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factory_boy-2.12.0 | factory_boy-2.12.0//factory/base.pyclass:BaseFactory/create_batch | @classmethod
def create_batch(cls, size, **kwargs):
"""Create a batch of instances of the given class, with overriden attrs.
Args:
size (int): the number of instances to create
Returns:
object list: the created instances
"""
return [cls.create(**kwargs) for _ in range(size)]
|
PyQtPurchasing-5.14.0 | PyQtPurchasing-5.14.0//configure.pyclass:ModuleConfiguration/get_qmake_configuration | @staticmethod
def get_qmake_configuration(target_configuration):
""" Return a dict of qmake configuration values for CONFIG, DEFINES,
INCLUDEPATH, LIBS and QT. If value names (i.e. dict keys) have either
'Qt4' or 'Qt5' prefixes then they are specific to the corresponding
version of Qt. target_configuration is the target configuration.
"""
return {'QT': 'purchasing'}
|
bitcoinX-0.2.4 | bitcoinX-0.2.4//bitcoinx/keys.pyclass:PublicKey/from_hex | @classmethod
def from_hex(cls, hex_str):
"""Construct a PublicKey from a hexadecimal string."""
return cls.from_bytes(bytes.fromhex(hex_str))
|
Kivy-1.11.1 | Kivy-1.11.1//kivy/parser.pyfile:/kivy/parser.py:function:parse_bool/parse_bool | def parse_bool(text):
"""Parse a string to a boolean, ignoring case. "true"/"1" is True,
"false"/"0" is False. Anything else throws an exception."""
if text.lower() in ('true', '1'):
return True
elif text.lower() in ('false', '0'):
return False
raise Exception('Invalid boolean: %s' % text)
|
dronin-pyqtgraph-20160825.3 | dronin-pyqtgraph-20160825.3//dronin_pyqtgraph/exceptionHandling.pyfile:/dronin_pyqtgraph/exceptionHandling.py:function:setTracebackClearing/setTracebackClearing | def setTracebackClearing(clear=True):
"""
Enable or disable traceback clearing.
By default, clearing is disabled and Python will indefinitely store unhandled exception stack traces.
This function is provided since Python's default behavior can cause unexpected retention of
large memory-consuming objects.
"""
global clear_tracebacks
clear_tracebacks = clear
|
mercurial | mercurial//interfaces/repository.pyclass:imanifeststorage/strip | def strip(minlink, transaction):
"""Remove storage of items starting at a linkrev.
See the documentation in ``ifilemutation`` for more.
"""
|
wxPython-4.1.0 | wxPython-4.1.0//wx/lib/agw/cubecolourdialog.pyfile:/wx/lib/agw/cubecolourdialog.py:function:FindC/FindC | def FindC(line):
""" Internal function. """
if line.slope is None:
c = line.y
else:
c = line.y - line.slope * line.x
return c
|
cluster_pack | cluster_pack//_version.pyfile:/_version.py:function:render_pep440_pre/render_pep440_pre | def render_pep440_pre(pieces):
"""TAG[.post.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post.devDISTANCE
"""
if pieces['closest-tag']:
rendered = pieces['closest-tag']
if pieces['distance']:
rendered += '.post.dev%d' % pieces['distance']
else:
rendered = '0.post.dev%d' % pieces['distance']
return rendered
|
memote-0.10.2 | memote-0.10.2//src/memote/support/helpers.pyfile:/src/memote/support/helpers.py:function:open_exchanges/open_exchanges | def open_exchanges(model):
"""
Open all exchange reactions.
Parameters
----------
model : cobra.Model
The metabolic model under investigation.
"""
for rxn in model.exchanges:
rxn.bounds = -1000, 1000
|
hapic_apispec-0.37.0 | hapic_apispec-0.37.0//apispec/auto_ref_strategy.pyfile:/apispec/auto_ref_strategy.py:function:get_excluded_params/get_excluded_params | def get_excluded_params(schema):
"""
Get all params excluded in this schema,
if "only" is provided in schema instance,
consider all not included params as excluded.
:param schema: instance or cls schema
:return: set of excluded params
"""
if isinstance(schema, type):
return set()
exclude = set()
only = set()
if getattr(schema, 'exclude', ()):
exclude = set(getattr(schema, 'exclude', ()))
if getattr(schema, 'only', ()):
only = set(getattr(schema, 'only', ()))
if only:
for field in schema._declared_fields:
if field not in only:
exclude.add(str(field))
return exclude
|
hg-evolve-9.3.1 | hg-evolve-9.3.1//hgext3rd/topic/common.pyfile:/hgext3rd/topic/common.py:function:hastopicext/hastopicext | def hastopicext(repo):
"""True if the repo use the topic extension"""
return getattr(repo, 'hastopicext', False)
|
zillion | zillion//sql_utils.pyfile:/sql_utils.py:function:column_fullname/column_fullname | def column_fullname(column, prefix=None):
"""Get a fully qualified name for a column
Parameters
----------
column : SQLAlchemy column
A SQLAlchemy column object to get the full name for
prefix : str, optional
If specified, a manual prefix to prepend to the output string. This
will automatically be separted with a ".".
Returns
-------
str
A fully qualified column name. The exact format will vary depending on
your SQLAlchemy metadata, but an example would be: schema.table.column
"""
name = '%s.%s' % (column.table.fullname, column.name)
if prefix:
name = prefix + '.' + name
return name
|
ppp_cas-0.8 | ppp_cas-0.8//ppp_cas/calchasYacc.pyfile:/ppp_cas/calchasYacc.py:function:p_expression_parentheses/p_expression_parentheses | def p_expression_parentheses(p):
"""expression : LPAREN expression RPAREN"""
p[0] = p[2]
|
abipy-0.7.0 | abipy-0.7.0//abipy/tools/plotting.pyclass:GenericDataFilesPlotter/from_files | @classmethod
def from_files(cls, filepaths):
"""
Build object from a list of `filenames`.
"""
new = cls()
for filepath in filepaths:
new.add_file(filepath)
return new
|
synapse_pay_rest_native-3.4.8 | synapse_pay_rest_native-3.4.8//synapse_pay_rest/models/transactions/transaction.pyclass:Transaction/all | @classmethod
def all(cls, node=None, **kwargs):
"""Retrieve all trans records (limited by pagination) as Transactions.
Args:
node (BaseNode): the node from which to send funds
per_page (int, str): (opt) number of records to retrieve
page (int, str): (opt) page number to retrieve
Returns:
list: containing 0 or more Transaction instances
"""
response = node.user.client.trans.get(node.user.id, node.id, **kwargs)
return cls.multiple_from_response(node, response['trans'])
|
pda | pda//utils.pyfile:/utils.py:function:print_header/print_header | def print_header():
"""Print pretty header of list content
The head contains 5 columns, each column has a differnt string length.
"""
headers = ['TASK#', 'SUMMARY', 'LIST TYPE', 'DUE TIME', 'PRIORITY']
print()
print('{0:<5} {1:<60} {2:<9} {3:<8} {4:<8}'.format(*headers))
print('{0:=<5} {1:=<60} {2:=<9} {3:=<8} {4:=<8}'.format(*['', '',
'', '', '']))
|
plone.app.standardtiles-2.3.2 | plone.app.standardtiles-2.3.2//plone/app/standardtiles/rss.pyclass:IFeed/update_failed | def update_failed():
"""Return if the last update failed or not."""
|
nlpnet | nlpnet//arguments.pyfile:/arguments.py:function:fill_defaults/fill_defaults | def fill_defaults(args, defaults_per_task):
"""
This function fills arguments not explicitly set (left as None)
with default values according to the chosen task.
We can't rely on argparse to it because using subparsers with
set_defaults and a parent parser overwrites the defaults.
"""
task = args.task
defaults = defaults_per_task[task]
for arg in args.__dict__:
if getattr(args, arg) is None and arg in defaults:
setattr(args, arg, defaults[arg])
|
pyflux_docker | pyflux_docker//families/normal.pyclass:Normal/reg_score_function | @staticmethod
def reg_score_function(X, y, mean, scale, shape, skewness):
""" GAS Normal Regression Update term using gradient only - native Python function
Parameters
----------
X : float
datapoint for the right hand side variable
y : float
datapoint for the time series
mean : float
location parameter for the Normal distribution
scale : float
scale parameter for the Normal distribution
shape : float
tail thickness parameter for the Normal distribution
skewness : float
skewness parameter for the Normal distribution
Returns
----------
- Score of the Normal family
"""
return X * (y - mean)
|
wvpy-0.1.9 | wvpy-0.1.9//wvpy/util.pyfile:/wvpy/util.py:function:cross_predict_model/cross_predict_model | def cross_predict_model(fitter, X, Y, plan):
"""train a model Y~X using the cross validation plan and return predictions"""
preds = [None] * X.shape[0]
for g in range(len(plan)):
pi = plan[g]
model = fitter.fit(X.iloc[pi['train']], Y.iloc[pi['train']])
predg = model.predict(X.iloc[pi['test']])
for i in range(len(pi['test'])):
preds[pi['test'][i]] = predg[i]
return preds
|
distributed | distributed//profile.pyfile:/profile.py:function:identifier/identifier | def identifier(frame):
""" A string identifier from a frame
Strings are cheaper to use as indexes into dicts than tuples or dicts
"""
if frame is None:
return 'None'
else:
return ';'.join((frame.f_code.co_name, frame.f_code.co_filename,
str(frame.f_code.co_firstlineno)))
|
pyNastran | pyNastran//bdf/cards/materials.pyclass:MAT8/export_to_hdf5 | @classmethod
def export_to_hdf5(cls, h5_file, model, mids):
"""exports the materials in a vectorized way"""
comments = []
e11 = []
e22 = []
nu12 = []
g12 = []
g1z = []
g2z = []
rho = []
a1 = []
a2 = []
tref = []
Xt = []
Xc = []
Yt = []
Yc = []
S = []
ge = []
F12 = []
strn = []
for mid in mids:
material = model.materials[mid]
e11.append(material.e11)
e22.append(material.e22)
nu12.append(material.nu12)
g12.append(material.g12)
g1z.append(material.g1z)
g2z.append(material.g2z)
rho.append(material.rho)
a1.append(material.a1)
a2.append(material.a2)
tref.append(material.tref)
ge.append(material.ge)
Xt.append(material.Xt)
Xc.append(material.Xc)
Yt.append(material.Yt)
Yc.append(material.Yc)
S.append(material.S)
F12.append(material.F12)
strn.append(material.strn)
h5_file.create_dataset('mid', data=mids)
h5_file.create_dataset('E11', data=e11)
h5_file.create_dataset('E22', data=e22)
h5_file.create_dataset('Nu12', data=nu12)
h5_file.create_dataset('G12', data=g12)
h5_file.create_dataset('G1z', data=g1z)
h5_file.create_dataset('G2z', data=g2z)
h5_file.create_dataset('A1', data=a1)
h5_file.create_dataset('A2', data=a2)
h5_file.create_dataset('rho', data=rho)
h5_file.create_dataset('tref', data=tref)
h5_file.create_dataset('ge', data=ge)
h5_file.create_dataset('Xt', data=Xt)
h5_file.create_dataset('Xc', data=Xc)
h5_file.create_dataset('Yt', data=Yt)
h5_file.create_dataset('Yc', data=Yc)
h5_file.create_dataset('S', data=S)
h5_file.create_dataset('F12', data=F12)
h5_file.create_dataset('strn', data=strn)
|
eo-learn-visualization-0.7.3 | eo-learn-visualization-0.7.3//eolearn/visualization/eoexecutor_visualization.pyclass:EOExecutorVisualization/_format_timedelta | @staticmethod
def _format_timedelta(value1, value2):
""" Method for formatting time delta into report
"""
return str(value2 - value1)
|
systemrdl | systemrdl//rdltypes.pyclass:UserStruct/get_parent_scope | @classmethod
def get_parent_scope(cls):
"""
Returns reference to parent component that contains this type definition.
"""
return getattr(cls, '_parent_scope', None)
|
plone.app.imagecropping-2.2.2 | plone.app.imagecropping-2.2.2//src/plone/app/imagecropping/interfaces.pyclass:IImageCroppingUtils/get_image_field | def get_image_field(fieldname):
"""Returns the image field"""
|
mmvec-1.0.4 | mmvec-1.0.4//mmvec/util.pyfile:/mmvec/util.py:function:embeddings2ranks/embeddings2ranks | def embeddings2ranks(embeddings):
""" Converts embeddings to ranks"""
microbes = embeddings.loc[embeddings.embed_type == 'microbe']
metabolites = embeddings.loc[embeddings.embed_type == 'metabolite']
U = microbes.pivot(index='feature_id', columns='axis', values='values')
V = metabolites.pivot(index='feature_id', columns='axis', values='values')
pc_ids = sorted(list(set(U.columns) - {'bias'}))
U['ones'] = 1
V['ones'] = 1
ranks = U[pc_ids + ['ones', 'bias']] @ V[pc_ids + ['bias', 'ones']].T
ranks = ranks - ranks.mean(axis=1).values.reshape(-1, 1)
return ranks
|
horae.subscription-1.0a1 | horae.subscription-1.0a1//horae/subscription/interfaces.pyclass:IMessage/message | def message(html=False):
""" Returns the notification message to be sent
"""
|
ib_insync-0.9.60 | ib_insync-0.9.60//ib_insync/util.pyfile:/ib_insync/util.py:function:isNan/isNan | def isNan(x: float) ->bool:
"""Not a number test."""
return x != x
|
failure | failure//failure.pyclass:Failure/from_dict | @classmethod
def from_dict(cls, data):
"""Converts this from a dictionary to a object."""
data = dict(data)
cause = data.get('cause')
if cause is not None:
data['cause'] = cls.from_dict(cause)
return cls(**data)
|
simfin-0.6.0 | simfin-0.6.0//simfin/transform.pyfile:/simfin/transform.py:function:avg_ttm/avg_ttm | def avg_ttm(df, years):
"""
Calculate multi-year averages from TTM financial data, which has 4
data-points per year, that each covers the Trailing Twelve Months.
This is different from using a rolling average on TTM data, which
over-weighs the most recent quarters in the average.
This function should only be used on DataFrames for a single stock.
Use :obj:`~simfin.utils.apply` with this function on DataFrames for
multiple stocks.
:param df:
Pandas DataFrame with TTM financial data sorted ascendingly by date.
:param years:
Integer for the number of years.
:return:
Pandas DataFrame with the averages.
"""
df_result = df.copy()
for i in range(1, years):
df_result += df.shift(4 * i)
df_result /= years
return df_result
|
ftw.mopage-1.0 | ftw.mopage-1.0//ftw/mopage/interfaces.pyclass:IMopageObjectLookup/get_objects | def get_objects():
"""
Return objects providing data for the xml export
"""
|
pyboto3-1.4.4 | pyboto3-1.4.4//pyboto3/iam.pyfile:/pyboto3/iam.py:function:update_open_id_connect_provider_thumbprint/update_open_id_connect_provider_thumbprint | def update_open_id_connect_provider_thumbprint(OpenIDConnectProviderArn=
None, ThumbprintList=None):
"""
Replaces the existing list of server certificate thumbprints associated with an OpenID Connect (OIDC) provider resource object with a new list of thumbprints.
The list that you pass with this action completely replaces the existing list of thumbprints. (The lists are not merged.)
Typically, you need to update a thumbprint only when the identity provider's certificate changes, which occurs rarely. However, if the provider's certificate does change, any attempt to assume an IAM role that specifies the OIDC provider as a principal fails until the certificate thumbprint is updated.
See also: AWS API Documentation
:example: response = client.update_open_id_connect_provider_thumbprint(
OpenIDConnectProviderArn='string',
ThumbprintList=[
'string',
]
)
:type OpenIDConnectProviderArn: string
:param OpenIDConnectProviderArn: [REQUIRED]
The Amazon Resource Name (ARN) of the IAM OIDC provider resource object for which you want to update the thumbprint. You can get a list of OIDC provider ARNs by using the ListOpenIDConnectProviders action.
For more information about ARNs, see Amazon Resource Names (ARNs) and AWS Service Namespaces in the AWS General Reference .
:type ThumbprintList: list
:param ThumbprintList: [REQUIRED]
A list of certificate thumbprints that are associated with the specified IAM OpenID Connect provider. For more information, see CreateOpenIDConnectProvider .
(string) --Contains a thumbprint for an identity provider's server certificate.
The identity provider's server certificate thumbprint is the hex-encoded SHA-1 hash value of the self-signed X.509 certificate used by the domain where the OpenID Connect provider makes its keys available. It is always a 40-character string.
"""
pass
|
skultrafast-2.0.5 | skultrafast-2.0.5//skultrafast/data_io.pyfile:/skultrafast/data_io.py:function:messpy_example_path/messpy_example_path | def messpy_example_path():
"""
Returns the path to the messpy example data shipped with skultrafast.
Returns
-------
str
The full path
"""
import skultrafast
return skultrafast.__path__[0] + '/examples/data/messpyv1_data.npz'
|
sunriset | sunriset//calc.pyfile:/calc.py:function:sunset_float/sunset_float | def sunset_float(solar_noon_float, hour_angle_sunrise):
"""Returns Sunset as float with Solar Noon Float, solar_noon_float
and Hour Angle Deg, hour_angle_deg"""
sunset_float = (solar_noon_float * 1440 + hour_angle_sunrise * 4) / 1440
return sunset_float
|
network2tikz-0.1.8 | network2tikz-0.1.8//network2tikz/units.pyclass:UnitConverter/mm_to_pt | @staticmethod
def mm_to_pt(measure):
"""Convert millimeters to points."""
return measure * 2.83465
|
black_widow | black_widow//app/managers/sniffer/pcap_sniffer.pyclass:PcapSniffer/_merge_addr | @staticmethod
def _merge_addr(host1: dict, host2: dict):
"""
Merge host1 and host2 by preferring host2
:param host1: {
'mac': <mac_addr>,
'mac_manufacturer': tuple,
'ip': <ip_addr>,
'ip_host': list
}
:param host2: //
:return: The host1 merged with host2
"""
if host1 is None:
return host2
if host2 is None:
return host1
host = host2.copy()
for key, val in host2.items():
if val is not None:
continue
host[key] = host1.get(key)
ip = host.get('ip')
ip_host = host.get('ip_host')
mac = host.get('mac')
mac_manufacturer = host.get('mac_manufacturer')
if ip is not None:
host['label'] = ip
host['title'] = ip_host
elif mac_manufacturer is None:
host['label'] = mac
else:
host['label'] = mac_manufacturer
host['title'] = mac
return host
|
ht-0.1.54 | ht-0.1.54//ht/conv_external.pyfile:/ht/conv_external.py:function:Nu_cylinder_Fand/Nu_cylinder_Fand | def Nu_cylinder_Fand(Re, Pr):
"""Calculates Nusselt number for crossflow across a single tube
at a specified `Re` and `Pr`, both evaluated at the film temperature. No
other wall correction is necessary for this formulation. Also shown in
[2]_.
.. math::
Nu = (0.35 + 0.34Re^{0.5} + 0.15Re^{0.58})Pr^{0.3}
Parameters
----------
Re : float
Reynolds number with respect to cylinder diameter, [-]
Pr : float
Prandtl number at film temperature, [-]
Returns
-------
Nu : float
Nusselt number with respect to cylinder diameter, [-]
Notes
-----
Developed with test results for water, and Re from 1E4 to 1E5, but also
compared with other data in the literature. Claimed validity of Re from
1E-1 to 1E5.
This method applies to both the laminar and turbulent regimes.
Examples
--------
>>> Nu_cylinder_Fand(6071, 0.7)
45.19984325481126
References
----------
.. [1] Fand, R. M. "Heat Transfer by Forced Convection from a Cylinder to
Water in Crossflow." International Journal of Heat and Mass Transfer 8,
no. 7 (July 1, 1965): 995-1010. doi:10.1016/0017-9310(65)90084-0.
.. [2] Sanitjai, S., and R. J. Goldstein. "Forced Convection Heat Transfer
from a Circular Cylinder in Crossflow to Air and Liquids." International
Journal of Heat and Mass Transfer 47, no. 22 (October 2004): 4795-4805.
doi:10.1016/j.ijheatmasstransfer.2004.05.012.
"""
return (0.35 + 0.34 * Re ** 0.5 + 0.15 * Re ** 0.58) * Pr ** 0.3
|
plotly | plotly//basewidget.pyclass:BaseFigureWidget/_display_frames_error | @staticmethod
def _display_frames_error():
"""
Display an informative error when user attempts to set frames on a
FigureWidget
Raises
------
ValueError
always
"""
msg = """
Frames are not supported by the plotly.graph_objs.FigureWidget class.
Note: Frames are supported by the plotly.graph_objs.Figure class"""
raise ValueError(msg)
|
pyRSD-0.1.19 | pyRSD-0.1.19//pyRSD/rsd/hzpt/P11.pyclass:HaloZeldovichP11/default_parameters | @staticmethod
def default_parameters():
"""
The default parameters
References
----------
These parameters are from:
file: ``mcmc_fit_kmin-0.005_kmax-1.0.npz``
directory: ``$RSD_DIR/SimCalibrations/P11HaloZeldovich/results``
git hash: 8e1304e6
"""
d = {}
d['_A0_amp'] = 658.9
d['_A0_alpha'] = 3.91
d['_A0_beta'] = 1.917
d['_R_amp'] = 18.95
d['_R_alpha'] = -0.3657
d['_R_beta'] = -0.2585
d['_R1h_amp'] = 0.8473
d['_R1h_alpha'] = -0.1524
d['_R1h_beta'] = 0.7769
return d
|
dama | dama//measures.pyfile:/measures.py:function:f1/f1 | def f1(labels, predictions):
"""
weighted average presicion and recall
"""
from sklearn.metrics import f1_score
return f1_score(labels, predictions, average='macro', pos_label=None)
|
FFC-2017.1.0 | FFC-2017.1.0//ffc/cpp.pyfile:/ffc/cpp.py:function:indent/indent | def indent(block, num_spaces):
"""Indent each row of the given string block with n spaces."""
indentation = ' ' * num_spaces
return indentation + ('\n' + indentation).join(block.split('\n'))
|
csv_detective | csv_detective//detection.pyfile:/detection.py:function:detect_trailing_columns/detect_trailing_columns | def detect_trailing_columns(file, sep, heading_columns):
""" Tests first 10 lines to see if there are empty trailing columns"""
file.seek(0)
return_int = float('Inf')
for i in range(10):
line = file.readline()
return_int = min(return_int, len(line.replace('\n', '')) - len(line
.replace('\n', '').strip(sep)) - heading_columns)
if return_int == 0:
return 0
return return_int
|
marathon_acme | marathon_acme//marathon_util.pyfile:/marathon_util.py:function:_is_legacy_ip_per_task/_is_legacy_ip_per_task | def _is_legacy_ip_per_task(app):
"""
Return whether the application is using IP-per-task on Marathon < 1.5.
:param app: The application to check.
:return: True if using IP per task, False otherwise.
"""
return app.get('ipAddress') is not None
|
pyboto3-1.4.4 | pyboto3-1.4.4//pyboto3/cognitoidentityprovider.pyfile:/pyboto3/cognitoidentityprovider.py:function:admin_disable_user/admin_disable_user | def admin_disable_user(UserPoolId=None, Username=None):
"""
Disables the specified user as an administrator. Works on any user.
Requires developer credentials.
See also: AWS API Documentation
:example: response = client.admin_disable_user(
UserPoolId='string',
Username='string'
)
:type UserPoolId: string
:param UserPoolId: [REQUIRED]
The user pool ID for the user pool where you want to disable the user.
:type Username: string
:param Username: [REQUIRED]
The user name of the user you wish to disable.
:rtype: dict
:return: {}
"""
pass
|
pyphinb-2.9.4 | pyphinb-2.9.4//pyphinb/tpm.pyfile:/pyphinb/tpm.py:function:is_state_by_state/is_state_by_state | def is_state_by_state(tpm):
"""Return ``True`` if ``tpm`` is in state-by-state form, otherwise
``False``.
"""
return tpm.ndim == 2 and tpm.shape[0] == tpm.shape[1]
|
grimoire_elk | grimoire_elk//enriched/mattermost.pyclass:Mapping/get_elastic_mappings | @staticmethod
def get_elastic_mappings(es_major):
"""Get Elasticsearch mapping.
:param es_major: major version of Elasticsearch, as string
:returns: dictionary with a key, 'items', with the mapping
"""
mapping = """
{
"properties": {
"text_analyzed": {
"type": "text",
"fielddata": true,
"index": true
}
}
} """
return {'items': mapping}
|
gns3-server-2.2.8 | gns3-server-2.2.8//gns3server/controller/ports/gigabitethernet_port.pyclass:GigabitEthernetPort/long_name_type | @staticmethod
def long_name_type():
"""
Returns the long name type for this port.
:returns: string
"""
return 'GigabitEthernet'
|
mtcli | mtcli//views.pyfile:/views.py:function:var_view/var_view | def var_view(ch_trend, var, num_bar):
""" Retorna view com a variação percentual de duas barras."""
return '%s %s %s' % (num_bar, ch_trend, var)
|
handprint-1.2.2 | handprint-1.2.2//handprint/services/amazon.pyfile:/handprint/services/amazon.py:function:corner_list/corner_list | def corner_list(polygon, width, height):
"""Takes a boundingBox value from Google vision's JSON output and returns
a condensed version, in the form [x y x y x y x y], with the first x, y
pair representing the upper left corner."""
corners = []
for index in [0, 1, 2, 3]:
if 'X' in polygon[index] and 'Y' in polygon[index]:
corners.append(int(round(polygon[index]['X'] * width)))
corners.append(int(round(polygon[index]['Y'] * height)))
else:
return []
return corners
|
lunar-0.0.1 | lunar-0.0.1//lunar/template.pyfile:/lunar/template.py:function:unescape/unescape | def unescape(s):
""" unescape html tokens.
<p>{{ unescape(content) }}</p>
"""
return s.replace('&', '&').replace('<', '<').replace('>', '>'
).replace('"', '"').replace(''', "'")
|
process_improve | process_improve//plotting.pyfile:/plotting.py:function:get_plot_title/get_plot_title | def get_plot_title(main, model, prefix=''):
"""
Constructs a sensible plot title from the ``model``.
"""
if main is not None:
main = prefix
title = model.get_title()
if title:
main += f': {title}'
return main
|
lifelib-0.0.14 | lifelib-0.0.14//lifelib/projects/solvency2/projection.pyfile:/lifelib/projects/solvency2/projection.py:function:PolsAccHosp/PolsAccHosp | def PolsAccHosp(t):
"""Number of policies: Accidental Hospitalization"""
return 0
|
inmembrane-0.95.0 | inmembrane-0.95.0//inmembrane/protocols/gram_neg.pyfile:/inmembrane/protocols/gram_neg.py:function:summary_table/summary_table | def summary_table(params, proteins):
"""
Returns a string representing a simple summary table of
protein classifcations.
"""
out = ''
counts = {}
for seqid in proteins:
category = proteins[seqid]['category']
if category not in counts:
counts[category] = 1
else:
counts[category] += 1
out += '\n\n# Number of proteins in each class:\n'
for c in counts:
out += '# %-15s %i\n' % (c, counts[c])
return out
|
openplc_editor | openplc_editor//graphics/GraphicCommons.pyfile:/graphics/GraphicCommons.py:function:GetScaledEventPosition/GetScaledEventPosition | def GetScaledEventPosition(event, dc, scaling):
"""
Function that calculates the nearest point of the grid defined by scaling for the given point
"""
pos = event.GetLogicalPosition(dc)
if scaling:
pos.x = round(pos.x / scaling[0]) * scaling[0]
pos.y = round(pos.y / scaling[1]) * scaling[1]
return pos
|
carpedm | carpedm//data/ops.pyfile:/data/ops.py:function:in_line/in_line | def in_line(xmin_line, xmax_line, ymin_line, xmin_new, xmax_new, ymax_new):
"""Heuristic for determining whether a character is in a line.
Note:
Currently dependent on the order in which characters are
added. For example, a character may vertically overlap with a
line, but adding it to the line would be out of reading order.
This should be fixed in a future version.
Args:
xmin_line (:obj:`list` of :obj:`int`): Minimum x-coordinate of
characters in the line the new character is tested against.
xmax_line (:obj:`list` of :obj:`int`): Maximum x-coordinate of
characters in the line the new character is tested against.
ymin_line (int): Minimum y-coordinate of line the new character
is tested against.
xmin_new (int): Minimum x-coordinate of new character.
xmax_new (int): Maximum x-coordinate of new character.
ymax_new (int): Maximum y-coordinate of new character.
Returns:
bool:
The new character vertically overlaps with the
"average" character in the line.
"""
xmin_avg = sum(xmin_line) / len(xmin_line)
xmax_avg = sum(xmax_line) / len(xmax_line)
return (xmin_avg <= xmax_new and xmax_avg >= xmin_new and ymax_new >=
ymin_line)
|
numpydoc-0.9.2 | numpydoc-0.9.2//numpydoc/docscrape.pyfile:/numpydoc/docscrape.py:function:strip_blank_lines/strip_blank_lines | def strip_blank_lines(l):
"""Remove leading and trailing blank lines from a list of lines"""
while l and not l[0].strip():
del l[0]
while l and not l[-1].strip():
del l[-1]
return l
|
cleo-0.8.1 | cleo-0.8.1//cleo/parser.pyclass:Parser/_parameters | @classmethod
def _parameters(cls, tokens):
"""
Extract all of the parameters from the tokens.
:param tokens: The tokens to extract the parameters from
:type tokens: list
:rtype: dict
"""
arguments = []
options = []
for token in tokens:
if not token.startswith('--'):
arguments.append(cls._parse_argument(token))
else:
options.append(cls._parse_option(token))
return {'arguments': arguments, 'options': options}
|
nipy | nipy//core/image/image.pyfile:/core/image/image.py:function:synchronized_order/synchronized_order | def synchronized_order(img, target_img, axes=True, reference=True):
""" Reorder reference and axes of `img` to match target_img.
Parameters
----------
img : Image
target_img : Image
axes : bool, optional
If True, synchronize the order of the axes.
reference : bool, optional
If True, synchronize the order of the reference coordinates.
Returns
-------
newimg : Image
An Image satisfying newimg.axes == target.axes (if axes == True),
newimg.reference == target.reference (if reference == True).
Examples
--------
>>> data = np.random.standard_normal((3,4,7,5))
>>> im = Image(data, AffineTransform.from_params('ijkl', 'xyzt', np.diag([1,2,3,4,1])))
>>> im_scrambled = im.reordered_axes('iljk').reordered_reference('txyz')
>>> im == im_scrambled
False
>>> im_unscrambled = synchronized_order(im_scrambled, im)
>>> im == im_unscrambled
True
The images don't have to be the same shape
>>> data2 = np.random.standard_normal((3,11,9,4))
>>> im2 = Image(data, AffineTransform.from_params('ijkl', 'xyzt', np.diag([1,2,3,4,1])))
>>> im_scrambled2 = im2.reordered_axes('iljk').reordered_reference('xtyz')
>>> im_unscrambled2 = synchronized_order(im_scrambled2, im)
>>> im_unscrambled2.coordmap == im.coordmap
True
or have the same coordmap
>>> data3 = np.random.standard_normal((3,11,9,4))
>>> im3 = Image(data3, AffineTransform.from_params('ijkl', 'xyzt', np.diag([1,9,3,-2,1])))
>>> im_scrambled3 = im3.reordered_axes('iljk').reordered_reference('xtyz')
>>> im_unscrambled3 = synchronized_order(im_scrambled3, im)
>>> im_unscrambled3.axes == im.axes
True
>>> im_unscrambled3.reference == im.reference
True
>>> im_unscrambled4 = synchronized_order(im_scrambled3, im, axes=False)
>>> im_unscrambled4.axes == im.axes
False
>>> im_unscrambled4.axes == im_scrambled3.axes
True
>>> im_unscrambled4.reference == im.reference
True
"""
target_axes = target_img.axes
target_reference = target_img.coordmap.function_range
if axes:
img = img.reordered_axes(target_axes.coord_names)
if reference:
img = img.reordered_reference(target_reference.coord_names)
return img
|
CDS-1.0.1 | CDS-1.0.1//cds/modules/deposit/api.pyfile:/cds/modules/deposit/api.py:function:is_deposit/is_deposit | def is_deposit(url):
"""Check if it's a deposit or a record."""
try:
return 'deposit' in url
except TypeError:
return False
|
ldapper | ldapper//utils.pyfile:/utils.py:function:dn_attribute/dn_attribute | def dn_attribute(dn, attr):
"""Given a full DN return the value of the attribute given"""
for rdn in dn.split(','):
if rdn.startswith('%s=' % attr):
return rdn.split('=', 1)[1]
|
haggis | haggis//recipes.pyclass:KeyedSingleton/__call__ | def __call__(cls, *args, **kwargs):
"""
The constructor/initializer require at least one argument since
the first argument is the singleton key.
If an instance was already created with the requested key, it is
returned without being re-allocated or re-initialized.
"""
if not args:
raise ValueError('Instance key must be first argument in constructor')
key = args[0]
if key in cls._instances:
instance = cls._instances[key]
else:
instance = super().__call__(*args, **kwargs)
cls._instances[key] = instance
return instance
|
rcgrep-0.1.0 | rcgrep-0.1.0//versioneer.pyfile:/versioneer.py:function:render_git_describe/render_git_describe | 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:
rendered = pieces['short']
if pieces['dirty']:
rendered += '-dirty'
return rendered
|
maestro | maestro//utils/text.pyfile:/utils/text.py:function:make_string/make_string | def make_string(x):
"""
Force object into string and raises a TypeError if object is not of a
compatible type.
"""
if isinstance(x, str):
return str
elif isinstance(x, bytes):
return x.decode('utf8')
else:
cls_name = type(x).__name__
raise TypeError(f'expect string type, got {cls_name}')
|
xblock | xblock//mixins.pyclass:HandlersMixin/handler | @classmethod
def handler(cls, func):
"""
A decorator to indicate a function is usable as a handler.
The wrapped function must return a `webob.Response` object.
"""
func._is_xblock_handler = True
return func
|
dkPYUtils-0.1.10 | dkPYUtils-0.1.10//src/functionapi.pyclass:functionapi/getConfig | @staticmethod
def getConfig(conf, section, key):
"""
获取指定section下面的key
:param conf:
:param section:
:param key:
:return:
"""
return conf.get(section, key)
|
pando-0.47 | pando-0.47//pando/state_chain.pyfile:/pando/state_chain.py:function:request_available/request_available | def request_available():
"""No-op placeholder for easy hookage"""
pass
|
fake-bpy-module-2.80-20200428 | fake-bpy-module-2.80-20200428//bpy/ops/armature.pyfile:/bpy/ops/armature.py:function:select_more/select_more | def select_more():
"""Select those bones connected to the initial selection
"""
pass
|
kipoi | kipoi//readers.pyclass:ZarrReader/load | @classmethod
def load(cls, file_path, unflatten=True):
"""Load the data all at once (classmethod).
# Arguments
file_path: Zarr file path
unflatten: see `load_all`
"""
with cls(file_path) as f:
return f.load_all(unflatten=unflatten)
|
mf_horizon_client-2.1.1.1 | mf_horizon_client-2.1.1.1//versioneer.pyfile:/versioneer.py:function:render_git_describe/render_git_describe | 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:
rendered = pieces['short']
if pieces['dirty']:
rendered += '-dirty'
return rendered
|
nmrglue | nmrglue//fileio/glue.pyfile:/fileio/glue.py:function:get_dic/get_dic | def get_dic(f, dataset='spectrum'):
"""
Get a dictionary from dataset in a HDF5 File
"""
dset = f[dataset]
dic = {}
for key, value in dset.attrs.items():
if '_' in key:
axis, subkey = key.split('_', 1)
axis = int(axis)
if axis not in dic:
dic[axis] = {}
dic[axis][subkey] = value
else:
dic[key] = value
return dic
|
bpy | bpy//ops/mask.pyfile:/ops/mask.py:function:select_linked/select_linked | def select_linked():
"""Select all curve points linked to already selected ones
"""
pass
|
pyboto3-1.4.4 | pyboto3-1.4.4//pyboto3/rds.pyfile:/pyboto3/rds.py:function:describe_source_regions/describe_source_regions | def describe_source_regions(RegionName=None, MaxRecords=None, Marker=None,
Filters=None):
"""
Returns a list of the source AWS regions where the current AWS region can create a Read Replica or copy a DB snapshot from. This API action supports pagination.
See also: AWS API Documentation
Examples
To list the AWS regions where a Read Replica can be created.
Expected Output:
:example: response = client.describe_source_regions(
RegionName='string',
MaxRecords=123,
Marker='string',
Filters=[
{
'Name': 'string',
'Values': [
'string',
]
},
]
)
:type RegionName: string
:param RegionName: The source region name. For example, us-east-1 .
Constraints:
Must specify a valid AWS Region name.
:type MaxRecords: integer
:param MaxRecords: The maximum number of records to include in the response. If more records exist than the specified MaxRecords value, a pagination token called a marker is included in the response so that the remaining results can be retrieved.
Default: 100
Constraints: Minimum 20, maximum 100.
:type Marker: string
:param Marker: An optional pagination token provided by a previous DescribeSourceRegions request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by MaxRecords .
:type Filters: list
:param Filters: This parameter is not currently supported.
(dict) --This type is not currently supported.
Name (string) -- [REQUIRED]This parameter is not currently supported.
Values (list) -- [REQUIRED]This parameter is not currently supported.
(string) --
:rtype: dict
:return: {
'Marker': 'string',
'SourceRegions': [
{
'RegionName': 'string',
'Endpoint': 'string',
'Status': 'string'
},
]
}
"""
pass
|
scikit-discovery-0.9.18 | scikit-discovery-0.9.18//skdiscovery/utilities/patterns/polygon_utils.pyfile:/skdiscovery/utilities/patterns/polygon_utils.py:function:findPolygon/findPolygon | def findPolygon(in_data, in_point):
"""
Find the polygon that a point resides in
@param in_data: Input data containing polygons as read in by parseBasemapShape
@param in_point: Shapely point
@return: Index of shape in in_data that contains in_point
"""
result_num = None
for index, data in enumerate(in_data):
if data['polygon'].contains(in_point):
if result_num == None:
result_num = index
else:
raise RuntimeError('Multiple polygons contains point')
if result_num == None:
return -1
return result_num
|
ytrss-0.2.6 | ytrss-0.2.6//ytrss/core/settings.pyclass:YTSettings/__print_url_name | @staticmethod
def __print_url_name(elem):
"""
Print infromation from dictionary
@param elem: url information
@type elem: L{dict}
@return: formated name of url
@rtype: str
"""
name = ''
try:
name = '{} ({})'.format(elem['name'], elem['code'])
except ValueError:
name = elem['code']
return name
|
pdftotree-0.4.0 | pdftotree-0.4.0//pdftotree/utils/pdf/node.pyfile:/pdftotree/utils/pdf/node.py:function:_one_contains_other/_one_contains_other | def _one_contains_other(s1, s2):
"""
Whether one set contains the other
"""
return min(len(s1), len(s2)) == len(s1 & s2)
|
hesong | hesong//utils/jsonrpc.pyclass:Local/get | @classmethod
def get(cls, method):
"""Get RPC implementation stub by `method` name.
:param str method:
:rtype: Local
"""
return cls._stubs.get(method)
|
coala_utils | coala_utils//string_processing/Core.pyfile:/string_processing/Core.py:function:convert_to_raw/convert_to_raw | def convert_to_raw(string, exceptions=''):
"""
Converts a string to its raw form, converting all backslash to double
backslash except when the backslash escapes a character given in
exceptions.
:param string: The given string that needs to be converted
:param exceptions: A list of characters that if escaped with backslash
should not be converted to double backslash.
:return: Returns the corresponding raw string.
"""
i = 0
length = len(string)
output = ''
while i < length:
if string[i] == '\\' and i + 1 < length and string[i + 1
] not in exceptions:
output += '\\'
if string[i + 1] == '\\':
i += 1
output += string[i]
i += 1
return output
|
dtrspnsy-0.0.2 | dtrspnsy-0.0.2//dtrspnsy/parse_wikiRU.pyfile:/dtrspnsy/parse_wikiRU.py:function:get_hero_name/get_hero_name | def get_hero_name(hero_page):
"""Method that parses hero name from its responses page.
Pages for heroes are in the form of `Hero name/Responses`. We need only the `Hero name` part for heroes.
:param hero_page: hero's responses page as string.
:return: Hero name as parsed
"""
return hero_page.split('/')[0]
|
phenopy | phenopy//score.pyclass:Scorer/maximum | @staticmethod
def maximum(df):
"""Returns the maximum similarity value between to term lists"""
return df.values.max()
|
rootpy-1.0.1 | rootpy-1.0.1//rootpy/plotting/contrib/plot_corrcoef_matrix.pyfile:/rootpy/plotting/contrib/plot_corrcoef_matrix.py:function:cov/cov | def cov(m, y=None, rowvar=1, bias=0, ddof=None, weights=None, repeat_weights=0
):
"""
Estimate a covariance matrix, given data.
Covariance indicates the level to which two variables vary together.
If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`,
then the covariance matrix element :math:`C_{ij}` is the covariance of
:math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance
of :math:`x_i`.
Parameters
----------
m : array_like
A 1-D or 2-D array containing multiple variables and observations.
Each row of `m` represents a variable, and each column a single
observation of all those variables. Also see `rowvar` below.
y : array_like, optional
An additional set of variables and observations. `y` has the same
form as that of `m`.
rowvar : int, optional
If `rowvar` is non-zero (default), then each row represents a
variable, with observations in the columns. Otherwise, the relationship
is transposed: each column represents a variable, while the rows
contain observations.
bias : int, optional
Default normalization is by ``(N - 1)``, where ``N`` is the number of
observations given (unbiased estimate). If `bias` is 1, then
normalization is by ``N``. These values can be overridden by using
the keyword ``ddof`` in numpy versions >= 1.5.
ddof : int, optional
.. versionadded:: 1.5
If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is
the number of observations; this overrides the value implied by
``bias``. The default value is ``None``.
weights : array-like, optional
A 1-D array of weights with a length equal to the number of
observations.
repeat_weights : int, optional
The default treatment of weights in the weighted covariance is to first
normalize them to unit sum and use the biased weighted covariance
equation. If `repeat_weights` is 1 then the weights must represent an
integer number of occurrences of each observation and both a biased and
unbiased weighted covariance is defined because the total sample size
can be determined.
Returns
-------
out : ndarray
The covariance matrix of the variables.
See Also
--------
corrcoef : Normalized covariance matrix
Examples
--------
Consider two variables, :math:`x_0` and :math:`x_1`, which
correlate perfectly, but in opposite directions:
>>> x = np.array([[0, 2], [1, 1], [2, 0]]).T
>>> x
array([[0, 1, 2],
[2, 1, 0]])
Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance
matrix shows this clearly:
>>> np.cov(x)
array([[ 1., -1.],
[-1., 1.]])
Note that element :math:`C_{0,1}`, which shows the correlation between
:math:`x_0` and :math:`x_1`, is negative.
Further, note how `x` and `y` are combined:
>>> x = [-2.1, -1, 4.3]
>>> y = [3, 1.1, 0.12]
>>> X = np.vstack((x,y))
>>> print np.cov(X)
[[ 11.71 -4.286 ]
[ -4.286 2.14413333]]
>>> print np.cov(x, y)
[[ 11.71 -4.286 ]
[ -4.286 2.14413333]]
>>> print np.cov(x)
11.71
"""
import numpy as np
if ddof is not None and ddof != int(ddof):
raise ValueError('ddof must be integer')
X = np.array(m, ndmin=2, dtype=float)
if X.size == 0:
return np.array(m)
if X.shape[0] == 1:
rowvar = 1
if rowvar:
axis = 0
tup = slice(None), np.newaxis
else:
axis = 1
tup = np.newaxis, slice(None)
if y is not None:
y = np.array(y, copy=False, ndmin=2, dtype=float)
X = np.concatenate((X, y), axis)
if ddof is None:
if bias == 0:
ddof = 1
else:
ddof = 0
if weights is not None:
weights = np.array(weights, dtype=float)
weights_sum = weights.sum()
if weights_sum <= 0:
raise ValueError('sum of weights is non-positive')
X -= np.average(X, axis=1 - axis, weights=weights)[tup]
if repeat_weights:
fact = weights_sum - ddof
else:
weights /= weights_sum
fact = 1.0 - np.power(weights, 2).sum()
else:
weights = 1
X -= X.mean(axis=1 - axis)[tup]
if rowvar:
N = X.shape[1]
else:
N = X.shape[0]
fact = float(N - ddof)
if not rowvar:
return (np.dot(weights * X.T, X.conj()) / fact).squeeze()
else:
return (np.dot(weights * X, X.T.conj()) / fact).squeeze()
|
dynamorm | dynamorm//types/base.pyclass:DynamORMSchema/base_field_type | @staticmethod
def base_field_type():
"""Returns the class that all fields in the schema will inherit from"""
raise NotImplementedError('Child class must implement base_field_type')
|
rickshaw-1.5.3 | rickshaw-1.5.3//rickshaw/simspec.pyfile:/rickshaw/simspec.py:function:def_archetypes/def_archetypes | def def_archetypes():
"""
Produces the default niche-archetype links for a rickshaw simspec.
Returns
----------
spec : dict
Dictionary representation of the niche-archetype links.
"""
arches = {'mine': {':cycamore:Source'}, 'conversion': {
':cycamore:Storage'}, 'enrichment': {':cycamore:Enrichment'},
'fuel_fab': {':cycamore:FuelFab'}, 'fuel_fab:uo2': {
':cycamore:FuelFab'}, 'fuel_fab:triso': {':cycamore:FuelFab'},
'fuel_fab:mox': {':cycamore:FuelFab'}, 'reactor': {
':cycamore:Reactor'}, 'reactor:fr': {':cycamore:Reactor'},
'reactor:lwr': {':cycamore:Reactor'}, 'reactor:hwr': {
':cycamore:Reactor'}, 'reactor:htgr': {':cycamore:Reactor'},
'reactor:rbmk': {':cycamore:Reactor'}, 'reactor:pb': {
':cycamore:Reactor'}, 'storage': {':cycamore:Storage'},
'storage:wet': {':cycamore:Storage'}, 'storage:dry': {
':cycamore:Storage'}, 'storage:interim': {':cycamore:Storage'},
'separations': {':cycamore:Separations'}, 'repository': {
':cycamore:Sink'}}
return arches
|
dropbox-10.1.2 | dropbox-10.1.2//dropbox/team_log.pyclass:EventType/tfa_add_backup_phone | @classmethod
def tfa_add_backup_phone(cls, val):
"""
Create an instance of this class set to the ``tfa_add_backup_phone`` tag
with value ``val``.
:param TfaAddBackupPhoneType val:
:rtype: EventType
"""
return cls('tfa_add_backup_phone', val)
|
sdcflows-1.3.0 | sdcflows-1.3.0//sdcflows/workflows/gre.pyfile:/sdcflows/workflows/gre.py:function:_demean/_demean | def _demean(in_file, in_mask=None, usemode=True):
"""
Subtract the median (since it is robuster than the mean) from a map.
Parameters
----------
usemode : bool
Use the mode instead of the median (should be even more robust
against outliers).
"""
from os import getcwd
import numpy as np
import nibabel as nb
from nipype.utils.filemanip import fname_presuffix
nii = nb.load(in_file)
data = nii.get_fdata(dtype='float32')
msk = np.ones_like(data, dtype=bool)
if in_mask is not None:
msk[nb.load(in_mask).get_fdata(dtype='float32') < 0.0001] = False
if usemode:
from scipy.stats import mode
data[msk] -= mode(data[msk], axis=None)[0][0]
else:
data[msk] -= np.median(data[msk], axis=None)
out_file = fname_presuffix(in_file, suffix='_demean', newpath=getcwd())
nb.Nifti1Image(data, nii.affine, nii.header).to_filename(out_file)
return out_file
|
pyphinb-2.9.4 | pyphinb-2.9.4//pyphinb/validate.pyfile:/pyphinb/validate.py:function:is_network/is_network | def is_network(network):
"""Validate that the argument is a |Network|."""
from . import Network
if not isinstance(network, Network):
raise ValueError(
'Input must be a Network (perhaps you passed a Subsystem instead?')
|
metaknowledge | metaknowledge//medline/tagProcessing/tagFunctions.pyfile:/medline/tagProcessing/tagFunctions.py:function:TT/TT | def TT(val):
"""TransliteratedTitle"""
return val
|
pyboto3-1.4.4 | pyboto3-1.4.4//pyboto3/apigateway.pyfile:/pyboto3/apigateway.py:function:get_request_validators/get_request_validators | def get_request_validators(restApiId=None, position=None, limit=None):
"""
Gets the RequestValidators collection of a given RestApi .
See also: AWS API Documentation
:example: response = client.get_request_validators(
restApiId='string',
position='string',
limit=123
)
:type restApiId: string
:param restApiId: [REQUIRED]
[Required] The identifier of a RestApi to which the RequestValidators collection belongs.
:type position: string
:param position: The current pagination position in the paged result set.
:type limit: integer
:param limit: The maximum number of returned results per page.
:rtype: dict
:return: {
'position': 'string',
'items': [
{
'id': 'string',
'name': 'string',
'validateRequestBody': True|False,
'validateRequestParameters': True|False
},
]
}
"""
pass
|
pyphs-0.5.1 | pyphs-0.5.1//pyphs/misc/tools.pyfile:/pyphs/misc/tools.py:function:remove_duplicates/remove_duplicates | def remove_duplicates(lis):
"""
Remove duplicate entries from a given list, preserving ordering.
"""
out_list = []
for el in lis:
if el not in out_list:
out_list.append(el)
return out_list
|
fake-bpy-module-2.80-20200428 | fake-bpy-module-2.80-20200428//bpy/ops/object.pyfile:/bpy/ops/object.py:function:posemode_toggle/posemode_toggle | def posemode_toggle():
"""Enable or disable posing/selecting bones
"""
pass
|
SpiffWorkflow | SpiffWorkflow//bpmn/parser/util.pyfile:/bpmn/parser/util.py:function:first/first | def first(nodes):
"""
Return the first node in the given list, or None, if the list is empty.
"""
if len(nodes) >= 1:
return nodes[0]
else:
return None
|
pyboto3-1.4.4 | pyboto3-1.4.4//pyboto3/iam.pyfile:/pyboto3/iam.py:function:delete_login_profile/delete_login_profile | def delete_login_profile(UserName=None):
"""
Deletes the password for the specified IAM user, which terminates the user's ability to access AWS services through the AWS Management Console.
See also: AWS API Documentation
Examples
The following command deletes the password for the IAM user named Bob.
Expected Output:
:example: response = client.delete_login_profile(
UserName='string'
)
:type UserName: string
:param UserName: [REQUIRED]
The name of the user whose password you want to delete.
This parameter allows (per its regex pattern ) a string of characters consisting of upper and lowercase alphanumeric characters with no spaces. You can also include any of the following characters: =,.@-
:return: response = client.delete_login_profile(
UserName='Bob',
)
print(response)
"""
pass
|
win32comext | win32comext//axscript/client/framework.pyfile:/axscript/client/framework.py:function:trace/trace | def trace(*args):
"""A function used instead of "print" for debugging output.
"""
for arg in args:
print(arg, end=' ')
print()
|
fake-bpy-module-2.78-20200428 | fake-bpy-module-2.78-20200428//bpy/ops/clip.pyfile:/bpy/ops/clip.py:function:set_center_principal/set_center_principal | def set_center_principal():
"""Set optical center to center of footage
"""
pass
|
acitoolkit-0.4 | acitoolkit-0.4//acitoolkit/acibaseobject.pyclass:BaseACIObject/get_table | @staticmethod
def get_table(aci_object, title=''):
"""
Abstract method that should be replaced by a version that is specific to
the object
:param aci_object:
:param title: String containing the table title
:return: list of Table objects
"""
return [None]
|
nlg_yongzhuo | nlg_yongzhuo//text_summarization/extractive_sum/graph_base/textrank/textrank_gensim.pyfile:/text_summarization/extractive_sum/graph_base/textrank/textrank_gensim.py:function:_get_sentences_with_word_count/_get_sentences_with_word_count | def _get_sentences_with_word_count(sentences, word_count):
"""Get list of sentences. Total number of returned words close to specified `word_count`.
Parameters
----------
sentences : list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
Given sentences.
word_count : int or None
Number of returned words. If None full most important sentences will be returned.
Returns
-------
list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
Most important sentences.
"""
length = 0
selected_sentences = []
for sentence in sentences:
words_in_sentence = len(sentence.text.split())
if abs(word_count - length - words_in_sentence) > abs(word_count -
length):
return selected_sentences
selected_sentences.append(sentence)
length += words_in_sentence
return selected_sentences
|
jqfactor_analyzer-1.0.6 | jqfactor_analyzer-1.0.6//jqfactor_analyzer/performance.pyfile:/jqfactor_analyzer/performance.py:function:factor_autocorrelation/factor_autocorrelation | def factor_autocorrelation(factor_data, period=1, rank=True):
"""
计算指定时间跨度内平均因子排名/因子值的自相关性.
该指标对于衡量因子的换手率非常有用.
如果每个因子值在一个周期内随机变化，我们预计自相关为 0.
参数
----------
factor_data : pd.DataFrame - MultiIndex
一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
values 包括因子的值, 各期因子远期收益, 因子分位数,
因子分组(可选), 因子权重(可选)
period: int, optional
对应的因子远期收益时间跨度
Returns
-------
autocorr : pd.Series
滞后一期的因子自相关性
"""
grouper = [factor_data.index.get_level_values('date')]
if rank:
ranks = factor_data.groupby(grouper)[['factor']].rank()
else:
ranks = factor_data[['factor']]
asset_factor_rank = ranks.reset_index().pivot(index='date', columns=
'asset', values='factor')
autocorr = asset_factor_rank.corrwith(asset_factor_rank.shift(period),
axis=1)
autocorr.name = period
return autocorr
|
telethon | telethon//utils.pyfile:/utils.py:function:get_message_id/get_message_id | def get_message_id(message):
"""Sanitizes the 'reply_to' parameter a user may send"""
if message is None:
return None
if isinstance(message, int):
return message
if hasattr(message, 'original_message'):
return message.original_message.id
try:
if message.SUBCLASS_OF_ID == 2030045667:
return message.id
except AttributeError:
pass
raise TypeError('Invalid message type: {}'.format(type(message)))
|
hyperparameter_hunter-3.0.0 | hyperparameter_hunter-3.0.0//hyperparameter_hunter/keys/makers.pyclass:KeyMaker/_filter_parameters_to_hash | @staticmethod
def _filter_parameters_to_hash(parameters):
"""Produce a filtered version of `parameters` that does not include values that should be
ignored during hashing
Parameters
----------
parameters: Dict
The full dictionary of initial parameters to be filtered
Returns
-------
parameters: Dict
The filtered version of the given `parameters`"""
return parameters
|
edx-sga-0.10.0 | edx-sga-0.10.0//edx_sga/utils.pyfile:/edx_sga/utils.py:function:is_finalized_submission/is_finalized_submission | def is_finalized_submission(submission_data):
"""
Helper function to determine whether or not a Submission was finalized by the student
"""
if submission_data and submission_data.get('answer') is not None:
return submission_data['answer'].get('finalized', True)
return False
|
bismuthclient-0.0.49 | bismuthclient-0.0.49//bismuthclient/bismuthutil.pyclass:BismuthUtil/height_to_supply | @staticmethod
def height_to_supply(height):
"""Gives total supply at a given block height"""
R0 = 11680000.4
delta = 2e-06
pos = 0.8
pow = 12.6
N = height - 800000.0
dev_rew = 1.1
R = dev_rew * R0 + N * (pos + dev_rew * (pow - N / 2 * delta))
return R
|