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pywren/pywren | d898af6418a2d915c3984152de07d95e32c9789a | pywren/storage/s3_backend.py | python | S3Backend.put_object | (self, key, data) | Put an object in S3. Override the object if the key already exists.
:param key: key of the object.
:param data: data of the object
:type data: str/bytes
:return: None | Put an object in S3. Override the object if the key already exists.
:param key: key of the object.
:param data: data of the object
:type data: str/bytes
:return: None | [
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"""
Put an object in S3. Override the object if the key already exists.
:param key: key of the object.
:param data: data of the object
:type data: str/bytes
:return: None
"""
self.s3client.put_object(Bucket=self.s3_bucket, Key=key, Body=data) | [
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shamangary/FSA-Net | 4361d0e48103bb215d15734220c9d17e6812bb48 | lib/FSANET_model.py | python | BaseFSANet.ssr_S_model_build | (self, num_primcaps, m_dim) | return Model(inputs=[input_s1_preS, input_s2_preS, input_s3_preS],outputs=primcaps, name='ssr_S_model') | [] | def ssr_S_model_build(self, num_primcaps, m_dim):
input_s1_preS = Input((self.map_xy_size,self.map_xy_size,64))
input_s2_preS = Input((self.map_xy_size,self.map_xy_size,64))
input_s3_preS = Input((self.map_xy_size,self.map_xy_size,64))
feat_S_model = self.ssr_feat_S_model_build(m_dim)
SR_matrix_s1,feat_s1_preS = feat_S_model(input_s1_preS)
SR_matrix_s2,feat_s2_preS = feat_S_model(input_s2_preS)
SR_matrix_s3,feat_s3_preS = feat_S_model(input_s3_preS)
feat_pre_concat = Concatenate()([feat_s1_preS,feat_s2_preS,feat_s3_preS])
SL_matrix = Dense(int(num_primcaps/3)*m_dim,activation='sigmoid')(feat_pre_concat)
SL_matrix = Reshape((int(num_primcaps/3),m_dim))(SL_matrix)
S_matrix_s1 = MatrixMultiplyLayer(name="S_matrix_s1")([SL_matrix,SR_matrix_s1])
S_matrix_s2 = MatrixMultiplyLayer(name='S_matrix_s2')([SL_matrix,SR_matrix_s2])
S_matrix_s3 = MatrixMultiplyLayer(name='S_matrix_s3')([SL_matrix,SR_matrix_s3])
# Very important!!! Without this training won't converge.
# norm_S_s1 = Lambda(lambda x: K.tile(K.sum(x,axis=-1,keepdims=True),(1,1,64)))(S_matrix_s1)
norm_S_s1 = MatrixNormLayer(tile_count=64)(S_matrix_s1)
norm_S_s2 = MatrixNormLayer(tile_count=64)(S_matrix_s2)
norm_S_s3 = MatrixNormLayer(tile_count=64)(S_matrix_s3)
feat_s1_pre = Reshape((self.map_xy_size*self.map_xy_size,64))(input_s1_preS)
feat_s2_pre = Reshape((self.map_xy_size*self.map_xy_size,64))(input_s2_preS)
feat_s3_pre = Reshape((self.map_xy_size*self.map_xy_size,64))(input_s3_preS)
feat_pre_concat = Concatenate(axis=1)([feat_s1_pre, feat_s2_pre, feat_s3_pre])
# Warining: don't use keras's 'K.dot'. It is very weird when high dimension is used.
# https://github.com/keras-team/keras/issues/9779
# Make sure 'tf.matmul' is used
# primcaps = Lambda(lambda x: tf.matmul(x[0],x[1])/x[2])([S_matrix,feat_pre_concat, norm_S])
primcaps_s1 = PrimCapsLayer()([S_matrix_s1,feat_pre_concat, norm_S_s1])
primcaps_s2 = PrimCapsLayer()([S_matrix_s2,feat_pre_concat, norm_S_s2])
primcaps_s3 = PrimCapsLayer()([S_matrix_s3,feat_pre_concat, norm_S_s3])
primcaps = Concatenate(axis=1)([primcaps_s1,primcaps_s2,primcaps_s3])
return Model(inputs=[input_s1_preS, input_s2_preS, input_s3_preS],outputs=primcaps, name='ssr_S_model') | [
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statsmodels/statsmodels | debbe7ea6ba28fe5bdb78f09f8cac694bef98722 | statsmodels/tsa/ardl/model.py | python | UECM.from_ardl | (
cls, ardl: ARDL, missing: Literal["none", "drop", "raise"] = "none"
) | return cls(
ardl.data.orig_endog,
ar_lags,
ardl.data.orig_exog,
uecm_lags,
trend=ardl.trend,
fixed=ardl.fixed,
seasonal=ardl.seasonal,
hold_back=ardl.hold_back,
period=ardl.period,
causal=ardl.causal,
missing=missing,
deterministic=ardl.deterministic,
) | Construct a UECM from an ARDL model
Parameters
----------
ardl : ARDL
The ARDL model instance
missing : {"none", "drop", "raise"}, default "none"
How to treat missing observations.
Returns
-------
UECM
The UECM model instance
Notes
-----
The lag requirements for a UECM are stricter than for an ARDL.
Any variable that is included in the UECM must have a lag length
of at least 1. Additionally, the included lags must be contiguous
starting at 0 if non-causal or 1 if causal. | Construct a UECM from an ARDL model | [
"Construct",
"a",
"UECM",
"from",
"an",
"ARDL",
"model"
] | def from_ardl(
cls, ardl: ARDL, missing: Literal["none", "drop", "raise"] = "none"
):
"""
Construct a UECM from an ARDL model
Parameters
----------
ardl : ARDL
The ARDL model instance
missing : {"none", "drop", "raise"}, default "none"
How to treat missing observations.
Returns
-------
UECM
The UECM model instance
Notes
-----
The lag requirements for a UECM are stricter than for an ARDL.
Any variable that is included in the UECM must have a lag length
of at least 1. Additionally, the included lags must be contiguous
starting at 0 if non-causal or 1 if causal.
"""
err = (
"UECM can only be created from ARDL models that include all "
"{var_typ} lags up to the maximum lag in the model."
)
uecm_lags = {}
dl_lags = ardl.dl_lags
for key, val in dl_lags.items():
max_val = max(val)
if len(dl_lags[key]) < (max_val + int(not ardl.causal)):
raise ValueError(err.format(var_typ="exogenous"))
uecm_lags[key] = max_val
if ardl.ar_lags is None:
ar_lags = None
else:
max_val = max(ardl.ar_lags)
if len(ardl.ar_lags) != max_val:
raise ValueError(err.format(var_typ="endogenous"))
ar_lags = max_val
return cls(
ardl.data.orig_endog,
ar_lags,
ardl.data.orig_exog,
uecm_lags,
trend=ardl.trend,
fixed=ardl.fixed,
seasonal=ardl.seasonal,
hold_back=ardl.hold_back,
period=ardl.period,
causal=ardl.causal,
missing=missing,
deterministic=ardl.deterministic,
) | [
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rizar/attention-lvcsr | 1ae52cafdd8419874846f9544a299eef9c758f3b | libs/Theano/theano/tensor/opt.py | python | local_useless_inc_subtensor | (node) | Remove IncSubtensor, when we overwrite the full inputs with the
new value. | Remove IncSubtensor, when we overwrite the full inputs with the
new value. | [
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] | def local_useless_inc_subtensor(node):
"""
Remove IncSubtensor, when we overwrite the full inputs with the
new value.
"""
if not isinstance(node.op, IncSubtensor):
return
if node.op.set_instead_of_inc is False:
# This is an IncSubtensor, so the init value must be zeros
try:
c = get_scalar_constant_value(node.inputs[0])
if c != 0:
return
except NotScalarConstantError:
return
if (node.inputs[0].ndim != node.inputs[1].ndim or
node.inputs[0].broadcastable != node.inputs[1].broadcastable):
# FB: I didn't check if this case can happen, but this opt
# don't support it.
return
# We have a SetSubtensor or an IncSubtensor on zeros
# If is this IncSubtensor useful?
# Check that we keep all the original data.
# Put the constant inputs in the slice.
idx_cst = get_idx_list(node.inputs[1:], node.op.idx_list)
if all(isinstance(e, slice) and e.start is None and
e.stop is None and (e.step is None or T.extract_constant(e.step) == -1)
for e in idx_cst):
# IncSubtensor broadcast node.inputs[1] on node.inputs[0]
# based on run time shapes, so we must check they are the same.
if not hasattr(node.fgraph, 'shape_feature'):
return
if not node.fgraph.shape_feature.same_shape(node.inputs[0],
node.inputs[1]):
return
# There is no reverse, so we don't need a replacement.
if all(e.step is None
for e in node.op.idx_list):
# They are the same shape, so we can remore this IncSubtensor
return [node.inputs[1]]
ret = Subtensor(node.op.idx_list)(*node.inputs[1:])
# Copy over previous output stacktrace
copy_stack_trace(node.outputs, ret)
return [ret] | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Tools/scripts/patchcheck.py | python | normalize_c_whitespace | (file_paths) | return fixed | Report if any C files | Report if any C files | [
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] | def normalize_c_whitespace(file_paths):
"""Report if any C files """
fixed = []
for path in file_paths:
abspath = os.path.join(SRCDIR, path)
with open(abspath, 'r') as f:
if '\t' not in f.read():
continue
untabify.process(abspath, 8, verbose=False)
fixed.append(path)
return fixed | [
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JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | packages/source/swift/swift/common/middleware/recon.py | python | ReconMiddleware.get_socket_info | (self, openr=open) | return sockstat | get info from /proc/net/sockstat and sockstat6
Note: The mem value is actually kernel pages, but we return bytes
allocated based on the systems page size. | get info from /proc/net/sockstat and sockstat6 | [
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] | def get_socket_info(self, openr=open):
"""
get info from /proc/net/sockstat and sockstat6
Note: The mem value is actually kernel pages, but we return bytes
allocated based on the systems page size.
"""
sockstat = {}
try:
with openr('/proc/net/sockstat', 'r') as proc_sockstat:
for entry in proc_sockstat:
if entry.startswith("TCP: inuse"):
tcpstats = entry.split()
sockstat['tcp_in_use'] = int(tcpstats[2])
sockstat['orphan'] = int(tcpstats[4])
sockstat['time_wait'] = int(tcpstats[6])
sockstat['tcp_mem_allocated_bytes'] = \
int(tcpstats[10]) * getpagesize()
except IOError as e:
if e.errno != errno.ENOENT:
raise
try:
with openr('/proc/net/sockstat6', 'r') as proc_sockstat6:
for entry in proc_sockstat6:
if entry.startswith("TCP6: inuse"):
sockstat['tcp6_in_use'] = int(entry.split()[2])
except IOError as e:
if e.errno != errno.ENOENT:
raise
return sockstat | [
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asterisk/ari-py | 1647d79176d9ac0dacf4655ca6cb07bd70351f62 | ari/model.py | python | Channel.__init__ | (self, client, channel_json) | [] | def __init__(self, client, channel_json):
super(Channel, self).__init__(
client, client.swagger.channels, channel_json,
client.on_channel_event) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/manifolds/differentiable/de_rham_cohomology.py | python | DeRhamCohomologyRing._element_constructor_ | (self, x) | return self.element_class(self, x) | r"""
Construct an element of ``self``.
TESTS::
sage: M = Manifold(2, 'M')
sage: X.<x,y> = M.chart()
sage: C = M.de_rham_complex()
sage: H = C.cohomology()
sage: H._element_constructor_(C.one())
[one]
Non-cycle element::
sage: omega = M.diff_form(1, name='omega')
sage: omega[0] = y
sage: omega.display()
omega = y dx
sage: H(omega)
Traceback (most recent call last):
...
ValueError: Mixed differential form omega on the 2-dimensional
differentiable manifold M must be a closed form | r"""
Construct an element of ``self``. | [
"r",
"Construct",
"an",
"element",
"of",
"self",
"."
] | def _element_constructor_(self, x):
r"""
Construct an element of ``self``.
TESTS::
sage: M = Manifold(2, 'M')
sage: X.<x,y> = M.chart()
sage: C = M.de_rham_complex()
sage: H = C.cohomology()
sage: H._element_constructor_(C.one())
[one]
Non-cycle element::
sage: omega = M.diff_form(1, name='omega')
sage: omega[0] = y
sage: omega.display()
omega = y dx
sage: H(omega)
Traceback (most recent call last):
...
ValueError: Mixed differential form omega on the 2-dimensional
differentiable manifold M must be a closed form
"""
if isinstance(x, CharacteristicCohomologyClassRingElement):
x = x.representative()
elif x not in self._module:
raise TypeError(f"{x} must be an element of {self._module}")
x = self._module(x)
if x.derivative() != 0:
raise ValueError(f"{x} must be a closed form")
return self.element_class(self, x) | [
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deanishe/alfred-vpn-manager | f5d0dd1433ea69b1517d4866a12b1118097057b9 | src/vpn.py | python | timed | (name=None) | Context manager that logs execution time. | Context manager that logs execution time. | [
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] | def timed(name=None):
"""Context manager that logs execution time."""
name = name or ''
start_time = time()
yield
log.debug('[%0.2fs] %s', time() - start_time, name) | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.2/django/utils/translation/trans_real.py | python | to_locale | (language, to_lower=False) | Turns a language name (en-us) into a locale name (en_US). If 'to_lower' is
True, the last component is lower-cased (en_us). | Turns a language name (en-us) into a locale name (en_US). If 'to_lower' is
True, the last component is lower-cased (en_us). | [
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] | def to_locale(language, to_lower=False):
"""
Turns a language name (en-us) into a locale name (en_US). If 'to_lower' is
True, the last component is lower-cased (en_us).
"""
p = language.find('-')
if p >= 0:
if to_lower:
return language[:p].lower()+'_'+language[p+1:].lower()
else:
# Get correct locale for sr-latn
if len(language[p+1:]) > 2:
return language[:p].lower()+'_'+language[p+1].upper()+language[p+2:].lower()
return language[:p].lower()+'_'+language[p+1:].upper()
else:
return language.lower() | [
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dump247/aws-mock-metadata | ec85bc8c6f41afa8fa624898d6ba1ee5315ebcc2 | metadata/bottle.py | python | BaseRequest.json | (self) | return None | If the ``Content-Type`` header is ``application/json``, this
property holds the parsed content of the request body. Only requests
smaller than :attr:`MEMFILE_MAX` are processed to avoid memory
exhaustion. | If the ``Content-Type`` header is ``application/json``, this
property holds the parsed content of the request body. Only requests
smaller than :attr:`MEMFILE_MAX` are processed to avoid memory
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smaller than :attr:`MEMFILE_MAX` are processed to avoid memory
exhaustion. '''
if 'application/json' in self.environ.get('CONTENT_TYPE', ''):
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digidotcom/xbee-python | 0757f4be0017530c205175fbee8f9f61be9614d1 | digi/xbee/filesystem.py | python | LocalXBeeFileSystemManager._is_function_supported | (self, function) | return function.cmd_name in self._supported_functions | Returns whether the specified file system function is supported or not.
Args:
function (:class:`._FilesystemFunction`): The file system function
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"""
Returns whether the specified file system function is supported or not.
Args:
function (:class:`._FilesystemFunction`): The file system function
to check.
Returns:
Boolean: `True` if the specified file system function is supported,
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tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/models/v2alpha1_cron_job_spec.py | python | V2alpha1CronJobSpec.suspend | (self, suspend) | Sets the suspend of this V2alpha1CronJobSpec.
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:param suspend: The suspend of this V2alpha1CronJobSpec. # noqa: E501
:type: bool
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orbingol/NURBS-Python | 8ae8b127eb0b130a25a6c81e98e90f319733bca0 | geomdl/compatibility.py | python | generate_ctrlpts_weights | (ctrlpts) | return new_ctrlpts | Generates unweighted control points from weighted ones in 1-D.
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#. Takes in 1-D control points list whose coordinates are organized in (x*w, y*w, z*w, w) format
#. Converts the input control points list into (x, y, z, w) format
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:param ctrlpts: 1-D control points (P)
:type ctrlpts: list
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:rtype: list
"""
# Divide control points by weight
new_ctrlpts = []
for cpt in ctrlpts:
temp = [float(pt / cpt[-1]) for pt in cpt]
temp[-1] = float(cpt[-1])
new_ctrlpts.append(temp)
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django/django | 0a17666045de6739ae1c2ac695041823d5f827f7 | django/template/base.py | python | DebugLexer.tokenize | (self) | return result | Split a template string into tokens and annotates each token with its
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AutodeskRoboticsLab/Mimic | 85447f0d346be66988303a6a054473d92f1ed6f4 | mimic/scripts/extern/pyqtgraph_0_11_0_dev0/pyqtgraph/widgets/SpinBox.py | python | SpinBox.setRange | (self, r0, r1) | Set the upper and lower limits for values in the spinbox. | Set the upper and lower limits for values in the spinbox. | [
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pulp/pulp | a0a28d804f997b6f81c391378aff2e4c90183df9 | nodes/common/pulp_node/error.py | python | ErrorList.update | (self, **details) | Update the details of all contained errors.
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giantbranch/python-hacker-code | addbc8c73e7e6fb9e4fcadcec022fa1d3da4b96d | 我手敲的代码(中文注释)/chapter11/volatility-2.3/build/lib/volatility/plugins/malware/apihooks.py | python | Hook._module_name | (self, module) | return str(module.BaseDllName or '') or str(module.FullDllName or '') or '<unknown>' | Return a sanitized module name | Return a sanitized module name | [
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# The module is a string name like "ntdll.dll"
if isinstance(module, basic.String) or isinstance(module, str):
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# The module is a _LDR_DATA_TABLE_ENTRY
return str(module.BaseDllName or '') or str(module.FullDllName or '') or '<unknown>' | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/pyinotify-0.9.4-py2.7.egg/pyinotify.py | python | command_line | () | By default the watched path is '/tmp' and all types of events are
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"""
By default the watched path is '/tmp' and all types of events are
monitored. Events monitoring serves forever, type c^c to stop it.
"""
from optparse import OptionParser
usage = "usage: %prog [options] [path1] [path2] [pathn]"
parser = OptionParser(usage=usage)
parser.add_option("-v", "--verbose", action="store_true",
dest="verbose", help="Verbose mode")
parser.add_option("-r", "--recursive", action="store_true",
dest="recursive",
help="Add watches recursively on paths")
parser.add_option("-a", "--auto_add", action="store_true",
dest="auto_add",
help="Automatically add watches on new directories")
parser.add_option("-g", "--glob", action="store_true",
dest="glob",
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parser.add_option("-e", "--events-list", metavar="EVENT[,...]",
dest="events_list",
help=("A comma-separated list of events to watch for - "
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parser.add_option("-s", "--stats", action="store_true",
dest="stats",
help="Display dummy statistics")
parser.add_option("-V", "--version", action="store_true",
dest="version", help="Pyinotify version")
parser.add_option("-f", "--raw-format", action="store_true",
dest="raw_format",
help="Disable enhanced output format.")
parser.add_option("-c", "--command", action="store",
dest="command",
help="Shell command to run upon event")
(options, args) = parser.parse_args()
if options.verbose:
log.setLevel(10)
if options.version:
print(__version__)
if not options.raw_format:
global output_format
output_format = ColoredOutputFormat()
if len(args) < 1:
path = '/tmp' # default watched path
else:
path = args
# watch manager instance
wm = WatchManager()
# notifier instance and init
if options.stats:
notifier = Notifier(wm, default_proc_fun=Stats(), read_freq=5)
else:
notifier = Notifier(wm, default_proc_fun=PrintAllEvents())
# What mask to apply
mask = 0
if options.events_list:
events_list = options.events_list.split(',')
for ev in events_list:
evcode = EventsCodes.ALL_FLAGS.get(ev, 0)
if evcode:
mask |= evcode
else:
parser.error("The event '%s' specified with option -e"
" is not valid" % ev)
else:
mask = ALL_EVENTS
# stats
cb_fun = None
if options.stats:
def cb(s):
sys.stdout.write(repr(s.proc_fun()))
sys.stdout.write('\n')
sys.stdout.write(str(s.proc_fun()))
sys.stdout.write('\n')
sys.stdout.flush()
cb_fun = cb
# External command
if options.command:
def cb(s):
subprocess.Popen(options.command, shell=True)
cb_fun = cb
log.debug('Start monitoring %s, (press c^c to halt pyinotify)' % path)
wm.add_watch(path, mask, rec=options.recursive, auto_add=options.auto_add, do_glob=options.glob)
# Loop forever (until sigint signal get caught)
notifier.loop(callback=cb_fun) | [
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abcminiuser/python-elgato-streamdeck | 681c7e9c084981e9286a41abb05d85713cd16317 | src/StreamDeck/Devices/StreamDeckXL.py | python | StreamDeckXL._reset_key_stream | (self) | Sends a blank key report to the StreamDeck, resetting the key image
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skarra/ASynK | e908a1ad670a2d79f791a6a7539392e078a64add | asynk/contact_ex.py | python | EXContact._add_email_helper | (self, ews_con, emails, n, key_start) | return i | From the list of emails, add at most n emails to ews_con. Return
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | plexpy/webserve.py | python | WebInterface.get_server_pref | (self, pref=None, **kwargs) | Get a specified PMS server preference.
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/ZSI-2.0-py2.7.egg/ZSI/generate/wsdl2dispatch.py | python | ServiceModuleWriter.getClassName | (self, name) | return NCName_to_ClassName(name) | return class name. | return class name. | [
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nansencenter/DAPPER | 406f5a526919286aa73b017add507f5b0cb98233 | dapper/mods/Ikeda/__init__.py | python | aux | (x, y) | return s, t, x1, y1 | Comps used both by step and its jacobian. | Comps used both by step and its jacobian. | [
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t = 0.4 - 6 / s
# x1= x*cos(t) + y*cos(t) # Colin's mod
x1 = x*cos(t) - y*sin(t)
y1 = x*sin(t) + y*cos(t)
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axcore/tartube | 36dd493642923fe8b9190a41db596c30c043ae90 | tartube/mainwin.py | python | MainWin.on_video_index_empty_folder | (self, menu_item, media_data_obj) | Called from a callback in self.video_index_popup_menu().
Empties the folder.
Args:
menu_item (Gtk.MenuItem): The clicked menu item
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Empties the folder.
Args:
menu_item (Gtk.MenuItem): The clicked menu item
media_data_obj (media.Folder): The clicked media data object
"""
if DEBUG_FUNC_FLAG:
utils.debug_time('mwn 12148 on_video_index_empty_folder')
# The True flag tells the function to empty the container, rather than
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self.app_obj.delete_container(media_data_obj, True) | [
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quantumlib/Cirq | 89f88b01d69222d3f1ec14d649b7b3a85ed9211f | cirq-web/cirq_web/circuits/symbols.py | python | resolve_operation | (operation: cirq.Operation, resolvers: Iterable[SymbolResolver]) | return symbol_info | Builds a SymbolInfo object based off of a designated operation
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researchmm/tasn | 5dba8ccc096cedc63913730eeea14a9647911129 | tasn-mxnet/python/mxnet/ndarray/ndarray.py | python | NDArray.arcsin | (self, *args, **kwargs) | return op.arcsin(self, *args, **kwargs) | Convenience fluent method for :py:func:`arcsin`.
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | thirdparty_libs/openvas_lib/data.py | python | OpenVASOverride.orphan | (self) | return self.__orphan | :return: indicates if the NVT is orphan
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/mutagen/_util.py | python | fileobj_name | (fileobj) | return value | Returns:
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asyml/texar | a23f021dae289a3d768dc099b220952111da04fd | texar/tf/data/data/mono_text_data.py | python | MonoTextData.dataset | (self) | return self._dataset | The dataset, an instance of
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abhi2610/ohem | 1f07dd09b50c8c21716ae36aede92125fe437579 | tools/demo.py | python | parse_args | () | return args | Parse input arguments. | Parse input arguments. | [
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"""Parse input arguments."""
parser = argparse.ArgumentParser(description='Faster R-CNN demo')
parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
default=0, type=int)
parser.add_argument('--cpu', dest='cpu_mode',
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action='store_true')
parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
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args = parser.parse_args()
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duanhongyi/dwebsocket | 8beb7fb5c05fb53eda612cea573692dfc5f0ed69 | dwebsocket/backends/default/websocket.py | python | DefaultWebSocket.close | (self, code=None, reason=None) | Forcibly close the websocket. | Forcibly close the websocket. | [
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kamalgill/flask-appengine-template | 11760f83faccbb0d0afe416fc58e67ecfb4643c2 | src/lib/click/core.py | python | Parameter.handle_parse_result | (self, ctx, opts, args) | return value, args | [] | def handle_parse_result(self, ctx, opts, args):
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value = self.consume_value(ctx, opts)
try:
value = self.full_process_value(ctx, value)
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sympy/sympy | d822fcba181155b85ff2b29fe525adbafb22b448 | sympy/polys/polyclasses.py | python | DMF.add | (f, g) | return per(num, den) | Add two multivariate fractions ``f`` and ``g``. | Add two multivariate fractions ``f`` and ``g``. | [
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num, den = dmp_add_mul(F_num, F_den, G, lev, dom), F_den
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lev, dom, per, F, G = f.frac_unify(g)
(F_num, F_den), (G_num, G_den) = F, G
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/visualization/stretch.py | python | AsinhStretch.inverse | (self) | return SinhStretch(a=1. / np.arcsinh(1. / self.a)) | A stretch object that performs the inverse operation. | A stretch object that performs the inverse operation. | [
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/api/apps_v1_api.py | python | AppsV1Api.read_namespaced_replica_set_status | (self, name, namespace, **kwargs) | return self.read_namespaced_replica_set_status_with_http_info(name, namespace, **kwargs) | read_namespaced_replica_set_status # noqa: E501
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"""read_namespaced_replica_set_status # noqa: E501
read status of the specified ReplicaSet # noqa: E501
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ifwe/digsby | f5fe00244744aa131e07f09348d10563f3d8fa99 | digsby/lib/pyxmpp/interface_micro_impl.py | python | InterfaceClass.implementedBy | (self, cls) | return self in implementedBy(cls) | Do instances of the given class implement the interface? | Do instances of the given class implement the interface? | [
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fzlee/alipay | 0f8eab30fea7adb43284182cc6bcac08f51b2e08 | alipay/__init__.py | python | ISVAliPay.api_alipay_open_auth_token_app_query | (self) | return self.verified_sync_response(data, response_type) | [] | def api_alipay_open_auth_token_app_query(self):
biz_content = {"app_auth_token": self.app_auth_token}
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zhanghan1990/zipline-chinese | 86904cac4b6e928271f640910aa83675ce945b8b | zipline/finance/performance/position_tracker.py | python | PositionTracker.pay_dividends | (self, dividend_frame) | return net_cash_payment | Given a frame of dividends whose pay_dates are all the next trading
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"""
payments = dividend_frame.apply(self._maybe_pay_dividend, axis=1)\
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self._unpaid_dividends.drop(payments.index)
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stock_payments = payments[payments['payment_sid'].notnull()]
for _, row in stock_payments.iterrows():
stock = row['payment_sid']
share_count = row['share_count']
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position = self.positions[stock]
position.amount += share_count
self._update_asset(stock)
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net_cash_payment = payments['cash_amount'].fillna(0).sum()
return net_cash_payment | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/messaging/smsbackends/airtel_tcl/models.py | python | AirtelTCLBackend.get_url | (self) | return 'https://%s/BULK_API/InstantJsonPush' % self.config.host_and_port | [] | def get_url(self):
return 'https://%s/BULK_API/InstantJsonPush' % self.config.host_and_port | [
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craigmacartney/Wave-U-Net-For-Speech-Enhancement | c8ccbd286cbe73d7539e5703e4407762304e3068 | Utils.py | python | crop | (tensor, target_shape, match_feature_dim=True) | return tensor[:,crop_start[1]:-crop_end[1],:] | Crops a 3D tensor [batch_size, width, channels] along the width axes to a target shape.
Performs a centre crop. If the dimension difference is uneven, crop last dimensions first.
:param tensor: 4D tensor [batch_size, width, height, channels] that should be cropped.
:param target_shape: Target shape (4D tensor) that the tensor should be cropped to
:return: Cropped tensor | Crops a 3D tensor [batch_size, width, channels] along the width axes to a target shape.
Performs a centre crop. If the dimension difference is uneven, crop last dimensions first.
:param tensor: 4D tensor [batch_size, width, height, channels] that should be cropped.
:param target_shape: Target shape (4D tensor) that the tensor should be cropped to
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Crops a 3D tensor [batch_size, width, channels] along the width axes to a target shape.
Performs a centre crop. If the dimension difference is uneven, crop last dimensions first.
:param tensor: 4D tensor [batch_size, width, height, channels] that should be cropped.
:param target_shape: Target shape (4D tensor) that the tensor should be cropped to
:return: Cropped tensor
'''
shape = np.array(tensor.get_shape().as_list())
diff = shape - np.array(target_shape)
assert(diff[0] == 0 and (diff[2] == 0 or not match_feature_dim))# Only width axis can differ
if (diff[1] % 2 != 0):
print("WARNING: Cropping with uneven number of extra entries on one side")
assert diff[1] >= 0 # Only positive difference allowed
if diff[1] == 0:
return tensor
crop_start = diff // 2
crop_end = diff - crop_start
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-50/skeinforge_application/skeinforge_plugins/craft_plugins/bottom.py | python | getCraftedTextFromText | (fileName, svgText, repository=None) | return BottomSkein().getCraftedGcode(fileName, repository, svgText) | Bottom and convert an svgText. | Bottom and convert an svgText. | [
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if repository == None:
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pika/pika | 12dcdf15d0932c388790e0fa990810bfd21b1a32 | examples/asynchronous_consumer_example.py | python | ExampleConsumer.acknowledge_message | (self, delivery_tag) | Acknowledge the message delivery from RabbitMQ by sending a
Basic.Ack RPC method for the delivery tag.
:param int delivery_tag: The delivery tag from the Basic.Deliver frame | Acknowledge the message delivery from RabbitMQ by sending a
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"""Acknowledge the message delivery from RabbitMQ by sending a
Basic.Ack RPC method for the delivery tag.
:param int delivery_tag: The delivery tag from the Basic.Deliver frame
"""
LOGGER.info('Acknowledging message %s', delivery_tag)
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wmliang/pe-afl | 4036d2f41da20ff12ecac43a076de5d60ce68bd9 | lighthouse/lighthouse/painting/ida_painter.py | python | IDAPainter.clear_nodes | (self, nodes_metadata) | Clear paint from the given graph nodes. | Clear paint from the given graph nodes. | [
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"""
Clear paint from the given graph nodes.
"""
# create a node info object as our vehicle for resetting the node color
node_info = idaapi.node_info_t()
node_info.bg_color = idc.DEFCOLOR
# NOTE/COMPAT:
if disassembler.USING_IDA7API:
set_node_info = idaapi.set_node_info
else:
set_node_info = idaapi.set_node_info2
#
# loop through every node that we have metadata data for, clearing
# their paint (color) in the IDA graph view as applicable.
#
for node_metadata in nodes_metadata:
# do the *actual* painting of a single node instance
set_node_info(
node_metadata.function.address,
node_metadata.id,
node_info,
idaapi.NIF_BG_COLOR | idaapi.NIF_FRAME_COLOR
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self._painted_nodes.discard(node_metadata.address) | [
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natea/django-deployer | 5ce7d972db2f8500ec53ad89e7eb312d3360d074 | django_deployer/tasks.py | python | deploy | (provider=None) | Deploys your project | Deploys your project | [
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"""
Deploys your project
"""
if os.path.exists(DEPLOY_YAML):
site = yaml.safe_load(_read_file(DEPLOY_YAML))
provider_class = PROVIDERS[site['provider']]
provider_class.deploy() | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | rpython/rlib/rbigint.py | python | _loghelper | (func, arg) | return func(x) + (e * float(SHIFT) * func(2.0)) | A decent logarithm is easy to compute even for huge bigints, but libm can't
do that by itself -- loghelper can. func is log or log10.
Note that overflow isn't possible: a bigint can contain
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"""
x, e = _AsScaledDouble(arg)
if x <= 0.0:
raise ValueError
# Value is ~= x * 2**(e*SHIFT), so the log ~=
# log(x) + log(2) * e * SHIFT.
# CAUTION: e*SHIFT may overflow using int arithmetic,
# so force use of double. */
return func(x) + (e * float(SHIFT) * func(2.0)) | [
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w3h/isf | 6faf0a3df185465ec17369c90ccc16e2a03a1870 | lib/thirdparty/bitstring.py | python | BitArray.__irshift__ | (self, n) | return self._irshift(n) | Shift bits by n to the right in place. Return self.
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"""Shift bits by n to the right in place. Return self.
n -- the number of bits to shift. Must be >= 0.
"""
if n < 0:
raise ValueError("Cannot shift by a negative amount.")
if not self.len:
raise ValueError("Cannot shift an empty bitstring.")
if not n:
return self
n = min(n, self.len)
return self._irshift(n) | [
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mars-project/mars | 6afd7ed86db77f29cc9470485698ef192ecc6d33 | mars/lib/version.py | python | parse | (version: str) | Parse the given version string and return either a :class:`Version` object
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Parse the given version string and return either a :class:`Version` object
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try:
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nodejs/node-gyp | a2f298870692022302fa27a1d42363c4a72df407 | gyp/pylib/gyp/MSVSSettings.py | python | _MSVSOnly | (tool, name, setting_type) | Defines a setting that is only found in MSVS.
Args:
tool: a dictionary that gives the names of the tool for MSVS and MSBuild.
name: the name of the setting.
setting_type: the type of this setting. | Defines a setting that is only found in MSVS. | [
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"""Defines a setting that is only found in MSVS.
Args:
tool: a dictionary that gives the names of the tool for MSVS and MSBuild.
name: the name of the setting.
setting_type: the type of this setting.
"""
def _Translate(unused_value, unused_msbuild_settings):
# Since this is for MSVS only settings, no translation will happen.
pass
_msvs_validators[tool.msvs_name][name] = setting_type.ValidateMSVS
_msvs_to_msbuild_converters[tool.msvs_name][name] = _Translate | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/psutil-0.6.1-py2.7-linux-x86_64.egg/psutil/_psosx.py | python | get_system_cpu_times | () | return _cputimes_ntuple(user, nice, system, idle) | Return system CPU times as a namedtuple. | Return system CPU times as a namedtuple. | [
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] | def get_system_cpu_times():
"""Return system CPU times as a namedtuple."""
user, nice, system, idle = _psutil_osx.get_system_cpu_times()
return _cputimes_ntuple(user, nice, system, idle) | [
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/api/v2010/account/short_code.py | python | ShortCodeInstance.api_version | (self) | return self._properties['api_version'] | :returns: The API version used to start a new TwiML session
:rtype: unicode | :returns: The API version used to start a new TwiML session
:rtype: unicode | [
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"""
:returns: The API version used to start a new TwiML session
:rtype: unicode
"""
return self._properties['api_version'] | [
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ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/random.py | python | Random.sample | (self, population, k) | return result | Chooses k unique random elements from a population sequence or set.
Returns a new list containing elements from the population while
leaving the original population unchanged. The resulting list is
in selection order so that all sub-slices will also be valid random
samples. This allows raffle winners (the sample) to be partitioned
into grand prize and second place winners (the subslices).
Members of the population need not be hashable or unique. If the
population contains repeats, then each occurrence is a possible
selection in the sample.
To choose a sample in a range of integers, use range as an argument.
This is especially fast and space efficient for sampling from a
large population: sample(range(10000000), 60) | Chooses k unique random elements from a population sequence or set. | [
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] | def sample(self, population, k):
"""Chooses k unique random elements from a population sequence or set.
Returns a new list containing elements from the population while
leaving the original population unchanged. The resulting list is
in selection order so that all sub-slices will also be valid random
samples. This allows raffle winners (the sample) to be partitioned
into grand prize and second place winners (the subslices).
Members of the population need not be hashable or unique. If the
population contains repeats, then each occurrence is a possible
selection in the sample.
To choose a sample in a range of integers, use range as an argument.
This is especially fast and space efficient for sampling from a
large population: sample(range(10000000), 60)
"""
# Sampling without replacement entails tracking either potential
# selections (the pool) in a list or previous selections in a set.
# When the number of selections is small compared to the
# population, then tracking selections is efficient, requiring
# only a small set and an occasional reselection. For
# a larger number of selections, the pool tracking method is
# preferred since the list takes less space than the
# set and it doesn't suffer from frequent reselections.
if isinstance(population, _Set):
population = tuple(population)
if not isinstance(population, _Sequence):
raise TypeError("Population must be a sequence or set. For dicts, use list(d).")
randbelow = self._randbelow
n = len(population)
if not 0 <= k <= n:
raise ValueError("Sample larger than population or is negative")
result = [None] * k
setsize = 21 # size of a small set minus size of an empty list
if k > 5:
setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets
if n <= setsize:
# An n-length list is smaller than a k-length set
pool = list(population)
for i in range(k): # invariant: non-selected at [0,n-i)
j = randbelow(n-i)
result[i] = pool[j]
pool[j] = pool[n-i-1] # move non-selected item into vacancy
else:
selected = set()
selected_add = selected.add
for i in range(k):
j = randbelow(n)
while j in selected:
j = randbelow(n)
selected_add(j)
result[i] = population[j]
return result | [
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"# population, then tra... | https://github.com/ales-tsurko/cells/blob/4cf7e395cd433762bea70cdc863a346f3a6fe1d0/packaging/macos/python/lib/python3.7/random.py#L286-L342 | |
rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/cherrypy/_cptools.py | python | DeprecatedTool.__init__ | (self, point, warnmsg=None) | [] | def __init__(self, point, warnmsg=None):
self.point = point
if warnmsg is not None:
self.warnmsg = warnmsg | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/bleach/_vendor/html5lib/filters/base.py | python | Filter.__init__ | (self, source) | [] | def __init__(self, source):
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hudson-and-thames/mlfinlab | 79dcc7120ec84110578f75b025a75850eb72fc73 | mlfinlab/bet_sizing/ch10_snippets.py | python | limit_price | (target_pos, pos, forecast_price, w_param, max_pos, func) | Derived from SNIPPET 10.4
Calculates the limit price. The 'func' argument allows the user to choose between bet sizing functions.
:param target_pos: (int) Target position.
:param pos: (int) Current position.
:param forecast_price: (float) Forecast price.
:param w_param: (float) Coefficient regulating the width of the bet size function.
:param max_pos: (int) Maximum absolute position size.
:param func: (string) Function to use for dynamic calculation. Valid options are: 'sigmoid', 'power'.
:return: (float) Limit price. | Derived from SNIPPET 10.4
Calculates the limit price. The 'func' argument allows the user to choose between bet sizing functions. | [
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"""
Derived from SNIPPET 10.4
Calculates the limit price. The 'func' argument allows the user to choose between bet sizing functions.
:param target_pos: (int) Target position.
:param pos: (int) Current position.
:param forecast_price: (float) Forecast price.
:param w_param: (float) Coefficient regulating the width of the bet size function.
:param max_pos: (int) Maximum absolute position size.
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/Pulsedive/Integrations/Pulsedive/Pulsedive.py | python | test_module | (client: Client, api_key) | return 'ok' | Tests API connectivity and authentication | Tests API connectivity and authentication | [
"Tests",
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"and",
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] | def test_module(client: Client, api_key) -> str:
"""Tests API connectivity and authentication"""
try:
client.test_connect(api_key)
except DemistoException:
return 'Could not connect to Pulsedive'
return 'ok' | [
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python-diamond/Diamond | 7000e16cfdf4508ed9291fc4b3800592557b2431 | src/collectors/elasticsearch/elasticsearch.py | python | ElasticSearchCollector.collect_instance_index_stats | (self, scheme, host, port, metrics) | [] | def collect_instance_index_stats(self, scheme, host, port, metrics):
result = self._get(scheme, host, port, '_stats', '_all')
if not result:
return
_all = result['_all']
self._index_metrics(metrics, 'indices._all', _all['primaries'])
if 'indices' in _all:
indices = _all['indices']
elif 'indices' in result: # elasticsearch >= 0.90RC2
indices = result['indices']
else:
return
for name, index in indices.iteritems():
self._index_metrics(metrics, 'indices.%s' % name,
index['primaries']) | [
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jiaruncao/Readers | ddef597db761fad90eaf694a7f3ed570d4cf9ce8 | Self-MatchingReader/Helper.py | python | merge_two_dicts | (x, y) | return z | Given two dicts, merge them into a new dict as a shallow copy. | Given two dicts, merge them into a new dict as a shallow copy. | [
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] | def merge_two_dicts(x, y):
"""Given two dicts, merge them into a new dict as a shallow copy."""
z = x.copy()
z.update(y)
return z | [
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/27/Lib/site-packages/pythonwin/pywin/framework/interact.py | python | CreateMDIInteractiveWindow | (makeDoc = None, makeFrame = None) | Create a standard (non-docked) interactive window unconditionally | Create a standard (non-docked) interactive window unconditionally | [
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] | def CreateMDIInteractiveWindow(makeDoc = None, makeFrame = None):
"""Create a standard (non-docked) interactive window unconditionally
"""
global edit
if makeDoc is None: makeDoc = InteractiveDocument
if makeFrame is None: makeFrame = InteractiveFrame
edit = CInteractivePython(makeDoc=makeDoc,makeFrame=makeFrame) | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | plexpy/helpers.py | python | radio | (variable, pos) | [] | def radio(variable, pos):
if variable == pos:
return 'Checked'
else:
return '' | [
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apple/ccs-calendarserver | 13c706b985fb728b9aab42dc0fef85aae21921c3 | twistedcaldav/database.py | python | AbstractADBAPIDatabase.queryList | (self, sql, *query_params) | [] | def queryList(self, sql, *query_params):
# Re-try at least once
for _ignore in (0, 1):
if not self.initialized:
yield self.open()
try:
result = (yield self._db_values_for_sql(sql, *query_params))
except Exception, e:
log.error("Error in database queryList: {ex}", ex=e)
self.close()
else:
break
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daoluan/decode-Django | d46a858b45b56de48b0355f50dd9e45402d04cfd | Django-1.5.1/django/contrib/localflavor/ro/forms.py | python | ROPhoneNumberField.clean | (self, value) | return value | Strips -, (, ) and spaces. Checks the final length. | Strips -, (, ) and spaces. Checks the final length. | [
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] | def clean(self, value):
"""
Strips -, (, ) and spaces. Checks the final length.
"""
value = super(ROPhoneNumberField, self).clean(value)
if value in EMPTY_VALUES:
return ''
value = value.replace('-', '')
value = value.replace('(', '')
value = value.replace(')', '')
value = value.replace(' ', '')
if len(value) != 10:
raise ValidationError(self.error_messages['invalid'])
return value | [
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sensepost/snoopy-ng | eac73f545952583af96c6ae8980e9ffeb8b972bc | includes/wigle_api.py | python | Wigle._haversine | (self, lat1, lon1, lat2, lon2) | return R * c * 1000.0 | Calculate distance between points on a sphere | Calculate distance between points on a sphere | [
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] | def _haversine(self, lat1, lon1, lat2, lon2):
"""Calculate distance between points on a sphere"""
R = 6372.8 # In kilometers
dLat = math.radians(lat2 - lat1)
dLon = math.radians(lon2 - lon1)
lat1 = math.radians(lat1)
lat2 = math.radians(lat2)
a = math.sin(dLat / 2) * math.sin(dLat / 2) + math.sin(dLon / 2) * math.sin(dLon / 2) * math.cos(lat1) * math.cos(lat2)
c = 2 * math.asin(math.sqrt(a))
return R * c * 1000.0 | [
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apple/coremltools | 141a83af482fcbdd5179807c9eaff9a7999c2c49 | coremltools/converters/sklearn/_converter_internal.py | python | _convert_sklearn_model | (
input_sk_obj, input_features=None, output_feature_names=None, class_labels=None
) | return pipeline.spec | Converts a generic sklearn pipeline, transformer, classifier, or regressor
into an coreML specification. | Converts a generic sklearn pipeline, transformer, classifier, or regressor
into an coreML specification. | [
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] | def _convert_sklearn_model(
input_sk_obj, input_features=None, output_feature_names=None, class_labels=None
):
"""
Converts a generic sklearn pipeline, transformer, classifier, or regressor
into an coreML specification.
"""
if not (_HAS_SKLEARN):
raise RuntimeError(
"scikit-learn not found. scikit-learn conversion API is disabled."
)
from sklearn.pipeline import Pipeline as sk_Pipeline
if input_features is None:
input_features = "input"
if isinstance(input_sk_obj, sk_Pipeline):
sk_obj_list = input_sk_obj.steps
else:
sk_obj_list = [("SKObj", input_sk_obj)]
if len(sk_obj_list) == 0:
raise ValueError("No SKLearn transformers supplied.")
# Put the transformers into a pipeline list to hold them so that they can
# later be added to a pipeline object. (Hold off adding them to the
# pipeline now in case it's a single model at the end, in which case it
# gets returned as is.)
#
# Each member of the pipeline list is a tuple of the proto spec for that
# model, the input features, and the output features.
pipeline_list = []
# These help us keep track of what's going on a bit easier.
Input = _namedtuple("InputTransformer", ["name", "sk_obj", "module"])
Output = _namedtuple(
"CoreMLTransformer", ["spec", "input_features", "output_features"]
)
# Get a more information rich representation of the list for convenience.
# obj_list is a list of tuples of (name, sk_obj, and the converter module for
# that step in the list.
obj_list = [
Input(sk_obj_name, sk_obj, _get_converter_module(sk_obj))
for sk_obj_name, sk_obj in sk_obj_list
]
# Various preprocessing steps.
# If the first component of the object list is the sklearn dict vectorizer,
# which is unique in that it accepts a list of dictionaries, then we can
# get the feature type mapping from that. This then may require the addition
# of several OHE steps, so those need to be processed in the first stage.
if isinstance(obj_list[0].sk_obj, _dict_vectorizer.sklearn_class):
dv_obj = obj_list[0].sk_obj
output_dim = len(_dict_vectorizer.get_input_feature_names(dv_obj))
if not isinstance(input_features, str):
raise TypeError(
"If the first transformer in a pipeline is a "
"DictVectorizer, then the input feature must be the name "
"of the input dictionary."
)
input_features = [(input_features, datatypes.Dictionary(str))]
if len(obj_list) > 1:
output_feature_name = _PIPELINE_INTERNAL_FEATURE_NAME
else:
if output_feature_names is None:
output_feature_name = "transformed_features"
elif isinstance(output_feature_names, str):
output_feature_name = output_feature_names
else:
raise TypeError(
"For a transformer pipeline, the "
"output_features needs to be None or a string "
"for the predicted value."
)
output_features = [(output_feature_name, datatypes.Array(output_dim))]
spec = _dict_vectorizer.convert(dv_obj, input_features, output_features)._spec
pipeline_list.append(Output(spec, input_features, output_features))
# Set up the environment for the rest of the pipeline
current_input_features = output_features
current_num_dimensions = output_dim
# In the corner case that it's only the dict vectorizer here, just return
# and exit with that at this point.
if len(obj_list) == 1:
return spec
else:
del obj_list[0]
else:
# First, we need to resolve the input feature types as the sklearn pipeline
# expects just an array as input, but what we want to expose to the coreML
# user is an interface with named variables. This resolution has to handle
# a number of cases.
# Can we get the number of features from the model? If so, pass that
# information into the feature resolution function. If we can't, then this
# function should return None.
first_sk_obj = obj_list[0].sk_obj
num_dimensions = _get_converter_module(first_sk_obj).get_input_dimension(
first_sk_obj
)
# Resolve the input features.
features = _fm.process_or_validate_features(input_features, num_dimensions)
current_num_dimensions = _fm.dimension_of_array_features(features)
# Add in a feature vectorizer that consolodates all of the feature inputs
# into the form expected by scipy's pipelines. Essentially this is a
# translation layer between the coreML form with named arguments and the
# scikit learn variable form.
if len(features) == 1 and isinstance(features[0][1], datatypes.Array):
current_input_features = features
else:
spec, _output_dimension = create_feature_vectorizer(
features, _PIPELINE_INTERNAL_FEATURE_NAME
)
assert _output_dimension == current_num_dimensions
ft_out_features = [
(
_PIPELINE_INTERNAL_FEATURE_NAME,
datatypes.Array(current_num_dimensions),
)
]
pipeline_list.append(Output(spec, features, ft_out_features))
current_input_features = ft_out_features
# Now, validate the sequence of transformers to make sure we have something
# that can work with all of this.
for i, (_, _, m) in enumerate(obj_list[:-1]):
if m.model_type != "transformer":
raise ValueError(
"Only a sequence of transformer classes followed by a "
"single transformer, regressor, or classifier is currently supported. "
"(object in position %d interpreted as %s)" % (i, m.model_type)
)
overall_mode = obj_list[-1].module.model_type
assert overall_mode in ("transformer", "regressor", "classifier")
# Now, go through each transformer in the sequence of transformers and add
# it to the pipeline.
for _, sk_obj, sk_m in obj_list[:-1]:
next_dimension = sk_m.update_dimension(sk_obj, current_num_dimensions)
output_features = [
(_PIPELINE_INTERNAL_FEATURE_NAME, datatypes.Array(next_dimension))
]
spec = sk_m.convert(sk_obj, current_input_features, output_features)._spec
pipeline_list.append(Output(spec, current_input_features, output_features))
current_input_features = output_features
current_num_dimensions = next_dimension
# Now, handle the final transformer. This is where we need to have different
# behavior depending on whether it's a classifier, transformer, or regressor.
_, last_sk_obj, last_sk_m = obj_list[-1]
if overall_mode == "classifier":
supports_output_scores = last_sk_m.supports_output_scores(last_sk_obj)
_internal_output_classes = list(last_sk_m.get_output_classes(last_sk_obj))
if class_labels is None:
class_labels = _internal_output_classes
output_features = _fm.process_or_validate_classifier_output_features(
output_feature_names, class_labels, supports_output_scores
)
elif overall_mode == "regressor":
if output_feature_names is None:
output_features = [("prediction", datatypes.Double())]
elif isinstance(output_feature_names, str):
output_features = [(output_feature_names, datatypes.Double())]
else:
raise TypeError(
"For a regressor object or regressor pipeline, the "
"output_features needs to be None or a string for the predicted value."
)
else: # transformer
final_output_dimension = last_sk_m.update_dimension(
last_sk_obj, current_num_dimensions
)
if output_feature_names is None:
output_features = [
("transformed_features", datatypes.Array(final_output_dimension))
]
elif isinstance(output_feature_names, str):
output_features = [
(output_feature_names, datatypes.Array(final_output_dimension))
]
else:
raise TypeError(
"For a transformer object or transformer pipeline, the "
"output_features needs to be None or a string for the "
"name of the transformed value."
)
last_spec = last_sk_m.convert(
last_sk_obj, current_input_features, output_features
)._spec
pipeline_list.append(Output(last_spec, current_input_features, output_features))
# Now, create the pipeline and return the spec for it.
# If it's just one element, we can return it.
if len(pipeline_list) == 1:
return pipeline_list[0].spec
original_input_features = pipeline_list[0].input_features
if overall_mode == "regressor":
pipeline = PipelineRegressor(original_input_features, output_features)
elif overall_mode == "classifier":
pipeline = PipelineClassifier(
original_input_features, class_labels, output_features
)
else:
pipeline = Pipeline(original_input_features, output_features)
# Okay, now we can build the pipeline spec.
for spec, input_features, output_features in pipeline_list:
pipeline.add_model(spec)
return pipeline.spec | [
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hubo1016/vlcp | 61c4c2595b610675ac0cbc4dbc46f70ec40090d3 | vlcp/utils/dataobject.py | python | dump | (obj, attributes = True, _refset = None) | Show full value of a data object | Show full value of a data object | [
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] | def dump(obj, attributes = True, _refset = None):
"Show full value of a data object"
if _refset is None:
_refset = set()
if obj is None:
return None
elif isinstance(obj, DataObject):
if id(obj) in _refset:
attributes = False
else:
_refset.add(id(obj))
cls = type(obj)
clsname = getattr(cls, '__module__', '<unknown>') + '.' + getattr(cls, '__name__', '<unknown>')
baseresult = {'_type': clsname, '_key': obj.getkey()}
if not attributes:
return baseresult
else:
baseresult.update((k,dump(v, attributes, _refset)) for k,v in vars(obj).items() if k[:1] != '_')
_refset.remove(id(obj))
return baseresult
elif isinstance(obj, ReferenceObject):
if obj._ref is not None:
return dump(obj._ref, attributes, _refset)
else:
return {'_ref':obj.getkey()}
elif isinstance(obj, WeakReferenceObject):
return {'_weakref':obj.getkey()}
elif isinstance(obj, DataObjectSet):
return dump(list(obj.dataset()))
elif isinstance(obj, dict):
return dict((k, dump(v, attributes, _refset)) for k,v in obj.items())
elif isinstance(obj, list) or isinstance(obj, tuple) or isinstance(obj, set):
return [dump(v, attributes, _refset) for v in obj]
else:
return obj | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/modelcluster/forms.py | python | ClusterForm.is_valid | (self) | return form_is_valid and formsets_are_valid | [] | def is_valid(self):
form_is_valid = super(ClusterForm, self).is_valid()
formsets_are_valid = all([formset.is_valid() for formset in self.formsets.values()])
return form_is_valid and formsets_are_valid | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/states/hg.py | python | _update_repo | (ret, name, target, clean, user, identity, rev, opts, update_head) | return ret | Update the repo to a given revision. Using clean passes -C to the hg up | Update the repo to a given revision. Using clean passes -C to the hg up | [
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"""
Update the repo to a given revision. Using clean passes -C to the hg up
"""
log.debug('target %s is found, "hg pull && hg up is probably required"', target)
current_rev = __salt__["hg.revision"](target, user=user, rev=".")
if not current_rev:
return _fail(ret, "Seems that {} is not a valid hg repo".format(target))
if __opts__["test"]:
return _neutral_test(
ret,
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try:
pull_out = __salt__["hg.pull"](
target, user=user, identity=identity, opts=opts, repository=name
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except CommandExecutionError as err:
ret["result"] = False
ret["comment"] = err
return ret
if update_head is False:
changes = "no changes found" not in pull_out
if changes:
ret["comment"] = (
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else:
ret[
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return ret
if rev:
try:
__salt__["hg.update"](target, rev, force=clean, user=user)
except CommandExecutionError as err:
ret["result"] = False
ret["comment"] = err
return ret
else:
try:
__salt__["hg.update"](target, "tip", force=clean, user=user)
except CommandExecutionError as err:
ret["result"] = False
ret["comment"] = err
return ret
new_rev = __salt__["hg.revision"](cwd=target, user=user, rev=".")
if current_rev != new_rev:
revision_text = "{} => {}".format(current_rev, new_rev)
log.info("Repository %s updated: %s", target, revision_text)
ret["comment"] = "Repository {} updated.".format(target)
ret["changes"]["revision"] = revision_text
elif "error:" in pull_out:
return _fail(ret, "An error was thrown by hg:\n{}".format(pull_out))
return ret | [
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pypa/setuptools_scm | 22724eb6c6613ba84f64b399d31f11333dbf3e4a | src/setuptools_scm/_overrides.py | python | _read_pretended_version_for | (config: Configuration) | read a a overridden version from the environment
tries ``SETUPTOOLS_SCM_PRETEND_VERSION``
and ``SETUPTOOLS_SCM_PRETEND_VERSION_FOR_$UPPERCASE_DIST_NAME`` | read a a overridden version from the environment | [
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"""read a a overridden version from the environment
tries ``SETUPTOOLS_SCM_PRETEND_VERSION``
and ``SETUPTOOLS_SCM_PRETEND_VERSION_FOR_$UPPERCASE_DIST_NAME``
"""
trace("dist name:", config.dist_name)
pretended: Optional[str]
if config.dist_name is not None:
pretended = os.environ.get(
PRETEND_KEY_NAMED.format(name=config.dist_name.upper())
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else:
pretended = None
if pretended is None:
pretended = os.environ.get(PRETEND_KEY)
if pretended is not None:
# we use meta here since the pretended version
# must adhere to the pep to begin with
return meta(tag=pretended, preformatted=True, config=config)
else:
return None | [
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vektort13/AntiOS | a4632c94e2106370a8cc8c8bd3e29ff0900bfe9b | system_fingerprint.py | python | WinFingerprint.random_ie_service_update | (self) | return self.ie_service_update | Internet Explorer Service Update (SvcKBNumber)
:return: String in format "KBNNNNNNN" | Internet Explorer Service Update (SvcKBNumber)
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"""
Internet Explorer Service Update (SvcKBNumber)
:return: String in format "KBNNNNNNN"
"""
return self.ie_service_update | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pip/_vendor/distlib/util.py | python | cached_property.__get__ | (self, obj, cls=None) | return value | [] | def __get__(self, obj, cls=None):
if obj is None:
return self
value = self.func(obj)
object.__setattr__(obj, self.func.__name__, value)
#obj.__dict__[self.func.__name__] = value = self.func(obj)
return value | [
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XanaduAI/strawberryfields | 298601e409528f22c6717c2d816ab68ae8bda1fa | strawberryfields/backends/bosonicbackend/backend.py | python | BosonicBackend.gaussian_cptp | (self, modes, X, Y=None) | r"""Transforms the state according to a deterministic Gaussian CPTP map.
Args:
modes (list): list of modes on which ``(X,Y)`` act
X (array): matrix for multiplicative part of transformation
Y (array): matrix for additive part of transformation | r"""Transforms the state according to a deterministic Gaussian CPTP map. | [
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r"""Transforms the state according to a deterministic Gaussian CPTP map.
Args:
modes (list): list of modes on which ``(X,Y)`` act
X (array): matrix for multiplicative part of transformation
Y (array): matrix for additive part of transformation
"""
if isinstance(modes, int):
modes = [modes]
if Y is not None:
X2, Y2 = self.circuit.expandXY(modes, X, Y)
self.circuit.apply_channel(X2, Y2)
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X2 = self.circuit.expandS(modes, X)
self.circuit.apply_channel(X, Y) | [
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google/caliban | 56f96e7e05b1d33ebdebc01620dc867f7ec54df3 | tutorials/uv-metrics/trainer/train.py | python | local_reporter | (folder: str, job_name: str) | return FSReporter(local_path).stepped() | Returns a reporter implementation that persists metrics on the local
filesystem in jsonl format. | Returns a reporter implementation that persists metrics on the local
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] | def local_reporter(folder: str, job_name: str):
"""Returns a reporter implementation that persists metrics on the local
filesystem in jsonl format.
"""
local_path = fs.path.join(folder, job_name)
return FSReporter(local_path).stepped() | [
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yongzhuo/Macropodus | 1d7b8f9938cb8b6d7744e9caabc3eb41c8891283 | macropodus/network/service/server_streamer.py | python | ServiceNer.streamer_init | (self) | ner初始化
:param model: class, like "ner_model"
:param cuda_devices: str, like "processing", "thread"
:param stream_type: str, like "0,1"
:param batch_size: int, like 32
:param max_latency: float, 0-1, like 0.01
:param worker_num: int, like 2
:return: | ner初始化
:param model: class, like "ner_model"
:param cuda_devices: str, like "processing", "thread"
:param stream_type: str, like "0,1"
:param batch_size: int, like 32
:param max_latency: float, 0-1, like 0.01
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"""
ner初始化
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:param stream_type: str, like "0,1"
:param batch_size: int, like 32
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:return:
"""
model = AlbertBilstmPredict(self.path_abs)
if self.stream_type == "thread":
self.streamer = ThreadedStreamer(model, self.batch_size, self.max_latency)
else:
self.streamer = Streamer(predict_function_or_model=model,
cuda_devices=self.cuda_devices,
max_latency=self.max_latency,
worker_num=self.worker_num,
batch_size=self.batch_size)
self.streamer._wait_for_worker_ready() | [
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tracim/tracim | a0e9746fde5a4c45b4e0f0bfa2caf9522b8c4e21 | backend/tracim_backend/config.py | python | CFG.load_and_check_json_file_path_param | (self, param_name: str, path: str,) | Check if path is valid json file and load it
:param param_name: name of parameter to check
:param path: path (value of parameter) which is check as a file path
:return: json content as dictionnary | Check if path is valid json file and load it
:param param_name: name of parameter to check
:param path: path (value of parameter) which is check as a file path
:return: json content as dictionnary | [
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"""
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:param param_name: name of parameter to check
:param path: path (value of parameter) which is check as a file path
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raise ConfigurationError(not_a_valid_json_file_msg.format(path, param_name)) from exc | [
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hyperledger/aries-cloudagent-python | 2f36776e99f6053ae92eed8123b5b1b2e891c02a | aries_cloudagent/vc/ld_proofs/purposes/proof_purpose.py | python | ProofPurpose.__eq__ | (self, o: object) | return False | Check if object is same as ProofPurpose. | Check if object is same as ProofPurpose. | [
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"""Check if object is same as ProofPurpose."""
if isinstance(o, ProofPurpose):
return (
self.date == o.date
and self.term == o.term
and self.max_timestamp_delta == o.max_timestamp_delta
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return False | [
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CPqD/RouteFlow | 3f406b9c1a0796f40a86eb1194990cdd2c955f4d | pox/pox/openflow/topology.py | python | OFSyncFlowTable.remove_strict | (self, entries=[]) | asynchronously remove entries in the flow table.
will raise a FlowTableModification event when the change has been
processed by the switch | asynchronously remove entries in the flow table.
will raise a FlowTableModification event when the change has been
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"""
asynchronously remove entries in the flow table.
will raise a FlowTableModification event when the change has been
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self._mod(entries, OFSyncFlowTable.REMOVE_STRICT) | [
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/pip/pip/_vendor/cachecontrol/cache.py | python | DictCache.get | (self, key) | return self.data.get(key, None) | [] | def get(self, key):
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tanghaibao/goatools | 647e9dd833695f688cd16c2f9ea18f1692e5c6bc | goatools/gosubdag/gosubdag_init.py | python | InitFields.__init__ | (self, ini_main, **kws) | [] | def __init__(self, ini_main, **kws):
self.go2obj_orig = ini_main.go2obj_orig
self.go2obj = ini_main.go2obj
self.kws = get_kwargs(kws, self.exp_keys, None)
if 'rcntobj' not in kws:
self.kws['rcntobj'] = True
self.kw_elems = self._init_kwelems()
self.relationships = ini_main.relationships
self.prt_flds = self._init_prt_flds()
self.go2reldepth = self._init_go2reldepth() | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/violin/_box.py | python | Box.width | (self) | return self["width"] | Sets the width of the inner box plots relative to the violins'
width. For example, with 1, the inner box plots are as wide as
the violins.
The 'width' property is a number and may be specified as:
- An int or float in the interval [0, 1]
Returns
-------
int|float | Sets the width of the inner box plots relative to the violins'
width. For example, with 1, the inner box plots are as wide as
the violins.
The 'width' property is a number and may be specified as:
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duo-labs/isthislegit | 5d51fd2e0fe070cacd1ee169ca8a371a72e005ef | dashboard/lib/flanker/mime/message/headers/headers.py | python | MimeHeaders.__contains__ | (self, key) | return normalize(key) in self._v | [] | def __contains__(self, key):
return normalize(key) in self._v | [
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/lib-tk/Tkinter.py | python | PhotoImage.zoom | (self,x,y='') | return destImage | Return a new PhotoImage with the same image as this widget
but zoom it with X and Y. | Return a new PhotoImage with the same image as this widget
but zoom it with X and Y. | [
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"""Return a new PhotoImage with the same image as this widget
but zoom it with X and Y."""
destImage = PhotoImage()
if y=='': y=x
self.tk.call(destImage, 'copy', self.name, '-zoom',x,y)
return destImage | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/com.oracle.graal.python.benchmarks/python/meso/pathfinder_rodinia.py | python | __benchmark__ | (iteration=10) | [] | def __benchmark__(iteration=10):
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Dash-Industry-Forum/dash-live-source-simulator | 23cb15c35656a731d9f6d78a30f2713eff2ec20d | dashlivesim/dashlib/mp4.py | python | tfra_box.__init__ | (self, *args) | [] | def __init__(self, *args):
super().__init__(*args)
self.random_access_time = []
self.random_access_moof_offset = [] | [
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yuxiaokui/Intranet-Penetration | f57678a204840c83cbf3308e3470ae56c5ff514b | proxy/XX-Net/code/default/python27/1.0/lib/noarch/pycparser/c_parser.py | python | CParser.p_specifier_qualifier_list_1 | (self, p) | specifier_qualifier_list : type_qualifier specifier_qualifier_list_opt | specifier_qualifier_list : type_qualifier specifier_qualifier_list_opt | [
"specifier_qualifier_list",
":",
"type_qualifier",
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] | def p_specifier_qualifier_list_1(self, p):
""" specifier_qualifier_list : type_qualifier specifier_qualifier_list_opt
"""
p[0] = self._add_declaration_specifier(p[2], p[1], 'qual') | [
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mozman/ezdxf | 59d0fc2ea63f5cf82293428f5931da7e9f9718e9 | src/ezdxf/entities/image.py | python | ImageBase.set_boundary_path | (self, vertices: Iterable["Vertex"]) | Set boundary path to `vertices`. Two vertices describe a rectangle
(lower left and upper right corner), more than two vertices is a polygon
as clipping path. | Set boundary path to `vertices`. Two vertices describe a rectangle
(lower left and upper right corner), more than two vertices is a polygon
as clipping path. | [
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"""Set boundary path to `vertices`. Two vertices describe a rectangle
(lower left and upper right corner), more than two vertices is a polygon
as clipping path.
"""
_vertices = Vec2.list(vertices)
if len(_vertices):
if len(_vertices) > 2 and not _vertices[-1].isclose(_vertices[0]):
# Close path, otherwise AutoCAD crashes
_vertices.append(_vertices[0])
self._boundary_path = _vertices
self.set_flag_state(self.USE_CLIPPING_BOUNDARY, state=True)
self.dxf.clipping = 1
self.dxf.clipping_boundary_type = 1 if len(_vertices) < 3 else 2
self.dxf.count_boundary_points = len(self._boundary_path)
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self.reset_boundary_path() | [
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aws-samples/aws-kube-codesuite | ab4e5ce45416b83bffb947ab8d234df5437f4fca | src/networkx/algorithms/bipartite/basic.py | python | degrees | (B, nodes, weight=None) | return (B.degree(top,weight),B.degree(bottom,weight)) | Return the degrees of the two node sets in the bipartite graph B.
Parameters
----------
G : NetworkX graph
nodes: list or container
Nodes in one node set of the bipartite graph.
weight : string or None, optional (default=None)
The edge attribute that holds the numerical value used as a weight.
If None, then each edge has weight 1.
The degree is the sum of the edge weights adjacent to the node.
Returns
-------
(degX,degY) : tuple of dictionaries
The degrees of the two bipartite sets as dictionaries keyed by node.
Examples
--------
>>> from networkx.algorithms import bipartite
>>> G = nx.complete_bipartite_graph(3,2)
>>> Y=set([3,4])
>>> degX,degY=bipartite.degrees(G,Y)
>>> dict(degX)
{0: 2, 1: 2, 2: 2}
Notes
-----
The container of nodes passed as argument must contain all nodes
in one of the two bipartite node sets to avoid ambiguity in the
case of disconnected graphs.
See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
for further details on how bipartite graphs are handled in NetworkX.
See Also
--------
color, density | Return the degrees of the two node sets in the bipartite graph B. | [
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"""Return the degrees of the two node sets in the bipartite graph B.
Parameters
----------
G : NetworkX graph
nodes: list or container
Nodes in one node set of the bipartite graph.
weight : string or None, optional (default=None)
The edge attribute that holds the numerical value used as a weight.
If None, then each edge has weight 1.
The degree is the sum of the edge weights adjacent to the node.
Returns
-------
(degX,degY) : tuple of dictionaries
The degrees of the two bipartite sets as dictionaries keyed by node.
Examples
--------
>>> from networkx.algorithms import bipartite
>>> G = nx.complete_bipartite_graph(3,2)
>>> Y=set([3,4])
>>> degX,degY=bipartite.degrees(G,Y)
>>> dict(degX)
{0: 2, 1: 2, 2: 2}
Notes
-----
The container of nodes passed as argument must contain all nodes
in one of the two bipartite node sets to avoid ambiguity in the
case of disconnected graphs.
See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
for further details on how bipartite graphs are handled in NetworkX.
See Also
--------
color, density
"""
bottom=set(nodes)
top=set(B)-bottom
return (B.degree(top,weight),B.degree(bottom,weight)) | [
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aliyun/aliyun-openapi-python-sdk | bda53176cc9cf07605b1cf769f0df444cca626a0 | aliyun-python-sdk-core/aliyunsdkcore/vendored/requests/adapters.py | python | HTTPAdapter.send | (self, request, stream=False, timeout=None, verify=True, cert=None, proxies=None) | return self.build_response(request, resp) | Sends PreparedRequest object. Returns Response object.
:param request: The :class:`PreparedRequest <PreparedRequest>` being sent.
:param stream: (optional) Whether to stream the request content.
:param timeout: (optional) How long to wait for the server to send
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:type timeout: float or tuple or urllib3 Timeout object
:param verify: (optional) Either a boolean, in which case it controls whether
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must be a path to a CA bundle to use
:param cert: (optional) Any user-provided SSL certificate to be trusted.
:param proxies: (optional) The proxies dictionary to apply to the request.
:rtype: requests.Response | Sends PreparedRequest object. Returns Response object. | [
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"""Sends PreparedRequest object. Returns Response object.
:param request: The :class:`PreparedRequest <PreparedRequest>` being sent.
:param stream: (optional) Whether to stream the request content.
:param timeout: (optional) How long to wait for the server to send
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:type timeout: float or tuple or urllib3 Timeout object
:param verify: (optional) Either a boolean, in which case it controls whether
we verify the server's TLS certificate, or a string, in which case it
must be a path to a CA bundle to use
:param cert: (optional) Any user-provided SSL certificate to be trusted.
:param proxies: (optional) The proxies dictionary to apply to the request.
:rtype: requests.Response
"""
conn = self.get_connection(request.url, proxies)
self.cert_verify(conn, request.url, verify, cert)
url = self.request_url(request, proxies)
self.add_headers(request)
chunked = not (request.body is None or 'Content-Length' in request.headers)
if isinstance(timeout, tuple):
try:
connect, read = timeout
timeout = TimeoutSauce(connect=connect, read=read)
except ValueError as e:
# this may raise a string formatting error.
err = ("Invalid timeout {0}. Pass a (connect, read) "
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raise ValueError(err)
elif isinstance(timeout, TimeoutSauce):
pass
else:
timeout = TimeoutSauce(connect=timeout, read=timeout)
try:
if not chunked:
resp = conn.urlopen(
method=request.method,
url=url,
body=request.body,
headers=request.headers,
redirect=False,
assert_same_host=False,
preload_content=False,
decode_content=False,
retries=self.max_retries,
timeout=timeout
)
# Send the request.
else:
if hasattr(conn, 'proxy_pool'):
conn = conn.proxy_pool
low_conn = conn._get_conn(timeout=DEFAULT_POOL_TIMEOUT)
try:
low_conn.putrequest(request.method,
url,
skip_accept_encoding=True)
for header, value in request.headers.items():
low_conn.putheader(header, value)
low_conn.endheaders()
for i in request.body:
low_conn.send(hex(len(i))[2:].encode('utf-8'))
low_conn.send(b'\r\n')
low_conn.send(i)
low_conn.send(b'\r\n')
low_conn.send(b'0\r\n\r\n')
# Receive the response from the server
try:
# For Python 2.7+ versions, use buffering of HTTP
# responses
r = low_conn.getresponse(buffering=True)
except TypeError:
# For compatibility with Python 2.6 versions and back
r = low_conn.getresponse()
resp = HTTPResponse.from_httplib(
r,
pool=conn,
connection=low_conn,
preload_content=False,
decode_content=False
)
except:
# If we hit any problems here, clean up the connection.
# Then, reraise so that we can handle the actual exception.
low_conn.close()
raise
except (ProtocolError, socket.error) as err:
raise ConnectionError(err, request=request)
except MaxRetryError as e:
if isinstance(e.reason, ConnectTimeoutError):
# TODO: Remove this in 3.0.0: see #2811
if not isinstance(e.reason, NewConnectionError):
raise ConnectTimeout(e, request=request)
if isinstance(e.reason, ResponseError):
raise RetryError(e, request=request)
if isinstance(e.reason, _ProxyError):
raise ProxyError(e, request=request)
if isinstance(e.reason, _SSLError):
# This branch is for urllib3 v1.22 and later.
raise SSLError(e, request=request)
raise ConnectionError(e, request=request)
except ClosedPoolError as e:
raise ConnectionError(e, request=request)
except _ProxyError as e:
raise ProxyError(e)
except (_SSLError, _HTTPError) as e:
if isinstance(e, _SSLError):
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kanzure/nanoengineer | 874e4c9f8a9190f093625b267f9767e19f82e6c4 | cad/src/graphics/widgets/GLPane_minimal.py | python | GLPane_minimal.__init__ | (self, parent, shareWidget, useStencilBuffer) | return | #doc
@note: If shareWidget is specified, useStencilBuffer is ignored:
set it in the widget you're sharing with. | #doc | [
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] | def __init__(self, parent, shareWidget, useStencilBuffer):
"""
#doc
@note: If shareWidget is specified, useStencilBuffer is ignored:
set it in the widget you're sharing with.
"""
if shareWidget:
self.shareWidget = shareWidget #bruce 051212
glformat = shareWidget.format()
QGLWidget.__init__(self, glformat, parent, shareWidget)
if not self.isSharing():
assert 0, "%r refused to share GL display list namespace " \
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return
else:
glformat = QGLFormat()
if (useStencilBuffer):
glformat.setStencil(True)
# set gl format to request stencil buffer
# (needed for mouseover-highlighting of objects of general
# shape in BuildAtoms_Graphicsmode.bareMotion) [bruce 050610]
if (self.useMultisample):
# use full scene anti-aliasing on hardware that supports it
# (note: setting this True works around bug 2961 on some systems)
glformat.setSampleBuffers(True)
QGLWidget.__init__(self, glformat, parent)
pass
self.glprefs = drawing_globals.glprefs
# future: should be ok if this differs for different glpanes,
# even between self and self.shareWidget. AFAIK, the refactoring
# I'm doing yesterday and today means this would work fine,
# or at least it does most of what would be required for that.
# [bruce 090304]
self._initialize_view_attributes()
# Initial value of depth "constant" (changeable by prefs.)
self.DEPTH_TWEAK = DEPTH_TWEAK_UNITS * DEPTH_TWEAK_VALUE
self.trackball = Trackball(10, 10)
self._functions_to_call_when_gl_context_is_current = []
# piotr 080714: Defined this attribute here in case
# chunk.py accesses it in ThumbView.
self.lastNonReducedDisplayMode = default_display_mode
# piotr 080807
# Most recent quaternion to be used in animation timer.
self.last_quat = None
self.transforms = [] ### TODO: clear this at start of frame, complain if not clear
# stack of current transforms (or Nodes that generate them)
# [bruce 090220]
# Note: this may be revised once we have TransformNode,
# e.g. we might push their dt and st separately;
# note we might need to push a "slot" for them if they might
# change before being popped, or perhaps even if they might
# change between the time pushed and the time later used
# (by something that collects them from here in association
# with a ColorSortedDisplayList).
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Qiskit/qiskit-terra | b66030e3b9192efdd3eb95cf25c6545fe0a13da4 | qiskit/dagcircuit/dagcircuit.py | python | DAGCircuit.collect_runs | (self, namelist) | return {tuple(x) for x in group_list} | Return a set of non-conditional runs of "op" nodes with the given names.
For example, "... h q[0]; cx q[0],q[1]; cx q[0],q[1]; h q[1]; .."
would produce the tuple of cx nodes as an element of the set returned
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"""Return a set of non-conditional runs of "op" nodes with the given names.
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Nodes must have only one successor to continue the run.
"""
def filter_fn(node):
return (
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group_list = rx.collect_runs(self._multi_graph, filter_fn)
return {tuple(x) for x in group_list} | [
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"o... | https://github.com/Qiskit/qiskit-terra/blob/b66030e3b9192efdd3eb95cf25c6545fe0a13da4/qiskit/dagcircuit/dagcircuit.py#L1624-L1645 | |
xlcnd/isbntools | e7c85f0f4b3dd023b43b0b5daccbd8f6f62250f9 | isbntools/bin/repl.py | python | ISBNRepl.complete_meta | (self, text, line, begidx, endidx) | return self._provandfmts(text) | Autocomplete providers. | Autocomplete providers. | [
"Autocomplete",
"providers",
"."
] | def complete_meta(self, text, line, begidx, endidx):
"""Autocomplete providers."""
return self._provandfmts(text) | [
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openstack/python-neutronclient | 517bef2c5454dde2eba5cc2194ee857be6be7164 | neutronclient/v2_0/client.py | python | Client.show_flavor | (self, flavor, **_params) | return self.get(self.flavor_path % (flavor), params=_params) | Fetches information for a certain Neutron service flavor. | Fetches information for a certain Neutron service flavor. | [
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] | def show_flavor(self, flavor, **_params):
"""Fetches information for a certain Neutron service flavor."""
return self.get(self.flavor_path % (flavor), params=_params) | [
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jaywink/socialhome | c3178b044936a5c57a502ab6ed2b4f43c8e076ca | socialhome/federate/views.py | python | webfinger_view | (request) | return HttpResponse(webfinger, content_type="application/xrd+xml") | Generate a webfinger document. | Generate a webfinger document. | [
"Generate",
"a",
"webfinger",
"document",
"."
] | def webfinger_view(request):
"""Generate a webfinger document."""
q = request.GET.get("q")
if not q:
raise Http404()
username = q.split("@")[0]
if username.startswith("acct:"):
username = username.replace("acct:", "", 1)
user = get_object_or_404(User, username=username)
# Create webfinger document
webfinger = generate_legacy_webfinger(
"diaspora",
handle="{username}@{domain}".format(username=user.username, domain=settings.SOCIALHOME_DOMAIN),
host=settings.SOCIALHOME_URL,
guid=str(user.profile.uuid),
public_key=user.profile.rsa_public_key
)
return HttpResponse(webfinger, content_type="application/xrd+xml") | [
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algorhythms/LeetCode | 3fb14aeea62a960442e47dfde9f964c7ffce32be | 670 Maximum Swap.py | python | Solution.maximumSwap | (self, num: int) | return int("".join(nums)) | stk maintain a increasing stack from right to left | stk maintain a increasing stack from right to left | [
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] | def maximumSwap(self, num: int) -> int:
"""
stk maintain a increasing stack from right to left
"""
stk = []
nums = list(str(num))
n = len(nums)
for i in range(n-1, -1, -1):
if stk and stk[-1][1] >= nums[i]: # only keep the rightmost duplicate
continue
stk.append((i, nums[i]))
for i in range(n):
while stk and stk[-1][0] <= i:
stk.pop()
if stk and stk[-1][1] > nums[i]:
j = stk[-1][0]
nums[i], nums[j] = nums[j], nums[i]
break
return int("".join(nums)) | [
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hubo1016/vlcp | 61c4c2595b610675ac0cbc4dbc46f70ec40090d3 | vlcp/event/runnable.py | python | RoutineContainer.wait_for_all | (self, *matchers, eventlist = None, eventdict = None, callback = None) | return eventlist, eventdict | Wait until each matcher matches an event. When this coroutine method returns,
`eventlist` is set to the list of events in the arriving order (may not
be the same as the matchers); `eventdict` is set to a dictionary
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:param eventdict: use external event dict
:param callback: if not None, the callback should be a callable callback(event, matcher)
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:return: (eventlist, eventdict) | Wait until each matcher matches an event. When this coroutine method returns,
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`{matcher1: event1, matcher2: event2, ...}`
:param eventlist: use external event list, so when an exception occurs
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:param eventdict: use external event dict
:param callback: if not None, the callback should be a callable callback(event, matcher)
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:return: (eventlist, eventdict) | [
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"""
Wait until each matcher matches an event. When this coroutine method returns,
`eventlist` is set to the list of events in the arriving order (may not
be the same as the matchers); `eventdict` is set to a dictionary
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:param eventlist: use external event list, so when an exception occurs
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:param eventdict: use external event dict
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:return: (eventlist, eventdict)
"""
if eventdict is None:
eventdict = {}
if eventlist is None:
eventlist = []
ms = len(matchers)
last_matchers = Diff_(matchers)
while ms:
ev, m = await last_matchers
ms -= 1
if callback:
callback(ev, m)
eventlist.append(ev)
eventdict[m] = ev
last_matchers = Diff_(last_matchers, remove=(m,))
return eventlist, eventdict | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/Django-1.11.29/django/contrib/gis/geos/point.py | python | Point.y | (self, value) | Sets the Y component of the Point. | Sets the Y component of the Point. | [
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"Sets the Y component of the Point."
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/simulated/sensor.py | python | SimulatedSensor.time_delta | (self) | return dt1 - dt0 | Return the time delta. | Return the time delta. | [
"Return",
"the",
"time",
"delta",
"."
] | def time_delta(self):
"""Return the time delta."""
dt0 = self._start_time
dt1 = dt_util.utcnow()
return dt1 - dt0 | [
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PaddlePaddle/PGL | e48545f2814523c777b8a9a9188bf5a7f00d6e52 | apps/Graph4KG/utils.py | python | print_log | (step, interval, log, timer, time_sum) | Print log to logger. | Print log to logger. | [
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"."
] | def print_log(step, interval, log, timer, time_sum):
"""Print log to logger.
"""
logging.info(
'step: %d, loss: %.5f, reg: %.4e, speed: %.2f steps/s, time: %.2f s' %
(step, log['loss'] / interval, log['reg'] / interval,
interval / time_sum, time_sum))
logging.info('sample: %f, forward: %f, backward: %f, update: %f' % (
timer['sample'], timer['forward'], timer['backward'], timer['update'])) | [
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"... | https://github.com/PaddlePaddle/PGL/blob/e48545f2814523c777b8a9a9188bf5a7f00d6e52/apps/Graph4KG/utils.py#L64-L72 | ||
brendano/tweetmotif | 1b0b1e3a941745cd5a26eba01f554688b7c4b27e | everything_else/djfrontend/django-1.0.2/dispatch/saferef.py | python | BoundNonDescriptorMethodWeakref.__call__ | (self) | return None | Return a strong reference to the bound method
If the target cannot be retrieved, then will
return None, otherwise returns a bound instance
method for our object and function.
Note:
You may call this method any number of times,
as it does not invalidate the reference. | Return a strong reference to the bound method | [
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] | def __call__(self):
"""Return a strong reference to the bound method
If the target cannot be retrieved, then will
return None, otherwise returns a bound instance
method for our object and function.
Note:
You may call this method any number of times,
as it does not invalidate the reference.
"""
target = self.weakSelf()
if target is not None:
function = self.weakFunc()
if function is not None:
# Using curry() would be another option, but it erases the
# "signature" of the function. That is, after a function is
# curried, the inspect module can't be used to determine how
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return getattr(target, function.__name__)
return None | [
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NaturalHistoryMuseum/inselect | 196a3ae2a0ed4e2c7cb667aaba9a6be1bcd90ca6 | inselect/gui/main_window.py | python | MainWindow._sync_recent_documents_actions | (self) | Synchronises the 'recent documents' actions | Synchronises the 'recent documents' actions | [
"Synchronises",
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] | def _sync_recent_documents_actions(self):
"Synchronises the 'recent documents' actions"
debug_print('MainWindow._sync_recent_documents_actions')
recent = RecentDocuments().read_paths()
if not recent:
# No recent documents - a single disabled action with placeholder
# text
self.recent_doc_actions[0].setEnabled(False)
self.recent_doc_actions[0].setText('No recent documents')
self.recent_doc_actions[0].setVisible(True)
hide_actions_after = 1
elif len(recent) > len(self.recent_doc_actions):
msg = 'Unexpected number of recent documents [{0}]'
raise ValueError(msg.format(len(recent)))
else:
# Show as many actions as there are recent documents
for index, path, action in zip(count(), recent, self.recent_doc_actions):
action.setEnabled(True)
action.setText(path.stem)
action.setToolTip(str(path))
action.setVisible(True)
hide_actions_after = 1 + index
# Hide all actions after and including 'hide_actions_after'
for action in self.recent_doc_actions[hide_actions_after:]:
action.setVisible(False)
action.setText('') | [
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secdev/scapy | 65089071da1acf54622df0b4fa7fc7673d47d3cd | scapy/utils.py | python | fletcher16_checkbytes | (binbuf, offset) | return chb(x) + chb(y) | Calculates the Fletcher-16 checkbytes returned as 2 byte binary-string.
Including the bytes into the buffer (at the position marked by offset) the # noqa: E501
global Fletcher-16 checksum of the buffer will be 0. Thus it is easy to verify # noqa: E501
the integrity of the buffer on the receiver side.
For details on the algorithm, see RFC 2328 chapter 12.1.7 and RFC 905 Annex B. # noqa: E501 | Calculates the Fletcher-16 checkbytes returned as 2 byte binary-string. | [
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] | def fletcher16_checkbytes(binbuf, offset):
# type: (bytes, int) -> bytes
"""Calculates the Fletcher-16 checkbytes returned as 2 byte binary-string.
Including the bytes into the buffer (at the position marked by offset) the # noqa: E501
global Fletcher-16 checksum of the buffer will be 0. Thus it is easy to verify # noqa: E501
the integrity of the buffer on the receiver side.
For details on the algorithm, see RFC 2328 chapter 12.1.7 and RFC 905 Annex B. # noqa: E501
"""
# This is based on the GPLed C implementation in Zebra <http://www.zebra.org/> # noqa: E501
if len(binbuf) < offset:
raise Exception("Packet too short for checkbytes %d" % len(binbuf))
binbuf = binbuf[:offset] + b"\x00\x00" + binbuf[offset + 2:]
(c0, c1) = _fletcher16(binbuf)
x = ((len(binbuf) - offset - 1) * c0 - c1) % 255
if (x <= 0):
x += 255
y = 510 - c0 - x
if (y > 255):
y -= 255
return chb(x) + chb(y) | [
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