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Azure/azure-devops-cli-extension | 11334cd55806bef0b99c3bee5a438eed71e44037 | azure-devops/azext_devops/dev/boards/relations.py | python | show_work_item | (id, organization=None, detect=None) | return work_item | Get work item, fill relations with friendly name | Get work item, fill relations with friendly name | [
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""" Get work item, fill relations with friendly name
"""
organization = resolve_instance(detect=detect, organization=organization)
client = get_work_item_tracking_client(organization)
work_item = client.get_work_item(id, expand='All')
relation_types_from_service = client.get_relation_types()
work_item = fill_friendly_name_for_relations_in_work_item(relation_types_from_service, work_item)
return work_item | [
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apache/bloodhound | c3e31294e68af99d4e040e64fbdf52394344df9e | trac/trac/web/api.py | python | Request.remote_addr | (self) | return self.environ.get('REMOTE_ADDR') | IP address of the remote user | IP address of the remote user | [
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sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | modules/templates/historic/ARC/menus.py | python | S3OptionsMenu.admin | (self) | return menu | ADMIN menu | ADMIN menu | [
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""" ADMIN menu """
menu = super(S3OptionsMenu, self).admin()
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dipy/dipy | be956a529465b28085f8fc435a756947ddee1c89 | dipy/reconst/qtdmri.py | python | QtdmriFit.odf | (self, sphere, tau, s=2) | return odf | r""" Calculates the analytical Orientation Distribution Function (ODF)
for a given diffusion time tau from the signal, [1]_ Eq. (32). The
qtdmri coefficients are first converted to mapmri coefficients
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Parameters
----------
sphere : dipy sphere object
sphere object with vertice orientations to compute the ODF on.
tau : float
diffusion time (big_delta - small_delta / 3.) in seconds
s : unsigned int
radial moment of the ODF
References
----------
.. [1] Ozarslan E. et. al, "Mean apparent propagator (MAP) MRI: A novel
diffusion imaging method for mapping tissue microstructure",
NeuroImage, 2013.
.. [2] Fick, Rutger HJ, et al. "Non-Parametric GraphNet-Regularized
Representation of dMRI in Space and Time", Medical Image Analysis,
2017. | r""" Calculates the analytical Orientation Distribution Function (ODF)
for a given diffusion time tau from the signal, [1]_ Eq. (32). The
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qtdmri coefficients are first converted to mapmri coefficients
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Parameters
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sphere : dipy sphere object
sphere object with vertice orientations to compute the ODF on.
tau : float
diffusion time (big_delta - small_delta / 3.) in seconds
s : unsigned int
radial moment of the ODF
References
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.. [1] Ozarslan E. et. al, "Mean apparent propagator (MAP) MRI: A novel
diffusion imaging method for mapping tissue microstructure",
NeuroImage, 2013.
.. [2] Fick, Rutger HJ, et al. "Non-Parametric GraphNet-Regularized
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mapmri_coef = self.qtdmri_to_mapmri_coef(tau)
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v = np.dot(v_, self.R)
I_s = mapmri.mapmri_odf_matrix(self.model.radial_order, self.us,
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odf = np.dot(I_s, mapmri_coef)
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II = self.model.cache_get('ODF_matrix', key=(sphere, s))
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II = mapmri.mapmri_isotropic_odf_matrix(
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self.model.cache_set('ODF_matrix', (sphere, s), II)
odf = self.us[0] ** s * np.dot(II, mapmri_coef)
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/idlelib/rpc.py | python | SocketIO.asyncreturn | (self, seq) | return self.decoderesponse(response) | [] | def asyncreturn(self, seq):
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ctxis/canape | 5f0e03424577296bcc60c2008a60a98ec5307e4b | CANAPE.Scripting/Lib/fractions.py | python | Fraction._richcmp | (self, other, op) | Helper for comparison operators, for internal use only.
Implement comparison between a Rational instance `self`, and
either another Rational instance or a float `other`. If
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either another Rational instance or a float `other`. If
`other` is not a Rational instance or a float, return
NotImplemented. `op` should be one of the six standard
comparison operators.
"""
# convert other to a Rational instance where reasonable.
if isinstance(other, Rational):
return op(self._numerator * other.denominator,
self._denominator * other.numerator)
# comparisons with complex should raise a TypeError, for consistency
# with int<->complex, float<->complex, and complex<->complex comparisons.
if isinstance(other, complex):
raise TypeError("no ordering relation is defined for complex numbers")
if isinstance(other, float):
if math.isnan(other) or math.isinf(other):
return op(0.0, other)
else:
return op(self, self.from_float(other))
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pwnieexpress/pwn_plug_sources | 1a23324f5dc2c3de20f9c810269b6a29b2758cad | src/voiper/sulley/impacket/dcerpc/dcom.py | python | RemoteActivationRequestHeader.get_mode | (self) | return self.get_long(60, '<') | [] | def get_mode(self):
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devitocodes/devito | 6abd441e3f5f091775ad332be6b95e017b8cbd16 | devito/types/sparse.py | python | SparseFunction._coordinate_symbols | (self) | return tuple([self.coordinates.indexify((p_dim, i))
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/modular/local_comp/smoothchar.py | python | SmoothCharacterGeneric.restrict_to_Qp | (self) | return G.character(self.level(), [self(x) for x in ugs]) | r"""
Return the restriction of this character to `\QQ_p^\times`, embedded as
a subfield of `F^\times`.
EXAMPLES::
sage: from sage.modular.local_comp.smoothchar import SmoothCharacterGroupRamifiedQuadratic
sage: SmoothCharacterGroupRamifiedQuadratic(3, 0, QQ).character(0, [2]).restrict_to_Qp()
Character of Q_3*, of level 0, mapping 3 |--> 4 | r"""
Return the restriction of this character to `\QQ_p^\times`, embedded as
a subfield of `F^\times`. | [
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r"""
Return the restriction of this character to `\QQ_p^\times`, embedded as
a subfield of `F^\times`.
EXAMPLES::
sage: from sage.modular.local_comp.smoothchar import SmoothCharacterGroupRamifiedQuadratic
sage: SmoothCharacterGroupRamifiedQuadratic(3, 0, QQ).character(0, [2]).restrict_to_Qp()
Character of Q_3*, of level 0, mapping 3 |--> 4
"""
G = SmoothCharacterGroupQp(self.parent().prime(), self.base_ring())
ugs = G.unit_gens(self.level())
return G.character(self.level(), [self(x) for x in ugs]) | [
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google/macops | 8442745359c0c941cd4e4e7d243e43bd16b40dec | gmacpyutil/gmacpyutil/profiles.py | python | Profile.Get | (self, key) | return self._profile.get(key) | [] | def Get(self, key):
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wbond/asn1crypto | 9ae350f212532dfee7f185f6b3eda24753249cf3 | asn1crypto/_iri.py | python | _iri_utf8_errors_handler | (exc) | return (''.join(replacements), exc.end) | Error handler for decoding UTF-8 parts of a URI into an IRI. Leaves byte
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The UnicodeDecodeError exception
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:param exc:
The UnicodeDecodeError exception
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bytes_as_ints = bytes_to_list(exc.object[exc.start:exc.end])
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onnx/onnx-coreml | 141fc33d7217674ea8bda36494fa8089a543a3f3 | onnx_coreml/_operators.py | python | _convert_neg | (builder, node, graph, err) | [] | def _convert_neg(builder, node, graph, err): # type: (NeuralNetworkBuilder, Node, Graph, ErrorHandling) -> None
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PaddlePaddle/PaddleX | 2bab73f81ab54e328204e7871e6ae4a82e719f5d | static/deploy/openvino/python/transforms/seg_transforms.py | python | Compose.__call__ | (self, im, im_info=None, label=None) | return outputs | Args:
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respeaker/get_started_with_respeaker | ec859759fcec7e683a5e09328a8ea307046f353d | files/usr/lib/python2.7/site-packages/sockjs/tornado/session.py | python | BaseSession.send_message | (self, msg, stats=True, binary=False) | Send or queue outgoing message
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] | https://github.com/respeaker/get_started_with_respeaker/blob/ec859759fcec7e683a5e09328a8ea307046f353d/files/usr/lib/python2.7/site-packages/sockjs/tornado/session.py#L177-L185 | ||
qntm/greenery | da23f57b737e19338777f4d327e58f95bb59556a | greenery/fsm.py | python | fsm.ispropersuperset | (self, other) | return self >= other and self != other | Treat `self` and `other` as sets of strings and see if `self` is a proper
superset of `other`. | Treat `self` and `other` as sets of strings and see if `self` is a proper
superset of `other`. | [
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] | def ispropersuperset(self, other):
'''
Treat `self` and `other` as sets of strings and see if `self` is a proper
superset of `other`.
'''
return self >= other and self != other | [
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/Axonius/Integrations/Axonius/Axonius.py | python | parse_key | (key: str) | return key | Parse fields into required format. | Parse fields into required format. | [
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] | def parse_key(key: str) -> str:
"""Parse fields into required format."""
if key.startswith("specific_data.data."):
key = strip_left(obj=key, fix="specific_data.data.")
key = f"aggregated_{key}"
if key.startswith("adapters_data."):
key = strip_left(obj=key, fix="adapters_data.")
key = key.replace(".", "_")
return key | [
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magenta/ddsp | 8536a366c7834908f418a6721547268e8f2083cc | ddsp/training/ddsp_run.py | python | allow_memory_growth | () | Sets the GPUs to grow the memory usage as is needed by the process. | Sets the GPUs to grow the memory usage as is needed by the process. | [
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] | def allow_memory_growth():
"""Sets the GPUs to grow the memory usage as is needed by the process."""
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs.
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized.
print(e) | [
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timonwong/OmniMarkupPreviewer | 21921ac7a99d2b5924a2219b33679a5b53621392 | OmniMarkupLib/libs/cherrypy/wsgiserver/wsgiserver2.py | python | HTTPServer.stop | (self) | Gracefully shutdown a server that is serving forever. | Gracefully shutdown a server that is serving forever. | [
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] | def stop(self):
"""Gracefully shutdown a server that is serving forever."""
self.ready = False
if self._start_time is not None:
self._run_time += (time.time() - self._start_time)
self._start_time = None
sock = getattr(self, "socket", None)
if sock:
if not isinstance(self.bind_addr, basestring):
# Touch our own socket to make accept() return immediately.
try:
host, port = sock.getsockname()[:2]
except socket.error:
x = sys.exc_info()[1]
if x.args[0] not in socket_errors_to_ignore:
# Changed to use error code and not message
# See https://bitbucket.org/cherrypy/cherrypy/issue/860.
raise
else:
# Note that we're explicitly NOT using AI_PASSIVE,
# here, because we want an actual IP to touch.
# localhost won't work if we've bound to a public IP,
# but it will if we bound to '0.0.0.0' (INADDR_ANY).
for res in socket.getaddrinfo(host, port, socket.AF_UNSPEC,
socket.SOCK_STREAM):
af, socktype, proto, canonname, sa = res
s = None
try:
s = socket.socket(af, socktype, proto)
# See http://groups.google.com/group/cherrypy-users/
# browse_frm/thread/bbfe5eb39c904fe0
s.settimeout(1.0)
s.connect((host, port))
s.close()
except socket.error:
if s:
s.close()
if hasattr(sock, "close"):
sock.close()
self.socket = None
self.requests.stop(self.shutdown_timeout) | [
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avrae/avrae | 6ebe46a1ec3d4dfaa2f9b18fac948325f39f87de | ddb/dice/tree.py | python | RollContext.from_character | (cls, character) | return cls(character.upstream_id, 'character', character.name, character.image) | Returns a context associated with a DDB character. | Returns a context associated with a DDB character. | [
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] | def from_character(cls, character):
"""Returns a context associated with a DDB character."""
return cls(character.upstream_id, 'character', character.name, character.image) | [
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apple/ccs-calendarserver | 13c706b985fb728b9aab42dc0fef85aae21921c3 | txdav/caldav/datastore/sql.py | python | Calendar.updateShareeGroupLink | (self, groupUID, mode=None) | update schema.GROUP_SHAREE | update schema.GROUP_SHAREE | [
"update",
"schema",
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"GROUP_SHAREE"
] | def updateShareeGroupLink(self, groupUID, mode=None):
"""
update schema.GROUP_SHAREE
"""
changed = False
group = yield self._txn.groupByUID(groupUID)
gs = schema.GROUP_SHAREE
rows = yield Select(
[gs.MEMBERSHIP_HASH, gs.GROUP_BIND_MODE],
From=gs,
Where=(gs.CALENDAR_ID == self._resourceID).And(
gs.GROUP_ID == group.groupID)
).on(self._txn)
if rows:
[[gsMembershipHash, gsMode]] = rows
updateMap = {}
if gsMembershipHash != group.membershipHash:
updateMap[gs.MEMBERSHIP_HASH] = group.membershipHash
if mode is not None and gsMode != mode:
updateMap[gs.GROUP_BIND_MODE] = mode
if updateMap:
yield Update(
updateMap,
Where=(gs.CALENDAR_ID == self._resourceID).And(
gs.GROUP_ID == group.groupID
)
).on(self._txn)
changed = True
else:
yield Insert({
gs.MEMBERSHIP_HASH: group.membershipHash,
gs.GROUP_BIND_MODE: mode,
gs.CALENDAR_ID: self._resourceID,
gs.GROUP_ID: group.groupID,
}).on(self._txn)
changed = True
returnValue(changed) | [
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PaddlePaddle/PaddleX | 2bab73f81ab54e328204e7871e6ae4a82e719f5d | paddlex/ppdet/data/source/category.py | python | _visdrone_category | () | return clsid2catid, catid2name | [] | def _visdrone_category():
clsid2catid = {i: i for i in range(10)}
catid2name = {
0: 'pedestrian',
1: 'people',
2: 'bicycle',
3: 'car',
4: 'van',
5: 'truck',
6: 'tricycle',
7: 'awning-tricycle',
8: 'bus',
9: 'motor'
}
return clsid2catid, catid2name | [
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CoinCheung/BiSeNet | f9231b7c971413e6ebdfcd961fbea53417b18851 | old/model.py | python | SpatialPath.init_weight | (self) | [] | def init_weight(self):
for ly in self.children():
if isinstance(ly, nn.Conv2d):
nn.init.kaiming_normal_(ly.weight, a=1)
if not ly.bias is None: nn.init.constant_(ly.bias, 0) | [
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okigan/awscurl | ea2a9b192e80053a4edf7dbfe7b390e249a92527 | awscurl/awscurl.py | python | inner_main | (argv) | return 0 | Awscurl CLI main entry point | Awscurl CLI main entry point | [
"Awscurl",
"CLI",
"main",
"entry",
"point"
] | def inner_main(argv):
"""
Awscurl CLI main entry point
"""
# note EC2 ignores Accept header and responds in xml
default_headers = ['Accept: application/xml',
'Content-Type: application/json']
parser = configargparse.ArgumentParser(
description='Curl AWS request signing',
formatter_class=configargparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('-v', '--verbose', action='store_true',
help='verbose flag', default=False)
parser.add_argument('-i', '--include', action='store_true',
help='include headers in the output', default=False)
parser.add_argument('-X', '--request',
help='Specify request command to use',
default='GET')
parser.add_argument('-d', '--data', help='HTTP POST data', default='')
parser.add_argument('-H', '--header', help='HTTP header', action='append')
parser.add_argument('-k', '--insecure', action='store_true', default=False,
help='Allow insecure server connections when using SSL')
parser.add_argument('--data-binary', action='store_true',
help='Process HTTP POST data exactly as specified with '
'no extra processing whatsoever.', default=False)
parser.add_argument('--region', help='AWS region', default='us-east-1',
env_var='AWS_DEFAULT_REGION')
parser.add_argument('--profile', help='AWS profile', default='default', env_var='AWS_PROFILE')
parser.add_argument('--service', help='AWS service', default='execute-api')
parser.add_argument('--access_key', env_var='AWS_ACCESS_KEY_ID')
parser.add_argument('--secret_key', env_var='AWS_SECRET_ACCESS_KEY')
# AWS_SECURITY_TOKEN is deprecated, but kept for backward compatibility
# https://github.com/boto/botocore/blob/c76553d3158b083d818f88c898d8f6d7918478fd/botocore/credentials.py#L260-262
parser.add_argument('--security_token', env_var='AWS_SECURITY_TOKEN')
parser.add_argument('--session_token', env_var='AWS_SESSION_TOKEN')
parser.add_argument('-L', '--location', action='store_true', default=False,
help="Follow redirects")
parser.add_argument('uri')
args = parser.parse_args(argv)
# pylint: disable=global-statement
global IS_VERBOSE
IS_VERBOSE = args.verbose
if args.verbose:
__log(vars(args))
data = args.data
if data is not None and data.startswith("@"):
filename = data[1:]
with open(filename, "r") as post_data_file:
data = post_data_file.read()
if args.header is None:
args.header = default_headers
if args.security_token is not None:
args.session_token = args.security_token
del args.security_token
# pylint: disable=deprecated-lambda
headers = {k: v for (k, v) in map(lambda s: s.split(": "), args.header)}
headers = CaseInsensitiveDict(headers)
credentials_path = os.path.expanduser("~") + "/.aws/credentials"
args.access_key, args.secret_key, args.session_token = load_aws_config(args.access_key,
args.secret_key,
args.session_token,
credentials_path,
args.profile)
if args.access_key is None:
raise ValueError('No access key is available')
if args.secret_key is None:
raise ValueError('No secret key is available')
response = make_request(args.request,
args.service,
args.region,
args.uri,
headers,
data,
args.access_key,
args.secret_key,
args.session_token,
args.data_binary,
verify=not args.insecure,
allow_redirects=args.location)
if args.include or IS_VERBOSE:
print(response.headers, end='\n\n')
print(response.text)
response.raise_for_status()
return 0 | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/polys/subresultants_qq_zz.py | python | row2poly | (row, deg, x) | return Poly(poly, x) | Converts the row of a matrix to a poly of degree deg and variable x.
Some entries at the beginning and/or at the end of the row may be zero. | Converts the row of a matrix to a poly of degree deg and variable x.
Some entries at the beginning and/or at the end of the row may be zero. | [
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] | def row2poly(row, deg, x):
'''
Converts the row of a matrix to a poly of degree deg and variable x.
Some entries at the beginning and/or at the end of the row may be zero.
'''
k = 0
poly = []
leng = len(row)
# find the beginning of the poly ; i.e. the first
# non-zero element of the row
while row[k] == 0:
k = k + 1
# append the next deg + 1 elements to poly
for j in range( deg + 1):
if k + j <= leng:
poly.append(row[k + j])
return Poly(poly, x) | [
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shazow/workerpool | 2c5b29ec64ffbc94fc3623a4531eaf7c7c1a9ab5 | workerpool/pools.py | python | WorkerPool.grow | (self) | Add another worker to the pool. | Add another worker to the pool. | [
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] | def grow(self):
"Add another worker to the pool."
t = self.worker_factory(self)
t.start()
self._size += 1 | [
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tensorflow/lattice | 784eca50cbdfedf39f183cc7d298c9fe376b69c0 | tensorflow_lattice/python/premade_lib.py | python | _verify_prefitting_model | (prefitting_model, feature_names) | Checks that prefitting_model has the proper input layer. | Checks that prefitting_model has the proper input layer. | [
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] | def _verify_prefitting_model(prefitting_model, feature_names):
"""Checks that prefitting_model has the proper input layer."""
if isinstance(prefitting_model, tf.keras.Model):
layer_names = [layer.name for layer in prefitting_model.layers]
elif isinstance(prefitting_model, tf.estimator.Estimator):
layer_names = prefitting_model.get_variable_names()
else:
raise ValueError('Invalid model type for prefitting_model: {}'.format(
type(prefitting_model)))
for feature_name in feature_names:
if isinstance(prefitting_model, tf.keras.Model):
input_layer_name = '{}_{}'.format(INPUT_LAYER_NAME, feature_name)
if input_layer_name not in layer_names:
raise ValueError(
'prefitting_model does not match prefitting_model_config. Make '
'sure that prefitting_model is the proper type and constructed '
'from the prefitting_model_config: {}'.format(
type(prefitting_model)))
else:
pwl_input_layer_name = '{}_{}/{}'.format(
CALIB_LAYER_NAME, feature_name,
pwl_calibration_layer.PWL_CALIBRATION_KERNEL_NAME)
cat_input_layer_name = '{}_{}/{}'.format(
CALIB_LAYER_NAME, feature_name,
categorical_calibration_layer.CATEGORICAL_CALIBRATION_KERNEL_NAME)
if (pwl_input_layer_name not in layer_names and
cat_input_layer_name not in layer_names):
raise ValueError(
'prefitting_model does not match prefitting_model_config. Make '
'sure that prefitting_model is the proper type and constructed '
'from the prefitting_model_config: {}'.format(
type(prefitting_model))) | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/Babel-0.9.6-py2.7.egg/babel/messages/catalog.py | python | Catalog._key_for | (self, id) | return key | The key for a message is just the singular ID even for pluralizable
messages. | The key for a message is just the singular ID even for pluralizable
messages. | [
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] | def _key_for(self, id):
"""The key for a message is just the singular ID even for pluralizable
messages.
"""
key = id
if isinstance(key, (list, tuple)):
key = id[0]
return key | [
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] | https://github.com/buke/GreenOdoo/blob/3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df/runtime/python/lib/python2.7/site-packages/Babel-0.9.6-py2.7.egg/babel/messages/catalog.py#L761-L768 | |
marshmallow-code/django-rest-marshmallow | 117f89bd5f06de6049dd51d4705d0ccafcc351d7 | setup.py | python | get_packages | (package) | return [dirpath
for dirpath, dirnames, filenames in os.walk(package)
if os.path.exists(os.path.join(dirpath, '__init__.py'))] | Return root package and all sub-packages. | Return root package and all sub-packages. | [
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"-",
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] | def get_packages(package):
"""
Return root package and all sub-packages.
"""
return [dirpath
for dirpath, dirnames, filenames in os.walk(package)
if os.path.exists(os.path.join(dirpath, '__init__.py'))] | [
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ChenglongChen/tensorflow-XNN | 6534a832f5b4461cbdf1ebbdf5620a0cd80f0aa9 | code/nn_module.py | python | word_dropout | (x, training, dropout=0, seed=0) | return x | tf.layers.Dropout doesn't work as it can't switch training or inference | tf.layers.Dropout doesn't work as it can't switch training or inference | [
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# word dropout (dropout the entire embedding for some words)
"""
tf.layers.Dropout doesn't work as it can't switch training or inference
"""
if dropout > 0:
input_shape = tf.shape(x)
noise_shape = [input_shape[0], input_shape[1], 1]
x = tf.layers.Dropout(rate=dropout, noise_shape=noise_shape, seed=seed)(x, training=training)
return x | [
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c-w/gutenberg | df8fcfab87e9d8f5b97bb44857beb9af5eb9e96c | gutenberg/acquire/metadata.py | python | MetadataCache._download_metadata_archive | (self) | Makes a remote call to the Project Gutenberg servers and downloads
the entire Project Gutenberg meta-data catalog. The catalog describes
the texts on Project Gutenberg in RDF. The function returns a
file-pointer to the catalog. | Makes a remote call to the Project Gutenberg servers and downloads
the entire Project Gutenberg meta-data catalog. The catalog describes
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file-pointer to the catalog. | [
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"""
with tempfile.NamedTemporaryFile(delete=False) as metadata_archive:
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remove(metadata_archive.name) | [
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rll/rllab | ba78e4c16dc492982e648f117875b22af3965579 | rllab/distributions/recurrent_categorical.py | python | RecurrentCategorical.kl | (self, old_dist_info, new_dist_info) | return np.sum(
old_prob * (np.log(old_prob + TINY) - np.log(new_prob + TINY)),
axis=2
) | Compute the KL divergence of two categorical distributions | Compute the KL divergence of two categorical distributions | [
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] | def kl(self, old_dist_info, new_dist_info):
"""
Compute the KL divergence of two categorical distributions
"""
old_prob = old_dist_info["prob"]
new_prob = new_dist_info["prob"]
return np.sum(
old_prob * (np.log(old_prob + TINY) - np.log(new_prob + TINY)),
axis=2
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Nuitka/Nuitka | 39262276993757fa4e299f497654065600453fc9 | nuitka/utils/Utils.py | python | getUserName | () | return pwd.getpwuid(os.getuid())[0] | Return the user name.
Notes: Currently doesn't work on Windows. | Return the user name. | [
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] | def getUserName():
"""Return the user name.
Notes: Currently doesn't work on Windows.
"""
import pwd # pylint: disable=I0021,import-error
return pwd.getpwuid(os.getuid())[0] | [
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suavecode/SUAVE | 4f83c467c5662b6cc611ce2ab6c0bdd25fd5c0a5 | trunk/SUAVE/Methods/Geometry/Three_Dimensional/compute_span_location_from_chord_length.py | python | compute_span_location_from_chord_length | (wing,chord_length) | return span_location | Computes the location along the half-span given a chord length.
Assumptions:
Linear variation of chord with span. Returns 0 if constant chord wing.
Source:
None
Inputs:
wing.chords.
root [m]
tip [m]
wing.spans.projected [m]
chord_length [m]
Outputs:
span_location [m]
Properties Used:
N/A | Computes the location along the half-span given a chord length. | [
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] | def compute_span_location_from_chord_length(wing,chord_length):
"""Computes the location along the half-span given a chord length.
Assumptions:
Linear variation of chord with span. Returns 0 if constant chord wing.
Source:
None
Inputs:
wing.chords.
root [m]
tip [m]
wing.spans.projected [m]
chord_length [m]
Outputs:
span_location [m]
Properties Used:
N/A
"""
#unpack
ct = wing.chords.tip
cr = wing.chords.root
b = wing.spans.projected
b_2 = b/2.
if (cr-ct)==0:
span_location = 0.
else:
span_location = b_2*(1-(chord_length-ct)/(cr-ct))
return span_location | [
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kexinyi/ns-vqa | df357618af224723acffb66a17ce3e94298642a7 | scene_parse/mask_rcnn/lib/utils/resnet_weights_helper.py | python | convert_state_dict | (src_dict) | return dst_dict | Return the correct mapping of tensor name and value
Mapping from the names of torchvision model to our resnet conv_body and box_head. | Return the correct mapping of tensor name and value | [
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] | def convert_state_dict(src_dict):
"""Return the correct mapping of tensor name and value
Mapping from the names of torchvision model to our resnet conv_body and box_head.
"""
dst_dict = {}
for k, v in src_dict.items():
toks = k.split('.')
if k.startswith('layer'):
assert len(toks[0]) == 6
res_id = int(toks[0][5]) + 1
name = '.'.join(['res%d' % res_id] + toks[1:])
dst_dict[name] = v
elif k.startswith('fc'):
continue
else:
name = '.'.join(['res1'] + toks)
dst_dict[name] = v
return dst_dict | [
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mongodb/docs | 03c2e030a97da6a818dab6e1b3e5301cb0f8eb43 | bin/archive/errorcodes.py | python | genErrorOutput | () | Sort and iterate through codes printing out error codes and messages in RST format. | Sort and iterate through codes printing out error codes and messages in RST format. | [
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] | def genErrorOutput():
"""Sort and iterate through codes printing out error codes and messages in RST format."""
sys.stderr.write("Generating RST files\n");
separatefiles = False
if errorsFormat == 'single':
errorsrst = resultsRoot + "/errors.txt"
if os.path.exists(errorsrst ):
i = open(errorsrst , "r" )
out = open( errorsrst , 'wb' )
sys.stderr.write("Generating single file: {}\n".format(errorsrst))
titleLen = len(errorsTitle)
out.write(":orphan:\n")
out.write("=" * titleLen + "\n")
out.write(errorsTitle + "\n")
out.write("=" * titleLen + "\n")
out.write(default_domain);
elif errorsFormat == 'separate':
separatefiles = True
else:
raise Exception("Unknown output format: {}".format(errorsFormat))
prev = ""
seen = {}
sourcerootOffset = len(sourceroot)
stripChars = " " + "\n"
# codes.sort( key=lambda x: x[0]+"-"+x[3] )
codes.sort( key=lambda x: int(x[3]) )
for f,l,line,num,message,severity in codes:
if num in seen:
continue
seen[num] = True
if f.startswith(sourceroot):
f = f[sourcerootOffset+1:]
fn = f.rpartition("/")[2]
url = ":source:`" + f + "#L" + str(l) + "`"
if separatefiles:
outputFile = "{}/{:d}.txt".format(resultsRoot,int(num))
out = open(outputFile, 'wb')
out.write(default_domain)
sys.stderr.write("Generating file: {}\n".format(outputFile))
out.write(".. line: {}\n\n".format(line.strip(stripChars)))
out.write(".. error:: {}\n\n".format(num))
if message != '':
out.write(" :message: {}\n".format(message.strip(stripChars)))
else:
message = getBestMessage(line,str(num)).strip(stripChars)
if message != '':
out.write(" :message: {}\n".format(message))
if severity:
if severity in severityTexts:
out.write(" :severity: {}\n".format(severityTexts[severity]))
elif severity in exceptionTexts:
out.write(" :throws: {}\n".format(exceptionTexts[severity]))
else:
out.write(" :severity: {}\n".format(severity))
out.write(" :module: {}\n".format(url) )
if separatefiles:
out.write("\n")
out.close()
if separatefiles==False:
out.write( "\n" )
out.close() | [
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kovidgoyal/calibre | 2b41671370f2a9eb1109b9ae901ccf915f1bd0c8 | src/calibre/devices/errors.py | python | OpenFeedback.custom_dialog | (self, parent) | If you need to show the user a custom dialog, instead of just
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displaying the feedback_msg, create and return it here. | [
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'''
If you need to show the user a custom dialog, instead of just
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odlgroup/odl | 0b088df8dc4621c68b9414c3deff9127f4c4f11d | odl/diagnostics/space.py | python | SpaceTest._lincomb | (self) | Verify linear combination. | Verify linear combination. | [
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] | def _lincomb(self):
"""Verify linear combination."""
self.log('\nTesting lincomb')
self._lincomb_aliased() | [
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CouchPotato/CouchPotatoServer | 7260c12f72447ddb6f062367c6dfbda03ecd4e9c | libs/CodernityDB/database.py | python | Database._compact_indexes | (self) | Runs compact on all indexes | Runs compact on all indexes | [
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] | def _compact_indexes(self):
"""
Runs compact on all indexes
"""
for index in self.indexes:
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google/trax | d6cae2067dedd0490b78d831033607357e975015 | trax/rl/actor_critic_joint.py | python | ActorCriticJointAgent.train_epoch | (self) | Trains RL for one epoch. | Trains RL for one epoch. | [
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] | def train_epoch(self):
"""Trains RL for one epoch."""
n_evals = rl_training.remaining_evals(
self._trainer.step,
self._epoch,
self._train_steps_per_epoch,
self._supervised_evals_per_epoch)
for _ in range(n_evals):
self._trainer.train_epoch(
self._train_steps_per_epoch // self._supervised_evals_per_epoch,
self._supervised_eval_steps) | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/pysaml2-4.9.0/src/saml2/ident.py | python | IdentDB.handle_name_id_mapping_request | (self, name_id, name_id_policy) | return self.construct_nameid(_id, name_id_policy=name_id_policy) | :param name_id: The NameID that specifies the principal
:param name_id_policy: The NameIDPolicy of the requester
:return: If an old name_id exists that match the name-id policy
that is return otherwise if a new one can be created it
will be and returned. If no old matching exists and a new
is not allowed to be created None is returned. | [] | def handle_name_id_mapping_request(self, name_id, name_id_policy):
"""
:param name_id: The NameID that specifies the principal
:param name_id_policy: The NameIDPolicy of the requester
:return: If an old name_id exists that match the name-id policy
that is return otherwise if a new one can be created it
will be and returned. If no old matching exists and a new
is not allowed to be created None is returned.
"""
_id = self.find_local_id(name_id)
if not _id:
raise Unknown("Unknown entity")
# return an old one if present
for val in self.db[_id].split(" "):
_nid = decode(val)
if _nid.format == name_id_policy.format:
if _nid.sp_name_qualifier == name_id_policy.sp_name_qualifier:
return _nid
if name_id_policy.allow_create == "false":
raise PolicyError("Not allowed to create new identifier")
# else create and return a new one
return self.construct_nameid(_id, name_id_policy=name_id_policy) | [
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iopsgroup/imoocc | de810eb6d4c1697b7139305925a5b0ba21225f3f | scanhosts/modules/paramiko_old/sftp_attr.py | python | SFTPAttributes.__init__ | (self) | Create a new (empty) SFTPAttributes object. All fields will be empty. | Create a new (empty) SFTPAttributes object. All fields will be empty. | [
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"""
Create a new (empty) SFTPAttributes object. All fields will be empty.
"""
self._flags = 0
self.st_size = None
self.st_uid = None
self.st_gid = None
self.st_mode = None
self.st_atime = None
self.st_mtime = None
self.attr = {} | [
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DLR-RM/BlenderProc | e04e03f34b66702bbca45d1ac701599b6d764609 | blenderproc/python/modules/utility/ConfigParser.py | python | ConfigParser._show_help | (self) | Print out help message which describes the placeholders that are used in the given config file | Print out help message which describes the placeholders that are used in the given config file | [
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isl-org/MultiObjectiveOptimization | d45eb262ec61c0dafecebfb69027ff6de280dbb3 | multi_task/loaders/celeba_loader.py | python | CELEBA.__getitem__ | (self, index) | return [img] + label | __getitem__
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/packages/IronPython.StdLib.2.7.4/content/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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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | marklogic/datadog_checks/marklogic/api.py | python | MarkLogicApi.get_health | (self) | return self.http_get(params=params) | Return the cluster health querying http://localhost:8002/manage/v2?view=health&format=json.
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googledatalab/pydatalab | 1c86e26a0d24e3bc8097895ddeab4d0607be4c40 | google/datalab/contrib/mlworkbench/commands/_ml.py | python | _TextLimeExplainerInstance.visualize | (self, label_index) | [] | def visualize(self, label_index):
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/requests/utils.py | python | parse_dict_header | (value) | return result | Parse lists of key, value pairs as described by RFC 2068 Section 2 and
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>>> type(d) is dict
True
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>>> type(d) is dict
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/Python3/_osx_support.py | python | customize_compiler | (_config_vars) | return _config_vars | Customize compiler path and configuration variables.
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"""
# Find a compiler to use for extension module builds
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/google/appengine/datastore/datastore_stub_util.py | python | StubQueryConverter.v4_to_v3_compiled_cursor | (self, v4_cursor, v3_compiled_cursor) | Converts a v4 cursor string to a v3 CompiledCursor.
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v4_cursor: a string representing a v4 query cursor
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v3_compiled_cursor.Clear()
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/pkg_resources.py | python | _handle_ns | (packageName, path_item) | return subpath | Ensure that named package includes a subpath of path_item (if needed) | Ensure that named package includes a subpath of path_item (if needed) | [
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if importer is None:
return None
loader = importer.find_module(packageName)
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module.__path__ = []
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/idlelib/IOBinding.py | python | IOBinding.set_filename | (self, filename) | [] | def set_filename(self, filename):
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_vendored_deps/library/oc_env.py | python | Yedit.valid_key | (key, sep='.') | return True | validate the incoming key | validate the incoming key | [
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RichardFrangenberg/Prism | 09283b5146d9cdf9d489dcf252f7927083534a48 | Prism/Plugins/ProjectManagers/Shotgun/external_modules/shotgun_api3/shotgun.py | python | Shotgun._visit_data | (self, data, visitor) | return visitor(data) | Walk the data (simple python types) and call the visitor. | Walk the data (simple python types) and call the visitor. | [
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andsens/bootstrap-vz | fcdc6993f59e521567fb101302b02312e741b88c | bootstrapvz/base/fs/partitionmaps/gpt.py | python | GPTPartitionMap._before_create | (self, event) | Creates the partition map | Creates the partition map | [
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researchmm/tasn | 5dba8ccc096cedc63913730eeea14a9647911129 | tasn-mxnet/3rdparty/tvm/tutorials/nnvm/from_mxnet_to_webgl.py | python | transform_image | (image) | return image | Perform necessary preprocessing to input image.
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image : numpy.ndarray
The raw image.
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image : numpy.ndarray
The preprocessed image.
"""
image = np.array(image) - np.array([123., 117., 104.])
image /= np.array([58.395, 57.12, 57.375])
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image = image[np.newaxis, :]
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coreemu/core | 7e18a7a72023a69a92ad61d87461bd659ba27f7c | daemon/core/api/tlv/coreapi.py | python | CoreMessage.repack | (self) | Invoke after updating self.tlv_data[] to rebuild self.raw_message.
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Chaffelson/nipyapi | d3b186fd701ce308c2812746d98af9120955e810 | nipyapi/nifi/models/templates_entity.py | python | TemplatesEntity.generated | (self, generated) | Sets the generated of this TemplatesEntity.
When this content was generated.
:param generated: The generated of this TemplatesEntity.
:type: str | Sets the generated of this TemplatesEntity.
When this content was generated. | [
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] | def generated(self, generated):
"""
Sets the generated of this TemplatesEntity.
When this content was generated.
:param generated: The generated of this TemplatesEntity.
:type: str
"""
self._generated = generated | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/wagtail/wagtailsearch/backends/elasticsearch2.py | python | Elasticsearch2Mapping.get_content_type | (self) | return self.model._meta.app_label + '.' + self.model.__name__ | Returns the content type as a string for the model.
For example: "wagtailcore.Page"
"myapp.MyModel" | Returns the content type as a string for the model. | [
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] | def get_content_type(self):
"""
Returns the content type as a string for the model.
For example: "wagtailcore.Page"
"myapp.MyModel"
"""
return self.model._meta.app_label + '.' + self.model.__name__ | [
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reviewboard/reviewboard | 7395902e4c181bcd1d633f61105012ffb1d18e1b | reviewboard/diffviewer/chunk_generator.py | python | DiffChunkGenerator.normalize_path_for_display | (self, filename) | return self.tool.normalize_path_for_display(
filename,
extra_data=self.filediff.extra_data) | Normalize a file path for display to the user.
This uses the associated :py:class:`~reviewboard.scmtools.core.SCMTool`
to normalize the filename.
Args:
filename (unicode):
The filename to normalize.
Returns:
unicode:
The normalized filename. | Normalize a file path for display to the user. | [
"Normalize",
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"path",
"for",
"display",
"to",
"the",
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] | def normalize_path_for_display(self, filename):
"""Normalize a file path for display to the user.
This uses the associated :py:class:`~reviewboard.scmtools.core.SCMTool`
to normalize the filename.
Args:
filename (unicode):
The filename to normalize.
Returns:
unicode:
The normalized filename.
"""
return self.tool.normalize_path_for_display(
filename,
extra_data=self.filediff.extra_data) | [
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materialsproject/pymatgen | 8128f3062a334a2edd240e4062b5b9bdd1ae6f58 | pymatgen/phonon/bandstructure.py | python | PhononBandStructure.has_imaginary_freq | (self, tol=1e-5) | return self.min_freq()[1] + tol < 0 | True if imaginary frequencies are present in the BS. | True if imaginary frequencies are present in the BS. | [
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] | def has_imaginary_freq(self, tol=1e-5):
"""
True if imaginary frequencies are present in the BS.
"""
return self.min_freq()[1] + tol < 0 | [
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GoogleCloudPlatform/PerfKitBenchmarker | 6e3412d7d5e414b8ca30ed5eaf970cef1d919a67 | perfkitbenchmarker/linux_packages/mkl.py | python | Install | (vm) | Installs the MKL package on the VM. | Installs the MKL package on the VM. | [
"Installs",
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"MKL",
"package",
"on",
"the",
"VM",
"."
] | def Install(vm):
"""Installs the MKL package on the VM."""
if UseMklRepo():
vm.Install('intel_repo')
if intel_repo.UseOneApi():
vm.InstallPackages(f'intel-oneapi-mkl-{MKL_VERSION.value}')
# do not need to symlink the vars file
return
vm.InstallPackages(f'intel-mkl-{MKL_VERSION.value}')
else:
_InstallFromPreprovisionedData(vm)
# Restore the /opt/intel/mkl/bin/mklvars.sh symlink that is missing if
# Intel MPI > 2018 installed.
if not vm.TryRemoteCommand(f'test -e {_MKL_VARS_FILE}'):
txt, _ = vm.RemoteCommand('realpath /opt/intel/*/linux/mkl | sort | uniq')
vm.RemoteCommand(f'sudo ln -s {txt.strip()} /opt/intel/mkl')
_LogEnvVariables(vm)
_CompileInterfaces(vm) | [
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deepfakes/faceswap | 09c7d8aca3c608d1afad941ea78e9fd9b64d9219 | lib/gui/wrapper.py | python | FaceswapControl.capture_tqdm | (self, string) | return True | Capture tqdm output for progress bar | Capture tqdm output for progress bar | [
"Capture",
"tqdm",
"output",
"for",
"progress",
"bar"
] | def capture_tqdm(self, string):
""" Capture tqdm output for progress bar """
logger.trace("Capturing tqdm")
tqdm = self.consoleregex["tqdm"].match(string)
if not tqdm:
return False
tqdm = tqdm.groupdict()
if any("?" in val for val in tqdm.values()):
logger.trace("tqdm initializing. Skipping")
return True
description = tqdm["dsc"].strip()
description = description if description == "" else "{} | ".format(description[:-1])
processtime = "Elapsed: {} Remaining: {}".format(tqdm["tme"].split("<")[0],
tqdm["tme"].split("<")[1])
message = "{}{} | {} | {} | {}".format(description,
processtime,
tqdm["rte"],
tqdm["itm"],
tqdm["pct"])
position = tqdm["pct"].replace("%", "")
position = int(position) if position.isdigit() else 0
self.statusbar.progress_update(message, position, True)
logger.trace("Succesfully captured tqdm message: %s", message)
return True | [
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"... | https://github.com/deepfakes/faceswap/blob/09c7d8aca3c608d1afad941ea78e9fd9b64d9219/lib/gui/wrapper.py#L324-L349 | |
puremourning/vimspector | bc57b1dd14214cf3e3a476ef75e9dcb56cf0c76d | python3/vimspector/vendor/cpuinfo.py | python | CPUID.get_max_extension_support | (self) | return max_extension_support | [] | def get_max_extension_support(self):
# Check for extension support
max_extension_support = self._run_asm(
b"\xB8\x00\x00\x00\x80" # mov ax,0x80000000
b"\x0f\xa2" # cpuid
b"\xC3" # ret
)
return max_extension_support | [
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raghakot/keras-vis | 90ae5565951b5e6a90d706b8205c2c4dfc271505 | vis/grad_modifiers.py | python | get | (identifier) | return utils.get_identifier(identifier, globals(), __name__) | [] | def get(identifier):
return utils.get_identifier(identifier, globals(), __name__) | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/compiler/ast.py | python | Mod.getChildNodes | (self) | return self.left, self.right | [] | def getChildNodes(self):
return self.left, self.right | [
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naftaliharris/tauthon | 5587ceec329b75f7caf6d65a036db61ac1bae214 | Lib/plat-mac/pimp.py | python | main | () | Minimal commandline tool to drive pimp. | Minimal commandline tool to drive pimp. | [
"Minimal",
"commandline",
"tool",
"to",
"drive",
"pimp",
"."
] | def main():
"""Minimal commandline tool to drive pimp."""
import getopt
def _help():
print "Usage: pimp [options] -s [package ...] List installed status"
print " pimp [options] -l [package ...] Show package information"
print " pimp [options] -i package ... Install packages"
print " pimp -d Dump database to stdout"
print " pimp -V Print version number"
print "Options:"
print " -v Verbose"
print " -f Force installation"
print " -D dir Set destination directory"
print " (default: %s)" % DEFAULT_INSTALLDIR
print " -u url URL for database"
sys.exit(1)
class _Watcher:
def update(self, msg):
sys.stderr.write(msg + '\r')
return 1
try:
opts, args = getopt.getopt(sys.argv[1:], "slifvdD:Vu:")
except getopt.GetoptError:
_help()
if not opts and not args:
_help()
mode = None
force = 0
verbose = 0
prefargs = {}
watcher = None
for o, a in opts:
if o == '-s':
if mode:
_help()
mode = 'status'
if o == '-l':
if mode:
_help()
mode = 'list'
if o == '-d':
if mode:
_help()
mode = 'dump'
if o == '-V':
if mode:
_help()
mode = 'version'
if o == '-i':
mode = 'install'
if o == '-f':
force = 1
if o == '-v':
verbose = 1
watcher = _Watcher()
if o == '-D':
prefargs['installDir'] = a
if o == '-u':
prefargs['pimpDatabase'] = a
if not mode:
_help()
if mode == 'version':
print 'Pimp version %s; module name is %s' % (PIMP_VERSION, __name__)
else:
_run(mode, verbose, force, args, prefargs, watcher) | [
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theotherp/nzbhydra | 4b03d7f769384b97dfc60dade4806c0fc987514e | libs/cmd.py | python | Cmd.postcmd | (self, stop, line) | return stop | Hook method executed just after a command dispatch is finished. | Hook method executed just after a command dispatch is finished. | [
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"""Hook method executed just after a command dispatch is finished."""
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fkie-cad/FACT_core | 034ed39cff092b4970f10d3e3ad117ae6baa0192 | src/plugins/installer.py | python | AbstractPluginInstaller.build | (self) | Build and install projects that can't be installed through a package
manager | Build and install projects that can't be installed through a package
manager | [
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"projects",
"that",
"can",
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] | def build(self):
'''
Build and install projects that can't be installed through a package
manager
''' | [
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":"
] | https://github.com/fkie-cad/FACT_core/blob/034ed39cff092b4970f10d3e3ad117ae6baa0192/src/plugins/installer.py#L89-L93 | ||
akfamily/akshare | 590e50eece9ec067da3538c7059fd660b71f1339 | akshare/stock/stock_zh_b_sina.py | python | stock_zh_b_daily | (
symbol: str = "sh900901",
start_date: str = "19900101",
end_date: str = "21000118",
adjust: str = "",
) | 新浪财经-B 股-个股的历史行情数据, 大量抓取容易封 IP
https://finance.sina.com.cn/realstock/company/sh689009/nc.shtml
:param start_date: 20201103; 开始日期
:type start_date: str
:param end_date: 20201103; 结束日期
:type end_date: str
:param symbol: sh600000
:type symbol: str
:param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子
:type adjust: str
:return: specific data
:rtype: pandas.DataFrame | 新浪财经-B 股-个股的历史行情数据, 大量抓取容易封 IP
https://finance.sina.com.cn/realstock/company/sh689009/nc.shtml
:param start_date: 20201103; 开始日期
:type start_date: str
:param end_date: 20201103; 结束日期
:type end_date: str
:param symbol: sh600000
:type symbol: str
:param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子
:type adjust: str
:return: specific data
:rtype: pandas.DataFrame | [
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"20201103",
";",... | def stock_zh_b_daily(
symbol: str = "sh900901",
start_date: str = "19900101",
end_date: str = "21000118",
adjust: str = "",
) -> pd.DataFrame:
"""
新浪财经-B 股-个股的历史行情数据, 大量抓取容易封 IP
https://finance.sina.com.cn/realstock/company/sh689009/nc.shtml
:param start_date: 20201103; 开始日期
:type start_date: str
:param end_date: 20201103; 结束日期
:type end_date: str
:param symbol: sh600000
:type symbol: str
:param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子
:type adjust: str
:return: specific data
:rtype: pandas.DataFrame
"""
def _fq_factor(method: str) -> pd.DataFrame:
if method == "hfq":
res = requests.get(zh_sina_a_stock_hfq_url.format(symbol))
hfq_factor_df = pd.DataFrame(
eval(res.text.split("=")[1].split("\n")[0])["data"]
)
if hfq_factor_df.shape[0] == 0:
raise ValueError("sina hfq factor not available")
hfq_factor_df.columns = ["date", "hfq_factor"]
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df["date"]
hfq_factor_df.reset_index(inplace=True)
return hfq_factor_df
else:
res = requests.get(zh_sina_a_stock_qfq_url.format(symbol))
qfq_factor_df = pd.DataFrame(
eval(res.text.split("=")[1].split("\n")[0])["data"]
)
if qfq_factor_df.shape[0] == 0:
raise ValueError("sina hfq factor not available")
qfq_factor_df.columns = ["date", "qfq_factor"]
qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
del qfq_factor_df["date"]
qfq_factor_df.reset_index(inplace=True)
return qfq_factor_df
if adjust in ("hfq-factor", "qfq-factor"):
return _fq_factor(adjust.split("-")[0])
res = requests.get(zh_sina_a_stock_hist_url.format(symbol))
js_code = py_mini_racer.MiniRacer()
js_code.eval(hk_js_decode)
dict_list = js_code.call(
"d", res.text.split("=")[1].split(";")[0].replace('"', "")
) # 执行js解密代码
data_df = pd.DataFrame(dict_list)
data_df.index = pd.to_datetime(data_df["date"]).dt.date
del data_df["date"]
data_df = data_df.astype("float")
r = requests.get(zh_sina_a_stock_amount_url.format(symbol, symbol))
amount_data_json = demjson.decode(r.text[r.text.find("["): r.text.rfind("]") + 1])
amount_data_df = pd.DataFrame(amount_data_json)
amount_data_df.index = pd.to_datetime(amount_data_df.date)
del amount_data_df["date"]
temp_df = pd.merge(
data_df, amount_data_df, left_index=True, right_index=True, how="outer"
)
temp_df.fillna(method="ffill", inplace=True)
temp_df = temp_df.astype(float)
temp_df["amount"] = temp_df["amount"] * 10000
temp_df["turnover"] = temp_df["volume"] / temp_df["amount"]
temp_df.columns = [
"open",
"high",
"low",
"close",
"volume",
"outstanding_share",
"turnover",
]
if adjust == "":
temp_df = temp_df[start_date:end_date]
temp_df.drop_duplicates(subset=["open", "high", "low", "close", "volume"], inplace=True)
temp_df["open"] = round(temp_df["open"], 2)
temp_df["high"] = round(temp_df["high"], 2)
temp_df["low"] = round(temp_df["low"], 2)
temp_df["close"] = round(temp_df["close"], 2)
temp_df.dropna(inplace=True)
temp_df.drop_duplicates(inplace=True)
temp_df.reset_index(inplace=True)
return temp_df
if adjust == "hfq":
res = requests.get(zh_sina_a_stock_hfq_url.format(symbol))
hfq_factor_df = pd.DataFrame(
eval(res.text.split("=")[1].split("\n")[0])["data"]
)
hfq_factor_df.columns = ["date", "hfq_factor"]
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df["date"]
temp_df = pd.merge(
temp_df, hfq_factor_df, left_index=True, right_index=True, how="outer"
)
temp_df.fillna(method="ffill", inplace=True)
temp_df = temp_df.astype(float)
temp_df.dropna(inplace=True)
temp_df.drop_duplicates(subset=["open", "high", "low", "close", "volume"], inplace=True)
temp_df["open"] = temp_df["open"] * temp_df["hfq_factor"]
temp_df["high"] = temp_df["high"] * temp_df["hfq_factor"]
temp_df["close"] = temp_df["close"] * temp_df["hfq_factor"]
temp_df["low"] = temp_df["low"] * temp_df["hfq_factor"]
temp_df = temp_df.iloc[:, :-1]
temp_df = temp_df[start_date:end_date]
temp_df["open"] = round(temp_df["open"], 2)
temp_df["high"] = round(temp_df["high"], 2)
temp_df["low"] = round(temp_df["low"], 2)
temp_df["close"] = round(temp_df["close"], 2)
temp_df.dropna(inplace=True)
temp_df.reset_index(inplace=True)
return temp_df
if adjust == "qfq":
res = requests.get(zh_sina_a_stock_qfq_url.format(symbol))
qfq_factor_df = pd.DataFrame(
eval(res.text.split("=")[1].split("\n")[0])["data"]
)
qfq_factor_df.columns = ["date", "qfq_factor"]
qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
del qfq_factor_df["date"]
temp_df = pd.merge(
temp_df, qfq_factor_df, left_index=True, right_index=True, how="outer"
)
temp_df.fillna(method="ffill", inplace=True)
temp_df = temp_df.astype(float)
temp_df.dropna(inplace=True)
temp_df.drop_duplicates(subset=["open", "high", "low", "close", "volume"], inplace=True)
temp_df["open"] = temp_df["open"] / temp_df["qfq_factor"]
temp_df["high"] = temp_df["high"] / temp_df["qfq_factor"]
temp_df["close"] = temp_df["close"] / temp_df["qfq_factor"]
temp_df["low"] = temp_df["low"] / temp_df["qfq_factor"]
temp_df = temp_df.iloc[:, :-1]
temp_df = temp_df[start_date:end_date]
temp_df["open"] = round(temp_df["open"], 2)
temp_df["high"] = round(temp_df["high"], 2)
temp_df["low"] = round(temp_df["low"], 2)
temp_df["close"] = round(temp_df["close"], 2)
temp_df.dropna(inplace=True)
temp_df.reset_index(inplace=True)
return temp_df | [
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sunpy/sunpy | 528579df0a4c938c133bd08971ba75c131b189a7 | sunpy/extern/inflect.py | python | engine.plural | (self, text: str, count: Optional[Union[str, int]] = None) | return f"{pre}{plural}{post}" | Return the plural of text.
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fablabnbg/inkscape-silhouette | e0ad65115b4b2b0a5b60dfecd9a3ca03ec1d6bec | silhouette/pyusb-1.0.2/usb/util.py | python | claim_interface | (device, interface) | r"""Explicitly claim an interface.
PyUSB users normally do not have to worry about interface claiming,
as the library takes care of it automatically. But there are situations
where you need deterministic interface claiming. For these uncommon
cases, you can use claim_interface.
If the interface is already claimed, either through a previously call
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r"""Explicitly claim an interface.
PyUSB users normally do not have to worry about interface claiming,
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spulec/moto | a688c0032596a7dfef122b69a08f2bec3be2e481 | moto/route53resolver/models.py | python | Route53ResolverBackend._matched_arn | (self, resource_arn) | Given ARN, raise exception if there is no corresponding resource. | Given ARN, raise exception if there is no corresponding resource. | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/users/dbaccessors.py | python | get_mobile_user_count | (domain, include_inactive=True) | return sum([
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microsoft/azure-devops-python-api | 451cade4c475482792cbe9e522c1fee32393139e | azure-devops/azure/devops/released/service_hooks/service_hooks_client.py | python | ServiceHooksClient.get_publisher | (self, publisher_id) | return self._deserialize('Publisher', response) | GetPublisher.
Get a specific service hooks publisher.
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/vendor/distlib/_backport/shutil.py | python | _find_unpack_format | (filename) | return None | [] | def _find_unpack_format(filename):
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trezor/python-trezor | 2813522b05cef4e0e545a101f8b3559a3183b45b | trezorlib/firmware.py | python | update | (client, data) | [] | def update(client, data):
if client.features.bootloader_mode is False:
raise RuntimeError("Device must be in bootloader mode")
resp = client.call(messages.FirmwareErase(length=len(data)))
# TREZORv1 method
if isinstance(resp, messages.Success):
resp = client.call(messages.FirmwareUpload(payload=data))
if isinstance(resp, messages.Success):
return
else:
raise RuntimeError("Unexpected result %s" % resp)
# TREZORv2 method
while isinstance(resp, messages.FirmwareRequest):
payload = data[resp.offset : resp.offset + resp.length]
digest = pyblake2.blake2s(payload).digest()
resp = client.call(messages.FirmwareUpload(payload=payload, hash=digest))
if isinstance(resp, messages.Success):
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else:
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CedricGuillemet/Imogen | ee417b42747ed5b46cb11b02ef0c3630000085b3 | bin/Lib/bdb.py | python | Bdb.get_stack | (self, f, t) | return stack, i | Return a list of (frame, lineno) in a stack trace and a size.
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/twisted/twisted/internet/posixbase.py | python | PosixReactorBase.connectTCP | (self, host, port, factory, timeout=30, bindAddress=None) | return c | @see: twisted.internet.interfaces.IReactorTCP.connectTCP | [] | def connectTCP(self, host, port, factory, timeout=30, bindAddress=None):
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/scripts/sshbackdoors/backdoors/shell/pupy/pupy/packages/windows/amd64/psutil/__init__.py | python | Process.nice | (self, value=None) | Get or set process niceness (priority). | Get or set process niceness (priority). | [
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openembedded/openembedded-core | 9154f71c7267e9731156c1dfd57397103e9e6a2b | meta/lib/oe/recipeutils.py | python | split_var_value | (value, assignment=True) | return outlist | Split a space-separated variable's value into a list of items,
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The string value to split
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llSourcell/AI_Artist | 3038c06c2e389b9c919c881c9a169efe2fd7810e | lib/python2.7/site-packages/pip/_vendor/lockfile/__init__.py | python | LockBase.break_lock | (self) | Remove a lock. Useful if a locking thread failed to unlock. | Remove a lock. Useful if a locking thread failed to unlock. | [
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runawayhorse001/LearningApacheSpark | 67f3879dce17553195f094f5728b94a01badcf24 | pyspark/shuffle.py | python | _get_local_dirs | (sub) | return [os.path.join(d, "python", str(os.getpid()), sub) for d in dirs] | Get all the directories | Get all the directories | [
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path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")
dirs = path.split(",")
if len(dirs) > 1:
# different order in different processes and instances
rnd = random.Random(os.getpid() + id(dirs))
random.shuffle(dirs, rnd.random)
return [os.path.join(d, "python", str(os.getpid()), sub) for d in dirs] | [
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maas/maas | db2f89970c640758a51247c59bf1ec6f60cf4ab5 | src/provisioningserver/drivers/hardware/virsh.py | python | VirshSSH.get_arch | (self, machine) | return ARCH_FIX.get(arch, arch) | Gets the VM architecture. | Gets the VM architecture. | [
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] | def get_arch(self, machine):
"""Gets the VM architecture."""
output = self.get_machine_xml(machine)
if output is None:
maaslog.error("%s: Failed to get VM architecture", machine)
return None
doc = etree.XML(output)
evaluator = etree.XPathEvaluator(doc)
arch = evaluator(XPATH_ARCH)[0]
# Fix architectures that need to be referenced by a different
# name, that MAAS understands.
return ARCH_FIX.get(arch, arch) | [
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pyroscope/pyrocore | e8ededb6d9f3702ede6cd5396e6b473915637b64 | src/pyrocore/torrent/engine.py | python | FieldDefinition.lookup | (cls, name) | return {"matcher": field._matcher} if field else None | Try to find field C{name}.
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try:
field = cls.FIELDS[name]
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/core/data/parsers/pynarcissus/jsparser.py | python | tokenstr | (tt) | return t.upper() | [] | def tokenstr(tt):
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ansible-collections/community.general | 3faffe8f47968a2400ba3c896c8901c03001a194 | plugins/modules/system/cronvar.py | python | CronVar.write | (self, backup_file=None) | Write the crontab to the system. Saves all information. | Write the crontab to the system. Saves all information. | [
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"""
Write the crontab to the system. Saves all information.
"""
if backup_file:
fileh = open(backup_file, 'w')
elif self.cron_file:
fileh = open(self.cron_file, 'w')
else:
filed, path = tempfile.mkstemp(prefix='crontab')
fileh = os.fdopen(filed, 'w')
fileh.write(self.render())
fileh.close()
# return if making a backup
if backup_file:
return
# Add the entire crontab back to the user crontab
if not self.cron_file:
# quoting shell args for now but really this should be two non-shell calls. FIXME
(rc, out, err) = self.module.run_command(self._write_execute(path), use_unsafe_shell=True)
os.unlink(path)
if rc != 0:
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/core/decorators.py | python | call_highest_priority | (method_name) | return priority_decorator | A decorator for binary special methods to handle _op_priority.
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# Call other.__rmul__ if other._op_priority > self._op_priority
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def __mul__(self, other):
...
# Call other.__mul__ if other._op_priority > self._op_priority
@call_highest_priority('__mul__')
def __rmul__(self, other):
... | A decorator for binary special methods to handle _op_priority. | [
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"""A decorator for binary special methods to handle _op_priority.
Binary special methods in Expr and its subclasses use a special attribute
'_op_priority' to determine whose special method will be called to
handle the operation. In general, the object having the highest value of
'_op_priority' will handle the operation. Expr and subclasses that define
custom binary special methods (__mul__, etc.) should decorate those
methods with this decorator to add the priority logic.
The ``method_name`` argument is the name of the method of the other class
that will be called. Use this decorator in the following manner::
# Call other.__rmul__ if other._op_priority > self._op_priority
@call_highest_priority('__rmul__')
def __mul__(self, other):
...
# Call other.__mul__ if other._op_priority > self._op_priority
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def __rmul__(self, other):
...
"""
def priority_decorator(func):
@wraps(func)
def binary_op_wrapper(self, other):
if hasattr(other, '_op_priority'):
if other._op_priority > self._op_priority:
try:
f = getattr(other, method_name)
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pass
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return f(self)
return func(self, other)
return binary_op_wrapper
return priority_decorator | [
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wagtail/Willow | 31fe99cd4f5988aa4366629f7fcc6f2029b474da | willow/plugins/pillow.py | python | PillowImage.rotate | (self, angle) | return PillowImage(rotated) | Accept a multiple of 90 to pass to the underlying Pillow function
to rotate the image. | Accept a multiple of 90 to pass to the underlying Pillow function
to rotate the image. | [
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] | def rotate(self, angle):
"""
Accept a multiple of 90 to pass to the underlying Pillow function
to rotate the image.
"""
Image = _PIL_Image()
ORIENTATION_TO_TRANSPOSE = {
90: Image.ROTATE_90,
180: Image.ROTATE_180,
270: Image.ROTATE_270,
}
modulo_angle = angle % 360
# is we're rotating a multiple of 360, it's the same as a no-op
if not modulo_angle:
return self
transpose_code = ORIENTATION_TO_TRANSPOSE.get(modulo_angle)
if not transpose_code:
raise UnsupportedRotation(
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)
# We call "transpose", as it rotates the image,
# updating the height and width, whereas using 'rotate'
# only changes the contents of the image.
rotated = self.image.transpose(transpose_code)
return PillowImage(rotated) | [
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redhat-imaging/imagefactory | 176f6e045e1df049d50f33a924653128d5ab8b27 | imgfac/rest/bottle.py | python | MultiDict.__init__ | (self, *a, **k) | [] | def __init__(self, *a, **k):
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datitran/object_detector_app | 44e8eddeb931cced5d8cf1e283383c720a5706bf | object_detection/meta_architectures/faster_rcnn_meta_arch.py | python | FasterRCNNMetaArch._sample_box_classifier_minibatch | (self,
proposal_boxlist,
groundtruth_boxlist,
groundtruth_classes_with_background) | return box_list_ops.boolean_mask(proposal_boxlist, sampled_indices) | Samples a mini-batch of proposals to be sent to the box classifier.
Helper function for self._postprocess_rpn.
Args:
proposal_boxlist: A BoxList containing K proposal boxes in absolute
coordinates.
groundtruth_boxlist: A Boxlist containing N groundtruth object boxes in
absolute coordinates.
groundtruth_classes_with_background: A tensor with shape
`[N, self.num_classes + 1]` representing groundtruth classes. The
classes are assumed to be k-hot encoded, and include background as the
zero-th class.
Returns:
a BoxList contained sampled proposals. | Samples a mini-batch of proposals to be sent to the box classifier. | [
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proposal_boxlist,
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groundtruth_classes_with_background):
"""Samples a mini-batch of proposals to be sent to the box classifier.
Helper function for self._postprocess_rpn.
Args:
proposal_boxlist: A BoxList containing K proposal boxes in absolute
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groundtruth_boxlist: A Boxlist containing N groundtruth object boxes in
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groundtruth_classes_with_background: A tensor with shape
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Returns:
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"""
(cls_targets, cls_weights, _, _, _) = self._detector_target_assigner.assign(
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groundtruth_classes_with_background)
# Selects all boxes as candidates if none of them is selected according
# to cls_weights. This could happen as boxes within certain IOU ranges
# are ignored. If triggered, the selected boxes will still be ignored
# during loss computation.
cls_weights += tf.to_float(tf.equal(tf.reduce_sum(cls_weights), 0))
positive_indicator = tf.greater(tf.argmax(cls_targets, axis=1), 0)
sampled_indices = self._second_stage_sampler.subsample(
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return box_list_ops.boolean_mask(proposal_boxlist, sampled_indices) | [
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ilius/pyglossary | d599b3beda3ae17642af5debd83bb991148e6425 | pyglossary/ui/ui_gtk.py | python | FormatDialog.updateTree | (self) | [] | def updateTree(self):
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FederatedAI/FATE | 32540492623568ecd1afcb367360133616e02fa3 | python/federatedml/framework/homo/blocks/has_converged.py | python | Client.__init__ | (self, trans_var: HasConvergedTransVar = None) | [] | def __init__(self, trans_var: HasConvergedTransVar = None):
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trans_var = HasConvergedTransVar()
self._broadcaster = trans_var.has_converged
self._server_parties = trans_var.server_parties | [
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nfvlabs/openmano | b09eabec0a168aeda8adc3ea99f734e45e810205 | openvim/utils/RADclass.py | python | ProcessorNode.set_eligible_cores | (self) | return | Set the default eligible cores, this is all cores non used by the host operating system | Set the default eligible cores, this is all cores non used by the host operating system | [
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not_first = False
for iterator in self.cores:
if not_first:
self.eligible_cores.append(iterator)
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not_first = True
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rlgraph/rlgraph | 428fc136a9a075f29a397495b4226a491a287be2 | rlgraph/components/policies/dueling_policy.py | python | DuelingPolicy.get_adapter_outputs | (self, nn_inputs) | return dict(
nn_outputs=nn_outputs,
adapter_outputs=q_values,
advantages=advantages,
q_values=q_values
) | Args:
nn_inputs (any): The input to our neural network.
Returns:
Dict:
nn_outputs: The raw NN outputs.
adapter_outputs: The q-values after adding advantages to state values (and subtracting the
mean advantage).
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q_values: | Args:
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nn_outputs: The raw NN outputs.
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advantages:
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"""
nn_outputs = self.get_nn_outputs(nn_inputs)
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state_values_tmp = self.dense_layer_state_value_stream.call(nn_outputs)
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compas-dev/compas | 0b33f8786481f710115fb1ae5fe79abc2a9a5175 | src/compas/datastructures/halfedge/halfedge.py | python | HalfEdge.face_vertices | (self, fkey) | return self.face[fkey] | The vertices of a face.
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fkey : int
Identifier of the face.
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-------
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Ordered vertex identifiers. | The vertices of a face. | [
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fkey : int
Identifier of the face.
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list
Ordered vertex identifiers.
"""
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dulwich/dulwich | 1f66817d712e3563ce1ff53b1218491a2eae39da | dulwich/protocol.py | python | Protocol.send_cmd | (self, cmd, *args) | Send a command and some arguments to a git server.
Only used for the TCP git protocol (git://).
Args:
cmd: The remote service to access.
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Only used for the TCP git protocol (git://).
Args:
cmd: The remote service to access.
args: List of arguments to send to remove service.
"""
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ManiacalLabs/BiblioPixel | afb993fbbe56e75e7c98f252df402b0f3e83bb6e | bibliopixel/builder/sections.py | python | set_one | (desc, name, value) | Set one section in a Project description | Set one section in a Project description | [
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if old_value is None:
raise KeyError('No section "%s"' % name)
if value is None:
value = type(old_value)()
elif name in CLASS_SECTIONS:
if isinstance(value, str):
value = {'typename': aliases.resolve(value)}
elif isinstance(value, type):
value = {'typename': class_name.class_name(value)}
elif not isinstance(value, dict):
raise TypeError('Expected dict, str or type, got "%s"' % value)
typename = value.get('typename')
if typename:
s = 's' if name == 'driver' else ''
path = 'bibliopixel.' + name + s
importer.import_symbol(typename, path)
elif name == 'shape':
if not isinstance(value, (list, int, tuple, str)):
raise TypeError('Expected shape, got "%s"' % value)
elif type(old_value) is not type(value):
raise TypeError('Expected %s but got "%s" of type %s' %
(type(old_value), value, type(value)))
desc[name] = value | [
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... | https://github.com/ManiacalLabs/BiblioPixel/blob/afb993fbbe56e75e7c98f252df402b0f3e83bb6e/bibliopixel/builder/sections.py#L8-L39 | ||
analyticalmindsltd/smote_variants | dedbc3d00b266954fedac0ae87775e1643bc920a | smote_variants/_smote_variants.py | python | Evaluation.do_evaluation | (self) | return list(evaluations.values()) | Does the evaluation or reads it from file
Returns:
dict: all metrics | Does the evaluation or reads it from file | [
"Does",
"the",
"evaluation",
"or",
"reads",
"it",
"from",
"file"
] | def do_evaluation(self):
"""
Does the evaluation or reads it from file
Returns:
dict: all metrics
"""
if self.n_threads is not None:
try:
import mkl
mkl.set_num_threads(self.n_threads)
message = " mkl thread number set to %d successfully"
message = message % self.n_threads
_logger.info(self.__class__.__name__ + message)
except Exception as e:
message = " setting mkl thread number didn't succeed"
_logger.info(self.__class__.__name__ + message)
evaluations = {}
if os.path.isfile(os.path.join(self.cache_path, self.filename)):
evaluations = pickle.load(
open(os.path.join(self.cache_path, self.filename), 'rb'))
already_evaluated = np.array([li in evaluations for li in self.labels])
if not np.all(already_evaluated):
samp = self.sampling.do_sampling()
else:
return list(evaluations.values())
# setting random states
for i in range(len(self.classifiers)):
clf_params = self.classifiers[i].get_params()
if 'random_state' in clf_params:
clf_params['random_state'] = self.random_state
self.classifiers[i] = self.classifiers[i].__class__(
**clf_params)
if isinstance(self.classifiers[i], CalibratedClassifierCV):
clf_params = self.classifiers[i].base_estimator.get_params()
clf_params['random_state'] = self.random_state
class_inst = self.classifiers[i].base_estimator.__class__
new_inst = class_inst(**clf_params)
self.classifiers[i].base_estimator = new_inst
for i in range(len(self.classifiers)):
if not already_evaluated[i]:
message = " do the evaluation %s %s %s"
message = message % (self.sampling.db_name,
self.sampling.sampler.__name__,
self.classifiers[i].__class__.__name__)
_logger.info(self.__class__.__name__ + message)
all_preds, all_tests, all_folds = [], [], []
minority_class_label = None
majority_class_label = None
fold_idx = -1
for X_train, y_train, X_test, y_test in samp['sampling']:
fold_idx += 1
# X_train[X_train == np.inf]= 0
# X_train[X_train == -np.inf]= 0
# X_test[X_test == np.inf]= 0
# X_test[X_test == -np.inf]= 0
class_labels = np.unique(y_train)
min_class_size = np.min(
[np.sum(y_train == c) for c in class_labels])
ss = StandardScaler()
X_train_trans = ss.fit_transform(X_train)
nonzero_var_idx = np.where(ss.var_ > 1e-8)[0]
X_test_trans = ss.transform(X_test)
enough_minority_samples = min_class_size > 4
y_train_big_enough = len(y_train) > 4
two_classes = len(class_labels) > 1
at_least_one_feature = (len(nonzero_var_idx) > 0)
if not enough_minority_samples:
message = " not enough minority samples: %d"
message = message % min_class_size
_logger.warning(
self.__class__.__name__ + message)
elif not y_train_big_enough:
message = (" number of minority training samples is "
"not enough: %d")
message = message % len(y_train)
_logger.warning(self.__class__.__name__ + message)
elif not two_classes:
message = " there is only 1 class in training data"
_logger.warning(self.__class__.__name__ + message)
elif not at_least_one_feature:
_logger.warning(self.__class__.__name__ +
(" no information in features"))
else:
all_tests.append(y_test)
if (minority_class_label is None or
majority_class_label is None):
class_labels = np.unique(y_train)
n_0 = sum(class_labels[0] == y_test)
n_1 = sum(class_labels[1] == y_test)
if n_0 < n_1:
minority_class_label = int(class_labels[0])
majority_class_label = int(class_labels[1])
else:
minority_class_label = int(class_labels[1])
majority_class_label = int(class_labels[0])
X_fit = X_train_trans[:, nonzero_var_idx]
self.classifiers[i].fit(X_fit, y_train)
clf = self.classifiers[i]
X_pred = X_test_trans[:, nonzero_var_idx]
pred = clf.predict_proba(X_pred)
all_preds.append(pred)
all_folds.append(
np.repeat(fold_idx, len(all_preds[-1])))
if len(all_tests) > 0:
all_preds = np.vstack(all_preds)
all_tests = np.hstack(all_tests)
all_folds = np.hstack(all_folds)
evaluations[self.labels[i]] = self.calculate_metrics(
all_preds, all_tests, all_folds)
else:
evaluations[self.labels[i]] = self.calculate_metrics(
None, None, None)
evaluations[self.labels[i]]['runtime'] = samp['runtime']
sampler_name = self.sampling.sampler.__name__
evaluations[self.labels[i]]['sampler'] = sampler_name
clf_name = self.classifiers[i].__class__.__name__
evaluations[self.labels[i]]['classifier'] = clf_name
sampler_parameters = self.sampling.sampler_parameters.copy()
evaluations[self.labels[i]]['sampler_parameters'] = str(
sampler_parameters)
evaluations[self.labels[i]]['classifier_parameters'] = str(
self.classifiers[i].get_params())
evaluations[self.labels[i]]['sampler_categories'] = str(
self.sampling.sampler.categories)
evaluations[self.labels[i]
]['db_name'] = self.sampling.folding.db_name
evaluations[self.labels[i]]['db_size'] = samp['db_size']
evaluations[self.labels[i]]['db_n_attr'] = samp['db_n_attr']
evaluations[self.labels[i]
]['imbalanced_ratio'] = samp['imbalanced_ratio']
if not np.all(already_evaluated):
_logger.info(self.__class__.__name__ +
(" dumping to file %s" % self.filename))
random_filename = os.path.join(self.cache_path, str(
np.random.randint(1000000)) + '.pickle')
pickle.dump(evaluations, open(random_filename, "wb"))
os.rename(random_filename, os.path.join(
self.cache_path, self.filename))
return list(evaluations.values()) | [
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"\" mkl thread number set to %d successfull... | https://github.com/analyticalmindsltd/smote_variants/blob/dedbc3d00b266954fedac0ae87775e1643bc920a/smote_variants/_smote_variants.py#L19826-L19983 | |
google/closure-linter | c09c885b4e4fec386ff81cebeb8c66c2b0643d49 | closure_linter/aliaspass.py | python | AliasPass._ProcessRootContext | (self, root_context) | Processes all goog.scope blocks under the root context. | Processes all goog.scope blocks under the root context. | [
"Processes",
"all",
"goog",
".",
"scope",
"blocks",
"under",
"the",
"root",
"context",
"."
] | def _ProcessRootContext(self, root_context):
"""Processes all goog.scope blocks under the root context."""
assert root_context.type is ecmametadatapass.EcmaContext.ROOT
# Process aliases in statements in the root scope for goog.module-style
# aliases.
global_alias_map = {}
for context in root_context.children:
if context.type == ecmametadatapass.EcmaContext.STATEMENT:
for statement_child in context.children:
if statement_child.type == ecmametadatapass.EcmaContext.VAR:
match = scopeutil.MatchModuleAlias(statement_child)
if match:
# goog.require aliases cannot use further aliases, the symbol is
# the second part of match, directly.
symbol = match[1]
if scopeutil.IsInClosurizedNamespace(symbol,
self._closurized_namespaces):
global_alias_map[match[0]] = symbol
# Process each block to find aliases.
for context in root_context.children:
self._ProcessBlock(context, global_alias_map) | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.5/django/contrib/formtools/wizard/views.py | python | WizardView.get_form_step_files | (self, form) | return form.files | Is used to return the raw form files. You may use this method to
manipulate the data. | Is used to return the raw form files. You may use this method to
manipulate the data. | [
"Is",
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"to",
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"the",
"data",
"."
] | def get_form_step_files(self, form):
"""
Is used to return the raw form files. You may use this method to
manipulate the data.
"""
return form.files | [
"def",
"get_form_step_files",
"(",
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")",
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] | https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/lib/django-1.5/django/contrib/formtools/wizard/views.py#L431-L436 |
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