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jobovy/galpy | 8e6a230bbe24ce16938db10053f92eb17fe4bb52 | galpy/actionAngle/actionAngleAdiabatic.py | python | actionAngleAdiabatic.calczmax | (self,*args,**kwargs) | return aAAxi.calczmax(**kwargs) | NAME:
calczmax
PURPOSE:
calculate the maximum height
INPUT:
Either:
a) R,vR,vT,z,vz
b) Orbit instance: initial condition used if that's it, orbit(t)
if there is a time given as well
OUTPUT:
zmax
HISTORY:
2012-06-01 - Written - Bovy (IAS) | NAME:
calczmax
PURPOSE:
calculate the maximum height
INPUT:
Either:
a) R,vR,vT,z,vz
b) Orbit instance: initial condition used if that's it, orbit(t)
if there is a time given as well
OUTPUT:
zmax
HISTORY:
2012-06-01 - Written - Bovy (IAS) | [
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"""
NAME:
calczmax
PURPOSE:
calculate the maximum height
INPUT:
Either:
a) R,vR,vT,z,vz
b) Orbit instance: initial condition used if that's it, orbit(t)
if there is a time given as well
OUTPUT:
zmax
HISTORY:
2012-06-01 - Written - Bovy (IAS)
"""
warnings.warn("actionAngleAdiabatic.calczmax function will soon be deprecated; please contact galpy's maintainer if you require this function")
#Set up the actionAngleAxi object
self._parse_eval_args(*args)
thispot= toPlanarPotential(self._pot)
thisverticalpot= toVerticalPotential(self._pot,self._eval_R)
aAAxi= actionAngleAxi(*args,pot=thispot,
verticalPot=thisverticalpot,
gamma=self._gamma)
return aAAxi.calczmax(**kwargs) | [
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ansible/ansible-modules-core | 00911a75ad6635834b6d28eef41f197b2f73c381 | packaging/os/yum.py | python | local_nvra | (module, path) | return '%s-%s-%s.%s' % (header[rpm.RPMTAG_NAME],
header[rpm.RPMTAG_VERSION],
header[rpm.RPMTAG_RELEASE],
header[rpm.RPMTAG_ARCH]) | return nvra of a local rpm passed in | return nvra of a local rpm passed in | [
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ts = rpm.TransactionSet()
ts.setVSFlags(rpm._RPMVSF_NOSIGNATURES)
fd = os.open(path, os.O_RDONLY)
try:
header = ts.hdrFromFdno(fd)
finally:
os.close(fd)
return '%s-%s-%s.%s' % (header[rpm.RPMTAG_NAME],
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mrkipling/maraschino | c6be9286937783ae01df2d6d8cebfc8b2734a7d7 | lib/sqlalchemy/event.py | python | remove | (target, identifier, fn) | Remove an event listener.
Note that some event removals, particularly for those event dispatchers
which create wrapper functions and secondary even listeners, may not yet
be supported. | Remove an event listener. | [
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"""Remove an event listener.
Note that some event removals, particularly for those event dispatchers
which create wrapper functions and secondary even listeners, may not yet
be supported.
"""
for evt_cls in _registrars[identifier]:
for tgt in evt_cls._accept_with(target):
tgt.dispatch._remove(identifier, tgt, fn, *args, **kw)
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v7/services/services/feed_item_target_service/client.py | python | FeedItemTargetServiceClientMeta.get_transport_class | (
cls, label: str = None,
) | return next(iter(cls._transport_registry.values())) | Return an appropriate transport class.
Args:
label: The name of the desired transport. If none is
provided, then the first transport in the registry is used.
Returns:
The transport class to use. | Return an appropriate transport class. | [
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cls, label: str = None,
) -> Type[FeedItemTargetServiceTransport]:
"""Return an appropriate transport class.
Args:
label: The name of the desired transport. If none is
provided, then the first transport in the registry is used.
Returns:
The transport class to use.
"""
# If a specific transport is requested, return that one.
if label:
return cls._transport_registry[label]
# No transport is requested; return the default (that is, the first one
# in the dictionary).
return next(iter(cls._transport_registry.values())) | [
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garywiz/chaperone | 9ff2c3a5b9c6820f8750320a564ea214042df06f | chaperone/cproc/pt/inetd.py | python | InetdService._create_server | (self) | return asyncio.get_event_loop().create_server(InetdServiceProtocol.buildProtocol(self, process=self.process),
'0.0.0.0',
self.process.port) | [] | def _create_server(self):
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weinbe58/QuSpin | 5bbc3204dbf5c227a87a44f0dacf39509cba580c | quspin/basis/basis_general/fermion.py | python | spinful_fermion_basis_general.__init__ | (self,N,Nf=None,nf=None,Ns_block_est=None,simple_symm=True,make_basis=True,block_order=None,double_occupancy=True,**blocks) | Intializes the `spinful_fermion_basis_general` object (basis for fermionic operators).
Parameters
-----------
L: int
Length of chain/number of sites.
Nf: tuple(int), optional
Number of fermions in chain. Can be tupe of integers or list of tuples of integers `[(Nup,Ndown),...]` to specify one or more particle sectors.
nf: float, optional
Density of fermions in chain (fermions per site).
Ns_block_est: int, optional
Overwrites the internal estimate of the size of the reduced Hilbert space for the given symmetries. This can be used to help conserve memory if the exact size of the H-space is known ahead of time.
simple_sym: bool, optional
Flags whidh toggles the setting for the types of mappings and operator strings the basis will use. See this tutorial for more details.
make_basis: bool, optional
Boolean to control whether to make the basis. Allows the use to use some functionality of the basis constructor without constructing the entire basis.
double_occupancy: bool, optional
Boolean to toggle the presence of doubly-occupied sites (both a spin up and a spin-down fermion present on the same lattice site) in the basis. Default is `double_occupancy=True`, for which doubly-occupied states are present.
block_order: list of strings, optional
A list of strings containing the names of the symmetry blocks which specifies the order in which the symmetries will be applied to the state when calculating the basis. The first element in the list is applied to the state first followed by the second element, etc. If the list is not specificed the ordering is such that the symmetry with the largest cycle is the first, followed by the second largest, etc.
**blocks: optional
keyword arguments which pass the symmetry generator arrays. For instance:
>>> basis(...,kxblock=(Q,q),...)
The keys of the symmetry sector, e.g. `kxblock`, can be chosen arbitrarily by the user. The
values are tuples where the first entry contains the symmetry transformation :math:`Q` acting on the
lattice sites (see class example), and the second entry is an integer :math:`q` to label the symmetry
sector. | Intializes the `spinful_fermion_basis_general` object (basis for fermionic operators). | [
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"""Intializes the `spinful_fermion_basis_general` object (basis for fermionic operators).
Parameters
-----------
L: int
Length of chain/number of sites.
Nf: tuple(int), optional
Number of fermions in chain. Can be tupe of integers or list of tuples of integers `[(Nup,Ndown),...]` to specify one or more particle sectors.
nf: float, optional
Density of fermions in chain (fermions per site).
Ns_block_est: int, optional
Overwrites the internal estimate of the size of the reduced Hilbert space for the given symmetries. This can be used to help conserve memory if the exact size of the H-space is known ahead of time.
simple_sym: bool, optional
Flags whidh toggles the setting for the types of mappings and operator strings the basis will use. See this tutorial for more details.
make_basis: bool, optional
Boolean to control whether to make the basis. Allows the use to use some functionality of the basis constructor without constructing the entire basis.
double_occupancy: bool, optional
Boolean to toggle the presence of doubly-occupied sites (both a spin up and a spin-down fermion present on the same lattice site) in the basis. Default is `double_occupancy=True`, for which doubly-occupied states are present.
block_order: list of strings, optional
A list of strings containing the names of the symmetry blocks which specifies the order in which the symmetries will be applied to the state when calculating the basis. The first element in the list is applied to the state first followed by the second element, etc. If the list is not specificed the ordering is such that the symmetry with the largest cycle is the first, followed by the second largest, etc.
**blocks: optional
keyword arguments which pass the symmetry generator arrays. For instance:
>>> basis(...,kxblock=(Q,q),...)
The keys of the symmetry sector, e.g. `kxblock`, can be chosen arbitrarily by the user. The
values are tuples where the first entry contains the symmetry transformation :math:`Q` acting on the
lattice sites (see class example), and the second entry is an integer :math:`q` to label the symmetry
sector.
"""
# Nf = [(Nup,Ndown),...]
# Nup is left side of basis sites 0 - N-1
# Ndown is right side of basis sites N - 2*N-1
_Np = blocks.get("_Np")
if _Np is not None:
blocks.pop("_Np")
if Nf is not None and nf is not None:
raise ValueError("cannot use 'nf' and 'Nf' simultaineously.")
if Nf is None and nf is not None:
Nf = (int(nf[0]*N),int(nf[1]*N))
if any((type(map) is not tuple) and (len(map)!=2) for map in blocks.values() if map is not None):
raise ValueError("blocks must contain tuple: (map,q).")
self._simple_symm = simple_symm
if simple_symm:
if any(len(item[0]) != N for item in blocks.values() if item is not None):
raise ValueError("Simple symmetries must have mapping of length N.")
else:
if any(len(item[0]) != 2*N for item in blocks.values() if item is not None):
raise ValueError("Simple symmetries must have mapping of length N.")
new_blocks = {key:process_spinful_map(N,*item) for key,item in blocks.items() if item is not None}
blocks.update(new_blocks)
basis_general.__init__(self,2*N,block_order=block_order,**blocks)
# self._check_pcon = False
self._count_particles = False
if _Np is not None and Nf is None:
self._count_particles = True
if type(_Np) is not int:
raise ValueError("_Np must be integer")
if _Np >= -1:
if _Np+1 > N:
Nf = []
for n in range(N+1):
Nf.extend((n-i,i)for i in range(n+1))
Nf = tuple(Nf)
elif _Np==-1:
Nf = None
else:
Nf=[]
for n in range(_Np+1):
Nf.extend((n-i,i)for i in range(n+1))
Nf = tuple(Nf)
else:
raise ValueError("_Np == -1 for no particle conservation, _Np >= 0 for particle conservation")
if Nf is None:
self._Ns = (1<<N)**2
else:
if type(Nf) is tuple:
if len(Nf)==2:
self._get_proj_pcon = True
Nup,Ndown = Nf
if (type(Nup) is int) and type(Ndown) is int:
self._Ns = comb(N,Nup,exact=True)*comb(N,Ndown,exact=True)
else:
raise ValueError("Nf must be tuple of integers or iterable object of tuples.")
Nf = [Nf]
else:
Nf = list(Nf)
if any((type(tup)is not tuple) and len(tup)!=2 for tup in Nf):
raise ValueError("Nf must be tuple of integers or iterable object of tuples.")
self._Ns = 0
for Nup,Ndown in Nf:
if Nup > N or Nup < 0:
raise ValueError("particle numbers in Nf must satisfy: 0 <= n <= N")
if Ndown > N or Ndown < 0:
raise ValueError("particle numbers in Nf must satisfy: 0 <= n <= N")
self._Ns += comb(N,Nup,exact=True)*comb(N,Ndown,exact=True)
else:
try:
Nf_iter = iter(Nf)
except TypeError:
raise ValueError("Nf must be tuple of integers or iterable object of tuples.")
if any((type(tup)is not tuple) and len(tup)!=2 for tup in Nf):
raise ValueError("Nf must be tuple of integers or iterable object of tuples.")
Nf = list(Nf)
self._Ns = 0
for Nup,Ndown in Nf:
if Nup > N or Nup < 0:
raise ValueError("particle numbers in Nf must satisfy: 0 <= n <= N")
if Ndown > N or Ndown < 0:
raise ValueError("particle numbers in Nf must satisfy: 0 <= n <= N")
self._Ns += comb(N,Nup,exact=True)*comb(N,Ndown,exact=True)
self._pcon_args = dict(N=N,Nf=Nf)
if len(self._pers)>0 or not double_occupancy:
if Ns_block_est is None:
self._Ns = int(float(self._Ns)/_np.multiply.reduce(self._pers))*4
else:
if type(Ns_block_est) is not int:
raise TypeError("Ns_block_est must be integer value.")
if Ns_block_est <= 0:
raise ValueError("Ns_block_est must be an integer > 0")
self._Ns = Ns_block_est
self._basis_dtype = get_basis_type(2*N,None,2)
self._core = spinful_fermion_basis_core_wrap(self._basis_dtype,N,self._maps,self._pers,self._qs,double_occupancy)
self._sps = 2
self._N = 2*N
self._Ns_block_est=self._Ns
self._Np = Nf
self._double_occupancy = double_occupancy
self._noncommuting_bits = [(_np.arange(self.N),_np.array(-1,dtype=_np.int8))]
# make the basis; make() is function method of base_general
if make_basis:
self.make()
else:
self._Ns=1
self._basis=basis_zeros(self._Ns,dtype=self._basis_dtype)
self._n=_np.zeros(self._Ns,dtype=self._n_dtype)
self._operators = ("availible operators for ferion_basis_1d:"+
"\n\tI: identity "+
"\n\t+: raising operator"+
"\n\t-: lowering operator"+
"\n\tn: number operator"+
"\n\tz: c-symm number operator"+
"\n\tx: majorana x-operator"+
"\n\ty: majorana y-operator")
self._allowed_ops=set(["I","n","+","-","z","x","y"]) | [
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yangxudong/deeplearning | 1b925c2171902d9a352d3747495030e058220204 | telepath/mobilenet_v2.py | python | mobilenet | (input_tensor,
num_classes=1001,
depth_multiplier=1.0,
scope='MobilenetV2',
conv_defs=None,
finegrain_classification_mode=False,
min_depth=None,
divisible_by=None,
**kwargs) | Creates mobilenet V2 network.
Inference mode is created by default. To create training use training_scope
below.
with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
Args:
input_tensor: The input tensor
num_classes: number of classes
depth_multiplier: The multiplier applied to scale number of
channels in each layer. Note: this is called depth multiplier in the
paper but the name is kept for consistency with slim's model builder.
scope: Scope of the operator
conv_defs: Allows to override default conv def.
finegrain_classification_mode: When set to True, the model
will keep the last layer large even for small multipliers. Following
https://arxiv.org/abs/1801.04381
suggests that it improves performance for ImageNet-type of problems.
*Note* ignored if final_endpoint makes the builder exit earlier.
min_depth: If provided, will ensure that all layers will have that
many channels after application of depth multiplier.
divisible_by: If provided will ensure that all layers # channels
will be divisible by this number.
**kwargs: passed directly to mobilenet.mobilenet:
prediction_fn- what prediction function to use.
reuse-: whether to reuse variables (if reuse set to true, scope
must be given).
Returns:
logits/endpoints pair
Raises:
ValueError: On invalid arguments | Creates mobilenet V2 network. | [
"Creates",
"mobilenet",
"V2",
"network",
"."
] | def mobilenet(input_tensor,
num_classes=1001,
depth_multiplier=1.0,
scope='MobilenetV2',
conv_defs=None,
finegrain_classification_mode=False,
min_depth=None,
divisible_by=None,
**kwargs):
"""Creates mobilenet V2 network.
Inference mode is created by default. To create training use training_scope
below.
with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
Args:
input_tensor: The input tensor
num_classes: number of classes
depth_multiplier: The multiplier applied to scale number of
channels in each layer. Note: this is called depth multiplier in the
paper but the name is kept for consistency with slim's model builder.
scope: Scope of the operator
conv_defs: Allows to override default conv def.
finegrain_classification_mode: When set to True, the model
will keep the last layer large even for small multipliers. Following
https://arxiv.org/abs/1801.04381
suggests that it improves performance for ImageNet-type of problems.
*Note* ignored if final_endpoint makes the builder exit earlier.
min_depth: If provided, will ensure that all layers will have that
many channels after application of depth multiplier.
divisible_by: If provided will ensure that all layers # channels
will be divisible by this number.
**kwargs: passed directly to mobilenet.mobilenet:
prediction_fn- what prediction function to use.
reuse-: whether to reuse variables (if reuse set to true, scope
must be given).
Returns:
logits/endpoints pair
Raises:
ValueError: On invalid arguments
"""
if conv_defs is None:
conv_defs = V2_DEF
if 'multiplier' in kwargs:
raise ValueError('mobilenetv2 doesn\'t support generic '
'multiplier parameter use "depth_multiplier" instead.')
if finegrain_classification_mode:
conv_defs = copy.deepcopy(conv_defs)
if depth_multiplier < 1:
conv_defs['spec'][-1].params['num_outputs'] /= depth_multiplier
depth_args = {}
# NB: do not set depth_args unless they are provided to avoid overriding
# whatever default depth_multiplier might have thanks to arg_scope.
if min_depth is not None:
depth_args['min_depth'] = min_depth
if divisible_by is not None:
depth_args['divisible_by'] = divisible_by
with slim.arg_scope((lib.depth_multiplier,), **depth_args):
return lib.mobilenet(
input_tensor,
num_classes=num_classes,
conv_defs=conv_defs,
scope=scope,
multiplier=depth_multiplier,
**kwargs) | [
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galaxyproject/galaxy | 4c03520f05062e0f4a1b3655dc0b7452fda69943 | lib/galaxy/web_stack/__init__.py | python | ApplicationStack._set_default_job_handler_assignment_methods | (self, job_config, base_pool) | Override in subclasses to set default job handler assignment methods if not explicitly configured by the administrator.
Called once per job_config. | Override in subclasses to set default job handler assignment methods if not explicitly configured by the administrator. | [
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] | def _set_default_job_handler_assignment_methods(self, job_config, base_pool):
"""Override in subclasses to set default job handler assignment methods if not explicitly configured by the administrator.
Called once per job_config.
""" | [
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web2py/web2py | 095905c4e010a1426c729483d912e270a51b7ba8 | gluon/contrib/login_methods/openid_auth.py | python | OpenIDAuth._form_with_notification | (self) | return lambda: form | Render the form for normal login with a notice of OpenID authenticated | Render the form for normal login with a notice of OpenID authenticated | [
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"""
Render the form for normal login with a notice of OpenID authenticated
"""
form = DIV()
# TODO: check when will happen
if self.auth.settings.login_form in (self.auth, self):
self.auth.settings.login_form = self.auth
form = DIV(self.auth())
register_note = DIV(P(self.messages.p_openid_not_registered))
form.components.append(register_note)
return lambda: form | [
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fooying/3102 | 0faee38c30b2e24154f41e68457cfd8f7a61c040 | thirdparty/dns/tokenizer.py | python | Tokenizer.get_identifier | (self, origin=None) | return token.value | Read the next token and raise an exception if it is not an identifier.
@raises dns.exception.SyntaxError:
@rtype: string | Read the next token and raise an exception if it is not an identifier. | [
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"""Read the next token and raise an exception if it is not an identifier.
@raises dns.exception.SyntaxError:
@rtype: string
"""
token = self.get().unescape()
if not token.is_identifier():
raise dns.exception.SyntaxError('expecting an identifier')
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/plistlib.py | python | _is_fmt_xml | (header) | return False | [] | def _is_fmt_xml(header):
prefixes = (b'<?xml', b'<plist')
for pfx in prefixes:
if header.startswith(pfx):
return True
# Also check for alternative XML encodings, this is slightly
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#(codecs.BOM_UTF32_BE, "utf-32-be"),
#(codecs.BOM_UTF32_LE, "utf-32-le"),
):
if not header.startswith(bom):
continue
for start in prefixes:
prefix = bom + start.decode('ascii').encode(encoding)
if header[:len(prefix)] == prefix:
return True
return False | [
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Blizzard/heroprotocol | 3d36eaf44fc4c8ff3331c2ae2f1dc08a94535f1c | heroprotocol/versions/protocol52351.py | python | decode_replay_game_events | (contents) | Decodes and yields each game event from the contents byte string. | Decodes and yields each game event from the contents byte string. | [
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] | def decode_replay_game_events(contents):
"""Decodes and yields each game event from the contents byte string."""
decoder = BitPackedDecoder(contents, typeinfos)
for event in _decode_event_stream(decoder,
game_eventid_typeid,
game_event_types,
decode_user_id=True):
yield event | [
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certtools/intelmq | 7c1ab88b77bcbb30c7cabca5bb3525ad4aaac138 | intelmq/lib/bot.py | python | Bot.__handle_sighup_signal | (self, signum: int, stack: Optional[object]) | Called when signal is received and postpone. | Called when signal is received and postpone. | [
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Called when signal is received and postpone.
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self.__sighup.set()
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/venv/lib/python3.7/site-packages/pip/_vendor/distlib/metadata.py | python | LegacyMetadata.is_field | (self, name) | return name in _ALL_FIELDS | return True if name is a valid metadata key | return True if name is a valid metadata key | [
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"""return True if name is a valid metadata key"""
name = self._convert_name(name)
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loicland/superpoint_graph | 9dfa370542aed1f817c6267e5af7921747da30a6 | partition/provider.py | python | object_name_to_label | (object_class) | return object_label | convert from object name in S3DIS to an int | convert from object name in S3DIS to an int | [
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"""convert from object name in S3DIS to an int"""
object_label = {
'ceiling': 1,
'floor': 2,
'wall': 3,
'column': 4,
'beam': 5,
'window': 6,
'door': 7,
'table': 8,
'chair': 9,
'bookcase': 10,
'sofa': 11,
'board': 12,
'clutter': 13,
'stairs': 0,
}.get(object_class, 0)
return object_label | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_openshift/library/oc_user.py | python | Yedit.update | (self, path, value, index=None, curr_value=None) | return (False, self.yaml_dict) | put path, value into a dict | put path, value into a dict | [
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''' put path, value into a dict '''
try:
entry = Yedit.get_entry(self.yaml_dict, path, self.separator)
except KeyError:
entry = None
if isinstance(entry, dict):
# AUDIT:maybe-no-member makes sense due to fuzzy types
# pylint: disable=maybe-no-member
if not isinstance(value, dict):
raise YeditException('Cannot replace key, value entry in dict with non-dict type. ' +
'value=[{}] type=[{}]'.format(value, type(value)))
entry.update(value)
return (True, self.yaml_dict)
elif isinstance(entry, list):
# AUDIT:maybe-no-member makes sense due to fuzzy types
# pylint: disable=maybe-no-member
ind = None
if curr_value:
try:
ind = entry.index(curr_value)
except ValueError:
return (False, self.yaml_dict)
elif index is not None:
ind = index
if ind is not None and entry[ind] != value:
entry[ind] = value
return (True, self.yaml_dict)
# see if it exists in the list
try:
ind = entry.index(value)
except ValueError:
# doesn't exist, append it
entry.append(value)
return (True, self.yaml_dict)
# already exists, return
if ind is not None:
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return (False, self.yaml_dict) | [
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NVIDIA/DeepLearningExamples | 589604d49e016cd9ef4525f7abcc9c7b826cfc5e | TensorFlow2/Classification/ConvNets/efficientnet/utils/augment.py | python | unwrap | (image: tf.Tensor, replace: int) | return image | Unwraps an image produced by wrap.
Where there is a 0 in the last channel for every spatial position,
the rest of the three channels in that spatial dimension are grayed
(set to 128). Operations like translate and shear on a wrapped
Tensor will leave 0s in empty locations. Some transformations look
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Args:
image: A 3D Image Tensor with 4 channels.
replace: A one or three value 1D tensor to fill empty pixels.
Returns:
image: A 3D image Tensor with 3 channels. | Unwraps an image produced by wrap. | [
"Unwraps",
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] | def unwrap(image: tf.Tensor, replace: int) -> tf.Tensor:
"""Unwraps an image produced by wrap.
Where there is a 0 in the last channel for every spatial position,
the rest of the three channels in that spatial dimension are grayed
(set to 128). Operations like translate and shear on a wrapped
Tensor will leave 0s in empty locations. Some transformations look
at the intensity of values to do preprocessing, and we want these
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Args:
image: A 3D Image Tensor with 4 channels.
replace: A one or three value 1D tensor to fill empty pixels.
Returns:
image: A 3D image Tensor with 3 channels.
"""
image_shape = tf.shape(image)
# Flatten the spatial dimensions.
flattened_image = tf.reshape(image, [-1, image_shape[2]])
# Find all pixels where the last channel is zero.
alpha_channel = tf.expand_dims(flattened_image[:, 3], axis=-1)
replace = tf.concat([replace, tf.ones([1], image.dtype)], 0)
# Where they are zero, fill them in with 'replace'.
flattened_image = tf.where(
tf.equal(alpha_channel, 0),
tf.ones_like(flattened_image, dtype=image.dtype) * replace,
flattened_image)
image = tf.reshape(flattened_image, image_shape)
image = tf.slice(image, [0, 0, 0], [image_shape[0], image_shape[1], 3])
return image | [
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pandas-dev/pandas | 5ba7d714014ae8feaccc0dd4a98890828cf2832d | pandas/core/apply.py | python | Apply.normalize_dictlike_arg | (
self, how: str, obj: DataFrame | Series, func: AggFuncTypeDict
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Ensures that necessary columns exist if obj is a DataFrame, and
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self, how: str, obj: DataFrame | Series, func: AggFuncTypeDict
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"""
Handler for dict-like argument.
Ensures that necessary columns exist if obj is a DataFrame, and
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when values consists of a mix of list and non-lists.
"""
assert how in ("apply", "agg", "transform")
# Can't use func.values(); wouldn't work for a Series
if (
how == "agg"
and isinstance(obj, ABCSeries)
and any(is_list_like(v) for _, v in func.items())
) or (any(is_dict_like(v) for _, v in func.items())):
# GH 15931 - deprecation of renaming keys
raise SpecificationError("nested renamer is not supported")
if obj.ndim != 1:
# Check for missing columns on a frame
cols = set(func.keys()) - set(obj.columns)
if len(cols) > 0:
cols_sorted = list(safe_sort(list(cols)))
raise KeyError(f"Column(s) {cols_sorted} do not exist")
is_aggregator = lambda x: isinstance(x, (list, tuple, dict))
# if we have a dict of any non-scalars
# eg. {'A' : ['mean']}, normalize all to
# be list-likes
# Cannot use func.values() because arg may be a Series
if any(is_aggregator(x) for _, x in func.items()):
new_func: AggFuncTypeDict = {}
for k, v in func.items():
if not is_aggregator(v):
# mypy can't realize v is not a list here
new_func[k] = [v] # type:ignore[list-item]
else:
new_func[k] = v
func = new_func
return func | [
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ClementPinard/DepthNet | 3c753fc21b06c9be307d73c8e7a0c61f2ea56cc3 | datasets/listdataset.py | python | default_loader | (root, path_imgs, path_depth) | return [imgs, depth] | [] | def default_loader(root, path_imgs, path_depth):
imgs = [imread(root/path) for path in path_imgs]
depth = np.load(root/path_depth)
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awslabs/aws-lambda-powertools-python | 0c6ac0fe183476140ee1df55fe9fa1cc20925577 | aws_lambda_powertools/utilities/parameters/base.py | python | BaseProvider._get | (self, name: str, **sdk_options) | Retrieve parameter value from the underlying parameter store | Retrieve parameter value from the underlying parameter store | [
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1012598167/flask_mongodb_game | 60c7e0351586656ec38f851592886338e50b4110 | python_flask/venv/Lib/site-packages/jinja2/environment.py | python | Template.stream | (self, *args, **kwargs) | return TemplateStream(self.generate(*args, **kwargs)) | Works exactly like :meth:`generate` but returns a
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mozilla/kitsune | 7c7cf9baed57aa776547aea744243ccad6ca91fb | kitsune/groups/views.py | python | remove_leader | (request, group_slug, user_id) | return render(request, "groups/confirm_remove_leader.html", {"profile": prof, "leader": user}) | Remove a leader from the group. | Remove a leader from the group. | [
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] | def remove_leader(request, group_slug, user_id):
"""Remove a leader from the group."""
prof = get_object_or_404(GroupProfile, slug=group_slug)
user = get_object_or_404(User, id=user_id)
if not _user_can_manage_leaders(request.user, prof):
raise PermissionDenied
if request.method == "POST":
prof.leaders.remove(user)
msg = _("{user} removed from the group leaders successfully!").format(user=user.username)
messages.add_message(request, messages.SUCCESS, msg)
return HttpResponseRedirect(prof.get_absolute_url())
return render(request, "groups/confirm_remove_leader.html", {"profile": prof, "leader": user}) | [
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mysql/mysql-utilities | 08276b2258bf9739f53406b354b21195ff69a261 | mysql/utilities/common/replication.py | python | Slave.get_delay | (self) | return (state, sec_behind, delay_remaining,
read_log_file, read_log_pos) | Return slave delay values
This method retrieves the slave's delay parameters.
Returns tuple - slave delay values or None if not connected | Return slave delay values | [
"Return",
"slave",
"delay",
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] | def get_delay(self):
"""Return slave delay values
This method retrieves the slave's delay parameters.
Returns tuple - slave delay values or None if not connected
"""
res = self.get_status()
if res == []:
return None
# slave IO state
state = res[0][_SLAVE_IO_STATE]
# seconds behind master
if res[0][_SLAVE_DELAY] is None:
sec_behind = 0
else:
sec_behind = int(res[0][_SLAVE_DELAY])
# remaining delay
delay_remaining = res[0][_SLAVE_REMAINING_DELAY]
# master's log file read
read_log_file = res[0][_SLAVE_MASTER_LOG_FILE]
# position in master's binlog
read_log_pos = res[0][_SLAVE_MASTER_LOG_FILE_POS]
return (state, sec_behind, delay_remaining,
read_log_file, read_log_pos) | [
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KalleHallden/AutoTimer | 2d954216700c4930baa154e28dbddc34609af7ce | env/lib/python2.7/site-packages/pkg_resources/__init__.py | python | get_distribution | (dist) | return dist | Return a current distribution object for a Requirement or string | Return a current distribution object for a Requirement or string | [
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"""Return a current distribution object for a Requirement or string"""
if isinstance(dist, six.string_types):
dist = Requirement.parse(dist)
if isinstance(dist, Requirement):
dist = get_provider(dist)
if not isinstance(dist, Distribution):
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modin-project/modin | 0d9d14e6669be3dd6bb3b72222dbe6a6dffe1bee | modin/pandas/indexing.py | python | boolean_mask_to_numeric | (indexer) | Convert boolean mask to numeric indices.
Parameters
----------
indexer : list-like of booleans
Returns
-------
np.ndarray of ints
Numerical positions of ``True`` elements in the passed `indexer`. | Convert boolean mask to numeric indices. | [
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"""
Convert boolean mask to numeric indices.
Parameters
----------
indexer : list-like of booleans
Returns
-------
np.ndarray of ints
Numerical positions of ``True`` elements in the passed `indexer`.
"""
if isinstance(indexer, (np.ndarray, Series, pandas.Series)):
return np.where(indexer)[0]
else:
# It's faster to build the resulting numpy array from the reduced amount of data via
# `compress` iterator than convert non-numpy-like `indexer` to numpy and apply `np.where`.
return np.fromiter(
# `itertools.compress` masks `data` with the `selectors` mask,
# works about ~10% faster than a pure list comprehension
itertools.compress(data=range(len(indexer)), selectors=indexer),
dtype=np.int64,
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pillone/usntssearch | 24b5e5bc4b6af2589d95121c4d523dc58cb34273 | NZBmegasearch/mechanize/_pullparser.py | python | _AbstractParser.get_tag | (self, *names) | Return the next Token that represents an opening or closing tag.
If arguments are given, they are taken to be element names in which the
caller is interested: tags representing other elements will be skipped.
Element names must be given in lower case.
Raises NoMoreTokensError. | Return the next Token that represents an opening or closing tag. | [
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"""Return the next Token that represents an opening or closing tag.
If arguments are given, they are taken to be element names in which the
caller is interested: tags representing other elements will be skipped.
Element names must be given in lower case.
Raises NoMoreTokensError.
"""
while 1:
tok = self.get_token()
if tok.type not in ["starttag", "endtag", "startendtag"]:
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if names:
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else:
return tok | [
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OpenCobolIDE/OpenCobolIDE | c78d0d335378e5fe0a5e74f53c19b68b55e85388 | open_cobol_ide/extlibs/pyqode/core/api/utils.py | python | TextHelper.selection_range | (self) | return start, end | Returns the selected lines boundaries (start line, end line)
:return: tuple(int, int) | Returns the selected lines boundaries (start line, end line) | [
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] | def selection_range(self):
"""
Returns the selected lines boundaries (start line, end line)
:return: tuple(int, int)
"""
editor = self._editor
doc = editor.document()
start = doc.findBlock(
editor.textCursor().selectionStart()).blockNumber()
end = doc.findBlock(
editor.textCursor().selectionEnd()).blockNumber()
text_cursor = QtGui.QTextCursor(editor.textCursor())
text_cursor.setPosition(editor.textCursor().selectionEnd())
if text_cursor.columnNumber() == 0 and start != end:
end -= 1
return start, end | [
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cloudera/impyla | 0c736af4cad2bade9b8e313badc08ec50e81c948 | impala/_thrift_gen/hive_metastore/ThriftHiveMetastore.py | python | open_txns_args.__init__ | (self, rqst=None,) | [] | def __init__(self, rqst=None,):
self.rqst = rqst | [
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mozman/ezdxf | 59d0fc2ea63f5cf82293428f5931da7e9f9718e9 | src/ezdxf/document.py | python | Drawing.dxfversion | (self) | return self._dxfversion | Get current DXF version. | Get current DXF version. | [
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"""Get current DXF version."""
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holland-backup/holland | 77dcfe9f23d4254e4c351cdc18f29a8d34945812 | holland/core/util/path.py | python | disk_capacity | (target_path) | return info.f_frsize * info.f_blocks | Find the total capacity of the filesystem that target_path is on
:returns: integer number of input_bytes | Find the total capacity of the filesystem that target_path is on | [
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] | def disk_capacity(target_path):
"""Find the total capacity of the filesystem that target_path is on
:returns: integer number of input_bytes
"""
path = getmount(target_path)
info = os.statvfs(path)
return info.f_frsize * info.f_blocks | [
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brainiak/brainiak | ee093597c6c11597b0a59e95b48d2118e40394a5 | brainiak/fcma/classifier.py | python | Classifier._prepare_test_data | (self, corr_data) | return data | Prepare the data to be applied to the predict function.
if the classifier is SVM, do kernel precomputation,
otherwise the test data is the reshaped corr_data
Parameters
----------
corr_data: the (normalized) correlation data
in shape [num_samples, num_voxels, num_voxels2]
Returns
-------
data: the data to be predicted, in shape of [num_samples, num_dim] | Prepare the data to be applied to the predict function. | [
"Prepare",
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"predict",
"function",
"."
] | def _prepare_test_data(self, corr_data):
"""Prepare the data to be applied to the predict function.
if the classifier is SVM, do kernel precomputation,
otherwise the test data is the reshaped corr_data
Parameters
----------
corr_data: the (normalized) correlation data
in shape [num_samples, num_voxels, num_voxels2]
Returns
-------
data: the data to be predicted, in shape of [num_samples, num_dim]
"""
num_test_samples = corr_data.shape[0]
assert num_test_samples > 0, \
'at least one test sample is needed'
if isinstance(self.clf, sklearn.svm.SVC) \
and self.clf.kernel == 'precomputed':
assert self.training_data_ is not None, \
'when using precomputed kernel of SVM, ' \
'all training data must be provided'
num_training_samples = self.training_data_.shape[0]
data = np.zeros((num_test_samples, num_training_samples),
np.float32,
order='C')
num_features = self.num_features_
corr_data = corr_data.reshape(num_test_samples,
num_features)
# compute the similarity vectors using corr_data and training_data
blas.compute_single_matrix_multiplication('T', 'N',
num_training_samples,
num_test_samples,
num_features,
1.0,
self.training_data_,
num_features,
corr_data,
num_features,
0.0,
data,
num_training_samples)
# shrink the values for getting more stable alpha values
# in SVM training iteration
num_digits = self.num_digits_
if num_digits > 2:
proportion = 10**(2-num_digits)
data *= proportion
logger.debug(
'similarity vectors computation done'
)
else:
data = corr_data.reshape(num_test_samples,
self.num_features_)
return data | [
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RiotGames/cloud-inquisitor | 29a26c705381fdba3538b4efedb25b9e09b387ed | plugins/public/cinq-auditor-required-tags/cinq_auditor_required_tags/__init__.py | python | RequiredTagsAuditor.process_actions | (self, actions) | return notices | Process the actions we want to take
Args:
actions (`list`): List of actions we want to take
Returns:
`list` of notifications | Process the actions we want to take | [
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] | def process_actions(self, actions):
"""Process the actions we want to take
Args:
actions (`list`): List of actions we want to take
Returns:
`list` of notifications
"""
notices = {}
notification_contacts = {}
for action in actions:
resource = action['resource']
action_status = ActionStatus.SUCCEED
try:
if action['action'] == AuditActions.REMOVE:
action_status = self.process_action(
resource,
AuditActions.REMOVE
)
if action_status == ActionStatus.SUCCEED:
db.session.delete(action['issue'].issue)
elif action['action'] == AuditActions.STOP:
action_status = self.process_action(
resource,
AuditActions.STOP
)
if action_status == ActionStatus.SUCCEED:
action['issue'].update({
'missing_tags': action['missing_tags'],
'notes': action['notes'],
'last_alert': action['last_alert'],
'state': action['action']
})
elif action['action'] == AuditActions.FIXED:
db.session.delete(action['issue'].issue)
elif action['action'] == AuditActions.ALERT:
action['issue'].update({
'missing_tags': action['missing_tags'],
'notes': action['notes'],
'last_alert': action['last_alert'],
'state': action['action']
})
db.session.commit()
if action_status == ActionStatus.SUCCEED:
for owner in [
dict(t) for t in {tuple(d.items()) for d in (action['owners'] + self.permanent_emails)}
]:
if owner['value'] not in notification_contacts:
contact = NotificationContact(type=owner['type'], value=owner['value'])
notification_contacts[owner['value']] = contact
notices[contact] = {
'fixed': [],
'not_fixed': []
}
else:
contact = notification_contacts[owner['value']]
if action['action'] == AuditActions.FIXED:
notices[contact]['fixed'].append(action)
else:
notices[contact]['not_fixed'].append(action)
except Exception as ex:
self.log.exception('Unexpected error while processing resource {}/{}/{}/{}'.format(
action['resource'].account.account_name,
action['resource'].id,
action['resource'],
ex
))
return notices | [
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grnet/synnefo | d06ec8c7871092131cdaabf6b03ed0b504c93e43 | snf-pithos-backend/pithos/backends/lib/sqlalchemy/node.py | python | Node.node_get_properties | (self, node) | return l | Return the node's (parent, path).
Return None if the node is not found. | Return the node's (parent, path).
Return None if the node is not found. | [
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"""Return the node's (parent, path).
Return None if the node is not found.
"""
s = select([self.nodes.c.parent, self.nodes.c.path])
s = s.where(self.nodes.c.node == node)
r = self.conn.execute(s)
l = r.fetchone()
r.close()
return l | [
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mayank93/Twitter-Sentiment-Analysis | f095c6ca6bf69787582b5dabb140fefaf278eb37 | front-end/web2py/gluon/contrib/feedparser.py | python | _stripDoctype | (data) | return version, data, dict(replacement and [(k.decode('utf-8'), v.decode('utf-8')) for k, v in safe_pattern.findall(replacement)]) | Strips DOCTYPE from XML document, returns (rss_version, stripped_data)
rss_version may be 'rss091n' or None
stripped_data is the same XML document, minus the DOCTYPE | Strips DOCTYPE from XML document, returns (rss_version, stripped_data) | [
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'''Strips DOCTYPE from XML document, returns (rss_version, stripped_data)
rss_version may be 'rss091n' or None
stripped_data is the same XML document, minus the DOCTYPE
'''
start = re.search(_s2bytes('<\w'), data)
start = start and start.start() or -1
head,data = data[:start+1], data[start+1:]
entity_pattern = re.compile(_s2bytes(r'^\s*<!ENTITY([^>]*?)>'), re.MULTILINE)
entity_results=entity_pattern.findall(head)
head = entity_pattern.sub(_s2bytes(''), head)
doctype_pattern = re.compile(_s2bytes(r'^\s*<!DOCTYPE([^>]*?)>'), re.MULTILINE)
doctype_results = doctype_pattern.findall(head)
doctype = doctype_results and doctype_results[0] or _s2bytes('')
if doctype.lower().count(_s2bytes('netscape')):
version = u'rss091n'
else:
version = None
# only allow in 'safe' inline entity definitions
replacement=_s2bytes('')
if len(doctype_results)==1 and entity_results:
safe_pattern=re.compile(_s2bytes('\s+(\w+)\s+"(&#\w+;|[^&"]*)"'))
safe_entities=filter(lambda e: safe_pattern.match(e),entity_results)
if safe_entities:
replacement=_s2bytes('<!DOCTYPE feed [\n <!ENTITY') + _s2bytes('>\n <!ENTITY ').join(safe_entities) + _s2bytes('>\n]>')
data = doctype_pattern.sub(replacement, head) + data
return version, data, dict(replacement and [(k.decode('utf-8'), v.decode('utf-8')) for k, v in safe_pattern.findall(replacement)]) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/tornado-6.0.1-py3.7-macosx-12.1-iPad6,7.egg/tornado/netutil.py | python | Resolver.close | (self) | Closes the `Resolver`, freeing any resources used.
.. versionadded:: 3.1 | Closes the `Resolver`, freeing any resources used. | [
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"""Closes the `Resolver`, freeing any resources used.
.. versionadded:: 3.1
"""
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rafellerc/Pytorch-SiamFC | d64d0fee08c7972726337be07e783d44bd9c275e | training/summary_utils.py | python | SummaryMaker.__init__ | (self, log_dir, params, up_factor) | Args:
log_dir: (str) The path to the folder where the summary files are
going to be written. The summary object creates a train and a
val folders to store the summary files.
params: (train.utils.Params) The parameters loaded from the
parameters.json file.
up_factor: (int) The upscale factor that indicates how much the
scores maps need to be upscaled to match the original scale
(used when superposing the embeddings and score maps to the
input images).
Attributes:
writer_train: (tensorboardX.writer.SummaryWriter) The tensorboardX
writer that writes the training informations.
writer_val: (tensorboardX.writer.SummaryWriter) The tensorboardX
writer that writes the validation informations.
epoch: (int) Stores the current epoch.
ref_sz: (int) The size in pixels of the reference image.
srch_sz: (int) The size in pixels of the search image.
up_factor: (int) The upscale factor. See Args. | Args:
log_dir: (str) The path to the folder where the summary files are
going to be written. The summary object creates a train and a
val folders to store the summary files.
params: (train.utils.Params) The parameters loaded from the
parameters.json file.
up_factor: (int) The upscale factor that indicates how much the
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writer_val: (tensorboardX.writer.SummaryWriter) The tensorboardX
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epoch: (int) Stores the current epoch.
ref_sz: (int) The size in pixels of the reference image.
srch_sz: (int) The size in pixels of the search image.
up_factor: (int) The upscale factor. See Args.
"""
# We use two different summary writers so we can plot both curves in
# the same plot, as suggested in https://www.quora.com/How-do-you-plot-training-and-validation-loss-on-the-same-graph-using-TensorFlow%E2%80%99s-TensorBoard
self.writer_train = SummaryWriter(join(log_dir, 'train'))
self.writer_val = SummaryWriter(join(log_dir, 'val'))
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self.ref_sz = params.reference_sz
self.srch_sz = params.search_sz
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-linux/x64/pyasn1/type/univ.py | python | Real.isMinusInfinity | (self) | return self.isMinusInf | [] | def isMinusInfinity(self):
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iagcl/watchmen | d329b357e6fde3ad91e972988b160a33c12afc2a | elasticsearch/roll_indexes/packages/requests/sessions.py | python | Session.prepare_request | (self, request) | return p | Constructs a :class:`PreparedRequest <PreparedRequest>` for
transmission and returns it. The :class:`PreparedRequest` has settings
merged from the :class:`Request <Request>` instance and those of the
:class:`Session`.
:param request: :class:`Request` instance to prepare with this
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:rtype: requests.PreparedRequest | Constructs a :class:`PreparedRequest <PreparedRequest>` for
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:class:`Session`.
:param request: :class:`Request` instance to prepare with this
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:rtype: requests.PreparedRequest
"""
cookies = request.cookies or {}
# Bootstrap CookieJar.
if not isinstance(cookies, cookielib.CookieJar):
cookies = cookiejar_from_dict(cookies)
# Merge with session cookies
merged_cookies = merge_cookies(
merge_cookies(RequestsCookieJar(), self.cookies), cookies)
# Set environment's basic authentication if not explicitly set.
auth = request.auth
if self.trust_env and not auth and not self.auth:
auth = get_netrc_auth(request.url)
p = PreparedRequest()
p.prepare(
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url=request.url,
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data=request.data,
json=request.json,
headers=merge_setting(request.headers, self.headers, dict_class=CaseInsensitiveDict),
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/schemes/toric/divisor.py | python | ToricDivisor_generic._sheaf_complex | (self, m) | return SimplicialComplex([c.ambient_ray_indices() for c in negative_cones]) | r"""
Return a simplicial complex whose cohomology is isomorphic to the
`m\in M`-graded piece of the sheaf cohomology.
Helper for :meth:`cohomology`.
INPUT:
- `m` -- a point in ``self.scheme().fan().dual_lattice()``.
OUTPUT:
- :class:`simplicial complex <sage.topology.simplicial_complex.SimplicialComplex>`.
EXAMPLES::
sage: dP6 = toric_varieties.dP6()
sage: D0 = dP6.divisor(0)
sage: D2 = dP6.divisor(2)
sage: D3 = dP6.divisor(3)
sage: D = -D0 + 2*D2 - D3
sage: M = dP6.fan().dual_lattice()
sage: D._sheaf_complex( M(1,0) )
Simplicial complex with vertex set (0, 1, 3) and facets {(3,), (0, 1)} | r"""
Return a simplicial complex whose cohomology is isomorphic to the
`m\in M`-graded piece of the sheaf cohomology. | [
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r"""
Return a simplicial complex whose cohomology is isomorphic to the
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Helper for :meth:`cohomology`.
INPUT:
- `m` -- a point in ``self.scheme().fan().dual_lattice()``.
OUTPUT:
- :class:`simplicial complex <sage.topology.simplicial_complex.SimplicialComplex>`.
EXAMPLES::
sage: dP6 = toric_varieties.dP6()
sage: D0 = dP6.divisor(0)
sage: D2 = dP6.divisor(2)
sage: D3 = dP6.divisor(3)
sage: D = -D0 + 2*D2 - D3
sage: M = dP6.fan().dual_lattice()
sage: D._sheaf_complex( M(1,0) )
Simplicial complex with vertex set (0, 1, 3) and facets {(3,), (0, 1)}
"""
fan = self.parent().scheme().fan()
ray_is_negative = [ m*ray + self.coefficient(i) < 0
for i, ray in enumerate(fan.rays()) ]
def cone_is_negative(cone): # and non-trivial
if cone.is_trivial():
return False
return all(ray_is_negative[i] for i in cone.ambient_ray_indices())
negative_cones = [cone for cone in flatten(fan.cones()) if cone_is_negative(cone)]
return SimplicialComplex([c.ambient_ray_indices() for c in negative_cones]) | [
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weewx/weewx | cb594fce224560bd8696050fc5c7843c7839320e | bin/weewx/wxxtypes.py | python | StdWXXTypes.__init__ | (self, engine, config_dict) | Initialize an instance of StdWXXTypes | Initialize an instance of StdWXXTypes | [
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"""Initialize an instance of StdWXXTypes"""
super(StdWXXTypes, self).__init__(engine, config_dict)
altitude_vt = engine.stn_info.altitude_vt
latitude_f = engine.stn_info.latitude_f
longitude_f = engine.stn_info.longitude_f
# These options were never documented. They have moved. Fail hard if they are present.
if 'StdWXCalculate' in config_dict \
and any(key in config_dict['StdWXCalculate']
for key in ['rain_period', 'et_period', 'wind_height',
'atc', 'nfac', 'max_delta_12h']):
raise ValueError("Undocumented options for [StdWXCalculate] have moved. "
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# Get any user-defined overrides
try:
override_dict = config_dict['StdWXCalculate']['WXXTypes']
except KeyError:
override_dict = {}
# Get the default values, then merge the user overrides into it
option_dict = weeutil.config.deep_copy(defaults_dict['StdWXCalculate']['WXXTypes'])
option_dict.merge(override_dict)
# Get force_null from the option dictionary
force_null = to_bool(option_dict['windDir'].get('force_null', True))
# Option ignore_zero_wind has also moved, but we will support it in a backwards-compatible
# way, provided that it doesn't conflict with any setting of force_null.
try:
# Is there a value for ignore_zero_wind as well?
ignore_zero_wind = to_bool(config_dict['StdWXCalculate']['ignore_zero_wind'])
except KeyError:
# No. We're done
pass
else:
# No exception, so there must be a value for ignore_zero_wind.
# Is there an explicit value for 'force_null'? That is, a default was not used?
if 'force_null' in override_dict:
# Yes. Make sure they match
if ignore_zero_wind != to_bool(override_dict['force_null']):
raise ValueError("Conflicting values for "
"ignore_zero_wind (%s) and force_null (%s)"
% (ignore_zero_wind, force_null))
else:
# No explicit value for 'force_null'. Use 'ignore_zero_wind' in its place
force_null = ignore_zero_wind
# maxSolarRad-related options
maxSolarRad_algo = option_dict['maxSolarRad'].get('algorithm', 'rs').lower()
# atmospheric transmission coefficient [0.7-0.91]
atc = to_float(option_dict['maxSolarRad'].get('atc', 0.8))
# atmospheric turbidity (2=clear, 4-5=smoggy)
nfac = to_float(option_dict['maxSolarRad'].get('nfac', 2))
# ET-related options
# height above ground at which wind is measured, in meters
wind_height = to_float(weeutil.config.search_up(option_dict['ET'], 'wind_height', 2.0))
# window of time for evapotranspiration calculation, in seconds
et_period = to_int(option_dict['ET'].get('et_period', 3600))
# heatindex-related options
heatindex_algo = option_dict['heatindex'].get('algorithm', 'new').lower()
self.wxxtypes = WXXTypes(altitude_vt, latitude_f, longitude_f,
et_period,
atc,
nfac,
wind_height,
force_null,
maxSolarRad_algo,
heatindex_algo)
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weewx.xtypes.xtypes.append(self.wxxtypes) | [
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thiagopena/djangoSIGE | e32186b27bfd8acf21b0fa400e699cb5c73e5433 | djangosige/apps/base/custom_views.py | python | CustomUpdateView.post | (self, request, *args, **kwargs) | return self.form_invalid(form) | [] | def post(self, request, *args, **kwargs):
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kennethreitz-archive/requests3 | 69eb662703b40db58fdc6c095d0fe130c56649bb | requests3/core/_http/exceptions.py | python | BadVersionError.__init__ | (self, version) | [] | def __init__(self, version):
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theislab/anndata | 664e32b0aa6625fe593370d37174384c05abfd4e | anndata/_core/anndata.py | python | AnnData.__getitem__ | (self, index: Index) | return AnnData(self, oidx=oidx, vidx=vidx, asview=True) | Returns a sliced view of the object. | Returns a sliced view of the object. | [
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F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idaapi.py | python | vdloc_t.reg1 | (self, *args) | return _idaapi.vdloc_t_reg1(self, *args) | reg1(self) -> int | reg1(self) -> int | [
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digidotcom/xbee-python | 0757f4be0017530c205175fbee8f9f61be9614d1 | digi/xbee/comm_interface.py | python | XBeeCommunicationInterface.close | (self) | Terminates the underlying hardware communication interface.
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Terminates the underlying hardware communication interface.
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vanheeringen-lab/genomepy | 4c10e69b6886cf52381caf6498395391834a675b | genomepy/functions.py | python | _provider_selection | (name, localname, genomes_dir, provider=None) | return _lazy_provider_selection(name, provider) | Return a provider object
First tries to return a specified provider,
Second tries to return the provider from the README
Third tries to return the first provider which has the genome (Ensembl>UCSC>NCBI) | Return a provider object | [
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"""
Return a provider object
First tries to return a specified provider,
Second tries to return the provider from the README
Third tries to return the first provider which has the genome (Ensembl>UCSC>NCBI)
"""
readme = os.path.join(genomes_dir, localname, "README.txt")
if provider is None and os.path.exists(readme):
m, _ = read_readme(readme)
p = m["provider"].lower()
if p in ["ensembl", "ucsc", "ncbi"]:
provider = p
return _lazy_provider_selection(name, provider) | [
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ronf/asyncssh | ee1714c598d8c2ea6f5484e465443f38b68714aa | asyncssh/kex.py | python | register_gss_kex_alg | (alg: bytes, handler: Type[Kex], hash_alg: HashType,
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_gss_kex_algs.append(alg)
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joke2k/faker | 0ebe46fc9b9793fe315cf0fce430258ce74df6f8 | faker/providers/address/az_AZ/__init__.py | python | Provider.village | (self) | return self.random_element(self.villages) | :example 'Didivar' | :example 'Didivar' | [
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"""
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/op2/tables/geom/ept.py | python | EPT._read_nsm1 | (self, data: bytes, n: int) | return n | NSM1(3301, 33, 992)
Defines the properties of a nonstructural mass.
Word Name Type Description
1 SID I Set identification number
2 PROP CHAR4 Set of properties
3 TYPE CHAR4 Set of elements
4 ORIGIN I Entry origin
5 VALUE RS Nonstructural mass value
6 SPECOPT I Specification option
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Words 7 through 13 repeat until -1 occurs
data = (3, PCOMP, 0, 0.37, 2, ALL, -1,
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")"
] | def _read_nsm1(self, data: bytes, n: int) -> int:
"""
NSM1(3301, 33, 992)
Defines the properties of a nonstructural mass.
Word Name Type Description
1 SID I Set identification number
2 PROP CHAR4 Set of properties
3 TYPE CHAR4 Set of elements
4 ORIGIN I Entry origin
5 VALUE RS Nonstructural mass value
6 SPECOPT I Specification option
SPECOPT=1 By IDs
7 ID I
Word 7 repeats until -1 occurs
SPECOPT=2 All
7 ALL(2) CHAR4
Words 7 and 8 repeat until -1 occurs
SPECOPT=3 Thru range
7 ID I
8 THRU(2) CHAR4
10 ID I
Words 7 through 10 repeat until -1 occurs
SPECOPT=4 Thru range with by
7 ID I
8 THRU(2) CHAR4
10 ID I
11 BY(2) CHAR4
13 N I
Words 7 through 13 repeat until -1 occurs
data = (3, PCOMP, 0, 0.37, 2, ALL, -1,
4, ELEMENT, 2, 2.1, 1, 3301, -1)
"""
op2 = self.op2
#op2.show_data(data[n:], types='ifs')
n0 = n
#op2.show_data(data[n:])
ints = np.frombuffer(data[n:], op2.idtype8).copy()
floats = np.frombuffer(data[n:], op2.fdtype8).copy()
istart, iend = get_minus1_start_end(ints)
ncards = 0
size = self.size
for (i0, i1) in zip(istart, iend):
assert ints[i1] == -1, ints[i1]
# 1 SID I Set identification number
sid = ints[i0]
# 2 PROP CHAR4 Set of properties
# 3 TYPE CHAR4 Set of elements
# 4 ORIGIN I Entry origin
# 5 VALUE RS Nonstructural mass value
# 6 SPECOPT I Specification option
nsm_type = data[n0+(i0+1)*size:n0+(i0+3)*size].decode('latin1').rstrip()
zero_two = ints[i0+3]
value = float(floats[i0+4])
spec_opt = ints[i0+5]
assert zero_two in [0, 2], zero_two
#nii = 6
#print(ints[i0+nii:i1])
#print(floats[i0+nii:i1])
#print(sid, nsm_type, value, spec_opt)
iminus1 = i0 + np.where(ints[i0:i1] == -1)[0]
#print('-1', iminus1)
#print('-2', iminus2)
istart2 = [i0 + 5] + list(iminus1[:-1] + 1)
iend2 = iminus1
#print(istart2, iend2)
if spec_opt == 1:
# 7 ID I
ids = ints[i0+6:i1]
elif spec_opt == 2:
word = data[n0+(i0+6)*size:n0+i1*size]
ids = word
elif spec_opt == 3: # thru
# datai = (249311, 'THRU ', 250189)
#datai = data[n0+(i0+6)*size:n0+i1*size]
ids = ints[i0+6:i1]
istart = ids[0]
iend = ids[-1]
ids = list(range(istart, iend+1))
else:
raise NotImplementedError(spec_opt)
#print(sid, nsm_type, zero_two, value, ids)
#if nsm_type == 'ELEM':
#nsm_type = 'ELEMENT'
#for pid_eid in pid_eids:
#nsml = self.add_nsml1(sid, nsm_type, pid_eids, [value])
nsm1 = op2.add_nsm1(sid, nsm_type, value, ids)
#print(nsm1)
str(nsm1)
n += (i1 - i0 + 1) * size
ncards += 1
op2.card_count['NSM1'] = ncards
return n | [
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cyberdelia/metrology | a029ec45228f59dfa82d8068698a3fa60ea8f7c2 | metrology/instruments/counter.py | python | Counter.decrement | (self, value=1) | Decrement the counter. By default it will decrement by 1.
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kanzure/nanoengineer | 874e4c9f8a9190f093625b267f9767e19f82e6c4 | cad/src/dna/operations/crossovers.py | python | Pl5_recognizer._C_strand_direction_well_defined | (self) | return self.ordered_bases is not None | [compute method for self.strand_direction_well_defined]
###doc | [compute method for self.strand_direction_well_defined]
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###doc
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charlesq34/frustum-pointnets | 2ffdd345e1fce4775ecb508d207e0ad465bcca80 | kitti/kitti_util.py | python | Calibration.project_velo_to_image | (self, pts_3d_velo) | return self.project_rect_to_image(pts_3d_rect) | Input: nx3 points in velodyne coord.
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''' Input: nx3 points in velodyne coord.
Output: nx2 points in image2 coord.
'''
pts_3d_rect = self.project_velo_to_rect(pts_3d_velo)
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/27/Doc/jinja2/compiler.py | python | CodeGenerator.macro_def | (self, node, frame) | Dump the macro definition for the def created by macro_body. | Dump the macro definition for the def created by macro_body. | [
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arg_tuple = ', '.join(repr(x.name) for x in node.args)
name = getattr(node, 'name', None)
if len(node.args) == 1:
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self.write('Macro(environment, macro, %r, (%s), (' %
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for arg in node.defaults:
self.visit(arg, frame)
self.write(', ')
self.write('), %r, %r, %r)' % (
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xtiankisutsa/MARA_Framework | ac4ac88bfd38f33ae8780a606ed09ab97177c562 | tools/AndroBugs/tools/modified/androguard/core/bytecode.py | python | method2jpg | (output, mx, raw=False) | Export method to a jpg file format
:param output: output filename
:type output: string
:param mx: specify the MethodAnalysis object
:type mx: :class:`MethodAnalysis` object
:param raw: use directly a dot raw buffer (optional)
:type raw: string | Export method to a jpg file format | [
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] | def method2jpg(output, mx, raw=False):
"""
Export method to a jpg file format
:param output: output filename
:type output: string
:param mx: specify the MethodAnalysis object
:type mx: :class:`MethodAnalysis` object
:param raw: use directly a dot raw buffer (optional)
:type raw: string
"""
buff = raw
if raw == False:
buff = method2dot(mx)
method2format(output, "jpg", mx, buff) | [
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kadalu/kadalu | 5d91d92830553b47d3729adc3c7f69ce6fd45e2d | kadalulib.py | python | ProcState.restart | (self) | Restart a Process | Restart a Process | [
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pyqteval/evlal_win | ce7fc77ac5cdf864cd6aa9b04b5329501f8f4c92 | pyinstaller-2.0/PyInstaller/compat.py | python | exec_python_all | (*args, **kwargs) | return exec_command_all(*cmdargs, **kwargs) | Wrap running python script in a subprocess.
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"""
Wrap running python script in a subprocess.
Return tuple (exit_code, stdout, stderr) of the invoked command.
"""
cmdargs, kwargs = __wrap_python(args, kwargs)
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beetbox/beets | 2fea53c34dd505ba391cb345424e0613901c8025 | beets/plugins.py | python | BeetsPlugin.add_media_field | (self, name, descriptor) | Add a field that is synchronized between media files and items.
When a media field is added ``item.write()`` will set the name
property of the item's MediaFile to ``item[name]`` and save the
changes. Similarly ``item.read()`` will set ``item[name]`` to
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"""Add a field that is synchronized between media files and items.
When a media field is added ``item.write()`` will set the name
property of the item's MediaFile to ``item[name]`` and save the
changes. Similarly ``item.read()`` will set ``item[name]`` to
the value of the name property of the media file.
``descriptor`` must be an instance of ``mediafile.MediaField``.
"""
# Defer import to prevent circular dependency
from beets import library
mediafile.MediaFile.add_field(name, descriptor)
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brython-dev/brython | 9cba5fb7f43a9b52fff13e89b403e02a1dfaa5f3 | www/src/Lib/importlib/_bootstrap_external.py | python | decode_source | (source_bytes) | return newline_decoder.decode(source_bytes.decode(encoding[0])) | Decode bytes representing source code and return the string.
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newline_decoder = _io.IncrementalNewlineDecoder(None, True)
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v2beta2_horizontal_pod_autoscaler_status.py | python | V2beta2HorizontalPodAutoscalerStatus.__init__ | (self, conditions=None, current_metrics=None, current_replicas=None, desired_replicas=None, last_scale_time=None, observed_generation=None, local_vars_configuration=None) | V2beta2HorizontalPodAutoscalerStatus - a model defined in OpenAPI | V2beta2HorizontalPodAutoscalerStatus - a model defined in OpenAPI | [
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"""V2beta2HorizontalPodAutoscalerStatus - a model defined in OpenAPI""" # noqa: E501
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/utils/dummy_flax_objects.py | python | FlaxGPT2PreTrainedModel.from_pretrained | (cls, *args, **kwargs) | [] | def from_pretrained(cls, *args, **kwargs):
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ReactionMechanismGenerator/RMG-Py | 2b7baf51febf27157def58fb3f6cee03fb6a684c | rmgpy/__init__.py | python | Settings.report | (self) | return '\n'.join(lines) | Returns a string saying what is set and where things came from, suitable for logging | Returns a string saying what is set and where things came from, suitable for logging | [
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | tokumx/datadog_checks/tokumx/vendor/pymongo/server_selectors.py | python | any_server_selector | (selection) | return selection | [] | def any_server_selector(selection):
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BMW-InnovationLab/BMW-TensorFlow-Training-GUI | 4f10d1f00f9ac312ca833e5b28fd0f8952cfee17 | training_api/research/object_detection/utils/vrd_evaluation.py | python | _VRDDetectionEvaluation.clear_detections | (self) | Clears detections. | Clears detections. | [
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"""Clears detections."""
self._detection_keys = set()
self._scores = []
self._relation_field_values = []
self._tp_fp_labels = []
self._average_precisions = {}
self._precisions = []
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/group/binary_sensor.py | python | BinarySensorGroup.async_added_to_hass | (self) | Register callbacks. | Register callbacks. | [
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await super().async_added_to_hass() | [
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Cadene/tensorflow-model-zoo.torch | 990b10ffc22d4c8eacb2a502f20415b4f70c74c2 | models/research/object_detection/data_decoders/tf_example_decoder.py | python | TfExampleDecoder.__init__ | (self,
load_instance_masks=False,
label_map_proto_file=None,
use_display_name=False) | Constructor sets keys_to_features and items_to_handlers.
Args:
load_instance_masks: whether or not to load and handle instance masks.
label_map_proto_file: a file path to a
object_detection.protos.StringIntLabelMap proto. If provided, then the
mapped IDs of 'image/object/class/text' will take precedence over the
existing 'image/object/class/label' ID. Also, if provided, it is
assumed that 'image/object/class/text' will be in the data.
use_display_name: whether or not to use the `display_name` for label
mapping (instead of `name`). Only used if label_map_proto_file is
provided. | Constructor sets keys_to_features and items_to_handlers. | [
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] | def __init__(self,
load_instance_masks=False,
label_map_proto_file=None,
use_display_name=False):
"""Constructor sets keys_to_features and items_to_handlers.
Args:
load_instance_masks: whether or not to load and handle instance masks.
label_map_proto_file: a file path to a
object_detection.protos.StringIntLabelMap proto. If provided, then the
mapped IDs of 'image/object/class/text' will take precedence over the
existing 'image/object/class/label' ID. Also, if provided, it is
assumed that 'image/object/class/text' will be in the data.
use_display_name: whether or not to use the `display_name` for label
mapping (instead of `name`). Only used if label_map_proto_file is
provided.
"""
self.keys_to_features = {
'image/encoded':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/format':
tf.FixedLenFeature((), tf.string, default_value='jpeg'),
'image/filename':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/key/sha256':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/source_id':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/height':
tf.FixedLenFeature((), tf.int64, 1),
'image/width':
tf.FixedLenFeature((), tf.int64, 1),
# Object boxes and classes.
'image/object/bbox/xmin':
tf.VarLenFeature(tf.float32),
'image/object/bbox/xmax':
tf.VarLenFeature(tf.float32),
'image/object/bbox/ymin':
tf.VarLenFeature(tf.float32),
'image/object/bbox/ymax':
tf.VarLenFeature(tf.float32),
'image/object/class/label':
tf.VarLenFeature(tf.int64),
'image/object/class/text':
tf.VarLenFeature(tf.string),
'image/object/area':
tf.VarLenFeature(tf.float32),
'image/object/is_crowd':
tf.VarLenFeature(tf.int64),
'image/object/difficult':
tf.VarLenFeature(tf.int64),
'image/object/group_of':
tf.VarLenFeature(tf.int64),
}
self.items_to_handlers = {
fields.InputDataFields.image: slim_example_decoder.Image(
image_key='image/encoded', format_key='image/format', channels=3),
fields.InputDataFields.source_id: (
slim_example_decoder.Tensor('image/source_id')),
fields.InputDataFields.key: (
slim_example_decoder.Tensor('image/key/sha256')),
fields.InputDataFields.filename: (
slim_example_decoder.Tensor('image/filename')),
# Object boxes and classes.
fields.InputDataFields.groundtruth_boxes: (
slim_example_decoder.BoundingBox(
['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/')),
fields.InputDataFields.groundtruth_area: slim_example_decoder.Tensor(
'image/object/area'),
fields.InputDataFields.groundtruth_is_crowd: (
slim_example_decoder.Tensor('image/object/is_crowd')),
fields.InputDataFields.groundtruth_difficult: (
slim_example_decoder.Tensor('image/object/difficult')),
fields.InputDataFields.groundtruth_group_of: (
slim_example_decoder.Tensor('image/object/group_of'))
}
if load_instance_masks:
self.keys_to_features['image/object/mask'] = tf.VarLenFeature(tf.float32)
self.items_to_handlers[
fields.InputDataFields.groundtruth_instance_masks] = (
slim_example_decoder.ItemHandlerCallback(
['image/object/mask', 'image/height', 'image/width'],
self._reshape_instance_masks))
# TODO: Add label_handler that decodes from 'image/object/class/text'
# primarily after the recent tf.contrib.slim changes make into a release
# supported by cloudml.
label_handler = slim_example_decoder.Tensor('image/object/class/label')
self.items_to_handlers[
fields.InputDataFields.groundtruth_classes] = label_handler | [
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python-control/python-control | df6b35212f8f657469627c227c893a175a6902cc | control/ctrlutil.py | python | unwrap | (angle, period=2*math.pi) | return angle | Unwrap a phase angle to give a continuous curve
Parameters
----------
angle : array_like
Array of angles to be unwrapped
period : float, optional
Period (defaults to `2*pi`)
Returns
-------
angle_out : array_like
Output array, with jumps of period/2 eliminated
Examples
--------
>>> import numpy as np
>>> theta = [5.74, 5.97, 6.19, 0.13, 0.35, 0.57]
>>> unwrap(theta, period=2 * np.pi)
[5.74, 5.97, 6.19, 6.413185307179586, 6.633185307179586, 6.8531853071795865] | Unwrap a phase angle to give a continuous curve | [
"Unwrap",
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] | def unwrap(angle, period=2*math.pi):
"""Unwrap a phase angle to give a continuous curve
Parameters
----------
angle : array_like
Array of angles to be unwrapped
period : float, optional
Period (defaults to `2*pi`)
Returns
-------
angle_out : array_like
Output array, with jumps of period/2 eliminated
Examples
--------
>>> import numpy as np
>>> theta = [5.74, 5.97, 6.19, 0.13, 0.35, 0.57]
>>> unwrap(theta, period=2 * np.pi)
[5.74, 5.97, 6.19, 6.413185307179586, 6.633185307179586, 6.8531853071795865]
"""
dangle = np.diff(angle)
dangle_desired = (dangle + period/2.) % period - period/2.
correction = np.cumsum(dangle_desired - dangle)
angle[1:] += correction
return angle | [
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zacharyvoase/urlobject | 5699ad545648fb976fef8905f3defbcba7cf04ba | urlobject/urlobject.py | python | URLObject.with_username | (self, username) | return self.with_netloc(self.netloc.with_username(username)) | Add or replace this URL's :attr:`.username`.
>>> print(URLObject("http://user@www.google.com").with_username("user2"))
http://user2@www.google.com | Add or replace this URL's :attr:`.username`. | [
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] | def with_username(self, username):
"""
Add or replace this URL's :attr:`.username`.
>>> print(URLObject("http://user@www.google.com").with_username("user2"))
http://user2@www.google.com
"""
return self.with_netloc(self.netloc.with_username(username)) | [
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john-kurkowski/tldextract | bdaa6f309c62465daa50a7759c9508f4a9472279 | tldextract/suffix_list.py | python | extract_tlds_from_suffix_list | (suffix_list_text) | return public_tlds, private_tlds | Parse the raw suffix list text for its different designations of
suffixes. | Parse the raw suffix list text for its different designations of
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] | def extract_tlds_from_suffix_list(suffix_list_text):
"""Parse the raw suffix list text for its different designations of
suffixes."""
public_text, _, private_text = suffix_list_text.partition(
PUBLIC_PRIVATE_SUFFIX_SEPARATOR
)
public_tlds = [m.group("suffix") for m in PUBLIC_SUFFIX_RE.finditer(public_text)]
private_tlds = [m.group("suffix") for m in PUBLIC_SUFFIX_RE.finditer(private_text)]
return public_tlds, private_tlds | [
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ialbert/biostar-central | 2dc7bd30691a50b2da9c2833ba354056bc686afa | biostar/forum/util.py | python | datetime_to_iso | (date) | return date.isoformat() | Converts a datetime to the ISO8601 format, like: 2014-05-20T06:11:41.733900.
Parameters:
date -- a `datetime` instance. | Converts a datetime to the ISO8601 format, like: 2014-05-20T06:11:41.733900. | [
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] | def datetime_to_iso(date):
"""
Converts a datetime to the ISO8601 format, like: 2014-05-20T06:11:41.733900.
Parameters:
date -- a `datetime` instance.
"""
if not isinstance(date, datetime):
date = datetime.combine(date, datetime.min.time())
return date.isoformat() | [
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google/grr | 8ad8a4d2c5a93c92729206b7771af19d92d4f915 | grr/core/grr_response_core/lib/utils.py | python | ShellQuote | (value) | return pipes.quote(SmartUnicode(value)) | Escapes the string for the safe use inside shell command line. | Escapes the string for the safe use inside shell command line. | [
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] | def ShellQuote(value):
"""Escapes the string for the safe use inside shell command line."""
# TODO(user): replace pipes.quote with shlex.quote when time comes.
return pipes.quote(SmartUnicode(value)) | [
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opengapps/apkcrawler | e33ab5d1f37c54139ebf9fa93214adbe037bc679 | googleplayapi/googleplay.py | python | GooglePlayAPI.details | (self, packageName) | return RequestResult(status_code, None) | Get app details from a package name.
packageName is the app unique ID (usually starting with 'com.'). | Get app details from a package name.
packageName is the app unique ID (usually starting with 'com.'). | [
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] | def details(self, packageName):
"""Get app details from a package name.
packageName is the app unique ID (usually starting with 'com.')."""
path = "details?doc=%s" % requests.utils.quote(packageName)
(status_code, message) = self.executeRequestApi2(path)
if status_code == http.client.OK:
return RequestResult(status_code, message.payload.detailsResponse)
return RequestResult(status_code, None) | [
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oaubert/python-vlc | 908ffdbd0844dc1849728c456e147788798c99da | generated/dev/vlc.py | python | Instance.media_new_fd | (self, fd) | return libvlc_media_new_fd(self, fd) | Create a media for an already open file descriptor.
The file descriptor shall be open for reading (or reading and writing).
Regular file descriptors, pipe read descriptors and character device
descriptors (including TTYs) are supported on all platforms.
Block device descriptors are supported where available.
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@note: This library will B{not} automatically close the file descriptor
under any circumstance. Nevertheless, a file descriptor can usually only be
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descriptor should probably be rewound to the beginning with lseek().
See L{media_release}.
@param fd: open file descriptor.
@return: the newly created media or None on error.
@version: LibVLC 1.1.5 and later. | Create a media for an already open file descriptor.
The file descriptor shall be open for reading (or reading and writing).
Regular file descriptors, pipe read descriptors and character device
descriptors (including TTYs) are supported on all platforms.
Block device descriptors are supported where available.
Directory descriptors are supported on systems that provide fdopendir().
Sockets are supported on all platforms where they are file descriptors,
i.e. all except Windows. | [
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Regular file descriptors, pipe read descriptors and character device
descriptors (including TTYs) are supported on all platforms.
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return libvlc_media_new_fd(self, fd) | [
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tendenci/tendenci | 0f2c348cc0e7d41bc56f50b00ce05544b083bf1d | tendenci/apps/helpdesk/management/commands/create_escalation_exclusions.py | python | usage | () | [] | def usage():
print("Options:")
print(" --days, -d: Days of week (monday, tuesday, etc)")
print(" --occurrences, -o: Occurrences: How many weeks ahead to exclude this day")
print(" --queues, -q: Queues to include (default: all). Use queue slugs")
print(" --verbose, -v: Display a list of dates excluded") | [
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hamcrest/PyHamcrest | 11aafabd688af2db23e8614f89da3015fd39f611 | src/hamcrest/core/description.py | python | Description.append_text | (self, text: str) | Appends some plain text to the description.
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"""Appends some plain text to the description.
:returns: ``self``, for chaining
"""
raise NotImplementedError("append_text") | [
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ProgVal/Limnoria | 181e34baf90a8cabc281e8349da6e36e1e558608 | plugins/PluginDownloader/plugin.py | python | PluginDownloader.info | (self, irc, msg, args, repository, plugin) | <repository> <plugin>
Displays informations on the <plugin> in the <repository>. | <repository> <plugin> | [
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] | def info(self, irc, msg, args, repository, plugin):
"""<repository> <plugin>
Displays informations on the <plugin> in the <repository>."""
global repositories
if repository not in repositories:
irc.error(_(
'This repository does not exist or is not known by '
'this bot.'
))
elif plugin not in repositories[repository].getPluginList():
irc.error(_('This plugin does not exist in this repository.'))
else:
info = repositories[repository].getInfo(plugin)
if info is None:
irc.error(_('No README found for this plugin.'))
else:
if info.startswith('Insert a description of your plugin here'):
irc.error(_('This plugin has no description.'))
else:
info = info.split('\n\n')[0]
irc.reply(info.replace('\n', ' ')) | [
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tensorflow/tensor2tensor | 2a33b152d7835af66a6d20afe7961751047e28dd | tensor2tensor/utils/cloud_mlengine.py | python | flags_as_args | () | return args | Convert FLAGS to list of args suitable for passing on cmd line. | Convert FLAGS to list of args suitable for passing on cmd line. | [
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"""Convert FLAGS to list of args suitable for passing on cmd line."""
if hasattr(FLAGS, "flag_values_dict"):
args_dict = FLAGS.flag_values_dict()
else:
args_dict = dict(FLAGS.__dict__["__flags"])
del args_dict["cloud_mlengine"]
# Configured later
del args_dict["t2t_usr_dir"]
args_dict.pop("h", None)
args_dict.pop("helpfull", None)
args_dict.pop("helpshort", None)
args_dict.pop("help", None)
args = []
for name, val in args_dict.items():
if val is None:
continue
if name.startswith("autotune"):
continue
args.extend(["--%s=%s" % (name, str(val))])
return args | [
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patrickdessalle/periscope | 74537da58c3f930ef61cb04d3f8f964aedde88ef | periscope/periscope.py | python | Periscope.listSubtitles | (self, filename, langs=None) | return subtitles | Searches subtitles within the active plugins and returns all found matching subtitles ordered by language then by plugin. | Searches subtitles within the active plugins and returns all found matching subtitles ordered by language then by plugin. | [
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'''Searches subtitles within the active plugins and returns all found matching subtitles ordered by language then by plugin.'''
#if not os.path.isfile(filename):
#raise InvalidFileException(filename, "does not exist")
log.info("Searching subtitles for %s with langs %s" %(filename, langs))
subtitles = []
q = Queue()
for name in self.pluginNames:
try :
plugin = getattr(plugins, name)(self.config, self.cache_path)
log.info("Searching on %s " %plugin.__class__.__name__)
thread = threading.Thread(target=plugin.searchInThread, args=(q, filename, langs))
thread.start()
except ImportError :
log.error("Plugin %s is not a valid plugin name. Skipping it.")
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for name in self.pluginNames:
subs = q.get(True)
if subs and len(subs) > 0:
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subtitles += subs
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subtitles += [sub] # Add an array with just that sub
if len(subtitles) == 0:
return []
return subtitles | [
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biolab/orange3 | 41685e1c7b1d1babe680113685a2d44bcc9fec0b | Orange/widgets/utils/itemmodels.py | python | ContinuousPalettesModel.indexOf | (self, x) | return None | [] | def indexOf(self, x):
if isinstance(x, str):
for i, item in enumerate(self.items):
if not isinstance(item, str) \
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VirtueSecurity/aws-extender | d123b7e1a845847709ba3a481f11996bddc68a1c | BappModules/boto/ec2/connection.py | python | EC2Connection.get_all_reservations | (self, instance_ids=None, filters=None,
dry_run=False, max_results=None, next_token=None) | return self.get_list('DescribeInstances', params,
[('item', Reservation)], verb='POST') | Retrieve all the instance reservations associated with your account.
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:param instance_ids: A list of strings of instance IDs
:type filters: dict
:param filters: Optional filters that can be used to limit the
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:param dry_run: Set to True if the operation should not actually run.
:type max_results: int
:param max_results: The maximum number of paginated instance
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:type next_token: str
:param next_token: A string specifying the next paginated set
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:rtype: list
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] | def get_all_reservations(self, instance_ids=None, filters=None,
dry_run=False, max_results=None, next_token=None):
"""
Retrieve all the instance reservations associated with your account.
:type instance_ids: list
:param instance_ids: A list of strings of instance IDs
:type filters: dict
:param filters: Optional filters that can be used to limit the
results returned. Filters are provided in the form of a
dictionary consisting of filter names as the key and
filter values as the value. The set of allowable filter
names/values is dependent on the request being performed.
Check the EC2 API guide for details.
:type dry_run: bool
:param dry_run: Set to True if the operation should not actually run.
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:type next_token: str
:param next_token: A string specifying the next paginated set
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:rtype: list
:return: A list of :class:`boto.ec2.instance.Reservation`
"""
params = {}
if instance_ids:
self.build_list_params(params, instance_ids, 'InstanceId')
if filters:
if 'group-id' in filters:
gid = filters.get('group-id')
if not gid.startswith('sg-') or len(gid) != 11:
warnings.warn(
"The group-id filter now requires a security group "
"identifier (sg-*) instead of a group name. To filter "
"by group name use the 'group-name' filter instead.",
UserWarning)
self.build_filter_params(params, filters)
if dry_run:
params['DryRun'] = 'true'
if max_results is not None:
params['MaxResults'] = max_results
if next_token:
params['NextToken'] = next_token
return self.get_list('DescribeInstances', params,
[('item', Reservation)], verb='POST') | [
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mesalock-linux/mesapy | ed546d59a21b36feb93e2309d5c6b75aa0ad95c9 | lib-python/2.7/mhlib.py | python | Folder._copysequences | (self, fromfolder, refileditems) | Helper for refilemessages() to copy sequences. | Helper for refilemessages() to copy sequences. | [
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fromsequences = fromfolder.getsequences()
tosequences = self.getsequences()
changed = 0
for name, seq in fromsequences.items():
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toseq = tosequences[name]
new = 0
except KeyError:
toseq = []
new = 1
for fromn, ton in refileditems:
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openvinotoolkit/training_extensions | e7aa33af94a1f8004d3ea2df259d99234dfca046 | ote_sdk/ote_sdk/entities/annotation.py | python | AnnotationSceneEntity.editor_name | (self) | return self.__editor | Returns the editor's name that made the AnnotationSceneEntity object. | Returns the editor's name that made the AnnotationSceneEntity object. | [
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"""
Returns the editor's name that made the AnnotationSceneEntity object.
"""
return self.__editor | [
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sapruash/RecursiveNN | 0ec71d3762625d1f72c0af42127d3d588f33c539 | tf_seq_lstm.py | python | tf_seqLSTM.add_placeholders | (self) | [] | def add_placeholders(self):
self.batch_len = tf.placeholder(tf.int32,name="batch_len")
self.max_time = tf.placeholder(tf.int32,name="max_time")
dim1=self.config.batch_size*(1+self.internal)
self.input = tf.placeholder(tf.int32,shape=[None,self.config.maxseqlen],name="input")
self.labels = tf.placeholder(tf.int32,shape=None
,name="labels")
self.dropout = tf.placeholder(tf.float32,name="dropout")
self.lngths = tf.placeholder(tf.int32,shape=None
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peterbrittain/asciimatics | 9a490faddf484ee5b9b845316f921f5888b23b18 | asciimatics/paths.py | python | Path._add_step | (self, pos) | Add a step to the end of the current recorded path.
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self._rec_x = pos[0]
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google/grr | 8ad8a4d2c5a93c92729206b7771af19d92d4f915 | grr/client/grr_response_client/osx/objc.py | python | CFBoolean.__bool__ | (self) | return self.value | [] | def __bool__(self):
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nipy/nibabel | 4703f4d8e32be4cec30e829c2d93ebe54759bb62 | nibabel/_version.py | python | render_git_describe | (pieces) | return rendered | TAG[-DISTANCE-gHEX][-dirty].
Like 'git describe --tags --dirty --always'.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix) | TAG[-DISTANCE-gHEX][-dirty]. | [
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] | def render_git_describe(pieces):
"""TAG[-DISTANCE-gHEX][-dirty].
Like 'git describe --tags --dirty --always'.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered | [
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TengXiaoDai/DistributedCrawling | f5c2439e6ce68dd9b49bde084d76473ff9ed4963 | Lib/site-packages/setuptools/package_index.py | python | open_with_auth | (url, opener=urllib.request.urlopen) | return fp | Open a urllib2 request, handling HTTP authentication | Open a urllib2 request, handling HTTP authentication | [
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"""Open a urllib2 request, handling HTTP authentication"""
scheme, netloc, path, params, query, frag = urllib.parse.urlparse(url)
# Double scheme does not raise on Mac OS X as revealed by a
# failing test. We would expect "nonnumeric port". Refs #20.
if netloc.endswith(':'):
raise http_client.InvalidURL("nonnumeric port: ''")
if scheme in ('http', 'https'):
auth, host = splituser(netloc)
else:
auth = None
if not auth:
cred = PyPIConfig().find_credential(url)
if cred:
auth = str(cred)
info = cred.username, url
log.info('Authenticating as %s for %s (from .pypirc)', *info)
if auth:
auth = "Basic " + _encode_auth(auth)
parts = scheme, host, path, params, query, frag
new_url = urllib.parse.urlunparse(parts)
request = urllib.request.Request(new_url)
request.add_header("Authorization", auth)
else:
request = urllib.request.Request(url)
request.add_header('User-Agent', user_agent)
fp = opener(request)
if auth:
# Put authentication info back into request URL if same host,
# so that links found on the page will work
s2, h2, path2, param2, query2, frag2 = urllib.parse.urlparse(fp.url)
if s2 == scheme and h2 == host:
parts = s2, netloc, path2, param2, query2, frag2
fp.url = urllib.parse.urlunparse(parts)
return fp | [
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enthought/traitsui | b7c38c7a47bf6ae7971f9ddab70c8a358647dd25 | traitsui/examples/demo/Advanced/Tabular_editor_with_context_menu_demo.py | python | PlayerAdapter.sub | (self, object, column) | Decrement the affected player statistic. | Decrement the affected player statistic. | [
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] | def sub(self, object, column):
"""Decrement the affected player statistic."""
column_name = self.column_map[column]
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rsmusllp/termineter | 9311d6d995a7bf0f80853a00a115a8fa16aa0727 | lib/termineter/interface.py | python | InteractiveInterpreter.do_reload | (self, args) | Reload a module in to the framework | Reload a module in to the framework | [
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] | def do_reload(self, args):
"""Reload a module in to the framework"""
if args.module is not None:
if args.module not in self.frmwk.modules:
self.print_error('Invalid Module Selected.')
return
module = self.frmwk.modules[args.module]
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self.print_error('Must \'use\' module first')
return
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/Python3/asyncio/sslproto.py | python | _SSLProtocolTransport.can_write_eof | (self) | return False | Return True if this transport supports write_eof(), False if not. | Return True if this transport supports write_eof(), False if not. | [
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"""Return True if this transport supports write_eof(), False if not."""
return False | [
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lvpengyuan/masktextspotter.caffe2 | da99ef31f5ccb4de5248bb881d5b4a291910c8ae | lib/datasets/roidb.py | python | filter_for_training | (roidb) | return filtered_roidb | Remove roidb entries that have no usable RoIs based on config settings. | Remove roidb entries that have no usable RoIs based on config settings. | [
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"""Remove roidb entries that have no usable RoIs based on config settings.
"""
def is_valid(entry):
# Valid images have:
# (1) At least one foreground RoI OR
# (2) At least one background RoI
overlaps = entry['max_overlaps']
# find boxes with sufficient overlap
fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
(overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# image is only valid if such boxes exist
valid = len(fg_inds) > 0 or len(bg_inds) > 0
if cfg.MODEL.KEYPOINTS_ON:
# If we're training for keypoints, exclude images with no keypoints
valid = valid and entry['has_visible_keypoints']
return valid
num = len(roidb)
filtered_roidb = [entry for entry in roidb if is_valid(entry)]
num_after = len(filtered_roidb)
logger.info('Filtered {} roidb entries: {} -> {}'.
format(num - num_after, num, num_after))
return filtered_roidb | [
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reclosedev/requests-cache | a29b85c3df40f874f80d06c949463b9532dada40 | requests_cache/patcher.py | python | install_cache | (
cache_name: str = 'http_cache',
backend: BackendSpecifier = None,
expire_after: ExpirationTime = -1,
urls_expire_after: Dict[str, ExpirationTime] = None,
allowable_codes: Iterable[int] = (200,),
allowable_methods: Iterable['str'] = ('GET', 'HEAD'),
filter_fn: Callable = None,
stale_if_error: bool = False,
session_factory: Type[OriginalSession] = CachedSession,
**kwargs,
) | Install the cache for all ``requests`` functions by monkey-patching :py:class:`requests.Session`
Example:
>>> requests_cache.install_cache('demo_cache')
Accepts all the same parameters as :py:class:`.CachedSession`. Additional parameters:
Args:
session_factory: Session class to use. It must inherit from either
:py:class:`.CachedSession` or :py:class:`.CacheMixin` | Install the cache for all ``requests`` functions by monkey-patching :py:class:`requests.Session` | [
"Install",
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"all",
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"functions",
"by",
"monkey",
"-",
"patching",
":",
"py",
":",
"class",
":",
"requests",
".",
"Session"
] | def install_cache(
cache_name: str = 'http_cache',
backend: BackendSpecifier = None,
expire_after: ExpirationTime = -1,
urls_expire_after: Dict[str, ExpirationTime] = None,
allowable_codes: Iterable[int] = (200,),
allowable_methods: Iterable['str'] = ('GET', 'HEAD'),
filter_fn: Callable = None,
stale_if_error: bool = False,
session_factory: Type[OriginalSession] = CachedSession,
**kwargs,
):
"""
Install the cache for all ``requests`` functions by monkey-patching :py:class:`requests.Session`
Example:
>>> requests_cache.install_cache('demo_cache')
Accepts all the same parameters as :py:class:`.CachedSession`. Additional parameters:
Args:
session_factory: Session class to use. It must inherit from either
:py:class:`.CachedSession` or :py:class:`.CacheMixin`
"""
class _ConfiguredCachedSession(session_factory): # type: ignore # See mypy issue #5865
def __init__(self):
super().__init__(
cache_name=cache_name,
backend=backend,
expire_after=expire_after,
urls_expire_after=urls_expire_after,
allowable_codes=allowable_codes,
allowable_methods=allowable_methods,
filter_fn=filter_fn,
stale_if_error=stale_if_error,
**kwargs,
)
_patch_session_factory(_ConfiguredCachedSession) | [
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trakt/Plex-Trakt-Scrobbler | aeb0bfbe62fad4b06c164f1b95581da7f35dce0b | Trakttv.bundle/Contents/Libraries/Shared/requests/packages/urllib3/packages/ordered_dict.py | python | OrderedDict.keys | (self) | return list(self) | od.keys() -> list of keys in od | od.keys() -> list of keys in od | [
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"-",
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'od.keys() -> list of keys in od'
return list(self) | [
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inducer/loopy | 55143b21711a534c07bbb14aaa63ff3879a93433 | loopy/symbolic.py | python | AffineConditionToISLSetMapper._map_logical_reduce | (self, expr, f) | return reduce(f, sets) | :arg f: Reduction callable. | :arg f: Reduction callable. | [
":",
"arg",
"f",
":",
"Reduction",
"callable",
"."
] | def _map_logical_reduce(self, expr, f):
"""
:arg f: Reduction callable.
"""
sets = [self.rec(child) for child in expr.children]
return reduce(f, sets) | [
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AutodeskRoboticsLab/Mimic | 85447f0d346be66988303a6a054473d92f1ed6f4 | mimic/scripts/extern/pyqtgraph_0_11_0/pyqtgraph/widgets/DataFilterWidget.py | python | DataFilterParameter.setFields | (self, fields) | Set the list of fields that are available to be filtered.
*fields* must be a dict or list of tuples that maps field names
to a specification describing the field. Each specification is
itself a dict with either ``'mode':'range'`` or ``'mode':'enum'``::
filter.setFields([
('field1', {'mode': 'range'}),
('field2', {'mode': 'enum', 'values': ['val1', 'val2', 'val3']}),
('field3', {'mode': 'enum', 'values': {'val1':True, 'val2':False, 'val3':True}}),
]) | Set the list of fields that are available to be filtered. | [
"Set",
"the",
"list",
"of",
"fields",
"that",
"are",
"available",
"to",
"be",
"filtered",
"."
] | def setFields(self, fields):
"""Set the list of fields that are available to be filtered.
*fields* must be a dict or list of tuples that maps field names
to a specification describing the field. Each specification is
itself a dict with either ``'mode':'range'`` or ``'mode':'enum'``::
filter.setFields([
('field1', {'mode': 'range'}),
('field2', {'mode': 'enum', 'values': ['val1', 'val2', 'val3']}),
('field3', {'mode': 'enum', 'values': {'val1':True, 'val2':False, 'val3':True}}),
])
"""
with fn.SignalBlock(self.sigTreeStateChanged, self.filterChanged):
self.fields = OrderedDict(fields)
names = self.fieldNames()
self.setAddList(names)
# update any existing filters
for ch in self.children():
name = ch.fieldName
if name in fields:
ch.updateFilter(fields[name])
self.sigFilterChanged.emit(self) | [
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misterch0c/shadowbroker | e3a069bea47a2c1009697941ac214adc6f90aa8d | windows/Resources/Python/Core/Lib/numbers.py | python | Real.__divmod__ | (self, other) | return (
self // other, self % other) | divmod(self, other): The pair (self // other, self % other).
Sometimes this can be computed faster than the pair of
operations. | divmod(self, other): The pair (self // other, self % other).
Sometimes this can be computed faster than the pair of
operations. | [
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"the",
"pair",
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] | def __divmod__(self, other):
"""divmod(self, other): The pair (self // other, self % other).
Sometimes this can be computed faster than the pair of
operations.
"""
return (
self // other, self % other) | [
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conansherry/detectron2 | 72c935d9aad8935406b1038af408aa06077d950a | detectron2/utils/visualizer.py | python | Visualizer.draw_sem_seg | (self, sem_seg, area_threshold=None, alpha=0.8) | return self.output | Draw semantic segmentation predictions/labels.
Args:
sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
area_threshold (int): segments with less than `area_threshold` are not drawn.
alpha (float): the larger it is, the more opaque the segmentations are.
Returns:
output (VisImage): image object with visualizations. | Draw semantic segmentation predictions/labels. | [
"Draw",
"semantic",
"segmentation",
"predictions",
"/",
"labels",
"."
] | def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):
"""
Draw semantic segmentation predictions/labels.
Args:
sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
area_threshold (int): segments with less than `area_threshold` are not drawn.
alpha (float): the larger it is, the more opaque the segmentations are.
Returns:
output (VisImage): image object with visualizations.
"""
if isinstance(sem_seg, torch.Tensor):
sem_seg = sem_seg.numpy()
labels, areas = np.unique(sem_seg, return_counts=True)
sorted_idxs = np.argsort(-areas).tolist()
labels = labels[sorted_idxs]
for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):
try:
mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]
except (AttributeError, IndexError):
mask_color = None
binary_mask = (sem_seg == label).astype(np.uint8)
text = self.metadata.stuff_classes[label]
self.draw_binary_mask(
binary_mask,
color=mask_color,
edge_color=_OFF_WHITE,
text=text,
alpha=alpha,
area_threshold=area_threshold,
)
return self.output | [
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... | https://github.com/conansherry/detectron2/blob/72c935d9aad8935406b1038af408aa06077d950a/detectron2/utils/visualizer.py#L372-L405 | |
MontrealCorpusTools/Montreal-Forced-Aligner | 63473f9a4fabd31eec14e1e5022882f85cfdaf31 | montreal_forced_aligner/language_modeling/trainer.py | python | LmCorpusTrainer.normalized_text_iter | (self, min_count: int = 1) | Construct an iterator over the normalized texts in the corpus
Parameters
----------
min_count: int
Minimum word count to include in the output, otherwise will use OOV code, defaults to 1
Yields
-------
str
Normalized text | Construct an iterator over the normalized texts in the corpus | [
"Construct",
"an",
"iterator",
"over",
"the",
"normalized",
"texts",
"in",
"the",
"corpus"
] | def normalized_text_iter(self, min_count: int = 1) -> Generator:
"""
Construct an iterator over the normalized texts in the corpus
Parameters
----------
min_count: int
Minimum word count to include in the output, otherwise will use OOV code, defaults to 1
Yields
-------
str
Normalized text
"""
unk_words = {k for k, v in self.word_counts.items() if v <= min_count}
for u in self.utterances:
text = u.text.split()
new_text = []
for t in text:
if u.speaker.dictionary is not None:
u.speaker.dictionary.to_int(t)
lookup = u.speaker.dictionary.split_clitics(t)
if lookup is None:
continue
else:
lookup = [t]
for item in lookup:
if item in unk_words:
new_text.append(self.oov_word)
self.oovs_found[item] += 1
elif (
u.speaker.dictionary is not None and item not in u.speaker.dictionary.words
):
new_text.append(self.oov_word)
else:
new_text.append(item)
yield " ".join(new_text) | [
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OpenNMT/OpenNMT-py | 4815f07fcd482af9a1fe1d3b620d144197178bc5 | onmt/translate/greedy_search.py | python | GreedySearch.__init__ | (self, pad, bos, eos, unk, batch_size, global_scorer,
min_length, block_ngram_repeat, exclusion_tokens,
return_attention, max_length, sampling_temp, keep_topk,
keep_topp, beam_size, ban_unk_token) | [] | def __init__(self, pad, bos, eos, unk, batch_size, global_scorer,
min_length, block_ngram_repeat, exclusion_tokens,
return_attention, max_length, sampling_temp, keep_topk,
keep_topp, beam_size, ban_unk_token):
super(GreedySearch, self).__init__(
pad, bos, eos, unk, batch_size, beam_size, global_scorer,
min_length, block_ngram_repeat, exclusion_tokens,
return_attention, max_length, ban_unk_token)
self.sampling_temp = sampling_temp
self.keep_topk = keep_topk
self.keep_topp = keep_topp
self.topk_scores = None
self.beam_size = beam_size | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/setuptools/pep425tags.py | python | get_flag | (var, fallback, expected=True, warn=True) | return val == expected | Use a fallback method for determining SOABI flags if the needed config
var is unset or unavailable. | Use a fallback method for determining SOABI flags if the needed config
var is unset or unavailable. | [
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"is",
"unset",
"or",
"unavailable",
"."
] | def get_flag(var, fallback, expected=True, warn=True):
"""Use a fallback method for determining SOABI flags if the needed config
var is unset or unavailable."""
val = get_config_var(var)
if val is None:
if warn:
warnings.warn("Config variable '{0}' is unset, Python ABI tag may "
"be incorrect".format(var), RuntimeWarning, 2)
return fallback()
return val == expected | [
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madduck/reclass | 9c3478498a5dfa3d1e5cf7aa3b602ca3b53ee15b | reclass/storage/__init__.py | python | NodeStorageBase.enumerate_nodes | (self) | [] | def enumerate_nodes(self):
msg = "Storage class '{0}' does not implement node enumeration."
raise NotImplementedError(msg.format(self.name)) | [
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] | https://github.com/madduck/reclass/blob/9c3478498a5dfa3d1e5cf7aa3b602ca3b53ee15b/reclass/storage/__init__.py#L25-L27 |
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