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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/dataclasses.py | python | is_dataclass | (obj) | return hasattr(cls, _FIELDS) | Returns True if obj is a dataclass or an instance of a
dataclass. | Returns True if obj is a dataclass or an instance of a
dataclass. | [
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"""Returns True if obj is a dataclass or an instance of a
dataclass."""
cls = obj if isinstance(obj, type) and not isinstance(obj, GenericAlias) else type(obj)
return hasattr(cls, _FIELDS) | [
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/tkinter/colorchooser.py | python | Chooser._fixresult | (self, widget, result) | return (r//256, g//256, b//256), str(result) | Adjust result returned from call to tk_chooseColor.
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"""Adjust result returned from call to tk_chooseColor.
Return both an RGB tuple of ints in the range (0, 255) and the
tk color string in the form #rrggbb.
"""
# Result can be many things: an empty tuple, an empty string, or
# a _tkinter.Tcl_Obj, so this somewhat weird check handles that.
if not result or not str(result):
return None, None # canceled
# To simplify application code, the color chooser returns
# an RGB tuple together with the Tk color string.
r, g, b = widget.winfo_rgb(result)
return (r//256, g//256, b//256), str(result) | [
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guildai/guildai | 1665985a3d4d788efc1a3180ca51cc417f71ca78 | guild/external/setuptools/command/build_py.py | python | build_py.check_package | (self, package, package_dir) | return init_py | Check namespace packages' __init__ for declare_namespace | Check namespace packages' __init__ for declare_namespace | [
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try:
return self.packages_checked[package]
except KeyError:
pass
init_py = orig.build_py.check_package(self, package, package_dir)
self.packages_checked[package] = init_py
if not init_py or not self.distribution.namespace_packages:
return init_py
for pkg in self.distribution.namespace_packages:
if pkg == package or pkg.startswith(package + '.'):
break
else:
return init_py
with io.open(init_py, 'rb') as f:
contents = f.read()
if b'declare_namespace' not in contents:
raise distutils.errors.DistutilsError(
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wbenbihi/hourglasstensorlfow | 356e0638a1bf28ecc92a121812e74d3fd0e353f5 | hourglass_tiny.py | python | HourglassModel._accur | (self, pred, gtMap, num_image) | return tf.subtract(tf.to_float(1), err/num_image) | Given a Prediction batch (pred) and a Ground Truth batch (gtMaps),
returns one minus the mean distance.
Args:
pred : Prediction Batch (shape = num_image x 64 x 64)
gtMaps : Ground Truth Batch (shape = num_image x 64 x 64)
num_image : (int) Number of images in batch
Returns:
(float) | Given a Prediction batch (pred) and a Ground Truth batch (gtMaps),
returns one minus the mean distance.
Args:
pred : Prediction Batch (shape = num_image x 64 x 64)
gtMaps : Ground Truth Batch (shape = num_image x 64 x 64)
num_image : (int) Number of images in batch
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err = tf.to_float(0)
for i in range(num_image):
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pydata/pandas-datareader | 3f1d590e6e67cf30aa516d3b1f1921b5c45ccc4b | pandas_datareader/av/__init__.py | python | AlphaVantage.__init__ | (
self,
symbols=None,
start=None,
end=None,
retry_count=3,
pause=0.1,
session=None,
api_key=None,
) | [] | def __init__(
self,
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start=None,
end=None,
retry_count=3,
pause=0.1,
session=None,
api_key=None,
):
super(AlphaVantage, self).__init__(
symbols=symbols,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
)
if api_key is None:
api_key = os.getenv("ALPHAVANTAGE_API_KEY")
if not api_key or not isinstance(api_key, str):
raise ValueError(
"The AlphaVantage API key must be provided "
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self.api_key = api_key | [
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cortex-lab/phy | 9a330b9437a3d0b40a37a201d147224e6e7fb462 | phy/apps/base.py | python | TemplateMixin.get_mean_spike_template_amplitudes | (self, cluster_id) | return np.mean(self.get_spike_template_amplitudes(spike_ids)) | Return the average of the spike template amplitudes. | Return the average of the spike template amplitudes. | [
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"""Return the average of the spike template amplitudes."""
spike_ids = self._get_amplitude_spike_ids(cluster_id)
return np.mean(self.get_spike_template_amplitudes(spike_ids)) | [
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | golismero/api/data/information/dns.py | python | DnsRegisterLOC.longitude | (self) | return self.__longitude | :return: tuple specifying the degrees, minutes, seconds, and milliseconds of the coordinate.
:rtype: (int, int, int, int) | :return: tuple specifying the degrees, minutes, seconds, and milliseconds of the coordinate.
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"""
:return: tuple specifying the degrees, minutes, seconds, and milliseconds of the coordinate.
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deepmipt/DeepPavlov | 08555428388fed3c7b036c0a82a70a25efcabcff | deeppavlov/utils/connector/conversation.py | python | BaseConversation._rearm_self_destruct | (self) | Rearms self-destruct timer. | Rearms self-destruct timer. | [
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] | def _rearm_self_destruct(self) -> None:
"""Rearms self-destruct timer."""
self._timer.cancel()
self._start_timer() | [
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spywhere/Javatar | e273ec40c209658247a71b109bb90cd126984a29 | core/java_utils.py | python | JavaClassPath.get_package | (self) | return self.package | Returns a package within class path | Returns a package within class path | [
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pysmt/pysmt | ade4dc2a825727615033a96d31c71e9f53ce4764 | pysmt/rewritings.py | python | CNFizer.walk_function | (self, formula, **kwargs) | [] | def walk_function(self, formula, **kwargs):
ty = formula.function_symbol().symbol_type()
if ty.return_type.is_bool_type():
return formula, CNFizer.TRUE_CNF
else:
return CNFizer.THEORY_PLACEHOLDER | [
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pyqt/examples | 843bb982917cecb2350b5f6d7f42c9b7fb142ec1 | src/pyqt-official/sql/querymodel.py | python | createView | (title, model) | [] | def createView(title, model):
global offset, views
view = QTableView()
views.append(view)
view.setModel(model)
view.setWindowTitle(title)
view.move(100 + offset, 100 + offset)
offset += 20
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/scipy/spatial/distance.py | python | cosine | (u, v) | return dist | Computes the Cosine distance between 1-D arrays.
The Cosine distance between `u` and `v`, is defined as
.. math::
1 - \\frac{u \\cdot v}
{||u||_2 ||v||_2}.
where :math:`u \\cdot v` is the dot product of :math:`u` and
:math:`v`.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
Returns
-------
cosine : double
The Cosine distance between vectors `u` and `v`. | Computes the Cosine distance between 1-D arrays. | [
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] | def cosine(u, v):
"""
Computes the Cosine distance between 1-D arrays.
The Cosine distance between `u` and `v`, is defined as
.. math::
1 - \\frac{u \\cdot v}
{||u||_2 ||v||_2}.
where :math:`u \\cdot v` is the dot product of :math:`u` and
:math:`v`.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
Returns
-------
cosine : double
The Cosine distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
dist = 1.0 - np.dot(u, v) / (norm(u) * norm(v))
return dist | [
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ganeti/ganeti | d340a9ddd12f501bef57da421b5f9b969a4ba905 | lib/tools/burnin.py | python | FeedbackAccumulator.GetFeedbackBuf | (self) | return self._feed_buf.getvalue() | Return the contents of the buffer. | Return the contents of the buffer. | [
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merenlab/anvio | 9b792e2cedc49ecb7c0bed768261595a0d87c012 | anvio/trnaseq.py | python | TRNASeqDataset.report_sub_stats | (self) | Report to terminal stats on potential modification-induced substitutions. | Report to terminal stats on potential modification-induced substitutions. | [
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"""Report to terminal stats on potential modification-induced substitutions."""
count_M = len(self.dict_M)
dict_Nf = self.dict_Nf
total_sub_count = 0
total_length_M = 0
for seq_M in self.dict_M.values():
length_M = len(dict_Nf[seq_M.name].string)
total_sub_count += len(seq_M.sub_positions)
total_length_M += length_M
mean_sub_per_seq = total_sub_count / count_M
mean_sub_per_nt = total_sub_count / total_length_M
warning = self.run.warning
info = self.run.info
warning(None, "SUBSTITUTION SEARCH RESULTS", lc='green', nl_before=1)
info("Modified seqs", count_M)
info("Mean (*potential*) subs per modified seq", round(mean_sub_per_seq, 1))
info("Mean subs per nt in modified seq", round(mean_sub_per_nt, 3), nl_after=2) | [
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eirannejad/pyRevit | 49c0b7eb54eb343458ce1365425e6552d0c47d44 | site-packages/sqlalchemy/orm/session.py | python | _state_session | (state) | return None | Given an :class:`.InstanceState`, return the :class:`.Session`
associated, if any. | Given an :class:`.InstanceState`, return the :class:`.Session`
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bnpy/bnpy | d5b311e8f58ccd98477f4a0c8a4d4982e3fca424 | bnpy/suffstats/ParamBag.py | python | ParamBag.setAllFieldsToZero | (self) | Update every field to be an array of all zeros. | Update every field to be an array of all zeros. | [
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for key, dims in list(self._FieldDims.items()):
curShape = getattr(self, key).shape
self.setField(key, np.zeros(curShape), dims=dims) | [
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gnes-ai/gnes | b4d2c8cf863664a9322f866a72eab8246175ebef | gnes/proto/gnes_pb2_grpc.py | python | GnesRPCStub.__init__ | (self, channel) | Constructor.
Args:
channel: A grpc.Channel. | Constructor. | [
"Constructor",
"."
] | def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.Train = channel.unary_unary(
'/gnes.GnesRPC/Train',
request_serializer=gnes__pb2.Request.SerializeToString,
response_deserializer=gnes__pb2.Response.FromString,
)
self.Index = channel.unary_unary(
'/gnes.GnesRPC/Index',
request_serializer=gnes__pb2.Request.SerializeToString,
response_deserializer=gnes__pb2.Response.FromString,
)
self.Query = channel.unary_unary(
'/gnes.GnesRPC/Query',
request_serializer=gnes__pb2.Request.SerializeToString,
response_deserializer=gnes__pb2.Response.FromString,
)
self.Call = channel.unary_unary(
'/gnes.GnesRPC/Call',
request_serializer=gnes__pb2.Request.SerializeToString,
response_deserializer=gnes__pb2.Response.FromString,
)
self.StreamCall = channel.stream_stream(
'/gnes.GnesRPC/StreamCall',
request_serializer=gnes__pb2.Request.SerializeToString,
response_deserializer=gnes__pb2.Response.FromString,
) | [
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ansible-collections/community.general | 3faffe8f47968a2400ba3c896c8901c03001a194 | plugins/modules/monitoring/nagios.py | python | Nagios.disable_servicegroup_svc_notifications | (self, servicegroup) | This command is used to prevent notifications from being sent
out for all services in the specified servicegroup.
Note that this does not prevent notifications from being sent
out about the hosts in this servicegroup.
Syntax: DISABLE_SERVICEGROUP_SVC_NOTIFICATIONS;<servicegroup_name> | This command is used to prevent notifications from being sent
out for all services in the specified servicegroup. | [
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"for",
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"services",
"in",
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"specified",
"servicegroup",
"."
] | def disable_servicegroup_svc_notifications(self, servicegroup):
"""
This command is used to prevent notifications from being sent
out for all services in the specified servicegroup.
Note that this does not prevent notifications from being sent
out about the hosts in this servicegroup.
Syntax: DISABLE_SERVICEGROUP_SVC_NOTIFICATIONS;<servicegroup_name>
"""
cmd = "DISABLE_SERVICEGROUP_SVC_NOTIFICATIONS"
notif_str = self._fmt_notif_str(cmd, servicegroup)
self._write_command(notif_str) | [
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ashdnazg/pyreshark | 77a84ef1d7858defe8a5bb4579d10f3dc254f82d | python/cal/cal_types.py | python | PyFunctionItem.generate_filter_name | (self, prefix) | @summary: See ItemBase. | [] | def generate_filter_name(self, prefix):
'''
@summary: See ItemBase.
'''
for subitem in getattr(self, "_subitems", []):
subitem.generate_filter_name(prefix) | [
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] | https://github.com/ashdnazg/pyreshark/blob/77a84ef1d7858defe8a5bb4579d10f3dc254f82d/python/cal/cal_types.py#L509-L514 | |||
zalando/patroni | 3e1076a5746b4d7f313eaba0ff374824644c8ab1 | patroni/ctl.py | python | set_defaults | (config, cluster_name) | fill-in some basic configuration parameters if config file is not set | fill-in some basic configuration parameters if config file is not set | [
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"parameters",
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] | def set_defaults(config, cluster_name):
"""fill-in some basic configuration parameters if config file is not set """
config['postgresql'].setdefault('name', cluster_name)
config['postgresql'].setdefault('scope', cluster_name)
config['postgresql'].setdefault('listen', '127.0.0.1')
config['postgresql']['authentication'] = {'replication': None}
config['restapi']['listen'] = ':' in config['restapi']['listen'] and config['restapi']['listen'] or '127.0.0.1:8008' | [
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AlessandroZ/LaZagne | 30623c9138e2387d10f6631007f954f2a4ead06d | Windows/lazagne/config/DPAPI/masterkey.py | python | MasterKeyFile.jhash | (self, sid=None, context='local') | return '$DPAPImk${version}*{context}*{sid}*{cipher_algo}*{hmac_algo}*{rounds}*{iv}*{size}*{ciphertext}'.format(
version=version,
context=context_int,
sid=sid,
cipher_algo=cipher_algo,
hmac_algo=hmac_algo,
rounds=self.masterkey.rounds,
iv=self.masterkey.iv.encode("hex"),
size=len(self.masterkey.ciphertext.encode("hex")),
ciphertext=self.masterkey.ciphertext.encode("hex")
) | Compute the hash used to be bruteforced.
From the masterkey field of the mk file => mk variable. | Compute the hash used to be bruteforced.
From the masterkey field of the mk file => mk variable. | [
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">",
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"."
] | def jhash(self, sid=None, context='local'):
"""
Compute the hash used to be bruteforced.
From the masterkey field of the mk file => mk variable.
"""
if 'des3' in str(self.masterkey.cipherAlgo).lower() and 'hmac' in str(self.masterkey.hashAlgo).lower():
version = 1
hmac_algo = 'sha1'
cipher_algo = 'des3'
elif 'aes-256' in str(self.masterkey.cipherAlgo).lower() and 'sha512' in str(self.masterkey.hashAlgo).lower():
version = 2
hmac_algo = 'sha512'
cipher_algo = 'aes256'
else:
return 'Unsupported combination of cipher {cipher_algo} and hash algorithm {algo} found!'.format(
cipher_algo=self.masterkey.cipherAlgo, algo=self.masterkey.hashAlgo)
context_int = 0
if context == "domain":
context_int = 2
elif context == "local":
context_int = 1
return '$DPAPImk${version}*{context}*{sid}*{cipher_algo}*{hmac_algo}*{rounds}*{iv}*{size}*{ciphertext}'.format(
version=version,
context=context_int,
sid=sid,
cipher_algo=cipher_algo,
hmac_algo=hmac_algo,
rounds=self.masterkey.rounds,
iv=self.masterkey.iv.encode("hex"),
size=len(self.masterkey.ciphertext.encode("hex")),
ciphertext=self.masterkey.ciphertext.encode("hex")
) | [
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CalebBell/fluids | dbbdd1d5ea3c458bd68dd8e21cf6281a0ec3fc80 | fluids/friction.py | python | Round_1980 | (Re, eD) | return 4*ff | r'''Calculates Darcy friction factor using the method in Round (1980) [2]_
as shown in [1]_.
.. math::
\frac{1}{\sqrt{f_f}} = -3.6\log_{10}\left[\frac{Re}{0.135Re
\frac{\epsilon}{D}+6.5}\right]
Parameters
----------
Re : float
Reynolds number, [-]
eD : float
Relative roughness, [-]
Returns
-------
fd : float
Darcy friction factor [-]
Notes
-----
Range is 4E3 <= Re <= 4E8; 0 <= eD <= 5E-2.
Examples
--------
>>> Round_1980(1E5, 1E-4)
0.01831475391244354
References
----------
.. [1] Winning, H. and T. Coole. "Explicit Friction Factor Accuracy and
Computational Efficiency for Turbulent Flow in Pipes." Flow, Turbulence
and Combustion 90, no. 1 (January 1, 2013): 1-27.
doi:10.1007/s10494-012-9419-7
.. [2] Round, G. F."An Explicit Approximation for the Friction
Factor-Reynolds Number Relation for Rough and Smooth Pipes." The
Canadian Journal of Chemical Engineering 58, no. 1 (February 1, 1980):
122-23. doi:10.1002/cjce.5450580119. | r'''Calculates Darcy friction factor using the method in Round (1980) [2]_
as shown in [1]_. | [
"r",
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")",
"[",
"2",
"]",
"_",
"as",
"shown",
"in",
"[",
"1",
"]",
"_",
"."
] | def Round_1980(Re, eD):
r'''Calculates Darcy friction factor using the method in Round (1980) [2]_
as shown in [1]_.
.. math::
\frac{1}{\sqrt{f_f}} = -3.6\log_{10}\left[\frac{Re}{0.135Re
\frac{\epsilon}{D}+6.5}\right]
Parameters
----------
Re : float
Reynolds number, [-]
eD : float
Relative roughness, [-]
Returns
-------
fd : float
Darcy friction factor [-]
Notes
-----
Range is 4E3 <= Re <= 4E8; 0 <= eD <= 5E-2.
Examples
--------
>>> Round_1980(1E5, 1E-4)
0.01831475391244354
References
----------
.. [1] Winning, H. and T. Coole. "Explicit Friction Factor Accuracy and
Computational Efficiency for Turbulent Flow in Pipes." Flow, Turbulence
and Combustion 90, no. 1 (January 1, 2013): 1-27.
doi:10.1007/s10494-012-9419-7
.. [2] Round, G. F."An Explicit Approximation for the Friction
Factor-Reynolds Number Relation for Rough and Smooth Pipes." The
Canadian Journal of Chemical Engineering 58, no. 1 (February 1, 1980):
122-23. doi:10.1002/cjce.5450580119.
'''
ff = (-3.6*log10(Re/(0.135*Re*eD+6.5)))**-2
return 4*ff | [
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] | https://github.com/CalebBell/fluids/blob/dbbdd1d5ea3c458bd68dd8e21cf6281a0ec3fc80/fluids/friction.py#L902-L943 | |
felipecode/coiltraine | 29060ab5fd2ea5531686e72c621aaaca3b23f4fb | carla08/image_converter.py | python | depth_to_logarithmic_grayscale | (image) | return numpy.repeat(logdepth[:, :, numpy.newaxis], 3, axis=2) | Convert an image containing CARLA encoded depth-map to a logarithmic
grayscale image array.
"max_depth" is used to omit the points that are far enough. | Convert an image containing CARLA encoded depth-map to a logarithmic
grayscale image array.
"max_depth" is used to omit the points that are far enough. | [
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"that",
"are",
"far",
"enough",
"."
] | def depth_to_logarithmic_grayscale(image):
"""
Convert an image containing CARLA encoded depth-map to a logarithmic
grayscale image array.
"max_depth" is used to omit the points that are far enough.
"""
normalized_depth = depth_to_array(image)
# Convert to logarithmic depth.
logdepth = numpy.ones(normalized_depth.shape) + \
(numpy.log(normalized_depth) / 5.70378)
logdepth = numpy.clip(logdepth, 0.0, 1.0)
logdepth *= 255.0
# Expand to three colors.
return numpy.repeat(logdepth[:, :, numpy.newaxis], 3, axis=2) | [
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... | https://github.com/felipecode/coiltraine/blob/29060ab5fd2ea5531686e72c621aaaca3b23f4fb/carla08/image_converter.py#L94-L107 | |
wakatime/legacy-python-cli | 9b64548b16ab5ef16603d9a6c2620a16d0df8d46 | wakatime/packages/py26/pygments/lexer.py | python | RegexLexerMeta._process_new_state | (cls, new_state, unprocessed, processed) | Preprocess the state transition action of a token definition. | Preprocess the state transition action of a token definition. | [
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] | def _process_new_state(cls, new_state, unprocessed, processed):
"""Preprocess the state transition action of a token definition."""
if isinstance(new_state, str):
# an existing state
if new_state == '#pop':
return -1
elif new_state in unprocessed:
return (new_state,)
elif new_state == '#push':
return new_state
elif new_state[:5] == '#pop:':
return -int(new_state[5:])
else:
assert False, 'unknown new state %r' % new_state
elif isinstance(new_state, combined):
# combine a new state from existing ones
tmp_state = '_tmp_%d' % cls._tmpname
cls._tmpname += 1
itokens = []
for istate in new_state:
assert istate != new_state, 'circular state ref %r' % istate
itokens.extend(cls._process_state(unprocessed,
processed, istate))
processed[tmp_state] = itokens
return (tmp_state,)
elif isinstance(new_state, tuple):
# push more than one state
for istate in new_state:
assert (istate in unprocessed or
istate in ('#pop', '#push')), \
'unknown new state ' + istate
return new_state
else:
assert False, 'unknown new state def %r' % new_state | [
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/runpy.py | python | run_module | (mod_name, init_globals=None,
run_name=None, alter_sys=False) | Execute a module's code without importing it
Returns the resulting top level namespace dictionary | Execute a module's code without importing it | [
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"a",
"module",
"s",
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"without",
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] | def run_module(mod_name, init_globals=None,
run_name=None, alter_sys=False):
"""Execute a module's code without importing it
Returns the resulting top level namespace dictionary
"""
mod_name, loader, code, fname = _get_module_details(mod_name)
if run_name is None:
run_name = mod_name
pkg_name = mod_name.rpartition('.')[0]
if alter_sys:
return _run_module_code(code, init_globals, run_name,
fname, loader, pkg_name)
else:
# Leave the sys module alone
return _run_code(code, {}, init_globals, run_name,
fname, loader, pkg_name) | [
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... | https://github.com/replit-archive/empythoned/blob/977ec10ced29a3541a4973dc2b59910805695752/cpython/Lib/runpy.py#L164-L180 | ||
trigger/trigger | c089f761a150f537f7e3201422a3964ec52ff245 | trigger/twister.py | python | TriggerSSHJunoscriptChannel._send_next | (self) | Send the next command in the stack. | Send the next command in the stack. | [
"Send",
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"."
] | def _send_next(self):
"""Send the next command in the stack."""
self.resetTimeout()
if self.incremental:
self.incremental(self.results)
try:
next_command = self.commanditer.next()
log.msg('[%s] COMMAND: next command %s' % (self.device,
next_command))
except StopIteration:
log.msg('[%s] CHANNEL: out of commands, closing connection...' %
self.device)
self.loseConnection()
return None
if next_command is None:
self.results.append(None)
self._send_next()
else:
rpc = Element('rpc')
rpc.append(next_command)
ElementTree(rpc).write(self) | [
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"n... | https://github.com/trigger/trigger/blob/c089f761a150f537f7e3201422a3964ec52ff245/trigger/twister.py#L1566-L1590 | ||
IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/27/Doc/docutils/parsers/rst/states.py | python | RSTState.inline_text | (self, text, lineno) | return self.inliner.parse(text, lineno, self.memo, self.parent) | Return 2 lists: nodes (text and inline elements), and system_messages. | Return 2 lists: nodes (text and inline elements), and system_messages. | [
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] | def inline_text(self, text, lineno):
"""
Return 2 lists: nodes (text and inline elements), and system_messages.
"""
return self.inliner.parse(text, lineno, self.memo, self.parent) | [
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spack/spack | 675210bd8bd1c5d32ad1cc83d898fb43b569ed74 | lib/spack/spack/relocate.py | python | _patchelf | () | return patchelf.path | Return the full path to the patchelf binary, if available, else None. | Return the full path to the patchelf binary, if available, else None. | [
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] | def _patchelf():
"""Return the full path to the patchelf binary, if available, else None."""
if is_macos:
return None
patchelf = executable.which('patchelf')
if patchelf is None:
with spack.bootstrap.ensure_bootstrap_configuration():
patchelf = spack.bootstrap.ensure_patchelf_in_path_or_raise()
return patchelf.path | [
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wikilinks/neleval | 68f68414f6a82e3d86761b8d3083e537582bfa7f | neleval/summary.py | python | PlotSystems._plot1d | (self, ax, data, group_sizes, tick_labels, score_label, sys_label=None) | [] | def _plot1d(self, ax, data, group_sizes, tick_labels, score_label, sys_label=None):
small_font = make_small_font()
ordinate = np.repeat(np.arange(len(group_sizes)), group_sizes)
for scores, kwargs in data:
if kwargs.pop('score_only', False):
try:
scores = scores['score']
except Exception:
pass
if self.secondary == 'rows':
self._plot(ax, scores, ordinate[::-1], **kwargs)
#, marker=marker, color=color, label=self._t(label), markeredgecolor=color)
else:
self._plot(ax, ordinate, scores, **kwargs)
ticks = np.arange(len(tick_labels))
tick_labels = [self._t(label) for label in tick_labels]
score_label = self._t(score_label)
if self.secondary == 'rows':
plt.yticks(ticks[::-1], tick_labels, fontproperties=small_font)
self._set_lim(plt.xlim)
plt.ylim(-.5, len(tick_labels) - .5)
plt.xlabel(score_label)
if sys_label is not None:
plt.ylabel(self._t(sys_label))
elif self.secondary == 'columns':
plt.xticks(ticks, tick_labels, fontproperties=small_font, **XTICK_ROTATION)
plt.xlim(-.5, len(tick_labels) - .5)
self._set_lim(plt.ylim)
plt.ylabel(score_label)
if sys_label is not None:
plt.xlabel(self._t(sys_label))
else:
raise ValueError('Unexpected secondary: {!r}'.format(self.secondary))
plt.tight_layout()
if len(data) > 1:
plt.legend(loc='best', prop=small_font, ncol=self._ncol(len(data))) | [
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elastic/eland | 72856e2c3f827a0b71d140323009a7a9a3df6e1d | eland/ndframe.py | python | NDFrame.dtypes | (self) | return self._query_compiler.dtypes | Return the pandas dtypes in the DataFrame. Elasticsearch types are mapped
to pandas dtypes via Mappings._es_dtype_to_pd_dtype.__doc__
Returns
-------
pandas.Series
The data type of each column.
See Also
--------
:pandas_api_docs:`pandas.DataFrame.dtypes`
Examples
--------
>>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=['Origin', 'AvgTicketPrice', 'timestamp', 'dayOfWeek'])
>>> df.dtypes
Origin object
AvgTicketPrice float64
timestamp datetime64[ns]
dayOfWeek int64
dtype: object | Return the pandas dtypes in the DataFrame. Elasticsearch types are mapped
to pandas dtypes via Mappings._es_dtype_to_pd_dtype.__doc__ | [
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"""
Return the pandas dtypes in the DataFrame. Elasticsearch types are mapped
to pandas dtypes via Mappings._es_dtype_to_pd_dtype.__doc__
Returns
-------
pandas.Series
The data type of each column.
See Also
--------
:pandas_api_docs:`pandas.DataFrame.dtypes`
Examples
--------
>>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=['Origin', 'AvgTicketPrice', 'timestamp', 'dayOfWeek'])
>>> df.dtypes
Origin object
AvgTicketPrice float64
timestamp datetime64[ns]
dayOfWeek int64
dtype: object
"""
return self._query_compiler.dtypes | [
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autotest/autotest | 4614ae5f550cc888267b9a419e4b90deb54f8fae | client/setup.py | python | _get_files | (path) | return flist | Given a path, return all the files in there to package | Given a path, return all the files in there to package | [
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'''
Given a path, return all the files in there to package
'''
flist = []
for root, _, files in sorted(os.walk(path)):
for name in files:
fullname = os.path.join(root, name)
flist.append(fullname)
return flist | [
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pathak22/noreward-rl | 3e220c2177fc253916f12d980957fc40579d577a | src/a3c.py | python | A3C.__init__ | (self, env, task, visualise, unsupType, envWrap=False, designHead='universe', noReward=False) | An implementation of the A3C algorithm that is reasonably well-tuned for the VNC environments.
Below, we will have a modest amount of complexity due to the way TensorFlow handles data parallelism.
But overall, we'll define the model, specify its inputs, and describe how the policy gradients step
should be computed. | An implementation of the A3C algorithm that is reasonably well-tuned for the VNC environments.
Below, we will have a modest amount of complexity due to the way TensorFlow handles data parallelism.
But overall, we'll define the model, specify its inputs, and describe how the policy gradients step
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"""
An implementation of the A3C algorithm that is reasonably well-tuned for the VNC environments.
Below, we will have a modest amount of complexity due to the way TensorFlow handles data parallelism.
But overall, we'll define the model, specify its inputs, and describe how the policy gradients step
should be computed.
"""
self.task = task
self.unsup = unsupType is not None
self.envWrap = envWrap
self.env = env
predictor = None
numaction = env.action_space.n
worker_device = "/job:worker/task:{}/cpu:0".format(task)
with tf.device(tf.train.replica_device_setter(1, worker_device=worker_device)):
with tf.variable_scope("global"):
self.network = LSTMPolicy(env.observation_space.shape, numaction, designHead)
self.global_step = tf.get_variable("global_step", [], tf.int32, initializer=tf.constant_initializer(0, dtype=tf.int32),
trainable=False)
if self.unsup:
with tf.variable_scope("predictor"):
if 'state' in unsupType:
self.ap_network = StatePredictor(env.observation_space.shape, numaction, designHead, unsupType)
else:
self.ap_network = StateActionPredictor(env.observation_space.shape, numaction, designHead)
with tf.device(worker_device):
with tf.variable_scope("local"):
self.local_network = pi = LSTMPolicy(env.observation_space.shape, numaction, designHead)
pi.global_step = self.global_step
if self.unsup:
with tf.variable_scope("predictor"):
if 'state' in unsupType:
self.local_ap_network = predictor = StatePredictor(env.observation_space.shape, numaction, designHead, unsupType)
else:
self.local_ap_network = predictor = StateActionPredictor(env.observation_space.shape, numaction, designHead)
# Computing a3c loss: https://arxiv.org/abs/1506.02438
self.ac = tf.placeholder(tf.float32, [None, numaction], name="ac")
self.adv = tf.placeholder(tf.float32, [None], name="adv")
self.r = tf.placeholder(tf.float32, [None], name="r")
log_prob_tf = tf.nn.log_softmax(pi.logits)
prob_tf = tf.nn.softmax(pi.logits)
# 1) the "policy gradients" loss: its derivative is precisely the policy gradient
# notice that self.ac is a placeholder that is provided externally.
# adv will contain the advantages, as calculated in process_rollout
pi_loss = - tf.reduce_mean(tf.reduce_sum(log_prob_tf * self.ac, 1) * self.adv) # Eq (19)
# 2) loss of value function: l2_loss = (x-y)^2/2
vf_loss = 0.5 * tf.reduce_mean(tf.square(pi.vf - self.r)) # Eq (28)
# 3) entropy to ensure randomness
entropy = - tf.reduce_mean(tf.reduce_sum(prob_tf * log_prob_tf, 1))
# final a3c loss: lr of critic is half of actor
self.loss = pi_loss + 0.5 * vf_loss - entropy * constants['ENTROPY_BETA']
# compute gradients
grads = tf.gradients(self.loss * 20.0, pi.var_list) # batchsize=20. Factored out to make hyperparams not depend on it.
# computing predictor loss
if self.unsup:
if 'state' in unsupType:
self.predloss = constants['PREDICTION_LR_SCALE'] * predictor.forwardloss
else:
self.predloss = constants['PREDICTION_LR_SCALE'] * (predictor.invloss * (1-constants['FORWARD_LOSS_WT']) +
predictor.forwardloss * constants['FORWARD_LOSS_WT'])
predgrads = tf.gradients(self.predloss * 20.0, predictor.var_list) # batchsize=20. Factored out to make hyperparams not depend on it.
# do not backprop to policy
if constants['POLICY_NO_BACKPROP_STEPS'] > 0:
grads = [tf.scalar_mul(tf.to_float(tf.greater(self.global_step, constants['POLICY_NO_BACKPROP_STEPS'])), grads_i)
for grads_i in grads]
self.runner = RunnerThread(env, pi, constants['ROLLOUT_MAXLEN'], visualise,
predictor, envWrap, noReward)
# storing summaries
bs = tf.to_float(tf.shape(pi.x)[0])
if use_tf12_api:
tf.summary.scalar("model/policy_loss", pi_loss)
tf.summary.scalar("model/value_loss", vf_loss)
tf.summary.scalar("model/entropy", entropy)
tf.summary.image("model/state", pi.x) # max_outputs=10
tf.summary.scalar("model/grad_global_norm", tf.global_norm(grads))
tf.summary.scalar("model/var_global_norm", tf.global_norm(pi.var_list))
if self.unsup:
tf.summary.scalar("model/predloss", self.predloss)
if 'action' in unsupType:
tf.summary.scalar("model/inv_loss", predictor.invloss)
tf.summary.scalar("model/forward_loss", predictor.forwardloss)
tf.summary.scalar("model/predgrad_global_norm", tf.global_norm(predgrads))
tf.summary.scalar("model/predvar_global_norm", tf.global_norm(predictor.var_list))
self.summary_op = tf.summary.merge_all()
else:
tf.scalar_summary("model/policy_loss", pi_loss)
tf.scalar_summary("model/value_loss", vf_loss)
tf.scalar_summary("model/entropy", entropy)
tf.image_summary("model/state", pi.x)
tf.scalar_summary("model/grad_global_norm", tf.global_norm(grads))
tf.scalar_summary("model/var_global_norm", tf.global_norm(pi.var_list))
if self.unsup:
tf.scalar_summary("model/predloss", self.predloss)
if 'action' in unsupType:
tf.scalar_summary("model/inv_loss", predictor.invloss)
tf.scalar_summary("model/forward_loss", predictor.forwardloss)
tf.scalar_summary("model/predgrad_global_norm", tf.global_norm(predgrads))
tf.scalar_summary("model/predvar_global_norm", tf.global_norm(predictor.var_list))
self.summary_op = tf.merge_all_summaries()
# clip gradients
grads, _ = tf.clip_by_global_norm(grads, constants['GRAD_NORM_CLIP'])
grads_and_vars = list(zip(grads, self.network.var_list))
if self.unsup:
predgrads, _ = tf.clip_by_global_norm(predgrads, constants['GRAD_NORM_CLIP'])
pred_grads_and_vars = list(zip(predgrads, self.ap_network.var_list))
grads_and_vars = grads_and_vars + pred_grads_and_vars
# update global step by batch size
inc_step = self.global_step.assign_add(tf.shape(pi.x)[0])
# each worker has a different set of adam optimizer parameters
# TODO: make optimizer global shared, if needed
print("Optimizer: ADAM with lr: %f" % (constants['LEARNING_RATE']))
print("Input observation shape: ",env.observation_space.shape)
opt = tf.train.AdamOptimizer(constants['LEARNING_RATE'])
self.train_op = tf.group(opt.apply_gradients(grads_and_vars), inc_step)
# copy weights from the parameter server to the local model
sync_var_list = [v1.assign(v2) for v1, v2 in zip(pi.var_list, self.network.var_list)]
if self.unsup:
sync_var_list += [v1.assign(v2) for v1, v2 in zip(predictor.var_list, self.ap_network.var_list)]
self.sync = tf.group(*sync_var_list)
# initialize extras
self.summary_writer = None
self.local_steps = 0 | [
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Chandlercjy/OnePy | 9bd43b721d3f7352495b6ccab76bd533a3d2e8f2 | OnePy/sys_module/models/base_series.py | python | SeriesBase.get_barly_cur_price | (self, ticker: str, order_executed: bool) | 若是订单执行，则返回成交价，否则只是更新信息，返回开盘价 | 若是订单执行，则返回成交价，否则只是更新信息，返回开盘价 | [
"若是订单执行，则返回成交价，否则只是更新信息，返回开盘价"
] | def get_barly_cur_price(self, ticker: str, order_executed: bool) -> float:
"""若是订单执行，则返回成交价，否则只是更新信息，返回开盘价"""
if order_executed:
return self.env.feeds[ticker].execute_price
else:
return self.env.feeds[ticker].open | [
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KhronosGroup/NNEF-Tools | c913758ca687dab8cb7b49e8f1556819a2d0ca25 | nnef_tools/conversion/nnef_to_tf.py | python | Converter._is_nxc | (self, format) | return format[0] == 'N' and format[-1] == 'C' and len(format) > 2 | [] | def _is_nxc(self, format):
return format[0] == 'N' and format[-1] == 'C' and len(format) > 2 | [
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missionpinball/mpf | 8e6b74cff4ba06d2fec9445742559c1068b88582 | mpf/devices/ball_device/ball_count_handler.py | python | BallCountHandler.entrance_during_eject | (self) | Received an entrance during eject. | Received an entrance during eject. | [
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"."
] | async def entrance_during_eject(self):
"""Received an entrance during eject."""
await self.ball_device.incoming_balls_handler.ball_arrived()
self._set_ball_count(self._ball_count + 1) | [
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BasioMeusPuga/Lector | 1b1d87739a8c14e0e22009435350b155fc3e3b77 | lector/rarfile/rarfile.py | python | CommonParser.parse | (self) | Process file. | Process file. | [
"Process",
"file",
"."
] | def parse(self):
"""Process file."""
self._fd = None
try:
self._parse_real()
finally:
if self._fd:
self._fd.close()
self._fd = None | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-35/fabmetheus_utilities/geometry/solids/trianglemesh.py | python | getIsPathEntirelyOutsideTriangle | (begin, center, end, vector3Path) | return True | Determine if a path is entirely outside another loop. | Determine if a path is entirely outside another loop. | [
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] | def getIsPathEntirelyOutsideTriangle(begin, center, end, vector3Path):
"Determine if a path is entirely outside another loop."
loop = [begin.dropAxis(), center.dropAxis(), end.dropAxis()]
for vector3 in vector3Path:
point = vector3.dropAxis()
if euclidean.isPointInsideLoop(loop, point):
return False
return True | [
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django-haystack/django-haystack | b6dd72e6b5c97b782f5436b7bb4e8227ba6e3b06 | haystack/backends/solr_backend.py | python | SolrSearchBackend.build_search_kwargs | (
self,
query_string,
sort_by=None,
start_offset=0,
end_offset=None,
fields="",
highlight=False,
facets=None,
date_facets=None,
query_facets=None,
narrow_queries=None,
spelling_query=None,
within=None,
dwithin=None,
distance_point=None,
models=None,
limit_to_registered_models=None,
result_class=None,
stats=None,
collate=None,
**extra_kwargs
) | return kwargs | [] | def build_search_kwargs(
self,
query_string,
sort_by=None,
start_offset=0,
end_offset=None,
fields="",
highlight=False,
facets=None,
date_facets=None,
query_facets=None,
narrow_queries=None,
spelling_query=None,
within=None,
dwithin=None,
distance_point=None,
models=None,
limit_to_registered_models=None,
result_class=None,
stats=None,
collate=None,
**extra_kwargs
):
index = haystack.connections[self.connection_alias].get_unified_index()
kwargs = {"fl": "* score", "df": index.document_field}
if fields:
if isinstance(fields, (list, set)):
fields = " ".join(fields)
kwargs["fl"] = fields
if sort_by is not None:
if sort_by in ["distance asc", "distance desc"] and distance_point:
# Do the geo-enabled sort.
lng, lat = distance_point["point"].coords
kwargs["sfield"] = distance_point["field"]
kwargs["pt"] = "%s,%s" % (lat, lng)
if sort_by == "distance asc":
kwargs["sort"] = "geodist() asc"
else:
kwargs["sort"] = "geodist() desc"
else:
if sort_by.startswith("distance "):
warnings.warn(
"In order to sort by distance, you must call the '.distance(...)' method."
)
# Regular sorting.
kwargs["sort"] = sort_by
if start_offset is not None:
kwargs["start"] = start_offset
if end_offset is not None:
kwargs["rows"] = end_offset - start_offset
if highlight:
# `highlight` can either be True or a dictionary containing custom parameters
# which will be passed to the backend and may override our default settings:
kwargs["hl"] = "true"
kwargs["hl.fragsize"] = "200"
if isinstance(highlight, dict):
# autoprefix highlighter options with 'hl.', all of them start with it anyway
# this makes option dicts shorter: {'maxAnalyzedChars': 42}
# and lets some of options be used as keyword arguments: `.highlight(preserveMulti=False)`
kwargs.update(
{
key if key.startswith("hl.") else ("hl." + key): highlight[key]
for key in highlight.keys()
}
)
if collate is None:
collate = self.collate
if self.include_spelling is True:
kwargs["spellcheck"] = "true"
kwargs["spellcheck.collate"] = str(collate).lower()
kwargs["spellcheck.count"] = 1
if spelling_query:
kwargs["spellcheck.q"] = spelling_query
if facets is not None:
kwargs["facet"] = "on"
kwargs["facet.field"] = facets.keys()
for facet_field, options in facets.items():
for key, value in options.items():
kwargs[
"f.%s.facet.%s" % (facet_field, key)
] = self.conn._from_python(value)
if date_facets is not None:
kwargs["facet"] = "on"
kwargs["facet.%s" % self.date_facet_field] = date_facets.keys()
kwargs["facet.%s.other" % self.date_facet_field] = "none"
for key, value in date_facets.items():
kwargs[
"f.%s.facet.%s.start" % (key, self.date_facet_field)
] = self.conn._from_python(value.get("start_date"))
kwargs[
"f.%s.facet.%s.end" % (key, self.date_facet_field)
] = self.conn._from_python(value.get("end_date"))
gap_by_string = value.get("gap_by").upper()
gap_string = "%d%s" % (value.get("gap_amount"), gap_by_string)
if value.get("gap_amount") != 1:
gap_string += "S"
kwargs[
"f.%s.facet.%s.gap" % (key, self.date_facet_field)
] = "+%s/%s" % (
gap_string,
gap_by_string,
)
if query_facets is not None:
kwargs["facet"] = "on"
kwargs["facet.query"] = [
"%s:%s" % (field, value) for field, value in query_facets
]
if limit_to_registered_models is None:
limit_to_registered_models = getattr(
settings, "HAYSTACK_LIMIT_TO_REGISTERED_MODELS", True
)
if models and len(models):
model_choices = sorted(get_model_ct(model) for model in models)
elif limit_to_registered_models:
# Using narrow queries, limit the results to only models handled
# with the current routers.
model_choices = self.build_models_list()
else:
model_choices = []
if len(model_choices) > 0:
if narrow_queries is None:
narrow_queries = set()
narrow_queries.add("%s:(%s)" % (DJANGO_CT, " OR ".join(model_choices)))
if narrow_queries is not None:
kwargs["fq"] = list(narrow_queries)
if stats:
kwargs["stats"] = "true"
for k in stats.keys():
kwargs["stats.field"] = k
for facet in stats[k]:
kwargs["f.%s.stats.facet" % k] = facet
if within is not None:
from haystack.utils.geo import generate_bounding_box
kwargs.setdefault("fq", [])
((min_lat, min_lng), (max_lat, max_lng)) = generate_bounding_box(
within["point_1"], within["point_2"]
)
# Bounding boxes are min, min TO max, max. Solr's wiki was *NOT*
# very clear on this.
bbox = "%s:[%s,%s TO %s,%s]" % (
within["field"],
min_lat,
min_lng,
max_lat,
max_lng,
)
kwargs["fq"].append(bbox)
if dwithin is not None:
kwargs.setdefault("fq", [])
lng, lat = dwithin["point"].coords
geofilt = "{!geofilt pt=%s,%s sfield=%s d=%s}" % (
lat,
lng,
dwithin["field"],
dwithin["distance"].km,
)
kwargs["fq"].append(geofilt)
# Check to see if the backend should try to include distances
# (Solr 4.X+) in the results.
if self.distance_available and distance_point:
# In early testing, you can't just hand Solr 4.X a proper bounding box
# & request distances. To enable native distance would take calculating
# a center point & a radius off the user-provided box, which kinda
# sucks. We'll avoid it for now, since Solr 4.x's release will be some
# time yet.
# kwargs['fl'] += ' _dist_:geodist()'
pass
if extra_kwargs:
kwargs.update(extra_kwargs)
return kwargs | [
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jwlodek/py_cui | bc2de6a30cfc894348306531db94d7edbdbf17f3 | py_cui/ui.py | python | UIElement.update_height_width | (self) | Function that refreshes position and dimensons on resize.
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"""
self._start_x, self._start_y = self.get_absolute_start_pos()
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deepgully/me | f7ad65edc2fe435310c6676bc2e322cfe5d4c8f0 | libs/sqlalchemy/orm/collections.py | python | CollectionAdapter.__iter__ | (self) | return iter(self._data()._sa_iterator()) | Iterate over entities in the collection. | Iterate over entities in the collection. | [
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"""Iterate over entities in the collection."""
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chen3feng/blade-build | 360b4c9ddb9087fb811af3aef2830301cf48805e | src/blade/pathlib.py | python | PurePath.parents | (self) | return _PathParents(self) | A sequence of this path's logical parents. | A sequence of this path's logical parents. | [
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_mutating_webhook.py | python | V1MutatingWebhook.object_selector | (self) | return self._object_selector | Gets the object_selector of this V1MutatingWebhook. # noqa: E501
:return: The object_selector of this V1MutatingWebhook. # noqa: E501
:rtype: V1LabelSelector | Gets the object_selector of this V1MutatingWebhook. # noqa: E501 | [
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] | def object_selector(self):
"""Gets the object_selector of this V1MutatingWebhook. # noqa: E501
:return: The object_selector of this V1MutatingWebhook. # noqa: E501
:rtype: V1LabelSelector
"""
return self._object_selector | [
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and3rson/clay | c271cecf6b6ea6465abcdd2444171b1a565a60a3 | clay/vlc.py | python | libvlc_audio_equalizer_get_preset_count | () | return f() | Get the number of equalizer presets.
@return: number of presets.
@version: LibVLC 2.2.0 or later. | Get the number of equalizer presets. | [
"Get",
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"number",
"of",
"equalizer",
"presets",
"."
] | def libvlc_audio_equalizer_get_preset_count():
'''Get the number of equalizer presets.
@return: number of presets.
@version: LibVLC 2.2.0 or later.
'''
f = _Cfunctions.get('libvlc_audio_equalizer_get_preset_count', None) or \
_Cfunction('libvlc_audio_equalizer_get_preset_count', (), None,
ctypes.c_uint)
return f() | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/db/models/deletion.py | python | ProtectedError.__init__ | (self, msg, protected_objects) | [] | def __init__(self, msg, protected_objects):
self.protected_objects = protected_objects
super(ProtectedError, self).__init__(msg, protected_objects) | [
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gkrizek/bash-lambda-layer | 703b0ade8174022d44779d823172ab7ac33a5505 | bin/botocore/credentials.py | python | AssumeRoleCredentialFetcher._get_credentials | (self) | return client.assume_role(**kwargs) | Get credentials by calling assume role. | Get credentials by calling assume role. | [
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"role",
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] | def _get_credentials(self):
"""Get credentials by calling assume role."""
kwargs = self._assume_role_kwargs()
client = self._create_client()
return client.assume_role(**kwargs) | [
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PaddlePaddle/PaddleX | 2bab73f81ab54e328204e7871e6ae4a82e719f5d | paddlex/ppcls/arch/backbone/model_zoo/mixnet.py | python | MixNet.forward | (self, x) | return x | [] | def forward(self, x):
x = self.features(x)
reshape_dim = reduce(lambda x, y: x * y, x.shape[1:])
x = x.reshape(shape=[x.shape[0], reshape_dim])
x = self.output(x)
return x | [
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/27/Lib/difflib.py | python | SequenceMatcher.__init__ | (self, isjunk=None, a='', b='', autojunk=True) | Construct a SequenceMatcher.
Optional arg isjunk is None (the default), or a one-argument
function that takes a sequence element and returns true iff the
element is junk. None is equivalent to passing "lambda x: 0", i.e.
no elements are considered to be junk. For example, pass
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Optional arg a is the first of two sequences to be compared. By
default, an empty string. The elements of a must be hashable. See
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Optional arg b is the second of two sequences to be compared. By
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also .set_seqs() and .set_seq2().
Optional arg autojunk should be set to False to disable the
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(see module documentation for more information). | Construct a SequenceMatcher. | [
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] | def __init__(self, isjunk=None, a='', b='', autojunk=True):
"""Construct a SequenceMatcher.
Optional arg isjunk is None (the default), or a one-argument
function that takes a sequence element and returns true iff the
element is junk. None is equivalent to passing "lambda x: 0", i.e.
no elements are considered to be junk. For example, pass
lambda x: x in " \\t"
if you're comparing lines as sequences of characters, and don't
want to synch up on blanks or hard tabs.
Optional arg a is the first of two sequences to be compared. By
default, an empty string. The elements of a must be hashable. See
also .set_seqs() and .set_seq1().
Optional arg b is the second of two sequences to be compared. By
default, an empty string. The elements of b must be hashable. See
also .set_seqs() and .set_seq2().
Optional arg autojunk should be set to False to disable the
"automatic junk heuristic" that treats popular elements as junk
(see module documentation for more information).
"""
# Members:
# a
# first sequence
# b
# second sequence; differences are computed as "what do
# we need to do to 'a' to change it into 'b'?"
# b2j
# for x in b, b2j[x] is a list of the indices (into b)
# at which x appears; junk elements do not appear
# fullbcount
# for x in b, fullbcount[x] == the number of times x
# appears in b; only materialized if really needed (used
# only for computing quick_ratio())
# matching_blocks
# a list of (i, j, k) triples, where a[i:i+k] == b[j:j+k];
# ascending & non-overlapping in i and in j; terminated by
# a dummy (len(a), len(b), 0) sentinel
# opcodes
# a list of (tag, i1, i2, j1, j2) tuples, where tag is
# one of
# 'replace' a[i1:i2] should be replaced by b[j1:j2]
# 'delete' a[i1:i2] should be deleted
# 'insert' b[j1:j2] should be inserted
# 'equal' a[i1:i2] == b[j1:j2]
# isjunk
# a user-supplied function taking a sequence element and
# returning true iff the element is "junk" -- this has
# subtle but helpful effects on the algorithm, which I'll
# get around to writing up someday <0.9 wink>.
# DON'T USE! Only __chain_b uses this. Use isbjunk.
# isbjunk
# for x in b, isbjunk(x) == isjunk(x) but much faster;
# it's really the __contains__ method of a hidden dict.
# DOES NOT WORK for x in a!
# isbpopular
# for x in b, isbpopular(x) is true iff b is reasonably long
# (at least 200 elements) and x accounts for more than 1 + 1% of
# its elements (when autojunk is enabled).
# DOES NOT WORK for x in a!
self.isjunk = isjunk
self.a = self.b = None
self.autojunk = autojunk
self.set_seqs(a, b) | [
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facebookresearch/ClassyVision | 309d4f12431c6b4d8540010a781dc2aa25fe88e7 | classy_vision/generic/distributed_util.py | python | all_reduce_sum | (tensor: torch.Tensor) | return all_reduce_op(tensor, torch.distributed.ReduceOp.SUM) | Wrapper over torch.distributed.all_reduce for performing sum
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"""
Wrapper over torch.distributed.all_reduce for performing sum
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"""
return all_reduce_op(tensor, torch.distributed.ReduceOp.SUM) | [
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ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/site-packages/setuptools/glob.py | python | iglob | (pathname, recursive=False) | return it | Return an iterator which yields the paths matching a pathname pattern.
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"""Return an iterator which yields the paths matching a pathname pattern.
The pattern may contain simple shell-style wildcards a la
fnmatch. However, unlike fnmatch, filenames starting with a
dot are special cases that are not matched by '*' and '?'
patterns.
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"""
it = _iglob(pathname, recursive)
if recursive and _isrecursive(pathname):
s = next(it) # skip empty string
assert not s
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beancount/fava | 69614956d3c01074403af0a07ddbaa986cf602a0 | src/fava/core/__init__.py | python | FavaLedger.load_file | (self) | Load the main file and all included files and set attributes. | Load the main file and all included files and set attributes. | [
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"""Load the main file and all included files and set attributes."""
# use the internal function to disable cache
if not self._is_encrypted:
# pylint: disable=protected-access
self.all_entries, self.errors, self.options = loader._load(
[(self.beancount_file_path, True)], None, None, None
)
else:
self.all_entries, self.errors, self.options = loader.load_file(
self.beancount_file_path
)
self.account_types = get_account_types(self.options)
self.price_map = build_price_map(self.all_entries)
self.all_root_account = realization.realize(
self.all_entries, self.account_types
)
self.all_entries_by_type = group_entries_by_type(self.all_entries)
self.accounts = AccountDict()
for open_entry in self.all_entries_by_type.Open:
self.accounts.setdefault(open_entry.account).meta = open_entry.meta
for close in self.all_entries_by_type.Close:
self.accounts.setdefault(close.account).close_date = close.date
self.commodities = {}
for commodity in self.all_entries_by_type.Commodity:
self.commodities[commodity.currency] = commodity
self.fava_options, errors = parse_options(
self.all_entries_by_type.Custom
)
self.errors.extend(errors)
if not self._is_encrypted:
self._watcher.update(*self.paths_to_watch())
for mod in MODULES:
getattr(self, mod).load_file()
self.filters = Filters(self.options, self.fava_options)
self.filter(True) | [
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... | https://github.com/beancount/fava/blob/69614956d3c01074403af0a07ddbaa986cf602a0/src/fava/core/__init__.py#L220-L264 | ||
eliostvs/tomate-gtk | 29e4db6b1e9bad34eee87bb64932c397eb2dffc0 | tomate/pomodoro/config.py | python | remove_duplicates | (original: List[str]) | return list(set(original)) | [] | def remove_duplicates(original: List[str]) -> List[str]:
return list(set(original)) | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/ext/importlib_metadata/__init__.py | python | distributions | (**kwargs) | return Distribution.discover(**kwargs) | Get all ``Distribution`` instances in the current environment.
:return: An iterable of ``Distribution`` instances. | Get all ``Distribution`` instances in the current environment. | [
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"""Get all ``Distribution`` instances in the current environment.
:return: An iterable of ``Distribution`` instances.
"""
return Distribution.discover(**kwargs) | [
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LexPredict/lexpredict-contraxsuite | 1d5a2540d31f8f3f1adc442cfa13a7c007319899 | sdk/python/sdk/openapi_client/model/project_document_similarity.py | python | ProjectDocumentSimilarity.additional_properties_type | () | return (bool, date, datetime, dict, float, int, list, str, none_type,) | This must be a method because a model may have properties that are
of type self, this must run after the class is loaded | This must be a method because a model may have properties that are
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"""
This must be a method because a model may have properties that are
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"""
return (bool, date, datetime, dict, float, int, list, str, none_type,) | [
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unsky/focal-loss | f238924eabc566e98d3c72ad1c9c40f72922bc3f | faster_rcnn_mxnet/faster_rcnn/core/module.py | python | Module.get_params | (self) | return (self._arg_params, self._aux_params) | Get current parameters.
Returns
-------
`(arg_params, aux_params)`, each a dictionary of name to parameters (in
`NDArray`) mapping. | Get current parameters.
Returns
-------
`(arg_params, aux_params)`, each a dictionary of name to parameters (in
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"""Get current parameters.
Returns
-------
`(arg_params, aux_params)`, each a dictionary of name to parameters (in
`NDArray`) mapping.
"""
assert self.binded and self.params_initialized
if self._params_dirty:
self._sync_params_from_devices()
return (self._arg_params, self._aux_params) | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/google_assistant/helpers.py | python | _get_entity_and_device | (
hass: HomeAssistant, entity_id: str
) | return entity_entry, device_entry | Fetch the entity and device entries for a entity_id. | Fetch the entity and device entries for a entity_id. | [
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hass: HomeAssistant, entity_id: str
) -> tuple[RegistryEntry, DeviceEntry] | None:
"""Fetch the entity and device entries for a entity_id."""
dev_reg, ent_reg = await gather(
hass.helpers.device_registry.async_get_registry(),
hass.helpers.entity_registry.async_get_registry(),
)
if not (entity_entry := ent_reg.async_get(entity_id)):
return None, None
device_entry = dev_reg.devices.get(entity_entry.device_id)
return entity_entry, device_entry | [
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/gui/menus/legend/animation.py | python | AnimationWindow.on_update_min_max_defaults | (self) | When the icase is changed, the min/max value default message is changed | When the icase is changed, the min/max value default message is changed | [
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"""
When the icase is changed, the min/max value default message is changed
"""
icase = self.icase_disp_start_edit.value()
min_value, max_value = self.get_min_max(icase)
self.min_value_button.setToolTip('Sets the min value to %g' % min_value)
self.max_value_button.setToolTip('Sets the max value to %g' % max_value) | [
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bungnoid/glTools | 8ff0899de43784a18bd4543285655e68e28fb5e5 | data/setData.py | python | SetData.rebuild | (self,mode='add',forceMembership=True) | return result | Rebuild the set from the stored setData.
@param mode: Membership mode if the specified set already exists. Accepted values are "add" and "replace".
@type mode: str
@param forceMembership: Forces addition of items to the set. If items are in another set which is in the same partition as the given set, the items will be removed from the other set in order to keep the sets in the partition mutually exclusive with respect to membership.
@type forceMembership: bool | Rebuild the set from the stored setData. | [
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] | def rebuild(self,mode='add',forceMembership=True):
'''
Rebuild the set from the stored setData.
@param mode: Membership mode if the specified set already exists. Accepted values are "add" and "replace".
@type mode: str
@param forceMembership: Forces addition of items to the set. If items are in another set which is in the same partition as the given set, the items will be removed from the other set in order to keep the sets in the partition mutually exclusive with respect to membership.
@type forceMembership: bool
'''
# ==========
# - Checks -
# ==========
# Set Name
if not self._data['name']:
raise Exception('SetData has not been initialized!')
# Member Items
memberList = self._data['membership'] or []
for obj in memberList:
if not mc.objExists(obj):
print('Set member item "'+obj+'" does not exist! Unable to add to set...')
memberList.remove(obj)
# Flatten Membership List
memberList = mc.ls(memberList,fl=True) or []
# Mode
if not mode in self.mode:
raise Exception('Invalid set membership mode "'+mode+'"! Use "add" or "replace"!')
# ===============
# - Rebuild Set -
# ===============
# Start timer
timer = mc.timerX()
# Create Set
setName = self._data['name']
# Delete Set (REPLACE only)
if mc.objExists(setName) and mode == 'replace': mc.delete(setName)
# Create Set
if not mc.objExists(setName): setName = mc.sets(n=setName)
# Add Members
if memberList:
if forceMembership:
for obj in memberList:
try: mc.sets(obj,e=True,fe=setName)
except Exception, e:
print('Error adding item "'+obj+'" to set "'+setName+'"! Skipping')
print(str(e))
else:
for obj in memberList:
try: mc.sets(obj,e=True,add=setName)
except Exception, e:
print('Error adding item "'+obj+'" to set "'+setName+'"! Skipping')
print(str(e))
# Print Timer Result
buildTime = mc.timerX(st=timer)
print('SetData: Rebuild time for set "'+setName+'": '+str(buildTime))
# =================
# - Return Result -
# =================
self.setName = setName
result = {}
result['set'] = setName
result['membership'] = memberList
return result | [
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Azure/azure-devops-cli-extension | 11334cd55806bef0b99c3bee5a438eed71e44037 | azure-devops/azext_devops/devops_sdk/v6_0/pipelines_checks/pipelines_checks_client.py | python | PipelinesChecksClient.delete_check_configuration | (self, project, id) | DeleteCheckConfiguration.
[Preview API]
:param str project: Project ID or project name
:param int id: | DeleteCheckConfiguration.
[Preview API]
:param str project: Project ID or project name
:param int id: | [
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"""DeleteCheckConfiguration.
[Preview API]
:param str project: Project ID or project name
:param int id:
"""
route_values = {}
if project is not None:
route_values['project'] = self._serialize.url('project', project, 'str')
if id is not None:
route_values['id'] = self._serialize.url('id', id, 'int')
self._send(http_method='DELETE',
location_id='86c8381e-5aee-4cde-8ae4-25c0c7f5eaea',
version='6.0-preview.1',
route_values=route_values) | [
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/oda/oda_client_composite_operations.py | python | OdaClientCompositeOperations.__init__ | (self, client, **kwargs) | Creates a new OdaClientCompositeOperations object
:param OdaClient client:
The service client which will be wrapped by this object | Creates a new OdaClientCompositeOperations object | [
"Creates",
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"OdaClientCompositeOperations",
"object"
] | def __init__(self, client, **kwargs):
"""
Creates a new OdaClientCompositeOperations object
:param OdaClient client:
The service client which will be wrapped by this object
"""
self.client = client | [
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jython/jython3 | def4f8ec47cb7a9c799ea4c745f12badf92c5769 | Lib/xml/sax/handler.py | python | ContentHandler.processingInstruction | (self, target, data) | Receive notification of a processing instruction.
The Parser will invoke this method once for each processing
instruction found: note that processing instructions may occur
before or after the main document element.
A SAX parser should never report an XML declaration (XML 1.0,
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"""Receive notification of a processing instruction.
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before or after the main document element.
A SAX parser should never report an XML declaration (XML 1.0,
section 2.8) or a text declaration (XML 1.0, section 4.3.1)
using this method.""" | [
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robhagemans/pcbasic | c3a043b46af66623a801e18a38175be077251ada | pcbasic/basic/parser/statements.py | python | Parser._parse_palette_using | (self, ins) | Parse PALETTE USING syntax. | Parse PALETTE USING syntax. | [
"Parse",
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"."
] | def _parse_palette_using(self, ins):
"""Parse PALETTE USING syntax."""
ins.require_read((tk.USING,))
array_name, start_indices = self._parse_variable(ins)
yield array_name, start_indices
# brackets are not optional
error.throw_if(not start_indices, error.STX) | [
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google-research/morph-net | be4d79dea816c473007c5967d45ab4036306c21c | morph_net/network_regularizers/resource_function.py | python | _calculate_bilinear_regularization | (
op, coeff, num_alive_inputs, num_alive_outputs, reg_inputs, reg_outputs,
batch_size) | Calculates bilinear regularization term for an op.
Args:
op: A tf.Operation.
coeff: A float coefficient for the bilinear function.
num_alive_inputs: Scalar Tensor indicating how many input channels are
considered alive.
num_alive_outputs: Scalar Tensor indicating how many output channels are
considered alive.
reg_inputs: Scalar Tensor which is the sum over the input regularization
vector.
reg_outputs: Scalar Tensor which is the sum over the output regularization
vector.
batch_size: Integer batch size to calculate cost/loss for.
Returns:
Tensor with the regularization loss of the op. | Calculates bilinear regularization term for an op. | [
"Calculates",
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"for",
"an",
"op",
"."
] | def _calculate_bilinear_regularization(
op, coeff, num_alive_inputs, num_alive_outputs, reg_inputs, reg_outputs,
batch_size):
"""Calculates bilinear regularization term for an op.
Args:
op: A tf.Operation.
coeff: A float coefficient for the bilinear function.
num_alive_inputs: Scalar Tensor indicating how many input channels are
considered alive.
num_alive_outputs: Scalar Tensor indicating how many output channels are
considered alive.
reg_inputs: Scalar Tensor which is the sum over the input regularization
vector.
reg_outputs: Scalar Tensor which is the sum over the output regularization
vector.
batch_size: Integer batch size to calculate cost/loss for.
Returns:
Tensor with the regularization loss of the op.
"""
if op.type == 'DepthwiseConv2dNative':
# reg_inputs and reg_outputs are often identical since they should
# come from the same regularizer. Duplicate them for symmetry.
# When the input doesn't have a regularizer (e.g. input), only the
# second term is used.
# TODO(b1): revisit this expression after experiments.
return batch_size * coeff * (reg_inputs + reg_outputs)
else:
# Handle normal ops.
return batch_size * coeff * (
num_alive_inputs * reg_outputs + num_alive_outputs * reg_inputs) | [
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intohole/moodstyle | 1d06fc565c0df4bf07196854f3efb94bbefd1bfb | moodstyle/classifier/Bp1.py | python | Neroun.update | (self , target, predict) | return error | [] | def update(self , target, predict):
error = target - predict
for i in self.weight_range:
self.weights[i] += self.rate * error
return error | [
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microsoft/azure-devops-python-api | 451cade4c475482792cbe9e522c1fee32393139e | azure-devops/azure/devops/v5_1/build/build_client.py | python | BuildClient.delete_folder | (self, project, path) | DeleteFolder.
[Preview API] Deletes a definition folder. Definitions and their corresponding builds will also be deleted.
:param str project: Project ID or project name
:param str path: The full path to the folder. | DeleteFolder.
[Preview API] Deletes a definition folder. Definitions and their corresponding builds will also be deleted.
:param str project: Project ID or project name
:param str path: The full path to the folder. | [
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"""DeleteFolder.
[Preview API] Deletes a definition folder. Definitions and their corresponding builds will also be deleted.
:param str project: Project ID or project name
:param str path: The full path to the folder.
"""
route_values = {}
if project is not None:
route_values['project'] = self._serialize.url('project', project, 'str')
query_parameters = {}
if path is not None:
query_parameters['path'] = self._serialize.query('path', path, 'str')
self._send(http_method='DELETE',
location_id='a906531b-d2da-4f55-bda7-f3e676cc50d9',
version='5.1-preview.2',
route_values=route_values,
query_parameters=query_parameters) | [
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kozec/sc-controller | ce92c773b8b26f6404882e9209aff212c4053170 | scc/gui/ring_editor.py | python | RingEditor.on_btCustomActionEditor_clicked | (self, *a) | Handler for 'Custom Editor' button | Handler for 'Custom Editor' button | [
"Handler",
"for",
"Custom",
"Editor",
"button"
] | def on_btCustomActionEditor_clicked(self, *a):
""" Handler for 'Custom Editor' button """
from scc.gui.action_editor import ActionEditor # Can't be imported on top
e = ActionEditor(self.app, self.ac_callback)
e.set_input(self.id, self._make_action(), mode = self.mode)
e.hide_action_buttons()
e.hide_advanced_settings()
e.set_title(_("Custom Action"))
e.force_page(e.load_component("custom"), True)
self.send_added_widget(e)
self.close()
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JasperSnoek/spearmint | b37a541be1ea035f82c7c82bbd93f5b4320e7d91 | spearmint/spearmint/chooser/cma.py | python | DerivedDictBase.__getitem__ | (self, key) | return self.data[key] | defines self[key] | defines self[key] | [
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euphrat1ca/fuzzdb-collect | f32552a4d5d84350552c68801aed281ca1f48e66 | ScriptShare/pacemake/pacemaker.py | python | PacemakerServer.serve_forever | (self) | [] | def serve_forever(self):
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home-assistant/supervisor | 69c2517d5211b483fdfe968b0a2b36b672ee7ab2 | supervisor/addons/addon.py | python | Addon.ingress_entry | (self) | return None | Return ingress external URL. | Return ingress external URL. | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/messaging/scheduling/forms.py | python | ScheduleForm.is_valid | (self) | [] | def is_valid(self):
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tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/models/v1beta1_volume_attachment_list.py | python | V1beta1VolumeAttachmentList.__eq__ | (self, other) | return self.to_dict() == other.to_dict() | Returns true if both objects are equal | Returns true if both objects are equal | [
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Unidata/MetPy | 1153590f397ae23adfea66c4adb0a206872fa0ad | tools/flake8-metpy/flake8_metpy.py | python | MetPyVisitor.error | (self, lineno, col, code) | Add an error to our output list. | Add an error to our output list. | [
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meetbill/zabbix_manager | 739e5b51facf19cc6bda2b50f29108f831cf833e | ZabbixTool/lib_zabbix/w_lib/mylib/xlwt/Style.py | python | add_palette_colour | (colour_str, colour_index) | [] | def add_palette_colour(colour_str, colour_index):
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tendenci/tendenci | 0f2c348cc0e7d41bc56f50b00ce05544b083bf1d | tendenci/apps/events/registration/formsets.py | python | RegistrantBaseFormSet.get_user_list | (self) | return users | returns a list of user registrants | returns a list of user registrants | [
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aws/aws-parallelcluster | f1fe5679a01c524e7ea904c329bd6d17318c6cd9 | cli/src/pcluster/config/cluster_config.py | python | HeadNode.is_ebs_optimized | (self) | return self.instance_type_info.is_ebs_optimized() | Return True if the instance has optimized EBS support. | Return True if the instance has optimized EBS support. | [
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kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python3-alpha/python-libs/gdata/auth.py | python | OAuthTokenFromHttpBody | (http_body) | return oauth_token | Parses the HTTP response body and returns an OAuth token.
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fluentpython/example-code-2e | 80f7f84274a47579e59c29a4657691525152c9d5 | 24-class-metaprog/bulkfood/model_v8.py | python | AutoStorage.__get__ | (self, instance, owner) | [] | def __get__(self, instance, owner):
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zzw922cn/Automatic_Speech_Recognition | 3fe0514d1377a16170923ff1ada6f808e907fcba | speechvalley/pipeline/big_input.py | python | _bytes_feature | (value) | return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) | [] | def _bytes_feature(value):
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pwnieexpress/raspberry_pwn | 86f80e781cfb9f130a21a7ff41ef3d5c0340f287 | src/pexpect-2.3/pexpect.py | python | searcher_re.__str__ | (self) | return '\n'.join(ss) | This returns a human-readable string that represents the state of
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Epistimio/orion | 732e739d99561020dbe620760acf062ade746006 | src/orion/algo/hyperband.py | python | Hyperband.state_dict | (self) | return {
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/encodings/iso8859_8.py | python | Codec.encode | (self,input,errors='strict') | return codecs.charmap_encode(input,errors,encoding_table) | [] | def encode(self,input,errors='strict'):
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pysmt/pysmt | ade4dc2a825727615033a96d31c71e9f53ce4764 | pysmt/smtlib/script.py | python | SmtLibScript.to_file | (self, fname, daggify=True) | [] | def to_file(self, fname, daggify=True):
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/shlex.py | python | shlex.push_source | (self, newstream, newfile=None) | Push an input source onto the lexer's input source stack. | Push an input source onto the lexer's input source stack. | [
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Anaconda-Platform/anaconda-project | df5ec33c12591e6512436d38d36c6132fa2e9618 | anaconda_project/archiver.py | python | _unarchive_project | (archive_filename, project_dir, frontend, parent_dir=None) | Unpack an archive of files in the project.
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"""
if project_dir is not None and os.path.isabs(project_dir) and parent_dir is not None:
raise ValueError("If supplying parent_dir to unarchive, project_dir must be relative or None")
frontend = _new_error_recorder(frontend)
list_files = None
extract_files = None
if archive_filename.endswith(".zip"):
list_files = _list_files_zip
extract_files = _extract_files_zip
elif any([archive_filename.endswith(suffix) for suffix in [".tar", ".tar.gz", ".tar.bz2"]]):
list_files = _list_files_tar
extract_files = _extract_files_tar
else:
frontend.error("Unsupported archive filename %s, must be a .zip, .tar.gz, or .tar.bz2" % (archive_filename))
return SimpleStatus(success=False,
description=("Could not unpack archive %s" % archive_filename),
errors=frontend.pop_errors())
try:
result = _get_source_and_dest_files(archive_filename, list_files, project_dir, parent_dir, frontend)
if result is None:
return SimpleStatus(success=False,
description=("Could not unpack archive %s" % archive_filename),
errors=frontend.pop_errors())
(canonical_project_dir, src_and_dest) = result
if len(src_and_dest) == 0:
frontend.error("Archive does not contain a project directory or is empty.")
return SimpleStatus(success=False,
description=("Could not unpack archive %s" % archive_filename),
errors=frontend.pop_errors())
if not os.path.exists(canonical_project_dir):
os.makedirs(canonical_project_dir)
try:
extract_files(archive_filename, src_and_dest, frontend)
except Exception as e:
try:
shutil.rmtree(canonical_project_dir)
except (IOError, OSError):
pass
raise e
return _UnarchiveStatus(success=True,
description=("Project archive unpacked to %s." % canonical_project_dir),
project_dir=canonical_project_dir)
except (IOError, OSError, zipfile.error, tarfile.TarError) as e:
frontend.error(str(e))
return SimpleStatus(success=False, description="Failed to read project archive.", errors=frontend.pop_errors()) | [
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amimo/dcc | 114326ab5a082a42c7728a375726489e4709ca29 | androguard/core/bytecodes/dvm.py | python | EncodedArray.get_size | (self) | return self.size | Return the number of elements in the array
:rtype: int | Return the number of elements in the array | [
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Return the number of elements in the array
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"""
return self.size | [
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idanr1986/cuckoo-droid | 1350274639473d3d2b0ac740cae133ca53ab7444 | analyzer/android/lib/api/androguard/dvm.py | python | Instruction.get_output | (self, idx=-1) | Return an additional output of the instruction
:rtype: string | Return an additional output of the instruction | [
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] | def get_output(self, idx=-1) :
"""
Return an additional output of the instruction
:rtype: string
"""
raise("not implemented") | [
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rll/rllab | ba78e4c16dc492982e648f117875b22af3965579 | rllab/policies/base.py | python | Policy.recurrent | (self) | return False | Indicates whether the policy is recurrent.
:return: | Indicates whether the policy is recurrent.
:return: | [
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"""
Indicates whether the policy is recurrent.
:return:
"""
return False | [
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ray-project/ray | 703c1610348615dcb8c2d141a0c46675084660f5 | python/ray/autoscaler/_private/aliyun/utils.py | python | AcsClient.describe_instances | (self, tags=None, instance_ids=None) | return None | Query the details of one or more Elastic Compute Service (ECS) instances.
:param tags: The tags of the instance.
:param instance_ids: The IDs of ECS instances
:return: ECS instance list | Query the details of one or more Elastic Compute Service (ECS) instances. | [
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] | def describe_instances(self, tags=None, instance_ids=None):
""" Query the details of one or more Elastic Compute Service (ECS) instances.
:param tags: The tags of the instance.
:param instance_ids: The IDs of ECS instances
:return: ECS instance list
"""
request = DescribeInstancesRequest()
if tags is not None:
request.set_Tags(tags)
if instance_ids is not None:
request.set_InstanceIds(instance_ids)
response = self._send_request(request)
if response is not None:
instance_list = response.get("Instances").get("Instance")
return instance_list
return None | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/pip/req/req_set.py | python | RequirementSet.is_download | (self) | return False | [] | def is_download(self):
if self.download_dir:
self.download_dir = expanduser(self.download_dir)
if os.path.exists(self.download_dir):
return True
else:
logger.critical('Could not find download directory')
raise InstallationError(
"Could not find or access download directory '%s'"
% display_path(self.download_dir))
return False | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/idlelib/ColorDelegator.py | python | ColorDelegator.toggle_colorize_event | (self, event) | return "break" | [] | def toggle_colorize_event(self, event):
if self.after_id:
after_id = self.after_id
self.after_id = None
if DEBUG: print "cancel scheduled recolorizer"
self.after_cancel(after_id)
if self.allow_colorizing and self.colorizing:
if DEBUG: print "stop colorizing"
self.stop_colorizing = True
self.allow_colorizing = not self.allow_colorizing
if self.allow_colorizing and not self.colorizing:
self.after_id = self.after(1, self.recolorize)
if DEBUG:
print "auto colorizing turned",\
self.allow_colorizing and "on" or "off"
return "break" | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/piglow/light.py | python | PiglowLight.brightness | (self) | return self._brightness | Return the brightness of the light. | Return the brightness of the light. | [
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] | def brightness(self):
"""Return the brightness of the light."""
return self._brightness | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/celery/loaders/base.py | python | BaseLoader.import_from_cwd | (self, module, imp=None, package=None) | return import_from_cwd(
module,
self.import_module if imp is None else imp,
package=package,
) | [] | def import_from_cwd(self, module, imp=None, package=None):
return import_from_cwd(
module,
self.import_module if imp is None else imp,
package=package,
) | [
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leancloud/satori | 701caccbd4fe45765001ca60435c0cb499477c03 | satori-rules/plugin/libs/gevent/lock.py | python | RLock.release | (self) | [] | def release(self):
if self._owner is not getcurrent():
raise RuntimeError("cannot release un-aquired lock")
self._count = count = self._count - 1
if not count:
self._owner = None
self._block.release() | [
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deepmind/learning-to-learn | f3c1a8d176b8ea7cc60478bfcfdd10a7a52fd296 | problems.py | python | simple_multi_optimizer | (num_dims=2) | return build | Multidimensional simple problem. | Multidimensional simple problem. | [
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"problem",
"."
] | def simple_multi_optimizer(num_dims=2):
"""Multidimensional simple problem."""
def get_coordinate(i):
return tf.get_variable("x_{}".format(i),
shape=[],
dtype=tf.float32,
initializer=tf.ones_initializer())
def build():
coordinates = [get_coordinate(i) for i in xrange(num_dims)]
x = tf.concat([tf.expand_dims(c, 0) for c in coordinates], 0)
return tf.reduce_sum(tf.square(x, name="x_squared"))
return build | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/tools/inject/extra/dbgtool/dbgtool.py | python | main | (inputFile, outputFile) | [] | def main(inputFile, outputFile):
if not os.path.isfile(inputFile):
print "ERROR: the provided input file '%s' is not a regular file" % inputFile
sys.exit(1)
script = convert(inputFile)
if outputFile:
fpOut = open(outputFile, "w")
sys.stdout = fpOut
sys.stdout.write(script)
sys.stdout.close()
else:
print script | [
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materialsproject/pymatgen | 8128f3062a334a2edd240e4062b5b9bdd1ae6f58 | pymatgen/analysis/defects/defect_compatibility.py | python | DefectCompatibility.process_entry | (self, defect_entry, perform_corrections=True) | return defect_entry | Process a given DefectEntry with qualifiers given from initialization of class.
Order of processing is:
1) perform all possible defect corrections with information given
2) consider delocalization analyses based on qualifier metrics
given initialization of class. If delocalized, flag entry as delocalized
3) update corrections to defect entry and flag as delocalized
Corrections are applied based on:
i) if free charges are more than free_chg_cutoff then will not apply charge correction,
because it no longer is applicable
ii) use charge correction set by preferred_cc
iii) only use BandFilling correction if use_bandfilling is set to True
iv) only use BandEdgeShift correction if use_bandedgeshift is set to True | Process a given DefectEntry with qualifiers given from initialization of class.
Order of processing is:
1) perform all possible defect corrections with information given
2) consider delocalization analyses based on qualifier metrics
given initialization of class. If delocalized, flag entry as delocalized
3) update corrections to defect entry and flag as delocalized | [
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"""
Process a given DefectEntry with qualifiers given from initialization of class.
Order of processing is:
1) perform all possible defect corrections with information given
2) consider delocalization analyses based on qualifier metrics
given initialization of class. If delocalized, flag entry as delocalized
3) update corrections to defect entry and flag as delocalized
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i) if free charges are more than free_chg_cutoff then will not apply charge correction,
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ii) use charge correction set by preferred_cc
iii) only use BandFilling correction if use_bandfilling is set to True
iv) only use BandEdgeShift correction if use_bandedgeshift is set to True
"""
for struct_key in [
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"initial_defect_structure",
"final_defect_structure",
]:
if struct_key in defect_entry.parameters.keys() and isinstance(defect_entry.parameters[struct_key], dict):
defect_entry.parameters[struct_key] = Structure.from_dict(defect_entry.parameters[struct_key])
if perform_corrections:
self.perform_all_corrections(defect_entry)
self.delocalization_analysis(defect_entry)
# apply corrections based on delocalization analysis
corrections = {}
skip_charge_corrections = False
if "num_hole_vbm" in defect_entry.parameters.keys():
if (self.free_chg_cutoff < defect_entry.parameters["num_hole_vbm"]) or (
self.free_chg_cutoff < defect_entry.parameters["num_elec_cbm"]
):
logger.info("Will not use charge correction because too many free charges")
skip_charge_corrections = True
if skip_charge_corrections:
corrections.update({"charge_correction": 0.0})
else:
if ("freysoldt" in self.preferred_cc.lower()) and ("freysoldt_meta" in defect_entry.parameters.keys()):
frey_meta = defect_entry.parameters["freysoldt_meta"]
frey_corr = frey_meta["freysoldt_electrostatic"] + frey_meta["freysoldt_potential_alignment_correction"]
corrections.update({"charge_correction": frey_corr})
elif "kumagai_meta" in defect_entry.parameters.keys():
kumagai_meta = defect_entry.parameters["kumagai_meta"]
kumagai_corr = (
kumagai_meta["kumagai_electrostatic"] + kumagai_meta["kumagai_potential_alignment_correction"]
)
corrections.update({"charge_correction": kumagai_corr})
else:
logger.info("Could not use any charge correction because insufficient metadata was supplied.")
if self.use_bandfilling:
if "bandfilling_meta" in defect_entry.parameters.keys():
bfc_corr = defect_entry.parameters["bandfilling_meta"]["bandfilling_correction"]
corrections.update({"bandfilling_correction": bfc_corr})
else:
logger.info("Could not use band filling correction because insufficient metadata was supplied.")
else:
corrections.update({"bandfilling_correction": 0.0})
if self.use_bandedgeshift and ("bandshift_meta" in defect_entry.parameters.keys()):
corrections.update(
{
"bandedgeshifting_correction": defect_entry.parameters["bandshift_meta"][
"bandedgeshifting_correction"
]
}
)
# also want to update relevant data for phase diagram
defect_entry.parameters.update(
{
"phasediagram_meta": {
"vbm": defect_entry.parameters["hybrid_vbm"],
"gap": defect_entry.parameters["hybrid_cbm"] - defect_entry.parameters["hybrid_vbm"],
}
}
)
else:
corrections.update({"bandedgeshifting_correction": 0.0})
if isinstance(defect_entry.parameters["vbm"], float) and isinstance(defect_entry.parameters["cbm"], float):
# still want to have vbm and gap ready for phase diagram
defect_entry.parameters.update(
{
"phasediagram_meta": {
"vbm": defect_entry.parameters["vbm"],
"gap": defect_entry.parameters["cbm"] - defect_entry.parameters["vbm"],
}
}
)
defect_entry.corrections.update(corrections)
return defect_entry | [
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WooYun/TangScan | f4fd60228ec09ad10bd3dd3ef3b67e58bcdd4aa5 | tangscan/thirdparty/requests/utils.py | python | should_bypass_proxies | (url) | return False | Returns whether we should bypass proxies or not. | Returns whether we should bypass proxies or not. | [
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] | def should_bypass_proxies(url):
"""
Returns whether we should bypass proxies or not.
"""
get_proxy = lambda k: os.environ.get(k) or os.environ.get(k.upper())
# First check whether no_proxy is defined. If it is, check that the URL
# we're getting isn't in the no_proxy list.
no_proxy = get_proxy('no_proxy')
netloc = urlparse(url).netloc
if no_proxy:
# We need to check whether we match here. We need to see if we match
# the end of the netloc, both with and without the port.
no_proxy = no_proxy.replace(' ', '').split(',')
ip = netloc.split(':')[0]
if is_ipv4_address(ip):
for proxy_ip in no_proxy:
if is_valid_cidr(proxy_ip):
if address_in_network(ip, proxy_ip):
return True
else:
for host in no_proxy:
if netloc.endswith(host) or netloc.split(':')[0].endswith(host):
# The URL does match something in no_proxy, so we don't want
# to apply the proxies on this URL.
return True
# If the system proxy settings indicate that this URL should be bypassed,
# don't proxy.
# The proxy_bypass function is incredibly buggy on OS X in early versions
# of Python 2.6, so allow this call to fail. Only catch the specific
# exceptions we've seen, though: this call failing in other ways can reveal
# legitimate problems.
try:
bypass = proxy_bypass(netloc)
except (TypeError, socket.gaierror):
bypass = False
if bypass:
return True
return False | [
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LinuxCNC/linuxcnc | d42398ede7c839b3ef76298a5d2d753b50b6620c | src/emc/usr_intf/gscreen/gscreen.py | python | Gscreen.on_tool_touchoff_clicked | (self,widget) | This is a callback function for a press of the tool_touchoff button
It calls tool_touchoff_checks() | This is a callback function for a press of the tool_touchoff button
It calls tool_touchoff_checks() | [
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"calls",
"tool_touchoff_checks",
"()"
] | def on_tool_touchoff_clicked(self,widget):
"""This is a callback function for a press of the tool_touchoff button
It calls tool_touchoff_checks()
"""
print("touch")
self.tool_touchoff_checks() | [
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] | https://github.com/LinuxCNC/linuxcnc/blob/d42398ede7c839b3ef76298a5d2d753b50b6620c/src/emc/usr_intf/gscreen/gscreen.py#L2513-L2518 | ||
merrychap/shellen | c0c5f8325dd3f0decf03e55d57c4714c797a2dea | shellen/opt/appearance.py | python | cprint | (text='', end='\n') | [] | def cprint(text='', end='\n'):
colored_text = make_colors(text)
print(colored_text, end=end) | [
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LMFDB/lmfdb | 6cf48a4c18a96e6298da6ae43f587f96845bcb43 | lmfdb/backend/statstable.py | python | PostgresStatsTable._approx_most_common | (self, col, n) | return [tuple(x) for x in cur] | Returns the n most common values for ``col``. Counts are only approximate,
but this functions should be quite fast. Note that the returned list
may have length less than ``n`` if there are not many common values.
Returns a list of pairs ``(value, count)`` where ``count`` is
the number of rows where ``col`` takes on the value ``value``.
INPUT:
- ``col`` -- a column name
- ``n`` -- an integer | Returns the n most common values for ``col``. Counts are only approximate,
but this functions should be quite fast. Note that the returned list
may have length less than ``n`` if there are not many common values. | [
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"tha... | def _approx_most_common(self, col, n):
"""
Returns the n most common values for ``col``. Counts are only approximate,
but this functions should be quite fast. Note that the returned list
may have length less than ``n`` if there are not many common values.
Returns a list of pairs ``(value, count)`` where ``count`` is
the number of rows where ``col`` takes on the value ``value``.
INPUT:
- ``col`` -- a column name
- ``n`` -- an integer
"""
if col not in self.table.search_cols:
raise ValueError("Column %s not a search column for %s" % (col, self.search_table))
selecter = SQL(
"""SELECT v.{0}, (c.reltuples * freq)::int as estimate_ct
FROM pg_stats s
CROSS JOIN LATERAL
unnest(s.most_common_vals::text::"""
+ self.table.col_type[col]
+ """[]
, s.most_common_freqs) WITH ORDINALITY v ({0}, freq, ord)
CROSS JOIN (
SELECT reltuples FROM pg_class
WHERE oid = regclass 'public.nf_fields') c
WHERE schemaname = 'public' AND tablename = %s AND attname = %s
ORDER BY v.ord LIMIT %s"""
).format(Identifier(col))
cur = self._execute(selecter, [self.search_table, col, n])
return [tuple(x) for x in cur] | [
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"... | https://github.com/LMFDB/lmfdb/blob/6cf48a4c18a96e6298da6ae43f587f96845bcb43/lmfdb/backend/statstable.py#L1345-L1376 | |
zihangdai/xlnet | bbaa3a6fa0b3a2ee694e8cf66167434f9eca9660 | modeling.py | python | rel_attn_core | (q_head, k_head_h, v_head_h, k_head_r, seg_embed, seg_mat,
r_w_bias, r_r_bias, r_s_bias, attn_mask, dropatt, is_training,
scale) | return attn_vec | Core relative positional attention operations. | Core relative positional attention operations. | [
"Core",
"relative",
"positional",
"attention",
"operations",
"."
] | def rel_attn_core(q_head, k_head_h, v_head_h, k_head_r, seg_embed, seg_mat,
r_w_bias, r_r_bias, r_s_bias, attn_mask, dropatt, is_training,
scale):
"""Core relative positional attention operations."""
# content based attention score
ac = tf.einsum('ibnd,jbnd->ijbn', q_head + r_w_bias, k_head_h)
# position based attention score
bd = tf.einsum('ibnd,jbnd->ijbn', q_head + r_r_bias, k_head_r)
bd = rel_shift(bd, klen=tf.shape(ac)[1])
# segment based attention score
if seg_mat is None:
ef = 0
else:
ef = tf.einsum('ibnd,snd->ibns', q_head + r_s_bias, seg_embed)
ef = tf.einsum('ijbs,ibns->ijbn', seg_mat, ef)
# merge attention scores and perform masking
attn_score = (ac + bd + ef) * scale
if attn_mask is not None:
# attn_score = attn_score * (1 - attn_mask) - 1e30 * attn_mask
attn_score = attn_score - 1e30 * attn_mask
# attention probability
attn_prob = tf.nn.softmax(attn_score, 1)
attn_prob = tf.layers.dropout(attn_prob, dropatt, training=is_training)
# attention output
attn_vec = tf.einsum('ijbn,jbnd->ibnd', attn_prob, v_head_h)
return attn_vec | [
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"# ... | https://github.com/zihangdai/xlnet/blob/bbaa3a6fa0b3a2ee694e8cf66167434f9eca9660/modeling.py#L128-L160 |
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