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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/sympy/polys/polytools.py | python | lcm | (f, g=None, *gens, **args) | Compute LCM of ``f`` and ``g``.
Examples
========
>>> from sympy import lcm
>>> from sympy.abc import x
>>> lcm(x**2 - 1, x**2 - 3*x + 2)
x**3 - 2*x**2 - x + 2 | Compute LCM of ``f`` and ``g``. | [
"Compute",
"LCM",
"of",
"f",
"and",
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"."
] | def lcm(f, g=None, *gens, **args):
"""
Compute LCM of ``f`` and ``g``.
Examples
========
>>> from sympy import lcm
>>> from sympy.abc import x
>>> lcm(x**2 - 1, x**2 - 3*x + 2)
x**3 - 2*x**2 - x + 2
"""
if hasattr(f, '__iter__'):
if g is not None:
gens = (g,) + gens
return lcm_list(f, *gens, **args)
elif g is None:
raise TypeError("lcm() takes 2 arguments or a sequence of arguments")
options.allowed_flags(args, ['polys'])
try:
(F, G), opt = parallel_poly_from_expr((f, g), *gens, **args)
except PolificationFailed as exc:
domain, (a, b) = construct_domain(exc.exprs)
try:
return domain.to_sympy(domain.lcm(a, b))
except NotImplementedError:
raise ComputationFailed('lcm', 2, exc)
result = F.lcm(G)
if not opt.polys:
return result.as_expr()
else:
return result | [
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JetBrains/python-skeletons | 95ad24b666e475998e5d1cc02ed53a2188036167 | builtins.py | python | complex.__rpow__ | (self, y) | return 0j | x to the power y.
:type y: numbers.Number
:rtype: complex | x to the power y. | [
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"""x to the power y.
:type y: numbers.Number
:rtype: complex
"""
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dropbox/stone | b7b64320631b3a4d2f10681dca64e0718ebe68ee | stone/ir/data_types.py | python | doc_unwrap | (raw_doc) | return docstring | Applies two transformations to raw_doc:
1. N consecutive newlines are converted into N-1 newlines.
2. A lone newline is converted to a space, which basically unwraps text.
Returns a new string, or None if the input was None. | Applies two transformations to raw_doc:
1. N consecutive newlines are converted into N-1 newlines.
2. A lone newline is converted to a space, which basically unwraps text. | [
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2. A lone newline is converted to a space, which basically unwraps text.
Returns a new string, or None if the input was None.
"""
if raw_doc is None:
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docstring = ''
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docstring += c
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QQuick/Transcrypt | 68b4b71d4ff3e4d58281b24e9e2dcc9fc766e822 | transcrypt/modules/datetime/__init__.py | python | datetime.utcnow | (cls) | return cls.utcfromtimestamp(t) | Construct a UTC datetime from time.time(). | Construct a UTC datetime from time.time(). | [
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t = _time.time()
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | mx.graalpython/mx_graalpython_benchmark.py | python | PythonJavaEmbeddingBenchmarkSuite.__init__ | (self, name, bench_path, benchmarks) | [] | def __init__(self, name, bench_path, benchmarks):
super(PythonJavaEmbeddingBenchmarkSuite, self).__init__(name, benchmarks)
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/plat-sunos5/STROPTS.py | python | hat_setrefmod | (pp) | return (hat_page_setattr(pp, P_REF|P_MOD)) | [] | def hat_setrefmod(pp): return (hat_page_setattr(pp, P_REF|P_MOD)) | [
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hatRiot/zarp | 2e772350a01c2aeed3f4da9685cd0cc5d6b3ecad | src/lib/scapy/layers/dot11.py | python | Dot11Elt.mysummary | (self) | [] | def mysummary(self):
if self.ID == 0:
return "SSID=%s"%repr(self.info),[Dot11]
else:
return "" | [
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | golismero/api/data/resource/url.py | python | URL.__init__ | (self, url, method = "GET", post_params = None, referer = None) | :param url: Absolute URL.
:type url: str
:param method: HTTP method.
:type method: str
:param post_params: POST parameters or raw data.
:type post_params: dict(str -> str) | str
:param referer: Referrer URL.
:type referer: str
:raises ValueError: Currently, relative URLs are not allowed. | :param url: Absolute URL.
:type url: str | [
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] | def __init__(self, url, method = "GET", post_params = None, referer = None):
"""
:param url: Absolute URL.
:type url: str
:param method: HTTP method.
:type method: str
:param post_params: POST parameters or raw data.
:type post_params: dict(str -> str) | str
:param referer: Referrer URL.
:type referer: str
:raises ValueError: Currently, relative URLs are not allowed.
"""
# Validate the arguments.
if method:
method = to_utf8(method)
else:
method = "GET"
if referer:
referer = to_utf8(referer)
else:
referer = None
if not isinstance(method, str):
raise TypeError("Expected string, got %r instead" % type(method))
if post_params is not None and not isinstance(post_params, dict):
raise TypeError("Expected dict, got %r instead" % type(post_params))
if referer is not None and not isinstance(referer, str):
raise TypeError("Expected string, got %r instead" % type(referer))
if post_params:
if hasattr(post_params, "iteritems"):
post_params = {
to_utf8(k): to_utf8(v) for k,v in post_params.iteritems()
}
post_data = '&'.join(
'%s=%s' % ( quote(k, safe=''), quote(v, safe='') )
for (k, v) in sorted(post_params.iteritems())
)
else:
post_data = to_utf8(post_params)
post_params = None
else:
post_data = None
post_params = None
# Save the properties.
self.__method = method
self.__post_data = post_data
self.__post_params = post_params
self.__referer = parse_url(referer).url if referer else None
# Call the parent constructor.
super(URL, self).__init__(url)
# Increment the crawling depth by one.
self.depth += 1 | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/config/__init__.py | python | _validate_pillar_roots | (pillar_roots) | return _normalize_roots(pillar_roots) | If the pillar_roots option has a key that is None then we will error out,
just replace it with an empty list | If the pillar_roots option has a key that is None then we will error out,
just replace it with an empty list | [
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] | def _validate_pillar_roots(pillar_roots):
"""
If the pillar_roots option has a key that is None then we will error out,
just replace it with an empty list
"""
if not isinstance(pillar_roots, dict):
log.warning(
"The pillar_roots parameter is not properly formatted, using defaults"
)
return {"base": _expand_glob_path([salt.syspaths.BASE_PILLAR_ROOTS_DIR])}
return _normalize_roots(pillar_roots) | [
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masyagin1998/robin | 6591a02f1bb71185480303aa99e83f43e801296e | src/dataset/stsl-download.py | python | save_img | (link: str, path: str) | Get image via HTTP and save it asynchronously. | Get image via HTTP and save it asynchronously. | [
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] | async def save_img(link: str, path: str):
"""Get image via HTTP and save it asynchronously."""
cv2.imwrite(path, cv2.imdecode(np.asarray(bytearray(urlopen(link).read()), dtype='uint8'), cv2.IMREAD_COLOR)) | [
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ctxis/CAPE | dae9fa6a254ecdbabeb7eb0d2389fa63722c1e82 | lib/cuckoo/common/peepdf/PDFUtils.py | python | encodeName | (name) | return (0,'/'+encodedName) | Encode the given PDF name
@param name: A PDFName string to encode
@return: A tuple (status,statusContent), where statusContent is the encoded PDF name in case status = 0 or an error in case status = -1 | Encode the given PDF name | [
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] | def encodeName(name):
'''
Encode the given PDF name
@param name: A PDFName string to encode
@return: A tuple (status,statusContent), where statusContent is the encoded PDF name in case status = 0 or an error in case status = -1
'''
encodedName = ''
if name[0] == '/':
name = name[1:]
for char in name:
if char == '\0':
encodedName += char
else:
try:
hex = '%x' % ord(char)
encodedName += '#'+hex
except:
return (-1,'Error encoding name')
return (0,'/'+encodedName) | [
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leonsio/YAHM | 949b748b22fe2da0f3aff5b5681147c18c06cb03 | share/tools/ubi_reader/ubi/block/sort.py | python | list_by_list | (blist, slist) | return slist_blocks | Sort list of block indexes, by another list.
Argument:
List:blist -- List of block indexes.
List:slist -- Secondary list of blocks.
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Argument:
List:blist -- List of block indexes.
List:slist -- Secondary list of blocks.
Returns:
List -- List of block indexes matching slist from blist.
"""
slist_blocks = []
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return slist_blocks | [
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secdev/scapy | 65089071da1acf54622df0b4fa7fc7673d47d3cd | scapy/contrib/automotive/gm/gmlanutils.py | python | GMLAN_InitDiagnostics | (
sock, # type: SuperSocket
broadcast_socket=None, # type: Optional[SuperSocket]
timeout=None, # type: Optional[int]
verbose=None, # type: Optional[bool]
retry=0 # type: int
) | return False | Send messages to put an ECU into diagnostic/programming state.
:param sock: socket for communication.
:param broadcast_socket: socket for broadcasting. If provided some message
will be sent as broadcast. Recommended when used
on a network with several ECUs.
:param timeout: timeout for sending, receiving or sniffing packages.
:param verbose: set verbosity level
:param retry: number of retries in case of failure.
:return: True on success else False | Send messages to put an ECU into diagnostic/programming state. | [
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] | def GMLAN_InitDiagnostics(
sock, # type: SuperSocket
broadcast_socket=None, # type: Optional[SuperSocket]
timeout=None, # type: Optional[int]
verbose=None, # type: Optional[bool]
retry=0 # type: int
):
# type: (...) -> bool
""" Send messages to put an ECU into diagnostic/programming state.
:param sock: socket for communication.
:param broadcast_socket: socket for broadcasting. If provided some message
will be sent as broadcast. Recommended when used
on a network with several ECUs.
:param timeout: timeout for sending, receiving or sniffing packages.
:param verbose: set verbosity level
:param retry: number of retries in case of failure.
:return: True on success else False
"""
# Helper function
def _send_and_check_response(sock, req, timeout, verbose):
# type: (SuperSocket, Packet, Optional[int], Optional[bool]) -> bool
if verbose:
print("Sending %s" % repr(req))
resp = sock.sr1(req, timeout=timeout, verbose=False)
return _check_response(resp, verbose)
if verbose is None:
verbose = conf.verb > 0
retry = abs(retry)
while retry >= 0:
retry -= 1
# DisableNormalCommunication
p = GMLAN(service="DisableNormalCommunication")
if broadcast_socket is None:
if not _send_and_check_response(sock, p, timeout, verbose):
continue
else:
if verbose:
print("Sending %s as broadcast" % repr(p))
broadcast_socket.send(p)
time.sleep(0.05)
# ReportProgrammedState
p = GMLAN(service="ReportProgrammingState")
if not _send_and_check_response(sock, p, timeout, verbose):
continue
# ProgrammingMode requestProgramming
p = GMLAN() / GMLAN_PM(subfunction="requestProgrammingMode")
if not _send_and_check_response(sock, p, timeout, verbose):
continue
time.sleep(0.05)
# InitiateProgramming enableProgramming
# No response expected
p = GMLAN() / GMLAN_PM(subfunction="enableProgrammingMode")
if verbose:
print("Sending %s" % repr(p))
sock.sr1(p, timeout=0.001, verbose=False)
return True
return False | [
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LiMeng95/MultiPoseNet.pytorch | 7b548fc20ae5b0141a2113669e8606103fb7e6d4 | network/fpn.py | python | FPN._upsample_add | (self, x, y) | return F.upsample(x, size=(H,W), mode='nearest', align_corners=None) + y | Upsample and add two feature maps.
Args:
x: top feature map to be upsampled.
y: lateral feature map.
Returns:
added feature map. | Upsample and add two feature maps. | [
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'''Upsample and add two feature maps.
Args:
x: top feature map to be upsampled.
y: lateral feature map.
Returns:
added feature map.
'''
_,_,H,W = y.size()
return F.upsample(x, size=(H,W), mode='nearest', align_corners=None) + y | [
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... | https://github.com/LiMeng95/MultiPoseNet.pytorch/blob/7b548fc20ae5b0141a2113669e8606103fb7e6d4/network/fpn.py#L84-L95 | |
ChineseGLUE/ChineseGLUE | 1591b85cf5427c2ff60f718d359ecb71d2b44879 | baselines/models/roberta/modeling.py | python | create_attention_mask_from_input_mask | (from_tensor, to_mask) | return mask | Create 3D attention mask from a 2D tensor mask.
Args:
from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...].
to_mask: int32 Tensor of shape [batch_size, to_seq_length].
Returns:
float Tensor of shape [batch_size, from_seq_length, to_seq_length]. | Create 3D attention mask from a 2D tensor mask. | [
"Create",
"3D",
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"mask",
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] | def create_attention_mask_from_input_mask(from_tensor, to_mask):
"""Create 3D attention mask from a 2D tensor mask.
Args:
from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...].
to_mask: int32 Tensor of shape [batch_size, to_seq_length].
Returns:
float Tensor of shape [batch_size, from_seq_length, to_seq_length].
"""
from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
batch_size = from_shape[0]
from_seq_length = from_shape[1]
to_shape = get_shape_list(to_mask, expected_rank=2)
to_seq_length = to_shape[1]
to_mask = tf.cast(
tf.reshape(to_mask, [batch_size, 1, to_seq_length]), tf.float32)
# We don't assume that `from_tensor` is a mask (although it could be). We
# don't actually care if we attend *from* padding tokens (only *to* padding)
# tokens so we create a tensor of all ones.
#
# `broadcast_ones` = [batch_size, from_seq_length, 1]
broadcast_ones = tf.ones(
shape=[batch_size, from_seq_length, 1], dtype=tf.float32)
# Here we broadcast along two dimensions to create the mask.
mask = broadcast_ones * to_mask
return mask | [
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mnemosyne-proj/mnemosyne | e39e364e56343437f2e485e0b06ca714de2f2d2e | mnemosyne/libmnemosyne/renderers/anki/utils.py | python | fmtFloat | (float_value, point=1) | return locale.format_string(fmt, float_value) | Return a string with decimal separator according to current locale | Return a string with decimal separator according to current locale | [
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"Return a string with decimal separator according to current locale"
fmt = '%' + "0.%(b)df" % {'b': point}
return locale.format_string(fmt, float_value) | [
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ChenglongChen/kaggle-HomeDepot | 55c1033d0af3b6cf2f033fe4bcf3e1e0ffda3445 | Code/Chenglong/feature_basic.py | python | AttrHasIndoorOutdoor.transform_one | (self, obs, target, id) | return 0 | obs is a list of attributes | obs is a list of attributes | [
"obs",
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] | def transform_one(self, obs, target, id):
"""obs is a list of attributes"""
for lst in obs:
if lst[0].find("indoor outdoor") != -1:
return 1
return 0 | [
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BEEmod/BEE2.4 | 02767f3cf476581789425ab308ca1bea978f6a74 | src/precomp/brushLoc.py | python | _conv_key | (pos: _grid_keys) | return x, y, z | Convert the key given in [] to a grid-position, as a x,y,z tuple. | Convert the key given in [] to a grid-position, as a x,y,z tuple. | [
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] | def _conv_key(pos: _grid_keys) -> tuple[float, float, float]:
"""Convert the key given in [] to a grid-position, as a x,y,z tuple."""
# TODO: Slices are assumed to be int by typeshed.
# type: ignore
if isinstance(pos, slice):
system, slice_pos = pos.start, pos.stop
if system == 'world':
return tuple(world_to_grid(Vec(slice_pos)))
else:
return tuple(slice_pos)
x, y, z = pos
return x, y, z | [
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mozilla/pontoon | d26999eea57902a30b5c15e9b77277fe7e76a60f | pontoon/batch/views.py | python | update_translation_memory | (changed_translation_pks, project, locale) | Update translation memory for a list of translations. | Update translation memory for a list of translations. | [
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memory_entries = [
TranslationMemoryEntry(
source=t.tm_source,
target=t.tm_target,
locale=locale,
entity=t.entity,
translation=t,
project=project,
)
for t in (
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feisuzhu/thbattle | ac0dee1b2d86de7664289cf432b157ef25427ba1 | src/pyglet/gl/glu_info.py | python | GLUInfo.get_version | (self) | return self.version | Get the current GLU version.
:return: the GLU version
:rtype: str | Get the current GLU version. | [
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] | def get_version(self):
'''Get the current GLU version.
:return: the GLU version
:rtype: str
'''
if not self.have_context:
warnings.warn('No GL context created yet.')
return self.version | [
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CryptoSignal/crypto-signal | 8769d0df2c50e5071b282300788a3860200b22c6 | app/notification.py | python | Notifier._indicator_message_templater | (self, new_analysis, template) | return new_message | Creates a message from a user defined template
Args:
new_analysis (dict): A dictionary of data related to the analysis to send a message about.
template (str): A Jinja formatted message template.
Returns:
str: The templated messages for the notifier. | Creates a message from a user defined template | [
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] | def _indicator_message_templater(self, new_analysis, template):
"""Creates a message from a user defined template
Args:
new_analysis (dict): A dictionary of data related to the analysis to send a message about.
template (str): A Jinja formatted message template.
Returns:
str: The templated messages for the notifier.
"""
if not self.last_analysis:
self.last_analysis = new_analysis
message_template = Template(template)
new_message = str()
for exchange in new_analysis:
for market in new_analysis[exchange]:
for indicator_type in new_analysis[exchange][market]:
if indicator_type == 'informants':
continue
for indicator in new_analysis[exchange][market][indicator_type]:
for index, analysis in enumerate(new_analysis[exchange][market][indicator_type][indicator]):
if analysis['result'].shape[0] == 0:
continue
values = dict()
if indicator_type == 'indicators':
for signal in analysis['config']['signal']:
latest_result = analysis['result'].iloc[-1]
values[signal] = analysis['result'].iloc[-1][signal]
if isinstance(values[signal], float):
values[signal] = format(values[signal], '.8f')
elif indicator_type == 'crossovers':
latest_result = analysis['result'].iloc[-1]
key_signal = '{}_{}'.format(
analysis['config']['key_signal'],
analysis['config']['key_indicator_index']
)
crossed_signal = '{}_{}'.format(
analysis['config']['crossed_signal'],
analysis['config']['crossed_indicator_index']
)
values[key_signal] = analysis['result'].iloc[-1][key_signal]
if isinstance(values[key_signal], float):
values[key_signal] = format(values[key_signal], '.8f')
values[crossed_signal] = analysis['result'].iloc[-1][crossed_signal]
if isinstance(values[crossed_signal], float):
values[crossed_signal] = format(values[crossed_signal], '.8f')
status = 'neutral'
if latest_result['is_hot']:
status = 'hot'
elif latest_result['is_cold']:
status = 'cold'
# Save status of indicator's new analysis
new_analysis[exchange][market][indicator_type][indicator][index]['status'] = status
if latest_result['is_hot'] or latest_result['is_cold']:
try:
last_status = self.last_analysis[exchange][market][indicator_type][indicator][index]['status']
except:
last_status = str()
should_alert = True
if analysis['config']['alert_frequency'] == 'once':
if last_status == status:
should_alert = False
if not analysis['config']['alert_enabled']:
should_alert = False
if should_alert:
base_currency, quote_currency = market.split('/')
new_message += message_template.render(
values=values,
exchange=exchange,
market=market,
base_currency=base_currency,
quote_currency=quote_currency,
indicator=indicator,
indicator_number=index,
analysis=analysis,
status=status,
last_status=last_status
)
# Merge changes from new analysis into last analysis
self.last_analysis = {**self.last_analysis, **new_analysis}
return new_message | [
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PaddlePaddle/Parakeet | 8705a2a8405e3c63f2174d69880d2b5525a6c9fd | parakeet/frontend/arpabet.py | python | ARPABET.phoneticize | (self, sentence, add_start_end=False) | return phonemes | Normalize the input text sequence and convert it into pronunciation sequence.
Parameters
-----------
sentence: str
The input text sequence.
Returns
----------
List[str]
The list of pronunciation sequence. | Normalize the input text sequence and convert it into pronunciation sequence.
Parameters
-----------
sentence: str
The input text sequence.
Returns
----------
List[str]
The list of pronunciation sequence. | [
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""" Normalize the input text sequence and convert it into pronunciation sequence.
Parameters
-----------
sentence: str
The input text sequence.
Returns
----------
List[str]
The list of pronunciation sequence.
"""
phonemes = [
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]
if add_start_end:
start = self.vocab.start_symbol
end = self.vocab.end_symbol
phonemes = [start] + phonemes + [end]
phonemes = [item for item in phonemes if item in self.vocab.stoi]
return phonemes | [
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facebookresearch/XLM | cd281d32612d145c6742b4d3f048f80df8669c30 | xlm/trainer.py | python | Trainer.pc_step | (self, lang1, lang2, lambda_coeff) | Parallel classification step. Predict if pairs of sentences are mutual translations of each other. | Parallel classification step. Predict if pairs of sentences are mutual translations of each other. | [
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] | def pc_step(self, lang1, lang2, lambda_coeff):
"""
Parallel classification step. Predict if pairs of sentences are mutual translations of each other.
"""
assert lambda_coeff >= 0
if lambda_coeff == 0:
return
params = self.params
name = 'model' if params.encoder_only else 'encoder'
model = getattr(self, name)
model.train()
lang1_id = params.lang2id[lang1]
lang2_id = params.lang2id[lang2]
# sample parallel sentences
(x1, len1), (x2, len2) = self.get_batch('align', lang1, lang2)
bs = len1.size(0)
if bs == 1: # can happen (although very rarely), which makes the negative loss fail
self.n_sentences += params.batch_size
return
# associate lang1 sentences with their translations, and random lang2 sentences
y = torch.LongTensor(bs).random_(2)
idx_pos = torch.arange(bs)
idx_neg = ((idx_pos + torch.LongTensor(bs).random_(1, bs)) % bs)
idx = (y == 1).long() * idx_pos + (y == 0).long() * idx_neg
x2, len2 = x2[:, idx], len2[idx]
# generate batch / cuda
x, lengths, positions, langs = concat_batches(x1, len1, lang1_id, x2, len2, lang2_id, params.pad_index, params.eos_index, reset_positions=False)
x, lengths, positions, langs, new_idx = self.round_batch(x, lengths, positions, langs)
if new_idx is not None:
y = y[new_idx]
x, lengths, positions, langs = to_cuda(x, lengths, positions, langs)
# get sentence embeddings
h = model('fwd', x=x, lengths=lengths, positions=positions, langs=langs, causal=False)[0]
# parallel classification loss
CLF_ID1, CLF_ID2 = 8, 9 # very hacky, use embeddings to make weights for the classifier
emb = (model.module if params.multi_gpu else model).embeddings.weight
pred = F.linear(h, emb[CLF_ID1].unsqueeze(0), emb[CLF_ID2, 0])
loss = F.binary_cross_entropy_with_logits(pred.view(-1), y.to(pred.device).type_as(pred))
self.stats['PC-%s-%s' % (lang1, lang2)].append(loss.item())
loss = lambda_coeff * loss
# optimize
self.optimize(loss)
# number of processed sentences / words
self.n_sentences += params.batch_size
self.stats['processed_s'] += bs
self.stats['processed_w'] += lengths.sum().item() | [
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lektor/lektor-archive | d2ab208c756b1e7092b2056108571719abd8d6cd | lektor/pagination.py | python | Pagination.pages | (self) | return pages | The total number of pages | The total number of pages | [
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"""The total number of pages"""
if self.per_page == 0:
pages = 0
else:
pages = int(ceil(self.total / float(self.per_page)))
return pages | [
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-darwin/x64/tornado/netutil.py | python | BlockingResolver.initialize | (self) | [] | def initialize(self):
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xiaolonw/adversarial-frcnn | 2a7bb96c9884c0f09ca5bde35a981087be28562b | lib/fast_rcnn/config.py | python | cfg_from_file | (filename) | Load a config file and merge it into the default options. | Load a config file and merge it into the default options. | [
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] | def cfg_from_file(filename):
"""Load a config file and merge it into the default options."""
import yaml
with open(filename, 'r') as f:
yaml_cfg = edict(yaml.load(f))
_merge_a_into_b(yaml_cfg, __C) | [
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IntelAI/models | 1d7a53ccfad3e6f0e7378c9e3c8840895d63df8c | models/image_segmentation/tensorflow/maskrcnn/inference/fp32/model.py | python | rpn_graph | (feature_map, anchors_per_location, anchor_stride) | return [rpn_class_logits, rpn_probs, rpn_bbox] | Builds the computation graph of Region Proposal Network.
feature_map: backbone features [batch, height, width, depth]
anchors_per_location: number of anchors per pixel in the feature map
anchor_stride: Controls the density of anchors. Typically 1 (anchors for
every pixel in the feature map), or 2 (every other pixel).
Returns:
rpn_logits: [batch, H, W, 2] Anchor classifier logits (before softmax)
rpn_probs: [batch, H, W, 2] Anchor classifier probabilities.
rpn_bbox: [batch, H, W, (dy, dx, log(dh), log(dw))] Deltas to be
applied to anchors. | Builds the computation graph of Region Proposal Network. | [
"Builds",
"the",
"computation",
"graph",
"of",
"Region",
"Proposal",
"Network",
"."
] | def rpn_graph(feature_map, anchors_per_location, anchor_stride):
"""Builds the computation graph of Region Proposal Network.
feature_map: backbone features [batch, height, width, depth]
anchors_per_location: number of anchors per pixel in the feature map
anchor_stride: Controls the density of anchors. Typically 1 (anchors for
every pixel in the feature map), or 2 (every other pixel).
Returns:
rpn_logits: [batch, H, W, 2] Anchor classifier logits (before softmax)
rpn_probs: [batch, H, W, 2] Anchor classifier probabilities.
rpn_bbox: [batch, H, W, (dy, dx, log(dh), log(dw))] Deltas to be
applied to anchors.
"""
# TODO: check if stride of 2 causes alignment issues if the featuremap
# is not even.
# Shared convolutional base of the RPN
shared = KL.Conv2D(512, (3, 3), padding='same', activation='relu',
strides=anchor_stride,
name='rpn_conv_shared')(feature_map)
# Anchor Score. [batch, height, width, anchors per location * 2].
x = KL.Conv2D(2 * anchors_per_location, (1, 1), padding='valid',
activation='linear', name='rpn_class_raw')(shared)
# Reshape to [batch, anchors, 2]
rpn_class_logits = KL.Lambda(
lambda t: tf.reshape(t, [tf.shape(t)[0], -1, 2]))(x)
# Softmax on last dimension of BG/FG.
rpn_probs = KL.Activation(
"softmax", name="rpn_class_xxx")(rpn_class_logits)
# Bounding box refinement. [batch, H, W, anchors per location, depth]
# where depth is [x, y, log(w), log(h)]
x = KL.Conv2D(anchors_per_location * 4, (1, 1), padding="valid",
activation='linear', name='rpn_bbox_pred')(shared)
# Reshape to [batch, anchors, 4]
rpn_bbox = KL.Lambda(lambda t: tf.reshape(t, [tf.shape(t)[0], -1, 4]))(x)
return [rpn_class_logits, rpn_probs, rpn_bbox] | [
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CouchPotato/CouchPotatoServer | 7260c12f72447ddb6f062367c6dfbda03ecd4e9c | libs/rsa/cli.py | python | BigfileOperation.__call__ | (self) | Runs the program. | Runs the program. | [
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'''Runs the program.'''
(cli, cli_args) = self.parse_cli()
key = self.read_key(cli_args[0], cli.keyform)
# Get the file handles
infile = self.get_infile(cli.input)
outfile = self.get_outfile(cli.output)
# Call the operation
print(self.operation_progressive.title(), file=sys.stderr)
self.perform_operation(infile, outfile, key, cli_args) | [
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emcconville/wand | 03682680c351645f16c3b8ea23bde79fbb270305 | wand/image.py | python | BaseImage.brightness_contrast | (self, brightness=0.0, contrast=0.0, channel=None) | return r | Converts ``brightness`` & ``contrast`` paramaters into a slope &
intercept, and applies a polynomial function.
:param brightness: between ``-100.0`` and ``100.0``. Default is ``0.0``
for unchanged.
:type brightness: :class:`numbers.Real`
:param contrast: between ``-100.0`` and ``100.0``. Default is ``0.0``
for unchanged.
:type contrast: :class:`numbers.Real`
:param channel: Isolate a single color channel to apply contrast.
See :const:`CHANNELS`.
.. versionadded:: 0.5.4
.. versionchanged:: 0.5.5
Optional ``channel`` argument added. | Converts ``brightness`` & ``contrast`` paramaters into a slope &
intercept, and applies a polynomial function. | [
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] | def brightness_contrast(self, brightness=0.0, contrast=0.0, channel=None):
"""Converts ``brightness`` & ``contrast`` paramaters into a slope &
intercept, and applies a polynomial function.
:param brightness: between ``-100.0`` and ``100.0``. Default is ``0.0``
for unchanged.
:type brightness: :class:`numbers.Real`
:param contrast: between ``-100.0`` and ``100.0``. Default is ``0.0``
for unchanged.
:type contrast: :class:`numbers.Real`
:param channel: Isolate a single color channel to apply contrast.
See :const:`CHANNELS`.
.. versionadded:: 0.5.4
.. versionchanged:: 0.5.5
Optional ``channel`` argument added.
"""
assertions.assert_real(brightness=brightness, contrast=contrast)
if channel is None:
r = library.MagickBrightnessContrastImage(self.wand,
brightness,
contrast)
else:
channel_ch = self._channel_to_mask(channel)
if MAGICK_VERSION_NUMBER < 0x700:
r = library.MagickBrightnessContrastImageChannel(self.wand,
channel_ch,
brightness,
contrast)
else: # pragma: no cover
mask = library.MagickSetImageChannelMask(self.wand, channel_ch)
r = library.MagickBrightnessContrastImage(self.wand,
brightness,
contrast)
library.MagickSetImageChannelMask(self.wand, mask)
return r | [
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ztosec/hunter | 4ee5cca8dc5fc5d7e631e935517bd0f493c30a37 | HunterCelery/common/plugins_util.py | python | modify_default_checkers | () | 修改插件状态，根据配置文件自动降级
:return: | 修改插件状态，根据配置文件自动降级
:return: | [
"修改插件状态，根据配置文件自动降级",
":",
"return",
":"
] | def modify_default_checkers():
"""
修改插件状态，根据配置文件自动降级
:return:
"""
from common.plugin_config.base_plugin_config import DegradablePluginConfig
checkers = load_default_checkers()
for (k, v) in DegradablePluginConfig.instance().get_plugin_config(True).items():
if k in checkers: # 确认配置文件和本地插件是否一致
checkers[k].useable = v["useable"]
logger.info('local found %d plugins, %d plugins useable' % (len(CHECKER_INSTANCE_DICT), len(get_useable_checkers()))) | [
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treeio/treeio | bae3115f4015aad2cbc5ab45572232ceec990495 | treeio/finance/templatetags/finance.py | python | finance_liability_list | (context, liabilities, skip_group=False) | return Markup(render_to_string('finance/tags/liability_list',
{'liabilities': liabilities,
'skip_group': skip_group},
context_instance=RequestContext(request),
response_format=response_format)) | Print a list of orders | Print a list of orders | [
"Print",
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] | def finance_liability_list(context, liabilities, skip_group=False):
"Print a list of orders"
request = context['request']
response_format = 'html'
if 'response_format' in context:
response_format = context['response_format']
return Markup(render_to_string('finance/tags/liability_list',
{'liabilities': liabilities,
'skip_group': skip_group},
context_instance=RequestContext(request),
response_format=response_format)) | [
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VirtueSecurity/aws-extender | d123b7e1a845847709ba3a481f11996bddc68a1c | BappModules/boto/sdb/db/sequence.py | python | SequenceGenerator.__init__ | (self, sequence_string, rollover=False) | Create a new SequenceGenerator using the sequence_string
as how to generate the next item.
:param sequence_string: The string or list that explains
how to generate the next item in the sequence
:type sequence_string: str,iterable
:param rollover: Rollover instead of incrementing when
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:type rollover: bool | Create a new SequenceGenerator using the sequence_string
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] | def __init__(self, sequence_string, rollover=False):
"""Create a new SequenceGenerator using the sequence_string
as how to generate the next item.
:param sequence_string: The string or list that explains
how to generate the next item in the sequence
:type sequence_string: str,iterable
:param rollover: Rollover instead of incrementing when
we hit the end of the sequence
:type rollover: bool
"""
self.sequence_string = sequence_string
self.sequence_length = len(sequence_string[0])
self.rollover = rollover
self.last_item = sequence_string[-1]
self.__name__ = "%s('%s')" % (self.__class__.__name__, sequence_string) | [
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thiagopena/djangoSIGE | e32186b27bfd8acf21b0fa400e699cb5c73e5433 | djangosige/apps/cadastro/views/base.py | python | EditarPessoaView.get | (self, request, form, *args, **kwargs) | return self.render_to_response(self.get_context_data(form=form,
pessoa_juridica_form=pessoa_juridica_form,
pessoa_fisica_form=pessoa_fisica_form,
endereco_form=endereco_form,
banco_form=banco_form,
documento_form=documento_form,
formsets=formsets,
logo_file=logo_file,
veiculo_form=veiculo_form,
object=self.object)) | [] | def get(self, request, form, *args, **kwargs):
if self.object.tipo_pessoa == 'PJ':
pessoa_juridica_form = PessoaJuridicaForm(
instance=self.object, prefix='pessoa_jur_form')
pessoa_fisica_form = PessoaFisicaForm(prefix='pessoa_fis_form')
else:
pessoa_juridica_form = PessoaJuridicaForm(prefix='pessoa_jur_form')
pessoa_fisica_form = PessoaFisicaForm(
instance=self.object, prefix='pessoa_fis_form')
endereco_form = EnderecoFormSet(
instance=self.object, prefix='endereco_form')
banco_form = BancoFormSet(instance=self.object, prefix='banco_form')
documento_form = DocumentoFormSet(
instance=self.object, prefix='documento_form')
telefone_form = TelefoneFormSet(
instance=self.object, prefix='telefone_form')
email_form = EmailFormSet(instance=self.object, prefix='email_form')
site_form = SiteFormSet(instance=self.object, prefix='site_form')
if Telefone.objects.filter(pessoa_tel=self.object.pk).count():
telefone_form.extra = 0
if Endereco.objects.filter(pessoa_end=self.object.pk).count():
endereco_form.extra = 0
if Email.objects.filter(pessoa_email=self.object.pk).count():
email_form.extra = 0
if Site.objects.filter(pessoa_site=self.object.pk).count():
site_form.extra = 0
if Banco.objects.filter(pessoa_banco=self.object.pk).count():
banco_form.extra = 0
if Documento.objects.filter(pessoa_documento=self.object.pk).count():
documento_form.extra = 0
formsets = [telefone_form, email_form, site_form]
# Caso Empresa
logo_file = kwargs.pop('logo_file', None)
# Caso Transportadora
veiculo_form = kwargs.pop('veiculo_form', None)
return self.render_to_response(self.get_context_data(form=form,
pessoa_juridica_form=pessoa_juridica_form,
pessoa_fisica_form=pessoa_fisica_form,
endereco_form=endereco_form,
banco_form=banco_form,
documento_form=documento_form,
formsets=formsets,
logo_file=logo_file,
veiculo_form=veiculo_form,
object=self.object)) | [
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/pandas/core/indexes/datetimes.py | python | DatetimeIndex.to_julian_date | (self) | return Float64Index(day +
np.fix((153 * month - 457) / 5) +
365 * year +
np.floor(year / 4) -
np.floor(year / 100) +
np.floor(year / 400) +
1721118.5 +
(self.hour +
self.minute / 60.0 +
self.second / 3600.0 +
self.microsecond / 3600.0 / 1e+6 +
self.nanosecond / 3600.0 / 1e+9
) / 24.0) | Convert DatetimeIndex to Float64Index of Julian Dates.
0 Julian date is noon January 1, 4713 BC.
http://en.wikipedia.org/wiki/Julian_day | Convert DatetimeIndex to Float64Index of Julian Dates.
0 Julian date is noon January 1, 4713 BC.
http://en.wikipedia.org/wiki/Julian_day | [
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] | def to_julian_date(self):
"""
Convert DatetimeIndex to Float64Index of Julian Dates.
0 Julian date is noon January 1, 4713 BC.
http://en.wikipedia.org/wiki/Julian_day
"""
# http://mysite.verizon.net/aesir_research/date/jdalg2.htm
year = np.asarray(self.year)
month = np.asarray(self.month)
day = np.asarray(self.day)
testarr = month < 3
year[testarr] -= 1
month[testarr] += 12
return Float64Index(day +
np.fix((153 * month - 457) / 5) +
365 * year +
np.floor(year / 4) -
np.floor(year / 100) +
np.floor(year / 400) +
1721118.5 +
(self.hour +
self.minute / 60.0 +
self.second / 3600.0 +
self.microsecond / 3600.0 / 1e+6 +
self.nanosecond / 3600.0 / 1e+9
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jupyterhub/repo2docker | a37a205c0e8a59240933d20c1c4fb80767e71db2 | repo2docker/contentproviders/hydroshare.py | python | Hydroshare.fetch | (self, spec, output_dir, yield_output=False, timeout=120) | Fetch and unpack a Hydroshare resource | Fetch and unpack a Hydroshare resource | [
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"Hydroshare",
"resource"
] | def fetch(self, spec, output_dir, yield_output=False, timeout=120):
"""Fetch and unpack a Hydroshare resource"""
resource_id = spec["resource"]
host = spec["host"]
bag_url = "{}{}".format(host["django_irods"], resource_id)
yield "Downloading {}.\n".format(bag_url)
# bag downloads are prepared on demand and may need some time
conn = self.urlopen(bag_url)
total_wait_time = 0
while (
conn.status_code == 200
and conn.headers["content-type"] != "application/zip"
):
wait_time = 10
total_wait_time += wait_time
if total_wait_time > timeout:
msg = "Bag taking too long to prepare, exiting now, try again later."
yield msg
raise ContentProviderException(msg)
yield "Bag is being prepared, requesting again in {} seconds.\n".format(
wait_time
)
time.sleep(wait_time)
conn = self.urlopen(bag_url)
if conn.status_code != 200:
msg = "Failed to download bag. status code {}.\n".format(conn.status_code)
yield msg
raise ContentProviderException(msg)
# Bag creation seems to need a small time buffer after it says it's ready.
time.sleep(1)
filehandle, _ = self._urlretrieve(bag_url)
zip_file_object = zipfile.ZipFile(filehandle, "r")
yield "Downloaded, unpacking contents.\n"
zip_file_object.extractall("temp")
# resources store the contents in the data/contents directory, which is all we want to keep
contents_dir = os.path.join("temp", self.resource_id, "data", "contents")
files = os.listdir(contents_dir)
for f in files:
shutil.move(os.path.join(contents_dir, f), output_dir)
yield "Finished, cleaning up.\n"
shutil.rmtree("temp") | [
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | golismero/managers/importmanager.py | python | ImportManager.close | (self) | Release all resources held by this manager. | Release all resources held by this manager. | [
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] | def close(self):
"""
Release all resources held by this manager.
"""
self.__config = None
self.__orchestrator = None
self.__plugins = None
self.__importers = None | [
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home-assistant/supervisor | 69c2517d5211b483fdfe968b0a2b36b672ee7ab2 | supervisor/store/utils.py | python | get_hash_from_repository | (name: str) | return hashlib.sha1(key).hexdigest()[:8] | Generate a hash from repository. | Generate a hash from repository. | [
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"repository",
"."
] | def get_hash_from_repository(name: str) -> str:
"""Generate a hash from repository."""
key = name.lower().encode()
return hashlib.sha1(key).hexdigest()[:8] | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/tkinter/__init__.py | python | Entry.get | (self) | return self.tk.call(self._w, 'get') | Return the text. | Return the text. | [
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] | def get(self):
"""Return the text."""
return self.tk.call(self._w, 'get') | [
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readthedocs/sphinx-autoapi | 71c6ceebe0b02c34027fcd3d56c8641e9b94c7af | autoapi/mappers/python/astroid_utils.py | python | _is_property_decorator | (decorator) | return False | [] | def _is_property_decorator(decorator):
def _is_property_class(class_node):
return (
class_node.name == "property"
and class_node.root().name == builtins.__name__
)
for inferred in decorator.infer():
if not isinstance(inferred, astroid.nodes.ClassDef):
continue
if _is_property_class(inferred):
return True
if any(_is_property_class(ancestor) for ancestor in inferred.ancestors()):
return True
return False | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/gevent/hub.py | python | Waiter.exc_info | (self) | Holds the exception info passed to :meth:`throw` if :meth:`throw` was called. Otherwise ``None``. | Holds the exception info passed to :meth:`throw` if :meth:`throw` was called. Otherwise ``None``. | [
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"Holds the exception info passed to :meth:`throw` if :meth:`throw` was called. Otherwise ``None``."
if self._exception is not _NONE:
return self._exception | [
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yuxiaokui/Intranet-Penetration | f57678a204840c83cbf3308e3470ae56c5ff514b | proxy/XX-Net/code/default/python27/1.0/lib/wsgiref/headers.py | python | Headers.get_all | (self, name) | return [kv[1] for kv in self._headers if kv[0].lower()==name] | Return a list of all the values for the named field.
These will be sorted in the order they appeared in the original header
list or were added to this instance, and may contain duplicates. Any
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] | def get_all(self, name):
"""Return a list of all the values for the named field.
These will be sorted in the order they appeared in the original header
list or were added to this instance, and may contain duplicates. Any
fields deleted and re-inserted are always appended to the header list.
If no fields exist with the given name, returns an empty list.
"""
name = name.lower()
return [kv[1] for kv in self._headers if kv[0].lower()==name] | [
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KvasirSecurity/Kvasir | a5b3775184a8343240e1154a1f762f75df04dc0a | modules/zenmapCore_Kvasir/ScriptMetadata.py | python | get_script_entries | (scripts_dir, nselib_dir) | return entries | Merge the information obtained so far into one single entry for
each script and return it. | Merge the information obtained so far into one single entry for
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"""Merge the information obtained so far into one single entry for
each script and return it."""
metadata = ScriptMetadata(scripts_dir, nselib_dir)
try:
scriptdb = ScriptDB(os.path.join(scripts_dir, "script.db"))
except IOError:
return []
entries = []
for dbentry in scriptdb.get_entries_list():
entry = metadata.get_metadata(dbentry["filename"])
# Categories is the only thing ScriptMetadata doesn't take care of.
entry.categories = dbentry["categories"]
entries.append(entry)
return entries | [
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youngsterxyf/mpdp-code | 282e13bac4b6c98036961e7d920b741509e92ab6 | chapter6/facade.py | python | ProcessServer.kill | (self, restart=True) | 杀死进程服务进程要求的操作 | 杀死进程服务进程要求的操作 | [
"杀死进程服务进程要求的操作"
] | def kill(self, restart=True):
print('Killing {}'.format(self))
'''杀死进程服务进程要求的操作'''
self.state = State.restart if restart else State.zombie | [
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/mutagen/asf/_objects.py | python | BaseObject.parse | (self, asf, data) | [] | def parse(self, asf, data):
self.data = data | [
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p2pool/p2pool | 53c438bbada06b9d4a9a465bc13f7694a7a322b7 | p2pool/p2p.py | python | Node.start | (self) | [] | def start(self):
if self.running:
raise ValueError('already running')
self.clientfactory.start()
self.serverfactory.start()
self.singleclientconnectors = [reactor.connectTCP(addr, port, SingleClientFactory(self)) for addr, port in self.connect_addrs]
self.running = True
self._stop_thinking = deferral.run_repeatedly(self._think) | [
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auth0/auth0-python | 511b016ac9853c7f4ee66769be7ad315c5585735 | auth0/v3/management/connections.py | python | Connections.update | (self, id, body) | return self.client.patch(self._url(id), data=body) | Modifies a connection.
Args:
id: Id of the connection.
body (dict): Specifies which fields are to be modified, and to what values.
See: https://auth0.com/docs/api/management/v2#!/Connections/patch_connections_by_id
Returns:
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] | def update(self, id, body):
"""Modifies a connection.
Args:
id: Id of the connection.
body (dict): Specifies which fields are to be modified, and to what values.
See: https://auth0.com/docs/api/management/v2#!/Connections/patch_connections_by_id
Returns:
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"""
return self.client.patch(self._url(id), data=body) | [
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gdraheim/docker-systemctl-replacement | 9cbe1a00eb4bdac6ff05b96ca34ec9ed3d8fc06c | files/docker/systemctl3.py | python | Systemctl.load_sysv_unit_conf | (self, module) | return conf | read the unit file with a UnitConfParser (sysv) | read the unit file with a UnitConfParser (sysv) | [
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""" read the unit file with a UnitConfParser (sysv) """
path = self.unit_sysv_file(module)
if not path: return None
assert self._loaded_file_sysv is not None
if path in self._loaded_file_sysv:
return self._loaded_file_sysv[path]
data = UnitConfParser()
data.read_sysv(path)
conf = SystemctlConf(data, module)
conf._root = self._root
self._loaded_file_sysv[path] = conf
return conf | [
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rhinstaller/anaconda | 63edc8680f1b05cbfe11bef28703acba808c5174 | pyanaconda/ui/gui/hubs/summary.py | python | SummaryHub._on_info_bar_clicked | (self, *args) | Call the callback to show a detailed message. | Call the callback to show a detailed message. | [
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"""Call the callback to show a detailed message."""
if self._show_details_callback:
self._show_details_callback() | [
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Stiivi/bubbles | b3c9332b8a9534655bd77821586e45a5086b1ddc | bubbles/objects.py | python | DataObject.records | (self) | Returns an iterator of records - dictionary-like objects that can
be acessed by field names. Default implementation returns
dictionaries, however other objects mith return their own structures.
For example the SQL object returns the same iterator as for rows, as
it can serve as key-value structure as well. | Returns an iterator of records - dictionary-like objects that can
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dictionaries, however other objects mith return their own structures.
For example the SQL object returns the same iterator as for rows, as
it can serve as key-value structure as well. | [
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For example the SQL object returns the same iterator as for rows, as
it can serve as key-value structure as well."""
names = [str(field) for field in fields]
for row in self.rows():
yield dict(zip(names, row)) | [
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miki725/django-url-filter | 1585da89acf9f182f8db2534ab8c72e89c5c92f2 | url_filter/utils.py | python | LookupConfig.name | (self) | return next(iter(self.data.keys())) | If the ``data`` is nested :class:`.LookupConfig`,
this gets its first lookup key. | If the ``data`` is nested :class:`.LookupConfig`,
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"""
If the ``data`` is nested :class:`.LookupConfig`,
this gets its first lookup key.
"""
return next(iter(self.data.keys())) | [
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zsdlove/Hades | f3d8c43a40ccd7a1bca2a855d8cccc110c34448a | cfg/instructions.py | python | InstructionsFinder.add | (self, blk) | 如果块不存在并且至少有一条指令，请将它添加到块列表中 | 如果块不存在并且至少有一条指令，请将它添加到块列表中 | [
"如果块不存在并且至少有一条指令，请将它添加到块列表中"
] | def add(self, blk):
'''
如果块不存在并且至少有一条指令，请将它添加到块列表中
'''
if (not (blk in self.ParsedResults)) and (len(blk.instructiones)>0):
self.ParsedResults.append(blk) | [
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openstack/cinder | 23494a6d6c51451688191e1847a458f1d3cdcaa5 | cinder/volume/drivers/hitachi/hbsd_rest_iscsi.py | python | HBSDRESTISCSI._set_target_portal | (self, port) | return True, ipv4_addr, tcp_port | Get port info and store it in an instance variable. | Get port info and store it in an instance variable. | [
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"""Get port info and store it in an instance variable."""
result = self.client.get_port(port)
ipv4_addr = result.get('ipv4Address')
tcp_port = result.get('tcpPort')
if not ipv4_addr or not tcp_port:
return False, ipv4_addr, tcp_port
self.storage_info['portals'][port] = '%(ip)s:%(port)s' % {
'ip': ipv4_addr,
'port': tcp_port,
}
return True, ipv4_addr, tcp_port | [
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littlecodersh/itchatmp | 6a8f01415d88874f179c9b50a063b2b338c309cf | itchatmp/views/crypto.py | python | encrypt_msg | (timestamp, nonce, signature, config, replyDict) | return construct_msg({
'FromUserName': replyDict['FromUserName'],
'ToUserName': replyDict['ToUserName'],
'MsgType': ENCRYPT,
'Encrypt': text.decode('utf8'),
'MsgSignature': hashlib.sha1(s).hexdigest(),
'TimeStamp': timestamp,
'Nonce': nonce,
}, ) | encrypt msg for sending to wechat
* use AES_CBC encryption
* return a string ready for sending
* as in construct_msg, string in replyDict should be unicode | encrypt msg for sending to wechat
* use AES_CBC encryption
* return a string ready for sending
* as in construct_msg, string in replyDict should be unicode | [
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''' encrypt msg for sending to wechat
* use AES_CBC encryption
* return a string ready for sending
* as in construct_msg, string in replyDict should be unicode
'''
text = construct_msg(replyDict).encode('utf8')
text = os.urandom(16) + struct.pack('>I', len(text)) +\
text + config.appId.encode('utf8')
paddingAmount = 32 - (len(text) % 32)
text += chr(paddingAmount).encode('utf8') * paddingAmount
text = aes_encode(config._encodingAesKey, text)
# Encrypt generated
s = [i.encode('utf8') for i in (timestamp, nonce, config.token)]
s += [text]; s.sort(); s = b''.join(s)
# Signature generated
return construct_msg({
'FromUserName': replyDict['FromUserName'],
'ToUserName': replyDict['ToUserName'],
'MsgType': ENCRYPT,
'Encrypt': text.decode('utf8'),
'MsgSignature': hashlib.sha1(s).hexdigest(),
'TimeStamp': timestamp,
'Nonce': nonce,
}, ) | [
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maxjiang93/ugscnn | 89cdd512e21a2d0cbb884e52ee75645c39ad6ed7 | experiments/exp3_2d3ds/model.py | python | Up.__init__ | (self, in_ch, out_ch, level, mesh_folder, bias=True) | use mesh_file for the mesh of one-level up | use mesh_file for the mesh of one-level up | [
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] | def __init__(self, in_ch, out_ch, level, mesh_folder, bias=True):
"""
use mesh_file for the mesh of one-level up
"""
super().__init__()
mesh_file = os.path.join(mesh_folder, "icosphere_{}.pkl".format(level))
half_in = int(in_ch/2)
self.up = MeshConv_transpose(half_in, half_in, mesh_file, stride=2)
self.conv = ResBlock(in_ch, out_ch, out_ch, level, False, mesh_folder) | [
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typeddjango/django-stubs | 7701957e4f5fac437b6b027a40e5aaeb26b87a5a | mypy_django_plugin/main.py | python | NewSemanalDjangoPlugin._get_current_model_bases | (self) | [] | def _get_current_model_bases(self) -> Dict[str, int]:
model_sym = self.lookup_fully_qualified(fullnames.MODEL_CLASS_FULLNAME)
if model_sym is not None and isinstance(model_sym.node, TypeInfo):
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mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | mesonbuild/compilers/compilers.py | python | Compiler.get_exelist | (self) | return self.exelist.copy() | [] | def get_exelist(self) -> T.List[str]:
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Azure/azure-cli | 6c1b085a0910c6c2139006fcbd8ade44006eb6dd | src/azure-cli/azure/cli/command_modules/serviceconnector/_validators.py | python | get_missing_connection_name | (namespace) | return missing_args | Get connection_name arg if user didn't provide it in command line | Get connection_name arg if user didn't provide it in command line | [
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missing_args = dict()
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missing_args['connection_name'] = {
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aiogram/aiogram | 4d2d81138681d730270819579f22b3a0001c43a5 | examples/webhook_example_old.py | python | cmd_id | (message: types.Message) | return SendMessage(message.chat.id, '\n'.join(result_msg), reply_to_message_id=message.message_id,
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Return info about user.
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chat = message.chat
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target = message.forward_from
chat = message.forward_from or message.chat
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target = message.from_user
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result_msg = [hbold('Info about user:'),
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result_msg.append(f"Last name: {target.last_name}")
if target.username:
result_msg.append(f"Username: {target.mention}")
result_msg.append(f"User ID: {target.id}")
result_msg.extend([hbold('Chat:'),
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result_msg.append(f"Title: {chat.title}")
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Blazemeter/taurus | 6e36b20397cf3e730e181cfebde0c8f19eb31fed | bzt/modules/blazemeter/blazemeter_reporter.py | python | BlazeMeterUploader._start_online | (self) | return self.results_url | Start online test | Start online test | [
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Xyntax/DirBrute | 84a54013f57a4588add9c2032c7c6c0902e6f504 | libs/requests/adapters.py | python | HTTPAdapter.init_poolmanager | (self, connections, maxsize, block=DEFAULT_POOLBLOCK, **pool_kwargs) | Initializes a urllib3 PoolManager.
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# save these values for pickling
self._pool_connections = connections
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/Werkzeug-0.8.3-py2.7.egg/werkzeug/datastructures.py | python | HeaderSet.clear | (self) | Clear the set. | Clear the set. | [
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leo-editor/leo-editor | 383d6776d135ef17d73d935a2f0ecb3ac0e99494 | leo/plugins/settings_finder.py | python | init | () | return True | Return True if the plugin has loaded successfully. | Return True if the plugin has loaded successfully. | [
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g.plugin_signon(__name__)
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tracim/tracim | a0e9746fde5a4c45b4e0f0bfa2caf9522b8c4e21 | backend/tracim_backend/views/core_api/call.py | python | CallController.update_incoming_call_state | (
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Update the state of a call.
"""
call_lib = CallLib(
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | tokumx/datadog_checks/tokumx/vendor/pymongo/monitoring.py | python | _EventListeners.enabled_for_server | (self) | return self.__enabled_for_server | Are any ServerListener instances registered? | Are any ServerListener instances registered? | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/nspawn.py | python | remove | (name, stop=False) | return True | Remove the named container
.. warning::
This function will remove all data associated with the container. It
will not, however, remove the btrfs subvolumes created by pulling
container images (:mod:`nspawn.pull_raw
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stop : False
If ``True``, the container will be destroyed even if it is
running/frozen.
CLI Examples:
.. code-block:: bash
salt '*' nspawn.remove foo
salt '*' nspawn.remove foo stop=True | Remove the named container | [
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] | def remove(name, stop=False):
"""
Remove the named container
.. warning::
This function will remove all data associated with the container. It
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container images (:mod:`nspawn.pull_raw
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stop : False
If ``True``, the container will be destroyed even if it is
running/frozen.
CLI Examples:
.. code-block:: bash
salt '*' nspawn.remove foo
salt '*' nspawn.remove foo stop=True
"""
if not stop and state(name) != "stopped":
raise CommandExecutionError("Container '{}' is not stopped".format(name))
def _failed_remove(name, exc):
raise CommandExecutionError(
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)
if _sd_version() >= 219:
ret = _machinectl("remove {}".format(name))
if ret["retcode"] != 0:
__context__["retcode"] = salt.defaults.exitcodes.EX_UNAVAILABLE
_failed_remove(name, ret["stderr"])
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nicolargo/glances | 00c65933ae1d0ebd3e72dc30fc3c215a83dfaae2 | glances/password.py | python | GlancesPassword.get_hash | (self, salt, plain_password) | return hashlib.sha256(salt.encode() + plain_password.encode()).hexdigest() | Return the hashed password, salt + SHA-256. | Return the hashed password, salt + SHA-256. | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/db/backends/base/base.py | python | BaseDatabaseWrapper.schema_editor | (self, *args, **kwargs) | return self.SchemaEditorClass(self, *args, **kwargs) | Returns a new instance of this backend's SchemaEditor. | Returns a new instance of this backend's SchemaEditor. | [
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phaethon/kamene | bf679a65d456411942ee4a907818ba3d6a183bfe | kamene/arch/windows/__init__.py | python | NetworkInterfaceDict.pcap_name | (self, devname) | Return pcap device name for given Windows device name. | Return pcap device name for given Windows device name. | [
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F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idc.py | python | GetFlags | (ea) | return idaapi.getFlags(ea) | Get internal flags
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mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | mesonbuild/mesonlib/universal.py | python | do_define | (regex: T.Pattern[str], line: str, confdata: 'ConfigurationData', variable_format: str) | [] | def do_define(regex: T.Pattern[str], line: str, confdata: 'ConfigurationData', variable_format: str) -> str:
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result = v
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result = get_cmake_define(line, confdata)
result = f'#define {varname} {result}\n'
(result, missing_variable) = do_replacement(regex, result, variable_format, confdata)
return result
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | pypy/module/_pypyjson/interp_decoder.py | python | JSONMap.should_cache_strings | (self) | return not (self.decoded_strings > JSONDecoder.STRING_CACHE_EVALUATION_SIZE and
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""" return whether strings parsed in the context of this map should be
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# we should cache if either we've seen few strings so far (less than
# STRING_CACHE_EVALUATION_SIZE), or if we've seen many, and the cache
# hit rate has been high enough
return not (self.decoded_strings > JSONDecoder.STRING_CACHE_EVALUATION_SIZE and
self.cache_hits * JSONDecoder.STRING_CACHE_USEFULNESS_FACTOR < self.decoded_strings) | [
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fonttools/fonttools | 892322aaff6a89bea5927379ec06bc0da3dfb7df | Lib/fontTools/ttLib/tables/otConverters.py | python | UInt8.read | (self, reader, font, tableDict) | return reader.readUInt8() | [] | def read(self, reader, font, tableDict):
return reader.readUInt8() | [
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cea-sec/miasm | 09376c524aedc7920a7eda304d6095e12f6958f4 | miasm/core/types.py | python | Ptr.__init__ | (self, fmt, dst_type, *type_args, **type_kwargs) | @fmt: (str) Num compatible format that will be the Ptr representation
in memory
@dst_type: (MemType or Type) the Type this Ptr points to.
If a Type is given, it is transformed into a MemType with
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*type_args, **type_kwargs: arguments to pass to the the pointed
MemType when instantiating it (e.g. for MemStr encoding or
MemArray field_type). | [] | def __init__(self, fmt, dst_type, *type_args, **type_kwargs):
"""
@fmt: (str) Num compatible format that will be the Ptr representation
in memory
@dst_type: (MemType or Type) the Type this Ptr points to.
If a Type is given, it is transformed into a MemType with
TheType.lval.
*type_args, **type_kwargs: arguments to pass to the the pointed
MemType when instantiating it (e.g. for MemStr encoding or
MemArray field_type).
"""
if (not isinstance(dst_type, Type) and
not (isinstance(dst_type, type) and
issubclass(dst_type, MemType)) and
not dst_type == MemSelf):
raise ValueError("dst_type of Ptr must be a MemType type, a "
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"name.")
super(Ptr, self).__init__(fmt)
if isinstance(dst_type, Type):
# Patch the field to propagate the MemSelf replacement
dst_type._get_self_type = lambda: self._get_self_type()
# dst_type cannot be patched here, since _get_self_type of the outer
# class has not yet been set. Patching dst_type involves calling
# dst_type.lval, which will only return a type that does not point
# on MemSelf but on the right class only when _get_self_type of the
# outer class has been replaced by _MetaMemStruct.
# In short, dst_type = dst_type.lval is not valid here, it is done
# lazily in _fix_dst_type
self._dst_type = dst_type
self._type_args = type_args
self._type_kwargs = type_kwargs | [
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pyg-team/pytorch_geometric | b920e9a3a64e22c8356be55301c88444ff051cae | torch_geometric/datasets/amazon_products.py | python | AmazonProducts.process | (self) | [] | def process(self):
f = np.load(osp.join(self.raw_dir, 'adj_full.npz'))
adj = sp.csr_matrix((f['data'], f['indices'], f['indptr']), f['shape'])
adj = adj.tocoo()
row = torch.from_numpy(adj.row).to(torch.long)
col = torch.from_numpy(adj.col).to(torch.long)
edge_index = torch.stack([row, col], dim=0)
x = np.load(osp.join(self.raw_dir, 'feats.npy'))
x = torch.from_numpy(x).to(torch.float)
ys = [-1] * x.size(0)
with open(osp.join(self.raw_dir, 'class_map.json')) as f:
class_map = json.load(f)
for key, item in class_map.items():
ys[int(key)] = item
y = torch.tensor(ys)
with open(osp.join(self.raw_dir, 'role.json')) as f:
role = json.load(f)
train_mask = torch.zeros(x.size(0), dtype=torch.bool)
train_mask[torch.tensor(role['tr'])] = True
val_mask = torch.zeros(x.size(0), dtype=torch.bool)
val_mask[torch.tensor(role['va'])] = True
test_mask = torch.zeros(x.size(0), dtype=torch.bool)
test_mask[torch.tensor(role['te'])] = True
data = Data(x=x, edge_index=edge_index, y=y, train_mask=train_mask,
val_mask=val_mask, test_mask=test_mask)
data = data if self.pre_transform is None else self.pre_transform(data)
torch.save(self.collate([data]), self.processed_paths[0]) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/db/backends/base/base.py | python | BaseDatabaseWrapper.create_cursor | (self) | Creates a cursor. Assumes that a connection is established. | Creates a cursor. Assumes that a connection is established. | [
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luyishisi/tensorflow | 448e72bfb64f826aff8672d74fd7e59c0112e924 | 4.Object_Detection/object_detection/core/keypoint_ops.py | python | change_coordinate_frame | (keypoints, window, scope=None) | Changes coordinate frame of the keypoints to be relative to window's frame.
Given a window of the form [y_min, x_min, y_max, x_max], changes keypoint
coordinates from keypoints of shape [num_instances, num_keypoints, 2]
to be relative to this window.
An example use case is data augmentation: where we are given groundtruth
keypoints and would like to randomly crop the image to some window. In this
case we need to change the coordinate frame of each groundtruth keypoint to be
relative to this new window.
Args:
keypoints: a tensor of shape [num_instances, num_keypoints, 2]
window: a tensor of shape [4] representing the [y_min, x_min, y_max, x_max]
window we should change the coordinate frame to.
scope: name scope.
Returns:
new_keypoints: a tensor of shape [num_instances, num_keypoints, 2] | Changes coordinate frame of the keypoints to be relative to window's frame. | [
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"""Changes coordinate frame of the keypoints to be relative to window's frame.
Given a window of the form [y_min, x_min, y_max, x_max], changes keypoint
coordinates from keypoints of shape [num_instances, num_keypoints, 2]
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keypoints: a tensor of shape [num_instances, num_keypoints, 2]
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scope: name scope.
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new_keypoints: a tensor of shape [num_instances, num_keypoints, 2]
"""
with tf.name_scope(scope, 'ChangeCoordinateFrame'):
win_height = window[2] - window[0]
win_width = window[3] - window[1]
new_keypoints = scale(keypoints - [window[0], window[1]], 1.0 / win_height,
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return new_keypoints | [
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wbond/package_control | cfaaeb57612023e3679ecb7f8cd7ceac9f57990d | package_control/deps/asn1crypto/util.py | python | extended_datetime.tzinfo | (self) | return self._y2k.tzinfo | :return:
If object is timezone aware, a datetime.tzinfo object, else None. | :return:
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internetarchive/warcprox | c027659001f76518b89055cea0d0a2d754f456d1 | warcprox/dedup.py | python | DedupDb.notify | (self, recorded_url, records) | [] | def notify(self, recorded_url, records):
if (records and records[0].type == b'response'
and self.should_dedup(recorded_url)):
digest_key = warcprox.digest_str(
recorded_url.payload_digest, self.options.base32)
if recorded_url.warcprox_meta and "dedup-buckets" in recorded_url.warcprox_meta:
for bucket, bucket_mode in recorded_url.warcprox_meta["dedup-buckets"].items():
if not bucket_mode == "ro":
self.save(
digest_key, records[0],
bucket=bucket)
else:
self.save(digest_key, records[0]) | [
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HymanLiuTS/flaskTs | 286648286976e85d9b9a5873632331efcafe0b21 | flasky/lib/python2.7/site-packages/pip/utils/__init__.py | python | dist_in_site_packages | (dist) | return normalize_path(
dist_location(dist)
).startswith(normalize_path(site_packages)) | Return True if given Distribution is installed in
distutils.sysconfig.get_python_lib(). | Return True if given Distribution is installed in
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"""
Return True if given Distribution is installed in
distutils.sysconfig.get_python_lib().
"""
return normalize_path(
dist_location(dist)
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Drakkar-Software/OctoBot | c80ed2270e5d085994213955c0f56b9e3b70b476 | octobot/logger.py | python | init_evaluator_chan_logger | (matrix_id: str) | [] | async def init_evaluator_chan_logger(matrix_id: str):
await evaluator_channels.get_chan(channels_name.OctoBotEvaluatorsChannelsName.MATRIX_CHANNEL.value,
matrix_id).new_consumer(
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openstack/openstacksdk | 58384268487fa854f21c470b101641ab382c9897 | openstack/network/v2/_proxy.py | python | Proxy.delete_conntrack_helper | (self, conntrack_helper, router,
ignore_missing=True) | Delete a L3 conntrack_helper
:param conntrack_helper: The value can be the ID of a L3 conntrack
helper or a
:class:`~openstack.network.v2.l3_conntrack_helper.ConntrackHelper`,
instance.
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:param bool ignore_missing: When set to ``False``
:class:`~openstack.exceptions.ResourceNotFound` will be
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When set to ``True``, no exception will be set when
attempting to delete a nonexistent ip.
:returns: ``None`` | Delete a L3 conntrack_helper | [
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ignore_missing=True):
"""Delete a L3 conntrack_helper
:param conntrack_helper: The value can be the ID of a L3 conntrack
helper or a
:class:`~openstack.network.v2.l3_conntrack_helper.ConntrackHelper`,
instance.
:param router: The value can be the ID of a Router or a
:class:`~openstack.network.v2.router.Router` instance.
:param bool ignore_missing: When set to ``False``
:class:`~openstack.exceptions.ResourceNotFound` will be
raised when the floating ip does not exist.
When set to ``True``, no exception will be set when
attempting to delete a nonexistent ip.
:returns: ``None``
"""
router = self._get_resource(_router.Router, router)
self._delete(_l3_conntrack_helper.ConntrackHelper,
conntrack_helper, router_id=router.id,
ignore_missing=ignore_missing) | [
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wxWidgets/Phoenix | b2199e299a6ca6d866aa6f3d0888499136ead9d6 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListCtrl.SetItemState | (self, item, state, stateMask) | return True | Sets the item state flags for the input item.
:param `item`: the index of the item; if defaulted to -1, the state flag
will be set for all the items;
:param `state`: any combination of the following bits:
============================ ========= ==============================
State Bits Hex Value Description
============================ ========= ==============================
``ULC_STATE_DONTCARE`` 0x0 Don't care what the state is
``ULC_STATE_DROPHILITED`` 0x1 The item is highlighted to receive a drop event
``ULC_STATE_FOCUSED`` 0x2 The item has the focus
``ULC_STATE_SELECTED`` 0x4 The item is selected
``ULC_STATE_CUT`` 0x8 The item is in the cut state
``ULC_STATE_DISABLED`` 0x10 The item is disabled
``ULC_STATE_FILTERED`` 0x20 The item has been filtered
``ULC_STATE_INUSE`` 0x40 The item is in use
``ULC_STATE_PICKED`` 0x80 The item has been picked
``ULC_STATE_SOURCE`` 0x100 The item is a drag and drop source
============================ ========= ==============================
:param `stateMask`: the bitmask for the state flag. | Sets the item state flags for the input item. | [
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"""
Sets the item state flags for the input item.
:param `item`: the index of the item; if defaulted to -1, the state flag
will be set for all the items;
:param `state`: any combination of the following bits:
============================ ========= ==============================
State Bits Hex Value Description
============================ ========= ==============================
``ULC_STATE_DONTCARE`` 0x0 Don't care what the state is
``ULC_STATE_DROPHILITED`` 0x1 The item is highlighted to receive a drop event
``ULC_STATE_FOCUSED`` 0x2 The item has the focus
``ULC_STATE_SELECTED`` 0x4 The item is selected
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``ULC_STATE_DISABLED`` 0x10 The item is disabled
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``ULC_STATE_INUSE`` 0x40 The item is in use
``ULC_STATE_PICKED`` 0x80 The item has been picked
``ULC_STATE_SOURCE`` 0x100 The item is a drag and drop source
============================ ========= ==============================
:param `stateMask`: the bitmask for the state flag.
"""
self._mainWin.SetItemState(item, state, stateMask)
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sethmlarson/virtualbox-python | 984a6e2cb0e8996f4df40f4444c1528849f1c70d | virtualbox/library.py | python | IHostDrivePartition.end_cylinder | (self) | return ret | Get int value for 'endCylinder'
The cylinder (0..1023) of the last sector (inclusive) in the partition on an MBR disk, zero for not an MBR disk. | Get int value for 'endCylinder'
The cylinder (0..1023) of the last sector (inclusive) in the partition on an MBR disk, zero for not an MBR disk. | [
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"""Get int value for 'endCylinder'
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"""
ret = self._get_attr("endCylinder")
return ret | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | examples/research_projects/longform-qa/eli5_app.py | python | load_indexes | () | return (wiki40b_passages, wiki40b_gpu_index_flat, es_client) | [] | def load_indexes():
if LOAD_DENSE_INDEX:
faiss_res = faiss.StandardGpuResources()
wiki40b_passages = datasets.load_dataset(path="wiki_snippets", name="wiki40b_en_100_0")["train"]
wiki40b_passage_reps = np.memmap(
"wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat",
dtype="float32",
mode="r",
shape=(wiki40b_passages.num_rows, 128),
)
wiki40b_index_flat = faiss.IndexFlatIP(128)
wiki40b_gpu_index_flat = faiss.index_cpu_to_gpu(faiss_res, 1, wiki40b_index_flat)
wiki40b_gpu_index_flat.add(wiki40b_passage_reps) # TODO fix for larger GPU
else:
wiki40b_passages, wiki40b_gpu_index_flat = (None, None)
es_client = Elasticsearch([{"host": "localhost", "port": "9200"}])
return (wiki40b_passages, wiki40b_gpu_index_flat, es_client) | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit /scripts/sshbackdoors/rpyc/utils/teleportation.py | python | decode_codeobj | (codeobj) | [] | def decode_codeobj(codeobj):
# adapted from dis.dis
extended_arg = 0
if is_py3k:
codestr = codeobj.co_code
else:
codestr = [ord(ch) for ch in codeobj.co_code]
free = None
i = 0
while i < len(codestr):
op = codestr[i]
opname = opcode.opname[op]
i += 1
argval = None
if op >= opcode.HAVE_ARGUMENT:
oparg = codestr[i] + codestr[i + 1] * 256 + extended_arg
i += 2
extended_arg = 0
if op == opcode.EXTENDED_ARG:
extended_arg = oparg * 65536
continue
if op in opcode.hasconst:
argval = codeobj.co_consts[oparg]
elif op in opcode.hasname:
argval = codeobj.co_names[oparg]
elif op in opcode.hasjrel:
argval = i + oparg
elif op in opcode.haslocal:
argval = codeobj.co_varnames[oparg]
elif op in opcode.hascompare:
argval = opcode.cmp_op[oparg]
elif op in opcode.hasfree:
if free is None:
free = codeobj.co_cellvars + codeobj.co_freevars
argval = free[oparg]
yield (opname, argval) | [
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leancloud/satori | 701caccbd4fe45765001ca60435c0cb499477c03 | satori-rules/plugin/libs/requests/cookies.py | python | RequestsCookieJar.keys | (self) | return list(self.iterkeys()) | Dict-like keys() that returns a list of names of cookies from the
jar. See values() and items(). | Dict-like keys() that returns a list of names of cookies from the
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confluentinc/confluent-kafka-python | 2ac0d72b24b14e5246445ad9ce66ec9c8828ef4e | examples/asyncio_example.py | python | Producer.produce | (self, topic, value, on_delivery=None) | [] | def produce(self, topic, value, on_delivery=None):
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runawayhorse001/LearningApacheSpark | 67f3879dce17553195f094f5728b94a01badcf24 | pyspark/mllib/util.py | python | MLUtils.loadLibSVMFile | (sc, path, numFeatures=-1, minPartitions=None, multiclass=None) | return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2]))) | Loads labeled data in the LIBSVM format into an RDD of
LabeledPoint. The LIBSVM format is a text-based format used by
LIBSVM and LIBLINEAR. Each line represents a labeled sparse
feature vector using the following format:
label index1:value1 index2:value2 ...
where the indices are one-based and in ascending order. This
method parses each line into a LabeledPoint, where the feature
indices are converted to zero-based.
:param sc: Spark context
:param path: file or directory path in any Hadoop-supported file
system URI
:param numFeatures: number of features, which will be determined
from the input data if a nonpositive value
is given. This is useful when the dataset is
already split into multiple files and you
want to load them separately, because some
features may not present in certain files,
which leads to inconsistent feature
dimensions.
:param minPartitions: min number of partitions
@return: labeled data stored as an RDD of LabeledPoint
>>> from tempfile import NamedTemporaryFile
>>> from pyspark.mllib.util import MLUtils
>>> from pyspark.mllib.regression import LabeledPoint
>>> tempFile = NamedTemporaryFile(delete=True)
>>> _ = tempFile.write(b"+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0")
>>> tempFile.flush()
>>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect()
>>> tempFile.close()
>>> examples[0]
LabeledPoint(1.0, (6,[0,2,4],[1.0,2.0,3.0]))
>>> examples[1]
LabeledPoint(-1.0, (6,[],[]))
>>> examples[2]
LabeledPoint(-1.0, (6,[1,3,5],[4.0,5.0,6.0])) | Loads labeled data in the LIBSVM format into an RDD of
LabeledPoint. The LIBSVM format is a text-based format used by
LIBSVM and LIBLINEAR. Each line represents a labeled sparse
feature vector using the following format: | [
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"""
Loads labeled data in the LIBSVM format into an RDD of
LabeledPoint. The LIBSVM format is a text-based format used by
LIBSVM and LIBLINEAR. Each line represents a labeled sparse
feature vector using the following format:
label index1:value1 index2:value2 ...
where the indices are one-based and in ascending order. This
method parses each line into a LabeledPoint, where the feature
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:param sc: Spark context
:param path: file or directory path in any Hadoop-supported file
system URI
:param numFeatures: number of features, which will be determined
from the input data if a nonpositive value
is given. This is useful when the dataset is
already split into multiple files and you
want to load them separately, because some
features may not present in certain files,
which leads to inconsistent feature
dimensions.
:param minPartitions: min number of partitions
@return: labeled data stored as an RDD of LabeledPoint
>>> from tempfile import NamedTemporaryFile
>>> from pyspark.mllib.util import MLUtils
>>> from pyspark.mllib.regression import LabeledPoint
>>> tempFile = NamedTemporaryFile(delete=True)
>>> _ = tempFile.write(b"+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0")
>>> tempFile.flush()
>>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect()
>>> tempFile.close()
>>> examples[0]
LabeledPoint(1.0, (6,[0,2,4],[1.0,2.0,3.0]))
>>> examples[1]
LabeledPoint(-1.0, (6,[],[]))
>>> examples[2]
LabeledPoint(-1.0, (6,[1,3,5],[4.0,5.0,6.0]))
"""
from pyspark.mllib.regression import LabeledPoint
if multiclass is not None:
warnings.warn("deprecated", DeprecationWarning)
lines = sc.textFile(path, minPartitions)
parsed = lines.map(lambda l: MLUtils._parse_libsvm_line(l))
if numFeatures <= 0:
parsed.cache()
numFeatures = parsed.map(lambda x: -1 if x[1].size == 0 else x[1][-1]).reduce(max) + 1
return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2]))) | [
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silverapp/silver | a59dbc7216733ab49dca2fae525d229bdba04420 | silver/currencies.py | python | DummyConverter.convert | (self, amount, from_currency, to_currency, date) | return amount | [] | def convert(self, amount, from_currency, to_currency, date):
if from_currency != to_currency:
raise RateNotFound(from_currency, to_currency, date)
return amount | [
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maozezhong/TIANCHI_XUELANG_AI | cfec006032e264cc77a56776b0ed237e6d07fe48 | code/main.py | python | mycrossentropy | (e = 0.1,nb_classes=2) | return mycrossentropy_fixed | https://spaces.ac.cn/archives/4493 | https://spaces.ac.cn/archives/4493 | [
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'''
https://spaces.ac.cn/archives/4493
'''
def mycrossentropy_fixed(y_true, y_pred):
return (1-e)*K.categorical_crossentropy(y_true,y_pred) + e*K.categorical_crossentropy(K.ones_like(y_pred)/nb_classes, y_pred)
return mycrossentropy_fixed | [
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dmlc/dgl | 8d14a739bc9e446d6c92ef83eafe5782398118de | python/dgl/backend/backend.py | python | ndim | (input) | Return the number of dimensions of the tensor.
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----------
input : Tensor
The input tensor.
Returns
-------
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The number of dimensions | Return the number of dimensions of the tensor. | [
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"""Return the number of dimensions of the tensor.
Parameters
----------
input : Tensor
The input tensor.
Returns
-------
int
The number of dimensions
"""
pass | [
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naftaliharris/tauthon | 5587ceec329b75f7caf6d65a036db61ac1bae214 | Lib/lib2to3/pgen2/tokenize.py | python | untokenize | (iterable) | return ut.untokenize(iterable) | Transform tokens back into Python source code.
Each element returned by the iterable must be a token sequence
with at least two elements, a token number and token value. If
only two tokens are passed, the resulting output is poor.
Round-trip invariant for full input:
Untokenized source will match input source exactly
Round-trip invariant for limited intput:
# Output text will tokenize the back to the input
t1 = [tok[:2] for tok in generate_tokens(f.readline)]
newcode = untokenize(t1)
readline = iter(newcode.splitlines(1)).next
t2 = [tok[:2] for tokin generate_tokens(readline)]
assert t1 == t2 | Transform tokens back into Python source code. | [
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"""Transform tokens back into Python source code.
Each element returned by the iterable must be a token sequence
with at least two elements, a token number and token value. If
only two tokens are passed, the resulting output is poor.
Round-trip invariant for full input:
Untokenized source will match input source exactly
Round-trip invariant for limited intput:
# Output text will tokenize the back to the input
t1 = [tok[:2] for tok in generate_tokens(f.readline)]
newcode = untokenize(t1)
readline = iter(newcode.splitlines(1)).next
t2 = [tok[:2] for tokin generate_tokens(readline)]
assert t1 == t2
"""
ut = Untokenizer()
return ut.untokenize(iterable) | [
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TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | 5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e | deep-learning/Deep-Reinforcement-Learning-Complete-Collection/DeepRL-Code/chapter10/mountain_car.py | python | tiles | (iht_or_size, num_tilings, floats, ints=None, read_only=False) | return tiles | returns num-tilings tile indices corresponding to the floats and ints | returns num-tilings tile indices corresponding to the floats and ints | [
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"""returns num-tilings tile indices corresponding to the floats and ints"""
if ints is None:
ints = []
qfloats = [floor(f * num_tilings) for f in floats]
tiles = []
for tiling in range(num_tilings):
tilingX2 = tiling * 2
coords = [tiling]
b = tiling
for q in qfloats:
coords.append((q + b) // num_tilings)
b += tilingX2
coords.extend(ints)
tiles.append(hash_coords(coords, iht_or_size, read_only))
return tiles | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/settings/views.py | python | EnableMobilePrivilegesView.dispatch | (self, request, *args, **kwargs) | return not_found(request) | [] | def dispatch(self, request, *args, **kwargs):
# raises a 404 if a user tries to access this page without the right authorizations
if self.is_user_authorized(request.couch_user):
return super(BaseMyAccountView, self).dispatch(request, *args, **kwargs)
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python-hyper/h2 | 53feb0e0d8ddd28fa2dbd534d519a8eefd441f14 | examples/curio/curio-server.py | python | H2Server.send_file | (self, file_path, stream_id) | Send a file, obeying the rules of HTTP/2 flow control. | Send a file, obeying the rules of HTTP/2 flow control. | [
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"""
filesize = os.stat(file_path).st_size
content_type, content_encoding = mimetypes.guess_type(file_path)
response_headers = [
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if content_type:
response_headers.append(('content-type', content_type))
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response_headers.append(('content-encoding', content_encoding))
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google/pytype | fa43edc95dd42ade6e3147d6580d63e778c9d506 | pytype/tools/merge_pyi/merge_pyi.py | python | Pyi._get_imports | (self, inserted_types) | Get the imports that provide the given types. | Get the imports that provide the given types. | [
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geaxgx/tello-openpose | a76c5e2dcd7d444207e79962e93fc1b4956442aa | CameraMorse.py | python | RollingGraph.__init__ | (self, window_name="Graph", width=640, height=250, step_width=5, y_min=0, y_max=255, colors=[(0,0,255)], thickness=[2], threshold=None, waitKey=True) | width, height: width and height in pixels of the OpenCv window in which the graph is draw
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colors : array of the colors used to draw the variables
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y_min, y_max : min and max of the variables
colors : array of the colors used to draw the variables
thickness: array of the thickness of the variable curves
waitKey : boolean. In OpenCv, to display a window, we must call cv2.waitKey(). This call can be done by RollingGraph (if True) or by the program who calls RollingGraph (if False) | [
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"consecutiv... | def __init__(self, window_name="Graph", width=640, height=250, step_width=5, y_min=0, y_max=255, colors=[(0,0,255)], thickness=[2], threshold=None, waitKey=True):
"""
width, height: width and height in pixels of the OpenCv window in which the graph is draw
step_width: width in pixels on the x-axis between each 2 consecutive points
y_min, y_max : min and max of the variables
colors : array of the colors used to draw the variables
thickness: array of the thickness of the variable curves
waitKey : boolean. In OpenCv, to display a window, we must call cv2.waitKey(). This call can be done by RollingGraph (if True) or by the program who calls RollingGraph (if False)
"""
self.window_name = window_name
self.width = width
self.height = height
self.step_width = step_width
self.y_min = y_min
self.y_max = y_max
self.waitKey = waitKey
assert len(colors) == len(thickness)
self.colors = colors
self.thickness = thickness
self.iter = 0
self.canvas = np.zeros((height,width,3),dtype=np.uint8)
self.nb_values = len(colors)
self.threshold = threshold | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/reportlab/lib/colors.py | python | CMYKColor.cmyk | (self) | return (self.cyan, self.magenta, self.yellow, self.black) | Returns a tuple of four color components - syntactic sugar | Returns a tuple of four color components - syntactic sugar | [
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] | def cmyk(self):
"Returns a tuple of four color components - syntactic sugar"
return (self.cyan, self.magenta, self.yellow, self.black) | [
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | tokumx/datadog_checks/tokumx/vendor/pymongo/monitoring.py | python | _EventListeners.publish_topology_closed | (self, topology_id) | Publish a TopologyClosedEvent to all topology listeners.
:Parameters:
- `topology_id`: A unique identifier for the topology this server
is a part of. | Publish a TopologyClosedEvent to all topology listeners. | [
"Publish",
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"TopologyClosedEvent",
"to",
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"topology",
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] | def publish_topology_closed(self, topology_id):
"""Publish a TopologyClosedEvent to all topology listeners.
:Parameters:
- `topology_id`: A unique identifier for the topology this server
is a part of.
"""
event = TopologyClosedEvent(topology_id)
for subscriber in self.__topology_listeners:
try:
subscriber.closed(event)
except Exception:
_handle_exception() | [
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microsoft/hummingbird | 2705c42585ac1d3ee69e65a93e5f59d7d42f44d5 | hummingbird/ml/_parse.py | python | _parse_sklearn_stacking | (topology, model, inputs) | return var_out | Taken from https://github.com/onnx/sklearn-onnx/blob/9939c089a467676f4ffe9f3cb91098c4841f89d8/skl2onnx/_parse.py#L238.
:param topology: Topology object
:param model: A *scikit-learn* *Stacking* object
:param inputs: A list of Variable objects
:return: A list of output variables produced by column transformer | Taken from https://github.com/onnx/sklearn-onnx/blob/9939c089a467676f4ffe9f3cb91098c4841f89d8/skl2onnx/_parse.py#L238.
:param topology: Topology object
:param model: A *scikit-learn* *Stacking* object
:param inputs: A list of Variable objects
:return: A list of output variables produced by column transformer | [
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"""
Taken from https://github.com/onnx/sklearn-onnx/blob/9939c089a467676f4ffe9f3cb91098c4841f89d8/skl2onnx/_parse.py#L238.
:param topology: Topology object
:param model: A *scikit-learn* *Stacking* object
:param inputs: A list of Variable objects
:return: A list of output variables produced by column transformer
"""
# Output variable name of each estimator. It's a list of variables.
transformed_result_names = []
# Encode each estimator as our IR object.
for op, method in zip(model.estimators_, model.stack_method_):
var_out = _parse_sklearn_api(topology, op, inputs)
if method not in ["predict_proba", "predict"]:
raise ValueError(
"Ensemble method {} not supported. Please fill an issue at https://github.com/microsoft/hummingbird.".format(
method
)
)
index = 0
if method == "predict_proba":
index = 1
array_feature_extractor_operator = topology.declare_logical_operator("SklearnArrayFeatureExtractor")
array_feature_extractor_operator.inputs = var_out
array_feature_extractor_operator.column_indices = [index]
output_variable_name = topology.declare_logical_variable("extracted_feature_columns", var_out[0].type)
array_feature_extractor_operator.outputs.append(output_variable_name)
transformed_result_names.append(output_variable_name)
if model.passthrough:
transformed_result_names.extend(inputs)
if len(transformed_result_names) > 1:
concat_operator = topology.declare_logical_operator("SklearnConcat")
concat_operator.inputs = transformed_result_names
# Declare output name of scikit-learn ColumnTransformer
transformed_column_name = topology.declare_logical_variable("transformed_column")
concat_operator.outputs.append(transformed_column_name)
transformed_result_names = [transformed_column_name]
op = model.final_estimator_
var_out = _parse_sklearn_api(topology, op, transformed_result_names)
return var_out | [
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... | https://github.com/microsoft/hummingbird/blob/2705c42585ac1d3ee69e65a93e5f59d7d42f44d5/hummingbird/ml/_parse.py#L476-L519 |
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