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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
IntelPython/sdc | 1ebf55c00ef38dfbd401a70b3945e352a5a38b87 | sdc/distributed_lower.py | python | setitem_req_array | (context, builder, sig, args) | return builder.call(fn, args) | [] | def setitem_req_array(context, builder, sig, args):
fnty = lir.FunctionType(lir.VoidType(), [lir.IntType(8).as_pointer(),
lir.IntType(64),
mpi_req_llvm_type])
fn = builder.module.get_or_insert_function(
fnty, name="req_array_setitem")
return builder.call(fn, args) | [
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bugy/script-server | 9a57ce15903c81bcb537b872f1330ee55ba31563 | src/utils/collection_utils.py | python | get_first_existing | (dict, *keys, default=None) | return default | [] | def get_first_existing(dict, *keys, default=None):
for key in keys:
if key in dict:
return dict[key]
return default | [
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bukun/TorCMS | f7b44e8650aa54774f6b57e7b178edebbbf57e8e | torcms/model/entity_model.py | python | MEntity.create_entity | (uid='', path='', desc='', kind='1') | create entity record in the database. | create entity record in the database. | [
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] | def create_entity(uid='', path='', desc='', kind='1'):
'''
create entity record in the database.
'''
if path:
pass
else:
return False
if uid:
pass
else:
uid = get_uuid()
try:
TabEntity.create(uid=uid,
path=path,
desc=desc,
time_create=time.time(),
kind=kind)
return True
except Exception as err:
print(repr(err))
return False | [
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racepwn/racepwn | 052bf06adb29edecd570ab577fd26e002e2ad9a9 | bindings/python/librace.py | python | UserString.index | (self, sub, start=0, end=sys.maxsize) | return self.data.index(sub, start, end) | [] | def index(self, sub, start=0, end=sys.maxsize):
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_vendored_deps/library/oc_adm_router.py | python | DeploymentConfig.add_volume | (self, volume) | add a volume or volume mount to the proper location | add a volume or volume mount to the proper location | [
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] | def add_volume(self, volume):
''' add a volume or volume mount to the proper location '''
exist_volumes = self.get_volumes()
if not volume:
return
if not exist_volumes:
self.put(DeploymentConfig.volumes_path, [volume])
else:
exist_volumes.append(volume) | [
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wbond/package_control | cfaaeb57612023e3679ecb7f8cd7ceac9f57990d | package_control/deps/asn1crypto/x509.py | python | Certificate.sha256 | (self) | return self._sha256 | :return:
The SHA-256 hash of the DER-encoded bytes of this complete
certificate | :return:
The SHA-256 hash of the DER-encoded bytes of this complete
certificate | [
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] | def sha256(self):
"""
:return:
The SHA-256 hash of the DER-encoded bytes of this complete
certificate
"""
if self._sha256 is None:
self._sha256 = hashlib.sha256(self.dump()).digest()
return self._sha256 | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | apps/beeswax/gen-py/hive_metastore/ThriftHiveMetastore.py | python | Iface.heartbeat_txn_range | (self, txns) | Parameters:
- txns | Parameters:
- txns | [
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] | def heartbeat_txn_range(self, txns):
"""
Parameters:
- txns
"""
pass | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/sympy/integrals/meijerint.py | python | _int0oo_1 | (g, x) | return combsimp(unpolarify(res)) | Evaluate int_0^\infty g dx using G functions,
assuming the necessary conditions are fulfilled.
>>> from sympy.abc import a, b, c, d, x, y
>>> from sympy import meijerg
>>> from sympy.integrals.meijerint import _int0oo_1
>>> _int0oo_1(meijerg([a], [b], [c], [d], x*y), x)
gamma(-a)*gamma(c + 1)/(y*gamma(-d)*gamma(b + 1)) | Evaluate int_0^\infty g dx using G functions,
assuming the necessary conditions are fulfilled. | [
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] | def _int0oo_1(g, x):
"""
Evaluate int_0^\infty g dx using G functions,
assuming the necessary conditions are fulfilled.
>>> from sympy.abc import a, b, c, d, x, y
>>> from sympy import meijerg
>>> from sympy.integrals.meijerint import _int0oo_1
>>> _int0oo_1(meijerg([a], [b], [c], [d], x*y), x)
gamma(-a)*gamma(c + 1)/(y*gamma(-d)*gamma(b + 1))
"""
# See [L, section 5.6.1]. Note that s=1.
from sympy import gamma, combsimp, unpolarify
eta, _ = _get_coeff_exp(g.argument, x)
res = 1/eta
# XXX TODO we should reduce order first
for b in g.bm:
res *= gamma(b + 1)
for a in g.an:
res *= gamma(1 - a - 1)
for b in g.bother:
res /= gamma(1 - b - 1)
for a in g.aother:
res /= gamma(a + 1)
return combsimp(unpolarify(res)) | [
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corenel/pytorch-adda | 96f2689dd418ef275fcd0b057e5dff89be5762c5 | datasets/usps.py | python | USPS.load_samples | (self) | return images, labels | Load sample images from dataset. | Load sample images from dataset. | [
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"from",
"dataset",
"."
] | def load_samples(self):
"""Load sample images from dataset."""
filename = os.path.join(self.root, self.filename)
f = gzip.open(filename, "rb")
data_set = pickle.load(f, encoding="bytes")
f.close()
if self.train:
images = data_set[0][0]
labels = data_set[0][1]
self.dataset_size = labels.shape[0]
else:
images = data_set[1][0]
labels = data_set[1][1]
self.dataset_size = labels.shape[0]
return images, labels | [
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phyllisstein/alp | cbc9e9fa2de19cfd72bc416b9c879b571dc92972 | alp/request/requests/cookies.py | python | get_cookie_header | (jar, request) | return r.get_new_headers().get('Cookie') | Produce an appropriate Cookie header string to be sent with `request`, or None. | Produce an appropriate Cookie header string to be sent with `request`, or None. | [
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] | def get_cookie_header(jar, request):
"""Produce an appropriate Cookie header string to be sent with `request`, or None."""
r = MockRequest(request)
jar.add_cookie_header(r)
return r.get_new_headers().get('Cookie') | [
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GluuFederation/community-edition-setup | d0c9427ed9e3ea3d95691677b73c1402ed9ca4db | pylib/ldif3/ldif3.py | python | LDIFParser._check_changetype | (self, dn, changetype, attr_value) | Check changetype attribute for issues. | Check changetype attribute for issues. | [
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"attribute",
"for",
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"."
] | def _check_changetype(self, dn, changetype, attr_value):
"""Check changetype attribute for issues."""
if dn is None:
self._error('Read changetype: before getting valid dn: line.')
if changetype is not None:
self._error('Two lines starting with changetype: in one record.')
if attr_value not in CHANGE_TYPES:
self._error('changetype value %s is invalid.' % attr_value) | [
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donnemartin/gitsome | d7c57abc7cb66e9c910a844f15d4536866da3310 | xonsh/ply/example/newclasscalc/calc.py | python | Calc.p_expression_name | (self, p) | expression : NAME | expression : NAME | [
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":",
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] | def p_expression_name(self, p):
'expression : NAME'
try:
p[0] = self.names[p[1]]
except LookupError:
print("Undefined name '%s'" % p[1])
p[0] = 0 | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/treebeard/al_tree.py | python | AL_Node.add_sibling | (self, pos=None, **kwargs) | return newobj | Adds a new node as a sibling to the current node object. | Adds a new node as a sibling to the current node object. | [
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] | def add_sibling(self, pos=None, **kwargs):
"""Adds a new node as a sibling to the current node object."""
pos = self._prepare_pos_var_for_add_sibling(pos)
if len(kwargs) == 1 and 'instance' in kwargs:
# adding the passed (unsaved) instance to the tree
newobj = kwargs['instance']
if newobj.pk:
raise NodeAlreadySaved("Attempted to add a tree node that is "\
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else:
# creating a new object
newobj = get_result_class(self.__class__)(**kwargs)
if not self.node_order_by:
newobj.sib_order = self.__class__._get_new_sibling_order(pos,
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newobj.parent_id = self.parent_id
newobj.save()
return newobj | [
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aws-samples/aws-kube-codesuite | ab4e5ce45416b83bffb947ab8d234df5437f4fca | src/pkg_resources/__init__.py | python | ZipManifests.build | (cls, path) | Build a dictionary similar to the zipimport directory
caches, except instead of tuples, store ZipInfo objects.
Use a platform-specific path separator (os.sep) for the path keys
for compatibility with pypy on Windows. | Build a dictionary similar to the zipimport directory
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"""
Build a dictionary similar to the zipimport directory
caches, except instead of tuples, store ZipInfo objects.
Use a platform-specific path separator (os.sep) for the path keys
for compatibility with pypy on Windows.
"""
with ContextualZipFile(path) as zfile:
items = (
(
name.replace('/', os.sep),
zfile.getinfo(name),
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for name in zfile.namelist()
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return dict(items) | [
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/cherrypy/lib/sessions.py | python | FileSession._get_file_path | (self) | return f | [] | def _get_file_path(self):
f = os.path.join(self.storage_path, self.SESSION_PREFIX + self.id)
if not os.path.abspath(f).startswith(self.storage_path):
raise cherrypy.HTTPError(400, "Invalid session id in cookie.")
return f | [
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Azure/aztk | 8f8e7b268bdbf82c3ae4ecdcd907077bd6fe69b6 | aztk/internal/configuration_base.py | python | ConfigurationBase.from_dict | (cls, args: dict) | Create a new model from a dict values
The dict is cleaned from null values and passed expanded to the constructor | Create a new model from a dict values
The dict is cleaned from null values and passed expanded to the constructor | [
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"""
Create a new model from a dict values
The dict is cleaned from null values and passed expanded to the constructor
"""
try:
return cls._from_dict(args)
except (ValueError, TypeError) as e:
pretty_args = yaml.dump(args, default_flow_style=False)
raise AztkError("{0} {1}\n{2}".format(cls.__name__, str(e), pretty_args)) | [
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andyzsf/TuShare | 92787ad0cd492614bdb6389b71a19c80d1c8c9ae | tushare/stock/classifying.py | python | get_concept_classified | () | return data | 获取概念分类数据
Return
--------
DataFrame
code :股票代码
name :股票名称
c_name :概念名称 | 获取概念分类数据
Return
--------
DataFrame
code :股票代码
name :股票名称
c_name :概念名称 | [
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] | def get_concept_classified():
"""
获取概念分类数据
Return
--------
DataFrame
code :股票代码
name :股票名称
c_name :概念名称
"""
ct._write_head()
df = _get_type_data(ct.SINA_CONCEPTS_INDEX_URL%(ct.P_TYPE['http'],
ct.DOMAINS['sf'], ct.PAGES['cpt']))
data = []
for row in df.values:
rowDf = _get_detail(row[0])
rowDf['c_name'] = row[1]
data.append(rowDf)
data = pd.concat(data,ignore_index=True)
return data | [
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javayhu/Gank-Alfred-Workflow | aca39bd0c7bc0c494eee204e10bca61dab760ab7 | source-v1/workflow/workflow.py | python | LockFile.locked | (self) | return self._locked | `True` if file is locked by this instance. | `True` if file is locked by this instance. | [
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"""`True` if file is locked by this instance."""
return self._locked | [
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volatilityfoundation/volatility3 | 168b0d0b053ab97a7cb096ef2048795cc54d885f | volatility3/framework/plugins/windows/registry/userassist.py | python | UserAssist.__init__ | (self, *args, **kwargs) | [] | def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._userassist_size = 0
self._userassist_type_name = "_VOL_USERASSIST_TYPES_7"
self._reg_table_name = None
self._win7 = None
# taken from http://msdn.microsoft.com/en-us/library/dd378457%28v=vs.85%29.aspx
self._folder_guids = json.load(open(os.path.join(os.path.dirname(__file__), "userassist.json"), "rb")) | [
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libretro/libretro-database | 22062a33c960798c5c0b63ac3fb048752154180d | scripts/FBNeo_dat_gen.py | python | setup_argparse | () | return parser | Set up the argparse arguments and return the argparse instance | Set up the argparse arguments and return the argparse instance | [
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] | def setup_argparse():
"""Set up the argparse arguments and return the argparse instance"""
parser = argparse.ArgumentParser(prog='FBgen.py', description='Generates Final Burn Neo .dat file needed for RetroArch/libretro-db/c_converter')
required_arguments = parser.add_argument_group('required arguments')
required_arguments.add_argument('-dat', help='Misc -> Generate dat file -> Generate dat (Arcade only)', required=True)
required_arguments.add_argument('-path', help='Path to a split, ClrMamePro verified and TorrentZipped ROM set matching the Arcade only dat', required=True)
parser.add_argument('-output_file', help='Path to the target output file; example: FBNeo - Arcade games.dat')
parser.add_argument('-header_name', help='Override the clrmamepro(name) in the output .dat')
parser.add_argument('-header_description', help='Override the clrmamepro(description) in the output .dat')
parser.add_argument('-header_version', help='Override the clrmamepro(version) in the output .dat')
if len(sys.argv[1:])==0:
parser.print_help()
print('\n'.join(['',
'example usage: python3 FBA_dat_gen.py -dat "FBNeo v0.2.97.42 (ClrMame Pro ',
' XML).dat" -path "/path/to/split/verified/torrentzipped/roms/" ',
' -output_file "FBNeo - Arcade Games.dat"']))
parser.exit()
return parser | [
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collinsctk/PyQYT | 7af3673955f94ff1b2df2f94220cd2dab2e252af | ExtentionPackages/pysnmp/smi/rfc1902.py | python | ObjectIdentity.addMibSource | (self, *mibSources) | return self | Adds path to repository to search PySNMP MIB files.
Parameters
----------
*mibSources :
one or more paths to search or Python package names to import
and search for PySNMP MIB modules.
Returns
-------
: :py:class:`~pysnmp.smi.rfc1902.ObjectIdentity`
reference to itself
Notes
-----
Normally, ASN.1-to-Python MIB modules conversion is performed
automatically through PySNMP/PySMI interaction. ASN1 MIB modules
could also be manually compiled into Python via the
`mibdump.py <http://pysmi.sourceforge.net/user-perspective.html>`_
tool.
Examples
--------
>>> ObjectIdentity('SNMPv2-MIB', 'sysDescr').addMibSource('/opt/pysnmp/mibs', 'pysnmp_mibs')
ObjectIdentity('SNMPv2-MIB', 'sysDescr')
>>> | Adds path to repository to search PySNMP MIB files. | [
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] | def addMibSource(self, *mibSources):
"""Adds path to repository to search PySNMP MIB files.
Parameters
----------
*mibSources :
one or more paths to search or Python package names to import
and search for PySNMP MIB modules.
Returns
-------
: :py:class:`~pysnmp.smi.rfc1902.ObjectIdentity`
reference to itself
Notes
-----
Normally, ASN.1-to-Python MIB modules conversion is performed
automatically through PySNMP/PySMI interaction. ASN1 MIB modules
could also be manually compiled into Python via the
`mibdump.py <http://pysmi.sourceforge.net/user-perspective.html>`_
tool.
Examples
--------
>>> ObjectIdentity('SNMPv2-MIB', 'sysDescr').addMibSource('/opt/pysnmp/mibs', 'pysnmp_mibs')
ObjectIdentity('SNMPv2-MIB', 'sysDescr')
>>>
"""
if self.__mibSourcesToAdd is None:
self.__mibSourcesToAdd = mibSources
else:
self.__mibSourcesToAdd += mibSources
return self | [
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numba/numba | bf480b9e0da858a65508c2b17759a72ee6a44c51 | numba/cpython/numbers.py | python | int_truediv_impl | (context, builder, sig, args) | return impl_ret_untracked(context, builder, sig.return_type, res) | [] | def int_truediv_impl(context, builder, sig, args):
[va, vb] = args
[ta, tb] = sig.args
a = context.cast(builder, va, ta, sig.return_type)
b = context.cast(builder, vb, tb, sig.return_type)
with cgutils.if_zero(builder, b):
context.error_model.fp_zero_division(builder, ("division by zero",))
res = builder.fdiv(a, b)
return impl_ret_untracked(context, builder, sig.return_type, res) | [
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CiscoDevNet/webexteamssdk | 673312779b8e05cf0535bea8b96599015cccbff1 | webexteamssdk/api/rooms.py | python | RoomsAPI.__init__ | (self, session, object_factory) | Initialize a new RoomsAPI object with the provided RestSession.
Args:
session(RestSession): The RESTful session object to be used for
API calls to the Webex Teams service.
Raises:
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Args:
session(RestSession): The RESTful session object to be used for
API calls to the Webex Teams service.
Raises:
TypeError: If the parameter types are incorrect.
"""
check_type(session, RestSession)
super(RoomsAPI, self).__init__()
self._session = session
self._object_factory = object_factory | [
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akulakov/django-mcbv | 68970eae1733b2b252265d6d1384946f80e23ed8 | dbe/mcbv/dates.py | python | WeekMixin.get_next_week | (self, date) | return _get_next_prev(self, date, is_previous=False, period='week') | Get the next valid week. | Get the next valid week. | [
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] | def get_next_week(self, date):
"""
Get the next valid week.
"""
return _get_next_prev(self, date, is_previous=False, period='week') | [
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enthought/traitsui | b7c38c7a47bf6ae7971f9ddab70c8a358647dd25 | traitsui/table_column.py | python | TableColumn.get_menu | (self, object) | return self.menu | Returns the context menu to display when the user right-clicks on
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"""Returns the context menu to display when the user right-clicks on
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return self.menu | [
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LINKIWI/modern-paste | ecc4168bda2a9e5981d495f9e0538258d9f727a2 | app/util/cryptography.py | python | secure_hash | (s, iterations=10000) | return hash_result | Performs several iterations of a SHA256 hash of a plain-text string to generate a secure hash.
:param s: Input string to hash
:param iterations: Number of hash iterations to use
:return: A string representing a secure hash of the string | Performs several iterations of a SHA256 hash of a plain-text string to generate a secure hash. | [
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] | def secure_hash(s, iterations=10000):
"""
Performs several iterations of a SHA256 hash of a plain-text string to generate a secure hash.
:param s: Input string to hash
:param iterations: Number of hash iterations to use
:return: A string representing a secure hash of the string
"""
hash_result = SHA256.new(data=str(s)).hexdigest()
for i in range(iterations):
hash_result = SHA256.new(data=hash_result).hexdigest()
return hash_result | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/sqlalchemy/sql/type_api.py | python | Variant.__init__ | (self, base, mapping) | Construct a new :class:`.Variant`.
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:param mapping: dictionary of string dialect names to
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"""
self.impl = base
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jython/jython3 | def4f8ec47cb7a9c799ea4c745f12badf92c5769 | lib-python/3.5.1/distutils/command/install.py | python | install.get_outputs | (self) | return outputs | Assembles the outputs of all the sub-commands. | Assembles the outputs of all the sub-commands. | [
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outputs = []
for cmd_name in self.get_sub_commands():
cmd = self.get_finalized_command(cmd_name)
# Add the contents of cmd.get_outputs(), ensuring
# that outputs doesn't contain duplicate entries
for filename in cmd.get_outputs():
if filename not in outputs:
outputs.append(filename)
if self.path_file and self.install_path_file:
outputs.append(os.path.join(self.install_libbase,
self.path_file + ".pth"))
return outputs | [
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nate-parrott/Flashlight | c3a7c7278a1cccf8918e7543faffc68e863ff5ab | flashlightsearchrelay/requests/packages/urllib3/util.py | python | parse_url | (url) | return Url(scheme, auth, host, port, path, query, fragment) | Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
performed to parse incomplete urls. Fields not provided will be None.
Partly backwards-compatible with :mod:`urlparse`.
Example: ::
>>> parse_url('http://google.com/mail/')
Url(scheme='http', host='google.com', port=None, path='/', ...)
>>> parse_url('google.com:80')
Url(scheme=None, host='google.com', port=80, path=None, ...)
>>> parse_url('/foo?bar')
Url(scheme=None, host=None, port=None, path='/foo', query='bar', ...) | Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
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"""
Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
performed to parse incomplete urls. Fields not provided will be None.
Partly backwards-compatible with :mod:`urlparse`.
Example: ::
>>> parse_url('http://google.com/mail/')
Url(scheme='http', host='google.com', port=None, path='/', ...)
>>> parse_url('google.com:80')
Url(scheme=None, host='google.com', port=80, path=None, ...)
>>> parse_url('/foo?bar')
Url(scheme=None, host=None, port=None, path='/foo', query='bar', ...)
"""
# While this code has overlap with stdlib's urlparse, it is much
# simplified for our needs and less annoying.
# Additionally, this implementations does silly things to be optimal
# on CPython.
scheme = None
auth = None
host = None
port = None
path = None
fragment = None
query = None
# Scheme
if '://' in url:
scheme, url = url.split('://', 1)
# Find the earliest Authority Terminator
# (http://tools.ietf.org/html/rfc3986#section-3.2)
url, path_, delim = split_first(url, ['/', '?', '#'])
if delim:
# Reassemble the path
path = delim + path_
# Auth
if '@' in url:
# Last '@' denotes end of auth part
auth, url = url.rsplit('@', 1)
# IPv6
if url and url[0] == '[':
host, url = url.split(']', 1)
host += ']'
# Port
if ':' in url:
_host, port = url.split(':', 1)
if not host:
host = _host
if port:
# If given, ports must be integers.
if not port.isdigit():
raise LocationParseError("Failed to parse: %s" % url)
port = int(port)
else:
# Blank ports are cool, too. (rfc3986#section-3.2.3)
port = None
elif not host and url:
host = url
if not path:
return Url(scheme, auth, host, port, path, query, fragment)
# Fragment
if '#' in path:
path, fragment = path.split('#', 1)
# Query
if '?' in path:
path, query = path.split('?', 1)
return Url(scheme, auth, host, port, path, query, fragment) | [
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rdiff-backup/rdiff-backup | 321e0cd6e5e47d4c158a0172e47ab38240a8b653 | src/rdiff_backup/rpath.py | python | RORPath.has_carbonfile | (self) | return 'carbonfile' in self.data | True if rpath has a carbonfile parameter | True if rpath has a carbonfile parameter | [
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"""True if rpath has a carbonfile parameter"""
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-darwin/x64/cryptography/hazmat/backends/openssl/aead.py | python | _aead_cipher_name | (cipher) | [] | def _aead_cipher_name(cipher):
from cryptography.hazmat.primitives.ciphers.aead import (
AESCCM, AESGCM, ChaCha20Poly1305
)
if isinstance(cipher, ChaCha20Poly1305):
return b"chacha20-poly1305"
elif isinstance(cipher, AESCCM):
return "aes-{0}-ccm".format(len(cipher._key) * 8).encode("ascii")
else:
assert isinstance(cipher, AESGCM)
return "aes-{0}-gcm".format(len(cipher._key) * 8).encode("ascii") | [
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YonghaoHe/LFFD-A-Light-and-Fast-Face-Detector-for-Edge-Devices | 529203dc07b1bd435a82907872f37d4c0c3b6807 | ChasingTrainFramework_GeneralOneClassDetection/data_provider_base/base_provider.py | python | ProviderBaseclass.write | (self) | Write a single sample to the files
:return: | Write a single sample to the files
:return: | [
"Write",
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"to",
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] | def write(self):
"""
Write a single sample to the files
:return:
"""
raise NotImplementedError() | [
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etetoolkit/ete | 2b207357dc2a40ccad7bfd8f54964472c72e4726 | ete3/tools/ete_build_lib/workflow/supermatrix.py | python | pipeline | (task, wkname, conf=None) | return new_tasks | [] | def pipeline(task, wkname, conf=None):
logindent(2)
# Points to npr parameters according to task properties
if not task:
source_seqtype = "aa" if "aa" in GLOBALS["seqtypes"] else "nt"
npr_conf = IterConfig(conf, wkname,
len(GLOBALS["target_species"]),
source_seqtype)
cogconf, cogclass = npr_conf.cog_selector
initial_task = cogclass(GLOBALS["target_species"], set(),
source_seqtype, conf, cogconf)
initial_task.main_tree = main_tree = None
initial_task.threadid = generate_runid()
initial_task.configid = initial_task.threadid
initial_task.target_wkname = wkname
# Register node
db.add_node(initial_task.threadid, initial_task.nodeid,
initial_task.cladeid, initial_task.targets,
initial_task.outgroups)
new_tasks = [initial_task]
else:
conf = GLOBALS[task.configid]
npr_conf = IterConfig(conf, wkname, task.size, task.seqtype)
new_tasks = process_task(task, wkname, npr_conf, conf['_nodeinfo'])
process_new_tasks(task, new_tasks, conf)
logindent(-2)
return new_tasks | [
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coreemu/core | 7e18a7a72023a69a92ad61d87461bd659ba27f7c | daemon/core/api/grpc/server.py | python | CoreGrpcServer.GetEmaneModelConfigs | (
self, request: GetEmaneModelConfigsRequest, context: ServicerContext
) | return GetEmaneModelConfigsResponse(configs=configs) | Retrieve all EMANE model configurations of a session
:param request:
get-EMANE-model-configurations request
:param context: context object
:return: get-EMANE-model-configurations response that has all the EMANE
configurations | Retrieve all EMANE model configurations of a session | [
"Retrieve",
"all",
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"model",
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"of",
"a",
"session"
] | def GetEmaneModelConfigs(
self, request: GetEmaneModelConfigsRequest, context: ServicerContext
) -> GetEmaneModelConfigsResponse:
"""
Retrieve all EMANE model configurations of a session
:param request:
get-EMANE-model-configurations request
:param context: context object
:return: get-EMANE-model-configurations response that has all the EMANE
configurations
"""
logging.debug("get emane model configs: %s", request)
session = self.get_session(request.session_id, context)
configs = grpcutils.get_emane_model_configs(session)
return GetEmaneModelConfigsResponse(configs=configs) | [
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RodrigoGantier/Mask_R_CNN_Keypoints | 6b22e72de01ae98eaa1acd8645e5dbe3a096459f | model.py | python | smooth_l1_loss | (y_true, y_pred) | return loss | Implements Smooth-L1 loss.
y_true and y_pred are typicallly: [N, 4], but could be any shape. | Implements Smooth-L1 loss.
y_true and y_pred are typicallly: [N, 4], but could be any shape. | [
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] | def smooth_l1_loss(y_true, y_pred):
"""Implements Smooth-L1 loss.
y_true and y_pred are typicallly: [N, 4], but could be any shape.
"""
diff = K.abs(y_true - y_pred)
less_than_one = K.cast(K.less(diff, 1.0), "float32")
loss = (less_than_one * 0.5 * diff ** 2) + (1 - less_than_one) * (diff - 0.5)
return loss | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/clb/v20180317/models.py | python | DescribeRewriteRequest.__init__ | (self) | r"""
:param LoadBalancerId: 负载均衡实例ID。
:type LoadBalancerId: str
:param SourceListenerIds: 负载均衡监听器ID数组。
:type SourceListenerIds: list of str
:param SourceLocationIds: 负载均衡转发规则的ID数组。
:type SourceLocationIds: list of str | r"""
:param LoadBalancerId: 负载均衡实例ID。
:type LoadBalancerId: str
:param SourceListenerIds: 负载均衡监听器ID数组。
:type SourceListenerIds: list of str
:param SourceLocationIds: 负载均衡转发规则的ID数组。
:type SourceLocationIds: list of str | [
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r"""
:param LoadBalancerId: 负载均衡实例ID。
:type LoadBalancerId: str
:param SourceListenerIds: 负载均衡监听器ID数组。
:type SourceListenerIds: list of str
:param SourceLocationIds: 负载均衡转发规则的ID数组。
:type SourceLocationIds: list of str
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self.LoadBalancerId = None
self.SourceListenerIds = None
self.SourceLocationIds = None | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/states/git.py | python | cloned | (
name,
target,
branch=None,
user=None,
password=None,
identity=None,
https_user=None,
https_pass=None,
output_encoding=None,
) | .. versionadded:: 2018.3.3,2019.2.0
Ensure that a repository has been cloned to the specified target directory.
If not, clone that repository. No fetches will be performed once cloned.
name
Address of the remote repository
target
Name of the target directory where repository should be cloned
branch
Remote branch to check out. If unspecified, the default branch (i.e.
the one to the remote HEAD points) will be checked out.
.. note::
The local branch name will match the remote branch name. If the
branch name is changed, then that branch will be checked out
locally, but keep in mind that remote repository will not be
fetched. If your use case requires that you keep the clone up to
date with the remote repository, then consider using
:py:func:`git.latest <salt.states.git.latest>`.
user
User under which to run git commands. By default, commands are run by
the user under which the minion is running.
password
Windows only. Required when specifying ``user``. This parameter will be
ignored on non-Windows platforms.
identity
Path to a private key to use for ssh URLs. Works the same way as in
:py:func:`git.latest <salt.states.git.latest>`, see that state's
documentation for more information.
https_user
HTTP Basic Auth username for HTTPS (only) clones
https_pass
HTTP Basic Auth password for HTTPS (only) clones
output_encoding
Use this option to specify which encoding to use to decode the output
from any git commands which are run. This should not be needed in most
cases.
.. note::
This should only be needed if the files in the repository were
created with filenames using an encoding other than UTF-8 to handle
Unicode characters. | .. versionadded:: 2018.3.3,2019.2.0 | [
"..",
"versionadded",
"::",
"2018",
".",
"3",
".",
"3",
"2019",
".",
"2",
".",
"0"
] | def cloned(
name,
target,
branch=None,
user=None,
password=None,
identity=None,
https_user=None,
https_pass=None,
output_encoding=None,
):
"""
.. versionadded:: 2018.3.3,2019.2.0
Ensure that a repository has been cloned to the specified target directory.
If not, clone that repository. No fetches will be performed once cloned.
name
Address of the remote repository
target
Name of the target directory where repository should be cloned
branch
Remote branch to check out. If unspecified, the default branch (i.e.
the one to the remote HEAD points) will be checked out.
.. note::
The local branch name will match the remote branch name. If the
branch name is changed, then that branch will be checked out
locally, but keep in mind that remote repository will not be
fetched. If your use case requires that you keep the clone up to
date with the remote repository, then consider using
:py:func:`git.latest <salt.states.git.latest>`.
user
User under which to run git commands. By default, commands are run by
the user under which the minion is running.
password
Windows only. Required when specifying ``user``. This parameter will be
ignored on non-Windows platforms.
identity
Path to a private key to use for ssh URLs. Works the same way as in
:py:func:`git.latest <salt.states.git.latest>`, see that state's
documentation for more information.
https_user
HTTP Basic Auth username for HTTPS (only) clones
https_pass
HTTP Basic Auth password for HTTPS (only) clones
output_encoding
Use this option to specify which encoding to use to decode the output
from any git commands which are run. This should not be needed in most
cases.
.. note::
This should only be needed if the files in the repository were
created with filenames using an encoding other than UTF-8 to handle
Unicode characters.
"""
ret = {"name": name, "result": False, "comment": "", "changes": {}}
if target is None:
ret["comment"] = "'target' argument is required"
return ret
elif not isinstance(target, str):
target = str(target)
if not os.path.isabs(target):
ret["comment"] = "'target' path must be absolute"
return ret
if branch is not None:
if not isinstance(branch, str):
branch = str(branch)
if not branch:
ret["comment"] = "Invalid 'branch' argument"
return ret
if not os.path.exists(target):
need_clone = True
else:
try:
__salt__["git.status"](
target, user=user, password=password, output_encoding=output_encoding
)
except Exception as exc: # pylint: disable=broad-except
ret["comment"] = str(exc)
return ret
else:
need_clone = False
comments = []
def _clone_changes(ret):
ret["changes"]["new"] = name + " => " + target
def _branch_changes(ret, old, new):
ret["changes"]["branch"] = {"old": old, "new": new}
if need_clone:
if __opts__["test"]:
_clone_changes(ret)
comment = "{} would be cloned to {}{}".format(
name,
target,
" with branch '{}'".format(branch) if branch is not None else "",
)
return _neutral_test(ret, comment)
clone_opts = ["--branch", branch] if branch is not None else None
try:
__salt__["git.clone"](
target,
name,
opts=clone_opts,
user=user,
password=password,
identity=identity,
https_user=https_user,
https_pass=https_pass,
output_encoding=output_encoding,
)
except CommandExecutionError as exc:
msg = "Clone failed: {}".format(_strip_exc(exc))
return _fail(ret, msg, comments)
comments.append(
"{} cloned to {}{}".format(
name,
target,
" with branch '{}'".format(branch) if branch is not None else "",
)
)
_clone_changes(ret)
ret["comment"] = _format_comments(comments)
ret["result"] = True
return ret
else:
if branch is None:
return _already_cloned(ret, target, branch, comments)
else:
current_branch = __salt__["git.current_branch"](
target, user=user, password=password, output_encoding=output_encoding
)
if current_branch == branch:
return _already_cloned(ret, target, branch, comments)
else:
if __opts__["test"]:
_branch_changes(ret, current_branch, branch)
return _neutral_test(
ret, "Branch would be changed to '{}'".format(branch)
)
try:
__salt__["git.rev_parse"](
target,
rev=branch,
user=user,
password=password,
ignore_retcode=True,
output_encoding=output_encoding,
)
except CommandExecutionError:
# Local head does not exist, so we need to check out a new
# branch at the remote rev
checkout_rev = "/".join(("origin", branch))
checkout_opts = ["-b", branch]
else:
# Local head exists, so we just need to check it out
checkout_rev = branch
checkout_opts = None
try:
__salt__["git.checkout"](
target,
rev=checkout_rev,
opts=checkout_opts,
user=user,
password=password,
output_encoding=output_encoding,
)
except CommandExecutionError as exc:
msg = "Failed to change branch to '{}': {}".format(branch, exc)
return _fail(ret, msg, comments)
else:
comments.append("Branch changed to '{}'".format(branch))
_branch_changes(ret, current_branch, branch)
ret["comment"] = _format_comments(comments)
ret["result"] = True
return ret | [
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facebookresearch/pytorch3d | fddd6a700fa9685c1ce2d4b266c111d7db424ecc | pytorch3d/transforms/se3.py | python | _se3_V_matrix | (
log_rotation: torch.Tensor,
log_rotation_hat: torch.Tensor,
log_rotation_hat_square: torch.Tensor,
rotation_angles: torch.Tensor,
eps: float = 1e-4,
) | return V | A helper function that computes the "V" matrix from [1], Sec 9.4.2.
[1] https://jinyongjeong.github.io/Download/SE3/jlblanco2010geometry3d_techrep.pdf | A helper function that computes the "V" matrix from [1], Sec 9.4.2.
[1] https://jinyongjeong.github.io/Download/SE3/jlblanco2010geometry3d_techrep.pdf | [
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log_rotation: torch.Tensor,
log_rotation_hat: torch.Tensor,
log_rotation_hat_square: torch.Tensor,
rotation_angles: torch.Tensor,
eps: float = 1e-4,
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"""
A helper function that computes the "V" matrix from [1], Sec 9.4.2.
[1] https://jinyongjeong.github.io/Download/SE3/jlblanco2010geometry3d_techrep.pdf
"""
V = (
torch.eye(3, dtype=log_rotation.dtype, device=log_rotation.device)[None]
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log_rotation_hat_square
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return V | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_vendored_deps/library/oc_configmap.py | python | OpenShiftCLI._replace | (self, fname, force=False) | return self.openshift_cmd(cmd) | replace the current object with oc replace | replace the current object with oc replace | [
"replace",
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"current",
"object",
"with",
"oc",
"replace"
] | def _replace(self, fname, force=False):
'''replace the current object with oc replace'''
# We are removing the 'resourceVersion' to handle
# a race condition when modifying oc objects
yed = Yedit(fname)
results = yed.delete('metadata.resourceVersion')
if results[0]:
yed.write()
cmd = ['replace', '-f', fname]
if force:
cmd.append('--force')
return self.openshift_cmd(cmd) | [
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sqlmapproject/sqlmap | 3b07b70864624dff4c29dcaa8a61c78e7f9189f7 | lib/core/common.py | python | firstNotNone | (*args) | return retVal | Returns first not-None value from a given list of arguments
>>> firstNotNone(None, None, 1, 2, 3)
1 | Returns first not-None value from a given list of arguments | [
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"""
Returns first not-None value from a given list of arguments
>>> firstNotNone(None, None, 1, 2, 3)
1
"""
retVal = None
for _ in args:
if _ is not None:
retVal = _
break
return retVal | [
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eirannejad/pyRevit | 49c0b7eb54eb343458ce1365425e6552d0c47d44 | pyrevitlib/pyrevit/coreutils/markdown/blockprocessors.py | python | BlockProcessor.test | (self, parent, block) | Test for block type. Must be overridden by subclasses.
As the parser loops through processors, it will call the ``test``
method on each to determine if the given block of text is of that
type. This method must return a boolean ``True`` or ``False``. The
actual method of testing is left to the needs of that particular
block type. It could be as simple as ``block.startswith(some_string)``
or a complex regular expression. As the block type may be different
depending on the parent of the block (i.e. inside a list), the parent
etree element is also provided and may be used as part of the test.
Keywords:
* ``parent``: A etree element which will be the parent of the block.
* ``block``: A block of text from the source which has been split at
blank lines. | Test for block type. Must be overridden by subclasses. | [
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] | def test(self, parent, block):
""" Test for block type. Must be overridden by subclasses.
As the parser loops through processors, it will call the ``test``
method on each to determine if the given block of text is of that
type. This method must return a boolean ``True`` or ``False``. The
actual method of testing is left to the needs of that particular
block type. It could be as simple as ``block.startswith(some_string)``
or a complex regular expression. As the block type may be different
depending on the parent of the block (i.e. inside a list), the parent
etree element is also provided and may be used as part of the test.
Keywords:
* ``parent``: A etree element which will be the parent of the block.
* ``block``: A block of text from the source which has been split at
blank lines.
"""
pass | [
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evennia/evennia | fa79110ba6b219932f22297838e8ac72ebc0be0e | evennia/web/website/views.py | python | ChannelDetailView.get_context_data | (self, **kwargs) | return context | Django hook; before we can display the channel logs, we need to recall
the logfile and read its lines.
Returns:
context (dict): Django context object | Django hook; before we can display the channel logs, we need to recall
the logfile and read its lines. | [
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"""
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Returns:
context (dict): Django context object
"""
# Get the parent context object, necessary first step
context = super(ChannelDetailView, self).get_context_data(**kwargs)
# Get the filename this Channel is recording to
filename = self.object.attributes.get(
"log_file", default="channel_%s.log" % self.object.key
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# Split log entries so we can filter by time
bucket = []
for log in (x.strip() for x in tail_log_file(filename, 0, self.max_num_lines)):
if not log:
continue
try:
time, msg = log.split(" [-] ")
time_key = time.split(":")[0]
except ValueError:
# malformed log line - skip line
continue
bucket.append({"key": time_key, "timestamp": time, "message": msg})
# Add the processed entries to the context
context["object_list"] = bucket
# Get a list of unique timestamps by hour and sort them
context["object_filters"] = sorted(set([x["key"] for x in bucket]))
return context | [
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter3-Decision_tree/decision_tree.py | python | createDataSet | () | return dataSet,labels | 函数说明：创建测试数据集
参数：无
return: dataSet-数据集
labels-分类属性 | 函数说明：创建测试数据集
参数：无
return: dataSet-数据集
labels-分类属性 | [
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] | def createDataSet():
"""
函数说明：创建测试数据集
参数：无
return: dataSet-数据集
labels-分类属性
"""
dataSet = [
[0,0,0,0,'no'],
[0,0,0,1,'no'],
[0,1,0,1,'yes'],
[0,1,1,0,'yes'],
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]
labels = ['年龄', '有工作', '有自己的房子', '信贷情况']
return dataSet,labels | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/whoosh/reading.py | python | MultiReader.generation | (self) | return self._gen | [] | def generation(self):
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JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | chap19/monitor/monitor/monitor/openstack/common/notifier/rpc_notifier2.py | python | notify | (context, message) | Sends a notification via RPC | Sends a notification via RPC | [
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] | def notify(context, message):
"""Sends a notification via RPC"""
if not context:
context = req_context.get_admin_context()
priority = message.get('priority',
CONF.default_notification_level)
priority = priority.lower()
for topic in CONF.rpc_notifier2.topics:
topic = '%s.%s' % (topic, priority)
try:
rpc.notify(context, topic, message, envelope=True)
except Exception:
LOG.exception(_("Could not send notification to %(topic)s. "
"Payload=%(message)s"), locals()) | [
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FSX/momoko | ad2f602c85b5df09e54dcb997c2f81ccc3d2b221 | momoko/connection.py | python | Connection.closed | (self) | return self.connection.closed > 0 if self.connection else True | Indicates whether the connection is closed or not. | Indicates whether the connection is closed or not. | [
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"""
Indicates whether the connection is closed or not.
"""
# 0 = open, 1 = closed, 2 = 'something horrible happened'
return self.connection.closed > 0 if self.connection else True | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/UniversalAnalytics/HTTPLog.py | python | LineBufferTranslator.__init__ | (self, *a, **kw) | [] | def __init__(self, *a, **kw):
self._linepending = []
super(LineBufferTranslator, self).__init__(*a, **kw) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/geometry/polyhedron/cdd_file_format.py | python | cdd_Hrepresentation | (cdd_type, ieqs, eqns, file_output=None) | r"""
Return a string containing the H-representation in cddlib's ine format.
INPUT:
- ``file_output`` (string; optional) -- a filename to which the
representation should be written. If set to ``None`` (default),
representation is returned as a string.
EXAMPLES::
sage: from sage.geometry.polyhedron.cdd_file_format import cdd_Hrepresentation
sage: cdd_Hrepresentation('rational', None, [[0,1]])
'H-representation\nlinearity 1 1\nbegin\n 1 2 rational\n 0 1\nend\n'
TESTS::
sage: from sage.misc.temporary_file import tmp_filename
sage: filename = tmp_filename(ext='.ine')
sage: cdd_Hrepresentation('rational', None, [[0,1]], file_output=filename) | r"""
Return a string containing the H-representation in cddlib's ine format. | [
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r"""
Return a string containing the H-representation in cddlib's ine format.
INPUT:
- ``file_output`` (string; optional) -- a filename to which the
representation should be written. If set to ``None`` (default),
representation is returned as a string.
EXAMPLES::
sage: from sage.geometry.polyhedron.cdd_file_format import cdd_Hrepresentation
sage: cdd_Hrepresentation('rational', None, [[0,1]])
'H-representation\nlinearity 1 1\nbegin\n 1 2 rational\n 0 1\nend\n'
TESTS::
sage: from sage.misc.temporary_file import tmp_filename
sage: filename = tmp_filename(ext='.ine')
sage: cdd_Hrepresentation('rational', None, [[0,1]], file_output=filename)
"""
ieqs = _set_to_None_if_empty(ieqs)
eqns = _set_to_None_if_empty(eqns)
num, ambient_dim = _common_length_of(ieqs, eqns)
ambient_dim -= 1
if cdd_type == 'real':
from sage.rings.real_double import RDF
base_ring = RDF
else:
base_ring = None
s = 'H-representation\n'
if eqns is not None:
assert len(eqns)>0
n = len(eqns)
s += "linearity " + repr(n) + ' '
s += _to_space_separated_string(range(1,n+1)) + '\n'
s += 'begin\n'
s += ' ' + repr(num) + ' ' + repr(ambient_dim+1) + ' ' + cdd_type + '\n'
if eqns is not None:
for e in eqns:
s += ' ' + _to_space_separated_string(e, base_ring) + '\n'
if ieqs is not None:
for i in ieqs:
s += ' ' + _to_space_separated_string(i, base_ring) + '\n'
s += 'end\n'
if file_output is not None:
in_file = open(file_output, 'w')
in_file.write(s)
in_file.close()
else:
return s | [
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hzlzh/AlfredWorkflow.com | 7055f14f6922c80ea5943839eb0caff11ae57255 | Sources/Workflows/Alfred-Time-Keeper/PyAl/Request/bs4/element.py | python | Tag.__repr__ | (self, encoding=DEFAULT_OUTPUT_ENCODING) | return self.encode(encoding) | Renders this tag as a string. | Renders this tag as a string. | [
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mgear-dev/mgear | 06ddc26c5adb5eab07ca470c7fafa77404c8a1de | scripts/mgear/maya/curve.py | python | createCurveFromOrderedEdges | (edgeLoop,
startVertex,
name,
parent=None,
degree=3) | return crv | Create a curve for a edgeloop ordering the list from starting vertex
Arguments:
edgeLoop (list ): List of edges
startVertex (vertex): Starting vertex
name (str): Name of the new curve.
parent (dagNode): Parent of the new curve.
degree (int): Degree of the new curve.
Returns:
dagNode: The newly created curve. | Create a curve for a edgeloop ordering the list from starting vertex | [
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startVertex,
name,
parent=None,
degree=3):
"""Create a curve for a edgeloop ordering the list from starting vertex
Arguments:
edgeLoop (list ): List of edges
startVertex (vertex): Starting vertex
name (str): Name of the new curve.
parent (dagNode): Parent of the new curve.
degree (int): Degree of the new curve.
Returns:
dagNode: The newly created curve.
"""
orderedEdges = []
for e in edgeLoop:
if startVertex in e.connectedVertices():
orderedEdges.append(e)
next = e
break
count = 0
while True:
for e in edgeLoop:
if e in next.connectedEdges() and e not in orderedEdges:
orderedEdges.append(e)
next = e
pass
if len(orderedEdges) == len(edgeLoop):
break
count += 1
if count > 100:
break
# return orderedEdges
orderedVertex = [startVertex]
orderedVertexPos = [startVertex.getPosition(space='world')]
for e in orderedEdges:
for v in e.connectedVertices():
if v not in orderedVertex:
orderedVertex.append(v)
orderedVertexPos.append(v.getPosition(space='world'))
crv = addCurve(parent, name, orderedVertexPos, degree=degree)
return crv | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | plexpy/newsletters.py | python | get_agent_class | (newsletter_id=None, newsletter_id_name=None, agent_id=None, config=None, email_config=None,
start_date=None, end_date=None, subject=None, body=None, message=None,
email_msg_id=None, email_reply_msg_id=None) | [] | def get_agent_class(newsletter_id=None, newsletter_id_name=None, agent_id=None, config=None, email_config=None,
start_date=None, end_date=None, subject=None, body=None, message=None,
email_msg_id=None, email_reply_msg_id=None):
if str(agent_id).isdigit():
agent_id = int(agent_id)
kwargs = {'newsletter_id': newsletter_id,
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'email_reply_msg_id': email_reply_msg_id}
if agent_id == 0:
return RecentlyAdded(**kwargs)
else:
return Newsletter(**kwargs)
else:
return None | [
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cyverse/atmosphere | 4a3e522f1f7b58abd9fa944c10b7455dc5cddac1 | api/v1/views/identity.py | python | get_provider | (user, provider_uuid) | Given the (request) user and a provider uuid,
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"""
Given the (request) user and a provider uuid,
return None or an Active provider
"""
try:
provider = user.current_providers.get(uuid=provider_uuid)
return provider
except Provider.DoesNotExist:
logger.warn(
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return None | [
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shadowmoose/RedditDownloader | ffe38afbee64e094fe80de2267ea049401bfb2f2 | redditdownloader/sources/source.py | python | Source.set_alias | (self, alias) | Set the alias of this Source. | Set the alias of this Source. | [
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CedricGuillemet/Imogen | ee417b42747ed5b46cb11b02ef0c3630000085b3 | bin/Lib/lib2to3/pytree.py | python | Leaf.prefix | (self) | return self._prefix | The whitespace and comments preceding this token in the input. | The whitespace and comments preceding this token in the input. | [
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CGCookie/retopoflow | 3d8b3a47d1d661f99ab0aeb21d31370bf15de35e | retopoflow/rf/rf_sources.py | python | RetopoFlow_Sources.raycast_sources_Point2D_all | (self, xy:Point2D) | return self.raycast_sources_Ray_all(self.Point2D_to_Ray(xy)) | [] | def raycast_sources_Point2D_all(self, xy:Point2D):
if xy is None: return None,None,None,None
return self.raycast_sources_Ray_all(self.Point2D_to_Ray(xy)) | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/redis/client.py | python | Redis.sentinel_get_master_addr_by_name | (self, service_name) | return self.execute_command('SENTINEL GET-MASTER-ADDR-BY-NAME',
service_name) | Returns a (host, port) pair for the given ``service_name`` | Returns a (host, port) pair for the given ``service_name`` | [
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] | def sentinel_get_master_addr_by_name(self, service_name):
"Returns a (host, port) pair for the given ``service_name``"
return self.execute_command('SENTINEL GET-MASTER-ADDR-BY-NAME',
service_name) | [
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/smtplib.py | python | SMTP.starttls | (self, keyfile=None, certfile=None, context=None) | return (resp, reply) | Puts the connection to the SMTP server into TLS mode.
If there has been no previous EHLO or HELO command this session, this
method tries ESMTP EHLO first.
If the server supports TLS, this will encrypt the rest of the SMTP
session. If you provide the keyfile and certfile parameters,
the identity of the SMTP server and client can be checked. This,
however, depends on whether the socket module really checks the
certificates.
This method may raise the following exceptions:
SMTPHeloError The server didn't reply properly to
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"""Puts the connection to the SMTP server into TLS mode.
If there has been no previous EHLO or HELO command this session, this
method tries ESMTP EHLO first.
If the server supports TLS, this will encrypt the rest of the SMTP
session. If you provide the keyfile and certfile parameters,
the identity of the SMTP server and client can be checked. This,
however, depends on whether the socket module really checks the
certificates.
This method may raise the following exceptions:
SMTPHeloError The server didn't reply properly to
the helo greeting.
"""
self.ehlo_or_helo_if_needed()
if not self.has_extn("starttls"):
raise SMTPNotSupportedError(
"STARTTLS extension not supported by server.")
(resp, reply) = self.docmd("STARTTLS")
if resp == 220:
if not _have_ssl:
raise RuntimeError("No SSL support included in this Python")
if context is not None and keyfile is not None:
raise ValueError("context and keyfile arguments are mutually "
"exclusive")
if context is not None and certfile is not None:
raise ValueError("context and certfile arguments are mutually "
"exclusive")
if keyfile is not None or certfile is not None:
import warnings
warnings.warn("keyfile and certfile are deprecated, use a "
"custom context instead", DeprecationWarning, 2)
if context is None:
context = ssl._create_stdlib_context(certfile=certfile,
keyfile=keyfile)
self.sock = context.wrap_socket(self.sock,
server_hostname=self._host)
self.file = None
# RFC 3207:
# The client MUST discard any knowledge obtained from
# the server, such as the list of SMTP service extensions,
# which was not obtained from the TLS negotiation itself.
self.helo_resp = None
self.ehlo_resp = None
self.esmtp_features = {}
self.does_esmtp = False
else:
# RFC 3207:
# 501 Syntax error (no parameters allowed)
# 454 TLS not available due to temporary reason
raise SMTPResponseException(resp, reply)
return (resp, reply) | [
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tensorflow/models | 6b8bb0cbeb3e10415c7a87448f08adc3c484c1d3 | research/object_detection/predictors/heads/keras_mask_head.py | python | ConvolutionalMaskHead.__init__ | (self,
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kernel_size,
num_predictions_per_location,
conv_hyperparams,
freeze_batchnorm,
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mask_height=7,
mask_width=7,
masks_are_class_agnostic=False,
name=None) | Constructor.
Args:
is_training: Indicates whether the BoxPredictor is in training mode.
num_classes: Number of classes.
use_dropout: Option to use dropout or not. Note that a single dropout
op is applied here prior to both box and class predictions, which stands
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dropout_keep_prob: Keep probability for dropout.
This is only used if use_dropout is True.
kernel_size: Size of final convolution kernel. If the
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then the kernel size is automatically set to be
min(feature_width, feature_height).
num_predictions_per_location: Number of box predictions to be made per
spatial location. Int specifying number of boxes per location.
conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
containing hyperparameters for convolution ops.
freeze_batchnorm: Bool. Whether to freeze batch norm parameters during
training or not. When training with a small batch size (e.g. 1), it is
desirable to freeze batch norm update and use pretrained batch norm
params.
use_depthwise: Whether to use depthwise convolutions for prediction
steps. Default is False.
mask_height: Desired output mask height. The default value is 7.
mask_width: Desired output mask width. The default value is 7.
masks_are_class_agnostic: Boolean determining if the mask-head is
class-agnostic or not.
name: A string name scope to assign to the model. If `None`, Keras
will auto-generate one from the class name.
Raises:
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kernel_size,
num_predictions_per_location,
conv_hyperparams,
freeze_batchnorm,
use_depthwise=False,
mask_height=7,
mask_width=7,
masks_are_class_agnostic=False,
name=None):
"""Constructor.
Args:
is_training: Indicates whether the BoxPredictor is in training mode.
num_classes: Number of classes.
use_dropout: Option to use dropout or not. Note that a single dropout
op is applied here prior to both box and class predictions, which stands
in contrast to the ConvolutionalBoxPredictor below.
dropout_keep_prob: Keep probability for dropout.
This is only used if use_dropout is True.
kernel_size: Size of final convolution kernel. If the
spatial resolution of the feature map is smaller than the kernel size,
then the kernel size is automatically set to be
min(feature_width, feature_height).
num_predictions_per_location: Number of box predictions to be made per
spatial location. Int specifying number of boxes per location.
conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
containing hyperparameters for convolution ops.
freeze_batchnorm: Bool. Whether to freeze batch norm parameters during
training or not. When training with a small batch size (e.g. 1), it is
desirable to freeze batch norm update and use pretrained batch norm
params.
use_depthwise: Whether to use depthwise convolutions for prediction
steps. Default is False.
mask_height: Desired output mask height. The default value is 7.
mask_width: Desired output mask width. The default value is 7.
masks_are_class_agnostic: Boolean determining if the mask-head is
class-agnostic or not.
name: A string name scope to assign to the model. If `None`, Keras
will auto-generate one from the class name.
Raises:
ValueError: if min_depth > max_depth.
"""
super(ConvolutionalMaskHead, self).__init__(name=name)
self._is_training = is_training
self._num_classes = num_classes
self._use_dropout = use_dropout
self._dropout_keep_prob = dropout_keep_prob
self._kernel_size = kernel_size
self._num_predictions_per_location = num_predictions_per_location
self._use_depthwise = use_depthwise
self._mask_height = mask_height
self._mask_width = mask_width
self._masks_are_class_agnostic = masks_are_class_agnostic
self._mask_predictor_layers = []
# Add a slot for the background class.
if self._masks_are_class_agnostic:
self._num_masks = 1
else:
self._num_masks = self._num_classes
num_mask_channels = self._num_masks * self._mask_height * self._mask_width
if self._use_dropout:
self._mask_predictor_layers.append(
# The Dropout layer's `training` parameter for the call method must
# be set implicitly by the Keras set_learning_phase. The object
# detection training code takes care of this.
tf.keras.layers.Dropout(rate=1.0 - self._dropout_keep_prob))
if self._use_depthwise:
self._mask_predictor_layers.append(
tf.keras.layers.DepthwiseConv2D(
[self._kernel_size, self._kernel_size],
padding='SAME',
depth_multiplier=1,
strides=1,
dilation_rate=1,
name='MaskPredictor_depthwise',
**conv_hyperparams.params()))
self._mask_predictor_layers.append(
conv_hyperparams.build_batch_norm(
training=(is_training and not freeze_batchnorm),
name='MaskPredictor_depthwise_batchnorm'))
self._mask_predictor_layers.append(
conv_hyperparams.build_activation_layer(
name='MaskPredictor_depthwise_activation'))
self._mask_predictor_layers.append(
tf.keras.layers.Conv2D(
num_predictions_per_location * num_mask_channels, [1, 1],
name='MaskPredictor',
**conv_hyperparams.params(use_bias=True)))
else:
self._mask_predictor_layers.append(
tf.keras.layers.Conv2D(
num_predictions_per_location * num_mask_channels,
[self._kernel_size, self._kernel_size],
padding='SAME',
name='MaskPredictor',
**conv_hyperparams.params(use_bias=True))) | [
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SanPen/GridCal | d3f4566d2d72c11c7e910c9d162538ef0e60df31 | src/GridCal/Gui/Main/GridCalMain.py | python | MainGUI.set_value_to_column | (self) | Set the value to all the column
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:return: Nothing
"""
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mdl = self.ui.dataStructureTableView.model() # is of type ObjectsModel
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if mdl is not None:
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GNOME/gnome-music | b7b55c8519f9cc613ca60c01a5ab8cef6b58c92e | gnomemusic/player.py | python | Player.current_song | (self) | return self._playlist.props.current_song | Get the current song.
:returns: The song being played. None if there is no playlist.
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:returns: The song being played. None if there is no playlist.
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/scrape/sensor.py | python | ScrapeSensor.__init__ | (
self,
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state_class,
):
"""Initialize a web scrape sensor."""
self.rest = rest
self._state = None
self._select = select
self._attr = attr
self._index = index
self._value_template = value_template
self._attr_name = name
self._attr_native_unit_of_measurement = unit
self._attr_device_class = device_class
self._attr_state_class = state_class | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-35/fabmetheus_utilities/geometry/creation/gear.py | python | getWidthMultipliedPath | (path, widthMultiplier) | return path | Get width multiplied path. | Get width multiplied path. | [
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] | def getWidthMultipliedPath(path, widthMultiplier):
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for pointIndex, point in enumerate(path):
path[pointIndex] = complex(point.real * widthMultiplier, point.imag)
return path | [
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cisco/mindmeld | 809c36112e9ea8019fe29d54d136ca14eb4fd8db | mindmeld/components/domain_classifier.py | python | DomainClassifier.load | (self, *args, **kwargs) | Loads the trained domain classification model from disk
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Args:
model_path (str): The location on disk where the model is stored
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logger.info("Loading domain classifier")
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nansencenter/nansat | 5700ec673fbf522c19b8dedcb01cc15f7cd29a6a | nansat/mappers/envisat.py | python | Envisat.get_ads_vrts | (self, gdalDataset, adsNames, zoomSize=500,
step=1, **kwargs) | return adsVRTs | Create list with VRTs with zoomed and resized ADS arrays
For given names of variables (which should match self.allADSParams):
Get VRT with zoomed ADS array
Get resized VRT
Parameters
----------
gdalDataset: GDAL Dataset
input dataset
adsNames: list with strings
names of varaiables from self.allADSParams['list']
zoomSize: int, 500
size to which the ADS array will be zoomed by scipy.zoom
step: int, 1
step, at which data will be given
Returns
--------
adsVRTs: list with VRT
list with resized VRT with zoomed arrays | Create list with VRTs with zoomed and resized ADS arrays | [
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step=1, **kwargs):
"""Create list with VRTs with zoomed and resized ADS arrays
For given names of variables (which should match self.allADSParams):
Get VRT with zoomed ADS array
Get resized VRT
Parameters
----------
gdalDataset: GDAL Dataset
input dataset
adsNames: list with strings
names of varaiables from self.allADSParams['list']
zoomSize: int, 500
size to which the ADS array will be zoomed by scipy.zoom
step: int, 1
step, at which data will be given
Returns
--------
adsVRTs: list with VRT
list with resized VRT with zoomed arrays
"""
XSize = gdalDataset.RasterXSize
YSize = gdalDataset.RasterYSize
# list with VRT with arrays of lon/lat
adsVRTs = []
for adsName in adsNames:
# create VRT with array from ADS
adsVRTs.append(self.create_VRT_from_ADS(adsName, zoomSize))
# resize the VRT to match <step>
adsVRTs[-1] = adsVRTs[-1].get_resized_vrt(XSize/step, YSize/step)
return adsVRTs | [
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mrlesmithjr/Ansible | d44f0dc0d942bdf3bf7334b307e6048f0ee16e36 | roles/ansible-vsphere-management/scripts/pdns/lib/python2.7/site-packages/pip/_vendor/distlib/database.py | python | DependencyGraph.__repr__ | (self) | return '\n'.join(output) | Representation of the graph | Representation of the graph | [
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jymcheong/AutoTTP | 617128fe71537de4579176d7170a3e8f1680b6a6 | EmpireAPIWrapper/wrapper.py | python | agents.agent_info | (self, name) | return utilties._getURL(self, full_url) | Returns JSON describing the agent specified by name.
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\n:rtype: dict | Returns JSON describing the agent specified by name.
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\n:rtype: dict | [
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"""
Returns JSON describing the agent specified by name.
\n:param name:
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full_url = '/api/agents/{}'.format(name)
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KalleHallden/AutoTimer | 2d954216700c4930baa154e28dbddc34609af7ce | env/lib/python2.7/site-packages/pip/_internal/req/req_install.py | python | InstallRequirement.assert_source_matches_version | (self) | [] | def assert_source_matches_version(self):
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facebookresearch/mobile-vision | f40401a44e86bb3ba9c1b66e7700e15f96b880cb | mobile_cv/arch/utils/fx_utils.py | python | get_input_nodes | (nodes: List[torch.fx.Node]) | return ret | Extract the input nodes from a list of nodes
Input nodes are the nodes that are not produced by other nodes | Extract the input nodes from a list of nodes
Input nodes are the nodes that are not produced by other nodes | [
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] | def get_input_nodes(nodes: List[torch.fx.Node]):
"""Extract the input nodes from a list of nodes
Input nodes are the nodes that are not produced by other nodes
"""
ret = []
for node in nodes:
all_inputs = node.all_input_nodes
for cur_input in all_inputs:
if cur_input not in nodes:
ret.append(cur_input)
assert len(ret) > 0
return ret | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/cherrypy/_helper.py | python | url | (path='', qs='', script_name=None, base=None, relative=None) | return newurl | Create an absolute URL for the given path.
If 'path' starts with a slash ('/'), this will return
(base + script_name + path + qs).
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If True, the output will instead be a URL that is relative to the
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"""Create an absolute URL for the given path.
If 'path' starts with a slash ('/'), this will return
(base + script_name + path + qs).
If it does not start with a slash, this returns
(base + script_name [+ request.path_info] + path + qs).
If script_name is None, cherrypy.request will be used
to find a script_name, if available.
If base is None, cherrypy.request.base will be used (if available).
Note that you can use cherrypy.tools.proxy to change this.
Finally, note that this function can be used to obtain an absolute URL
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If you call url(qs=cherrypy.request.query_string), you should get the
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If relative is None or not provided, request.app.relative_urls will
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current request path, perhaps including '..' atoms. If relative is
the string 'server', the output will instead be a URL that is
relative to the server root; i.e., it will start with a slash.
"""
if isinstance(qs, (tuple, list, dict)):
qs = urllib.parse.urlencode(qs)
if qs:
qs = '?' + qs
if cherrypy.request.app:
if not path.startswith('/'):
# Append/remove trailing slash from path_info as needed
# (this is to support mistyped URL's without redirecting;
# if you want to redirect, use tools.trailing_slash).
pi = cherrypy.request.path_info
if cherrypy.request.is_index is True:
if not pi.endswith('/'):
pi = pi + '/'
elif cherrypy.request.is_index is False:
if pi.endswith('/') and pi != '/':
pi = pi[:-1]
if path == '':
path = pi
else:
path = urllib.parse.urljoin(pi, path)
if script_name is None:
script_name = cherrypy.request.script_name
if base is None:
base = cherrypy.request.base
newurl = base + script_name + normalize_path(path) + qs
else:
# No request.app (we're being called outside a request).
# We'll have to guess the base from server.* attributes.
# This will produce very different results from the above
# if you're using vhosts or tools.proxy.
if base is None:
base = cherrypy.server.base()
path = (script_name or '') + path
newurl = base + normalize_path(path) + qs
# At this point, we should have a fully-qualified absolute URL.
if relative is None:
relative = getattr(cherrypy.request.app, 'relative_urls', False)
# See http://www.ietf.org/rfc/rfc2396.txt
if relative == 'server':
# "A relative reference beginning with a single slash character is
# termed an absolute-path reference, as defined by <abs_path>..."
# This is also sometimes called "server-relative".
newurl = '/' + '/'.join(newurl.split('/', 3)[3:])
elif relative:
# "A relative reference that does not begin with a scheme name
# or a slash character is termed a relative-path reference."
old = url(relative=False).split('/')[:-1]
new = newurl.split('/')
while old and new:
a, b = old[0], new[0]
if a != b:
break
old.pop(0)
new.pop(0)
new = (['..'] * len(old)) + new
newurl = '/'.join(new)
return newurl | [
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/patched/notpip/_vendor/distlib/metadata.py | python | Metadata.name_and_version | (self) | return _get_name_and_version(self.name, self.version, True) | [] | def name_and_version(self):
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/app_manager/suite_xml/post_process/workflow.py | python | WorkflowDatumMeta.clone_to_match | (self, source_id=None) | return new_meta | [] | def clone_to_match(self, source_id=None):
new_meta = WorkflowDatumMeta(self.id, self.nodeset, self.function)
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return new_meta | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/PIL/ImageStat.py | python | Stat._getcount | (self) | return v | Get total number of pixels in each layer | Get total number of pixels in each layer | [
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"Get total number of pixels in each layer"
v = []
for i in range(0, len(self.h), 256):
v.append(functools.reduce(operator.add, self.h[i:i+256]))
return v | [
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bloomberg/phabricator-tools | 09bd1587fe8945d93a891162fd4c89640c6fada7 | py/phl/phlsys_subprocess__t.py | python | PhlsysSubprocessTests.test_run_list | (self) | Passing valid list on stdin sorted in reverse order. | Passing valid list on stdin sorted in reverse order. | [
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"""Passing valid list on stdin sorted in reverse order."""
args = ("sort", "-r")
kwargs = {"stdin": "1\n2\n3"}
result = phlsys_subprocess.run(*args, **kwargs)
expect = phlsys_subprocess.RunResult(
stdout=kwargs['stdin'][::-1] + "\n", stderr='')
self.assertEqual(result, expect) | [
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google/containerregistry | 8a11dc8c53003ecf5b72ffaf035ba280109356ac | client/v2_2/docker_image_.py | python | FromDisk.blob_size | (self, digest) | return info.st_size | Override. | Override. | [
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"""Override."""
if digest not in self._layer_to_filename:
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info = os.stat(self._layer_to_filename[digest])
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DataIntegrationAlliance/data_integration_celery | 6775292030213dd1fa33a1ec0f542d5d2d2e612a | tasks/jqdata/stock/available_check/__init__.py | python | check_diff | (table_name, table_name_backup=None) | return df | 检查数据库两个表数据是否一致
:param table_name:
:param table_name_backup:
:return: | 检查数据库两个表数据是否一致
:param table_name:
:param table_name_backup:
:return: | [
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"""
检查数据库两个表数据是否一致
:param table_name:
:param table_name_backup:
:return:
"""
has_table = engine_md.has_table(table_name)
if not has_table:
logger.warning('%s 表不存在无需检查', table_name)
return None
if table_name_backup is None:
table_name_backup = get_bak_table_name(table_name)
has_table = engine_md.has_table(table_name_backup)
if not has_table:
logger.warning('%s 表不存在无需检查', table_name_backup)
return None
# table_name, table_name_backup = 'jq_stock_daily_md_pre', 'jq_stock_daily_md_pre_bak' # 测试使用
col_list = get_table_col(table_name, engine_md, config.DB_SCHEMA_MD)
# 主键字段名称列表
pk_col_set = {_[0] for _ in col_list if _[2]}
# 非主键字段名称列表
col_not_pk_list = [col_name for col_name, col_type, is_primary_key in col_list if not is_primary_key]
# selection clause
selection_clause_str = ', '.join([f"t.`{key}` {key}" for key in pk_col_set]) + ',\n'
selection_clause_str += ',\n'.join([
f"t.`{col_name}` {col_name}_t, bak.`{col_name}` {col_name}_bak"
for col_name in col_not_pk_list])
# join on condition
# t.jq_code = bak.jq_code and t.trade_date = bak.trade_date
on_condition_str = '\n and '.join([f"t.`{key}` = bak.`{key}`" for key in pk_col_set])
# where condition
# t.open <> bak.open or t.close <> bak.close or ...
where_condition_str = '\n or '.join([
f"t.`{col_name}` <> bak.`{col_name}`" for col_name in col_not_pk_list])
sql_str = f"""select {selection_clause_str} from {table_name} t
inner join {table_name_backup} bak
on {on_condition_str}
where {where_condition_str}"""
df = pd.read_sql(sql_str, engine_md)
data_count = df.shape[0]
if data_count > 0:
for num, (_, data_s) in enumerate(df.T.items(), start=1):
for col_name in col_not_pk_list:
col_name_t, col_name_bak = f'{col_name}_t', f'{col_name}_bak'
value_t, value_bak = data_s[col_name_t], data_s[col_name_bak]
is_nan_on_t, is_nan_on_bak = pd.isna(value_t), pd.isna(value_bak)
if is_nan_on_t and is_nan_on_bak:
continue
if value_t != value_bak:
# 两个字段一个为空，另一个不为空，或者两者不一致
logger.warning("%s %d/%d) 字段 %s 数值不一致 %f != %f",
table_name, num, data_count, ', '.join([str(data_s[key]) for key in pk_col_set]),
value_t, value_bak)
break
return df | [
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kirkthaker/investopedia-trading-api | 45049ac499d8eab71ee7634796c08ead5470f341 | InvestopediaApi/ita.py | python | Account.get_portfolio_status | (self) | return Status(
account_val=account_value,
buying_power=buying_power,
cash=cash,
annual_return=annual_return,
) | Returns a Status object containing account value,
buying power, cash on hand, and annual return.
Annual return is a percentage. | Returns a Status object containing account value,
buying power, cash on hand, and annual return.
Annual return is a percentage. | [
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"""
Returns a Status object containing account value,
buying power, cash on hand, and annual return.
Annual return is a percentage.
"""
response = self.fetch('/simulator/portfolio/')
parsed_html = response.soup
# The ids of all the account information values
acct_val_id = "ctl00_MainPlaceHolder_currencyFilter_ctrlPortfolioDetails_PortfolioSummary_lblAccountValue"
buying_power_id = "ctl00_MainPlaceHolder_currencyFilter_ctrlPortfolioDetails_PortfolioSummary_lblBuyingPower"
cash_id = "ctl00_MainPlaceHolder_currencyFilter_ctrlPortfolioDetails_PortfolioSummary_lblCash"
return_id = "ctl00_MainPlaceHolder_currencyFilter_ctrlPortfolioDetails_PortfolioSummary_lblAnnualReturn"
# Use BeautifulSoup to extract the relevant values based on html ID tags
account_value = parsed_html.find('span', attrs={'id': acct_val_id}).text
buying_power = parsed_html.find('span', attrs={'id': buying_power_id}).text
cash = parsed_html.find('span', attrs={'id': cash_id}).text
annual_return = parsed_html.find('span', attrs={'id': return_id}).text
# We want our returned values to be floats
# Use regex to remove non-numerical or decimal characters
# But keep - (negative sign)
regexp = "[^0-9.-]"
account_value = float(re.sub(regexp, '', account_value))
buying_power = float(re.sub(regexp, '', buying_power))
cash = float(re.sub(regexp, '', cash))
annual_return = float(re.sub(regexp, '', annual_return))
return Status(
account_val=account_value,
buying_power=buying_power,
cash=cash,
annual_return=annual_return,
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mne-tools/mne-python | f90b303ce66a8415e64edd4605b09ac0179c1ebf | mne/viz/utils.py | python | SelectFromCollection.style_sensors | (self, inds) | Style selected sensors as "active". | Style selected sensors as "active". | [
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"""Style selected sensors as "active"."""
# reset
self.fc[:, -1] = self.alpha_other
self.ec[:, -1] = self.alpha_other / 2
self.lw[:] = self.linewidth_other
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self.fc[inds, -1] = self.alpha_selected
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self.lw[inds] = self.linewidth_selected
self.collection.set_facecolors(self.fc)
self.collection.set_edgecolors(self.ec)
self.collection.set_linewidths(self.lw)
self.canvas.draw_idle() | [
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Patrowl/PatrowlEngines | 61dce987b662177396fa2f914ae07fd651179daf | engines/shhgit/libs/github.py | python | get_github_repositories | (logger, github_account) | return repositories | Retrieve repositories. | Retrieve repositories. | [
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] | def get_github_repositories(logger, github_account):
"""Retrieve repositories."""
github = Github(github_account['github_key'])
if github_account['is_internal']:
github = Github(
base_url=github_account['base_url'],
login_or_token=github_account['github_key']
)
raw_repositories = get_repositories(logger, github, github_account['organization'])
if raw_repositories is False:
logger.error('Error while getting repositories.')
return False
repositories = []
for raw_repository in raw_repositories:
repositories.append({
'id': raw_repository.id,
'name': raw_repository.name,
'clone_url': raw_repository.clone_url
})
return repositories | [
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/logging/__init__.py | python | _StderrHandler.__init__ | (self, level=NOTSET) | Initialize the handler. | Initialize the handler. | [
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mne-tools/mne-python | f90b303ce66a8415e64edd4605b09ac0179c1ebf | mne/io/brainvision/brainvision.py | python | _aux_vhdr_info | (vhdr_fname) | return settings, cfg, cinfostr, info | Aux function for _get_vhdr_info. | Aux function for _get_vhdr_info. | [
"Aux",
"function",
"for",
"_get_vhdr_info",
"."
] | def _aux_vhdr_info(vhdr_fname):
"""Aux function for _get_vhdr_info."""
with open(vhdr_fname, 'rb') as f:
# extract the first section to resemble a cfg
header = f.readline()
codepage = 'utf-8'
# we don't actually need to know the coding for the header line.
# the characters in it all belong to ASCII and are thus the
# same in Latin-1 and UTF-8
header = header.decode('ascii', 'ignore').strip()
_check_bv_version(header, 'header')
settings = f.read()
try:
# if there is an explicit codepage set, use it
# we pretend like it's ascii when searching for the codepage
cp_setting = re.search('Codepage=(.+)',
settings.decode('ascii', 'ignore'),
re.IGNORECASE & re.MULTILINE)
if cp_setting:
codepage = cp_setting.group(1).strip()
# BrainAmp Recorder also uses ANSI codepage
# an ANSI codepage raises a LookupError exception
# python recognize ANSI decoding as cp1252
if codepage == 'ANSI':
codepage = 'cp1252'
settings = settings.decode(codepage)
except UnicodeDecodeError:
# if UTF-8 (new standard) or explicit codepage setting fails,
# fallback to Latin-1, which is Windows default and implicit
# standard in older recordings
settings = settings.decode('latin-1')
if settings.find('[Comment]') != -1:
params, settings = settings.split('[Comment]')
else:
params, settings = settings, ''
cfg = configparser.ConfigParser()
with StringIO(params) as fid:
cfg.read_file(fid)
# get sampling info
# Sampling interval is given in microsec
cinfostr = 'Common Infos'
if not cfg.has_section(cinfostr):
cinfostr = 'Common infos' # NeurOne BrainVision export workaround
# get sampling info
# Sampling interval is given in microsec
sfreq = 1e6 / cfg.getfloat(cinfostr, 'SamplingInterval')
info = _empty_info(sfreq)
info._unlocked = False
return settings, cfg, cinfostr, info | [
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akfamily/akshare | 590e50eece9ec067da3538c7059fd660b71f1339 | akshare/fund/fund_amac.py | python | _get_pages | (url: str = "", payload: str = "") | return json_df["totalPages"] | 中国证券投资基金业协会-信息公示-私募基金管理人公示 页数
暂时不使用本函数, 直接可以获取所有数据 | 中国证券投资基金业协会-信息公示-私募基金管理人公示 页数
暂时不使用本函数, 直接可以获取所有数据 | [
"中国证券投资基金业协会",
"-",
"信息公示",
"-",
"私募基金管理人公示",
"页数",
"暂时不使用本函数",
"直接可以获取所有数据"
] | def _get_pages(url: str = "", payload: str = "") -> pd.DataFrame:
"""
中国证券投资基金业协会-信息公示-私募基金管理人公示 页数
暂时不使用本函数, 直接可以获取所有数据
"""
headers = {
"Content-Type": "application/json",
}
res = requests.post(url=url, json=payload, headers=headers, verify=False)
res.encoding = "utf-8"
json_df = res.json()
return json_df["totalPages"] | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/user_importer/helpers.py | python | BaseUserImporter.__init__ | (self, upload_domain, user_domain, user, upload_user, is_new_user, via, upload_record_id) | :param upload_domain: domain on which the bulk upload is being done
:param user_domain: domain user is being updated for
:param user: user to update
:param upload_user: user doing the upload
:param is_new_user: if user is a new user
:param via: USER_CHANGE_VIA_BULK_IMPORTER
:param upload_record_id: ID of the bulk upload record | :param upload_domain: domain on which the bulk upload is being done
:param user_domain: domain user is being updated for
:param user: user to update
:param upload_user: user doing the upload
:param is_new_user: if user is a new user
:param via: USER_CHANGE_VIA_BULK_IMPORTER
:param upload_record_id: ID of the bulk upload record | [
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"""
:param upload_domain: domain on which the bulk upload is being done
:param user_domain: domain user is being updated for
:param user: user to update
:param upload_user: user doing the upload
:param is_new_user: if user is a new user
:param via: USER_CHANGE_VIA_BULK_IMPORTER
:param upload_record_id: ID of the bulk upload record
"""
self.user_domain = user_domain
self.user = user
self.upload_user = upload_user
self.logger = UserChangeLogger(upload_domain=upload_domain, user_domain=user_domain, user=user,
is_new_user=is_new_user,
changed_by_user=upload_user, changed_via=via,
upload_record_id=upload_record_id)
self.role_updated = False | [
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/repackage/pip/pip/_vendor/distlib/locators.py | python | Locator.score_url | (self, url) | return (t.scheme != 'https', 'pypi.python.org' in t.netloc,
is_wheel, compatible, basename) | Give an url a score which can be used to choose preferred URLs
for a given project release. | Give an url a score which can be used to choose preferred URLs
for a given project release. | [
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] | def score_url(self, url):
"""
Give an url a score which can be used to choose preferred URLs
for a given project release.
"""
t = urlparse(url)
basename = posixpath.basename(t.path)
compatible = True
is_wheel = basename.endswith('.whl')
if is_wheel:
compatible = is_compatible(Wheel(basename), self.wheel_tags)
return (t.scheme != 'https', 'pypi.python.org' in t.netloc,
is_wheel, compatible, basename) | [
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matrix-org/synapse | 8e57584a5859a9002759963eb546d523d2498a01 | synapse/storage/databases/main/registration.py | python | RegistrationWorkerStore.set_renewal_mail_status | (self, user_id: str, email_sent: bool) | Sets or unsets the flag that indicates whether a renewal email has been sent
to the user (and the user hasn't renewed their account yet).
Args:
user_id: ID of the user to set/unset the flag for.
email_sent: Flag which indicates whether a renewal email has been sent
to this user. | Sets or unsets the flag that indicates whether a renewal email has been sent
to the user (and the user hasn't renewed their account yet). | [
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] | async def set_renewal_mail_status(self, user_id: str, email_sent: bool) -> None:
"""Sets or unsets the flag that indicates whether a renewal email has been sent
to the user (and the user hasn't renewed their account yet).
Args:
user_id: ID of the user to set/unset the flag for.
email_sent: Flag which indicates whether a renewal email has been sent
to this user.
"""
await self.db_pool.simple_update_one(
table="account_validity",
keyvalues={"user_id": user_id},
updatevalues={"email_sent": email_sent},
desc="set_renewal_mail_status",
) | [
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... | https://github.com/matrix-org/synapse/blob/8e57584a5859a9002759963eb546d523d2498a01/synapse/storage/databases/main/registration.py#L414-L428 | ||
anchore/anchore | 35d45722e6a97fb4172a607e8c7f57810b615119 | anchore/util/scripting.py | python | ScriptSetExecutor.get_scripts | (self) | return(ret) | Get the scripts at the path in sorted order as set in the module properties
:return: a sorted list of scripts | Get the scripts at the path in sorted order as set in the module properties
:return: a sorted list of scripts | [
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] | def get_scripts(self):
"""
Get the scripts at the path in sorted order as set in the module properties
:return: a sorted list of scripts
"""
ret = list()
for d in self.allpaths:
scripts = filter(lambda x: x.startswith(self.prefix), os.listdir(d))
scripts.sort(reverse=(not self.sort_ascending))
ret = ret + [os.path.join(d, x) for x in scripts]
return(ret) | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/concrete/summations.py | python | _eval_sum_hyper | (f, i, a) | return f.subs(i, 0)*hyperexpand(h), h.convergence_statement | Returns (res, cond). Sums from a to oo. | Returns (res, cond). Sums from a to oo. | [
"Returns",
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".",
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"."
] | def _eval_sum_hyper(f, i, a):
""" Returns (res, cond). Sums from a to oo. """
from sympy.functions import hyper
from sympy.simplify import hyperexpand, hypersimp, fraction, simplify
from sympy.polys.polytools import Poly, factor
if a != 0:
return _eval_sum_hyper(f.subs(i, i + a), i, 0)
if f.subs(i, 0) == 0:
if simplify(f.subs(i, Dummy('i', integer=True, positive=True))) == 0:
return S(0), True
return _eval_sum_hyper(f.subs(i, i + 1), i, 0)
hs = hypersimp(f, i)
if hs is None:
return None
numer, denom = fraction(factor(hs))
top, topl = numer.as_coeff_mul(i)
bot, botl = denom.as_coeff_mul(i)
ab = [top, bot]
factors = [topl, botl]
params = [[], []]
for k in range(2):
for fac in factors[k]:
mul = 1
if fac.is_Pow:
mul = fac.exp
fac = fac.base
if not mul.is_Integer:
return None
p = Poly(fac, i)
if p.degree() != 1:
return None
m, n = p.all_coeffs()
ab[k] *= m**mul
params[k] += [n/m]*mul
# Add "1" to numerator parameters, to account for implicit n! in
# hypergeometric series.
ap = params[0] + [1]
bq = params[1]
x = ab[0]/ab[1]
h = hyper(ap, bq, x)
return f.subs(i, 0)*hyperexpand(h), h.convergence_statement | [
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facebookresearch/Large-Scale-VRD | 7ababfe1023941c3653d7aebe9f835a47f5e8277 | lib/utils/vis.py | python | vis_mask | (img, mask, col, alpha=0.4, show_border=True, border_thick=1) | return img.astype(np.uint8) | Visualizes a single binary mask. | Visualizes a single binary mask. | [
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"mask",
"."
] | def vis_mask(img, mask, col, alpha=0.4, show_border=True, border_thick=1):
"""Visualizes a single binary mask."""
img = img.astype(np.float32)
idx = np.nonzero(mask)
img[idx[0], idx[1], :] *= 1.0 - alpha
img[idx[0], idx[1], :] += alpha * col
if show_border:
_, contours, _ = cv2.findContours(
mask.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
cv2.drawContours(img, contours, -1, _WHITE, border_thick, cv2.LINE_AA)
return img.astype(np.uint8) | [
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turicas/brasil.io | f1c371fe828a090510259a5027b49e2e651936b4 | brasilio_auth/scripts/migrate_wrong_usernames.py | python | migrate_usernames | (filepath) | [] | def migrate_usernames(filepath):
with open(filepath, mode="w") as fobj:
writer = csv.DictWriter(fobj, fieldnames=["old_username", "new_username", "email"])
writer.writeheader()
for user in User.objects.all():
if is_valid_username(user.username):
continue
# Define possible usernames based on current and remove any
# non-allowed chars
possible = [
slug(username, permitted_chars=possible_chars)
for username in possible_usernames(user.username, user.email)
]
for username in possible:
if not User.objects.filter(username=username).exists():
writer.writerow({"old_username": user.username, "new_username": username, "email": user.email})
user.username = username
user.save()
break
print(f"ERROR: could not migrate {user} (tried: {', '.join(possible)})") | [
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perseas/Pyrseas | 957860839c3b0047293a7a4fa982b1a2b85c7eb4 | pyrseas/dbobject/trigger.py | python | TriggerDict.from_map | (self, table, intriggers) | Initalize the dictionary of triggers by converting the input map
:param table: table owning the triggers
:param intriggers: YAML map defining the triggers | Initalize the dictionary of triggers by converting the input map | [
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] | def from_map(self, table, intriggers):
"""Initalize the dictionary of triggers by converting the input map
:param table: table owning the triggers
:param intriggers: YAML map defining the triggers
"""
for trg in intriggers:
inobj = intriggers[trg]
self[(table.schema, table.name, trg)] = Trigger.from_map(
trg, table, inobj) | [
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ssato/python-anyconfig | 09af1950f3226759932f5168d52f5e06ab88815c | src/anyconfig/parser.py | python | parse_attrlist_0 | (str_: str, avs_sep: str = ':', vs_sep: str = ',',
as_sep: str = ';') | return list(attr_val_itr(str_, avs_sep, vs_sep, as_sep)) | Simple parser to parse expressions in the form of
[ATTR1:VAL0,VAL1,...;ATTR2:VAL0,VAL2,..].
:param str_: input string
:param avs_sep: char to separate attribute and values
:param vs_sep: char to separate values
:param as_sep: char to separate attributes
:return:
a list of tuples of (key, value | [value])
where key = (Int | String | ...),
value = (Int | Bool | String | ...) | [Int | Bool | String | ...]
>>> parse_attrlist_0('a:1')
[('a', 1)]
>>> parse_attrlist_0('a:1;b:xyz')
[('a', 1), ('b', 'xyz')]
>>> parse_attrlist_0('requires:bash,zsh')
[('requires', ['bash', 'zsh'])]
>>> parse_attrlist_0('obsoletes:sysdata;conflicts:sysdata-old')
[('obsoletes', 'sysdata'), ('conflicts', 'sysdata-old')] | Simple parser to parse expressions in the form of
[ATTR1:VAL0,VAL1,...;ATTR2:VAL0,VAL2,..]. | [
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] | def parse_attrlist_0(str_: str, avs_sep: str = ':', vs_sep: str = ',',
as_sep: str = ';') -> typing.List[AttrValsT]:
"""
Simple parser to parse expressions in the form of
[ATTR1:VAL0,VAL1,...;ATTR2:VAL0,VAL2,..].
:param str_: input string
:param avs_sep: char to separate attribute and values
:param vs_sep: char to separate values
:param as_sep: char to separate attributes
:return:
a list of tuples of (key, value | [value])
where key = (Int | String | ...),
value = (Int | Bool | String | ...) | [Int | Bool | String | ...]
>>> parse_attrlist_0('a:1')
[('a', 1)]
>>> parse_attrlist_0('a:1;b:xyz')
[('a', 1), ('b', 'xyz')]
>>> parse_attrlist_0('requires:bash,zsh')
[('requires', ['bash', 'zsh'])]
>>> parse_attrlist_0('obsoletes:sysdata;conflicts:sysdata-old')
[('obsoletes', 'sysdata'), ('conflicts', 'sysdata-old')]
"""
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | lts/ctp/__init__.py | python | TraderApi.OnRtnFundInByBank | (self, pFundTransfer) | 银行发起入金通知 | 银行发起入金通知 | [
"银行发起入金通知"
] | def OnRtnFundInByBank(self, pFundTransfer):
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dawsonjon/Chips-2.0 | 57a986b8df36248bb4736bd84e3e68046b8665af | chips/compiler/types.py | python | is_int | (expression) | return expression.type_() == "int" | Expression object is of type int | Expression object is of type int | [
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hyperledger/sawtooth-core | 704cd5837c21f53642c06ffc97ba7978a77940b0 | cli/sawtooth_cli/format_utils.py | python | format_terminal_row | (headers, example_row) | return format_string | Uses headers and a row of example data to generate a format string
for printing a single row of data.
Args:
headers (tuple of strings): The headers for each column of data
example_row (tuple): A representative tuple of strings or ints
Returns
string: A format string with a size for each column | Uses headers and a row of example data to generate a format string
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"""Uses headers and a row of example data to generate a format string
for printing a single row of data.
Args:
headers (tuple of strings): The headers for each column of data
example_row (tuple): A representative tuple of strings or ints
Returns
string: A format string with a size for each column
"""
def format_column(col):
if isinstance(col, str):
return '{{:{w}.{w}}}'
return '{{:<{w}}}'
widths = [max(len(h), len(str(d))) for h, d in zip(headers, example_row)]
# Truncate last column to fit terminal width
original_last_width = widths[-1]
if sys.stdout.isatty():
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# console width - width of other columns and gutters - 3 for '...'
tty.width() - sum(w + 2 for w in widths[0:-1]) - 3)
# Build format string
cols = [format_column(c).format(w=w) for c, w in zip(example_row, widths)]
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if original_last_width > widths[-1]:
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JulianEberius/SublimePythonIDE | d70e40abc0c9f347af3204c7b910e0d6bfd6e459 | server/lib/python2/rope/base/utils.py | python | ignore_exception | (exception_class) | return _decorator | A decorator that ignores `exception_class` exceptions | A decorator that ignores `exception_class` exceptions | [
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def newfunc(*args, **kwds):
try:
return func(*args, **kwds)
except exception_class:
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return newfunc
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whtlkeep/BAT-algorithms | 1a339effd3719be5e94e742490e582bf4a03b7c0 | The beauty of programming/chapter-2/1-求二进制数中1的个数.py | python | count_one_bit_3 | (x) | return count | 解法三
时间复杂度是0(M), M是1的个数 | 解法三
时间复杂度是0(M), M是1的个数 | [
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""" 解法三
时间复杂度是0(M), M是1的个数
"""
count = 0
while x:
x = x & (x-1) # 此操作可以将最右边的1置为0
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edisonlz/fastor | 342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3 | base/site-packages/tencentcloud/mariadb/v20170312/mariadb_client.py | python | MariadbClient.DescribeBackupTime | (self, request) | 本接口（DescribeBackupTime）用于获取云数据库的备份时间。后台系统将根据此配置定期进行实例备份。
:param request: 调用DescribeBackupTime所需参数的结构体。
:type request: :class:`tencentcloud.mariadb.v20170312.models.DescribeBackupTimeRequest`
:rtype: :class:`tencentcloud.mariadb.v20170312.models.DescribeBackupTimeResponse` | 本接口（DescribeBackupTime）用于获取云数据库的备份时间。后台系统将根据此配置定期进行实例备份。 | [
"本接口（DescribeBackupTime）用于获取云数据库的备份时间。后台系统将根据此配置定期进行实例备份。"
] | def DescribeBackupTime(self, request):
"""本接口（DescribeBackupTime）用于获取云数据库的备份时间。后台系统将根据此配置定期进行实例备份。
:param request: 调用DescribeBackupTime所需参数的结构体。
:type request: :class:`tencentcloud.mariadb.v20170312.models.DescribeBackupTimeRequest`
:rtype: :class:`tencentcloud.mariadb.v20170312.models.DescribeBackupTimeResponse`
"""
try:
params = request._serialize()
body = self.call("DescribeBackupTime", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.DescribeBackupTimeResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise e
else:
raise TencentCloudSDKException(e.message, e.message) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/tensor/modules/ext_pow_free_module.py | python | ExtPowerFreeModule._an_element_ | (self) | return resu | r"""
Construct some (unamed) alternating contravariant tensor.
EXAMPLES::
sage: M = FiniteRankFreeModule(QQ, 4, name='M')
sage: e = M.basis('e')
sage: a = M.exterior_power(2)._an_element_() ; a
Alternating contravariant tensor of degree 2 on the 4-dimensional vector space M
over the Rational Field
sage: a.display()
1/2 e_0∧e_1
sage: a = M.exterior_power(3)._an_element_() ; a
Alternating contravariant tensor of degree 3 on the 4-dimensional vector space M
over the Rational Field
sage: a.display()
1/2 e_0∧e_1∧e_2
sage: a = M.exterior_power(4)._an_element_() ; a
Alternating contravariant tensor of degree 4 on the 4-dimensional vector space M
over the Rational Field
sage: a.display()
1/2 e_0∧e_1∧e_2∧e_3
TESTS:
When the base module has no default basis, a default
basis will be set for it::
sage: M2 = FiniteRankFreeModule(QQ, 4, name='M2')
sage: a = M2.exterior_power(2)._an_element_(); a
Alternating contravariant tensor of degree 2
on the 4-dimensional vector space M2 over the Rational Field
sage: a + a
Alternating contravariant tensor of degree 2
on the 4-dimensional vector space M2 over the Rational Field
sage: M2.default_basis()
Basis (e_0,e_1,e_2,e_3) on the 4-dimensional vector space M2 over the Rational Field | r"""
Construct some (unamed) alternating contravariant tensor. | [
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"tensor",
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r"""
Construct some (unamed) alternating contravariant tensor.
EXAMPLES::
sage: M = FiniteRankFreeModule(QQ, 4, name='M')
sage: e = M.basis('e')
sage: a = M.exterior_power(2)._an_element_() ; a
Alternating contravariant tensor of degree 2 on the 4-dimensional vector space M
over the Rational Field
sage: a.display()
1/2 e_0∧e_1
sage: a = M.exterior_power(3)._an_element_() ; a
Alternating contravariant tensor of degree 3 on the 4-dimensional vector space M
over the Rational Field
sage: a.display()
1/2 e_0∧e_1∧e_2
sage: a = M.exterior_power(4)._an_element_() ; a
Alternating contravariant tensor of degree 4 on the 4-dimensional vector space M
over the Rational Field
sage: a.display()
1/2 e_0∧e_1∧e_2∧e_3
TESTS:
When the base module has no default basis, a default
basis will be set for it::
sage: M2 = FiniteRankFreeModule(QQ, 4, name='M2')
sage: a = M2.exterior_power(2)._an_element_(); a
Alternating contravariant tensor of degree 2
on the 4-dimensional vector space M2 over the Rational Field
sage: a + a
Alternating contravariant tensor of degree 2
on the 4-dimensional vector space M2 over the Rational Field
sage: M2.default_basis()
Basis (e_0,e_1,e_2,e_3) on the 4-dimensional vector space M2 over the Rational Field
"""
resu = self.element_class(self._fmodule, self._degree)
# Make sure that the base module has a default basis
self._fmodule.an_element()
sindex = self._fmodule._sindex
ind = [sindex + i for i in range(resu._tensor_rank)]
resu.set_comp()[ind] = self._fmodule._ring.an_element()
return resu | [
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biolab/orange3 | 41685e1c7b1d1babe680113685a2d44bcc9fec0b | Orange/widgets/visualize/utils/__init__.py | python | CanvasText.setPos | (self, x, y) | setPos with adjustment for alignment | setPos with adjustment for alignment | [
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if self.vertical:
h, w = rect.height(), rect.width()
rect.setWidth(h)
rect.setHeight(-w)
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x -= rect.width()
elif int(self.alignment & Qt.AlignHCenter):
x -= rect.width() / 2.
if int(self.alignment & Qt.AlignBottom):
y -= rect.height()
elif int(self.alignment & Qt.AlignVCenter):
y -= rect.height() / 2.
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0vercl0k/z3-playground | 9496201c9a21e8f0ff43015af2527b69018b73ee | proof_unsigned_integer_overflow_chech.py | python | does_overflow_check | (a, b) | return If(
UGE((a_64 + b_64), BitVecVal(0x100000000, 64)), # UGE = >= unsigned
True,
False
) | The boring way, where you have to use a greater type to store
the temporary result | The boring way, where you have to use a greater type to store
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'''The boring way, where you have to use a greater type to store
the temporary result'''
a_64 = ZeroExt(32, a)
b_64 = ZeroExt(32, b)
return If(
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PennyLaneAI/pennylane | 1275736f790ced1d778858ed383448d4a43a4cdd | pennylane/gradients/vjp.py | python | batch_vjp | (tapes, dys, gradient_fn, reduction="append", gradient_kwargs=None) | return gradient_tapes, processing_fn | r"""Generate the gradient tapes and processing function required to compute
the vector-Jacobian products of a batch of tapes.
Consider a function :math:`\mathbf{f}(\mathbf{x})`. The Jacobian is given by
.. math::
\mathbf{J}_{\mathbf{f}}(\mathbf{x}) = \begin{pmatrix}
\frac{\partial f_1}{\partial x_1} &\cdots &\frac{\partial f_1}{\partial x_n}\\
\vdots &\ddots &\vdots\\
\frac{\partial f_m}{\partial x_1} &\cdots &\frac{\partial f_m}{\partial x_n}\\
\end{pmatrix}.
During backpropagation, the chain rule is applied. For example, consider the
cost function :math:`h = y\circ f: \mathbb{R}^n \rightarrow \mathbb{R}`,
where :math:`y: \mathbb{R}^m \rightarrow \mathbb{R}`.
The gradient is:
.. math::
\nabla h(\mathbf{x}) = \frac{\partial y}{\partial \mathbf{f}} \frac{\partial \mathbf{f}}{\partial \mathbf{x}}
= \frac{\partial y}{\partial \mathbf{f}} \mathbf{J}_{\mathbf{f}}(\mathbf{x}).
Denote :math:`d\mathbf{y} = \frac{\partial y}{\partial \mathbf{f}}`; we can write this in the form
of a matrix multiplication:
.. math:: \left[\nabla h(\mathbf{x})\right]_{j} = \sum_{i=0}^m d\mathbf{y}_i ~ \mathbf{J}_{ij}.
Thus, we can see that the gradient of the cost function is given by the so-called
**vector-Jacobian product**; the product of the row-vector :math:`d\mathbf{y}`, representing
the gradient of subsequent components of the cost function, and :math:`\mathbf{J}`,
the Jacobian of the current node of interest.
Args:
tapes (Sequence[.QuantumTape]): sequence of quantum tapes to differentiate
dys (Sequence[tensor_like]): Sequence of gradient-output vectors ``dy``. Must be the
same length as ``tapes``. Each ``dy`` tensor should have shape
matching the output shape of the corresponding tape.
gradient_fn (callable): the gradient transform to use to differentiate
the tapes
reduction (str): Determines how the vector-Jacobian products are returned.
If ``append``, then the output of the function will be of the form
``List[tensor_like]``, with each element corresponding to the VJP of each
input tape. If ``extend``, then the output VJPs will be concatenated.
gradient_kwargs (dict): dictionary of keyword arguments to pass when
determining the gradients of tapes
Returns:
List[tensor_like or None]: list of vector-Jacobian products. ``None`` elements corresponds
to tapes with no trainable parameters.
**Example**
Consider the following Torch-compatible quantum tapes:
.. code-block:: python
x = torch.tensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], requires_grad=True, dtype=torch.float64)
def ansatz(x):
qml.RX(x[0, 0], wires=0)
qml.RY(x[0, 1], wires=1)
qml.RZ(x[0, 2], wires=0)
qml.CNOT(wires=[0, 1])
qml.RX(x[1, 0], wires=1)
qml.RY(x[1, 1], wires=0)
qml.RZ(x[1, 2], wires=1)
with TorchInterface.apply(qml.tape.JacobianTape()) as tape1:
ansatz(x)
qml.expval(qml.PauliZ(0))
qml.probs(wires=1)
with TorchInterface.apply(qml.tape.JacobianTape()) as tape2:
ansatz(x)
qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))
tapes = [tape1, tape2]
Both tapes share the same circuit ansatz, but have different measurement outputs.
We can use the ``batch_vjp`` function to compute the vector-Jacobian product,
given a list of gradient-output vectors ``dys`` per tape:
>>> dys = [torch.tensor([1., 1., 1.], dtype=torch.float64),
... torch.tensor([1.], dtype=torch.float64)]
>>> vjp_tapes, fn = qml.gradients.batch_vjp(tapes, dys, qml.gradients.param_shift)
Note that each ``dy`` has shape matching the output dimension of the tape
(``tape1`` has 1 expectation and 2 probability values --- 3 outputs --- and ``tape2``
has 1 expectation value).
Executing the VJP tapes, and applying the processing function:
>>> dev = qml.device("default.qubit", wires=2)
>>> vjps = fn([t.execute(dev) for t in vjp_tapes])
>>> vjps
[tensor([-0.6069, -0.0451, 0.0451, -0.0139, -0.2809, 0.2809],
dtype=torch.float64, grad_fn=<ViewBackward>),
tensor([ 0.1739, -0.1641, -0.0054, -0.2937, -0.4008, 0.0000],
dtype=torch.float64, grad_fn=<ViewBackward>)]
We have two VJPs; one per tape. Each one corresponds to the number of parameters
on the tapes (6).
The output VJPs are also differentiable with respect to the tape parameters:
>>> cost = torch.sum(vjps[0] + vjps[1])
>>> cost.backward()
>>> x.grad
tensor([[-4.7924e-01, -9.0857e-01, -2.4198e-01],
[-9.2973e-02, -1.0772e+00, 4.7184e-09]], dtype=torch.float64) | r"""Generate the gradient tapes and processing function required to compute
the vector-Jacobian products of a batch of tapes. | [
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] | def batch_vjp(tapes, dys, gradient_fn, reduction="append", gradient_kwargs=None):
r"""Generate the gradient tapes and processing function required to compute
the vector-Jacobian products of a batch of tapes.
Consider a function :math:`\mathbf{f}(\mathbf{x})`. The Jacobian is given by
.. math::
\mathbf{J}_{\mathbf{f}}(\mathbf{x}) = \begin{pmatrix}
\frac{\partial f_1}{\partial x_1} &\cdots &\frac{\partial f_1}{\partial x_n}\\
\vdots &\ddots &\vdots\\
\frac{\partial f_m}{\partial x_1} &\cdots &\frac{\partial f_m}{\partial x_n}\\
\end{pmatrix}.
During backpropagation, the chain rule is applied. For example, consider the
cost function :math:`h = y\circ f: \mathbb{R}^n \rightarrow \mathbb{R}`,
where :math:`y: \mathbb{R}^m \rightarrow \mathbb{R}`.
The gradient is:
.. math::
\nabla h(\mathbf{x}) = \frac{\partial y}{\partial \mathbf{f}} \frac{\partial \mathbf{f}}{\partial \mathbf{x}}
= \frac{\partial y}{\partial \mathbf{f}} \mathbf{J}_{\mathbf{f}}(\mathbf{x}).
Denote :math:`d\mathbf{y} = \frac{\partial y}{\partial \mathbf{f}}`; we can write this in the form
of a matrix multiplication:
.. math:: \left[\nabla h(\mathbf{x})\right]_{j} = \sum_{i=0}^m d\mathbf{y}_i ~ \mathbf{J}_{ij}.
Thus, we can see that the gradient of the cost function is given by the so-called
**vector-Jacobian product**; the product of the row-vector :math:`d\mathbf{y}`, representing
the gradient of subsequent components of the cost function, and :math:`\mathbf{J}`,
the Jacobian of the current node of interest.
Args:
tapes (Sequence[.QuantumTape]): sequence of quantum tapes to differentiate
dys (Sequence[tensor_like]): Sequence of gradient-output vectors ``dy``. Must be the
same length as ``tapes``. Each ``dy`` tensor should have shape
matching the output shape of the corresponding tape.
gradient_fn (callable): the gradient transform to use to differentiate
the tapes
reduction (str): Determines how the vector-Jacobian products are returned.
If ``append``, then the output of the function will be of the form
``List[tensor_like]``, with each element corresponding to the VJP of each
input tape. If ``extend``, then the output VJPs will be concatenated.
gradient_kwargs (dict): dictionary of keyword arguments to pass when
determining the gradients of tapes
Returns:
List[tensor_like or None]: list of vector-Jacobian products. ``None`` elements corresponds
to tapes with no trainable parameters.
**Example**
Consider the following Torch-compatible quantum tapes:
.. code-block:: python
x = torch.tensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], requires_grad=True, dtype=torch.float64)
def ansatz(x):
qml.RX(x[0, 0], wires=0)
qml.RY(x[0, 1], wires=1)
qml.RZ(x[0, 2], wires=0)
qml.CNOT(wires=[0, 1])
qml.RX(x[1, 0], wires=1)
qml.RY(x[1, 1], wires=0)
qml.RZ(x[1, 2], wires=1)
with TorchInterface.apply(qml.tape.JacobianTape()) as tape1:
ansatz(x)
qml.expval(qml.PauliZ(0))
qml.probs(wires=1)
with TorchInterface.apply(qml.tape.JacobianTape()) as tape2:
ansatz(x)
qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))
tapes = [tape1, tape2]
Both tapes share the same circuit ansatz, but have different measurement outputs.
We can use the ``batch_vjp`` function to compute the vector-Jacobian product,
given a list of gradient-output vectors ``dys`` per tape:
>>> dys = [torch.tensor([1., 1., 1.], dtype=torch.float64),
... torch.tensor([1.], dtype=torch.float64)]
>>> vjp_tapes, fn = qml.gradients.batch_vjp(tapes, dys, qml.gradients.param_shift)
Note that each ``dy`` has shape matching the output dimension of the tape
(``tape1`` has 1 expectation and 2 probability values --- 3 outputs --- and ``tape2``
has 1 expectation value).
Executing the VJP tapes, and applying the processing function:
>>> dev = qml.device("default.qubit", wires=2)
>>> vjps = fn([t.execute(dev) for t in vjp_tapes])
>>> vjps
[tensor([-0.6069, -0.0451, 0.0451, -0.0139, -0.2809, 0.2809],
dtype=torch.float64, grad_fn=<ViewBackward>),
tensor([ 0.1739, -0.1641, -0.0054, -0.2937, -0.4008, 0.0000],
dtype=torch.float64, grad_fn=<ViewBackward>)]
We have two VJPs; one per tape. Each one corresponds to the number of parameters
on the tapes (6).
The output VJPs are also differentiable with respect to the tape parameters:
>>> cost = torch.sum(vjps[0] + vjps[1])
>>> cost.backward()
>>> x.grad
tensor([[-4.7924e-01, -9.0857e-01, -2.4198e-01],
[-9.2973e-02, -1.0772e+00, 4.7184e-09]], dtype=torch.float64)
"""
gradient_kwargs = gradient_kwargs or {}
reshape_info = []
gradient_tapes = []
processing_fns = []
# Loop through the tapes and dys vector
for tape, dy in zip(tapes, dys):
g_tapes, fn = vjp(tape, dy, gradient_fn, gradient_kwargs)
reshape_info.append(len(g_tapes))
processing_fns.append(fn)
gradient_tapes.extend(g_tapes)
def processing_fn(results, nums=None):
vjps = []
start = 0
if nums is None:
nums = [None] * len(tapes)
for t_idx in range(len(tapes)):
# extract the correct results from the flat list
res_len = reshape_info[t_idx]
res_t = results[start : start + res_len]
start += res_len
# postprocess results to compute the VJP
vjp_ = processing_fns[t_idx](res_t, num=nums[t_idx])
if vjp_ is None:
if reduction == "append":
vjps.append(None)
continue
if isinstance(reduction, str):
getattr(vjps, reduction)(vjp_)
elif callable(reduction):
reduction(vjps, vjp_)
return vjps
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... | https://github.com/PennyLaneAI/pennylane/blob/1275736f790ced1d778858ed383448d4a43a4cdd/pennylane/gradients/vjp.py#L186-L341 |
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