varorder)))
else:
varorder = [(self.variableDict[v] if isinstance(v,str) else v) for v in varorder]
iorder = [expr.argNames.index(v.name) for v in varorder]
varnames = [v.name for v in varorder]
def f(*args):
eargs = expr.argNames[:]
for i,a in zip(iorder,args):
eargs[i] = a
#print("Arguments",eargs)
return expr(*eargs).evalf(self)
return f,varorder
assert isinstance(expr,Expression)
res = expr.eval(self)
if isinstance(res,Expression):
rvars = res.vars(self,bound=False)
if varorder is None:
allorder = dict()
for i,v in enumerate(self.variables):
allorder[v.name] = i
varorder = sorted(rvars,key=lambda v:allorder[v.name])
else:
#convert strings to Variables
varorder = [(self.variableDict[v] if isinstance(v,str) else v)for v in varorder]
vnames = set(v.name for v in varorder)
for v in rvars:
if v.name not in vnames:
warnings.warn("Error while creating Python function corresponding to",res)
raise ValueError("Unbound variable "+v.name+" not in given variable order "+",".join([var.name for var in varorder]))
def f(*args):
#print("Evaluating with order",[str(v) for v in varorder],args)
for (v,val) in zip(varorder,args):
v.bind(val)
res = expr.evalf(self)
for (v,val) in zip(varorder,args):
v.unbind()
return res
return (f,varorder)
else:
if varorder is None:
varorder = []
return ((lambda *args: res),varorder) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/composite/multitype_ops/negative_impl.py | python | _negative_tensor | (x) | return F.neg_tensor(x) | Returns the negative value of tensor x by element-wise.
Returns:
Tensor, negative value of x by element-wise. | Returns the negative value of tensor x by element-wise. | [
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] | def _negative_tensor(x):
"""
Returns the negative value of tensor x by element-wise.
Returns:
Tensor, negative value of x by element-wise.
"""
return F.neg_tensor(x) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/propgrid.py | python | PGArrayEditorDialog.EnableCustomNewAction | (*args, **kwargs) | return _propgrid.PGArrayEditorDialog_EnableCustomNewAction(*args, **kwargs) | EnableCustomNewAction(self) | EnableCustomNewAction(self) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/lite/python/lite.py | python | TFLiteConverterBaseV2.convert | (self, graph_def, input_tensors, output_tensors) | return self._optimize_tflite_model(
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output_tensors: List of output tensors.
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output_tensors: List of output tensors.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/_pydecimal.py | python | Decimal.__abs__ | (self, round=True, context=None) | return ans | Returns the absolute value of self.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/pkg_resources/_vendor/pyparsing.py | python | countedArray | ( expr, intExpr=None ) | return ( intExpr + arrayExpr ).setName('(len) ' + _ustr(expr) + '...') | Helper to define a counted list of expressions.
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yrnkrn/zapcc | c6a8aa30006d997eff0d60fd37b0e62b8aa0ea50 | tools/clang/bindings/python/clang/cindex.py | python | Type.is_volatile_qualified | (self) | return conf.lib.clang_isVolatileQualifiedType(self) | Determine whether a Type has the "volatile" qualifier set.
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hakuna-m/wubiuefi | caec1af0a09c78fd5a345180ada1fe45e0c63493 | src/openpgp/sap/msg/KeyMsg.py | python | PublicKeyMsg.add_sig | (self, sigpkt, target) | Add a signature to one of the message's key blocks.
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- `target`: string target block leader
The signature packet's type will indicate the target type (key
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"""
block = self.get_block(target)
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smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/google/protobuf/descriptor_pool.py | python | DescriptorPool._MakeFieldDescriptor | (self, field_proto, message_name, index,
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field_proto: The proto describing the field.
message_name: The name of the containing message.
index: Index of the field
is_extension: Indication that this field is for an extension.
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message_name: The name of the containing message.
index: Index of the field
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"""
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kismetwireless/kismet | a7c0dc270c960fb1f58bd9cec4601c201885fd4e | capture_sdr_rtl433/KismetCaptureRtl433/kismetexternal/__init__.py | python | Datasource.set_configsource_cb | (self, cb) | Set callback for source configuring
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tracemalloc.py | python | Snapshot.compare_to | (self, old_snapshot, key_type, cumulative=False) | return statistics | Compute the differences with an old snapshot old_snapshot. Get
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_pydecimal.py | python | _convert_other | (other, raiseit=False, allow_float=False) | return NotImplemented | Convert other to Decimal.
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/perf/benchmarks/peacekeeper.py | python | _PeaceKeeperBenchmark.CreatePageSet | (self, options) | return page_set.PageSet.FromDict(page_set_dict, os.path.abspath(__file__)) | Makes a PageSet for PeaceKeeper benchmarks. | Makes a PageSet for PeaceKeeper benchmarks. | [
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/v7.9.317/third_party/jinja2/filters.py | python | do_mark_unsafe | (value) | return text_type(value) | Mark a value as unsafe. This is the reverse operation for :func:`safe`. | Mark a value as unsafe. This is the reverse operation for :func:`safe`. | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/distlib/index.py | python | PackageIndex.check_credentials | (self) | Check that ``username`` and ``password`` have been set, and raise an
exception if not. | Check that ``username`` and ``password`` have been set, and raise an
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_, netloc, _, _, _, _ = urlparse(self.url)
pm.add_password(self.realm, netloc, self.username, self.password)
self.password_handler = HTTPBasicAuthHandler(pm) | [
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hfinkel/llvm-project-cxxjit | 91084ef018240bbb8e24235ff5cd8c355a9c1a1e | clang/bindings/python/clang/cindex.py | python | Type.get_array_element_type | (self) | return conf.lib.clang_getArrayElementType(self) | Retrieve the type of the elements of the array type. | Retrieve the type of the elements of the array type. | [
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"""
Retrieve the type of the elements of the array type.
"""
return conf.lib.clang_getArrayElementType(self) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/sim/simulation.py | python | ActuatorEmulator.substep | (self,dt) | This is called every simulation substep, which occurs at a higher rate than
process() is called. dt is the simulation substep. | This is called every simulation substep, which occurs at a higher rate than
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Illumina/manta | 75b5c38d4fcd2f6961197b28a41eb61856f2d976 | src/python/lib/configureOptions.py | python | ConfigureWorkflowOptions.addWorkflowGroupOptions | (self,group) | Add options to OptionsGroup object which specify
parameters which commonly change from run to run | Add options to OptionsGroup object which specify
parameters which commonly change from run to run | [
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"""
Add options to OptionsGroup object which specify
parameters which commonly change from run to run
"""
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/incubate/fleet/base/fleet_base.py | python | Fleet.worker_endpoints | (self, to_string=False) | Get current server endpoints, such as ["127.0.0.1:1001", "127.0.0.1:1002"].
Returns:
list/string: server endpoints | Get current server endpoints, such as ["127.0.0.1:1001", "127.0.0.1:1002"]. | [
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"""
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Returns:
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"""
if to_string:
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else:
return self._role_maker.get_trainer_endpoints() | [
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nsnam/ns-3-dev-git | efdb2e21f45c0a87a60b47c547b68fa140a7b686 | src/visualizer/visualizer/ipython_view.py | python | ConsoleView.showPrompt | (self, prompt) | !
Prints prompt at start of line.
@param prompt: Prompt to print.
@return none | !
Prints prompt at start of line. | [
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] | def showPrompt(self, prompt):
"""!
Prints prompt at start of line.
@param prompt: Prompt to print.
@return none
"""
GObject.idle_add(self._showPrompt, prompt) | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | example/ctc/multiproc_data.py | python | MPData.reset | (self) | Resets the generator by stopping all processes | Resets the generator by stopping all processes | [
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"""Resets the generator by stopping all processes"""
self.alive.value = False
qsize = 0
try:
while True:
self.queue.get(timeout=0.1)
qsize += 1
except QEmptyExcept:
pass
print("Queue size on reset: {}".format(qsize))
for i, p in enumerate(self.proc):
p.join()
self.proc.clear() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListItem.SetFooterText | (self, text) | Sets the text label for the footer item.
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Sets the text label for the footer item.
:param `text`: the text label for the footer item.
"""
self._mask |= ULC_MASK_FOOTER_TEXT
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TGAC/KAT | e8870331de2b4bb0a1b3b91c6afb8fb9d59e9216 | deps/boost/tools/build/src/build/feature.py | python | __validate_feature | (feature) | Generates an error if the feature is unknown. | Generates an error if the feature is unknown. | [
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] | def __validate_feature (feature):
""" Generates an error if the feature is unknown.
"""
assert isinstance(feature, basestring)
if feature not in __all_features:
raise BaseException ('unknown feature "%s"' % feature) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/layers/pooling.py | python | average_pooling3d | (inputs,
pool_size, strides,
padding='valid', data_format='channels_last',
name=None) | return layer.apply(inputs) | Average pooling layer for 3D inputs (e.g. volumes).
Arguments:
inputs: The tensor over which to pool. Must have rank 5.
pool_size: An integer or tuple/list of 3 integers:
(pool_depth, pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 3 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
name: A string, the name of the layer.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled. | Average pooling layer for 3D inputs (e.g. volumes). | [
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pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Average pooling layer for 3D inputs (e.g. volumes).
Arguments:
inputs: The tensor over which to pool. Must have rank 5.
pool_size: An integer or tuple/list of 3 integers:
(pool_depth, pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 3 integers,
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Can be a single integer to specify the same value for
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padding: A string. The padding method, either 'valid' or 'same'.
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data_format: A string. The ordering of the dimensions in the inputs.
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`(batch, channels, depth, height, width)`.
name: A string, the name of the layer.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
if context.in_eager_mode():
raise ValueError(
'Functional layers are currently not compatible with eager execution.'
'Use tf.layers.AveragePooling3D instead.')
layer = AveragePooling3D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format,
name=name)
return layer.apply(inputs) | [
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baidu/bigflow | 449245016c0df7d1252e85581e588bfc60cefad3 | bigflow_python/python/bigflow/base.py | python | Transformer.process | (self, record, *side_inputs) | return [] | 此方法在处理数据之时被调用，以通知用户要开始处理数据了。
其中record即为待处理的数据。
用户必须返回一个可迭代的对象，其中值将会被放入结果的PCollection中。 | 此方法在处理数据之时被调用，以通知用户要开始处理数据了。
其中record即为待处理的数据。 | [
"此方法在处理数据之时被调用，以通知用户要开始处理数据了。",
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] | def process(self, record, *side_inputs):
"""
此方法在处理数据之时被调用，以通知用户要开始处理数据了。
其中record即为待处理的数据。
用户必须返回一个可迭代的对象，其中值将会被放入结果的PCollection中。
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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/chardet/__init__.py | python | detect | (byte_str) | return detector.close() | Detect the encoding of the given byte string.
:param byte_str: The byte sequence to examine.
:type byte_str: ``bytes`` or ``bytearray`` | Detect the encoding of the given byte string. | [
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] | def detect(byte_str):
"""
Detect the encoding of the given byte string.
:param byte_str: The byte sequence to examine.
:type byte_str: ``bytes`` or ``bytearray``
"""
if not isinstance(byte_str, bytearray):
if not isinstance(byte_str, bytes):
raise TypeError('Expected object of type bytes or bytearray, got: '
'{}'.format(type(byte_str)))
else:
byte_str = bytearray(byte_str)
detector = UniversalDetector()
detector.feed(byte_str)
return detector.close() | [
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CNugteren/CLBlast | 4500a03440e2cc54998c0edab366babf5e504d67 | scripts/generator/generator/routine.py | python | Routine.arguments_type | (self, flavour) | return (self.options_type() + self.sizes_type() +
list(chain(*[self.buffer_type(b) for b in self.scalar_buffers_first()])) +
self.scalar_type("alpha", flavour) +
list(chain(*[self.buffer_type(b) for b in self.buffers_first()])) +
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self.batch_count_type()) | Retrieves a combination of all the argument types | Retrieves a combination of all the argument types | [
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] | def arguments_type(self, flavour):
"""Retrieves a combination of all the argument types"""
return (self.options_type() + self.sizes_type() +
list(chain(*[self.buffer_type(b) for b in self.scalar_buffers_first()])) +
self.scalar_type("alpha", flavour) +
list(chain(*[self.buffer_type(b) for b in self.buffers_first()])) +
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list(chain(*[self.buffer_type(b) for b in self.scalar_buffers_second()])) +
list(chain(*[self.scalar_type(s, flavour) for s in self.other_scalars()])) +
self.batch_count_type()) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | App_CleanUp | (*args) | return _core_.App_CleanUp(*args) | App_CleanUp()
For internal use only, it is used to cleanup after wxWidgets when
Python shuts down. | App_CleanUp() | [
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] | def App_CleanUp(*args):
"""
App_CleanUp()
For internal use only, it is used to cleanup after wxWidgets when
Python shuts down.
"""
return _core_.App_CleanUp(*args) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/cgutils.py | python | _StructProxy.__setattr__ | (self, field, value) | Store the LLVM *value* into the named *field*. | Store the LLVM *value* into the named *field*. | [
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"""
Store the LLVM *value* into the named *field*.
"""
if field.startswith('_'):
return super(_StructProxy, self).__setattr__(field, value)
self[self._datamodel.get_field_position(field)] = value | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/distutils/fcompiler/gnu.py | python | Gnu95FCompiler.wrap_unlinkable_objects | (self, objects, output_dir, extra_dll_dir) | Convert a set of object files that are not compatible with the default
linker, to a file that is compatible. | Convert a set of object files that are not compatible with the default
linker, to a file that is compatible. | [
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"""
Convert a set of object files that are not compatible with the default
linker, to a file that is compatible.
"""
if self.c_compiler.compiler_type == "msvc":
# Compile a DLL and return the lib for the DLL as
# the object. Also keep track of previous DLLs that
# we have compiled so that we can link against them.
# If there are .a archives, assume they are self-contained
# static libraries, and build separate DLLs for each
archives = []
plain_objects = []
for obj in objects:
if obj.lower().endswith('.a'):
archives.append(obj)
else:
plain_objects.append(obj)
chained_libs = []
chained_dlls = []
for archive in archives[::-1]:
lib, dll = self._link_wrapper_lib(
[archive],
output_dir,
extra_dll_dir,
chained_dlls=chained_dlls,
is_archive=True)
chained_libs.insert(0, lib)
chained_dlls.insert(0, dll)
if not plain_objects:
return chained_libs
lib, dll = self._link_wrapper_lib(
plain_objects,
output_dir,
extra_dll_dir,
chained_dlls=chained_dlls,
is_archive=False)
return [lib] + chained_libs
else:
raise ValueError("Unsupported C compiler") | [
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ApolloAuto/apollo | 463fb82f9e979d02dcb25044e60931293ab2dba0 | modules/tools/routing/road_show.py | python | draw_id | (x, y, id_string) | Draw id_string on (x, y) | Draw id_string on (x, y) | [
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bbox=dict(boxstyle='round,pad=0.5', fc='green', alpha=0.5),
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tuttleofx/TuttleOFX | 36fc4cae15092a84ea8c29b9c6658c7cabfadb6e | tools/upload_on_drive.py | python | DriveHelper.create_arbo | (self, tree, parent_id=None) | Create arbo recursively in Drive
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/!/ --- As Arbo as already been generated ---
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Exception : changes in arbo structure. But in that
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Call : drive.create_arbo(ARBO.tree).
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/!/ --- As Arbo as already been generated ---
This function should'nt be used anymore.
Exception : changes in arbo structure. But in that
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build_parent = parent_id
for node in tree:
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build_parent = (self.create_directory(node, parent_id))
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/turtle.py | python | TNavigator.setx | (self, x) | Set the turtle's first coordinate to x
Argument:
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Set the turtle's first coordinate to x, leave second coordinate
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Example (for a Turtle instance named turtle):
>>> turtle.position()
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>>> turtle.setx(10)
>>> turtle.position()
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] | def setx(self, x):
"""Set the turtle's first coordinate to x
Argument:
x -- a number (integer or float)
Set the turtle's first coordinate to x, leave second coordinate
unchanged.
Example (for a Turtle instance named turtle):
>>> turtle.position()
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>>> turtle.setx(10)
>>> turtle.position()
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"""
self._goto(Vec2D(x, self._position[1])) | [
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