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eirannejad/pyRevit | 49c0b7eb54eb343458ce1365425e6552d0c47d44 | extensions/pyRevitTools.extension/pyRevit.tab/Selection.panel/Pick.splitpushbutton/Pick.pushbutton/script.py | python | PickByCategorySelectionFilter.AllowReference | (self, refer, point) | return False | Not used for selection | Not used for selection | [
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] | def AllowReference(self, refer, point): # pylint: disable=W0613
"""Not used for selection"""
return False | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/wagtail/utils/setup.py | python | sdist.run | (self) | [] | def run(self):
self.compile_assets()
base_sdist.run(self) | [
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akamai/api-kickstart | 209b9543054d03692c5883dc59e3203a0056736d | examples/python/papi_create_datastore_new.py | python | getGroup | () | return (groups_result) | Request the list of groups for the account. Print out
how many groups there are, then use the first group where
the test property lives. | Request the list of groups for the account. Print out
how many groups there are, then use the first group where
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"""
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"""
print
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hyperledger/sawtooth-core | 704cd5837c21f53642c06ffc97ba7978a77940b0 | validator/sawtooth_validator/state/merkle.py | python | MerkleDatabase.__contains__ | (self, item) | Does the tree contain an address.
Args:
item (str): An address.
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"""
try:
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holoviz/datashader | 25578abde75c7fa28c6633b33cb8d8a1e433da67 | datashader/bokeh_ext.py | python | patch_event | (image) | return json.dumps({'events': [{'attr': u'data',
'kind': 'ModelChanged',
'model': image.ds.ref,
'new': data}],
'references': []}) | Generates a bokeh patch event message given an InteractiveImage
instance. Uses the bokeh messaging protocol for bokeh>=0.12.10
and a custom patch for previous versions.
Parameters
----------
image: InteractiveImage
InteractiveImage instance with a plot
Returns
-------
msg: str
JSON message containing patch events to update the plot | Generates a bokeh patch event message given an InteractiveImage
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and a custom patch for previous versions.
Parameters
----------
image: InteractiveImage
InteractiveImage instance with a plot
Returns
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msg: str
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"""
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event_obj = image.doc.callbacks if bokeh_version >= '2.4' else image.doc
events = list(event_obj._held_events)
if not events:
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protocol = Protocol()
else:
protocol = Protocol("1.0")
msg = protocol.create("PATCH-DOC", events)
event_obj._held_events = []
return msg
data = dict(image.ds.data)
data['image'] = [data['image'][0].tolist()]
return json.dumps({'events': [{'attr': u'data',
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'model': image.ds.ref,
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wbond/packagecontrol.io | 9f5eb7e3392e6bc2ad979ad32d3dd27ef9c00b20 | app/lib/package_control/deps/oscrypto/_mac/_core_foundation_ctypes.py | python | CFHelpers.cf_dictionary_to_dict | (dictionary) | return output | Converts a CFDictionary object into a python dictionary
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:return:
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:param dictionary:
The CFDictionary to convert
:return:
A python dict
"""
dict_length = CoreFoundation.CFDictionaryGetCount(dictionary)
keys = (CFTypeRef * dict_length)()
values = (CFTypeRef * dict_length)()
CoreFoundation.CFDictionaryGetKeysAndValues(
dictionary,
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output = {}
for index in range(0, dict_length):
output[CFHelpers.native(keys[index])] = CFHelpers.native(values[index])
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box/box-python-sdk | e8abbb515cfe77d9533df77c807d55d6b494ceaa | boxsdk/client/client.py | python | Client.enterprise | (self, enterprise_id: str) | return self.translator.get('enterprise')(session=self._session, object_id=enterprise_id) | Initialize a :class:`Enterprise` object, whose box ID is enterprise_id.
:param enterprise_id:
The box id of the :class:`Enterprise` object.
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"""
Initialize a :class:`Enterprise` object, whose box ID is enterprise_id.
:param enterprise_id:
The box id of the :class:`Enterprise` object.
:return:
A :class:`Enterprise` object with the given enterprise ID.
"""
return self.translator.get('enterprise')(session=self._session, object_id=enterprise_id) | [
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twisted/twisted | dee676b040dd38b847ea6fb112a712cb5e119490 | src/twisted/mail/_pop3client.py | python | POP3Client.serverGreeting | (self, greeting) | Handle the server greeting.
@type greeting: L{bytes}
@param greeting: The server greeting minus the status indicator.
For servers implementing APOP authentication, this will contain a
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Handle the server greeting.
@type greeting: L{bytes}
@param greeting: The server greeting minus the status indicator.
For servers implementing APOP authentication, this will contain a
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hatRiot/zarp | 2e772350a01c2aeed3f4da9685cd0cc5d6b3ecad | src/lib/scapy/layers/inet6.py | python | IP6ListField.i2len | (self, pkt, i) | return 16*len(i) | [] | def i2len(self, pkt, i):
return 16*len(i) | [
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citronneur/rdpy | cef16a9f64d836a3221a344ca7d571644280d829 | rdpy/core/filetimes.py | python | dt_to_filetime | (dt) | return ft + (dt.microsecond * 10) | Converts a datetime to Microsoft filetime format. If the object is
time zone-naive, it is forced to UTC before conversion.
>>> "%.0f" % dt_to_filetime(datetime(2009, 7, 25, 23, 0))
'128930364000000000'
>>> "%.0f" % dt_to_filetime(datetime(1970, 1, 1, 0, 0, tzinfo=utc))
'116444736000000000'
>>> "%.0f" % dt_to_filetime(datetime(1970, 1, 1, 0, 0))
'116444736000000000'
>>> dt_to_filetime(datetime(2009, 7, 25, 23, 0, 0, 100))
128930364000001000 | Converts a datetime to Microsoft filetime format. If the object is
time zone-naive, it is forced to UTC before conversion. | [
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"""Converts a datetime to Microsoft filetime format. If the object is
time zone-naive, it is forced to UTC before conversion.
>>> "%.0f" % dt_to_filetime(datetime(2009, 7, 25, 23, 0))
'128930364000000000'
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'116444736000000000'
>>> "%.0f" % dt_to_filetime(datetime(1970, 1, 1, 0, 0))
'116444736000000000'
>>> dt_to_filetime(datetime(2009, 7, 25, 23, 0, 0, 100))
128930364000001000
"""
if (dt.tzinfo is None) or (dt.tzinfo.utcoffset(dt) is None):
dt = dt.replace(tzinfo=utc)
ft = EPOCH_AS_FILETIME + (timegm(dt.timetuple()) * HUNDREDS_OF_NANOSECONDS)
return ft + (dt.microsecond * 10) | [
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spulec/moto | a688c0032596a7dfef122b69a08f2bec3be2e481 | moto/dynamodb2/parsing/expressions.py | python | UpdateExpressionFunctionParser._is_possible_start | (cls, token) | Check whether a token is supposed to be a function
Args:
token(Token): the token to check
Returns:
bool: True if token is the start of a function. | Check whether a token is supposed to be a function
Args:
token(Token): the token to check | [
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Check whether a token is supposed to be a function
Args:
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bool: True if token is the start of a function.
"""
if token.type == Token.ATTRIBUTE:
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/min/operator.py | python | inv | (a) | return ~a | Same as ~a. | Same as ~a. | [
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openstack/python-neutronclient | 517bef2c5454dde2eba5cc2194ee857be6be7164 | neutronclient/v2_0/client.py | python | Client.show_subnetpool | (self, subnetpool, **_params) | return self.get(self.subnetpool_path % (subnetpool), params=_params) | Fetches information of a certain subnetpool. | Fetches information of a certain subnetpool. | [
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"""Fetches information of a certain subnetpool."""
return self.get(self.subnetpool_path % (subnetpool), params=_params) | [
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rowliny/DiffHelper | ab3a96f58f9579d0023aed9ebd785f4edf26f8af | Tool/SitePackages/nltk/corpus/reader/timit.py | python | TimitCorpusReader.spkrutteranceids | (self, speaker) | return [
utterance
for utterance in self._utterances
if utterance.startswith(speaker + "/")
] | :return: A list of all utterances associated with a given
speaker. | :return: A list of all utterances associated with a given
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"""
:return: A list of all utterances associated with a given
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"""
return [
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cronyo/cronyo | cd5abab0871b68bf31b18aac934303928130a441 | cronyo/vendor/urllib3/util/retry.py | python | Retry.__init__ | (
self,
total=10,
connect=None,
read=None,
redirect=None,
status=None,
method_whitelist=DEFAULT_METHOD_WHITELIST,
status_forcelist=None,
backoff_factor=0,
raise_on_redirect=True,
raise_on_status=True,
history=None,
respect_retry_after_header=True,
remove_headers_on_redirect=DEFAULT_REDIRECT_HEADERS_BLACKLIST,
) | [] | def __init__(
self,
total=10,
connect=None,
read=None,
redirect=None,
status=None,
method_whitelist=DEFAULT_METHOD_WHITELIST,
status_forcelist=None,
backoff_factor=0,
raise_on_redirect=True,
raise_on_status=True,
history=None,
respect_retry_after_header=True,
remove_headers_on_redirect=DEFAULT_REDIRECT_HEADERS_BLACKLIST,
):
self.total = total
self.connect = connect
self.read = read
self.status = status
if redirect is False or total is False:
redirect = 0
raise_on_redirect = False
self.redirect = redirect
self.status_forcelist = status_forcelist or set()
self.method_whitelist = method_whitelist
self.backoff_factor = backoff_factor
self.raise_on_redirect = raise_on_redirect
self.raise_on_status = raise_on_status
self.history = history or tuple()
self.respect_retry_after_header = respect_retry_after_header
self.remove_headers_on_redirect = frozenset(
[h.lower() for h in remove_headers_on_redirect]
) | [
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khanhnamle1994/natural-language-processing | 01d450d5ac002b0156ef4cf93a07cb508c1bcdc5 | assignment1/.env/lib/python2.7/site-packages/scipy/cluster/hierarchy.py | python | inconsistent | (Z, d=2) | return R | Calculates inconsistency statistics on a linkage.
Note: This function behaves similarly to the MATLAB(TM)
inconsistent function.
Parameters
----------
Z : ndarray
The :math:`(n-1)` by 4 matrix encoding the linkage
(hierarchical clustering). See ``linkage`` documentation
for more information on its form.
d : int, optional
The number of links up to `d` levels below each
non-singleton cluster.
Returns
-------
R : ndarray
A :math:`(n-1)` by 5 matrix where the ``i``'th row
contains the link statistics for the non-singleton cluster
``i``. The link statistics are computed over the link
heights for links :math:`d` levels below the cluster
``i``. ``R[i,0]`` and ``R[i,1]`` are the mean and standard
deviation of the link heights, respectively; ``R[i,2]`` is
the number of links included in the calculation; and
``R[i,3]`` is the inconsistency coefficient,
.. math:: \\frac{\\mathtt{Z[i,2]}-\\mathtt{R[i,0]}} {R[i,1]} | Calculates inconsistency statistics on a linkage. | [
"Calculates",
"inconsistency",
"statistics",
"on",
"a",
"linkage",
"."
] | def inconsistent(Z, d=2):
"""
Calculates inconsistency statistics on a linkage.
Note: This function behaves similarly to the MATLAB(TM)
inconsistent function.
Parameters
----------
Z : ndarray
The :math:`(n-1)` by 4 matrix encoding the linkage
(hierarchical clustering). See ``linkage`` documentation
for more information on its form.
d : int, optional
The number of links up to `d` levels below each
non-singleton cluster.
Returns
-------
R : ndarray
A :math:`(n-1)` by 5 matrix where the ``i``'th row
contains the link statistics for the non-singleton cluster
``i``. The link statistics are computed over the link
heights for links :math:`d` levels below the cluster
``i``. ``R[i,0]`` and ``R[i,1]`` are the mean and standard
deviation of the link heights, respectively; ``R[i,2]`` is
the number of links included in the calculation; and
``R[i,3]`` is the inconsistency coefficient,
.. math:: \\frac{\\mathtt{Z[i,2]}-\\mathtt{R[i,0]}} {R[i,1]}
"""
Z = np.asarray(Z, order='c')
Zs = Z.shape
is_valid_linkage(Z, throw=True, name='Z')
if (not d == np.floor(d)) or d < 0:
raise ValueError('The second argument d must be a nonnegative '
'integer value.')
# Since the C code does not support striding using strides.
# The dimensions are used instead.
[Z] = _copy_arrays_if_base_present([Z])
n = Zs[0] + 1
R = np.zeros((n - 1, 4), dtype=np.double)
_hierarchy_wrap.inconsistent_wrap(Z, R, int(n), int(d))
return R | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/Lib/inspect.py | python | iscode | (object) | return isinstance(object, types.CodeType) | Return true if the object is a code object.
Code objects provide these attributes:
co_argcount number of arguments (not including * or ** args)
co_code string of raw compiled bytecode
co_consts tuple of constants used in the bytecode
co_filename name of file in which this code object was created
co_firstlineno number of first line in Python source code
co_flags bitmap: 1=optimized | 2=newlocals | 4=*arg | 8=**arg
co_lnotab encoded mapping of line numbers to bytecode indices
co_name name with which this code object was defined
co_names tuple of names of local variables
co_nlocals number of local variables
co_stacksize virtual machine stack space required
co_varnames tuple of names of arguments and local variables | Return true if the object is a code object. | [
"Return",
"true",
"if",
"the",
"object",
"is",
"a",
"code",
"object",
"."
] | def iscode(object):
"""Return true if the object is a code object.
Code objects provide these attributes:
co_argcount number of arguments (not including * or ** args)
co_code string of raw compiled bytecode
co_consts tuple of constants used in the bytecode
co_filename name of file in which this code object was created
co_firstlineno number of first line in Python source code
co_flags bitmap: 1=optimized | 2=newlocals | 4=*arg | 8=**arg
co_lnotab encoded mapping of line numbers to bytecode indices
co_name name with which this code object was defined
co_names tuple of names of local variables
co_nlocals number of local variables
co_stacksize virtual machine stack space required
co_varnames tuple of names of arguments and local variables"""
return isinstance(object, types.CodeType) | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/pydoc.py | python | ttypager | (text) | Page through text on a text terminal. | Page through text on a text terminal. | [
"Page",
"through",
"text",
"on",
"a",
"text",
"terminal",
"."
] | def ttypager(text):
"""Page through text on a text terminal."""
lines = plain(_encode(plain(text), getattr(sys.stdout, 'encoding', _encoding))).split('\n')
try:
import tty
fd = sys.stdin.fileno()
old = tty.tcgetattr(fd)
tty.setcbreak(fd)
getchar = lambda: sys.stdin.read(1)
except (ImportError, AttributeError):
tty = None
getchar = lambda: sys.stdin.readline()[:-1][:1]
try:
try:
h = int(os.environ.get('LINES', 0))
except ValueError:
h = 0
if h <= 1:
h = 25
r = inc = h - 1
sys.stdout.write(join(lines[:inc], '\n') + '\n')
while lines[r:]:
sys.stdout.write('-- more --')
sys.stdout.flush()
c = getchar()
if c in ('q', 'Q'):
sys.stdout.write('\r \r')
break
elif c in ('\r', '\n'):
sys.stdout.write('\r \r' + lines[r] + '\n')
r = r + 1
continue
if c in ('b', 'B', '\x1b'):
r = r - inc - inc
if r < 0: r = 0
sys.stdout.write('\n' + join(lines[r:r+inc], '\n') + '\n')
r = r + inc
finally:
if tty:
tty.tcsetattr(fd, tty.TCSAFLUSH, old) | [
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nucleic/enaml | 65c2a2a2d765e88f2e1103046680571894bb41ed | enaml/core/compiler_nodes.py | python | TemplateInstanceNode.iternodes | (self) | return self.template.iternodes() | Iterate over the nodes of the instantiation.
Returns
-------
result : generator
A generator which yields the unrolled nodes of the template
instantiation. | Iterate over the nodes of the instantiation. | [
"Iterate",
"over",
"the",
"nodes",
"of",
"the",
"instantiation",
"."
] | def iternodes(self):
""" Iterate over the nodes of the instantiation.
Returns
-------
result : generator
A generator which yields the unrolled nodes of the template
instantiation.
"""
return self.template.iternodes() | [
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hardbyte/python-can | e7a2b040ee1f0cdd7fd77fbfef0454353166b333 | can/io/blf.py | python | BLFWriter.on_message_received | (self, msg) | [] | def on_message_received(self, msg):
channel = channel2int(msg.channel)
if channel is None:
channel = self.channel
else:
# Many interfaces start channel numbering at 0 which is invalid
channel += 1
arb_id = msg.arbitration_id
if msg.is_extended_id:
arb_id |= CAN_MSG_EXT
flags = REMOTE_FLAG if msg.is_remote_frame else 0
if not msg.is_rx:
flags |= DIR
can_data = bytes(msg.data)
if msg.is_error_frame:
data = CAN_ERROR_EXT_STRUCT.pack(
channel,
0, # length
0, # flags
0, # ecc
0, # position
len2dlc(msg.dlc),
0, # frame length
arb_id,
0, # ext flags
can_data,
)
self._add_object(CAN_ERROR_EXT, data, msg.timestamp)
elif msg.is_fd:
fd_flags = EDL
if msg.bitrate_switch:
fd_flags |= BRS
if msg.error_state_indicator:
fd_flags |= ESI
data = CAN_FD_MSG_STRUCT.pack(
channel,
flags,
len2dlc(msg.dlc),
arb_id,
0,
0,
fd_flags,
len(can_data),
can_data,
)
self._add_object(CAN_FD_MESSAGE, data, msg.timestamp)
else:
data = CAN_MSG_STRUCT.pack(channel, flags, msg.dlc, arb_id, can_data)
self._add_object(CAN_MESSAGE, data, msg.timestamp) | [
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NifTK/NiftyNet | 935bf4334cd00fa9f9d50f6a95ddcbfdde4031e0 | niftynet/network/se_resnet.py | python | BottleneckBlock.__init__ | (self, n_output_chns, stride, Conv, name='bottleneck') | :param n_output_chns: int, number of output channels
:param stride: int, stride to use in the convolutional layers
:param Conv: layer, convolutional layer
:param name: layer name | [] | def __init__(self, n_output_chns, stride, Conv, name='bottleneck'):
"""
:param n_output_chns: int, number of output channels
:param stride: int, stride to use in the convolutional layers
:param Conv: layer, convolutional layer
:param name: layer name
"""
self.n_output_chns = n_output_chns
self.stride = stride
self.bottle_neck_chns = n_output_chns // 4
self.Conv = Conv
super(BottleneckBlock, self).__init__(name=name) | [
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ctfs/write-ups-2014 | b02bcbb2737907dd0aa39c5d4df1d1e270958f54 | asis-ctf-finals-2014/xorqr/netcatlib/netcatlib.py | python | Netcat.mt_interact | (self) | Multithreaded version of interact(). | Multithreaded version of interact(). | [
"Multithreaded",
"version",
"of",
"interact",
"()",
"."
] | def mt_interact(self):
"""Multithreaded version of interact()."""
global mt_interact_exit
import thread
mt_interact_exit = False
threadid = thread.start_new_thread(self.listener, ())
while 1:
line = sys.stdin.readline()
if not line or mt_interact_exit:
break
self.write(line) | [
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/op2/tables/oes_stressStrain/real/oes_triax.py | python | RealTriaxArray.build | (self) | sizes the vectorized attributes of the RealTriaxArray | sizes the vectorized attributes of the RealTriaxArray | [
"sizes",
"the",
"vectorized",
"attributes",
"of",
"the",
"RealTriaxArray"
] | def build(self):
"""sizes the vectorized attributes of the RealTriaxArray"""
#print("self.ielement =", self.ielement)
#print('ntimes=%s nelements=%s ntotal=%s' % (self.ntimes, self.nelements, self.ntotal))
assert self.ntimes > 0, 'ntimes=%s' % self.ntimes
assert self.nelements > 0, 'nelements=%s' % self.nelements
assert self.ntotal > 0, 'ntotal=%s' % self.ntotal
unused_nnodes_per_element = self.nnodes_per_element
self.itime = 0
self.ielement = 0
self.itotal = 0
#self.ntimes = 0
#self.nelements = 0
#print("***name=%s type=%s nnodes_per_element=%s ntimes=%s nelements=%s ntotal=%s" % (
#self.element_name, self.element_type, nnodes_per_element, self.ntimes, self.nelements, self.ntotal))
dtype, idtype, fdtype = get_times_dtype(self.nonlinear_factor, self.size, self.analysis_fmt)
_times = zeros(self.ntimes, dtype=dtype)
element_node = zeros((self.ntotal, 2), dtype='int32')
# [radial, azimuthal, axial, shear, omax, oms, ovm]
data = zeros((self.ntimes, self.ntotal, 7), dtype='float32')
if self.load_as_h5:
#for key, value in sorted(self.data_code.items()):
#print(key, value)
group = self._get_result_group()
self._times = group.create_dataset('_times', data=_times)
self.element_node = group.create_dataset('element_node', data=element_node)
self.data = group.create_dataset('data', data=data)
else:
self._times = _times
self.element_node = element_node
self.data = data | [
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NordicSemiconductor/pc-nrfutil | d08e742128f2a3dac522601bc6b9f9b2b63952df | nordicsemi/dfu/package.py | python | Package.__create_temp_workspace | () | return tempfile.mkdtemp(prefix="nrf_dfu_pkg_") | [] | def __create_temp_workspace():
return tempfile.mkdtemp(prefix="nrf_dfu_pkg_") | [
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Qiskit/qiskit-terra | b66030e3b9192efdd3eb95cf25c6545fe0a13da4 | qiskit/transpiler/passes/routing/sabre_swap.py | python | SabreSwap.__init__ | (self, coupling_map, heuristic="basic", seed=None, fake_run=False) | r"""SabreSwap initializer.
Args:
coupling_map (CouplingMap): CouplingMap of the target backend.
heuristic (str): The type of heuristic to use when deciding best
swap strategy ('basic' or 'lookahead' or 'decay').
seed (int): random seed used to tie-break among candidate swaps.
fake_run (bool): if true, it only pretend to do routing, i.e., no
swap is effectively added.
Additional Information:
The search space of possible SWAPs on physical qubits is explored
by assigning a score to the layout that would result from each SWAP.
The goodness of a layout is evaluated based on how viable it makes
the remaining virtual gates that must be applied. A few heuristic
cost functions are supported
- 'basic':
The sum of distances for corresponding physical qubits of
interacting virtual qubits in the front_layer.
.. math::
H_{basic} = \sum_{gate \in F} D[\pi(gate.q_1)][\pi(gate.q2)]
- 'lookahead':
This is the sum of two costs: first is the same as the basic cost.
Second is the basic cost but now evaluated for the
extended set as well (i.e. :math:`|E|` number of upcoming successors to gates in
front_layer F). This is weighted by some amount EXTENDED_SET_WEIGHT (W) to
signify that upcoming gates are less important that the front_layer.
.. math::
H_{decay}=\frac{1}{\left|{F}\right|}\sum_{gate \in F} D[\pi(gate.q_1)][\pi(gate.q2)]
+ W*\frac{1}{\left|{E}\right|} \sum_{gate \in E} D[\pi(gate.q_1)][\pi(gate.q2)]
- 'decay':
This is the same as 'lookahead', but the whole cost is multiplied by a
decay factor. This increases the cost if the SWAP that generated the
trial layout was recently used (i.e. it penalizes increase in depth).
.. math::
H_{decay} = max(decay(SWAP.q_1), decay(SWAP.q_2)) {
\frac{1}{\left|{F}\right|} \sum_{gate \in F} D[\pi(gate.q_1)][\pi(gate.q2)]\\
+ W *\frac{1}{\left|{E}\right|} \sum_{gate \in E} D[\pi(gate.q_1)][\pi(gate.q2)]
} | r"""SabreSwap initializer. | [
"r",
"SabreSwap",
"initializer",
"."
] | def __init__(self, coupling_map, heuristic="basic", seed=None, fake_run=False):
r"""SabreSwap initializer.
Args:
coupling_map (CouplingMap): CouplingMap of the target backend.
heuristic (str): The type of heuristic to use when deciding best
swap strategy ('basic' or 'lookahead' or 'decay').
seed (int): random seed used to tie-break among candidate swaps.
fake_run (bool): if true, it only pretend to do routing, i.e., no
swap is effectively added.
Additional Information:
The search space of possible SWAPs on physical qubits is explored
by assigning a score to the layout that would result from each SWAP.
The goodness of a layout is evaluated based on how viable it makes
the remaining virtual gates that must be applied. A few heuristic
cost functions are supported
- 'basic':
The sum of distances for corresponding physical qubits of
interacting virtual qubits in the front_layer.
.. math::
H_{basic} = \sum_{gate \in F} D[\pi(gate.q_1)][\pi(gate.q2)]
- 'lookahead':
This is the sum of two costs: first is the same as the basic cost.
Second is the basic cost but now evaluated for the
extended set as well (i.e. :math:`|E|` number of upcoming successors to gates in
front_layer F). This is weighted by some amount EXTENDED_SET_WEIGHT (W) to
signify that upcoming gates are less important that the front_layer.
.. math::
H_{decay}=\frac{1}{\left|{F}\right|}\sum_{gate \in F} D[\pi(gate.q_1)][\pi(gate.q2)]
+ W*\frac{1}{\left|{E}\right|} \sum_{gate \in E} D[\pi(gate.q_1)][\pi(gate.q2)]
- 'decay':
This is the same as 'lookahead', but the whole cost is multiplied by a
decay factor. This increases the cost if the SWAP that generated the
trial layout was recently used (i.e. it penalizes increase in depth).
.. math::
H_{decay} = max(decay(SWAP.q_1), decay(SWAP.q_2)) {
\frac{1}{\left|{F}\right|} \sum_{gate \in F} D[\pi(gate.q_1)][\pi(gate.q2)]\\
+ W *\frac{1}{\left|{E}\right|} \sum_{gate \in E} D[\pi(gate.q_1)][\pi(gate.q2)]
}
"""
super().__init__()
# Assume bidirectional couplings, fixing gate direction is easy later.
if coupling_map is None or coupling_map.is_symmetric:
self.coupling_map = coupling_map
else:
self.coupling_map = deepcopy(coupling_map)
self.coupling_map.make_symmetric()
self.heuristic = heuristic
self.seed = seed
self.fake_run = fake_run
self.applied_predecessors = None
self.qubits_decay = None
self._bit_indices = None
self.dist_matrix = None | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/Lib/numbers.py | python | Complex.real | (self) | Retrieve the real component of this number.
This should subclass Real. | Retrieve the real component of this number. | [
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"""Retrieve the real component of this number.
This should subclass Real.
"""
raise NotImplementedError | [
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dcos/dcos | 79b9a39b4e639dc2c9435a869918399b50bfaf24 | pkgpanda/actions.py | python | remove_package | (install, repository, package_id) | Remove a package from the local repository.
Errors if any packages in package_ids are activated in install.
install: pkgpanda.Install
repository: pkgpanda.Repository
package_id: package ID to remove from repository | Remove a package from the local repository. | [
"Remove",
"a",
"package",
"from",
"the",
"local",
"repository",
"."
] | def remove_package(install, repository, package_id):
"""Remove a package from the local repository.
Errors if any packages in package_ids are activated in install.
install: pkgpanda.Install
repository: pkgpanda.Repository
package_id: package ID to remove from repository
"""
if package_id in install.get_active():
raise PackageConflict("Refusing to remove active package {0}".format(package_id))
sys.stdout.write("\rRemoving: {0}".format(package_id))
sys.stdout.flush()
try:
# Validate package id, that package is installed.
PackageId(package_id)
repository.remove(package_id)
except ValidationError as ex:
raise ValidationError("Invalid package id {0}".format(package_id)) from ex
except OSError as ex:
raise Exception("Error removing package {0}: {1}".format(package_id, ex)) from ex
else:
sys.stdout.write("\rRemoved: {0}".format(package_id))
finally:
sys.stdout.write("\n")
sys.stdout.flush() | [
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llSourcell/Make_Money_with_Tensorflow | 9bedf1af6cf91c099d915bc08253b464dcb2f205 | mnist_client.py | python | do_inference | (hostport, work_dir, concurrency, num_tests) | return result_counter.get_error_rate() | Tests PredictionService with concurrent requests.
Args:
hostport: Host:port address of the PredictionService.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
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"""Tests PredictionService with concurrent requests.
Args:
hostport: Host:port address of the PredictionService.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
IOError: An error occurred processing test data set.
"""
test_data_set = mnist_input_data.read_data_sets(work_dir).test
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
result_counter = _ResultCounter(num_tests, concurrency)
for _ in range(num_tests):
request = predict_pb2.PredictRequest()
request.model_spec.name = 'mnist'
request.model_spec.signature_name = 'predict'
image, label = test_data_set.next_batch(1)
request.inputs['inputs'].CopyFrom(
tf.contrib.util.make_tensor_proto(image[0], shape=[1, 28, 28, 1]))
result_counter.throttle()
result_future = stub.Predict.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
_create_rpc_callback(label[0], result_counter))
return result_counter.get_error_rate() | [
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pulp/pulp | a0a28d804f997b6f81c391378aff2e4c90183df9 | client_lib/pulp/client/commands/consumer/bind.py | python | ConsumerBindCommand._render_bind_header | (self) | Displays the task header for the bind task. | Displays the task header for the bind task. | [
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Displays the task header for the bind task.
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | example/ctp/option/__init__.py | python | TraderApi.OnRspForQuoteInsert | (self, pInputForQuote, pRspInfo, nRequestID, bIsLast) | 询价录入请求响应 | 询价录入请求响应 | [
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giantbranch/python-hacker-code | addbc8c73e7e6fb9e4fcadcec022fa1d3da4b96d | 我手敲的代码(中文注释)/chapter11/immlib/immlib.py | python | Debugger.getHeapsAddress | (self) | return self.HeapsAddr | Get a the process heaps
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self.HeapsAddr = []
peb = self.getPEB()
addr = peb.ProcessHeaps[0]
for ndx in range(0, peb.NumberOfHeaps):
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if l:
self.HeapsAddr.append( l )
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boston-dynamics/spot-sdk | 5ffa12e6943a47323c7279d86e30346868755f52 | python/bosdyn-client/src/bosdyn/client/robot_command.py | python | RobotCommandBuilder.stand_command | (params=None, body_height=0.0, footprint_R_body=geometry.EulerZXY()) | return command | Command robot to stand. If the robot is sitting, it will stand up. If the robot is
moving, it will come to a stop. Params can specify a trajectory for the body to follow
while standing. In the simplest case, this can be a specific position+orientation which the
body will hold at. The arguments body_height and footprint_R_body are ignored if params
argument is passed.
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params(spot.MobilityParams): Spot specific parameters for mobility commands. If not set,
this will be constructed using other args.
body_height(float): Height, meters, to stand at relative to a nominal stand height.
footprint_R_body(EulerZXY): The orientation of the body frame with respect to the
footprint frame (gravity aligned framed with yaw computed from the stance feet)
Returns:
RobotCommand, which can be issued to the robot command service. | Command robot to stand. If the robot is sitting, it will stand up. If the robot is
moving, it will come to a stop. Params can specify a trajectory for the body to follow
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body_height(float): Height, meters, to stand at relative to a nominal stand height.
footprint_R_body(EulerZXY): The orientation of the body frame with respect to the
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Returns:
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"""
if not params:
params = RobotCommandBuilder.mobility_params(body_height=body_height,
footprint_R_body=footprint_R_body)
any_params = RobotCommandBuilder._to_any(params)
mobility_command = mobility_command_pb2.MobilityCommand.Request(
stand_request=basic_command_pb2.StandCommand.Request(), params=any_params)
command = robot_command_pb2.RobotCommand(mobility_command=mobility_command)
return command | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | custom/inddex/food.py | python | FoodRow._set_conversion_factors | (self) | [] | def _set_conversion_factors(self):
self.conv_factor_gap_code = ConvFactorGaps.NOT_AVAILABLE
if (self.food_type == FOOD_ITEM and self._is_std_recipe_ingredient
or self.food_type == NON_STANDARD_RECIPE):
self.conv_factor_gap_code = ConvFactorGaps.NOT_APPLICABLE
elif self.food_type in (FOOD_ITEM, STANDARD_RECIPE) and self.conv_method_code:
self.conv_factor_food_code = self.fixtures.conversion_factors.get(
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self.conv_factor_base_term_food_code = self.fixtures.conversion_factors.get(
(self.base_term_food_code, self.conv_method_code, self.conv_option_code))
if self.conv_factor_food_code:
self.conv_factor_used = 'food_code'
self.conv_factor = self.conv_factor_food_code
self.conv_factor_gap_code = ConvFactorGaps.AVAILABLE
elif self.conv_factor_base_term_food_code:
self.conv_factor_used = 'base_term_food_code'
self.conv_factor = self.conv_factor_base_term_food_code
self.conv_factor_gap_code = ConvFactorGaps.BASE_TERM
self.conv_factor_gap_desc = ConvFactorGaps.DESCRIPTIONS[self.conv_factor_gap_code] | [
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JulianEberius/SublimePythonIDE | d70e40abc0c9f347af3204c7b910e0d6bfd6e459 | server/lib/python_all/jedi/cache.py | python | _invalidate_star_import_cache_module | (module, only_main=False) | Important if some new modules are being reparsed | Important if some new modules are being reparsed | [
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try:
t, modules = _time_caches['star_import_cache_validity'][module]
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pass
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tensorflow/models | 6b8bb0cbeb3e10415c7a87448f08adc3c484c1d3 | official/vision/beta/projects/yolo/modeling/decoders/yolo_decoder.py | python | YoloFPN.__init__ | (self,
fpn_depth=4,
max_fpn_depth=None,
max_csp_stack=None,
use_spatial_attention=False,
csp_stack=False,
activation='leaky',
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norm_momentum=0.99,
norm_epsilon=0.001,
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
**kwargs) | Yolo FPN initialization function (Yolo V4).
Args:
fpn_depth: `int`, number of layers to use in each FPN path
if you choose to use an FPN.
max_fpn_depth: `int`, number of layers to use in each FPN path
if you choose to use an FPN along the largest FPN level.
max_csp_stack: `int`, number of layers to use for CSP on the largest_path
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use_spatial_attention: `bool`, use the spatial attention module.
csp_stack: `bool`, CSPize the FPN.
activation: `str`, the activation function to use typically leaky or mish.
fpn_filter_scale: `int`, scaling factor for the FPN filters.
use_sync_bn: if True, use synchronized batch normalization.
use_separable_conv: `bool` whether to use separable convs.
norm_momentum: `float`, normalization momentum for the moving average.
norm_epsilon: `float`, small float added to variance to avoid dividing by
zero.
kernel_initializer: kernel_initializer for convolutional layers.
kernel_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
bias_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
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fpn_depth: `int`, number of layers to use in each FPN path
if you choose to use an FPN.
max_fpn_depth: `int`, number of layers to use in each FPN path
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max_csp_stack: `int`, number of layers to use for CSP on the largest_path
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use_spatial_attention: `bool`, use the spatial attention module.
csp_stack: `bool`, CSPize the FPN.
activation: `str`, the activation function to use typically leaky or mish.
fpn_filter_scale: `int`, scaling factor for the FPN filters.
use_sync_bn: if True, use synchronized batch normalization.
use_separable_conv: `bool` whether to use separable convs.
norm_momentum: `float`, normalization momentum for the moving average.
norm_epsilon: `float`, small float added to variance to avoid dividing by
zero.
kernel_initializer: kernel_initializer for convolutional layers.
kernel_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
bias_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
**kwargs: keyword arguments to be passed.
"""
super().__init__(**kwargs)
self._fpn_depth = fpn_depth
self._max_fpn_depth = max_fpn_depth or self._fpn_depth
self._activation = activation
self._use_sync_bn = use_sync_bn
self._use_separable_conv = use_separable_conv
self._norm_momentum = norm_momentum
self._norm_epsilon = norm_epsilon
self._kernel_initializer = kernel_initializer
self._kernel_regularizer = kernel_regularizer
self._bias_regularizer = bias_regularizer
self._use_spatial_attention = use_spatial_attention
self._filter_scale = fpn_filter_scale
self._csp_stack = csp_stack
self._max_csp_stack = max_csp_stack or min(self._max_fpn_depth, csp_stack)
self._base_config = dict(
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kernel_initializer=self._kernel_initializer,
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/coordinates/angles.py | python | Angle.signed_dms | (self) | return signed_dms_tuple(np.sign(self.degree),
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wxWidgets/Phoenix | b2199e299a6ca6d866aa6f3d0888499136ead9d6 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListMainWindow.FindItemData | (self, start, data) | return wx.NOT_FOUND | Find an item whose data matches this data.
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pyca/pyopenssl | fb26edde0aa27670c7bb24c0daeb05516e83d7b0 | src/OpenSSL/SSL.py | python | Context.use_privatekey | (self, pkey) | Load a private key from a PKey object
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if not isinstance(pkey, PKey):
raise TypeError("pkey must be a PKey instance")
use_result = _lib.SSL_CTX_use_PrivateKey(self._context, pkey._pkey)
if not use_result:
self._raise_passphrase_exception() | [
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qiueer/zabbix | 31983dedbd59d917ecd71bb6f36b35302673a783 | All In One/src/memcache.py | python | MCache.get_item_tval | (self, item) | return val | [] | def get_item_tval(self, item):
val = self.get_item_val(item)
if val == None:return 0
val = str(val)
try:
val = int(val)
except Exception:
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xiaobai1217/MBMD | 246f3434bccb9c8357e0f698995b659578bf1afb | lib/object_detection/core/box_list.py | python | BoxList.transpose_coordinates | (self, scope=None) | Transpose the coordinate representation in a boxlist.
Args:
scope: name scope of the function. | Transpose the coordinate representation in a boxlist. | [
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] | def transpose_coordinates(self, scope=None):
"""Transpose the coordinate representation in a boxlist.
Args:
scope: name scope of the function.
"""
with tf.name_scope(scope, 'transpose_coordinates'):
y_min, x_min, y_max, x_max = tf.split(
value=self.get(), num_or_size_splits=4, axis=1)
self.set(tf.concat([x_min, y_min, x_max, y_max], 1)) | [
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v7/services/services/keyword_plan_campaign_keyword_service/client.py | python | KeywordPlanCampaignKeywordServiceClient.common_project_path | (project: str,) | return "projects/{project}".format(project=project,) | Return a fully-qualified project string. | Return a fully-qualified project string. | [
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return "projects/{project}".format(project=project,) | [
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mik3y/django-db-multitenant | 07e52628007c3d4798122956cecb10adbb691682 | mapper_examples/mysql_hostname_tenant_mapper.py | python | SimpleTenantMapper.get_tenant_name | (self, request) | return hostname.split('.')[0] | Takes the first part of the hostname as the tenant | Takes the first part of the hostname as the tenant | [
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] | def get_tenant_name(self, request):
"""Takes the first part of the hostname as the tenant"""
hostname = request.get_host().split(':')[0].lower()
return hostname.split('.')[0] | [
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Telefonica/HomePWN | 080398174159f856f4155dcb155c6754d1f85ad8 | utils/npyscreen/wgwidget.py | python | Widget.edit | (self) | return self._post_edit() | Allow the user to edit the widget: ie. start handling keypresses. | Allow the user to edit the widget: ie. start handling keypresses. | [
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] | def edit(self):
"""Allow the user to edit the widget: ie. start handling keypresses."""
self.editing = 1
self._pre_edit()
self._edit_loop()
return self._post_edit() | [
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algorhythms/LintCode | 2520762a1cfbd486081583136396a2b2cac6e4fb | Subarray Sum II.py | python | Solution.subarraySumII | (self, A, start, end) | return cnt | O(n lg n) Binary Search
Bound:
f[i] - f[j] = start
f[i] - f[j'] = end
start < end
f[j] > f[j']
:param A: an integer array
:param start: start an integer
:param end: end an integer
:return: | O(n lg n) Binary Search
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f[i] - f[j] = start
f[i] - f[j'] = end
start < end
f[j] > f[j'] | [
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O(n lg n) Binary Search
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f[i] - f[j] = start
f[i] - f[j'] = end
start < end
f[j] > f[j']
:param A: an integer array
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:param end: end an integer
:return:
"""
n = len(A)
cnt = 0
f = [0 for _ in xrange(n+1)]
for i in xrange(1, n+1):
f[i] = f[i-1]+A[i-1] # from left
f.sort()
for i in xrange(n+1):
lo = bisect_left(f, f[i]-end, 0, i)
hi = bisect_right(f, f[i]-start, 0, i)
cnt += hi-lo # 0----lo----hi-----END
return cnt | [
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | riak/datadog_checks/riak/riak.py | python | Riak.safe_submit_metric | (self, name, value, tags=None) | [] | def safe_submit_metric(self, name, value, tags=None):
tags = [] if tags is None else tags
try:
self.gauge(name, float(value), tags=tags)
return
except ValueError:
self.log.debug("metric name %s cannot be converted to a float: %s", name, value)
pass
try:
self.gauge(name, unicodedata.numeric(value), tags=tags)
return
except (TypeError, ValueError):
self.log.debug("metric name %s cannot be converted to a float even using unicode tools: %s", name, value)
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/twill/twill/other_packages/pyparsing.py | python | ParseResults.items | ( self ) | return [(k,v[-1][0]) for k,v in self.__tokdict.items()] | Returns all named result keys and values as a list of tuples. | Returns all named result keys and values as a list of tuples. | [
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] | def items( self ):
"""Returns all named result keys and values as a list of tuples."""
return [(k,v[-1][0]) for k,v in self.__tokdict.items()] | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | SearchService2/appscale/search/solr_adapter.py | python | SolrAdapter.delete_documents | (self, app_id, namespace, index_name, ids) | Deletes documents with specified IDs from the index (asynchronously).
Args:
app_id: a str representing Application ID.
namespace: a str representing GAE namespace or None.
index_name: a str representing name of Search API index.
ids: a list of document IDs to delete. | Deletes documents with specified IDs from the index (asynchronously). | [
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] | async def delete_documents(self, app_id, namespace, index_name, ids):
""" Deletes documents with specified IDs from the index (asynchronously).
Args:
app_id: a str representing Application ID.
namespace: a str representing GAE namespace or None.
index_name: a str representing name of Search API index.
ids: a list of document IDs to delete.
"""
collection = get_collection_name(app_id, namespace, index_name)
await self.solr.delete_documents(collection, ids) | [
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kevoreilly/CAPEv2 | 6cf79c33264624b3604d4cd432cde2a6b4536de6 | modules/processing/parsers/CAPE/Fareit.py | python | config | (memdump_path, read=False) | return res | # Get the aPLib header + data
buf = re.findall(r"aPLib .*PWDFILE", cData, re.DOTALL|re.MULTILINE)
# Strip out the header
if buf and len(buf[0]) > 200:
cData = buf[0][200:] | # Get the aPLib header + data
buf = re.findall(r"aPLib .*PWDFILE", cData, re.DOTALL|re.MULTILINE)
# Strip out the header
if buf and len(buf[0]) > 200:
cData = buf[0][200:] | [
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res = False
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else:
F = memdump_path
"""
# Get the aPLib header + data
buf = re.findall(r"aPLib .*PWDFILE", cData, re.DOTALL|re.MULTILINE)
# Strip out the header
if buf and len(buf[0]) > 200:
cData = buf[0][200:]
"""
artifacts_raw = {
"controllers": [],
"downloads": [],
}
start = F.find(b"YUIPWDFILE0YUIPKDFILE0YUICRYPTED0YUI1.0")
if start:
F = F[start - 600 : start + 500]
output = re.findall(
b"(https?://.[A-Za-z0-9-\\.\\_\\~\\:\\/\\?\\#\\[\\]\\@\\!\\$\\&'\\(\\)\\*\\+\\,\\;\\=]+(?:\\.php|\\.exe|\\.dll))", F
)
for url in output:
try:
if b"\x00" not in url:
# url = self._check_valid_url(url)
if url is None:
continue
if gate_url.match(url):
artifacts_raw["controllers"].append(url.lower().decode())
elif exe_url.match(url):
artifacts_raw["downloads"].append(url.lower().decode())
elif dll_url.match(url):
artifacts_raw["downloads"].append(url.lower().decode())
except Exception as e:
print(e, sys.exc_info(), "PONY")
artifacts_raw["controllers"] = list(set(artifacts_raw["controllers"]))
artifacts_raw["downloads"] = list(set(artifacts_raw["downloads"]))
if len(artifacts_raw["controllers"]) != 0 or len(artifacts_raw["downloads"]) != 0:
res = artifacts_raw
return res | [
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aploium/zmirror | f3db3048f83d1ea1bf06a94df312dd30d54b96d3 | zmirror/utils.py | python | check_global_ua_pass | (ua_str) | 该user-agent是否满足全局白名单 | 该user-agent是否满足全局白名单 | [
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"""该user-agent是否满足全局白名单"""
if ua_str is None or not global_ua_white_name:
return False
ua_str = ua_str.lower()
if global_ua_white_name in ua_str:
return True
else:
return False | [
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rowliny/DiffHelper | ab3a96f58f9579d0023aed9ebd785f4edf26f8af | Tool/SitePackages/nltk/translate/gale_church.py | python | trace | (backlinks, source_sents_lens, target_sents_lens) | return links[::-1] | Traverse the alignment cost from the tracebacks and retrieves
appropriate sentence pairs.
:param backlinks: A dictionary where the key is the alignment points and value is the cost (referencing the LanguageIndependent.PRIORS)
:type backlinks: dict
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:type source_sents_lens: list(int)
:param target_sents_lens: A list of target sentences' lengths
:type target_sents_lens: list(int) | Traverse the alignment cost from the tracebacks and retrieves
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Traverse the alignment cost from the tracebacks and retrieves
appropriate sentence pairs.
:param backlinks: A dictionary where the key is the alignment points and value is the cost (referencing the LanguageIndependent.PRIORS)
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:type source_sents_lens: list(int)
:param target_sents_lens: A list of target sentences' lengths
:type target_sents_lens: list(int)
"""
links = []
position = (len(source_sents_lens), len(target_sents_lens))
while position != (0, 0) and all(p >= 0 for p in position):
try:
s, t = backlinks[position]
except TypeError:
position = (position[0] - 1, position[1] - 1)
continue
for i in range(s):
for j in range(t):
links.append((position[0] - i - 1, position[1] - j - 1))
position = (position[0] - s, position[1] - t)
return links[::-1] | [
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jay0lee/got-your-back | 54c6119c0e78502fa5c92d5354f80a4a95a6e3e6 | gyb.py | python | _createHttpObj | (cache=None, timeout=None) | return httplib2.Http(**http_args) | [] | def _createHttpObj(cache=None, timeout=None):
http_args = {'cache': cache, 'timeout': timeout}
if 'tls_maximum_version' in options:
http_args['tls_maximum_version'] = options.tls_maximum_version
if 'tls_minimum_version' in options:
http_args['tls_minimum_version'] = options.tls_minimum_version
return httplib2.Http(**http_args) | [
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SforAiDl/Neural-Voice-Cloning-With-Few-Samples | 33fb609427657c9492f46507184ecba4dcc272b0 | speaker_adaptatation-libri.py | python | MaskedL1Loss.__init__ | (self) | [] | def __init__(self):
super(MaskedL1Loss, self).__init__()
self.criterion = nn.L1Loss(size_average=False) | [
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ProtonVPN/linux-cli | 3f4bea1b8a12ed6a31654cc184021ddab6dd56e6 | protonvpn_cli/cli.py | python | ProtonVPNCLI.disconnect | (self) | return self.cli_wrapper.disconnect() | Disconnect from ProtonVPN. | Disconnect from ProtonVPN. | [
"Disconnect",
"from",
"ProtonVPN",
"."
] | def disconnect(self):
"""Disconnect from ProtonVPN."""
return self.cli_wrapper.disconnect() | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/boto-2.46.1/boto/dynamodb2/table.py | python | Table.batch_write | (self) | return BatchTable(self) | Allows the batching of writes to DynamoDB.
Since each write/delete call to DynamoDB has a cost associated with it,
when loading lots of data, it makes sense to batch them, creating as
few calls as possible.
This returns a context manager that will transparently handle creating
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object, sharing just the ``put_item`` & ``delete_item`` methods
(which are all that DynamoDB can batch in terms of writing data).
DynamoDB's maximum batch size is 25 items per request. If you attempt
to put/delete more than that, the context manager will batch as many
as it can up to that number, then flush them to DynamoDB & continue
batching as more calls come in.
Example::
# Assuming a table with one record...
>>> with users.batch_write() as batch:
... batch.put_item(data={
... 'username': 'johndoe',
... 'first_name': 'John',
... 'last_name': 'Doe',
... 'owner': 1,
... })
... # Nothing across the wire yet.
... batch.delete_item(username='bob')
... # Still no requests sent.
... batch.put_item(data={
... 'username': 'jane',
... 'first_name': 'Jane',
... 'last_name': 'Doe',
... 'date_joined': 127436192,
... })
... # Nothing yet, but once we leave the context, the
... # put/deletes will be sent. | Allows the batching of writes to DynamoDB. | [
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] | def batch_write(self):
"""
Allows the batching of writes to DynamoDB.
Since each write/delete call to DynamoDB has a cost associated with it,
when loading lots of data, it makes sense to batch them, creating as
few calls as possible.
This returns a context manager that will transparently handle creating
these batches. The object you get back lightly-resembles a ``Table``
object, sharing just the ``put_item`` & ``delete_item`` methods
(which are all that DynamoDB can batch in terms of writing data).
DynamoDB's maximum batch size is 25 items per request. If you attempt
to put/delete more than that, the context manager will batch as many
as it can up to that number, then flush them to DynamoDB & continue
batching as more calls come in.
Example::
# Assuming a table with one record...
>>> with users.batch_write() as batch:
... batch.put_item(data={
... 'username': 'johndoe',
... 'first_name': 'John',
... 'last_name': 'Doe',
... 'owner': 1,
... })
... # Nothing across the wire yet.
... batch.delete_item(username='bob')
... # Still no requests sent.
... batch.put_item(data={
... 'username': 'jane',
... 'first_name': 'Jane',
... 'last_name': 'Doe',
... 'date_joined': 127436192,
... })
... # Nothing yet, but once we leave the context, the
... # put/deletes will be sent.
"""
# PHENOMENAL COSMIC DOCS!!! itty-bitty code.
return BatchTable(self) | [
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carbonblack/cbapi-python | 24d677ffd99aee911c2c76ecb5528e4e9320c7cc | src/cbapi/six.py | python | add_metaclass | (metaclass) | return wrapper | Class decorator for creating a class with a metaclass. | Class decorator for creating a class with a metaclass. | [
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] | def add_metaclass(metaclass):
"""Class decorator for creating a class with a metaclass."""
def wrapper(cls):
orig_vars = cls.__dict__.copy()
slots = orig_vars.get('__slots__')
if slots is not None:
if isinstance(slots, str):
slots = [slots]
for slots_var in slots:
orig_vars.pop(slots_var)
orig_vars.pop('__dict__', None)
orig_vars.pop('__weakref__', None)
return metaclass(cls.__name__, cls.__bases__, orig_vars)
return wrapper | [
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/27/Lib/difflib.py | python | Differ.__init__ | (self, linejunk=None, charjunk=None) | Construct a text differencer, with optional filters.
The two optional keyword parameters are for filter functions:
- `linejunk`: A function that should accept a single string argument,
and return true iff the string is junk. The module-level function
`IS_LINE_JUNK` may be used to filter out lines without visible
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"""
Construct a text differencer, with optional filters.
The two optional keyword parameters are for filter functions:
- `linejunk`: A function that should accept a single string argument,
and return true iff the string is junk. The module-level function
`IS_LINE_JUNK` may be used to filter out lines without visible
characters, except for at most one splat ('#'). It is recommended
to leave linejunk None; as of Python 2.3, the underlying
SequenceMatcher class has grown an adaptive notion of "noise" lines
that's better than any static definition the author has ever been
able to craft.
- `charjunk`: A function that should accept a string of length 1. The
module-level function `IS_CHARACTER_JUNK` may be used to filter out
whitespace characters (a blank or tab; **note**: bad idea to include
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"""
self.linejunk = linejunk
self.charjunk = charjunk | [
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/Base/Scripts/CommonServerPython/CommonServerPython.py | python | GetDemistoVersion.__call__ | (self) | return self._version | Returns the Demisto version and build number.
:return: Demisto version object if Demisto class has attribute demistoVersion, else raises AttributeError
:rtype: ``dict`` | Returns the Demisto version and build number. | [
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] | def __call__(self):
"""Returns the Demisto version and build number.
:return: Demisto version object if Demisto class has attribute demistoVersion, else raises AttributeError
:rtype: ``dict``
"""
if self._version is None:
if hasattr(demisto, 'demistoVersion'):
self._version = demisto.demistoVersion()
else:
raise AttributeError('demistoVersion attribute not found.')
return self._version | [
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Zephery/weiboanalysis | bee35996ffdf9f6c4bb9af349f6d4dce6666b6ab | train/boostNB.py | python | setOfWordsToVecTor | (vocabularyList, moodWords) | return np.array(vocabMarked) | SMS内容匹配预料库，标记预料库的词汇出现的次数
:param vocabularyList:
:param moodWords:
:return: | SMS内容匹配预料库，标记预料库的词汇出现的次数
:param vocabularyList:
:param moodWords:
:return: | [
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":",
":",
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] | def setOfWordsToVecTor(vocabularyList, moodWords):
"""
SMS内容匹配预料库，标记预料库的词汇出现的次数
:param vocabularyList:
:param moodWords:
:return:
"""
vocabMarked = [0] * len(vocabularyList)
for smsWord in moodWords:
if smsWord in vocabularyList:
vocabMarked[vocabularyList.index(smsWord)] += 1
return np.array(vocabMarked) | [
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... | https://github.com/Zephery/weiboanalysis/blob/bee35996ffdf9f6c4bb9af349f6d4dce6666b6ab/train/boostNB.py#L66-L77 | |
oddt/oddt | 8cf555820d97a692ade81c101ebe10e28bcb3722 | oddt/toolkits/rdk.py | python | Residue.idx | (self) | return self._idx | Internal index (0-based) of the Residue | Internal index (0-based) of the Residue | [
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"""Internal index (0-based) of the Residue"""
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mne-tools/mne-python | f90b303ce66a8415e64edd4605b09ac0179c1ebf | mne/viz/_figure.py | python | _get_browser | (**kwargs) | return browser | Instantiate a new MNE browse-style figure. | Instantiate a new MNE browse-style figure. | [
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] | def _get_browser(**kwargs):
"""Instantiate a new MNE browse-style figure."""
from .utils import _get_figsize_from_config
figsize = kwargs.setdefault('figsize', _get_figsize_from_config())
if figsize is None or np.any(np.array(figsize) < 8):
kwargs['figsize'] = (8, 8)
# Initialize browser backend
_init_browser_backend()
# Initialize Browser
browser = backend._init_browser(**kwargs)
return browser | [
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TensorSpeech/TensorflowTTS | 34358d82a4c91fd70344872f8ea8a405ea84aedb | tensorflow_tts/models/mb_melgan.py | python | TFPQMF.synthesis | (self, x) | return tf.nn.conv1d(x, self.synthesis_filter, stride=1, padding="VALID") | Synthesis with PQMF.
Args:
x (Tensor): Input tensor (B, T // subbands, subbands).
Returns:
Tensor: Output tensor (B, T, 1). | Synthesis with PQMF.
Args:
x (Tensor): Input tensor (B, T // subbands, subbands).
Returns:
Tensor: Output tensor (B, T, 1). | [
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] | def synthesis(self, x):
"""Synthesis with PQMF.
Args:
x (Tensor): Input tensor (B, T // subbands, subbands).
Returns:
Tensor: Output tensor (B, T, 1).
"""
x = tf.nn.conv1d_transpose(
x,
self.updown_filter * self.subbands,
strides=self.subbands,
output_shape=(
tf.shape(x)[0],
tf.shape(x)[1] * self.subbands,
self.subbands,
),
)
x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]])
return tf.nn.conv1d(x, self.synthesis_filter, stride=1, padding="VALID") | [
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spyder-ide/spyder | 55da47c032dfcf519600f67f8b30eab467f965e7 | spyder/config/user.py | python | MultiUserConfig.remove_section | (self, section) | Remove `section` and all options within it. | Remove `section` and all options within it. | [
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] | def remove_section(self, section):
"""Remove `section` and all options within it."""
config = self._get_config(section, None)
config.remove_section(section) | [
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spectacles/CodeComplice | 8ca8ee4236f72b58caa4209d2fbd5fa56bd31d62 | libs/codeintel2/lang_javascript.py | python | JavaScriptCiler.incBlock | (self) | Increment the block (scope) count | Increment the block (scope) count | [
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"""Increment the block (scope) count"""
self.depth = self.depth+1
log.debug("incBlock: depth:%d, line:%d, currentScope:%r",
self.depth, self.lineno, self.currentScope.name)
if not self.currentScope:
log.debug(
"incBlock:: No currentScope available. Defaulting to global file scope.")
# Use the global file scope then
self.currentScope = self.cile
if len(self.objectStack) == 0 or self.currentScope != self.objectStack[-1]:
# Not the same scope...
self.objectStack.append(self.currentScope)
self._scopeStack.append(self.currentScope) | [
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danielfrg/copper | 956e9ae607aec461d4fe4f6e7b0ccd9ed556fc79 | copper/ml/gdbn/npmat.py | python | CUDAMatrix.set_selected_columns | (self, indices, source) | return self | copies all columns of source into some columns of self.
<indices> must be a row vector. Its elements are float32's representing
integers, e.g. "34.0" means the integer "34". after this call, for all
r,c, self[r,indices[c]]=source[r,c]. This returns self.
Negative indices are interpreted in the usual Python way: all elements
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This does bounds checking, but out of bounds indices do not raise an
exception (because the programmer was lazy). Instead, they result in NaN
values in <self>. | copies all columns of source into some columns of self.
<indices> must be a row vector. Its elements are float32's representing
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r,c, self[r,indices[c]]=source[r,c]. This returns self.
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This does bounds checking, but out of bounds indices do not raise an
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"""
assert self.shape[0]==source.shape[0]
assert indices.shape[0]==1
assert indices.shape[1]==source.shape[1]
for c in range(source.shape[1]):
try:
self.numpy_array[:,int(indices.numpy_array.ravel()[c])] = source.numpy_array[:,c]
except IndexError:
self.numpy_array[:,int(indices.numpy_array.ravel()[c])] = np.nan
return self | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/models/detr/modeling_detr.py | python | NestedTensor.to | (self, device) | return NestedTensor(cast_tensor, cast_mask) | [] | def to(self, device):
# type: (Device) -> NestedTensor # noqa
cast_tensor = self.tensors.to(device)
mask = self.mask
if mask is not None:
cast_mask = mask.to(device)
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cast_mask = None
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giantbranch/python-hacker-code | addbc8c73e7e6fb9e4fcadcec022fa1d3da4b96d | 我手敲的代码(中文注释)/chapter11/volatility-2.3/build/lib/volatility/plugins/vadinfo.py | python | VADInfo.write_vad_control | (self, outfd, vad) | Renders a text version of a (non-short) Vad's control information | Renders a text version of a (non-short) Vad's control information | [
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"version",
"of",
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"(",
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"-",
"short",
")",
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] | def write_vad_control(self, outfd, vad):
"""Renders a text version of a (non-short) Vad's control information"""
# even if the ControlArea is not NULL, it is only meaningful
# for shared (non private) memory sections.
if vad.u.VadFlags.PrivateMemory == 1:
return
control_area = vad.ControlArea
if not control_area:
return
outfd.write("ControlArea @{0:08x} Segment {1:08x}\n".format(control_area.dereference().obj_offset, control_area.Segment))
outfd.write("Dereference list: Flink {0:08x}, Blink {1:08x}\n".format(control_area.DereferenceList.Flink, control_area.DereferenceList.Blink))
outfd.write("NumberOfSectionReferences: {0:10} NumberOfPfnReferences: {1:10}\n".format(control_area.NumberOfSectionReferences, control_area.NumberOfPfnReferences))
outfd.write("NumberOfMappedViews: {0:10} NumberOfUserReferences: {1:10}\n".format(control_area.NumberOfMappedViews, control_area.NumberOfUserReferences))
outfd.write("WaitingForDeletion Event: {0:08x}\n".format(control_area.WaitingForDeletion))
outfd.write("Control Flags: {0}\n".format(str(control_area.u.Flags)))
file_object = vad.FileObject
if file_object:
outfd.write("FileObject @{0:08x}, Name: {1}\n".format(file_object.obj_offset, str(file_object.FileName or ''))) | [
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selfteaching/selfteaching-python-camp | 9982ee964b984595e7d664b07c389cddaf158f1e | 19100101/zhwycsz/d6_exercise_stats_word.py | python | stats_text_en | (string_en) | 统计英文词频
第一步：过滤英文字符，并将string拆分为list。
第二步：清理标点符号。
第三步：使用collections库中的Counter函数进行词频统计并输出结果。 | 统计英文词频
第一步：过滤英文字符，并将string拆分为list。
第二步：清理标点符号。
第三步：使用collections库中的Counter函数进行词频统计并输出结果。 | [
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"第二步：清理标点符号。",
"第三步：使用collections库中的Counter函数进行词频统计并输出结果。"
] | def stats_text_en(string_en):
''' 统计英文词频
第一步：过滤英文字符，并将string拆分为list。
第二步：清理标点符号。
第三步：使用collections库中的Counter函数进行词频统计并输出结果。
'''
result = re.sub("[^A-Za-z]", " ", string_en.strip()) #正则表达式 去匹配目标字符串中非a—z也非A—Z的字符
newList = result.split( ) #字符分割
i=0
for i in range(0,len(newList)): #len（）得到string的长度
newList[i]=newList[i].strip('*-,.?!')
if newList[i]==' ':
newList[i].remove(' ') #用于移除列表中某个值的第一个匹配项,去除标点
else:
i=i+1 #i+1 继续赋值给i
print('1.英文词频统计结果： ',collections.Counter(newList),'\t') | [
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WerWolv/EdiZon_CheatsConfigsAndScripts | d16d36c7509c01dca770f402babd83ff2e9ae6e7 | Scripts/lib/python3.5/datetime.py | python | date.__sub__ | (self, other) | return NotImplemented | Subtract two dates, or a date and a timedelta. | Subtract two dates, or a date and a timedelta. | [
"Subtract",
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] | def __sub__(self, other):
"""Subtract two dates, or a date and a timedelta."""
if isinstance(other, timedelta):
return self + timedelta(-other.days)
if isinstance(other, date):
days1 = self.toordinal()
days2 = other.toordinal()
return timedelta(days1 - days2)
return NotImplemented | [
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dictation-toolbox/Caster | 6c57587cbba783fd5b9e0a5e49e5957c9f73c22c | castervoice/lib/util/pathlib/__init__.py | python | PurePath.is_reserved | (self) | return self._flavour.is_reserved(self._parts) | Return True if the path contains one of the special names reserved
by the system, if any. | Return True if the path contains one of the special names reserved
by the system, if any. | [
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] | def is_reserved(self):
"""Return True if the path contains one of the special names reserved
by the system, if any."""
return self._flavour.is_reserved(self._parts) | [
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firedrakeproject/firedrake | 06ab4975c14c0d4dcb79be55821f8b9e41554125 | firedrake/functionspacedata.py | python | set_max_work_functions | (V, val) | Set the maximum number of work functions.
:arg V: The function space to set the number of work functions
for.
:arg val: The new maximum number of work functions.
This number is shared between all function spaces with the same
:meth:`~.FunctionSpace.ufl_element` and
:meth:`~FunctionSpace.mesh`. | Set the maximum number of work functions. | [
"Set",
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"maximum",
"number",
"of",
"work",
"functions",
"."
] | def set_max_work_functions(V, val):
"""Set the maximum number of work functions.
:arg V: The function space to set the number of work functions
for.
:arg val: The new maximum number of work functions.
This number is shared between all function spaces with the same
:meth:`~.FunctionSpace.ufl_element` and
:meth:`~FunctionSpace.mesh`.
"""
mesh = V.mesh()
assert hasattr(mesh, "_shared_data_cache")
cache = mesh._shared_data_cache["max_work_functions"]
cache[V.ufl_element()] = val | [
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mozilla/moztrap | 93b34a4cd21c9e08f73d3b1a7630cd873f8418a0 | moztrap/view/manage/runs/forms.py | python | RunForm.clean_build | (self) | If this is a series, then null out the build field. | If this is a series, then null out the build field. | [
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] | def clean_build(self):
"""If this is a series, then null out the build field."""
if self.cleaned_data["is_series"]:
return None | [
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joxeankoret/diaphora | dcb5a25ac9fe23a285b657e5389cf770de7ac928 | jkutils/graph_hashes.py | python | CKoretKaramitasHash.calculate | (self, f) | return str(hash) | [] | def calculate(self, f):
func = get_func(f)
if func is None:
return "NO-FUNCTION"
flow = FlowChart(func)
if flow is None:
return "NO-FLOW-GRAPH"
hash = 1
# Variables required for calculations of previous ones
bb_relations = {}
# Iterate through each basic block
for block in flow:
block_ea = block.start_ea
if block.end_ea == 0:
continue
succs = list(block.succs())
preds = list(block.preds())
hash *= self.get_node_value(len(succs), len(preds))
hash *= self.get_edges_value(block, succs, preds)
# ...and each instruction on each basic block
for ea in list(Heads(block.start_ea, block.end_ea)):
if self.is_call_insn(ea):
hash *= FEATURE_CALL
l = list(DataRefsFrom(ea))
if len(l) > 0:
hash *= FEATURE_DATA_REFS
for xref in CodeRefsFrom(ea, 0):
tmp_func = get_func(xref)
if tmp_func is None or tmp_func.start_ea != func.start_ea:
hash *= FEATURE_CALL_REF
# Remember the relationships
bb_relations[block_ea] = []
# Iterate the succesors of this basic block
for succ_block in block.succs():
bb_relations[block_ea].append(succ_block.start_ea)
# Iterate the predecessors of this basic block
for pred_block in block.preds():
try:
bb_relations[pred_block.start_ea].append(block.start_ea)
except KeyError:
bb_relations[pred_block.start_ea] = [block.start_ea]
# Calculate the strongly connected components
try:
strongly_connected = strongly_connected_components(bb_relations)
# ...and get the number of loops out of it
for sc in strongly_connected:
if len(sc) > 1:
hash *= FEATURE_LOOP
else:
if sc[0] in bb_relations and sc[0] in bb_relations[sc[0]]:
hash *= FEATURE_LOOP
# And, also, use the number of strongly connected components
# to calculate another part of the hash.
hash *= (FEATURE_STRONGLY_CONNECTED ** len(strongly_connected))
except:
print("Exception:", str(sys.exc_info()[1]))
flags = get_func_attr(f, FUNCATTR_FLAGS)
if flags & FUNC_NORET:
hash *= FEATURE_FUNC_NO_RET
if flags & FUNC_LIB:
hash *= FEATURE_FUNC_LIB
if flags & FUNC_THUNK:
hash *= FEATURE_FUNC_THUNK
return str(hash) | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/flask/app.py | python | Flask.url_value_preprocessor | (self, f) | return f | Registers a function as URL value preprocessor for all view
functions of the application. It's called before the view functions
are called and can modify the url values provided. | Registers a function as URL value preprocessor for all view
functions of the application. It's called before the view functions
are called and can modify the url values provided. | [
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"""Registers a function as URL value preprocessor for all view
functions of the application. It's called before the view functions
are called and can modify the url values provided.
"""
self.url_value_preprocessors.setdefault(None, []).append(f)
return f | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/asn1crypto-0.24.0/asn1crypto/core.py | python | ObjectIdentifier.native | (self) | return self._native | The a native Python datatype representation of this value
:return:
A unicode string or None. If _map is not defined, the unicode string
is a string of dotted integers. If _map is defined and the dotted
string is present in the _map, the mapped value is returned. | The a native Python datatype representation of this value | [
"The",
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"native",
"Python",
"datatype",
"representation",
"of",
"this",
"value"
] | def native(self):
"""
The a native Python datatype representation of this value
:return:
A unicode string or None. If _map is not defined, the unicode string
is a string of dotted integers. If _map is defined and the dotted
string is present in the _map, the mapped value is returned.
"""
if self.contents is None:
return None
if self._native is None:
self._native = self.dotted
if self._map is not None and self._native in self._map:
self._native = self._map[self._native]
return self._native | [
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donnemartin/viz | dc1ed0690ed3d8cb515c9dbac43bafc59a704789 | githubstats/github_stats.py | python | GitHubStats.generate_search_query | (self, language,
creation_date_filter=None,
stars_filter=None) | return query | Generates the GitHub search query.
:type language: str
:param language: The current language.
:type creation_date_filter: str (optional)
:param creation_date_filter: The repo creation date filter.
:type stars_filter: str
:param stars_filter: The stars filter.
:rtype: str
:return: The GitHub search query. | Generates the GitHub search query. | [
"Generates",
"the",
"GitHub",
"search",
"query",
"."
] | def generate_search_query(self, language,
creation_date_filter=None,
stars_filter=None):
"""Generates the GitHub search query.
:type language: str
:param language: The current language.
:type creation_date_filter: str (optional)
:param creation_date_filter: The repo creation date filter.
:type stars_filter: str
:param stars_filter: The stars filter.
:rtype: str
:return: The GitHub search query.
"""
if creation_date_filter is None:
creation_date_filter = 'created:>=2017-01-01'
if stars_filter is None:
stars_filter = 'stars:>=' + str(self.CFG_MIN_STARS)
query = (creation_date_filter + ' ' + stars_filter +
' language:' + language.lower())
return query | [
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/numpy/ma/timer_comparison.py | python | ModuleTester.test_2 | (self) | Tests conversions and indexing. | Tests conversions and indexing. | [
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] | def test_2(self):
"""
Tests conversions and indexing.
"""
x1 = np.array([1, 2, 4, 3])
x2 = self.array(x1, mask=[1, 0, 0, 0])
x3 = self.array(x1, mask=[0, 1, 0, 1])
x4 = self.array(x1)
# test conversion to strings, no errors
str(x2)
repr(x2)
# tests of indexing
assert type(x2[1]) is type(x1[1])
assert x1[1] == x2[1]
x1[2] = 9
x2[2] = 9
self.assert_array_equal(x1, x2)
x1[1:3] = 99
x2[1:3] = 99
x2[1] = self.masked
x2[1:3] = self.masked
x2[:] = x1
x2[1] = self.masked
x3[:] = self.masked_array([1, 2, 3, 4], [0, 1, 1, 0])
x4[:] = self.masked_array([1, 2, 3, 4], [0, 1, 1, 0])
x1 = np.arange(5)*1.0
x2 = self.masked_values(x1, 3.0)
x1 = self.array([1, 'hello', 2, 3], object)
x2 = np.array([1, 'hello', 2, 3], object)
# check that no error occurs.
x1[1]
x2[1]
assert x1[1:1].shape == (0,)
# Tests copy-size
n = [0, 0, 1, 0, 0]
m = self.make_mask(n)
m2 = self.make_mask(m)
assert(m is m2)
m3 = self.make_mask(m, copy=1)
assert(m is not m3) | [
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espeed/bulbs | 628e5b14f0249f9ca4fa1ceea6f2af2dca45f75a | bulbs/rexster/client.py | python | RexsterClient.gremlin | (self, script, params=None, load=None) | return self.request.post(gremlin_path, params) | Executes a Gremlin script and returns the Response.
:param script: Gremlin script to execute.
:type script: str
:param params: Param bindings for the script.
:type params: dict
:rtype: RexsterResponse | Executes a Gremlin script and returns the Response. | [
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] | def gremlin(self, script, params=None, load=None):
"""
Executes a Gremlin script and returns the Response.
:param script: Gremlin script to execute.
:type script: str
:param params: Param bindings for the script.
:type params: dict
:rtype: RexsterResponse
"""
params = dict(script=script, params=params)
if self.config.server_scripts is True:
params["load"] = load or [self.scripts.default_namespace]
return self.request.post(gremlin_path, params) | [
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databricks/databricks-cli | 6c429d4f581fc99f54cbf9239879434310037cc7 | databricks_cli/groups/api.py | python | GroupsApi.list_all | (self) | return self.client.get_groups() | Return all of the groups in an organization. | Return all of the groups in an organization. | [
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] | def list_all(self):
"""Return all of the groups in an organization."""
return self.client.get_groups() | [
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erevus-cn/pocscan | 5fef32b1abe22a9f666ad3aacfd1f99d784cb72d | pocscan/plugins/pocsuite/packages/requests/api.py | python | put | (url, data=None, **kwargs) | return request('put', url, data=data, **kwargs) | Sends a PUT request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param data: (optional) Dictionary, bytes, or file-like object to send in the body of the :class:`Request`.
:param \*\*kwargs: Optional arguments that ``request`` takes. | Sends a PUT request. Returns :class:`Response` object. | [
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"""Sends a PUT request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param data: (optional) Dictionary, bytes, or file-like object to send in the body of the :class:`Request`.
:param \*\*kwargs: Optional arguments that ``request`` takes.
"""
return request('put', url, data=data, **kwargs) | [
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter4-NaiveBayes/venv/Lib/site-packages/pip/_vendor/requests/packages/urllib3/packages/six.py | python | _SixMetaPathImporter._add_module | (self, mod, *fullnames) | [] | def _add_module(self, mod, *fullnames):
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yueyoum/social-oauth | 80600ea737355b20931c8a0b5223f5b68175d930 | example/_bottle.py | python | Route.reset | (self) | Forget any cached values. The next time :attr:`call` is accessed,
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google/clusterfuzz | f358af24f414daa17a3649b143e71ea71871ef59 | src/clusterfuzz/_internal/build_management/revisions.py | python | _get_revision | (component_revision_dict) | return component_revision_dict['rev'] | Return revision for a component revision dict. | Return revision for a component revision dict. | [
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MicrosoftResearch/Azimuth | 84eb013b8dde7132357a9b69206e99a4c65e2b89 | azimuth/metrics.py | python | precision_at_k | (r, k) | return np.mean(r) | Score is precision @ k
Relevance is binary (nonzero is relevant).
>>> r = [0, 0, 1]
>>> precision_at_k(r, 1)
0.0
>>> precision_at_k(r, 2)
0.0
>>> precision_at_k(r, 3)
0.33333333333333331
>>> precision_at_k(r, 4)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Relevance score length < k
Args:
r: Relevance scores (list or numpy) in rank order
(first element is the first item)
Returns:
Precision @ k
Raises:
ValueError: len(r) must be >= k | Score is precision @ k | [
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"""Score is precision @ k
Relevance is binary (nonzero is relevant).
>>> r = [0, 0, 1]
>>> precision_at_k(r, 1)
0.0
>>> precision_at_k(r, 2)
0.0
>>> precision_at_k(r, 3)
0.33333333333333331
>>> precision_at_k(r, 4)
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File "<stdin>", line 1, in ?
ValueError: Relevance score length < k
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assert k >= 1
r = np.asarray(r)[:k] != 0
if r.size != k:
raise ValueError('Relevance score length < k')
return np.mean(r) | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/ipmi.py | python | set_user_name | (uid, name, **kwargs) | Set user name
:param uid: user number [1:16]
:param name: username (limit of 16bytes)
:param kwargs:
- api_host=127.0.0.1
- api_user=admin
- api_pass=example
- api_port=623
- api_kg=None
CLI Examples:
.. code-block:: bash
salt-call ipmi.set_user_name uid=2 name='steverweber' | Set user name | [
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"""
Set user name
:param uid: user number [1:16]
:param name: username (limit of 16bytes)
:param kwargs:
- api_host=127.0.0.1
- api_user=admin
- api_pass=example
- api_port=623
- api_kg=None
CLI Examples:
.. code-block:: bash
salt-call ipmi.set_user_name uid=2 name='steverweber'
"""
with _IpmiCommand(**kwargs) as s:
return s.set_user_name(uid, name) | [
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playframework/play1 | 0ecac3bc2421ae2dbec27a368bf671eda1c9cba5 | python/Lib/bsddb/dbtables.py | python | bsdTableDB.ListTableColumns | (self, table) | Return a list of columns in the given table.
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assert isinstance(table, str)
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raise ValueError, "bad table name: contains reserved metastrings"
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anchore/anchore-engine | bb18b70e0cbcad58beb44cd439d00067d8f7ea8b | anchore_engine/services/apiext/api/controllers/image_imports.py | python | import_image_packages | (operation_id) | return content_upload(operation_id, "packages", request) | POST /imports/images/{operation_id}/packages
:param operation_id:
:param sbom:
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"""
POST /imports/images/{operation_id}/packages
:param operation_id:
:param sbom:
:return:
"""
return content_upload(operation_id, "packages", request) | [
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microsoft/unilm | 65f15af2a307ebb64cfb25adf54375b002e6fe8d | infoxlm/fairseq/fairseq/data/legacy/block_pair_dataset.py | python | BlockPairDataset._fetch_block | (self, start_ds_idx, offset, end_ds_idx, length) | return buffer[s:e] | Fetch a block of tokens based on its dataset idx | Fetch a block of tokens based on its dataset idx | [
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"""
Fetch a block of tokens based on its dataset idx
"""
buffer = torch.cat(
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s, e = offset, offset + length
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mapproxy/mapproxy | 45ae81b3dd6c8a1a0b473ba8c669afd0ec7ecd10 | mapproxy/grid.py | python | MetaGrid._full_tile_list | (self, tiles) | return list(_create_tile_list(xs, ys, z, (maxx+1, maxy+1))), grid_size, bounds | Return a complete list of all tiles that a minimal meta tile with `tiles` contains.
>>> mgrid = MetaGrid(grid=TileGrid(), meta_size=(2, 2))
>>> mgrid._full_tile_list([(0, 0, 2), (1, 1, 2)])
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>>> mgrid = MetaGrid(grid=TileGrid(), meta_size=(2, 2))
>>> mgrid._full_tile_list([(0, 0, 2), (1, 1, 2)])
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"""
tile = tiles.pop()
z = tile[2]
minx = maxx = tile[0]
miny = maxy = tile[1]
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return list(_create_tile_list(xs, ys, z, (maxx+1, maxy+1))), grid_size, bounds | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/modular/overconvergent/genus0.py | python | OverconvergentModularFormElement.prec | (self) | return self.gexp().prec() | r"""
Return the series expansion precision of this overconvergent modular
form. (This is not the same as the `p`-adic precision of the
coefficients.)
EXAMPLES::
sage: OverconvergentModularForms(5, 6, 1/3,prec=15).gen(1).prec()
15 | r"""
Return the series expansion precision of this overconvergent modular
form. (This is not the same as the `p`-adic precision of the
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Return the series expansion precision of this overconvergent modular
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EXAMPLES::
sage: OverconvergentModularForms(5, 6, 1/3,prec=15).gen(1).prec()
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return self.gexp().prec() | [
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kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python-build/python-libs/gdata/build/lib/gdata/tlslite/utils/cipherfactory.py | python | createTripleDES | (key, IV, implList=None) | Create a new 3DES object.
@type key: str
@param key: A 24 byte string.
@type IV: str
@param IV: An 8 byte string
@rtype: L{tlslite.utils.TripleDES}
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@type IV: str
@param IV: An 8 byte string
@rtype: L{tlslite.utils.TripleDES}
@return: A 3DES object.
"""
if implList == None:
implList = ["cryptlib", "openssl", "pycrypto"]
for impl in implList:
if impl == "cryptlib" and cryptomath.cryptlibpyLoaded:
return Cryptlib_TripleDES.new(key, 2, IV)
elif impl == "openssl" and cryptomath.m2cryptoLoaded:
return OpenSSL_TripleDES.new(key, 2, IV)
elif impl == "pycrypto" and cryptomath.pycryptoLoaded:
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pandera-dev/pandera | 9448d0a80b8dd02910f9cc553ce00349584b107f | pandera/schemas.py | python | DataFrameSchema.coerce_dtype | (self, obj: pd.DataFrame) | return obj | Coerce dataframe to the type specified in dtype.
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:returns: dataframe with coerced dtypes
"""
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return obj
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matched_columns = col_schema.get_regex_columns(obj.columns)
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if self.dtype is not None:
obj = _try_coercion(self._coerce_dtype, obj)
if self.index is not None and (self.index.coerce or self.coerce):
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# coercing at the dataframe-level should apply index coercion
# for both single- and multi-indexes.
index_schema._coerce = True
coerced_index = _try_coercion(index_schema.coerce_dtype, obj.index)
if coerced_index is not None:
obj.index = coerced_index
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_vendored_deps/library/oc_group.py | python | Utils.create_tmpfile_copy | (inc_file) | return tmpfile | create a temporary copy of a file | create a temporary copy of a file | [
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'''create a temporary copy of a file'''
tmpfile = Utils.create_tmpfile('lib_openshift-')
Utils._write(tmpfile, open(inc_file).read())
# Cleanup the tmpfile
atexit.register(Utils.cleanup, [tmpfile])
return tmpfile | [
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tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/models/v1_scale_io_volume_source.py | python | V1ScaleIOVolumeSource.to_str | (self) | return pprint.pformat(self.to_dict()) | Returns the string representation of the model | Returns the string representation of the model | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/octoprint/sensor.py | python | OctoPrintTemperatureSensor.available | (self) | return self.coordinator.last_update_success and self.coordinator.data["printer"] | Return if entity is available. | Return if entity is available. | [
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bert-nmt/bert-nmt | fcb616d28091ac23c9c16f30e6870fe90b8576d6 | fairseq/models/lstm.py | python | AttentionLayer.forward | (self, input, source_hids, encoder_padding_mask) | return x, attn_scores | [] | def forward(self, input, source_hids, encoder_padding_mask):
# input: bsz x input_embed_dim
# source_hids: srclen x bsz x output_embed_dim
# x: bsz x output_embed_dim
x = self.input_proj(input)
# compute attention
attn_scores = (source_hids * x.unsqueeze(0)).sum(dim=2)
# don't attend over padding
if encoder_padding_mask is not None:
attn_scores = attn_scores.float().masked_fill_(
encoder_padding_mask,
float('-inf')
).type_as(attn_scores) # FP16 support: cast to float and back
attn_scores = F.softmax(attn_scores, dim=0) # srclen x bsz
# sum weighted sources
x = (attn_scores.unsqueeze(2) * source_hids).sum(dim=0)
x = torch.tanh(self.output_proj(torch.cat((x, input), dim=1)))
return x, attn_scores | [
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aceisace/Inkycal | 552744bc5d80769c1015d48fd8b13201683ee679 | inkycal/custom/functions.py | python | internet_available | () | checks if the internet is available.
Attempts to connect to google.com with a timeout of 5 seconds to check
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Returns:
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- False if the internet could not be reached.
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Attempts to connect to google.com with a timeout of 5 seconds to check
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Returns:
- True if connection could be established.
- False if the internet could not be reached.
Returned output can be used to add a check for internet availability:
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>>> #...do something that requires internet connectivity
"""
try:
urlopen('https://google.com',timeout=5)
return True
except URLError as err:
return False | [
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david8862/keras-YOLOv3-model-set | e9f0f94109430973525219e66eeafe8a2f51363d | tracking/model/deep_sort/kalman_filter.py | python | KalmanFilter.initiate | (self, measurement) | return mean, covariance | Create track from unassociated measurement.
Parameters
----------
measurement : ndarray
Bounding box coordinates (x, y, a, h) with center position (x, y),
aspect ratio a, and height h.
Returns
-------
(ndarray, ndarray)
Returns the mean vector (8 dimensional) and covariance matrix (8x8
dimensional) of the new track. Unobserved velocities are initialized
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"""Create track from unassociated measurement.
Parameters
----------
measurement : ndarray
Bounding box coordinates (x, y, a, h) with center position (x, y),
aspect ratio a, and height h.
Returns
-------
(ndarray, ndarray)
Returns the mean vector (8 dimensional) and covariance matrix (8x8
dimensional) of the new track. Unobserved velocities are initialized
to 0 mean.
"""
mean_pos = measurement
mean_vel = np.zeros_like(mean_pos)
mean = np.r_[mean_pos, mean_vel]
std = [
2 * self._std_weight_position * measurement[3],
2 * self._std_weight_position * measurement[3],
1e-2,
2 * self._std_weight_position * measurement[3],
10 * self._std_weight_velocity * measurement[3],
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covariance = np.diag(np.square(std))
return mean, covariance | [
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allenai/allennlp | a3d71254fcc0f3615910e9c3d48874515edf53e0 | allennlp/confidence_checks/task_checklists/utils.py | python | random_handle | (n: int = 6) | return "@%s" % random_string(n) | Returns a random handle of length `n`. Eg. "@randomstr23` | Returns a random handle of length `n`. Eg. " | [
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ArtistScript/FastTextRank | 0af1f353b4ff3180b8cac2953196d84fe012f7bc | FastTextRank/util.py | python | two_sentences_similarity | (sents_1, sents_2) | return counter / (math.log(len(sents_1) + len(sents_2))) | 计算两个句子的相似性
:param sents_1:
:param sents_2:
:return: | 计算两个句子的相似性
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计算两个句子的相似性
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/bulkexports/v1/export/export_custom_job.py | python | ExportCustomJobInstance.resource_type | (self) | return self._properties['resource_type'] | :returns: The type of communication – Messages, Calls, Conferences, and Participants
:rtype: unicode | :returns: The type of communication – Messages, Calls, Conferences, and Participants
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"""
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"""
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