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SanPen/GridCal | d3f4566d2d72c11c7e910c9d162538ef0e60df31 | src/GridCal/ThirdParty/pulp/pulp.py | python | permutation | (orgset, k=None) | returns an iterator that lists the permutations of orgset of
length k
:param orgset: the list to be iterated
:param k: the cardinality of the subsets
:return: an iterator of the subsets
example:
>>> c = permutation([1,2,3,4],2)
>>> for s in c:
... print(s)
(1, 2)
(1, 3)
(1, 4)
(2, 1)
(2, 3)
(2, 4)
(3, 1)
(3, 2)
(3, 4)
(4, 1)
(4, 2)
(4, 3) | returns an iterator that lists the permutations of orgset of
length k | [
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] | def permutation(orgset, k=None):
"""
returns an iterator that lists the permutations of orgset of
length k
:param orgset: the list to be iterated
:param k: the cardinality of the subsets
:return: an iterator of the subsets
example:
>>> c = permutation([1,2,3,4],2)
>>> for s in c:
... print(s)
(1, 2)
(1, 3)
(1, 4)
(2, 1)
(2, 3)
(2, 4)
(3, 1)
(3, 2)
(3, 4)
(4, 1)
(4, 2)
(4, 3)
"""
try:
from itertools import permutation as _it_permutation
return _it_permutation(orgset, k)
except ImportError:
return __permutation(orgset, k) | [
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IronLanguages/ironpython2 | 51fdedeeda15727717fb8268a805f71b06c0b9f1 | Src/StdLib/repackage/setuptools/setuptools/_vendor/pyparsing.py | python | ParserElement.__or__ | (self, other ) | return MatchFirst( [ self, other ] ) | Implementation of | operator - returns C{L{MatchFirst}} | Implementation of | operator - returns C{L{MatchFirst}} | [
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] | def __or__(self, other ):
"""
Implementation of | operator - returns C{L{MatchFirst}}
"""
if isinstance( other, basestring ):
other = ParserElement._literalStringClass( other )
if not isinstance( other, ParserElement ):
warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
SyntaxWarning, stacklevel=2)
return None
return MatchFirst( [ self, other ] ) | [
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rigetti/grove | dc6bf6ec63e8c435fe52b1e00f707d5ce4cdb9b3 | grove/utils/utility_programs.py | python | ControlledProgramBuilder.with_operation | (self, operation) | return self | Sets the operation that defines the controlled gate.
:param numpy.ndarray operation: The unitary gate to be controlled, given as a numpy array.
:return: self, with operation set.
:rtype: ControlledProgramBuilder | Sets the operation that defines the controlled gate. | [
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"""Sets the operation that defines the controlled gate.
:param numpy.ndarray operation: The unitary gate to be controlled, given as a numpy array.
:return: self, with operation set.
:rtype: ControlledProgramBuilder
"""
self.operation = operation
return self | [
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F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idaapi.py | python | is_type_uchar | (*args) | return _idaapi.is_type_uchar(*args) | is_type_uchar(t) -> bool | is_type_uchar(t) -> bool | [
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] | def is_type_uchar(*args):
"""
is_type_uchar(t) -> bool
"""
return _idaapi.is_type_uchar(*args) | [
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allenai/document-qa | 2f9fa6878b60ed8a8a31bcf03f802cde292fe48b | docqa/scripts/train_bidaf.py | python | main | () | A close-as-possible impelemntation of BiDaF, its based on the `dev` tensorflow 1.1 branch of Ming's repo
which, in particular, uses Adam not Adadelta. I was not able to replicate the results in paper using Adadelta,
but with Adam i was able to get to 78.0 F1 on the dev set with this scripts. I believe this approach is
an exact reproduction up the code in the repo, up to initializations.
Notes: Exponential Moving Average is very important, as is early stopping. This is also in particualr best run
on a GPU due to the large number of parameters and batch size involved. | A close-as-possible impelemntation of BiDaF, its based on the `dev` tensorflow 1.1 branch of Ming's repo
which, in particular, uses Adam not Adadelta. I was not able to replicate the results in paper using Adadelta,
but with Adam i was able to get to 78.0 F1 on the dev set with this scripts. I believe this approach is
an exact reproduction up the code in the repo, up to initializations. | [
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"""
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which, in particular, uses Adam not Adadelta. I was not able to replicate the results in paper using Adadelta,
but with Adam i was able to get to 78.0 F1 on the dev set with this scripts. I believe this approach is
an exact reproduction up the code in the repo, up to initializations.
Notes: Exponential Moving Average is very important, as is early stopping. This is also in particualr best run
on a GPU due to the large number of parameters and batch size involved.
"""
out = get_output_name_from_cli()
train_params = TrainParams(SerializableOptimizer("Adam", dict(learning_rate=0.001)),
num_epochs=12, ema=0.999, async_encoding=10,
log_period=30, eval_period=1000, save_period=1000,
eval_samples=dict(dev=None, train=8000))
# recurrent_layer = BiRecurrentMapper(LstmCellSpec(100, keep_probs=0.8))
# recurrent_layer = FusedLstm()
recurrent_layer = SequenceMapperSeq(DropoutLayer(0.8), CudnnLstm(100))
model = Attention(
encoder=DocumentAndQuestionEncoder(SingleSpanAnswerEncoder()),
word_embed=FixedWordEmbedder(vec_name="glove.6B.100d", word_vec_init_scale=0, learn_unk=False),
char_embed=CharWordEmbedder(
embedder=LearnedCharEmbedder(16, 49, 8),
layer=ReduceLayer("max", Conv1d(100, 5, 0.8), mask=False),
shared_parameters=True
),
word_embed_layer=None,
embed_mapper=SequenceMapperSeq(
HighwayLayer(activation="relu"), HighwayLayer(activation="relu"),
recurrent_layer),
preprocess=None,
question_mapper=None,
context_mapper=None,
memory_builder=NullBiMapper(),
attention=BiAttention(TriLinear(bias=True), True),
match_encoder=NullMapper(),
predictor= BoundsPredictor(
ChainConcat(
start_layer=SequenceMapperSeq(
recurrent_layer,
recurrent_layer),
end_layer=recurrent_layer
)
),
)
with open(__file__, "r") as f:
notes = f.read()
eval = [LossEvaluator(), SpanEvaluator(bound=[17], text_eval="squad")]
corpus = SquadCorpus()
train_batching = ClusteredBatcher(60, ContextLenBucketedKey(3), True, False)
eval_batching = ClusteredBatcher(60, ContextLenKey(), False, False)
data = DocumentQaTrainingData(corpus, None, train_batching, eval_batching)
trainer.start_training(data, model, train_params, eval, model_dir.ModelDir(out), notes) | [
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SymbiFlow/symbiflow-arch-defs | f38793112ff78a06de9f1e3269bd22543e29729f | xc/common/libraries/parse_pdf_modules.py | python | find_pages | (pgs, start, stop) | Find all the pages between the resolved "start" and "stop" actions | Find all the pages between the resolved "start" and "stop" actions | [
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] | def find_pages(pgs, start, stop):
"""Find all the pages between the resolved "start" and "stop" actions"""
extract = False
bottom = PAGE_MARGIN
for pg in pgs:
if pg.pageid == start[0].objid:
extract = True
top = start[1] - HEADER_MARGIN
else:
top = pg.mediabox[3] - PAGE_MARGIN
if pg.pageid == stop[0].objid:
bottom = stop[1]
if extract:
yield pg, top, bottom
if pg.pageid == stop[0].objid:
return | [
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onaio/onadata | 89ad16744e8f247fb748219476f6ac295869a95f | onadata/apps/logger/models/xform.py | python | XFormMixin.get_unique_id_string | (self, id_string, count=0) | return id_string | [] | def get_unique_id_string(self, id_string, count=0):
# used to generate a new id_string for new data_dictionary object if
# id_string already existed
if self._id_string_already_exists_in_account(id_string):
if count != 0:
if re.match(r'\w+_\d+$', id_string):
a = id_string.split('_')
id_string = "_".join(a[:-1])
count += 1
id_string = "{}_{}".format(id_string, count)
return self.get_unique_id_string(id_string, count)
return id_string | [
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... | https://github.com/onaio/onadata/blob/89ad16744e8f247fb748219476f6ac295869a95f/onadata/apps/logger/models/xform.py#L291-L304 | |||
vulscanteam/vulscan | 787397e267c4e6469522ee0abe55b3e98f968d4a | pocsuite/lib/core/common.py | python | isListLike | (value) | return isinstance(value, (list, tuple, set)) | Returns True if the given value is a list-like instance
>>> isListLike([1, 2, 3])
True
>>> isListLike(u'2')
False | Returns True if the given value is a list-like instance | [
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] | def isListLike(value):
"""
Returns True if the given value is a list-like instance
>>> isListLike([1, 2, 3])
True
>>> isListLike(u'2')
False
"""
return isinstance(value, (list, tuple, set)) | [
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gevent/gevent | ae2cb5aeb2aea8987efcb90a4c50ca4e1ee12c31 | src/gevent/resolver/_addresses.py | python | _ipv6_inet_aton | (text,
_v4_ending=re.compile(br'(.*):(\d+\.\d+\.\d+\.\d+)$'),
_colon_colon_start=re.compile(br'::.*'),
_colon_colon_end=re.compile(br'.*::$')) | Convert an IPv6 address in text form to binary form.
*text*, a ``text``, the IPv6 address in textual form.
Returns a ``binary``. | Convert an IPv6 address in text form to binary form. | [
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] | def _ipv6_inet_aton(text,
_v4_ending=re.compile(br'(.*):(\d+\.\d+\.\d+\.\d+)$'),
_colon_colon_start=re.compile(br'::.*'),
_colon_colon_end=re.compile(br'.*::$')):
"""
Convert an IPv6 address in text form to binary form.
*text*, a ``text``, the IPv6 address in textual form.
Returns a ``binary``.
"""
# pylint:disable=too-many-branches
#
# Our aim here is not something fast; we just want something that works.
#
if not isinstance(text, bytes):
text = text.encode()
if text == b'::':
text = b'0::'
#
# Get rid of the icky dot-quad syntax if we have it.
#
m = _v4_ending.match(text)
if not m is None:
b = bytearray(_ipv4_inet_aton(m.group(2)))
text = (u"{}:{:02x}{:02x}:{:02x}{:02x}".format(m.group(1).decode(),
b[0], b[1], b[2],
b[3])).encode()
#
# Try to turn '::<whatever>' into ':<whatever>'; if no match try to
# turn '<whatever>::' into '<whatever>:'
#
m = _colon_colon_start.match(text)
if not m is None:
text = text[1:]
else:
m = _colon_colon_end.match(text)
if not m is None:
text = text[:-1]
#
# Now canonicalize into 8 chunks of 4 hex digits each
#
chunks = text.split(b':')
l = len(chunks)
if l > 8:
raise SyntaxError
seen_empty = False
canonical = []
for c in chunks:
if c == b'':
if seen_empty:
raise AddressSyntaxError(text)
seen_empty = True
for _ in range(0, 8 - l + 1):
canonical.append(b'0000')
else:
lc = len(c)
if lc > 4:
raise AddressSyntaxError(text)
if lc != 4:
c = (b'0' * (4 - lc)) + c
canonical.append(c)
if l < 8 and not seen_empty:
raise AddressSyntaxError(text)
text = b''.join(canonical)
#
# Finally we can go to binary.
#
try:
return binascii.unhexlify(text)
except (binascii.Error, TypeError):
raise AddressSyntaxError(text) | [
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berkeleydeeprlcourse/homework_fall2019 | e9725d8aba926e2fe1c743f881884111b44e33bb | hw3/cs285/infrastructure/dqn_utils.py | python | MemoryOptimizedReplayBuffer.can_sample | (self, batch_size) | return batch_size + 1 <= self.num_in_buffer | Returns true if `batch_size` different transitions can be sampled from the buffer. | Returns true if `batch_size` different transitions can be sampled from the buffer. | [
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] | def can_sample(self, batch_size):
"""Returns true if `batch_size` different transitions can be sampled from the buffer."""
return batch_size + 1 <= self.num_in_buffer | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/api/resources/v0_3.py | python | CommCareCaseResource.obj_get_list | (self, bundle, domain, **kwargs) | return ElasticAPIQuerySet(
payload=es_query,
model=ESCase,
es_client=self.case_es(domain)
).order_by('server_modified_on') | [] | def obj_get_list(self, bundle, domain, **kwargs):
try:
es_query = es_query_from_get_params(bundle.request.GET, domain, doc_type='case')
except Http400 as e:
raise BadRequest(str(e))
return ElasticAPIQuerySet(
payload=es_query,
model=ESCase,
es_client=self.case_es(domain)
).order_by('server_modified_on') | [
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dragonbook/V2V-PoseNet-pytorch | 90045b61c45f18dc20b410e2de14bd22be55fe0e | lib/accuracy.py | python | compute_mean_err | (pred, gt) | return np.mean(err_dist, axis=0) | pred: (N, K, 3)
gt: (N, K, 3)
mean_err: (K,) | pred: (N, K, 3)
gt: (N, K, 3) | [
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'''
pred: (N, K, 3)
gt: (N, K, 3)
mean_err: (K,)
'''
N, K = pred.shape[0], pred.shape[1]
err_dist = np.sqrt(np.sum((pred - gt)**2, axis=2)) # (N, K)
return np.mean(err_dist, axis=0) | [
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plasticityai/magnitude | 7ac0baeaf181263b661c3ae00643d21e3fd90216 | pymagnitude/third_party/_pysqlite/__init__.py | python | Magnitude._db_full_result_to_vec | (self, result, put_cache=True) | Converts a full database result to a vector. | Converts a full database result to a vector. | [
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"""Converts a full database result to a vector."""
result_key = result[0]
if self._query_is_cached(result_key):
return (result_key, self.query(result_key))
else:
vec = self._db_result_to_vec(result[1:])
if put_cache:
self._vector_for_key_cached._cache.put((result_key,), vec)
return (result_key, vec) | [
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"result_key",
",",
"self",
... | https://github.com/plasticityai/magnitude/blob/7ac0baeaf181263b661c3ae00643d21e3fd90216/pymagnitude/third_party/_pysqlite/__init__.py#L718-L727 | ||
HymanLiuTS/flaskTs | 286648286976e85d9b9a5873632331efcafe0b21 | flasky/lib/python2.7/site-packages/selenium/webdriver/remote/remote_connection.py | python | RemoteConnection.set_timeout | (cls, timeout) | Override the default timeout
:Args:
- timeout - timeout value for http requests in seconds | Override the default timeout | [
"Override",
"the",
"default",
"timeout"
] | def set_timeout(cls, timeout):
"""
Override the default timeout
:Args:
- timeout - timeout value for http requests in seconds
"""
cls._timeout = timeout | [
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aws-samples/aws-kube-codesuite | ab4e5ce45416b83bffb947ab8d234df5437f4fca | src/kubernetes/client/models/v1_security_context.py | python | V1SecurityContext.to_dict | (self) | return result | Returns the model properties as a dict | Returns the model properties as a dict | [
"Returns",
"the",
"model",
"properties",
"as",
"a",
"dict"
] | def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result | [
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bslatkin/effectivepython | 4ae6f3141291ea137eb29a245bf889dbc8091713 | example_code/item_49.py | python | Point2D.__repr__ | (self) | return f'Point2D({self.x}, {self.y})' | [] | def __repr__(self):
return f'Point2D({self.x}, {self.y})' | [
"def",
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"return",
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] | https://github.com/bslatkin/effectivepython/blob/4ae6f3141291ea137eb29a245bf889dbc8091713/example_code/item_49.py#L67-L68 | |||
newfies-dialer/newfies-dialer | 8168b3dd43e9f5ce73a2645b3229def1b2815d47 | newfies/appointment/utils.py | python | OccurrenceReplacer.get_additional_occurrences | (self, start, end) | return [occ for key, occ in self.lookup.items() if (occ.start < end and occ.end >= start and not occ.cancelled)] | Return persisted occurrences which are now in the period | Return persisted occurrences which are now in the period | [
"Return",
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"occurrences",
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"are",
"now",
"in",
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] | def get_additional_occurrences(self, start, end):
"""
Return persisted occurrences which are now in the period
"""
return [occ for key, occ in self.lookup.items() if (occ.start < end and occ.end >= start and not occ.cancelled)] | [
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samuelclay/NewsBlur | 2c45209df01a1566ea105e04d499367f32ac9ad2 | utils/S3.py | python | AWSAuthConnection.__init__ | (self, aws_access_key_id, aws_secret_access_key, is_secure=True,
server=DEFAULT_HOST, port=None, calling_format=CallingFormat.SUBDOMAIN) | [] | def __init__(self, aws_access_key_id, aws_secret_access_key, is_secure=True,
server=DEFAULT_HOST, port=None, calling_format=CallingFormat.SUBDOMAIN):
if not port:
port = PORTS_BY_SECURITY[is_secure]
self.aws_access_key_id = aws_access_key_id
self.aws_secret_access_key = aws_secret_access_key
self.is_secure = is_secure
self.server = server
self.port = port
self.calling_format = calling_format | [
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... | https://github.com/samuelclay/NewsBlur/blob/2c45209df01a1566ea105e04d499367f32ac9ad2/utils/S3.py#L145-L156 | ||||
oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/core/virtual_network_client.py | python | VirtualNetworkClient.create_security_list | (self, create_security_list_details, **kwargs) | Creates a new security list for the specified VCN. For more information
about security lists, see `Security Lists`__.
For information on the number of rules you can have in a security list, see
`Service Limits`__.
For the purposes of access control, you must provide the `OCID`__ of the compartment where you want the security
list to reside. Notice that the security list doesn't have to be in the same compartment as the VCN, subnets,
or other Networking Service components. If you're not sure which compartment to use, put the security
list in the same compartment as the VCN. For more information about compartments and access control, see
`Overview of the IAM Service`__. For information about OCIDs, see
`Resource Identifiers`__.
You may optionally specify a *display name* for the security list, otherwise a default is provided.
It does not have to be unique, and you can change it. Avoid entering confidential information.
__ https://docs.cloud.oracle.com/iaas/Content/Network/Concepts/securitylists.htm
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/servicelimits.htm
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm
__ https://docs.cloud.oracle.com/iaas/Content/Identity/Concepts/overview.htm
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm
:param oci.core.models.CreateSecurityListDetails create_security_list_details: (required)
Details regarding the security list to create.
:param str opc_retry_token: (optional)
A token that uniquely identifies a request so it can be retried in case of a timeout or
server error without risk of executing that same action again. Retry tokens expire after 24
hours, but can be invalidated before then due to conflicting operations (for example, if a resource
has been deleted and purged from the system, then a retry of the original creation request
may be rejected).
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.core.models.SecurityList`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/core/create_security_list.py.html>`__ to see an example of how to use create_security_list API. | Creates a new security list for the specified VCN. For more information
about security lists, see `Security Lists`__.
For information on the number of rules you can have in a security list, see
`Service Limits`__. | [
"Creates",
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"__",
".",
"For",
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"on",
"the",
"number",
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"rules",
"you",
"can",
"... | def create_security_list(self, create_security_list_details, **kwargs):
"""
Creates a new security list for the specified VCN. For more information
about security lists, see `Security Lists`__.
For information on the number of rules you can have in a security list, see
`Service Limits`__.
For the purposes of access control, you must provide the `OCID`__ of the compartment where you want the security
list to reside. Notice that the security list doesn't have to be in the same compartment as the VCN, subnets,
or other Networking Service components. If you're not sure which compartment to use, put the security
list in the same compartment as the VCN. For more information about compartments and access control, see
`Overview of the IAM Service`__. For information about OCIDs, see
`Resource Identifiers`__.
You may optionally specify a *display name* for the security list, otherwise a default is provided.
It does not have to be unique, and you can change it. Avoid entering confidential information.
__ https://docs.cloud.oracle.com/iaas/Content/Network/Concepts/securitylists.htm
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/servicelimits.htm
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm
__ https://docs.cloud.oracle.com/iaas/Content/Identity/Concepts/overview.htm
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm
:param oci.core.models.CreateSecurityListDetails create_security_list_details: (required)
Details regarding the security list to create.
:param str opc_retry_token: (optional)
A token that uniquely identifies a request so it can be retried in case of a timeout or
server error without risk of executing that same action again. Retry tokens expire after 24
hours, but can be invalidated before then due to conflicting operations (for example, if a resource
has been deleted and purged from the system, then a retry of the original creation request
may be rejected).
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.core.models.SecurityList`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/core/create_security_list.py.html>`__ to see an example of how to use create_security_list API.
"""
resource_path = "/securityLists"
method = "POST"
# Don't accept unknown kwargs
expected_kwargs = [
"retry_strategy",
"opc_retry_token"
]
extra_kwargs = [_key for _key in six.iterkeys(kwargs) if _key not in expected_kwargs]
if extra_kwargs:
raise ValueError(
"create_security_list got unknown kwargs: {!r}".format(extra_kwargs))
header_params = {
"accept": "application/json",
"content-type": "application/json",
"opc-retry-token": kwargs.get("opc_retry_token", missing)
}
header_params = {k: v for (k, v) in six.iteritems(header_params) if v is not missing and v is not None}
retry_strategy = self.base_client.get_preferred_retry_strategy(
operation_retry_strategy=kwargs.get('retry_strategy'),
client_retry_strategy=self.retry_strategy
)
if retry_strategy:
if not isinstance(retry_strategy, retry.NoneRetryStrategy):
self.base_client.add_opc_retry_token_if_needed(header_params)
self.base_client.add_opc_client_retries_header(header_params)
retry_strategy.add_circuit_breaker_callback(self.circuit_breaker_callback)
return retry_strategy.make_retrying_call(
self.base_client.call_api,
resource_path=resource_path,
method=method,
header_params=header_params,
body=create_security_list_details,
response_type="SecurityList")
else:
return self.base_client.call_api(
resource_path=resource_path,
method=method,
header_params=header_params,
body=create_security_list_details,
response_type="SecurityList") | [
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paralelo14/google_explorer | 0b21b57ef6fb7b9182fb13d00508164d007b2d19 | exploits/joomraa.py | python | set_media_options | (options, sess, data) | return True | Allow us to upload a .pht file | Allow us to upload a .pht file | [
"Allow",
"us",
"to",
"upload",
"a",
".",
"pht",
"file"
] | def set_media_options(options, sess, data):
"""
Allow us to upload a .pht file
"""
print("[+] Setting media options")
newdata = {
'jform[upload_extensions]': 'bmp,csv,doc,gif,ico,jpg,jpeg,odg,odp,ods,odt,pdf,png,ppt,swf,txt,xcf,xls,BMP,CSV,DOC,GIF,ICO,JPG,JPEG,ODG,ODP,ODS,ODT,PDF,PNG,PPT,SWF,TXT,XCF,XLS',
'jform[upload_maxsize]':10,
'jform[file_path]':'images',
'jform[image_path]':'images',
'jform[restrict_uploads]':1,
'jform[check_mime]':0,
'jform[image_extensions]':'bmp,gif,jpg,png',
'jform[ignore_extensions]': '',
'jform[upload_mime]': 'image/jpeg,image/gif,image/png,image/bmp,application/x-shockwave-flash,application/msword,application/excel,application/pdf,application/powerpoint,text/plain,application/x-zip',
'jform[upload_mime_illegal]':'text/html',
'id':13
}
newdata.update(data)
newdata['component'] = 'com_media'
newdata['task'] = 'config.save.component.apply'
config_url = options.url + '/administrator/index.php?option=com_config'
resp = sess.post(config_url, data=newdata)
if 'jform[upload_extensions]' not in resp.text:
print('[!] Maybe failed to set media options...')
return False
return True | [
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"'bmp,csv,doc,gif,ico,jpg,jpeg,odg,odp,ods,odt,pdf,png,ppt,swf,txt,xcf,xls,BMP,CSV,DOC,GIF... | https://github.com/paralelo14/google_explorer/blob/0b21b57ef6fb7b9182fb13d00508164d007b2d19/exploits/joomraa.py#L75-L101 | |
amirassov/kaggle-imaterialist | f1ae37100801203500d20119b9de7e19b0d89a1c | mmdetection/mmdet/core/post_processing/merge_augs.py | python | merge_aug_masks | (aug_masks, img_metas, rcnn_test_cfg, weights=None) | return merged_masks | Merge augmented mask prediction.
Args:
aug_masks (list[ndarray]): shape (n, #class, h, w)
img_shapes (list[ndarray]): shape (3, ).
rcnn_test_cfg (dict): rcnn test config.
Returns:
tuple: (bboxes, scores) | Merge augmented mask prediction. | [
"Merge",
"augmented",
"mask",
"prediction",
"."
] | def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None):
"""Merge augmented mask prediction.
Args:
aug_masks (list[ndarray]): shape (n, #class, h, w)
img_shapes (list[ndarray]): shape (3, ).
rcnn_test_cfg (dict): rcnn test config.
Returns:
tuple: (bboxes, scores)
"""
recovered_masks = [
mask if not img_info[0]['flip'] else mask[..., ::-1]
for mask, img_info in zip(aug_masks, img_metas)
]
if weights is None:
merged_masks = np.mean(recovered_masks, axis=0)
else:
merged_masks = np.average(
np.array(recovered_masks), axis=0, weights=np.array(weights))
return merged_masks | [
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OpenCobolIDE/OpenCobolIDE | c78d0d335378e5fe0a5e74f53c19b68b55e85388 | open_cobol_ide/controllers/cobol.py | python | CobolController._on_run_finished | (self) | [] | def _on_run_finished(self):
self.enable_compile(True)
self.enable_run(True) | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/gdata/src/gdata/apps/service.py | python | AppsService.GetGeneratorForAllNicknamesOfAUser | (
self, user_name, num_retries=gdata.service.DEFAULT_NUM_RETRIES,
delay=gdata.service.DEFAULT_DELAY, backoff=gdata.service.DEFAULT_BACKOFF) | return self.GetGeneratorFromLinkFinder(
first_page, gdata.apps.NicknameFeedFromString, num_retries=num_retries,
delay=delay, backoff=backoff) | Retrieve a generator for all nicknames of a particular user. | Retrieve a generator for all nicknames of a particular user. | [
"Retrieve",
"a",
"generator",
"for",
"all",
"nicknames",
"of",
"a",
"particular",
"user",
"."
] | def GetGeneratorForAllNicknamesOfAUser(
self, user_name, num_retries=gdata.service.DEFAULT_NUM_RETRIES,
delay=gdata.service.DEFAULT_DELAY, backoff=gdata.service.DEFAULT_BACKOFF):
"""Retrieve a generator for all nicknames of a particular user."""
uri = "%s/nickname/%s?username=%s" % (self._baseURL(), API_VER, user_name)
try:
first_page = gdata.apps.NicknameFeedFromString(str(self.GetWithRetries(
uri, num_retries=num_retries, delay=delay, backoff=backoff)))
except gdata.service.RequestError, e:
raise AppsForYourDomainException(e.args[0])
return self.GetGeneratorFromLinkFinder(
first_page, gdata.apps.NicknameFeedFromString, num_retries=num_retries,
delay=delay, backoff=backoff) | [
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aleju/imgaug | 0101108d4fed06bc5056c4a03e2bcb0216dac326 | imgaug/augmenters/geometric.py | python | _AffineSamplingResult.to_matrix | (
self,
idx,
arr_shape,
image_shape,
fit_output,
shift_add=(0.5, 0.5)
) | return matrix, arr_shape | [] | def to_matrix(
self,
idx,
arr_shape,
image_shape,
fit_output,
shift_add=(0.5, 0.5)
):
if 0 in image_shape:
return np.eye(3, dtype=np.float32), arr_shape
params = self.get_affine_parameters(
idx, arr_shape=arr_shape, image_shape=image_shape
)
matrix_gen = _AffineMatrixGenerator()
matrix_gen.centerize(arr_shape)
matrix_gen.translate(x_px=shift_add[1], y_px=shift_add[0])
matrix_gen.rotate(params["rotate_rad"])
matrix_gen.scale(x_frac=params["scale_x"], y_frac=params["scale_y"])
matrix_gen.shear(x_rad=params["shear_x_rad"],
y_rad=params["shear_y_rad"])
matrix_gen.translate(x_px=params["translate_x_px"],
y_px=params["translate_y_px"])
matrix_gen.translate(x_px=-shift_add[1], y_px=-shift_add[0])
matrix_gen.invert_centerize(arr_shape)
matrix = matrix_gen.matrix
if fit_output:
matrix, arr_shape = _compute_affine_warp_output_shape(
matrix, arr_shape
)
return matrix, arr_shape | [
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"dty... | https://github.com/aleju/imgaug/blob/0101108d4fed06bc5056c4a03e2bcb0216dac326/imgaug/augmenters/geometric.py#L742-L774 | |||
lad1337/XDM | 0c1b7009fe00f06f102a6f67c793478f515e7efe | site-packages/logilab/common/tree.py | python | Node.flatten | (self, _list=None) | return _list | return a list with all the nodes descendant from this node | return a list with all the nodes descendant from this node | [
"return",
"a",
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"with",
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"nodes",
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"from",
"this",
"node"
] | def flatten(self, _list=None):
"""
return a list with all the nodes descendant from this node
"""
if _list is None:
_list = []
_list.append(self)
for c in self.children:
c.flatten(_list)
return _list | [
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django-oscar/django-oscar | ffcc530844d40283b6b1552778a140536b904f5f | src/oscar/templatetags/display_tags.py | python | get_parameters | (context, except_field) | return '' | Renders current get parameters except for the specified parameter | Renders current get parameters except for the specified parameter | [
"Renders",
"current",
"get",
"parameters",
"except",
"for",
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"specified",
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] | def get_parameters(context, except_field):
"""
Renders current get parameters except for the specified parameter
"""
getvars = context['request'].GET.copy()
getvars.pop(except_field, None)
if len(getvars.keys()) > 0:
return "%s&" % getvars.urlencode()
return '' | [
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ansible-collections/community.general | 3faffe8f47968a2400ba3c896c8901c03001a194 | plugins/modules/nagios.py | python | Nagios.disable_svc_notifications | (self, host, services=None) | This command is used to prevent notifications from being sent
out for the specified service.
Note that this command does not disable notifications from
being sent out about the host.
Syntax: DISABLE_SVC_NOTIFICATIONS;<host_name>;<service_description> | This command is used to prevent notifications from being sent
out for the specified service. | [
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] | def disable_svc_notifications(self, host, services=None):
"""
This command is used to prevent notifications from being sent
out for the specified service.
Note that this command does not disable notifications from
being sent out about the host.
Syntax: DISABLE_SVC_NOTIFICATIONS;<host_name>;<service_description>
"""
cmd = "DISABLE_SVC_NOTIFICATIONS"
if services is None:
services = []
for service in services:
notif_str = self._fmt_notif_str(cmd, host, svc=service)
self._write_command(notif_str) | [
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abarker/pdfCropMargins | d9378cd6cba55ff137d80ff2d1f098bf6938f759 | src/pdfCropMargins/external_program_calls.py | python | call_external_subprocess | (command_list, stdin_filename=None, stdout_filename=None,
stderr_filename=None, env=None) | Run the command and arguments in the command_list. Will search the system
PATH for commands to execute, but no shell is started. Redirects any selected
outputs to the given filename. Waits for command completion. | Run the command and arguments in the command_list. Will search the system
PATH for commands to execute, but no shell is started. Redirects any selected
outputs to the given filename. Waits for command completion. | [
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... | def call_external_subprocess(command_list, stdin_filename=None, stdout_filename=None,
stderr_filename=None, env=None):
"""Run the command and arguments in the command_list. Will search the system
PATH for commands to execute, but no shell is started. Redirects any selected
outputs to the given filename. Waits for command completion."""
stdin = open(stdin_filename, "r") if stdin_filename else None
stdout = open(stdout_filename, "w") if stdout_filename else None
stderr = open(stderr_filename, "w") if stderr_filename else None
subprocess.check_call(command_list, stdin=stdin, stdout=stdout, stderr=stderr,
env=env)
if stdin_filename:
stdin.close()
if stdout_filename:
stdout.close()
if stderr_filename:
stderr.close() | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/dns/update.py | python | UpdateMessage.update | (self, v) | [] | def update(self, v):
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devopshq/tfs | 56644a36dd34457dec6922eb144c21db320f16e7 | tfs/connection.py | python | TFSAPI.run_saved_query | (self, id) | return self._find_resource(Wiql, ids=id) | Run saved query by query id
:param id: id of the query to run
:return: instance of the Wiql object with query results | Run saved query by query id | [
"Run",
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] | def run_saved_query(self, id):
""" Run saved query by query id
:param id: id of the query to run
:return: instance of the Wiql object with query results
"""
return self._find_resource(Wiql, ids=id) | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/Lib/csv.py | python | Sniffer._guess_quote_and_delimiter | (self, data, delimiters) | return (quotechar, doublequote, delim, skipinitialspace) | Looks for text enclosed between two identical quotes
(the probable quotechar) which are preceded and followed
by the same character (the probable delimiter).
For example:
,'some text',
The quote with the most wins, same with the delimiter.
If there is no quotechar the delimiter can't be determined
this way. | Looks for text enclosed between two identical quotes
(the probable quotechar) which are preceded and followed
by the same character (the probable delimiter).
For example:
,'some text',
The quote with the most wins, same with the delimiter.
If there is no quotechar the delimiter can't be determined
this way. | [
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"""
Looks for text enclosed between two identical quotes
(the probable quotechar) which are preceded and followed
by the same character (the probable delimiter).
For example:
,'some text',
The quote with the most wins, same with the delimiter.
If there is no quotechar the delimiter can't be determined
this way.
"""
matches = []
for restr in ('(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?P=delim)', # ,".*?",
'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?P<delim>[^\w\n"\'])(?P<space> ?)', # ".*?",
'(?P<delim>>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?:$|\n)', # ,".*?"
'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?:$|\n)'): # ".*?" (no delim, no space)
regexp = re.compile(restr, re.DOTALL | re.MULTILINE)
matches = regexp.findall(data)
if matches:
break
if not matches:
# (quotechar, doublequote, delimiter, skipinitialspace)
return ('', False, None, 0)
quotes = {}
delims = {}
spaces = 0
for m in matches:
n = regexp.groupindex['quote'] - 1
key = m[n]
if key:
quotes[key] = quotes.get(key, 0) + 1
try:
n = regexp.groupindex['delim'] - 1
key = m[n]
except KeyError:
continue
if key and (delimiters is None or key in delimiters):
delims[key] = delims.get(key, 0) + 1
try:
n = regexp.groupindex['space'] - 1
except KeyError:
continue
if m[n]:
spaces += 1
quotechar = reduce(lambda a, b, quotes = quotes:
(quotes[a] > quotes[b]) and a or b, quotes.keys())
if delims:
delim = reduce(lambda a, b, delims = delims:
(delims[a] > delims[b]) and a or b, delims.keys())
skipinitialspace = delims[delim] == spaces
if delim == '\n': # most likely a file with a single column
delim = ''
else:
# there is *no* delimiter, it's a single column of quoted data
delim = ''
skipinitialspace = 0
# if we see an extra quote between delimiters, we've got a
# double quoted format
dq_regexp = re.compile(r"((%(delim)s)|^)\W*%(quote)s[^%(delim)s\n]*%(quote)s[^%(delim)s\n]*%(quote)s\W*((%(delim)s)|$)" % \
{'delim':delim, 'quote':quotechar}, re.MULTILINE)
if dq_regexp.search(data):
doublequote = True
else:
doublequote = False
return (quotechar, doublequote, delim, skipinitialspace) | [
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vrenkens/nabu | 39deb62d182c7036c72f0f7eeb1d5c8eb0fb20fd | nabu/neuralnetworks/decoders/max_decoder.py | python | MaxDecoder.update_evaluation_loss | (self, loss, outputs, references,
reference_seq_length) | return update_loss | update the evaluation loss
args:
loss: the current evaluation loss
outputs: the outputs of the decoder as a dictionary
references: the references as a dictionary
reference_seq_length: the sequence lengths of the references
Returns:
an op to update the evalution loss | update the evaluation loss | [
"update",
"the",
"evaluation",
"loss"
] | def update_evaluation_loss(self, loss, outputs, references,
reference_seq_length):
'''update the evaluation loss
args:
loss: the current evaluation loss
outputs: the outputs of the decoder as a dictionary
references: the references as a dictionary
reference_seq_length: the sequence lengths of the references
Returns:
an op to update the evalution loss
'''
#create a valiable to hold the total number of reference targets
num_targets = tf.get_variable(
name='num_targets',
shape=[],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
trainable=False
)
#stack all the logits
stacked_outputs = {
t:ops.stack_seq(outputs[t][0], outputs[t][1])
for t in outputs}
#create the stacked targets
stacked_targets = {
t:tf.cast(ops.stack_seq(references[t],
reference_seq_length[t]), tf.int32)
for t in references}
#compute the number of errors
errors = [
tf.reduce_sum(tf.reduce_sum(tf.cast(tf.not_equal(
stacked_outputs[o], stacked_targets[o]), tf.float32)))
for o in outputs]
errors = tf.reduce_sum(errors)
#compute the number of targets in this batch
batch_targets = tf.reduce_sum([
tf.reduce_sum(lengths)
for lengths in reference_seq_length.values()])
new_num_targets = num_targets + tf.cast(batch_targets, tf.float32)
#an operation to update the loss
update_loss = loss.assign(
(loss*num_targets + errors)/new_num_targets).op
#add an operation to update the number of targets
with tf.control_dependencies([update_loss]):
update_loss = num_targets.assign(new_num_targets).op
return update_loss | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/plat-sunos5/TYPES.py | python | INT64_C | (c) | return __CONCAT__(c,ll) | [] | def INT64_C(c): return __CONCAT__(c,ll) | [
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/plugins/attack/xpath.py | python | xpath._get_delimiter | (self, vuln, is_error_resp) | :return: The delimiter to be used to terminate strings, one of
single quote or double quote. If an error is found, None is returned. | :return: The delimiter to be used to terminate strings, one of
single quote or double quote. If an error is found, None is returned. | [
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] | def _get_delimiter(self, vuln, is_error_resp):
"""
:return: The delimiter to be used to terminate strings, one of
single quote or double quote. If an error is found, None is returned.
"""
mutant = vuln.get_mutant()
orig_value = mutant.get_token_original_value()
true_sq = "%s' and '%i'='%i" % (orig_value, self.rnum, self.rnum)
false_sq = "%s' and '%i'='%i" % (orig_value, self.rnum, self.rnum + 1)
true_dq = '%s" and "%i"="%i' % (orig_value, self.rnum, self.rnum)
false_dq = '%s" and "%i"="%i' % (orig_value, self.rnum, self.rnum + 1)
to_test = [("'", true_sq, false_sq),
('"', true_dq, false_dq)]
for str_delim, true_xpath, false_xpath in to_test:
mutant_true = mutant.copy()
mutant_false = mutant.copy()
mutant_true.set_token_value(true_xpath)
mutant_false.set_token_value(false_xpath)
try:
true_resp = self._uri_opener.send_mutant(mutant_true)
false_resp = self._uri_opener.send_mutant(mutant_false)
except BaseFrameworkException, e:
om.out.debug('Error "%s"' % e)
else:
if (is_error_resp(false_resp.get_body())
and not is_error_resp(true_resp.get_body())):
return str_delim
else:
msg = 'Failed to identify XPATH injection string delimiter.'
raise BaseFrameworkException(msg) | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/http/cookiejar.py | python | DefaultCookiePolicy.return_ok_expires | (self, cookie, request) | return True | [] | def return_ok_expires(self, cookie, request):
if cookie.is_expired(self._now):
_debug(" cookie expired")
return False
return True | [
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/site-packages/django/contrib/localflavor/uy/util.py | python | get_validation_digit | (number) | return (10-sum) % 10 | Calculates the validation digit for the given number. | Calculates the validation digit for the given number. | [
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] | def get_validation_digit(number):
""" Calculates the validation digit for the given number. """
sum = 0
dvs = [4, 3, 6, 7, 8, 9, 2]
number = str(number)
for i in range(0, len(number)):
sum = (int(number[-1 - i]) * dvs[i] + sum) % 10
return (10-sum) % 10 | [
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metamorphose/metamorphose2 | d2bdd6a86340b9668e93b35a6a568894c9909d68 | src/operations/changeLength.py | python | OpPanel.on_config_load | (self) | Update GUI elements, settings after config load. | Update GUI elements, settings after config load. | [
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"settings",
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] | def on_config_load(self):
"""Update GUI elements, settings after config load."""
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | examples/list_resources_in_tenancy/list_limits_per_compartments.py | python | print_header | (name, category) | [] | def print_header(name, category):
options = {0: 120, 1: 100, 2: 90, 3: 85}
chars = int(options[category])
print("")
print('#' * chars)
print("#" + name.center(chars - 2, " ") + "#")
print('#' * chars) | [
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... | https://github.com/oracle/oci-python-sdk/blob/3c1604e4e212008fb6718e2f68cdb5ef71fd5793/examples/list_resources_in_tenancy/list_limits_per_compartments.py#L77-L83 | ||||
SpriteLink/NIPAP | de09cd7c4c55521f26f00201d694ea9e388b21a9 | pynipap/pynipap.py | python | Pynipap.__init__ | (self, id=None) | Creates logger and XML-RPC-connection. | Creates logger and XML-RPC-connection. | [
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"XML",
"-",
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"-",
"connection",
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] | def __init__(self, id=None):
""" Creates logger and XML-RPC-connection.
"""
self._logger = logging.getLogger(self.__class__.__name__)
self._auth_opts = AuthOptions()
self.id = id | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/contextlib.py | python | closing.__exit__ | (self, *exc_info) | [] | def __exit__(self, *exc_info):
self.thing.close() | [
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matrix-org/synapse | 8e57584a5859a9002759963eb546d523d2498a01 | synapse/storage/databases/main/media_repository.py | python | MediaRepositoryStore.mark_local_media_as_safe | (self, media_id: str, safe: bool = True) | Mark a local media as safe or unsafe from quarantining. | Mark a local media as safe or unsafe from quarantining. | [
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] | async def mark_local_media_as_safe(self, media_id: str, safe: bool = True) -> None:
"""Mark a local media as safe or unsafe from quarantining."""
await self.db_pool.simple_update_one(
table="local_media_repository",
keyvalues={"media_id": media_id},
updatevalues={"safe_from_quarantine": safe},
desc="mark_local_media_as_safe",
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ctxis/canape | 5f0e03424577296bcc60c2008a60a98ec5307e4b | CANAPE.Scripting/Lib/_pyio.py | python | TextIOBase.encoding | (self) | return None | Subclasses should override. | Subclasses should override. | [
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"""Subclasses should override."""
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cisco/mindmeld | 809c36112e9ea8019fe29d54d136ca14eb4fd8db | mindmeld/text_preparation/spacy_model_factory.py | python | SpacyModelFactory.validate_spacy_model_size | (spacy_model_size) | Check if the model size is valid.
Args:
spacy_model_size (str, optional): Size of the Spacy model to use. ("sm", "md", or "lg") | Check if the model size is valid. | [
"Check",
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"model",
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"valid",
"."
] | def validate_spacy_model_size(spacy_model_size):
"""Check if the model size is valid.
Args:
spacy_model_size (str, optional): Size of the Spacy model to use. ("sm", "md", or "lg")
"""
if spacy_model_size not in SPACY_MODEL_SIZES:
raise ValueError(
"{!r} is not a valid model size. Select from: {!r}.".format(
spacy_model_size, " ".join(SPACY_MODEL_SIZES)
)
) | [
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JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | packages/source/nova/nova/api/openstack/xmlutil.py | python | TemplateElement.get | (self, key) | return self.attrib[key] | Get an attribute.
Returns a callable which performs datum selection.
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] | def get(self, key):
"""Get an attribute.
Returns a callable which performs datum selection.
:param key: The name of the attribute to get.
"""
return self.attrib[key] | [
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snakemake/snakemake | 987282dde8a2db5174414988c134a39ae8836a61 | snakemake/io.py | python | expand | (*args, **wildcards) | Expand wildcards in given filepatterns.
Arguments
*args -- first arg: filepatterns as list or one single filepattern,
second arg (optional): a function to combine wildcard values
(itertools.product per default)
**wildcards -- the wildcards as keyword arguments
with their values as lists. If allow_missing=True is included
wildcards in filepattern without values will stay unformatted. | Expand wildcards in given filepatterns. | [
"Expand",
"wildcards",
"in",
"given",
"filepatterns",
"."
] | def expand(*args, **wildcards):
"""
Expand wildcards in given filepatterns.
Arguments
*args -- first arg: filepatterns as list or one single filepattern,
second arg (optional): a function to combine wildcard values
(itertools.product per default)
**wildcards -- the wildcards as keyword arguments
with their values as lists. If allow_missing=True is included
wildcards in filepattern without values will stay unformatted.
"""
filepatterns = args[0]
if len(args) == 1:
combinator = product
elif len(args) == 2:
combinator = args[1]
if isinstance(filepatterns, str) or isinstance(filepatterns, Path):
filepatterns = [filepatterns]
def path_to_str(f):
if isinstance(f, Path):
return str(f)
return f
filepatterns = list(map(path_to_str, filepatterns))
if any(map(lambda f: getattr(f, "flags", {}), filepatterns)):
raise WorkflowError(
"Flags in file patterns given to expand() are invalid. "
"Flags (e.g. temp(), directory()) have to be applied outside "
"of expand (e.g. 'temp(expand(\"plots/{sample}.pdf\", sample=SAMPLES))')."
)
# check if remove missing is provided
format_dict = dict
if "allow_missing" in wildcards and wildcards["allow_missing"] is True:
class FormatDict(dict):
def __missing__(self, key):
return "{" + key + "}"
format_dict = FormatDict
# check that remove missing is not a wildcard in the filepatterns
for filepattern in filepatterns:
if "allow_missing" in re.findall(r"{([^}\.[!:]+)", filepattern):
format_dict = dict
break
# remove unused wildcards to avoid duplicate filepatterns
wildcards = {
filepattern: {
k: v
for k, v in wildcards.items()
if k in re.findall(r"{([^}\.[!:]+)", filepattern)
}
for filepattern in filepatterns
}
def flatten(wildcards):
for wildcard, values in wildcards.items():
if isinstance(values, str) or not isinstance(
values, collections.abc.Iterable
):
values = [values]
yield [(wildcard, value) for value in values]
formatter = string.Formatter()
try:
return [
formatter.vformat(filepattern, (), comb)
for filepattern in filepatterns
for comb in map(format_dict, combinator(*flatten(wildcards[filepattern])))
]
except KeyError as e:
raise WildcardError("No values given for wildcard {}.".format(e)) | [
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errbotio/errbot | 66e1de8e1d7daa62be7f9ed1b2ac8832f09fa25d | tools/plugin-gen.py | python | rate_limit | (resp) | Wait enough to be in the budget for this request.
:param resp: the http response from github
:return: | Wait enough to be in the budget for this request.
:param resp: the http response from github
:return: | [
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":",
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] | def rate_limit(resp):
"""
Wait enough to be in the budget for this request.
:param resp: the http response from github
:return:
"""
if "X-RateLimit-Remaining" not in resp.headers:
log.info("No rate limit detected. Hum along...")
return
remain = int(resp.headers["X-RateLimit-Remaining"])
limit = int(resp.headers["X-RateLimit-Limit"])
log.info("Rate limiter: %s allowed out of %d", remain, limit)
if remain > 1: # margin by one request
return
reset = int(resp.headers["X-RateLimit-Reset"])
ts = datetime.fromtimestamp(reset)
delay = (ts - datetime.now()).total_seconds()
log.info("Hit rate limit. Have to wait for %d seconds", delay)
if delay < 0: # time drift
delay = 2
time.sleep(delay) | [
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deepmind/leo | de9a0c2a77dd7a42c1986b1eef18d184a86e294a | runner.py | python | _construct_training_summaries | (metatrain_loss, metatrain_accuracy,
model_grads, model_vars) | [] | def _construct_training_summaries(metatrain_loss, metatrain_accuracy,
model_grads, model_vars):
tf.summary.scalar("metatrain_loss", metatrain_loss)
tf.summary.scalar("metatrain_valid_accuracy", metatrain_accuracy)
for g, v in zip(model_grads, model_vars):
histogram_name = v.name.split(":")[0]
tf.summary.histogram(histogram_name, v)
histogram_name = "gradient/{}".format(histogram_name)
tf.summary.histogram(histogram_name, g) | [
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natashamjaques/neural_chat | ddb977bb4602a67c460d02231e7bbf7b2cb49a97 | ParlAI/parlai/agents/legacy_agents/seq2seq/dict_v1.py | python | find_ngrams | (token_dict, text, n) | return saved_tokens | Break text into ngrams that appear in ``token_dict``.
:param token_dict: ``dict`` to check for ngrams
:param text: ``str`` to look for ngrams in
:param n: ``int`` max size of ngrams | Break text into ngrams that appear in ``token_dict``. | [
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] | def find_ngrams(token_dict, text, n):
"""Break text into ngrams that appear in ``token_dict``.
:param token_dict: ``dict`` to check for ngrams
:param text: ``str`` to look for ngrams in
:param n: ``int`` max size of ngrams
"""
# base case
if n <= 1:
return text
# tokens committed to output
saved_tokens = []
# tokens remaining to be searched in sentence
search_tokens = text[:]
# tokens stored until next ngram found
next_search = []
while len(search_tokens) >= n:
ngram = ' '.join(search_tokens[:n])
if ngram in token_dict:
# first, search previous unmatched words for smaller ngrams
sub_n = min(len(next_search), n - 1)
saved_tokens.extend(find_ngrams(token_dict, next_search, sub_n))
next_search.clear()
# then add this ngram
saved_tokens.append(ngram)
# then pop this ngram from the remaining words to search
search_tokens = search_tokens[n:]
else:
next_search.append(search_tokens.pop(0))
remainder = next_search + search_tokens
sub_n = min(len(remainder), n - 1)
saved_tokens.extend(find_ngrams(token_dict, remainder, sub_n))
return saved_tokens | [
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bendmorris/static-python | 2e0f8c4d7ed5b359dc7d8a75b6fb37e6b6c5c473 | Parser/asdl_c.py | python | PrototypeVisitor.get_args | (self, fields) | return args | Return list of C argument into, one for each field.
Argument info is 3-tuple of a C type, variable name, and flag
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] | def get_args(self, fields):
"""Return list of C argument into, one for each field.
Argument info is 3-tuple of a C type, variable name, and flag
that is true if type can be NULL.
"""
args = []
unnamed = {}
for f in fields:
if f.name is None:
name = f.type
c = unnamed[name] = unnamed.get(name, 0) + 1
if c > 1:
name = "name%d" % (c - 1)
else:
name = f.name
# XXX should extend get_c_type() to handle this
if f.seq:
if f.type.value in ('cmpop',):
ctype = "asdl_int_seq *"
else:
ctype = "asdl_seq *"
else:
ctype = get_c_type(f.type)
args.append((ctype, name, f.opt or f.seq))
return args | [
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nottombrown/rl-teacher | b2c2201e9d2457b13185424a19da7209364f23df | rl_teacher/teach.py | python | ComparisonRewardPredictor.__init__ | (self, env, summary_writer, comparison_collector, agent_logger, label_schedule) | [] | def __init__(self, env, summary_writer, comparison_collector, agent_logger, label_schedule):
self.summary_writer = summary_writer
self.agent_logger = agent_logger
self.comparison_collector = comparison_collector
self.label_schedule = label_schedule
# Set up some bookkeeping
self.recent_segments = deque(maxlen=200) # Keep a queue of recently seen segments to pull new comparisons from
self._frames_per_segment = CLIP_LENGTH * env.fps
self._steps_since_last_training = 0
self._n_timesteps_per_predictor_training = 1e2 # How often should we train our predictor?
self._elapsed_predictor_training_iters = 0
# Build and initialize our predictor model
config = tf.ConfigProto(
device_count={'GPU': 0}
)
self.sess = tf.InteractiveSession(config=config)
self.obs_shape = env.observation_space.shape
self.discrete_action_space = not hasattr(env.action_space, "shape")
self.act_shape = (env.action_space.n,) if self.discrete_action_space else env.action_space.shape
self.graph = self._build_model()
self.sess.run(tf.global_variables_initializer()) | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/iotvideoindustry/v20201201/iotvideoindustry_client.py | python | IotvideoindustryClient.DescribeIPCChannels | (self, request) | 获取IPC设备下属通道
请使用DescribeChannels接口
:param request: Request instance for DescribeIPCChannels.
:type request: :class:`tencentcloud.iotvideoindustry.v20201201.models.DescribeIPCChannelsRequest`
:rtype: :class:`tencentcloud.iotvideoindustry.v20201201.models.DescribeIPCChannelsResponse` | 获取IPC设备下属通道
请使用DescribeChannels接口 | [
"获取IPC设备下属通道",
"请使用DescribeChannels接口"
] | def DescribeIPCChannels(self, request):
"""获取IPC设备下属通道
请使用DescribeChannels接口
:param request: Request instance for DescribeIPCChannels.
:type request: :class:`tencentcloud.iotvideoindustry.v20201201.models.DescribeIPCChannelsRequest`
:rtype: :class:`tencentcloud.iotvideoindustry.v20201201.models.DescribeIPCChannelsResponse`
"""
try:
params = request._serialize()
body = self.call("DescribeIPCChannels", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.DescribeIPCChannelsResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise
else:
raise TencentCloudSDKException(e.message, e.message) | [
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toxygen-project/toxygen | 0a54012cf5ee72434b923bcde7d8f1a4e575ce2f | toxygen/profile.py | python | Profile.send_friend_request | (self, tox_id, message) | Function tries to send request to contact with specified id
:param tox_id: id of new contact or tox dns 4 value
:param message: additional message
:return: True on success else error string | Function tries to send request to contact with specified id
:param tox_id: id of new contact or tox dns 4 value
:param message: additional message
:return: True on success else error string | [
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"""
Function tries to send request to contact with specified id
:param tox_id: id of new contact or tox dns 4 value
:param message: additional message
:return: True on success else error string
"""
try:
message = message or 'Hello! Add me to your contact list please'
if '@' in tox_id: # value like groupbot@toxme.io
tox_id = tox_dns(tox_id)
if tox_id is None:
raise Exception('TOX DNS lookup failed')
if len(tox_id) == TOX_PUBLIC_KEY_SIZE * 2: # public key
self.add_friend(tox_id)
msgBox = QtWidgets.QMessageBox()
msgBox.setWindowTitle(QtWidgets.QApplication.translate("MainWindow", "Friend added"))
text = (QtWidgets.QApplication.translate("MainWindow", 'Friend added without sending friend request'))
msgBox.setText(text)
msgBox.exec_()
else:
result = self._tox.friend_add(tox_id, message.encode('utf-8'))
tox_id = tox_id[:TOX_PUBLIC_KEY_SIZE * 2]
item = self.create_friend_item()
if not self._history.friend_exists_in_db(tox_id):
self._history.add_friend_to_db(tox_id)
message_getter = self._history.messages_getter(tox_id)
friend = Friend(message_getter, result, tox_id, '', item, tox_id)
self._contacts.append(friend)
data = self._tox.get_savedata()
ProfileHelper.get_instance().save_profile(data)
return True
except Exception as ex: # wrong data
log('Friend request failed with ' + str(ex))
return str(ex) | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-47/skeinforge_application/skeinforge_plugins/craft_plugins/smooth.py | python | getNewRepository | () | return SmoothRepository() | Get new repository. | Get new repository. | [
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fastnlp/fitlog | ba9547a56f4855a2d9cf26ae709d01922befa077 | fitlog/__init__.py | python | get_log_dir | (absolute=False) | return _logger.get_log_dir(absolute=absolute) | 返回的是存放所有log的文件夹。例如logs/，需要调用set_log_dir()先设置log的记录文件夹
:param bool absolute: 是否返回绝对路径
:return: | 返回的是存放所有log的文件夹。例如logs/，需要调用set_log_dir()先设置log的记录文件夹 | [
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"""
返回的是存放所有log的文件夹。例如logs/，需要调用set_log_dir()先设置log的记录文件夹
:param bool absolute: 是否返回绝对路径
:return:
"""
return _logger.get_log_dir(absolute=absolute) | [
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robotlearn/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | pyrobolearn/simulators/simulator.py | python | Simulator.get_link_state | (self, body_id, link_id, compute_velocity=False, compute_forward_kinematics=False) | Get the state of the associated link.
Args:
body_id (int): body unique id.
link_id (int): link index.
compute_velocity (bool): If True, the Cartesian world velocity will be computed and returned.
compute_forward_kinematics (bool): if True, the Cartesian world position/orientation will be recomputed
using forward kinematics.
Returns:
np.array[float[3]]: Cartesian world position of CoM
np.array[float[4]]: Cartesian world orientation of CoM, in quaternion [x,y,z,w]
np.array[float[3]]: local position offset of inertial frame (center of mass) expressed in the URDF link
frame
np.array[float[4]]: local orientation (quaternion [x,y,z,w]) offset of the inertial frame expressed in
URDF link frame
np.array[float[3]]: world position of the URDF link frame
np.array[float[4]]: world orientation of the URDF link frame
np.array[float[3]]: Cartesian world linear velocity. Only returned if `compute_velocity` is True.
np.array[float[3]]: Cartesian world angular velocity. Only returned if `compute_velocity` is True. | Get the state of the associated link. | [
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"""
Get the state of the associated link.
Args:
body_id (int): body unique id.
link_id (int): link index.
compute_velocity (bool): If True, the Cartesian world velocity will be computed and returned.
compute_forward_kinematics (bool): if True, the Cartesian world position/orientation will be recomputed
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Returns:
np.array[float[3]]: Cartesian world position of CoM
np.array[float[4]]: Cartesian world orientation of CoM, in quaternion [x,y,z,w]
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hhstore/annotated-py-projects | 4f4eca9a3913a19983cae496279a72699c083a0b | asyncio/asyncio-3.4.3/asyncio/events.py | python | set_event_loop | (loop) | Equivalent to calling get_event_loop_policy().set_event_loop(loop). | Equivalent to calling get_event_loop_policy().set_event_loop(loop). | [
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rlgraph/rlgraph | 428fc136a9a075f29a397495b4226a491a287be2 | rlgraph/components/explorations/epsilon_exploration.py | python | EpsilonExploration.__init__ | (self, decay_spec=None, scope="epsilon-exploration", **kwargs) | Args:
decay_spec (Optional[dict,DecayComponent]): The spec-dict for the DecayComponent to use or a DecayComponent
object directly.
Keyword Args:
Used as decay_spec (only if `decay_spec` not given) to construct the DecayComponent. | Args:
decay_spec (Optional[dict,DecayComponent]): The spec-dict for the DecayComponent to use or a DecayComponent
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Args:
decay_spec (Optional[dict,DecayComponent]): The spec-dict for the DecayComponent to use or a DecayComponent
object directly.
Keyword Args:
Used as decay_spec (only if `decay_spec` not given) to construct the DecayComponent.
"""
super(EpsilonExploration, self).__init__(scope=scope, **kwargs)
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self.sample_space = None
self.flat_sample_space = None
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self.decay = TimeDependentParameter.from_spec(decay_spec)
# Add the decay component and make time_step our (only) input.
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haiwen/seafile-docker | 2d2461d4c8cab3458ec9832611c419d47506c300 | cluster/image/pro_seafile_7.1/scripts_7.1/setup-seafile-mysql.py | python | Utils.write_config | (cp, fn) | Return a case sensitive ConfigParser by reading the file "fn" | Return a case sensitive ConfigParser by reading the file "fn" | [
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with open(fn, 'w') as fp:
cp.write(fp) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/interfaces/kenzo.py | python | KenzoSimplicialSet.join | (self, other) | return KenzoSimplicialSet(join_kenzo) | r"""
Return the join of ``self`` and ``other``.
INPUT:
- ``other`` -- the Kenzo simplicial set with which the join is made
OUTPUT:
- A :class:`KenzoSimplicialSet`
EXAMPLES::
sage: from sage.interfaces.kenzo import Sphere # optional - kenzo
sage: s2 = Sphere(2) # optional - kenzo
sage: s3 = Sphere(3) # optional - kenzo
sage: j = s2.join(s3) # optional - kenzo
sage: type(j) # optional - kenzo
<class 'sage.interfaces.kenzo.KenzoSimplicialSet'>
sage: [j.homology(i) for i in range(6)] # optional - kenzo
[Z, 0, 0, 0, 0, 0] | r"""
Return the join of ``self`` and ``other``. | [
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r"""
Return the join of ``self`` and ``other``.
INPUT:
- ``other`` -- the Kenzo simplicial set with which the join is made
OUTPUT:
- A :class:`KenzoSimplicialSet`
EXAMPLES::
sage: from sage.interfaces.kenzo import Sphere # optional - kenzo
sage: s2 = Sphere(2) # optional - kenzo
sage: s3 = Sphere(3) # optional - kenzo
sage: j = s2.join(s3) # optional - kenzo
sage: type(j) # optional - kenzo
<class 'sage.interfaces.kenzo.KenzoSimplicialSet'>
sage: [j.homology(i) for i in range(6)] # optional - kenzo
[Z, 0, 0, 0, 0, 0]
"""
join_kenzo = __join__(self._kenzo, other._kenzo)
return KenzoSimplicialSet(join_kenzo) | [
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | pgbouncer/datadog_checks/pgbouncer/config_models/shared.py | python | SharedConfig._run_validations | (cls, v, field) | return getattr(validators, f'shared_{field.name}', identity)(v, field=field) | [] | def _run_validations(cls, v, field):
if not v:
return v
return getattr(validators, f'shared_{field.name}', identity)(v, field=field) | [
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ewels/MultiQC | 9b953261d3d684c24eef1827a5ce6718c847a5af | multiqc/modules/mirtop/mirtop.py | python | MultiqcModule.mirtop_stats_table | (self) | Take the parsed stats from the mirtop report and add them to the
basic stats table at the top of the report | Take the parsed stats from the mirtop report and add them to the
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"""Take the parsed stats from the mirtop report and add them to the
basic stats table at the top of the report"""
headers = OrderedDict()
headers["ref_miRNA_sum"] = {
"title": "{} Ref miRNA reads".format(config.read_count_prefix),
"description": "Read counts summed over all reference miRNAs ({})".format(config.read_count_desc),
"modify": lambda x: x * config.read_count_multiplier,
"shared_key": "read_count",
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}
headers["isomiR_perc"] = {
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"scale": "YlOrRd",
}
headers["isomiR_sum"] = {
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"description": "Read counts summed over all isomiRs ({})".format(config.read_count_desc),
"modify": lambda x: x * config.read_count_multiplier,
"shared_key": "read_count",
"scale": "Oranges",
}
headers["read_count"] = {
"title": "{} Reads".format(config.read_count_prefix),
"description": "Total read counts - both isomiRs and reference miRNA ({})".format(config.read_count_desc),
"modify": lambda x: x * config.read_count_multiplier,
"shared_key": "read_count",
"scale": "BuGn",
}
self.general_stats_addcols(self.mirtop_data, headers) | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/mount.py | python | swaps | () | return ret | Return a dict containing information on active swap
.. versionchanged:: 2016.3.2
CLI Example:
.. code-block:: bash
salt '*' mount.swaps | Return a dict containing information on active swap | [
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"""
Return a dict containing information on active swap
.. versionchanged:: 2016.3.2
CLI Example:
.. code-block:: bash
salt '*' mount.swaps
"""
ret = {}
if __grains__["kernel"] == "SunOS":
for line in __salt__["cmd.run_stdout"]("swap -l").splitlines():
if line.startswith("swapfile"):
continue
comps = line.split()
ret[comps[0]] = {
"type": "device" if comps[0].startswith(("/dev", "swap")) else "file",
"size": int(comps[3]),
"used": (int(comps[3]) - int(comps[4])),
"priority": "-",
}
elif "AIX" in __grains__["kernel"]:
for line in __salt__["cmd.run_stdout"]("swap -l").splitlines():
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comps = line.split()
# AIX uses MB for units
ret[comps[0]] = {
"type": "device",
"size": int(comps[3][:-2]) * 1024,
"used": (int(comps[3][:-2]) - int(comps[4][:-2])) * 1024,
"priority": "-",
}
elif __grains__["os"] != "OpenBSD":
with salt.utils.files.fopen("/proc/swaps") as fp_:
for line in fp_:
line = salt.utils.stringutils.to_unicode(line)
if line.startswith("Filename"):
continue
comps = line.split()
ret[comps[0]] = {
"type": comps[1],
"size": comps[2],
"used": comps[3],
"priority": comps[4],
}
else:
for line in __salt__["cmd.run_stdout"]("swapctl -kl").splitlines():
if line.startswith(("Device", "Total")):
continue
swap_type = "file"
comps = line.split()
if comps[0].startswith("/dev/"):
swap_type = "partition"
ret[comps[0]] = {
"type": swap_type,
"size": comps[1],
"used": comps[2],
"priority": comps[5],
}
return ret | [
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/FreshDesk/Integrations/FreshDesk/FreshDesk.py | python | get_ticket_conversations_command | () | Lists all replies and notes for a specified ticket.
demisto parameter: (number) ticket_id
ID of the ticket for which you would like to list all of its conversations
returns:
Conversation Objects | Lists all replies and notes for a specified ticket. | [
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"""
Lists all replies and notes for a specified ticket.
demisto parameter: (number) ticket_id
ID of the ticket for which you would like to list all of its conversations
returns:
Conversation Objects
"""
# Get id number of ticket as cmd arg for which you want to see all the conversations
ticket_id = demisto.args().get('ticket_id')
# Make request and get raw response
conversations = get_ticket_conversations(ticket_id)
# Parse response into context
contexts = []
readable_contexts = []
for conversation in conversations:
context = {string_to_context_key(key): val for key, val in conversation.iteritems() if val}
context = reformat_conversation_context(context)
# Parse attachments into context
context, context_readable = attachments_into_context(conversation, context)
context = reformat_ticket_context(context)
context_readable = reformat_ticket_context(context_readable)
contexts.append(context)
readable_contexts.append(context_readable)
complete_context = {
'ID': int(ticket_id),
'Conversation': contexts
}
title = 'Conversations of Ticket #{}'.format(ticket_id)
human_readable = tableToMarkdown(title, readable_contexts, removeNull=True)
demisto.results({
'Type': entryTypes['note'],
'ContentsFormat': formats['json'],
'Contents': conversations,
'ReadableContentsFormat': formats['markdown'],
'HumanReadable': human_readable,
'EntryContext': {
'Freshdesk.Ticket(val.ID && val.ID === obj.ID)': complete_context
}
}) | [
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dephell/dephell | de96f01fcfd8dd620b049369a8ec30dde566c5de | dephell/cached_property.py | python | cached_property.__get__ | (self, obj, cls) | return value | [] | def __get__(self, obj, cls):
if obj is None:
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value = obj.__dict__[self.func.__name__] = self.func(obj)
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celiao/tmdbsimple | 2c046367866667102b78247db708c0ac275f805d | tmdbsimple/account.py | python | Account.favorite | (self, **kwargs) | return response | This method allows you to mark a movie or TV show as a favorite item.
Args:
media_type: 'movie' | 'tv'
media_id: The id of the media.
favorite: True (to add) | False (to remove).
Returns:
A dict respresentation of the JSON returned from the API. | This method allows you to mark a movie or TV show as a favorite item. | [
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"""
This method allows you to mark a movie or TV show as a favorite item.
Args:
media_type: 'movie' | 'tv'
media_id: The id of the media.
favorite: True (to add) | False (to remove).
Returns:
A dict respresentation of the JSON returned from the API.
"""
path = self._get_id_path('favorite')
kwargs.update({'session_id': self.session_id})
payload = {
'media_type': kwargs.pop('media_type', None),
'media_id': kwargs.pop('media_id', None),
'favorite': kwargs.pop('favorite', None),
}
response = self._POST(path, kwargs, payload)
self._set_attrs_to_values(response)
return response | [
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ChineseGLUE/ChineseGLUE | 1591b85cf5427c2ff60f718d359ecb71d2b44879 | baselines/models_pytorch/classifier_pytorch/transformers/modeling_bert.py | python | BertSelfAttention.transpose_for_scores | (self, x) | return x.permute(0, 2, 1, 3) | [] | def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3) | [
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TengXiaoDai/DistributedCrawling | f5c2439e6ce68dd9b49bde084d76473ff9ed4963 | Lib/site-packages/pip/_vendor/distlib/markers.py | python | Evaluator.__init__ | (self, context=None) | Initialise an instance.
:param context: If specified, names are looked up in this mapping. | Initialise an instance. | [
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] | def __init__(self, context=None):
"""
Initialise an instance.
:param context: If specified, names are looked up in this mapping.
"""
self.context = context or {}
self.source = None | [
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quantumlib/Cirq | 89f88b01d69222d3f1ec14d649b7b3a85ed9211f | cirq-core/cirq/sim/simulator.py | python | SimulatesAmplitudes.compute_amplitudes_sweep_iter | (
self,
program: 'cirq.AbstractCircuit',
bitstrings: Sequence[int],
params: 'cirq.Sweepable',
qubit_order: 'cirq.QubitOrderOrList' = ops.QubitOrder.DEFAULT,
) | Computes the desired amplitudes.
The initial state is assumed to be the all zeros state.
Args:
program: The circuit to simulate.
bitstrings: The bitstrings whose amplitudes are desired, input
as an integer array where each integer is formed from measured
qubit values according to `qubit_order` from most to least
significant qubit, i.e. in big-endian ordering.
params: Parameters to run with the program.
qubit_order: Determines the canonical ordering of the qubits. This
is often used in specifying the initial state, i.e. the
ordering of the computational basis states.
Returns:
An Iterator over lists of amplitudes. The outer dimension indexes
the circuit parameters and the inner dimension indexes bitstrings. | Computes the desired amplitudes. | [
"Computes",
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"desired",
"amplitudes",
"."
] | def compute_amplitudes_sweep_iter(
self,
program: 'cirq.AbstractCircuit',
bitstrings: Sequence[int],
params: 'cirq.Sweepable',
qubit_order: 'cirq.QubitOrderOrList' = ops.QubitOrder.DEFAULT,
) -> Iterator[Sequence[complex]]:
"""Computes the desired amplitudes.
The initial state is assumed to be the all zeros state.
Args:
program: The circuit to simulate.
bitstrings: The bitstrings whose amplitudes are desired, input
as an integer array where each integer is formed from measured
qubit values according to `qubit_order` from most to least
significant qubit, i.e. in big-endian ordering.
params: Parameters to run with the program.
qubit_order: Determines the canonical ordering of the qubits. This
is often used in specifying the initial state, i.e. the
ordering of the computational basis states.
Returns:
An Iterator over lists of amplitudes. The outer dimension indexes
the circuit parameters and the inner dimension indexes bitstrings.
"""
raise NotImplementedError() | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/nexia/sensor.py | python | NexiaThermostatSensor.native_value | (self) | return val | Return the state of the sensor. | Return the state of the sensor. | [
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"""Return the state of the sensor."""
val = getattr(self._thermostat, self._call)()
if self._modifier:
val = self._modifier(val)
if isinstance(val, float):
val = round(val, 1)
return val | [
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microsoft/azure-devops-python-api | 451cade4c475482792cbe9e522c1fee32393139e | azure-devops/azure/devops/v6_0/git/git_client_base.py | python | GitClientBase.update_pull_request_reviewers | (self, patch_votes, repository_id, pull_request_id, project=None) | UpdatePullRequestReviewers.
[Preview API] Reset the votes of multiple reviewers on a pull request. NOTE: This endpoint only supports updating votes, but does not support updating required reviewers (use policy) or display names.
:param [IdentityRefWithVote] patch_votes: IDs of the reviewers whose votes will be reset to zero
:param str repository_id: The repository ID of the pull request's target branch.
:param int pull_request_id: ID of the pull request
:param str project: Project ID or project name | UpdatePullRequestReviewers.
[Preview API] Reset the votes of multiple reviewers on a pull request. NOTE: This endpoint only supports updating votes, but does not support updating required reviewers (use policy) or display names.
:param [IdentityRefWithVote] patch_votes: IDs of the reviewers whose votes will be reset to zero
:param str repository_id: The repository ID of the pull request's target branch.
:param int pull_request_id: ID of the pull request
:param str project: Project ID or project name | [
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"""UpdatePullRequestReviewers.
[Preview API] Reset the votes of multiple reviewers on a pull request. NOTE: This endpoint only supports updating votes, but does not support updating required reviewers (use policy) or display names.
:param [IdentityRefWithVote] patch_votes: IDs of the reviewers whose votes will be reset to zero
:param str repository_id: The repository ID of the pull request's target branch.
:param int pull_request_id: ID of the pull request
:param str project: Project ID or project name
"""
route_values = {}
if project is not None:
route_values['project'] = self._serialize.url('project', project, 'str')
if repository_id is not None:
route_values['repositoryId'] = self._serialize.url('repository_id', repository_id, 'str')
if pull_request_id is not None:
route_values['pullRequestId'] = self._serialize.url('pull_request_id', pull_request_id, 'int')
content = self._serialize.body(patch_votes, '[IdentityRefWithVote]')
self._send(http_method='PATCH',
location_id='4b6702c7-aa35-4b89-9c96-b9abf6d3e540',
version='6.0-preview.1',
route_values=route_values,
content=content) | [
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zsdonghao/text-to-image | c1fbfa4349f5aa1c74c9fe17743cc0cff34f1aab | tensorlayer/iterate.py | python | ptb_iterator | (raw_data, batch_size, num_steps) | Generate a generator that iterates on a list of words, see PTB tutorial. Yields (Returns) the source contexts and
the target context by the given batch_size and num_steps (sequence_length).\n
see ``PTB tutorial``.
e.g. x = [0, 1, 2] y = [1, 2, 3] , when batch_size = 1, num_steps = 3,
raw_data = [i for i in range(100)]
In TensorFlow's tutorial, this generates batch_size pointers into the raw
PTB data, and allows minibatch iteration along these pointers.
Parameters
----------
raw_data : a list
the context in list format; note that context usually be
represented by splitting by space, and then convert to unique
word IDs.
batch_size : int
the batch size.
num_steps : int
the number of unrolls. i.e. sequence_length
Yields
------
Pairs of the batched data, each a matrix of shape [batch_size, num_steps].
The second element of the tuple is the same data time-shifted to the
right by one.
Raises
------
ValueError : if batch_size or num_steps are too high.
Examples
--------
>>> train_data = [i for i in range(20)]
>>> for batch in tl.iterate.ptb_iterator(train_data, batch_size=2, num_steps=3):
>>> x, y = batch
>>> print(x, y)
... [[ 0 1 2] <---x 1st subset/ iteration
... [10 11 12]]
... [[ 1 2 3] <---y
... [11 12 13]]
...
... [[ 3 4 5] <--- 1st batch input 2nd subset/ iteration
... [13 14 15]] <--- 2nd batch input
... [[ 4 5 6] <--- 1st batch target
... [14 15 16]] <--- 2nd batch target
...
... [[ 6 7 8] 3rd subset/ iteration
... [16 17 18]]
... [[ 7 8 9]
... [17 18 19]]
Code References
----------------
- ``tensorflow/models/rnn/ptb/reader.py`` | Generate a generator that iterates on a list of words, see PTB tutorial. Yields (Returns) the source contexts and
the target context by the given batch_size and num_steps (sequence_length).\n
see ``PTB tutorial``. | [
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"""
Generate a generator that iterates on a list of words, see PTB tutorial. Yields (Returns) the source contexts and
the target context by the given batch_size and num_steps (sequence_length).\n
see ``PTB tutorial``.
e.g. x = [0, 1, 2] y = [1, 2, 3] , when batch_size = 1, num_steps = 3,
raw_data = [i for i in range(100)]
In TensorFlow's tutorial, this generates batch_size pointers into the raw
PTB data, and allows minibatch iteration along these pointers.
Parameters
----------
raw_data : a list
the context in list format; note that context usually be
represented by splitting by space, and then convert to unique
word IDs.
batch_size : int
the batch size.
num_steps : int
the number of unrolls. i.e. sequence_length
Yields
------
Pairs of the batched data, each a matrix of shape [batch_size, num_steps].
The second element of the tuple is the same data time-shifted to the
right by one.
Raises
------
ValueError : if batch_size or num_steps are too high.
Examples
--------
>>> train_data = [i for i in range(20)]
>>> for batch in tl.iterate.ptb_iterator(train_data, batch_size=2, num_steps=3):
>>> x, y = batch
>>> print(x, y)
... [[ 0 1 2] <---x 1st subset/ iteration
... [10 11 12]]
... [[ 1 2 3] <---y
... [11 12 13]]
...
... [[ 3 4 5] <--- 1st batch input 2nd subset/ iteration
... [13 14 15]] <--- 2nd batch input
... [[ 4 5 6] <--- 1st batch target
... [14 15 16]] <--- 2nd batch target
...
... [[ 6 7 8] 3rd subset/ iteration
... [16 17 18]]
... [[ 7 8 9]
... [17 18 19]]
Code References
----------------
- ``tensorflow/models/rnn/ptb/reader.py``
"""
raw_data = np.array(raw_data, dtype=np.int32)
data_len = len(raw_data)
batch_len = data_len // batch_size
data = np.zeros([batch_size, batch_len], dtype=np.int32)
for i in range(batch_size):
data[i] = raw_data[batch_len * i:batch_len * (i + 1)]
epoch_size = (batch_len - 1) // num_steps
if epoch_size == 0:
raise ValueError("epoch_size == 0, decrease batch_size or num_steps")
for i in range(epoch_size):
x = data[:, i*num_steps:(i+1)*num_steps]
y = data[:, i*num_steps+1:(i+1)*num_steps+1]
yield (x, y) | [
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bokeh/bokeh | a00e59da76beb7b9f83613533cfd3aced1df5f06 | bokeh/application/handlers/function.py | python | FunctionHandler.safe_to_fork | (self) | return self._safe_to_fork | Whether it is still safe for the Bokeh server to fork new workers.
``False`` if ``modify_doc`` has already been called. | Whether it is still safe for the Bokeh server to fork new workers. | [
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''' Whether it is still safe for the Bokeh server to fork new workers.
``False`` if ``modify_doc`` has already been called.
'''
return self._safe_to_fork | [
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DamnWidget/anaconda | a9998fb362320f907d5ccbc6fcf5b62baca677c0 | anaconda_lib/jedi/_compatibility.py | python | cast_path | (obj) | return u(obj, errors='replace') | Take a bytes or str path and cast it to unicode.
Apparently it is perfectly fine to pass both byte and unicode objects into
the sys.path. This probably means that byte paths are normal at other
places as well.
Since this just really complicates everything and Python 2.7 will be EOL
soon anyway, just go with always strings. | Take a bytes or str path and cast it to unicode. | [
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] | def cast_path(obj):
"""
Take a bytes or str path and cast it to unicode.
Apparently it is perfectly fine to pass both byte and unicode objects into
the sys.path. This probably means that byte paths are normal at other
places as well.
Since this just really complicates everything and Python 2.7 will be EOL
soon anyway, just go with always strings.
"""
return u(obj, errors='replace') | [
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sunpy/sunpy | 528579df0a4c938c133bd08971ba75c131b189a7 | sunpy/io/special/genx.py | python | read_genx | (filename) | return skeleton | solarsoft genx file reader.
genx files have been used to store calibration data for multiple
instruments and distributed within solarsoft. They are stored in XDR
format; The External Data Representation Standard file format (XDR) is
described in `RFC 1014 <https://tools.ietf.org/html/rfc1014>`__,
written by Sun Microsystems, Inc. June 1987.
SolarSoft genx writer creates structures to store the values together with
the variable names. It use the ``size`` IDL function to include the data
type, dimension and number of elements that each variable contains.
Parameters
----------
filename : `str`
The genx file to be read
Returns
-------
output : `~collections.OrderedDict`
A dictionary with possibly nested dictionaries with the data in
the genx file.
Notes
-----
The reader aims to maintain the shape and type of the arrays, but take care with the
difference in indexing between Python and IDL (row mayor vs column mayor).
Regarding the type notice that single numbers are converted to python precision, therefore
a single integer is converted from 16 to 32/64 bits, and a float from 32 to 64.
**Strings** read from genx files are assumed to be UTF-8. | solarsoft genx file reader. | [
"solarsoft",
"genx",
"file",
"reader",
"."
] | def read_genx(filename):
"""
solarsoft genx file reader.
genx files have been used to store calibration data for multiple
instruments and distributed within solarsoft. They are stored in XDR
format; The External Data Representation Standard file format (XDR) is
described in `RFC 1014 <https://tools.ietf.org/html/rfc1014>`__,
written by Sun Microsystems, Inc. June 1987.
SolarSoft genx writer creates structures to store the values together with
the variable names. It use the ``size`` IDL function to include the data
type, dimension and number of elements that each variable contains.
Parameters
----------
filename : `str`
The genx file to be read
Returns
-------
output : `~collections.OrderedDict`
A dictionary with possibly nested dictionaries with the data in
the genx file.
Notes
-----
The reader aims to maintain the shape and type of the arrays, but take care with the
difference in indexing between Python and IDL (row mayor vs column mayor).
Regarding the type notice that single numbers are converted to python precision, therefore
a single integer is converted from 16 to 32/64 bits, and a float from 32 to 64.
**Strings** read from genx files are assumed to be UTF-8.
"""
with open(filename, mode='rb') as xdrfile:
xdrdata = SSWUnpacker(xdrfile.read())
# HEADER information
version, xdr = xdrdata.unpack_int(), xdrdata.unpack_int()
creation = xdrdata.unpack_string()
if version == 2:
arch = xdrdata.unpack_string()
os = xdrdata.unpack_string()
release = xdrdata.unpack_string()
text = xdrdata.unpack_string()
# TODO: I don't think will have dim>1 but need
# to check, if it's larger like savegen has run
# with a multidimensional structure, then
# probably the read_struct_skeleton will have to
# run as in multi-dim structure.
dim = xdrdata.unpack_int()
# [1, 8, 1] = Main structure for the data
arr_size = xdrdata.unpack_farray(dim + 2, xdrdata.unpack_int)
# the number of upper level strs
# This is used somehow
mainsize = arr_size[2] # NOQA
skeleton = read_struct_skeleton(xdrdata)
struct_to_data(xdrdata, skeleton)
xdrdata.done()
skeleton['HEADER'] = OrderedDict([('VERSION', version), ('XDR', xdr), ('CREATION', creation)])
if version == 2:
skeleton['HEADER']['IDL_VERSION'] = OrderedDict([('ARCH', arch),
('OS', os),
('RELEASE', release)])
skeleton['HEADER']['TEXT'] = text
# TODO: for python >= 3.2; so we can keep the original order as how it's stored in the file
# skeleton.move_to_end('HEADER', last=False)
return skeleton | [
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volatilityfoundation/volatility | a438e768194a9e05eb4d9ee9338b881c0fa25937 | volatility/plugins/addrspaces/amd64.py | python | AMD64PagedMemory.get_pdpi | (self, vaddr, pml4e) | return self.read_long_long_phys(pdpte_paddr) | This method returns the Page Directory Pointer entry for the
virtual address. Bits 32:30 are used to select the appropriate
8 byte entry in the Page Directory Pointer table.
"Bits 51:12 are from the PML4E" [Intel]
"Bits 11:3 are bits 38:30 of the linear address" [Intel]
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'''
This method returns the Page Directory Pointer entry for the
virtual address. Bits 32:30 are used to select the appropriate
8 byte entry in the Page Directory Pointer table.
"Bits 51:12 are from the PML4E" [Intel]
"Bits 11:3 are bits 38:30 of the linear address" [Intel]
"Bits 2:0 are all 0" [Intel]
'''
pdpte_paddr = (pml4e & 0xffffffffff000) | ((vaddr & 0x7FC0000000) >> 27)
return self.read_long_long_phys(pdpte_paddr) | [
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WoLpH/python-statsd | a757da04375c48d03d322246405b33382d37f03f | statsd/raw.py | python | Raw.send | (self, subname, value, timestamp=None) | return statsd.Client._send(self, {name: '%s|r|%s' % (value, ts)}) | Send the data to statsd via self.connection
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:keyword subname: The subname to report the data to (appended to the
client name)
:type subname: str
:keyword value: The raw value to send
'''
if timestamp is None:
ts = int(dt.datetime.now().strftime("%s"))
else:
ts = timestamp
name = self._get_name(self.name, subname)
self.logger.info('%s: %s %s' % (name, value, ts))
return statsd.Client._send(self, {name: '%s|r|%s' % (value, ts)}) | [
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wxWidgets/Phoenix | b2199e299a6ca6d866aa6f3d0888499136ead9d6 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListItemData.GetWidth | (self) | return self._rect.width | Returns the item width, in pixels. | Returns the item width, in pixels. | [
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Lab41/ipython-spark-docker | a73dca291447f4861a97a9716593306b148d0b2a | mesos/fabfile.py | python | start_masters | () | [] | def start_masters():
run('echo manual | sudo tee /etc/init/mesos-slave.override')
run('sudo stop mesos-slave; sudo restart zookeeper')
run('sudo start mesos-master')
run('sudo start marathon') | [
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xonsh/xonsh | b76d6f994f22a4078f602f8b386f4ec280c8461f | xonsh/parsers/base.py | python | xonsh_pathsearch | (pattern, pymode=False, lineno=None, col=None) | return xonsh_call(
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args=[func, pattern, pymode, pathobj],
lineno=lineno,
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return xonsh_call(
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lineno=lineno,
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marinho/geraldo | 868ebdce67176d9b6205cddc92476f642c783fff | site/newsite/site-geraldo/django/__init__.py | python | get_version | () | return v | Returns the version as a human-format string. | Returns the version as a human-format string. | [
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v = '.'.join([str(i) for i in VERSION[:-1]])
if VERSION[-1]:
from django.utils.version import get_svn_revision
v = '%s-%s-%s' % (v, VERSION[-1], get_svn_revision())
return v | [
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quic/aimet | dae9bae9a77ca719aa7553fefde4768270fc3518 | TrainingExtensions/tensorflow/src/python/aimet_tensorflow/common/connectedgraph.py | python | ConnectedGraph.get_op_from_module_name | (self, name: str) | return self._ops.get(op_info.module_name, None) | Given the name of a tf operation, return the op in ops dict corresponding to the tf operation | Given the name of a tf operation, return the op in ops dict corresponding to the tf operation | [
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tf_op = self._graph.get_operation_by_name(name)
op_info = self._module_identifier.get_op_info(tf_op)
return self._ops.get(op_info.module_name, None) | [
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mathics/Mathics | 318e06dea8f1c70758a50cb2f95c9900150e3a68 | mathics/builtin/numbers/integer.py | python | BitLength.apply | (self, n, evaluation) | return Integer(n.bit_length()) | BitLength[n_Integer] | BitLength[n_Integer] | [
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n = n.get_int_value()
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stopstalk/stopstalk-deployment | 10c3ab44c4ece33ae515f6888c15033db2004bb1 | aws_lambda/spoj_aws_lambda_function/lambda_code/setuptools/command/easy_install.py | python | easy_install.create_home_path | (self) | Create directories under ~. | Create directories under ~. | [
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"""Create directories under ~."""
if not self.user:
return
home = convert_path(os.path.expanduser("~"))
for name, path in six.iteritems(self.config_vars):
if path.startswith(home) and not os.path.isdir(path):
self.debug_print("os.makedirs('%s', 0o700)" % path)
os.makedirs(path, 0o700) | [
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avrae/avrae | 6ebe46a1ec3d4dfaa2f9b18fac948325f39f87de | cogs5e/models/sheet/base.py | python | Saves.update | (self, explicit_saves: dict) | Updates saves with an explicit dictionary of modifiers. All provided are assumed to have prof=1. | Updates saves with an explicit dictionary of modifiers. All provided are assumed to have prof=1. | [
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if save not in self.saves:
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self.saves[save].prof = 1 | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/set_partition.py | python | SetPartition.check | (self) | Check that we are a valid set partition.
EXAMPLES::
sage: S = SetPartitions(4)
sage: s = S([[1, 3], [2, 4]])
sage: s.check()
TESTS::
sage: s = S([[1, 2, 3]], check=False)
sage: s.check()
Traceback (most recent call last):
...
ValueError: {{1, 2, 3}} is not an element of Set partitions of {1, 2, 3, 4}
sage: s = S([1, 2, 3])
Traceback (most recent call last):
...
TypeError: 'sage.rings.integer.Integer' object is not iterable | Check that we are a valid set partition. | [
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"""
Check that we are a valid set partition.
EXAMPLES::
sage: S = SetPartitions(4)
sage: s = S([[1, 3], [2, 4]])
sage: s.check()
TESTS::
sage: s = S([[1, 2, 3]], check=False)
sage: s.check()
Traceback (most recent call last):
...
ValueError: {{1, 2, 3}} is not an element of Set partitions of {1, 2, 3, 4}
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Traceback (most recent call last):
...
TypeError: 'sage.rings.integer.Integer' object is not iterable
"""
if self not in self.parent():
raise ValueError("%s is not an element of %s"%(self, self.parent())) | [
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grayddq/PublicMonitors | 814bfe9662d23b64d089ff750aa6a5b3e152d3c2 | lib/nmap.py | python | PortScannerHostDict.sctp | (self, port) | return self['sctp'][port] | returns info for sctp port | returns info for sctp port | [
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"""
returns info for sctp port
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redis/redis-py | 0affa0ed3f3cbcb6dec29b34a580f769f69ae9f7 | redis/commands/core.py | python | BasicKeyCommands.getrange | (self, key, start, end) | return self.execute_command("GETRANGE", key, start, end) | Returns the substring of the string value stored at ``key``,
determined by the offsets ``start`` and ``end`` (both are inclusive)
For more information check https://redis.io/commands/getrange | Returns the substring of the string value stored at ``key``,
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For more information check https://redis.io/commands/getrange
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/lib2to3/pgen2/conv.py | python | Converter.run | (self, graminit_h, graminit_c) | Load the grammar tables from the text files written by pgen. | Load the grammar tables from the text files written by pgen. | [
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self.parse_graminit_h(graminit_h)
self.parse_graminit_c(graminit_c)
self.finish_off() | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/pycparser/c_ast.py | python | Node.__repr__ | (self) | return result | Generates a python representation of the current node | Generates a python representation of the current node | [
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""" Generates a python representation of the current node
"""
result = self.__class__.__name__ + '('
indent = ''
separator = ''
for name in self.__slots__[:-2]:
result += separator
result += indent
result += name + '=' + (_repr(getattr(self, name)).replace('\n', '\n ' + (' ' * (len(name) + len(self.__class__.__name__)))))
separator = ','
indent = '\n ' + (' ' * len(self.__class__.__name__))
result += indent + ')'
return result | [
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facebookresearch/detectron2 | cb92ae1763cd7d3777c243f07749574cdaec6cb8 | projects/Panoptic-DeepLab/panoptic_deeplab/target_generator.py | python | PanopticDeepLabTargetGenerator.__call__ | (self, panoptic, segments_info) | return dict(
sem_seg=torch.as_tensor(semantic.astype("long")),
center=torch.as_tensor(center.astype(np.float32)),
center_points=center_pts,
offset=torch.as_tensor(offset.astype(np.float32)),
sem_seg_weights=torch.as_tensor(semantic_weights.astype(np.float32)),
center_weights=torch.as_tensor(center_weights.astype(np.float32)),
offset_weights=torch.as_tensor(offset_weights.astype(np.float32)),
) | Generates the training target.
reference: https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/createPanopticImgs.py # noqa
reference: https://github.com/facebookresearch/detectron2/blob/main/datasets/prepare_panoptic_fpn.py#L18 # noqa
Args:
panoptic: numpy.array, panoptic label, we assume it is already
converted from rgb image by panopticapi.utils.rgb2id.
segments_info (list[dict]): see detectron2 documentation of "Use Custom Datasets".
Returns:
A dictionary with fields:
- sem_seg: Tensor, semantic label, shape=(H, W).
- center: Tensor, center heatmap, shape=(H, W).
- center_points: List, center coordinates, with tuple
(y-coord, x-coord).
- offset: Tensor, offset, shape=(2, H, W), first dim is
(offset_y, offset_x).
- sem_seg_weights: Tensor, loss weight for semantic prediction,
shape=(H, W).
- center_weights: Tensor, ignore region of center prediction,
shape=(H, W), used as weights for center regression 0 is
ignore, 1 is has instance. Multiply this mask to loss.
- offset_weights: Tensor, ignore region of offset prediction,
shape=(H, W), used as weights for offset regression 0 is
ignore, 1 is has instance. Multiply this mask to loss. | Generates the training target.
reference: https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/createPanopticImgs.py # noqa
reference: https://github.com/facebookresearch/detectron2/blob/main/datasets/prepare_panoptic_fpn.py#L18 # noqa | [
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"""Generates the training target.
reference: https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/createPanopticImgs.py # noqa
reference: https://github.com/facebookresearch/detectron2/blob/main/datasets/prepare_panoptic_fpn.py#L18 # noqa
Args:
panoptic: numpy.array, panoptic label, we assume it is already
converted from rgb image by panopticapi.utils.rgb2id.
segments_info (list[dict]): see detectron2 documentation of "Use Custom Datasets".
Returns:
A dictionary with fields:
- sem_seg: Tensor, semantic label, shape=(H, W).
- center: Tensor, center heatmap, shape=(H, W).
- center_points: List, center coordinates, with tuple
(y-coord, x-coord).
- offset: Tensor, offset, shape=(2, H, W), first dim is
(offset_y, offset_x).
- sem_seg_weights: Tensor, loss weight for semantic prediction,
shape=(H, W).
- center_weights: Tensor, ignore region of center prediction,
shape=(H, W), used as weights for center regression 0 is
ignore, 1 is has instance. Multiply this mask to loss.
- offset_weights: Tensor, ignore region of offset prediction,
shape=(H, W), used as weights for offset regression 0 is
ignore, 1 is has instance. Multiply this mask to loss.
"""
height, width = panoptic.shape[0], panoptic.shape[1]
semantic = np.zeros_like(panoptic, dtype=np.uint8) + self.ignore_label
center = np.zeros((height, width), dtype=np.float32)
center_pts = []
offset = np.zeros((2, height, width), dtype=np.float32)
y_coord, x_coord = np.meshgrid(
np.arange(height, dtype=np.float32), np.arange(width, dtype=np.float32), indexing="ij"
)
# Generate pixel-wise loss weights
semantic_weights = np.ones_like(panoptic, dtype=np.uint8)
# 0: ignore, 1: has instance
# three conditions for a region to be ignored for instance branches:
# (1) It is labeled as `ignore_label`
# (2) It is crowd region (iscrowd=1)
# (3) (Optional) It is stuff region (for offset branch)
center_weights = np.zeros_like(panoptic, dtype=np.uint8)
offset_weights = np.zeros_like(panoptic, dtype=np.uint8)
for seg in segments_info:
cat_id = seg["category_id"]
if not (self.ignore_crowd_in_semantic and seg["iscrowd"]):
semantic[panoptic == seg["id"]] = cat_id
if not seg["iscrowd"]:
# Ignored regions are not in `segments_info`.
# Handle crowd region.
center_weights[panoptic == seg["id"]] = 1
if not self.ignore_stuff_in_offset or cat_id in self.thing_ids:
offset_weights[panoptic == seg["id"]] = 1
if cat_id in self.thing_ids:
# find instance center
mask_index = np.where(panoptic == seg["id"])
if len(mask_index[0]) == 0:
# the instance is completely cropped
continue
# Find instance area
ins_area = len(mask_index[0])
if ins_area < self.small_instance_area:
semantic_weights[panoptic == seg["id"]] = self.small_instance_weight
center_y, center_x = np.mean(mask_index[0]), np.mean(mask_index[1])
center_pts.append([center_y, center_x])
# generate center heatmap
y, x = int(round(center_y)), int(round(center_x))
sigma = self.sigma
# upper left
ul = int(np.round(x - 3 * sigma - 1)), int(np.round(y - 3 * sigma - 1))
# bottom right
br = int(np.round(x + 3 * sigma + 2)), int(np.round(y + 3 * sigma + 2))
# start and end indices in default Gaussian image
gaussian_x0, gaussian_x1 = max(0, -ul[0]), min(br[0], width) - ul[0]
gaussian_y0, gaussian_y1 = max(0, -ul[1]), min(br[1], height) - ul[1]
# start and end indices in center heatmap image
center_x0, center_x1 = max(0, ul[0]), min(br[0], width)
center_y0, center_y1 = max(0, ul[1]), min(br[1], height)
center[center_y0:center_y1, center_x0:center_x1] = np.maximum(
center[center_y0:center_y1, center_x0:center_x1],
self.g[gaussian_y0:gaussian_y1, gaussian_x0:gaussian_x1],
)
# generate offset (2, h, w) -> (y-dir, x-dir)
offset[0][mask_index] = center_y - y_coord[mask_index]
offset[1][mask_index] = center_x - x_coord[mask_index]
center_weights = center_weights[None]
offset_weights = offset_weights[None]
return dict(
sem_seg=torch.as_tensor(semantic.astype("long")),
center=torch.as_tensor(center.astype(np.float32)),
center_points=center_pts,
offset=torch.as_tensor(offset.astype(np.float32)),
sem_seg_weights=torch.as_tensor(semantic_weights.astype(np.float32)),
center_weights=torch.as_tensor(center_weights.astype(np.float32)),
offset_weights=torch.as_tensor(offset_weights.astype(np.float32)),
) | [
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IntelAI/models | 1d7a53ccfad3e6f0e7378c9e3c8840895d63df8c | models/common/tensorflow/mlperf_utils/logs/logger.py | python | BenchmarkLogger.log_metric | (self, name, value, unit=None, global_step=None, extras=None) | Log the benchmark metric information to local file.
Currently the logging is done in a synchronized way. This should be updated
to log asynchronously.
Args:
name: string, the name of the metric to log.
value: number, the value of the metric. The value will not be logged if it
is not a number type.
unit: string, the unit of the metric, E.g "image per second".
global_step: int, the global_step when the metric is logged.
extras: map of string:string, the extra information about the metric. | Log the benchmark metric information to local file. | [
"Log",
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"benchmark",
"metric",
"information",
"to",
"local",
"file",
"."
] | def log_metric(self, name, value, unit=None, global_step=None, extras=None):
"""Log the benchmark metric information to local file.
Currently the logging is done in a synchronized way. This should be updated
to log asynchronously.
Args:
name: string, the name of the metric to log.
value: number, the value of the metric. The value will not be logged if it
is not a number type.
unit: string, the unit of the metric, E.g "image per second".
global_step: int, the global_step when the metric is logged.
extras: map of string:string, the extra information about the metric.
"""
if not isinstance(value, numbers.Number):
tf.compat.v1.logging.warning(
"Metric value to log should be a number. Got %s", type(value))
return
if extras:
extras = [{"name": k, "value": v} for k, v in sorted(extras.items())]
else:
extras = []
with tf.io.gfile.GFile(
os.path.join(self._logging_dir, METRIC_LOG_FILE_NAME), "a") as f:
metric = {
"name": name,
"value": float(value),
"unit": unit,
"global_step": global_step,
"timestamp": datetime.datetime.now().strftime(
_DATE_TIME_FORMAT_PATTERN),
"extras": extras}
try:
json.dump(metric, f)
f.write("\n")
except (TypeError, ValueError) as e:
tf.compat.v1.logging.warning("Failed to dump metric to log file: "
"name %s, value %s, error %s", name, value, e) | [
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tendenci/tendenci | 0f2c348cc0e7d41bc56f50b00ce05544b083bf1d | tendenci/apps/corporate_memberships/import_processor.py | python | CorpMembershipImportProcessor.process_corp_membership | (self, cmemb_data, **kwargs) | return corp_memb_display | Check if it's insert or update. If dry_run is False,
do the import to the corpmembership.
:param cmemb_data: a dictionary that includes the info
of a corp_membership | Check if it's insert or update. If dry_run is False,
do the import to the corpmembership. | [
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] | def process_corp_membership(self, cmemb_data, **kwargs):
"""
Check if it's insert or update. If dry_run is False,
do the import to the corpmembership.
:param cmemb_data: a dictionary that includes the info
of a corp_membership
"""
self.cmemb_data = cmemb_data
if 'id' not in self.cmemb_data:
if 'id' in self.corp_membership_fields:
del self.corp_membership_fields['id']
self.cmemb_data['name'] = self.cmemb_data['company_name']
del self.cmemb_data['company_name']
self.field_names = cmemb_data # csv field names
corp_memb_display = {}
corp_memb_display['error'] = ''
corp_memb_display['user'] = None
status_detail = self.cmemb_data.get('status_detail', 'active')
if status_detail == 'archived':
status_detail = 'archive'
if status_detail not in CorpMembership.VALID_STATUS_DETAIL:
status_detail = 'active'
self.cmemb_data['status_detail'] = status_detail
expiration_dt = self.cmemb_data.get('expiration_dt', None)
if expiration_dt:
expiration_dt = dparser.parse(expiration_dt)
error_msg = self.validate_fields(self.cmemb_data, self.key)
# don't process if we have missing value of required fields
if error_msg:
corp_memb_display['error'] = error_msg
corp_memb_display['action'] = 'skip'
if not self.dry_run:
self.summary_d['invalid'] += 1
else:
#if self.key == 'name':
[corp_profile] = CorpProfile.objects.filter(
name=self.cmemb_data['name'])[:1] or [None]
if corp_profile:
corp_membs = CorpMembership.objects.filter(
corp_profile=corp_profile,
status_detail=status_detail)
# there might be multiple archives, pick the one that
# matches with the expiration_dt
if status_detail == 'archive' and expiration_dt:
corp_membs = corp_membs.filter(
expiration_dt__year=expiration_dt.year,
expiration_dt__month=expiration_dt.month,
expiration_dt__day=expiration_dt.day
)
[corp_memb] = corp_membs.order_by('-id')[:1] or [None]
else:
corp_memb = None
if corp_profile:
if corp_memb:
corp_memb_display['action'] = 'update'
corp_memb_display['corp_profile_action'] = 'update'
corp_memb_display['corp_memb_action'] = 'update'
else:
corp_memb_display['action'] = 'mixed'
corp_memb_display['corp_profile_action'] = 'update'
corp_memb_display['corp_memb_action'] = 'insert'
else:
corp_memb_display['action'] = 'insert'
corp_memb_display['corp_profile_action'] = 'insert'
corp_memb_display['corp_memb_action'] = 'insert'
if not self.dry_run:
if corp_memb_display['action'] == 'insert':
self.summary_d['insert'] += 1
elif corp_memb_display['action'] == 'update':
self.summary_d['update'] += 1
else:
self.summary_d['update_insert'] += 1
# now do the update or insert
corp_profile, corp_memb = self.do_import_corp_membership(corp_profile, corp_memb,
corp_memb_display)
# handle authorized_domain
if 'authorized_domains' in self.field_names:
update_authorized_domains(corp_profile,
self.cmemb_data['authorized_domains'])
# handle dues_rep
if 'dues_rep' in self.field_names:
self.update_dues_reps(corp_profile,
self.cmemb_data['dues_rep'])
return
corp_memb_display.update({
'company_name': self.cmemb_data.get('name', ''),
'email': self.cmemb_data.get('email', ''),
'address': self.cmemb_data.get('address', ''),
'address2': self.cmemb_data.get('address2', ''),
'city': self.cmemb_data.get('city', ''),
'state': self.cmemb_data.get('state', ''),
'zip': self.cmemb_data.get('zip', ''),
'status_detail': self.cmemb_data.get('status_detail', ''),
})
return corp_memb_display | [
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cloudera/impyla | 0c736af4cad2bade9b8e313badc08ec50e81c948 | impala/_thrift_gen/hive_metastore/ThriftHiveMetastore.py | python | Client.alter_table | (self, dbname, tbl_name, new_tbl) | Parameters:
- dbname
- tbl_name
- new_tbl | Parameters:
- dbname
- tbl_name
- new_tbl | [
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] | def alter_table(self, dbname, tbl_name, new_tbl):
"""
Parameters:
- dbname
- tbl_name
- new_tbl
"""
self.send_alter_table(dbname, tbl_name, new_tbl)
self.recv_alter_table() | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/lib-tk/turtle.py | python | RawTurtle.reset | (self) | Delete the turtle's drawings and restore its default values.
No argument.
,
Delete the turtle's drawings from the screen, re-center the turtle
and set variables to the default values.
Example (for a Turtle instance named turtle):
>>> turtle.position()
(0.00,-22.00)
>>> turtle.heading()
100.0
>>> turtle.reset()
>>> turtle.position()
(0.00,0.00)
>>> turtle.heading()
0.0 | Delete the turtle's drawings and restore its default values. | [
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"turtle",
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"and",
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"values",
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] | def reset(self):
"""Delete the turtle's drawings and restore its default values.
No argument.
,
Delete the turtle's drawings from the screen, re-center the turtle
and set variables to the default values.
Example (for a Turtle instance named turtle):
>>> turtle.position()
(0.00,-22.00)
>>> turtle.heading()
100.0
>>> turtle.reset()
>>> turtle.position()
(0.00,0.00)
>>> turtle.heading()
0.0
"""
TNavigator.reset(self)
TPen._reset(self)
self._clear()
self._drawturtle()
self._update() | [
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apache/tvm | 6eb4ed813ebcdcd9558f0906a1870db8302ff1e0 | python/tvm/contrib/graph_executor.py | python | GraphModule.get_input_info | (self) | return shape_dict, dtype_dict | Return the 'shape' and 'dtype' dictionaries of the graph.
.. note::
We can't simply get the input tensors from a TVM graph
because weight tensors are treated equivalently. Therefore, to
find the input tensors we look at the 'arg_nodes' in the graph
(which are either weights or inputs) and check which ones don't
appear in the params (where the weights are stored). These nodes
are therefore inferred to be input tensors.
Returns
-------
shape_dict : Map
Shape dictionary - {input_name: tuple}.
dtype_dict : Map
dtype dictionary - {input_name: dtype}. | Return the 'shape' and 'dtype' dictionaries of the graph. | [
"Return",
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"dictionaries",
"of",
"the",
"graph",
"."
] | def get_input_info(self):
"""Return the 'shape' and 'dtype' dictionaries of the graph.
.. note::
We can't simply get the input tensors from a TVM graph
because weight tensors are treated equivalently. Therefore, to
find the input tensors we look at the 'arg_nodes' in the graph
(which are either weights or inputs) and check which ones don't
appear in the params (where the weights are stored). These nodes
are therefore inferred to be input tensors.
Returns
-------
shape_dict : Map
Shape dictionary - {input_name: tuple}.
dtype_dict : Map
dtype dictionary - {input_name: dtype}.
"""
input_info = self._get_input_info()
assert "shape" in input_info
shape_dict = input_info["shape"]
assert "dtype" in input_info
dtype_dict = input_info["dtype"]
return shape_dict, dtype_dict | [
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mrjoes/flask-admin | 402b56ea844dc5b215f6293e7dc63f39a6723692 | flask_admin/contrib/sqla/view.py | python | ModelView.__init__ | (self, model, session,
name=None, category=None, endpoint=None, url=None, static_folder=None,
menu_class_name=None, menu_icon_type=None, menu_icon_value=None) | Constructor.
:param model:
Model class
:param session:
SQLAlchemy session
:param name:
View name. If not set, defaults to the model name
:param category:
Category name
:param endpoint:
Endpoint name. If not set, defaults to the model name
:param url:
Base URL. If not set, defaults to '/admin/' + endpoint
:param menu_class_name:
Optional class name for the menu item.
:param menu_icon_type:
Optional icon. Possible icon types:
- `flask.ext.admin.consts.ICON_TYPE_GLYPH` - Bootstrap glyph icon
- `flask.ext.admin.consts.ICON_TYPE_IMAGE` - Image relative to Flask static directory
- `flask.ext.admin.consts.ICON_TYPE_IMAGE_URL` - Image with full URL
:param menu_icon_value:
Icon glyph name or URL, depending on `menu_icon_type` setting | Constructor. | [
"Constructor",
"."
] | def __init__(self, model, session,
name=None, category=None, endpoint=None, url=None, static_folder=None,
menu_class_name=None, menu_icon_type=None, menu_icon_value=None):
"""
Constructor.
:param model:
Model class
:param session:
SQLAlchemy session
:param name:
View name. If not set, defaults to the model name
:param category:
Category name
:param endpoint:
Endpoint name. If not set, defaults to the model name
:param url:
Base URL. If not set, defaults to '/admin/' + endpoint
:param menu_class_name:
Optional class name for the menu item.
:param menu_icon_type:
Optional icon. Possible icon types:
- `flask.ext.admin.consts.ICON_TYPE_GLYPH` - Bootstrap glyph icon
- `flask.ext.admin.consts.ICON_TYPE_IMAGE` - Image relative to Flask static directory
- `flask.ext.admin.consts.ICON_TYPE_IMAGE_URL` - Image with full URL
:param menu_icon_value:
Icon glyph name or URL, depending on `menu_icon_type` setting
"""
self.session = session
self._search_fields = None
self._search_joins = []
self._filter_joins = dict()
self._sortable_joins = dict()
if self.form_choices is None:
self.form_choices = {}
super(ModelView, self).__init__(model, name, category, endpoint, url, static_folder,
menu_class_name=menu_class_name,
menu_icon_type=menu_icon_type,
menu_icon_value=menu_icon_value)
# Primary key
self._primary_key = self.scaffold_pk()
if self._primary_key is None:
raise Exception('Model %s does not have primary key.' % self.model.__name__)
# Configuration
if not self.column_select_related_list:
self._auto_joins = self.scaffold_auto_joins()
else:
self._auto_joins = self.column_select_related_list | [
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... | https://github.com/mrjoes/flask-admin/blob/402b56ea844dc5b215f6293e7dc63f39a6723692/flask_admin/contrib/sqla/view.py#L245-L301 | ||
F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idaapi.py | python | AskUsingForm | (*args) | return r | Calls the AskUsingForm()
@param: Compiled Arguments obtain through the Form.Compile() function
@return: 1 = ok, 0 = cancel | Calls the AskUsingForm() | [
"Calls",
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"AskUsingForm",
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] | def AskUsingForm(*args):
"""
Calls the AskUsingForm()
@param: Compiled Arguments obtain through the Form.Compile() function
@return: 1 = ok, 0 = cancel
"""
old = set_script_timeout(0)
r = AskUsingForm__(*args)
set_script_timeout(old)
return r | [
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livid/v2ex-gae | 32be3a77d535e7c9df85a333e01ab8834d0e8581 | twitter/twitter.py | python | User.SetScreenName | (self, screen_name) | Set the short username of this user.
Args:
screen_name: the short username of this user | Set the short username of this user. | [
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] | def SetScreenName(self, screen_name):
'''Set the short username of this user.
Args:
screen_name: the short username of this user
'''
self._screen_name = screen_name | [
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F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idaapi.py | python | wait_for_next_event | (*args) | return _idaapi.wait_for_next_event(*args) | wait_for_next_event(wfne, timeout_in_secs) -> dbg_event_code_t | wait_for_next_event(wfne, timeout_in_secs) -> dbg_event_code_t | [
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"""
wait_for_next_event(wfne, timeout_in_secs) -> dbg_event_code_t
"""
return _idaapi.wait_for_next_event(*args) | [
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pwnieexpress/pwn_plug_sources | 1a23324f5dc2c3de20f9c810269b6a29b2758cad | src/metagoofil/hachoir_parser/network/tcpdump.py | python | Layer.parseNext | (self, parent) | return None | [] | def parseNext(self, parent):
return None | [
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