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x0rz/EQGRP_Lost_in_Translation | 6692b1486f562f027567a49523b8c151a4050988 | windows/fuzzbunch/pyreadline/console/ironpython_console.py | python | Console.pos | (self, x=None, y=None) | return x,y | Move or query the window cursor. | Move or query the window cursor. | [
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"query",
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"cursor",
"."
] | def pos(self, x=None, y=None):
'''Move or query the window cursor.'''
if x is not None:
System.Console.CursorLeft=x
else:
x=System.Console.CursorLeft
if y is not None:
System.Console.CursorTop=y
else:
y=System.Console.CursorTop
return x,y | [
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twisted/klein | 365f033010b76c6a060531d0abcd4146c726918f | src/klein/_plating.py | python | PlatedElement.lookupRenderMethod | (self, name) | @return: a renderer. | [] | def lookupRenderMethod(self, name):
"""
@return: a renderer.
"""
if name in self._renderers:
wrapped = self._renderers[name]
@modified("plated render wrapper", wrapped)
def renderWrapper(
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return _call(
self._boundInstance, wrapped, request, tag, *args, **kw
)
return renderWrapper
if ":" not in name:
raise MissingRenderMethod(self, name)
slot, type = name.split(":", 1)
def renderList(request, tag):
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yield tag.fillSlots(item=_extra_types(item))
types = {
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if type in types:
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raise MissingRenderMethod(self, name) | [
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haiwen/seahub | e92fcd44e3e46260597d8faa9347cb8222b8b10d | seahub/api2/models.py | python | TokenV2.save | (self, *args, **kwargs) | return super(TokenV2, self).save(*args, **kwargs) | [] | def save(self, *args, **kwargs):
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achael/eht-imaging | bbd3aeb06bef52bf89fa1c06de71e5509a5b0015 | ehtim/imager.py | python | Imager.out_last | (self) | return self._out_list[-1] | Return last result. | Return last result. | [
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"""Return last result.
"""
if self.nruns == 0:
print("No imager runs yet!")
return
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hakril/PythonForWindows | 61e027a678d5b87aa64fcf8a37a6661a86236589 | windows/winproxy/apis/advapi32.py | python | ConvertSecurityDescriptorToStringSecurityDescriptorW | (SecurityDescriptor, RequestedStringSDRevision, SecurityInformation, StringSecurityDescriptor, StringSecurityDescriptorLen) | return ConvertSecurityDescriptorToStringSecurityDescriptorW.ctypes_function(SecurityDescriptor, RequestedStringSDRevision, SecurityInformation, StringSecurityDescriptor, StringSecurityDescriptorLen) | [] | def ConvertSecurityDescriptorToStringSecurityDescriptorW(SecurityDescriptor, RequestedStringSDRevision, SecurityInformation, StringSecurityDescriptor, StringSecurityDescriptorLen):
return ConvertSecurityDescriptorToStringSecurityDescriptorW.ctypes_function(SecurityDescriptor, RequestedStringSDRevision, SecurityInformation, StringSecurityDescriptor, StringSecurityDescriptorLen) | [
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realpython/book2-exercises | cde325eac8e6d8cff2316601c2e5b36bb46af7d0 | web2py/venv/lib/python2.7/site-packages/pip/_vendor/distlib/_backport/tarfile.py | python | TarIter.__iter__ | (self) | return self | Return iterator object. | Return iterator object. | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/min/_pydecimal.py | python | Decimal._check_nans | (self, other=None, context=None) | return 0 | Returns whether the number is not actually one.
if self, other are sNaN, signal
if self, other are NaN return nan
return 0
Done before operations. | Returns whether the number is not actually one. | [
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] | def _check_nans(self, other=None, context=None):
"""Returns whether the number is not actually one.
if self, other are sNaN, signal
if self, other are NaN return nan
return 0
Done before operations.
"""
self_is_nan = self._isnan()
if other is None:
other_is_nan = False
else:
other_is_nan = other._isnan()
if self_is_nan or other_is_nan:
if context is None:
context = getcontext()
if self_is_nan == 2:
return context._raise_error(InvalidOperation, 'sNaN',
self)
if other_is_nan == 2:
return context._raise_error(InvalidOperation, 'sNaN',
other)
if self_is_nan:
return self._fix_nan(context)
return other._fix_nan(context)
return 0 | [
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TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | 5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e | deep-learning/fastai-docs/fastai_docs-master/dev_nb/nb_004b.py | python | to_fp16 | (learn:Learner, loss_scale:float=512., flat_master:bool=False) | return learn | Transforms the learner in FP16 precision | Transforms the learner in FP16 precision | [
"Transforms",
"the",
"learner",
"in",
"FP16",
"precision"
] | def to_fp16(learn:Learner, loss_scale:float=512., flat_master:bool=False)->Learner:
"Transforms the learner in FP16 precision"
learn.model = model2half(learn.model)
learn.mp_cb = MixedPrecision(learn, loss_scale=loss_scale, flat_master=flat_master)
learn.callbacks.append(learn.mp_cb)
return learn | [
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leo-editor/leo-editor | 383d6776d135ef17d73d935a2f0ecb3ac0e99494 | leo/plugins/obsolete/wxGui.py | python | wxLeoTree.item2vnode | (self,item) | Override baseNativeTreeWidget.item2vnode. | Override baseNativeTreeWidget.item2vnode. | [
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"."
] | def item2vnode (self,item):
'''Override baseNativeTreeWidget.item2vnode.'''
w = self.treeWidget
if item and item.IsOk():
v = self.getItemData(item)
return v
else:
return None | [
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holoviz/hvplot | 25f3285c8ac315e96929b57504e4674d40b947b5 | hvplot/interactive.py | python | Interactive.eval | (self) | return obj | Returns the currrent state of the interactive expression. The
returned object is no longer interactive. | Returns the currrent state of the interactive expression. The
returned object is no longer interactive. | [
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] | def eval(self):
"""
Returns the currrent state of the interactive expression. The
returned object is no longer interactive.
"""
obj = self._current
if self._method:
return getattr(obj, self._method, obj)
return obj | [
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numenta/numenta-apps | 02903b0062c89c2c259b533eea2df6e8bb44eaf3 | taurus_metric_collectors/taurus_metric_collectors/common_services/metric_maintenance_agent.py | python | _purgeDeprecatedCompanies | () | Purge cached data and Taurus Engine metrics/models corresponding to symbols
that are in xignite_security table, but not in metrics configuration. | Purge cached data and Taurus Engine metrics/models corresponding to symbols
that are in xignite_security table, but not in metrics configuration. | [
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] | def _purgeDeprecatedCompanies():
"""Purge cached data and Taurus Engine metrics/models corresponding to symbols
that are in xignite_security table, but not in metrics configuration.
"""
activeCompanySymbols = set(security[0] for security in
metric_utils.getAllMetricSecurities())
deprecatedSymbols = set(_queryCachedCompanySymbols()) - activeCompanySymbols
if deprecatedSymbols:
delete_companies.deleteCompanies(
tickerSymbols=deprecatedSymbols,
engineServer=_TAURUS_HTM_SERVER,
engineApiKey=_TAURUS_API_KEY,
warnAboutDestructiveAction=False)
else:
g_log.info("There were no deprecated companies to remove") | [
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cloudant/bigcouch | 8e9c1ec0ed1676ff152f10658f5c83a1a91fa8fe | couchjs/scons/scons-time.py | python | SConsTimer.doc_to_help | (self, obj) | return self.outdent(doc) | Translates an object's __doc__ string into help text.
This strips a consistent number of spaces from each line in the
help text, essentially "outdenting" the text to the left-most
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] | def doc_to_help(self, obj):
"""
Translates an object's __doc__ string into help text.
This strips a consistent number of spaces from each line in the
help text, essentially "outdenting" the text to the left-most
column.
"""
doc = obj.__doc__
if doc is None:
return ''
return self.outdent(doc) | [
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edisonlz/fastor | 342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3 | base/site-packages/redis_model/models/base.py | python | ModelBase._initialize_attributes | (self,model_class, name, bases, attrs) | Initialize the attributes of the model.
param:
model_class:object
name:string
bases:base model
attrs:attribute | Initialize the attributes of the model.
param:
model_class:object
name:string
bases:base model
attrs:attribute | [
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] | def _initialize_attributes(self,model_class, name, bases, attrs):
"""
Initialize the attributes of the model.
param:
model_class:object
name:string
bases:base model
attrs:attribute
"""
#主要功能：添加属性列表
model_class.attributes = {}
for k, v in attrs.iteritems():
if isinstance(v,Attribute):
model_class.attributes[k] = v
v.name = v.name or k | [
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pytorch/translate | 564d011b10b4cef4e110c092c2912277ed64c713 | pytorch_translate/transformer_aan.py | python | TransformerAANDecoder.max_positions | (self) | return min(self.max_target_positions, self.embed_positions.max_positions) | Maximum output length supported by the decoder. | Maximum output length supported by the decoder. | [
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] | def max_positions(self):
"""Maximum output length supported by the decoder."""
if self.embed_positions is None:
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/coding/relative_finite_field_extension.py | python | RelativeFiniteFieldExtension._latex_ | (self) | return "\\textnormal{Relative field extension between %s and %s}" % (self.absolute_field()._latex_(),
self.relative_field()._latex_()) | r"""
Returns a latex representation of ``self``.
EXAMPLES::
sage: from sage.coding.relative_finite_field_extension import *
sage: Fqm.<aa> = GF(16)
sage: Fq.<a> = GF(4)
sage: latex(RelativeFiniteFieldExtension(Fqm, Fq))
\textnormal{Relative field extension between \Bold{F}_{2^{4}} and \Bold{F}_{2^{2}}} | r"""
Returns a latex representation of ``self``. | [
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] | def _latex_(self):
r"""
Returns a latex representation of ``self``.
EXAMPLES::
sage: from sage.coding.relative_finite_field_extension import *
sage: Fqm.<aa> = GF(16)
sage: Fq.<a> = GF(4)
sage: latex(RelativeFiniteFieldExtension(Fqm, Fq))
\textnormal{Relative field extension between \Bold{F}_{2^{4}} and \Bold{F}_{2^{2}}}
"""
return "\\textnormal{Relative field extension between %s and %s}" % (self.absolute_field()._latex_(),
self.relative_field()._latex_()) | [
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thaines/helit | 04bd36ee0fb6b762c63d746e2cd8813641dceda9 | handwriting/corpus/extract_text.py | python | each_prefixed_paragraph | (lines, attribution, prefix) | Same as each_named_paragraph, but where each title has a prefix. | Same as each_named_paragraph, but where each title has a prefix. | [
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] | def each_prefixed_paragraph(lines, attribution, prefix):
"""Same as each_named_paragraph, but where each title has a prefix."""
buf = []
title = 'Unknown'
for line in lines:
line = line.strip()
line = line.replace('_','')
if has_letters(line):
buf.append(line)
elif len(buf)!=0:
text = '\n'.join(buf)
if has_lower(text):
yield Block(text, attribution%title)
elif text.startswith(prefix):
title = string.capwords(text[len(prefix):].replace('\n',''))
buf = []
if len(buf)!=0:
text = '\n'.join(buf)
if has_lower(text):
yield Block(text, attribution%title) | [
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hack12306/12306-booking | 726c830c2e771d5cd8e09772e7646e480dddb086 | booking/remind.py | python | remind_left_ticket | () | 有票提醒 | 有票提醒 | [
"有票提醒"
] | def remind_left_ticket():
"""
有票提醒
"""
cmd = 'open %s --hide --background' % settings.TRAIN_AUDIO_FILE
os.system(cmd) | [
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realpython/book2-exercises | cde325eac8e6d8cff2316601c2e5b36bb46af7d0 | web2py/venv/lib/python2.7/site-packages/pip/_vendor/requests/packages/urllib3/util/ssl_.py | python | ssl_wrap_socket | (sock, keyfile=None, certfile=None, cert_reqs=None,
ca_certs=None, server_hostname=None,
ssl_version=None, ciphers=None, ssl_context=None,
ca_cert_dir=None) | return context.wrap_socket(sock) | All arguments except for server_hostname, ssl_context, and ca_cert_dir have
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:param server_hostname:
When SNI is supported, the expected hostname of the certificate
:param ssl_context:
A pre-made :class:`SSLContext` object. If none is provided, one will
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:param ciphers:
A string of ciphers we wish the client to support. This is not
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:param ca_cert_dir:
A directory containing CA certificates in multiple separate files, as
supported by OpenSSL's -CApath flag or the capath argument to
SSLContext.load_verify_locations(). | All arguments except for server_hostname, ssl_context, and ca_cert_dir have
the same meaning as they do when using :func:`ssl.wrap_socket`. | [
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context = ssl_context
if context is None:
context = create_urllib3_context(ssl_version, cert_reqs,
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if ca_certs or ca_cert_dir:
try:
context.load_verify_locations(ca_certs, ca_cert_dir)
except IOError as e: # Platform-specific: Python 2.6, 2.7, 3.2
raise SSLError(e)
# Py33 raises FileNotFoundError which subclasses OSError
# These are not equivalent unless we check the errno attribute
except OSError as e: # Platform-specific: Python 3.3 and beyond
if e.errno == errno.ENOENT:
raise SSLError(e)
raise
if certfile:
context.load_cert_chain(certfile, keyfile)
if HAS_SNI: # Platform-specific: OpenSSL with enabled SNI
return context.wrap_socket(sock, server_hostname=server_hostname)
warnings.warn(
'An HTTPS request has been made, but the SNI (Subject Name '
'Indication) extension to TLS is not available on this platform. '
'This may cause the server to present an incorrect TLS '
'certificate, which can cause validation failures. You can upgrade to '
'a newer version of Python to solve this. For more information, see '
'https://urllib3.readthedocs.io/en/latest/security.html'
'#snimissingwarning.',
SNIMissingWarning
)
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wrye-bash/wrye-bash | d495c47cfdb44475befa523438a40c4419cb386f | Mopy/bash/basher/files_links.py | python | File_Redate._perform_refresh | (self) | Refreshes the data store - | Refreshes the data store - | [
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igraph/python-igraph | e9f83e8af08f24ea025596e745917197d8b44d94 | src/igraph/statistics.py | python | RunningMean.mean | (self) | return self._mean | Returns the current mean | Returns the current mean | [
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yhlleo/tensorflow.cifar10 | cd950323e7157e30391c123b665804b6276e38a6 | cifar10.py | python | loss | (logits, labels) | return tf.add_n(tf.get_collection('losses'), name='total_loss') | Add L2Loss to all the trainable variables.
Add summary for for "Loss" and "Loss/avg".
Args:
logits: Logits from inference().
labels: Labels from distorted_inputs or inputs(). 1-D tensor
of shape [batch_size]
Returns:
Loss tensor of type float. | Add L2Loss to all the trainable variables. | [
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"""Add L2Loss to all the trainable variables.
Add summary for for "Loss" and "Loss/avg".
Args:
logits: Logits from inference().
labels: Labels from distorted_inputs or inputs(). 1-D tensor
of shape [batch_size]
Returns:
Loss tensor of type float.
"""
# Reshape the labels into a dense Tensor of
# shape [batch_size, NUM_CLASSES].
sparse_labels = tf.reshape(labels, [FLAGS.batch_size, 1])
# indices = tf.reshape(tfrange(FLAGS.batch_size), [FLAGS.batch_size, 1])
indices = tf.reshape(range(FLAGS.batch_size), [FLAGS.batch_size, 1])
concated = tf.concat(1, [indices, sparse_labels])
dense_labels = tf.sparse_to_dense(concated,
[FLAGS.batch_size, NUM_CLASSES],
1.0, 0.0)
# Calculate the average cross entropy loss across the batch.
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits, dense_labels, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# The total loss is defined as the cross entropy loss plus all of the weight
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return tf.add_n(tf.get_collection('losses'), name='total_loss') | [
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robinhood/faust | 01b4c0ad8390221db71751d80001b0fd879291e2 | faust/channels.py | python | get_topic_name | (self) | Get the topic name, or raise if this is not a named channel. | Get the topic name, or raise if this is not a named channel. | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/solvers/ode.py | python | _nonlinear_3eq_order1_type1 | (x, y, z, t, eq) | return [sol1, sol2, sol3] | r"""
Equations:
.. math:: a x' = (b - c) y z, \enspace b y' = (c - a) z x, \enspace c z' = (a - b) x y
First Integrals:
.. math:: a x^{2} + b y^{2} + c z^{2} = C_1
.. math:: a^{2} x^{2} + b^{2} y^{2} + c^{2} z^{2} = C_2
where `C_1` and `C_2` are arbitrary constants. On solving the integrals for `y` and
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for other two equations, we will arrive at first order equation on `y` and `z` too.
References
==========
-http://eqworld.ipmnet.ru/en/solutions/sysode/sode0401.pdf | r"""
Equations: | [
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r"""
Equations:
.. math:: a x' = (b - c) y z, \enspace b y' = (c - a) z x, \enspace c z' = (a - b) x y
First Integrals:
.. math:: a x^{2} + b y^{2} + c z^{2} = C_1
.. math:: a^{2} x^{2} + b^{2} y^{2} + c^{2} z^{2} = C_2
where `C_1` and `C_2` are arbitrary constants. On solving the integrals for `y` and
`z` and on substituting the resulting expressions into the first equation of the
system, we arrives at a separable first-order equation on `x`. Similarly doing that
for other two equations, we will arrive at first order equation on `y` and `z` too.
References
==========
-http://eqworld.ipmnet.ru/en/solutions/sysode/sode0401.pdf
"""
C1, C2 = get_numbered_constants(eq, num=2)
u, v, w = symbols('u, v, w')
p = Wild('p', exclude=[x(t), y(t), z(t), t])
q = Wild('q', exclude=[x(t), y(t), z(t), t])
s = Wild('s', exclude=[x(t), y(t), z(t), t])
r = (diff(x(t),t) - eq[0]).match(p*y(t)*z(t))
r.update((diff(y(t),t) - eq[1]).match(q*z(t)*x(t)))
r.update((diff(z(t),t) - eq[2]).match(s*x(t)*y(t)))
n1, d1 = r[p].as_numer_denom()
n2, d2 = r[q].as_numer_denom()
n3, d3 = r[s].as_numer_denom()
val = solve([n1*u-d1*v+d1*w, d2*u+n2*v-d2*w, d3*u-d3*v-n3*w],[u,v])
vals = [val[v], val[u]]
c = lcm(vals[0].as_numer_denom()[1], vals[1].as_numer_denom()[1])
b = vals[0].subs(w, c)
a = vals[1].subs(w, c)
y_x = sqrt(((c*C1-C2) - a*(c-a)*x(t)**2)/(b*(c-b)))
z_x = sqrt(((b*C1-C2) - a*(b-a)*x(t)**2)/(c*(b-c)))
z_y = sqrt(((a*C1-C2) - b*(a-b)*y(t)**2)/(c*(a-c)))
x_y = sqrt(((c*C1-C2) - b*(c-b)*y(t)**2)/(a*(c-a)))
x_z = sqrt(((b*C1-C2) - c*(b-c)*z(t)**2)/(a*(b-a)))
y_z = sqrt(((a*C1-C2) - c*(a-c)*z(t)**2)/(b*(a-b)))
sol1 = dsolve(a*diff(x(t),t) - (b-c)*y_x*z_x)
sol2 = dsolve(b*diff(y(t),t) - (c-a)*z_y*x_y)
sol3 = dsolve(c*diff(z(t),t) - (a-b)*x_z*y_z)
return [sol1, sol2, sol3] | [
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JinnLynn/alfred-workflows | 1fc663b00f56b4dc3f9fa2212a8600e6da616fc1 | lib/six/__init__.py | python | with_metaclass | (meta, base=object) | return meta("NewBase", (base,), {}) | Create a base class with a metaclass. | Create a base class with a metaclass. | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/PIL/PngImagePlugin.py | python | PngImageFile.load_end | (self) | internal: finished reading image data | internal: finished reading image data | [
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WilsonWangTHU/mbbl | bb88a016de2fcd8ea0ed9c4d5c539817d2b476e7 | mbbl/env/gym_env/noise_gym_cartpole.py | python | env._set_groundtruth_api | (self) | @brief:
In this function, we could provide the ground-truth dynamics
and rewards APIs for the agent to call.
For the new environments, if we don't set their ground-truth
apis, then we cannot test the algorithm using ground-truth
dynamics or reward | [] | def _set_groundtruth_api(self):
""" @brief:
In this function, we could provide the ground-truth dynamics
and rewards APIs for the agent to call.
For the new environments, if we don't set their ground-truth
apis, then we cannot test the algorithm using ground-truth
dynamics or reward
"""
self._set_reward_api()
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ironport/shrapnel | 9496a64c46271b0c5cef0feb8f2cdf33cb752bb6 | coro/http/client.py | python | request.wake | (self) | signal that a reply to this request has been received | signal that a reply to this request has been received | [
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sefakilic/goodreads | 5187100d66b87c87db8b095bbf576e1b92422a23 | goodreads/event.py | python | GoodreadsEvent.start_at | (self) | return self._event_dict['start_at']['#text'] | Event starts at | Event starts at | [
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materialsproject/fireworks | 83a907c19baf2a5c9fdcf63996f9797c3c85b785 | fireworks/core/launchpad.py | python | LaunchPad.defuse_wf | (self, fw_id, defuse_all_states=True) | Defuse the workflow containing the given firework id.
Args:
fw_id (int): firework id
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Defuse the workflow containing the given firework id.
Args:
fw_id (int): firework id
defuse_all_states (bool)
"""
wf = self.get_wf_by_fw_id_lzyfw(fw_id)
for fw in wf:
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/scatter/_legendgrouptitle.py | python | Legendgrouptitle.__init__ | (self, arg=None, font=None, text=None, **kwargs) | Construct a new Legendgrouptitle object
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dict of properties compatible with this constructor or
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Sets this legend group's title font.
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Sets the title of the legend group.
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dict of properties compatible with this constructor or
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Sets this legend group's title font.
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# ----------------------------------
_v = arg.pop("font", None)
_v = font if font is not None else _v
if _v is not None:
self["font"] = _v
_v = arg.pop("text", None)
_v = text if text is not None else _v
if _v is not None:
self["text"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False | [
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robinhood/faust | 01b4c0ad8390221db71751d80001b0fd879291e2 | faust/streams.py | python | Stream.on_stop | (self) | Signal that the stream is stopping. | Signal that the stream is stopping. | [
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"""Signal that the stream is stopping."""
self._passive = False
self._passive_started.clear()
for table_or_stream in self.combined:
await table_or_stream.remove_from_stream(self) | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/tools/inject/plugins/generic/search.py | python | Search.searchTable | (self) | [] | def searchTable(self):
bruteForce = False
if Backend.isDbms(DBMS.MYSQL) and not kb.data.has_information_schema:
errMsg = "information_schema not available, "
errMsg += "back-end DBMS is MySQL < 5.0"
bruteForce = True
if bruteForce:
message = "do you want to use common table existence check? %s" % ("[Y/n/q]" if Backend.getIdentifiedDbms() in (DBMS.ACCESS,) else "[y/N/q]")
test = readInput(message, default="Y" if "Y" in message else "N")
if test[0] in ("n", "N"):
return
elif test[0] in ("q", "Q"):
raise SqlmapUserQuitException
else:
regex = "|".join(conf.tbl.split(","))
return tableExists(paths.COMMON_TABLES, regex)
foundTbls = {}
tblList = conf.tbl.split(",")
rootQuery = queries[Backend.getIdentifiedDbms()].search_table
tblCond = rootQuery.inband.condition
dbCond = rootQuery.inband.condition2
tblConsider, tblCondParam = self.likeOrExact("table")
for tbl in tblList:
values = []
tbl = safeSQLIdentificatorNaming(tbl, True)
if Backend.getIdentifiedDbms() in (DBMS.ORACLE, DBMS.DB2, DBMS.FIREBIRD):
tbl = tbl.upper()
infoMsg = "searching table"
if tblConsider == "1":
infoMsg += "s LIKE"
infoMsg += " '%s'" % unsafeSQLIdentificatorNaming(tbl)
if dbCond and conf.db and conf.db != CURRENT_DB:
_ = conf.db.split(",")
whereDbsQuery = " AND (" + " OR ".join("%s = '%s'" % (dbCond, unsafeSQLIdentificatorNaming(db)) for db in _) + ")"
infoMsg += " for database%s '%s'" % ("s" if len(_) > 1 else "", ", ".join(db for db in _))
elif conf.excludeSysDbs:
whereDbsQuery = "".join(" AND '%s' != %s" % (unsafeSQLIdentificatorNaming(db), dbCond) for db in self.excludeDbsList)
infoMsg2 = "skipping system database%s '%s'" % ("s" if len(self.excludeDbsList) > 1 else "", ", ".join(db for db in self.excludeDbsList))
logger.info(infoMsg2)
else:
whereDbsQuery = ""
logger.info(infoMsg)
tblQuery = "%s%s" % (tblCond, tblCondParam)
tblQuery = tblQuery % unsafeSQLIdentificatorNaming(tbl)
if any(isTechniqueAvailable(_) for _ in (PAYLOAD.TECHNIQUE.UNION, PAYLOAD.TECHNIQUE.ERROR, PAYLOAD.TECHNIQUE.QUERY)) or conf.direct:
query = rootQuery.inband.query
query = query % (tblQuery + whereDbsQuery)
values = inject.getValue(query, blind=False, time=False)
if values and Backend.getIdentifiedDbms() in (DBMS.SQLITE, DBMS.FIREBIRD):
newValues = []
if isinstance(values, basestring):
values = [values]
for value in values:
dbName = "SQLite" if Backend.isDbms(DBMS.SQLITE) else "Firebird"
newValues.append(["%s%s" % (dbName, METADB_SUFFIX), value])
values = newValues
for foundDb, foundTbl in filterPairValues(values):
foundDb = safeSQLIdentificatorNaming(foundDb)
foundTbl = safeSQLIdentificatorNaming(foundTbl, True)
if foundDb is None or foundTbl is None:
continue
if foundDb in foundTbls:
foundTbls[foundDb].append(foundTbl)
else:
foundTbls[foundDb] = [foundTbl]
if not values and isInferenceAvailable() and not conf.direct:
if Backend.getIdentifiedDbms() not in (DBMS.SQLITE, DBMS.FIREBIRD):
if len(whereDbsQuery) == 0:
infoMsg = "fetching number of databases with table"
if tblConsider == "1":
infoMsg += "s LIKE"
infoMsg += " '%s'" % unsafeSQLIdentificatorNaming(tbl)
logger.info(infoMsg)
query = rootQuery.blind.count
query = query % (tblQuery + whereDbsQuery)
count = inject.getValue(query, union=False, error=False, expected=EXPECTED.INT, charsetType=CHARSET_TYPE.DIGITS)
if not isNumPosStrValue(count):
warnMsg = "no databases have table"
if tblConsider == "1":
warnMsg += "s LIKE"
warnMsg += " '%s'" % unsafeSQLIdentificatorNaming(tbl)
logger.warn(warnMsg)
continue
indexRange = getLimitRange(count)
for index in indexRange:
query = rootQuery.blind.query
query = query % (tblQuery + whereDbsQuery)
query = agent.limitQuery(index, query)
foundDb = unArrayizeValue(inject.getValue(query, union=False, error=False))
foundDb = safeSQLIdentificatorNaming(foundDb)
if foundDb not in foundTbls:
foundTbls[foundDb] = []
if tblConsider == "2":
foundTbls[foundDb].append(tbl)
if tblConsider == "2":
continue
else:
for db in conf.db.split(",") if conf.db else (self.getCurrentDb(),):
db = safeSQLIdentificatorNaming(db)
if db not in foundTbls:
foundTbls[db] = []
else:
dbName = "SQLite" if Backend.isDbms(DBMS.SQLITE) else "Firebird"
foundTbls["%s%s" % (dbName, METADB_SUFFIX)] = []
for db in foundTbls.keys():
db = safeSQLIdentificatorNaming(db)
infoMsg = "fetching number of table"
if tblConsider == "1":
infoMsg += "s LIKE"
infoMsg += " '%s' in database '%s'" % (unsafeSQLIdentificatorNaming(tbl), unsafeSQLIdentificatorNaming(db))
logger.info(infoMsg)
query = rootQuery.blind.count2
if Backend.getIdentifiedDbms() not in (DBMS.SQLITE, DBMS.FIREBIRD):
query = query % unsafeSQLIdentificatorNaming(db)
query += " AND %s" % tblQuery
count = inject.getValue(query, union=False, error=False, expected=EXPECTED.INT, charsetType=CHARSET_TYPE.DIGITS)
if not isNumPosStrValue(count):
warnMsg = "no table"
if tblConsider == "1":
warnMsg += "s LIKE"
warnMsg += " '%s' " % unsafeSQLIdentificatorNaming(tbl)
warnMsg += "in database '%s'" % unsafeSQLIdentificatorNaming(db)
logger.warn(warnMsg)
continue
indexRange = getLimitRange(count)
for index in indexRange:
query = rootQuery.blind.query2
if query.endswith("'%s')"):
query = query[:-1] + " AND %s)" % tblQuery
else:
query += " AND %s" % tblQuery
if Backend.isDbms(DBMS.FIREBIRD):
query = safeStringFormat(query, index)
if Backend.getIdentifiedDbms() not in (DBMS.SQLITE, DBMS.FIREBIRD):
query = safeStringFormat(query, unsafeSQLIdentificatorNaming(db))
if not Backend.isDbms(DBMS.FIREBIRD):
query = agent.limitQuery(index, query)
foundTbl = unArrayizeValue(inject.getValue(query, union=False, error=False))
if not isNoneValue(foundTbl):
kb.hintValue = foundTbl
foundTbl = safeSQLIdentificatorNaming(foundTbl, True)
foundTbls[db].append(foundTbl)
for db in foundTbls.keys():
if isNoneValue(foundTbls[db]):
del foundTbls[db]
if not foundTbls:
warnMsg = "no databases contain any of the provided tables"
logger.warn(warnMsg)
return
conf.dumper.dbTables(foundTbls)
self.dumpFoundTables(foundTbls) | [
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jparkhill/TensorMol | d52104dc7ee46eec8301d332a95d672270ac0bd1 | TensorMol/TFNetworks/TFBehlerParinelloSymEE.py | python | MolInstance_DirectBP_Charge_SymFunction.train_step | (self, step) | return | Perform a single training step (complete processing of all input), using minibatches of size self.batch_size.
Training object including dipole, energy and gradient
Args:
step: the index of this step. | Perform a single training step (complete processing of all input), using minibatches of size self.batch_size.
Training object including dipole, energy and gradient | [
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".",
"batch_size",
".",
"Training",
"object",
"including",
"dipole",
"energy",
"and",
"gradient"
] | def train_step(self, step):
"""
Perform a single training step (complete processing of all input), using minibatches of size self.batch_size.
Training object including dipole, energy and gradient
Args:
step: the index of this step.
"""
Ncase_train = self.TData.NTrain
start_time = time.time()
train_loss = 0.0
num_of_mols = 0
pre_output = np.zeros((self.batch_size),dtype=np.float64)
for ministep in range (0, int(Ncase_train/self.batch_size)):
batch_data = self.TData.GetTrainBatch(self.batch_size)+ [self.keep_prob]
actual_mols = self.batch_size
t = time.time()
dump_2, total_loss_value, dipole_loss, dipole, charge, debug1 = self.sess.run([self.train_op, self.total_loss, self.dipole_loss, self.dipole, self.charge, self.debug1], feed_dict=self.fill_feed_dict(batch_data))
train_loss = train_loss + dipole_loss
duration = time.time() - start_time
num_of_mols += actual_mols
#print ("dipole:", dipole, dipole.shape, dipole[0], np.sum(dipole[0]))
#print ("charge:", charge, charge.shape, charge[-1], np.sum(charge[-1]))
#print ("debug1:", debug1, debug1.shape, debug1[-1], np.sum(debug1[-1]))
#xyz = batch_data[0][-1]*BOHRPERA
#natom = int(1/ batch_data[-2][-1])
#charge = charge[-1]
#field = np.zeros(3)
#for i in range(0, natom-1):
# field += charge[i]*(xyz[natom-1] - xyz[i])/np.linalg.norm(xyz[natom-1] - xyz[i])**2
#print ("xyz:", xyz,"charge:", charge, "field:", field)
self.print_training(step, train_loss, num_of_mols, duration)
return | [
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tribe29/checkmk | 6260f2512e159e311f426e16b84b19d0b8e9ad0c | cmk/gui/livestatus_utils/commands/downtimes.py | python | delete_downtime | (connection, downtime_id) | Delete a scheduled downtime based upon the downtime id | Delete a scheduled downtime based upon the downtime id | [
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"scheduled",
"downtime",
"based",
"upon",
"the",
"downtime",
"id"
] | def delete_downtime(connection, downtime_id):
"""Delete a scheduled downtime based upon the downtime id"""
with detailed_connection(connection) as conn:
entry = Query(
[Downtimes.is_service],
Downtimes.id == downtime_id,
).fetchone(conn)
if entry["is_service"]:
del_service_downtime(connection, downtime_id, entry["site"])
else:
del_host_downtime(connection, downtime_id, entry["site"]) | [
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Azure/azure-storage-python | 4306898850dd21617644fc537a57d025e833db74 | azure-storage-blob/azure/storage/blob/blockblobservice.py | python | BlockBlobService.__init__ | (self, account_name=None, account_key=None, sas_token=None, is_emulated=False,
protocol=DEFAULT_PROTOCOL, endpoint_suffix=SERVICE_HOST_BASE, custom_domain=None,
request_session=None, connection_string=None, socket_timeout=None, token_credential=None) | :param str account_name:
The storage account name. This is used to authenticate requests
signed with an account key and to construct the storage endpoint. It
is required unless a connection string is given, or if a custom
domain is used with anonymous authentication.
:param str account_key:
The storage account key. This is used for shared key authentication.
If neither account key or sas token is specified, anonymous access
will be used.
:param str sas_token:
A shared access signature token to use to authenticate requests
instead of the account key. If account key and sas token are both
specified, account key will be used to sign. If neither are
specified, anonymous access will be used.
:param bool is_emulated:
Whether to use the emulator. Defaults to False. If specified, will
override all other parameters besides connection string and request
session.
:param str protocol:
The protocol to use for requests. Defaults to https.
:param str endpoint_suffix:
The host base component of the url, minus the account name. Defaults
to Azure (core.windows.net). Override this to use the China cloud
(core.chinacloudapi.cn).
:param str custom_domain:
The custom domain to use. This can be set in the Azure Portal. For
example, 'www.mydomain.com'.
:param requests.Session request_session:
The session object to use for http requests.
:param str connection_string:
If specified, this will override all other parameters besides
request session. See
http://azure.microsoft.com/en-us/documentation/articles/storage-configure-connection-string/
for the connection string format.
:param int socket_timeout:
If specified, this will override the default socket timeout. The timeout specified is in seconds.
See DEFAULT_SOCKET_TIMEOUT in _constants.py for the default value.
:param token_credential:
A token credential used to authenticate HTTPS requests. The token value
should be updated before its expiration.
:type `~azure.storage.common.TokenCredential` | :param str account_name:
The storage account name. This is used to authenticate requests
signed with an account key and to construct the storage endpoint. It
is required unless a connection string is given, or if a custom
domain is used with anonymous authentication.
:param str account_key:
The storage account key. This is used for shared key authentication.
If neither account key or sas token is specified, anonymous access
will be used.
:param str sas_token:
A shared access signature token to use to authenticate requests
instead of the account key. If account key and sas token are both
specified, account key will be used to sign. If neither are
specified, anonymous access will be used.
:param bool is_emulated:
Whether to use the emulator. Defaults to False. If specified, will
override all other parameters besides connection string and request
session.
:param str protocol:
The protocol to use for requests. Defaults to https.
:param str endpoint_suffix:
The host base component of the url, minus the account name. Defaults
to Azure (core.windows.net). Override this to use the China cloud
(core.chinacloudapi.cn).
:param str custom_domain:
The custom domain to use. This can be set in the Azure Portal. For
example, 'www.mydomain.com'.
:param requests.Session request_session:
The session object to use for http requests.
:param str connection_string:
If specified, this will override all other parameters besides
request session. See
http://azure.microsoft.com/en-us/documentation/articles/storage-configure-connection-string/
for the connection string format.
:param int socket_timeout:
If specified, this will override the default socket timeout. The timeout specified is in seconds.
See DEFAULT_SOCKET_TIMEOUT in _constants.py for the default value.
:param token_credential:
A token credential used to authenticate HTTPS requests. The token value
should be updated before its expiration.
:type `~azure.storage.common.TokenCredential` | [
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protocol=DEFAULT_PROTOCOL, endpoint_suffix=SERVICE_HOST_BASE, custom_domain=None,
request_session=None, connection_string=None, socket_timeout=None, token_credential=None):
'''
:param str account_name:
The storage account name. This is used to authenticate requests
signed with an account key and to construct the storage endpoint. It
is required unless a connection string is given, or if a custom
domain is used with anonymous authentication.
:param str account_key:
The storage account key. This is used for shared key authentication.
If neither account key or sas token is specified, anonymous access
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:param str sas_token:
A shared access signature token to use to authenticate requests
instead of the account key. If account key and sas token are both
specified, account key will be used to sign. If neither are
specified, anonymous access will be used.
:param bool is_emulated:
Whether to use the emulator. Defaults to False. If specified, will
override all other parameters besides connection string and request
session.
:param str protocol:
The protocol to use for requests. Defaults to https.
:param str endpoint_suffix:
The host base component of the url, minus the account name. Defaults
to Azure (core.windows.net). Override this to use the China cloud
(core.chinacloudapi.cn).
:param str custom_domain:
The custom domain to use. This can be set in the Azure Portal. For
example, 'www.mydomain.com'.
:param requests.Session request_session:
The session object to use for http requests.
:param str connection_string:
If specified, this will override all other parameters besides
request session. See
http://azure.microsoft.com/en-us/documentation/articles/storage-configure-connection-string/
for the connection string format.
:param int socket_timeout:
If specified, this will override the default socket timeout. The timeout specified is in seconds.
See DEFAULT_SOCKET_TIMEOUT in _constants.py for the default value.
:param token_credential:
A token credential used to authenticate HTTPS requests. The token value
should be updated before its expiration.
:type `~azure.storage.common.TokenCredential`
'''
self.blob_type = _BlobTypes.BlockBlob
super(BlockBlobService, self).__init__(
account_name, account_key, sas_token, is_emulated, protocol, endpoint_suffix,
custom_domain, request_session, connection_string, socket_timeout, token_credential) | [
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PaddlePaddle/Research | 2da0bd6c72d60e9df403aff23a7802779561c4a1 | KG/DuEE_baseline/bin/utils/utils.py | python | read_by_lines | (path, encoding="utf-8") | return result | read the data by line | read the data by line | [
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] | def read_by_lines(path, encoding="utf-8"):
"""read the data by line"""
result = list()
with open(path, "r") as infile:
for line in infile:
result.append(line.strip().decode(encoding))
return result | [
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/sklearn/linear_model/stochastic_gradient.py | python | BaseSGDRegressor.fit | (self, X, y, coef_init=None, intercept_init=None,
sample_weight=None) | return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init,
intercept_init=intercept_init,
sample_weight=sample_weight) | Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_features,)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (1,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : returns an instance of self. | Fit linear model with Stochastic Gradient Descent. | [
"Fit",
"linear",
"model",
"with",
"Stochastic",
"Gradient",
"Descent",
"."
] | def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_features,)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (1,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : returns an instance of self.
"""
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init,
intercept_init=intercept_init,
sample_weight=sample_weight) | [
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microsoft/ptvsd | 99c8513921021d2cc7cd82e132b65c644c256768 | src/ptvsd/_vendored/pydevd/stubs/_django_manager_body.py | python | _update | (self, *args, **kwargs) | A version of update that accepts field objects instead of field names.
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/Python3/email/message.py | python | Message.keys | (self) | return [k for k, v in self._headers] | Return a list of all the message's header field names.
These will be sorted in the order they appeared in the original
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message, or were added to the message, and may contain duplicates.
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/matplotlib-3.0.3-py3.7-macosx-10.9-x86_64.egg/matplotlib/ticker.py | python | FixedLocator.set_params | (self, nbins=None) | Set parameters within this locator. | Set parameters within this locator. | [
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general03/flask-autoindex | 424246242c9f40aeb9ac2c8c63f4d2234024256e | .eggs/Werkzeug-1.0.1-py3.7.egg/werkzeug/wrappers/base_response.py | python | BaseResponse.get_app_iter | (self, environ) | return ClosingIterator(iterable, self.close) | Returns the application iterator for the given environ. Depending
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might be an empty response rather than the one from the response.
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Liusifei/UVC | 36bc6b2c99366a6d1a6033229d395f3c88ff656b | libs/vis_utils.py | python | compute_color | (u, v) | return img | compute optical flow color map
:param u: optical flow horizontal map
:param v: optical flow vertical map
:return: optical flow in color code | compute optical flow color map
:param u: optical flow horizontal map
:param v: optical flow vertical map
:return: optical flow in color code | [
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"""
compute optical flow color map
:param u: optical flow horizontal map
:param v: optical flow vertical map
:return: optical flow in color code
"""
[h, w] = u.shape
img = np.zeros([h, w, 3])
nanIdx = np.isnan(u) | np.isnan(v)
u[nanIdx] = 0
v[nanIdx] = 0
colorwheel = make_color_wheel()
ncols = np.size(colorwheel, 0)
rad = np.sqrt(u**2+v**2)
a = np.arctan2(-v, -u) / np.pi
fk = (a+1) / 2 * (ncols - 1) + 1
k0 = np.floor(fk).astype(int)
k1 = k0 + 1
k1[k1 == ncols+1] = 1
f = fk - k0
for i in range(0, np.size(colorwheel,1)):
tmp = colorwheel[:, i]
col0 = tmp[k0-1] / 255
col1 = tmp[k1-1] / 255
col = (1-f) * col0 + f * col1
idx = rad <= 1
col[idx] = 1-rad[idx]*(1-col[idx])
notidx = np.logical_not(idx)
col[notidx] *= 0.75
img[:, :, i] = np.uint8(np.floor(255 * col*(1-nanIdx)))
return img | [
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mikew/ss-plex.bundle | 031566c06205e08a8cb15c57a0c143fba5270493 | Contents/Libraries/Shared/ss/mechanize/_opener.py | python | OpenerDirector.retrieve | (self, fullurl, filename=None, reporthook=None, data=None,
timeout=_sockettimeout._GLOBAL_DEFAULT_TIMEOUT,
open=open_file) | return result | Returns (filename, headers).
For remote objects, the default filename will refer to a temporary
file. Temporary files are removed when the OpenerDirector.close()
method is called.
For file: URLs, at present the returned filename is None. This may
change in future.
If the actual number of bytes read is less than indicated by the
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] | def retrieve(self, fullurl, filename=None, reporthook=None, data=None,
timeout=_sockettimeout._GLOBAL_DEFAULT_TIMEOUT,
open=open_file):
"""Returns (filename, headers).
For remote objects, the default filename will refer to a temporary
file. Temporary files are removed when the OpenerDirector.close()
method is called.
For file: URLs, at present the returned filename is None. This may
change in future.
If the actual number of bytes read is less than indicated by the
Content-Length header, raises ContentTooShortError (a URLError
subclass). The exception's .result attribute contains the (filename,
headers) that would have been returned.
"""
req = self._request(fullurl, data, False, timeout)
scheme = req.get_type()
fp = self.open(req)
try:
headers = fp.info()
if filename is None and scheme == 'file':
# XXX req.get_selector() seems broken here, return None,
# pending sanity :-/
return None, headers
#return urllib.url2pathname(req.get_selector()), headers
if filename:
tfp = open(filename, 'wb')
else:
path = _rfc3986.urlsplit(req.get_full_url())[2]
suffix = os.path.splitext(path)[1]
fd, filename = tempfile.mkstemp(suffix)
self._tempfiles.append(filename)
tfp = os.fdopen(fd, 'wb')
try:
result = filename, headers
bs = self.BLOCK_SIZE
size = -1
read = 0
blocknum = 0
if reporthook:
if "content-length" in headers:
size = int(headers["Content-Length"])
reporthook(blocknum, bs, size)
while 1:
block = fp.read(bs)
if block == "":
break
read += len(block)
tfp.write(block)
blocknum += 1
if reporthook:
reporthook(blocknum, bs, size)
finally:
tfp.close()
finally:
fp.close()
# raise exception if actual size does not match content-length header
if size >= 0 and read < size:
raise ContentTooShortError(
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return result | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/gntp/core.py | python | GNTPRegister.validate | (self) | Validate required headers and validate notification headers | Validate required headers and validate notification headers | [
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'''Validate required headers and validate notification headers'''
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raise errors.ParseError('Missing Registration Header: ' + header)
for notice in self.notifications:
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delira-dev/delira | cd3ad277d6fad5f837d6c5147e6eee2ada648596 | delira/data_loading/augmenter.py | python | Augmenter.__iter__ | (self) | Makes the Augmenter iterable by generators
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Generator
a generator function yielding the arguments | Makes the Augmenter iterable by generators
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a generator function yielding the arguments | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/packages/IronPython.StdLib.2.7.4/content/Lib/mhlib.py | python | Folder.removefromallsequences | (self, list) | Remove one or more messages from all sequences (including last)
-- but not from 'cur'!!! | Remove one or more messages from all sequences (including last)
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"""Remove one or more messages from all sequences (including last)
-- but not from 'cur'!!!"""
if hasattr(self, 'last') and self.last in list:
del self.last
sequences = self.getsequences()
changed = 0
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for n in list:
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seq.remove(n)
changed = 1
if not seq:
del sequences[name]
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XX-net/XX-Net | a9898cfcf0084195fb7e69b6bc834e59aecdf14f | python3.8.2/Lib/_pyio.py | python | IOBase.writelines | (self, lines) | Write a list of lines to the stream.
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self._checkClosed()
for line in lines:
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selinon/selinon | 3613153566d454022a138639f0375c63f490c4cb | selinon/selective_run_function.py | python | SelectiveRunFunction.from_dict | (cls, dict_) | return cls(name, import_path) | Instantiate selective run function based on definition in a dictionary.
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"""Instantiate selective run function based on definition in a dictionary.
:return: selective run function that will be used based on configuration
:rtype: SelectiveRunFunction
"""
if not dict_:
return cls.get_default()
unknown_conf = check_conf_keys(dict_, known_conf_opts=('name', 'import'))
if unknown_conf:
raise ConfigurationError("Unknown configuration options for selective run function supplied: %s"
% (unknown_conf.keys()))
name = dict_.pop('name', cls._DEFAULT_FUNCTION_NAME)
import_path = dict_.pop('import', cls._DEFAULT_IMPORT_PATH)
return cls(name, import_path) | [
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lifting-bits/mcsema | b7492e2ffb9f2fa64b5ef269753ba1d134bdf2f2 | tools/mcsema_disass/ida7/util.py | python | get_destructor_segment | () | Returns the start address of the global destructor section | Returns the start address of the global destructor section | [
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for seg_ea in idautils.Segments():
seg_name = idc.get_segm_name(seg_ea).lower()
if seg_name in [".fini_array", ".dtor"]:
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armancohan/long-summarization | 1328d4f37b3a3a460f455e93e84ed4ddcd10dab1 | model.py | python | SummarizationModel._add_seq2seq | (self) | Add the whole sequence-to-sequence model to the graph. | Add the whole sequence-to-sequence model to the graph. | [
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"""Add the whole sequence-to-sequence model to the graph."""
hps = self._hps
vsize = self._vocab.size() # size of the vocabulary
with tf.variable_scope('seq2seq'):
# Some initializers
self.rand_unif_init = tf.random_uniform_initializer(-hps.rand_unif_init_mag, hps.rand_unif_init_mag, seed=123)
self.trunc_norm_init = tf.truncated_normal_initializer(stddev=hps.trunc_norm_init_std)
with tf.variable_scope('embedding'):
if hps.pretrained_embeddings:
word2vec = load_embeddings(hps.embeddings_path, self._vocab.word2id, hps.rand_unif_init_mag)
self.embedding = tf.get_variable('embedding', [vsize, hps.emb_dim],
dtype=tf.float32, initializer=tf.constant_initializer(word2vec))
# self.assign_embedding = tf.assign(self.embedding, word2vec)
else:
self.embedding = tf.get_variable('embedding', [vsize, hps.emb_dim],
dtype=tf.float32, initializer=self.trunc_norm_init)
if hps.mode=="train": self._add_emb_vis(self.embedding) # add to tensorboard
# tensor with shape (batch_size, max_enc_steps, emb_size)
emb_enc_inputs = tf.nn.embedding_lookup(self.embedding, self._enc_batch)
if self._hps.hier:
enc_batch_sections = tf.unstack(self._enc_batch_sections, axis=1)
sec_emb_enc_inputs = [tf.nn.embedding_lookup(self.embedding, section)
for section in enc_batch_sections]
# list length max_dec_steps containing shape (batch_size, emb_size)
emb_dec_inputs = [tf.nn.embedding_lookup(self.embedding, x)
for x in tf.unstack(self._dec_batch, axis=1)]
# Hierarchical attention model
if self._hps.hier:
with tf.variable_scope('encoder'), tf.device(self._next_device()):
sec_enc_outs = []
states_fw = []
states_bw = []
states = []
# level 1, encode words to sections
with tf.variable_scope("word_level_encoder", reuse=tf.AUTO_REUSE) as scope:
encoder_outputs_words = []
cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)
cell_bw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)
fw_st, bw_st = None, None
if self._hps.use_do: # DropOut
cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw, output_keep_prob=1.0 - self._hps.do_prob)
cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, output_keep_prob=1.0 - self._hps.do_prob)
for i in range(self._hps.num_sections):
encoder_tmp_output, (fw_st, bw_st) = tf.nn.bidirectional_dynamic_rnn(
cell_fw, cell_bw, inputs=sec_emb_enc_inputs[i], dtype=tf.float32,
sequence_length=self._batch_sections_len[:,i], swap_memory=True, initial_state_bw=bw_st, initial_state_fw=fw_st)
# concatenate the forwards and backwards states
encoder_tmp_output = tf.concat(axis=2, values=encoder_tmp_output) #shape=[batch x seq_len x hidden_size]
encoder_outputs_words.append(encoder_tmp_output)
# instead of concating the fw and bw states, we use a ff network
combined_state = self._reduce_states(fw_st, bw_st)
states.append(combined_state)
scope.reuse_variables()
# level 2, encode sections to doc
encoder_outputs_words = tf.stack(encoder_outputs_words, axis=1) # shape [batch x num_sections x seq_len x hidden_size]
shapes = encoder_outputs_words.shape
encoder_outputs_words = tf.reshape(encoder_outputs_words, (shapes[0].value, -1, shapes[-1].value)) #shape=[batch x (seq_len * num_sections) x hidden_size]
doc_sections_h = tf.stack([s.h for s in states], axis=1) # [batch x num_sections x hidden_size]
doc_sections_c = tf.stack([s.c for s in states], axis=1) # [batch x num_sections x hidden_size]
with tf.variable_scope("section_level_encoder"):
if FLAGS.section_level_encoder == 'RNN':
cell_fw_1 = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)
cell_bw_1 = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)
if self._hps.use_do:
cell_fw_1 = tf.contrib.rnn.DropoutWrapper(cell_fw_1, output_keep_prob=1.0 - self._hps.do_prob)
cell_bw_1 = tf.contrib.rnn.DropoutWrapper(cell_bw_1, output_keep_prob=1.0 - self._hps.do_prob)
encoder_output_sections, (fw_st_2, bw_st_2) =\
tf.nn.bidirectional_dynamic_rnn(cell_fw_1, cell_bw_1, inputs=doc_sections_h, sequence_length=self._doc_sec_lens, dtype=tf.float32, swap_memory=True)
encoder_output_sections = tf.concat(axis=2, values=encoder_output_sections)
doc_sections_state = self._reduce_states(fw_st_2, bw_st_2)
else:
if FLAGS.section_level_encoder == 'AVG': # average section cells
doc_sections_state_h = tf.reduce_mean(doc_sections_h, axis=1)
doc_sections_state_c = tf.reduce_mean(doc_sections_c, axis=1)
elif FLAGS.section_level_encoder == 'FF': # use a feedforward network to combine section cells
doc_sections_state_h = tf.reshape([doc_sections_h.shape[0].eval(), -1])
doc_sections_state_h = tf.layers.dense(
inputs=doc_sections_state_h,
units=self._hps.hidden,
activation=tf.nn.relu)
doc_sections_state_c = tf.reshape([doc_sections_c.shape[0].eval(), -1])
doc_sections_state_c = tf.layers.dense(
inputs=doc_sections_state_c,
units=self._hps.hidden,
activation=tf.nn.relu)
else:
raise AttributeError('FLAGS.section_level_encoder={} is not a valid option'.format(FLAGS.section_level_encoder))
doc_sections_state = tf.contrib.rnn.LSTMStateTuple(doc_sections_state_c, doc_sections_state_h)
encoder_output_sections = doc_sections_h
elif not self._hps.multi_layer_encoder:
with tf.variable_scope('encoder'):
with tf.variable_scope('word_level_encoder'):
cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)
cell_bw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)
(encoder_outputs, (fw_st, bw_st)) =\
tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs=emb_enc_inputs, dtype=tf.float32, sequence_length=self._enc_lens, swap_memory=True)
# concatenate the forwards and backwards states
encoder_outputs = tf.concat(axis=2, values=encoder_outputs)
# stack n layers of lstms for encoder
elif self._hps.multi_layer_encoder:
# TODO: check
for layer_i in xrange(self._hps.enc_layers):
with tf.variable_scope('encoder%d'%layer_i), tf.device(
self._next_device()):
cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)
cell_bw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)
if self._hps.use_do: # add dropout
cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw, output_keep_prob=1.0 - self._hps.do_prob)
cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, output_keep_prob=1.0 - self._hps.do_prob)
emb_enc_inputs, (fw_st, bw_st) =\
tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs=emb_enc_inputs, dtype=tf.float32, sequence_length=self._enc_lens, swap_memory=True)
emb_enc_inputs = tf.concat(axis=2, values=emb_enc_inputs)
encoder_outputs = emb_enc_inputs
if self._hps.hier:
self._enc_sec_states = encoder_output_sections
self._enc_states = encoder_outputs_words
else:
self._enc_states = encoder_outputs
self._enc_sec_states = None
# convert the encoder bidirectional hidden state to the decoder state
# (unidirectional) by an MLP
if self._hps.hier:
self._dec_in_state = doc_sections_state
else:
with tf.variable_scope('encoder'):
with tf.variable_scope('word_level_encoder'):
self._dec_in_state = self._reduce_states(fw_st, bw_st)
# Add the decoder
with tf.variable_scope('decoder'), tf.device(self._next_device()):
cell = tf.contrib.rnn.LSTMCell(
self._hps.hidden_dim,
state_is_tuple=True,
initializer=self.rand_unif_init)
# We need to pass in the previous step's coverage vector each time
prev_coverage = self.prev_coverage\
if hps.mode=="decode" and self._hps.coverage \
else None
if self._hps.hier:
decoder_outputs, self._dec_out_state, self.attn_dists, self.p_gens, self.coverage, self.attn_dists_sec =\
self.attn_decoder(emb_dec_inputs,
self._dec_in_state,
self._enc_states,
cell,
self._enc_sec_states,
num_words_section=self._batch_sections_len,
enc_padding_mask=self._enc_padding_mask,
enc_section_padding_mask=self._enc_section_padding_mask,
initial_state_attention=(self._hps.mode=="decode"),
pointer_gen=self._hps.pointer_gen,
use_coverage=self._hps.coverage,
prev_coverage=prev_coverage,
temperature=self._hps.temperature
)
else:
decoder_outputs, self._dec_out_state, self.attn_dists, self.p_gens, self.coverage, _ =\
self.attn_decoder(emb_dec_inputs,
self._dec_in_state,
self._enc_states,
cell,
encoder_section_states=None,
num_words_section=None,
enc_padding_mask=self._enc_padding_mask,
initial_state_attention=(self._hps.mode=="decode"),
pointer_gen=self._hps.pointer_gen,
use_coverage=self._hps.coverage,
prev_coverage=prev_coverage,
)
# Project decoder output to vocabulary
with tf.variable_scope('output_projection'), tf.device(self._next_device()):
if self._hps.output_weight_sharing:
# share weights of embedding layer with projection
# self.embedding is in shape [vsize, hps.emb_dim]
w_proj = tf.get_variable('w_proj', [self._hps.emb_dim, self._hps.hidden_dim],
dtype=tf.float32, initializer=self.trunc_norm_init)
w = tf.tanh(tf.transpose(tf.matmul(self.embedding, w_proj))) # shape = [vsize, hps.hidden_dim]
# w_t = tf.transpose(w)
b = tf.get_variable('b', [vsize],
dtype=tf.float32, initializer=self.trunc_norm_init)
else:
w = tf.get_variable('w', [self._hps.hidden_dim, vsize],
dtype=tf.float32, initializer=self.trunc_norm_init)
# w_t = tf.transpose(w)
b = tf.get_variable('b', [vsize],
dtype=tf.float32, initializer=self.trunc_norm_init)
# vocabulary score at each decoder step
vocab_scores = []
for i,output in enumerate(decoder_outputs):
if i > 0:
tf.get_variable_scope().reuse_variables()
vocab_scores.append(tf.nn.xw_plus_b(output, w, b)) # apply the linear layer
# the final vocab distribution for each decoder time step
# shape of each element is [batch_size, vsize]
vocab_dists = [tf.nn.softmax(s) for s in vocab_scores]
# pointing / generating
if FLAGS.pointer_gen:
final_dists = self._calc_final_dist(vocab_dists, self.attn_dists)
# log_dists = [tf.log(dist) for dist in final_dists]
else:
# log_dists = [tf.log(dist) for dist in vocab_dists]
final_dists = vocab_dists
# Calculate Losses:
if self._hps.mode in ['train', 'eval']:
# Calculate the loss
with tf.variable_scope('loss'), tf.device(self._next_device()):
if FLAGS.pointer_gen:
# Calculate the loss per step
# This is fiddly; we use tf.gather_nd to pick out the gold target words
# will be list length max_dec_steps containing shape (batch_size)
loss_per_step = []
batch_nums = tf.range(0, limit=hps.batch_size) # shape (batch_size)
for dec_step, dist in enumerate(final_dists):
# The indices of the target words. shape (batch_size)
targets = self._target_batch[:,dec_step]
indices = tf.stack( (batch_nums, targets), axis=1) # shape (batch_size, 2)
# shape (batch_size). loss on this step for each batch
gold_probs = tf.gather_nd(dist, indices)
losses = -tf.log(gold_probs)
loss_per_step.append(losses)
# Apply dec_padding_mask mask and get loss
self._loss = _mask_and_avg(loss_per_step, self._dec_padding_mask)
else: # baseline model
# this applies softmax internally
self._loss = tf.contrib.seq2seq.sequence_loss(
tf.stack(vocab_scores, axis=1), self._target_batch, self._dec_padding_mask) # this applies softmax internally
tf.summary.scalar('loss', self._loss)
# Calculate coverage loss from the attention distributions
if self._hps.coverage:
with tf.variable_scope('coverage_loss'):
self._coverage_loss = _coverage_loss(self.attn_dists, self._dec_padding_mask)
tf.summary.scalar('coverage_loss', self._coverage_loss)
self._total_loss = self._loss + self._hps.cov_loss_wt * self._coverage_loss
tf.summary.scalar('total_loss', self._total_loss)
# ---------------------------/
if self._hps.mode == "decode":
assert len(final_dists) == 1 # final_dists is a singleton list containing shape (batch_size, extended_vsize)
final_dists = final_dists[0]
topk_probs, self._topk_ids = tf.nn.top_k(final_dists, hps.batch_size*2) # take the k largest probs. note batch_size=beam_size in decode mode
self._topk_log_probs = tf.log(topk_probs) | [
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"... | https://github.com/armancohan/long-summarization/blob/1328d4f37b3a3a460f455e93e84ed4ddcd10dab1/model.py#L243-L519 | ||
sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | static/scripts/tools/pep8.py | python | Checker.check_logical | (self) | Build a line from tokens and run all logical checks on it. | Build a line from tokens and run all logical checks on it. | [
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] | def check_logical(self):
"""
Build a line from tokens and run all logical checks on it.
"""
options.counters['logical lines'] = \
options.counters.get('logical lines', 0) + 1
self.build_tokens_line()
first_line = self.lines[self.mapping[0][1][2][0] - 1]
indent = first_line[:self.mapping[0][1][2][1]]
self.previous_indent_level = self.indent_level
self.indent_level = expand_indent(indent)
if options.verbose >= 2:
print self.logical_line[:80].rstrip()
for name, check, argument_names in self.logical_checks:
if options.verbose >= 3:
print ' ', name
result = self.run_check(check, argument_names)
if result is not None:
offset, text = result
if type(offset) is tuple:
original_number, original_offset = offset
else:
for token_offset, token in self.mapping:
if offset >= token_offset:
original_number = token[2][0]
original_offset = (token[2][1]
+ offset - token_offset)
self.report_error(original_number, original_offset,
text, check)
self.previous_logical = self.logical_line | [
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... | https://github.com/sahana/eden/blob/1696fa50e90ce967df69f66b571af45356cc18da/static/scripts/tools/pep8.py#L577-L606 | ||
ihaveamac/ninfs | dee583ba3205c4e050e0b193a85a4177eb334159 | ninfs/mount/ncch.py | python | NCCHContainerMount.init | (self, path, _setup_romfs=True) | [] | def init(self, path, _setup_romfs=True):
decrypted_filename = '/decrypted.' + ('cxi' if self.reader.flags.executable else 'cfa')
self.files[decrypted_filename] = NCCHSection.FullDecrypted
self.files['/ncch.bin'] = NCCHSection.Header
if NCCHSection.ExtendedHeader in self.reader.sections:
self.files['/extheader.bin'] = NCCHSection.ExtendedHeader
if NCCHSection.Logo in self.reader.sections:
self.files['/logo.bin'] = NCCHSection.Logo
if NCCHSection.Plain in self.reader.sections:
self.files['/plain.bin'] = NCCHSection.Plain
if NCCHSection.ExeFS in self.reader.sections:
self.files['/exefs.bin'] = NCCHSection.ExeFS
self.exefs_fuse = ExeFSMount(self.reader.exefs, g_stat=self.g_stat, decompress_code=True)
self.exefs_fuse.init(path)
if NCCHSection.RomFS in self.reader.sections:
self.files['/romfs.bin'] = NCCHSection.RomFS
self.romfs_fuse = RomFSMount(self.reader.romfs, g_stat=self.g_stat) | [
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... | https://github.com/ihaveamac/ninfs/blob/dee583ba3205c4e050e0b193a85a4177eb334159/ninfs/mount/ncch.py#L52-L74 | ||||
securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/fileinput.py | python | isfirstline | () | return _state.isfirstline() | Returns true the line just read is the first line of its file,
otherwise returns false. | Returns true the line just read is the first line of its file,
otherwise returns false. | [
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] | def isfirstline():
"""
Returns true the line just read is the first line of its file,
otherwise returns false.
"""
if not _state:
raise RuntimeError("no active input()")
return _state.isfirstline() | [
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svip-lab/impersonator | b041dd415157c1e7f5b46e579a1ad4dffabb2e66 | thirdparty/his_evaluators/his_evaluators/evaluators/motion_imitation.py | python | MotionImitationRunnerProcessor.post_format_metric_file_list | (self, si_preds_files, si_ref_files, ci_preds_files, ci_ref_files) | return si_preds_ref_files, ci_preds_ref_files | make [(si_pred, si_ref), ...], and [(ci_pred, ci_ref), ...]
Args:
si_preds_files:
si_ref_files:
ci_preds_files:
ci_ref_files:
Returns:
si_preds_ref_files:
ci_preds_ref_files: | make [(si_pred, si_ref), ...], and [(ci_pred, ci_ref), ...]
Args:
si_preds_files:
si_ref_files:
ci_preds_files:
ci_ref_files: | [
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"""
make [(si_pred, si_ref), ...], and [(ci_pred, ci_ref), ...]
Args:
si_preds_files:
si_ref_files:
ci_preds_files:
ci_ref_files:
Returns:
si_preds_ref_files:
ci_preds_ref_files:
"""
si_preds_ref_files = list(zip(si_preds_files, si_ref_files))
ci_preds_ref_files = list(zip(ci_preds_files, ci_ref_files))
return si_preds_ref_files, ci_preds_ref_files | [
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openai/multiagent-competition | b2e081a192429913b7d21a89525901d59c85e7f3 | gym-compete/gym_compete/new_envs/agents/agent.py | python | Agent.set_env | (self, env) | [] | def set_env(self, env):
self.env = env
self._env_init = True
self._set_body()
self._set_joint()
if self.n_agents > 1:
self._set_other_joint()
self.set_observation_space()
self.set_action_space() | [
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BEEmod/BEE2.4 | 02767f3cf476581789425ab308ca1bea978f6a74 | src/app/sound.py | python | PygletSound.fx | (self, sound: str) | Play a sound effect. | Play a sound effect. | [
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"."
] | async def fx(self, sound: str) -> None:
"""Play a sound effect."""
global sounds
if play_sound:
try:
snd = self.sources[sound]
except KeyError:
# We were called before the BG thread loaded em, load it now.
LOGGER.warning('Sound "{}" couldn\'t be loaded in time!', sound)
snd = await self.load(sound)
try:
if snd is not None:
snd.play()
except Exception:
LOGGER.exception("Couldn't play sound {}:", sound)
LOGGER.info('UI sounds disabled.')
_nursery.cancel_scope.cancel()
sounds = NullSound() | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/http/multipartparser.py | python | ChunkIter.__next__ | (self) | [] | def __next__(self):
try:
data = self.flo.read(self.chunk_size)
except InputStreamExhausted:
raise StopIteration()
if data:
return data
else:
raise StopIteration() | [
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monero-ecosystem/monero-python | 9c3c841624f635a4fd154f1f278206af8a6d2c1d | monero/wallet.py | python | Wallet.address | (self) | return self.accounts[0].address() | Returns wallet's master address.
:rtype: :class:`Address <monero.address.Address>` | Returns wallet's master address. | [
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] | def address(self):
"""
Returns wallet's master address.
:rtype: :class:`Address <monero.address.Address>`
"""
return self.accounts[0].address() | [
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Buroni/booksoup | da4470654a820e6450fdb6759d6d625c5fd3b5eb | booksoup/BookSoup.py | python | BookSoup.load_conversation | (self, search_name, interval="month") | return None | [] | def load_conversation(self, search_name, interval="month"):
# If search_name is an integer, simply get the HTML file whose name is that integer.
if isinstance(search_name, int):
contact = Conversation(os.path.join(self.__path, "messages", str(search_name)+".html"), interval=interval)
self.conversations[contact.name] = contact
return contact
# If the search name is a string, get the list of links leading to conversations,
# find the relative link and load the conversation using the link path.
with open(os.path.join(self.__path,"html", "messages.htm"), "r") as infile:
soup = BeautifulSoup(infile.read(), "html.parser")
convo_links = soup.find_all("a", {"href": re.compile('.*messages.*')})
for link in convo_links:
if link.text != search_name:
continue
contact = Conversation(os.path.join(self.__path, link["href"]))
self.conversations[contact.name] = contact
return contact
return None | [
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returntocorp/bento | 05b365da71b65170d41fe92a702480ab76c1d17c | bento/git.py | python | user_email | (path: Optional[Path] = None) | [] | def user_email(path: Optional[Path] = None) -> Optional[str]:
r = repo(path)
if r is None:
return None
try:
return r.config_reader().get_value("user", "email").strip("\"'")
except configparser.NoSectionError:
return None
except configparser.NoOptionError:
return None | [
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nosmokingbandit/watcher | dadacd21a5790ee609058a98a17fcc8954d24439 | lib/sqlalchemy/sql/selectable.py | python | Select.self_group | (self, against=None) | return FromGrouping(self) | return a 'grouping' construct as per the ClauseElement
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] | def self_group(self, against=None):
"""return a 'grouping' construct as per the ClauseElement
specification.
This produces an element that can be embedded in an expression. Note
that this method is called automatically as needed when constructing
expressions and should not require explicit use.
"""
if isinstance(against, CompoundSelect) and \
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return self
return FromGrouping(self) | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/matplotlib/colors.py | python | LinearSegmentedColormap.__init__ | (self, name, segmentdata, N=256, gamma=1.0) | Create color map from linear mapping segments
segmentdata argument is a dictionary with a red, green and blue
entries. Each entry should be a list of *x*, *y0*, *y1* tuples,
forming rows in a table. Entries for alpha are optional.
Example: suppose you want red to increase from 0 to 1 over
the bottom half, green to do the same over the middle half,
and blue over the top half. Then you would use::
cdict = {'red': [(0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 1.0, 1.0)],
'green': [(0.0, 0.0, 0.0),
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(1.0, 1.0, 1.0)],
'blue': [(0.0, 0.0, 0.0),
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Each row in the table for a given color is a sequence of
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row i: x y0 y1
/
/
row i+1: x y0 y1
Hence y0 in the first row and y1 in the last row are never used.
.. seealso::
:meth:`LinearSegmentedColormap.from_list`
Static method; factory function for generating a
smoothly-varying LinearSegmentedColormap.
:func:`makeMappingArray`
For information about making a mapping array. | Create color map from linear mapping segments | [
"Create",
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"""Create color map from linear mapping segments
segmentdata argument is a dictionary with a red, green and blue
entries. Each entry should be a list of *x*, *y0*, *y1* tuples,
forming rows in a table. Entries for alpha are optional.
Example: suppose you want red to increase from 0 to 1 over
the bottom half, green to do the same over the middle half,
and blue over the top half. Then you would use::
cdict = {'red': [(0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 1.0, 1.0)],
'green': [(0.0, 0.0, 0.0),
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'blue': [(0.0, 0.0, 0.0),
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row i: x y0 y1
/
/
row i+1: x y0 y1
Hence y0 in the first row and y1 in the last row are never used.
.. seealso::
:meth:`LinearSegmentedColormap.from_list`
Static method; factory function for generating a
smoothly-varying LinearSegmentedColormap.
:func:`makeMappingArray`
For information about making a mapping array.
"""
# True only if all colors in map are identical; needed for contouring.
self.monochrome = False
Colormap.__init__(self, name, N)
self._segmentdata = segmentdata
self._gamma = gamma | [
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vicariousinc/science_rcn | 821d8ce4c7d3a7bf0827fc00a5a405a9f87cc67e | science_rcn/preproc.py | python | get_gabor_filters | (size=21, filter_scale=4., num_orients=16, weights=False) | return filts | Get Gabor filter bank. See Preproc for parameters and returns. | Get Gabor filter bank. See Preproc for parameters and returns. | [
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] | def get_gabor_filters(size=21, filter_scale=4., num_orients=16, weights=False):
"""Get Gabor filter bank. See Preproc for parameters and returns."""
def _get_sparse_gaussian():
"""Sparse Gaussian."""
size = 2 * np.ceil(np.sqrt(2.) * filter_scale) + 1
alt = np.zeros((int(size), int(size)), np.float32)
alt[int(size // 2), int(size // 2)] = 1
gaussian = gaussian_filter(alt, filter_scale / np.sqrt(2.), mode='constant')
gaussian[gaussian < 0.05 * gaussian.max()] = 0
return gaussian
gaussian = _get_sparse_gaussian()
filts = []
for angle in np.linspace(0., 2 * np.pi, num_orients, endpoint=False):
acts = np.zeros((size, size), np.float32)
x, y = np.cos(angle) * filter_scale, np.sin(angle) * filter_scale
acts[int(size / 2 + y), int(size / 2 + x)] = 1.
acts[int(size / 2 - y), int(size / 2 - x)] = -1.
filt = fftconvolve(acts, gaussian, mode='same')
filt /= np.abs(filt).sum() # Normalize to ensure the maximum output is 1
if weights:
filt = np.abs(filt)
filts.append(filt)
return filts | [
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gwpy/gwpy | 82becd78d166a32985cb657a54d0d39f6a207739 | gwpy/io/datafind.py | python | _error_missing_channels | (required, found, gpstime, allow_tape) | Raise an exception if required channels are not found | Raise an exception if required channels are not found | [
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"""Raise an exception if required channels are not found
"""
missing = set(required) - set(found)
if not missing: # success
return
# failure
msg = "Cannot locate the following channel(s) in any known frametype"
if gpstime:
msg += f" at GPS={gpstime}"
msg += ":\n " + "\n ".join(missing)
if not allow_tape:
msg += ("\n[files on tape have not been checked, use "
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raise ValueError(msg) | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/layout/_xaxis.py | python | XAxis.tickformatstopdefaults | (self) | return self["tickformatstopdefaults"] | When used in a template (as
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Supported dict properties:
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-------
plotly.graph_objs.layout.xaxis.Tickformatstop | When used in a template (as
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The 'tickformatstopdefaults' property is an instance of Tickformatstop
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- An instance of :class:`plotly.graph_objs.layout.xaxis.Tickformatstop`
- A dict of string/value properties that will be passed
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"""
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layout.xaxis.tickformatstops
The 'tickformatstopdefaults' property is an instance of Tickformatstop
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nlloyd/SubliminalCollaborator | 5c619e17ddbe8acb9eea8996ec038169ddcd50a1 | libs/twisted/spread/pb.py | python | Broker.dontNotifyOnDisconnect | (self, notifier) | Remove a callback from list of disconnect callbacks. | Remove a callback from list of disconnect callbacks. | [
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] | def dontNotifyOnDisconnect(self, notifier):
"""Remove a callback from list of disconnect callbacks."""
try:
self.disconnects.remove(notifier)
except ValueError:
pass | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | pypy/module/cpyext/stubs.py | python | PySys_ResetWarnOptions | (space) | Reset sys.warnoptions to an empty list. | Reset sys.warnoptions to an empty list. | [
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"""Reset sys.warnoptions to an empty list."""
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.2/django/core/serializers/base.py | python | Serializer.handle_fk_field | (self, obj, field) | Called to handle a ForeignKey field. | Called to handle a ForeignKey field. | [
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] | def handle_fk_field(self, obj, field):
"""
Called to handle a ForeignKey field.
"""
raise NotImplementedError | [
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lipiji/hierarchical-encoder-decoder | fa4ab3ddfbbb55ffebafedaa17b1b22472fc0936 | utils_pg.py | python | use_gpu | (gpu_id) | [] | def use_gpu(gpu_id):
if gpu_id > -1:
theano.sandbox.cuda.use("gpu" + str(gpu_id)) | [
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F-Secure/mittn | 7808552dc85a7998895038fdb2fa5c5bbed90c3e | mittn/httpfuzzer/steps.py | python | step_impl | (context) | Go through responses and store any that contain a string from
user-supplied list of strings into the database | Go through responses and store any that contain a string from
user-supplied list of strings into the database | [
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"""Go through responses and store any that contain a string from
user-supplied list of strings into the database
"""
# Create a regex from the error response list
error_list = []
for row in context.table:
error_list.append(row['string'])
error_list_regex = "(" + ")|(".join(error_list) + ")"
# For each response, check that it isn't in the error response list
new_findings = 0
for response in context.responses:
if re.search(error_list_regex, response.get('resp_body'),
re.IGNORECASE) is not None:
response['server_error_text_detected'] = True
if fuzzdb.known_false_positive(context, response) is False:
fuzzdb.add_false_positive(context, response)
new_findings += 1
if new_findings > 0:
context.new_findings += new_findings
assert True | [
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quantopian/zipline | 014f1fc339dc8b7671d29be2d85ce57d3daec343 | zipline/utils/memoize.py | python | weak_lru_cache | (maxsize=100) | return desc | Weak least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and the cache
can grow without bound.
Arguments to the cached function must be hashable. Any that are weak-
referenceable will be stored by weak reference. Once any of the args have
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View the cache statistics named tuple (hits, misses, maxsize, currsize)
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See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used | Weak least-recently-used cache decorator. | [
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] | def weak_lru_cache(maxsize=100):
"""Weak least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and the cache
can grow without bound.
Arguments to the cached function must be hashable. Any that are weak-
referenceable will be stored by weak reference. Once any of the args have
been garbage collected, the entry will be removed from the cache.
View the cache statistics named tuple (hits, misses, maxsize, currsize)
with f.cache_info(). Clear the cache and statistics with f.cache_clear().
See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
"""
class desc(lazyval):
def __get__(self, instance, owner):
if instance is None:
return self
try:
return self._cache[instance]
except KeyError:
inst = ref(instance)
@_weak_lru_cache(maxsize)
@wraps(self._get)
def wrapper(*args, **kwargs):
return self._get(inst(), *args, **kwargs)
self._cache[instance] = wrapper
return wrapper
@_weak_lru_cache(maxsize)
def __call__(self, *args, **kwargs):
return self._get(*args, **kwargs)
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inasafe/inasafe | 355eb2ce63f516b9c26af0c86a24f99e53f63f87 | safe/gui/tools/wizard/step_fc25_hazlayer_from_browser.py | python | StepFcHazLayerFromBrowser.get_next_step | (self) | return new_step | Find the proper step when user clicks the Next button.
:returns: The step to be switched to
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:returns: The step to be switched to
:rtype: WizardStep instance or None
"""
if self.parent.is_selected_layer_keywordless:
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self.parent.existing_keywords = None
self.parent.set_mode_label_to_keywords_creation()
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ncbi-nlp/bluebert | ccc828c74e0eed942fd1ececff3638babc38269c | bert/run_classifier.py | python | MrpcProcessor._create_examples | (self, lines, set_type) | return examples | Creates examples for the training and dev sets. | Creates examples for the training and dev sets. | [
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examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = tokenization.convert_to_unicode(line[3])
text_b = tokenization.convert_to_unicode(line[4])
if set_type == "test":
label = "0"
else:
label = tokenization.convert_to_unicode(line[0])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/scipy/_lib/_util.py | python | _lazywhere | (cond, arrays, f, fillvalue=None, f2=None) | return out | np.where(cond, x, fillvalue) always evaluates x even where cond is False.
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array([ nan, nan, 21., 32.])
Notice it assumes that all `arrays` are of the same shape, or can be
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>>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])
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array([ nan, nan, 21., 32.]) | [
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"""
if fillvalue is None:
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arrays = np.broadcast_arrays(*arrays)
temp = tuple(np.extract(cond, arr) for arr in arrays)
tcode = np.mintypecode([a.dtype.char for a in arrays])
out = _valarray(np.shape(arrays[0]), value=fillvalue, typecode=tcode)
np.place(out, cond, f(*temp))
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np.place(out, ~cond, f2(*temp))
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cudamat/cudamat | 21baa0a82f189fae40cab3ab607c11cabfee8899 | cudamat/cudamat.py | python | CUDAMatrix.add_row_vec | (self, vec, target=None) | return target | Add vector vec to every row of the matrix. If a target is provided,
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"""
if not target:
target = self
err_code = _cudamat.add_row_vec(self.p_mat, vec.p_mat, target.p_mat)
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return target | [
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deepmind/hanabi-learning-environment | 54e79594f4b6fb40ebb3004289c6db0e34a8b5fb | hanabi_learning_environment/pyhanabi.py | python | HanabiGame.hand_size | (self) | return lib.HandSize(self._game) | Returns the maximum number of cards in each player hand.
The number of cards in a player's hand may be smaller than this maximum
a) at the beginning of the game before cards are dealt out, b) after
any Play or Discard action and before the subsequent deal event, and c)
after the deck is empty and cards can no longer be dealt to a player. | Returns the maximum number of cards in each player hand. | [
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"""Returns the maximum number of cards in each player hand.
The number of cards in a player's hand may be smaller than this maximum
a) at the beginning of the game before cards are dealt out, b) after
any Play or Discard action and before the subsequent deal event, and c)
after the deck is empty and cards can no longer be dealt to a player.
"""
return lib.HandSize(self._game) | [
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SirVer/ultisnips | 2c83e40ce66814bf813457bb58ea96184ab9bb81 | pythonx/UltiSnips/snippet/definition/base.py | python | SnippetDefinition._re_match | (self, trigger) | return False | Test if a the current regex trigger matches `trigger`.
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"""
for match in re.finditer(self._trigger, trigger):
if match.end() != len(trigger):
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self._last_re = match
return match
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google/capirca | 679e3885e3a5e5e129dc2dfab204ec44d63b26a4 | capirca/lib/windows_ipsec.py | python | Term._ComposeFilter | (self, srcaddr, dstaddr, proto, srcmask, dstmask,
srcport, dstport) | return (self.CMD_PREFIX +
self._FILTER_FORMAT.substitute(
name=self.term_name + self._LIST_SUFFIX,
atoms=' '.join(atoms))) | Convert the given parameters to a netsh filter rule string. | Convert the given parameters to a netsh filter rule string. | [
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] | def _ComposeFilter(self, srcaddr, dstaddr, proto, srcmask, dstmask,
srcport, dstport):
"""Convert the given parameters to a netsh filter rule string."""
atoms = []
if srcmask == 0:
atoms.append(self._ADDR_ATOM.substitute(dir='src', addr='any', mask=''))
else:
mask_atom = self._MASK_ATOM.substitute(dir='src', mask=srcmask)
atoms.append(self._ADDR_ATOM.substitute(
dir='src', addr=srcaddr, mask=mask_atom))
if dstmask == 0:
atoms.append(self._ADDR_ATOM.substitute(dir='dst', addr='any', mask=''))
else:
mask_atom = self._MASK_ATOM.substitute(dir='dst', mask=dstmask)
atoms.append(self._ADDR_ATOM.substitute(
dir='dst', addr=dstaddr, mask=mask_atom))
if srcport:
atoms.append(self._PORT_ATOM.substitute(dir='src', port=srcport))
if dstport:
atoms.append(self._PORT_ATOM.substitute(dir='dst', port=dstport))
if proto:
atoms.append(self._PROTO_ATOM.substitute(protocol=proto))
return (self.CMD_PREFIX +
self._FILTER_FORMAT.substitute(
name=self.term_name + self._LIST_SUFFIX,
atoms=' '.join(atoms))) | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/tcr/v20190924/models.py | python | DescribeInstanceTokenResponse.__init__ | (self) | r"""
:param TotalCount: 长期访问凭证总数
:type TotalCount: int
:param Tokens: 长期访问凭证列表
:type Tokens: list of TcrInstanceToken
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str | r"""
:param TotalCount: 长期访问凭证总数
:type TotalCount: int
:param Tokens: 长期访问凭证列表
:type Tokens: list of TcrInstanceToken
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r"""
:param TotalCount: 长期访问凭证总数
:type TotalCount: int
:param Tokens: 长期访问凭证列表
:type Tokens: list of TcrInstanceToken
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-linux/x64/cryptography/x509/name.py | python | RelativeDistinguishedName.get_attributes_for_oid | (self, oid) | return [i for i in self if i.oid == oid] | [] | def get_attributes_for_oid(self, oid):
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pandas-dev/pandas | 5ba7d714014ae8feaccc0dd4a98890828cf2832d | pandas/core/ops/__init__.py | python | should_reindex_frame_op | (
left: DataFrame, right, op, axis, default_axis, fill_value, level
) | return False | Check if this is an operation between DataFrames that will need to reindex. | Check if this is an operation between DataFrames that will need to reindex. | [
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left: DataFrame, right, op, axis, default_axis, fill_value, level
) -> bool:
"""
Check if this is an operation between DataFrames that will need to reindex.
"""
assert isinstance(left, ABCDataFrame)
if op is operator.pow or op is roperator.rpow:
# GH#32685 pow has special semantics for operating with null values
return False
if not isinstance(right, ABCDataFrame):
return False
if fill_value is None and level is None and axis is default_axis:
# TODO: any other cases we should handle here?
# Intersection is always unique so we have to check the unique columns
left_uniques = left.columns.unique()
right_uniques = right.columns.unique()
cols = left_uniques.intersection(right_uniques)
if len(cols) and not (cols.equals(left_uniques) and cols.equals(right_uniques)):
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return True
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partho-maple/coding-interview-gym | f9b28916da31935a27900794cfb8b91be3b38b9b | leetcode.com/python/21_Merge_Two_Sorted_Lists.py | python | MyLinkedList.getNode | (self, index) | return node | :type index: int
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node = self.first
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luispedro/milk | abc2a28b526c199414d42c0a26092938968c3caf | milk/unsupervised/nnmf/hoyer.py | python | sparse_nnmf | (V, r, sparsenessW=None, sparsenessH=None, max_iter=10000, R=None) | return W,H | W,H = hoyer.sparse_nnmf(V, r, sparsenessW = None, sparsenessH = None, max_iter=10000, R=None)
Implement sparse nonnegative matrix factorisation.
Parameters
----------
V : 2-D matrix
input feature matrix
r : integer
number of latent features
sparsenessW : double, optional
sparseness contraint on W (default: no sparsity contraint)
sparsenessH : double, optional
sparseness contraint on H (default: no sparsity contraint)
max_iter : integer, optional
maximum nr of iterations (default: 10000)
R : integer, optional
source of randomness
Returns
-------
W : 2-ndarray
H : 2-ndarray
Reference
---------
"Non-negative Matrix Factorisation with Sparseness Constraints"
by Patrik Hoyer
in Journal of Machine Learning Research 5 (2004) 1457--1469 | W,H = hoyer.sparse_nnmf(V, r, sparsenessW = None, sparsenessH = None, max_iter=10000, R=None) | [
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'''
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Implement sparse nonnegative matrix factorisation.
Parameters
----------
V : 2-D matrix
input feature matrix
r : integer
number of latent features
sparsenessW : double, optional
sparseness contraint on W (default: no sparsity contraint)
sparsenessH : double, optional
sparseness contraint on H (default: no sparsity contraint)
max_iter : integer, optional
maximum nr of iterations (default: 10000)
R : integer, optional
source of randomness
Returns
-------
W : 2-ndarray
H : 2-ndarray
Reference
---------
"Non-negative Matrix Factorisation with Sparseness Constraints"
by Patrik Hoyer
in Journal of Machine Learning Research 5 (2004) 1457--1469
'''
n,m = V.shape
R = get_nprandom(R)
mu_W = .15
mu_H = .15
eps = 1e-8
W = R.standard_normal((n,r))**2
H = R.standard_normal((r,m))**2
def fix(X, sparseness):
for i in range(r):
row = X[i]
L2 = np.sqrt(np.dot(row, row))
row /= L2
X[i] = _project(row, _L1for(sparseness, row, 1.), 1.)
def fixW():
fix(W.T, sparsenessW)
def fixH():
fix(H, sparsenessH)
if sparsenessW is not None: fixW()
if sparsenessH is not None: fixH()
for i in range(max_iter):
if sparsenessW is not None:
W -= mu_W * np.dot(np.dot(W,H)-V,H.T)
fixW()
else:
updateW = np.dot(V,H.T)/(np.dot(W,np.dot(H,H.T))+eps)
W *= updateW
if sparsenessH is not None:
H -= mu_H * np.dot(W.T,np.dot(W,H)-V)
fixH()
else:
updateH = np.dot(W.T,V)/(np.dot(np.dot(W.T,W),H)+eps)
H *= updateH
return W,H | [
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astropy/photutils | 3caa48e4e4d139976ed7457dc41583fb2c56ba20 | photutils/psf/matching/windows.py | python | SplitCosineBellWindow.__call__ | (self, shape) | return f | Return a 2D split cosine bell.
Parameters
----------
shape : tuple of int
The size of the output array along each axis.
Returns
-------
result : `~numpy.ndarray`
A 2D array containing the cosine bell values. | Return a 2D split cosine bell. | [
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] | def __call__(self, shape):
"""
Return a 2D split cosine bell.
Parameters
----------
shape : tuple of int
The size of the output array along each axis.
Returns
-------
result : `~numpy.ndarray`
A 2D array containing the cosine bell values.
"""
radial_dist = _radial_distance(shape)
npts = (np.array(shape).min() - 1.) / 2.
r_inner = self.beta * npts
r = radial_dist - r_inner
r_taper = int(np.floor(self.alpha * npts))
if r_taper != 0:
f = 0.5 * (1.0 + np.cos(np.pi * r / r_taper))
else:
f = np.ones(shape)
f[radial_dist < r_inner] = 1.
r_cut = r_inner + r_taper
f[radial_dist > r_cut] = 0.
return f | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/users/models.py | python | UserReportingMetadataStaging.add_heartbeat | (cls, domain, user_id, app_id, build_id, sync_date, device_id,
app_version, num_unsent_forms, num_quarantined_forms, commcare_version, build_profile_id) | [] | def add_heartbeat(cls, domain, user_id, app_id, build_id, sync_date, device_id,
app_version, num_unsent_forms, num_quarantined_forms, commcare_version, build_profile_id):
params = {
'domain': domain,
'user_id': user_id,
'app_id': app_id,
'build_id': build_id,
'sync_date': sync_date,
'device_id': device_id,
'app_version': app_version,
'num_unsent_forms': num_unsent_forms,
'num_quarantined_forms': num_quarantined_forms,
'commcare_version': commcare_version,
'build_profile_id': build_profile_id,
}
with connection.cursor() as cursor:
cursor.execute(f"""
INSERT INTO {cls._meta.db_table} AS staging (
domain, user_id, app_id, modified_on, created_on,
build_id,
sync_date, device_id,
app_version, num_unsent_forms, num_quarantined_forms,
commcare_version, build_profile_id, last_heartbeat
) VALUES (
%(domain)s, %(user_id)s, %(app_id)s, CLOCK_TIMESTAMP(), CLOCK_TIMESTAMP(),
%(build_id)s,
%(sync_date)s, %(device_id)s,
%(app_version)s, %(num_unsent_forms)s, %(num_quarantined_forms)s,
%(commcare_version)s, %(build_profile_id)s, CLOCK_TIMESTAMP()
)
ON CONFLICT (domain, user_id, app_id)
DO UPDATE SET
modified_on = CLOCK_TIMESTAMP(),
build_id = COALESCE(EXCLUDED.build_id, staging.build_id),
sync_date = COALESCE(EXCLUDED.sync_date, staging.sync_date),
device_id = COALESCE(EXCLUDED.device_id, staging.device_id),
app_version = EXCLUDED.app_version,
num_unsent_forms = EXCLUDED.num_unsent_forms,
num_quarantined_forms = EXCLUDED.num_quarantined_forms,
commcare_version = EXCLUDED.commcare_version,
build_profile_id = EXCLUDED.build_profile_id,
last_heartbeat = CLOCK_TIMESTAMP()
WHERE staging.last_heartbeat is NULL or EXCLUDED.last_heartbeat > staging.last_heartbeat
""", params) | [
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Epistimio/orion | 732e739d99561020dbe620760acf062ade746006 | src/orion/core/evc/adapters.py | python | apply_if_valid | (name, trial, callback=None, raise_if_not=True) | return False | Detect a parameter in trial and call a callback on it if provided
Parameters
----------
name: str
Name of the param to look for
trial: `orion.core.worker.trial.Trial`
Instance of trial to investigate
callback: None of callable
Function to call with (trial, param) with a parameter is found.
Defaults to None
raise_if_not: bool
raises RuntimeError if no parameter is found.
Defaults to True.
Returns
-------
bool
False if parameter is not found and `raise_if_not is False`.
True if parameter is found and callback is None.
Else, output of callback(trial, item). | Detect a parameter in trial and call a callback on it if provided | [
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"call",
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"callback",
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] | def apply_if_valid(name, trial, callback=None, raise_if_not=True):
"""Detect a parameter in trial and call a callback on it if provided
Parameters
----------
name: str
Name of the param to look for
trial: `orion.core.worker.trial.Trial`
Instance of trial to investigate
callback: None of callable
Function to call with (trial, param) with a parameter is found.
Defaults to None
raise_if_not: bool
raises RuntimeError if no parameter is found.
Defaults to True.
Returns
-------
bool
False if parameter is not found and `raise_if_not is False`.
True if parameter is found and callback is None.
Else, output of callback(trial, item).
"""
for param in trial._params: # pylint: disable=protected-access
if param.name == name:
return callback is None or callback(trial, param)
if raise_if_not:
raise RuntimeError(
"Provided trial does not have a compatible configuration. "
"A dimension named '%s' should be present.\n %s" % (name, trial)
)
return False | [
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... | https://github.com/Epistimio/orion/blob/732e739d99561020dbe620760acf062ade746006/src/orion/core/evc/adapters.py#L228-L262 | |
optuna/optuna | 2c44c1a405ba059efd53f4b9c8e849d20fb95c0a | optuna/study/_optimize.py | python | _optimize | (
study: "optuna.Study",
func: "optuna.study.study.ObjectiveFuncType",
n_trials: Optional[int] = None,
timeout: Optional[float] = None,
n_jobs: int = 1,
catch: Tuple[Type[Exception], ...] = (),
callbacks: Optional[List[Callable[["optuna.Study", FrozenTrial], None]]] = None,
gc_after_trial: bool = False,
show_progress_bar: bool = False,
) | [] | def _optimize(
study: "optuna.Study",
func: "optuna.study.study.ObjectiveFuncType",
n_trials: Optional[int] = None,
timeout: Optional[float] = None,
n_jobs: int = 1,
catch: Tuple[Type[Exception], ...] = (),
callbacks: Optional[List[Callable[["optuna.Study", FrozenTrial], None]]] = None,
gc_after_trial: bool = False,
show_progress_bar: bool = False,
) -> None:
if not isinstance(catch, tuple):
raise TypeError(
"The catch argument is of type '{}' but must be a tuple.".format(type(catch).__name__)
)
if not study._optimize_lock.acquire(False):
raise RuntimeError("Nested invocation of `Study.optimize` method isn't allowed.")
# TODO(crcrpar): Make progress bar work when n_jobs != 1.
progress_bar = pbar_module._ProgressBar(show_progress_bar and n_jobs == 1, n_trials, timeout)
study._stop_flag = False
try:
if n_jobs == 1:
_optimize_sequential(
study,
func,
n_trials,
timeout,
catch,
callbacks,
gc_after_trial,
reseed_sampler_rng=False,
time_start=None,
progress_bar=progress_bar,
)
else:
if show_progress_bar:
warnings.warn("Progress bar only supports serial execution (`n_jobs=1`).")
if n_jobs == -1:
n_jobs = os.cpu_count() or 1
time_start = datetime.datetime.now()
futures: Set[Future] = set()
with ThreadPoolExecutor(max_workers=n_jobs) as executor:
for n_submitted_trials in itertools.count():
if study._stop_flag:
break
if (
timeout is not None
and (datetime.datetime.now() - time_start).total_seconds() > timeout
):
break
if n_trials is not None and n_submitted_trials >= n_trials:
break
if len(futures) >= n_jobs:
completed, futures = wait(futures, return_when=FIRST_COMPLETED)
# Raise if exception occurred in executing the completed futures.
for f in completed:
f.result()
futures.add(
executor.submit(
_optimize_sequential,
study,
func,
1,
timeout,
catch,
callbacks,
gc_after_trial,
True,
time_start,
None,
)
)
finally:
study._optimize_lock.release()
progress_bar.close() | [
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vita-epfl/monoloco | f5e82c48e8ee5af352f2c9b1690050f4e02fc1b6 | monoloco/prep/preprocess_nu.py | python | factory | (dataset, dir_nuscenes) | return nusc, scenes, split_train, split_val | Define dataset type and split training and validation | Define dataset type and split training and validation | [
"Define",
"dataset",
"type",
"and",
"split",
"training",
"and",
"validation"
] | def factory(dataset, dir_nuscenes):
"""Define dataset type and split training and validation"""
assert dataset in ['nuscenes', 'nuscenes_mini', 'nuscenes_teaser']
if dataset == 'nuscenes_mini':
version = 'v1.0-mini'
else:
version = 'v1.0-trainval'
nusc = NuScenes(version=version, dataroot=dir_nuscenes, verbose=True)
scenes = nusc.scene
if dataset == 'nuscenes_teaser':
with open("splits/nuscenes_teaser_scenes.txt", "r") as file:
teaser_scenes = file.read().splitlines()
scenes = [scene for scene in scenes if scene['token'] in teaser_scenes]
with open("splits/split_nuscenes_teaser.json", "r") as file:
dic_split = json.load(file)
split_train = [scene['name'] for scene in scenes if scene['token'] in dic_split['train']]
split_val = [scene['name'] for scene in scenes if scene['token'] in dic_split['val']]
else:
split_scenes = splits.create_splits_scenes()
split_train, split_val = split_scenes['train'], split_scenes['val']
return nusc, scenes, split_train, split_val | [
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yaml/pyyaml | 8cdff2c80573b8be8e8ad28929264a913a63aa33 | lib/yaml/__init__.py | python | add_multi_constructor | (tag_prefix, multi_constructor, Loader=None) | Add a multi-constructor for the given tag prefix.
Multi-constructor is called for a node if its tag starts with tag_prefix.
Multi-constructor accepts a Loader instance, a tag suffix,
and a node object and produces the corresponding Python object. | Add a multi-constructor for the given tag prefix.
Multi-constructor is called for a node if its tag starts with tag_prefix.
Multi-constructor accepts a Loader instance, a tag suffix,
and a node object and produces the corresponding Python object. | [
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"-",
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"... | def add_multi_constructor(tag_prefix, multi_constructor, Loader=None):
"""
Add a multi-constructor for the given tag prefix.
Multi-constructor is called for a node if its tag starts with tag_prefix.
Multi-constructor accepts a Loader instance, a tag suffix,
and a node object and produces the corresponding Python object.
"""
if Loader is None:
loader.Loader.add_multi_constructor(tag_prefix, multi_constructor)
loader.FullLoader.add_multi_constructor(tag_prefix, multi_constructor)
loader.UnsafeLoader.add_multi_constructor(tag_prefix, multi_constructor)
else:
Loader.add_multi_constructor(tag_prefix, multi_constructor) | [
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NervanaSystems/neon | 8c3fb8a93b4a89303467b25817c60536542d08bd | neon/layers/layer.py | python | BranchNode.bprop | (self, error, alpha=1.0, beta=0.0) | Branch nodes should be skipped in bprop, since their deltas are shared
Arguments:
error (Tensor): deltas back propagated from the adjacent higher layer
alpha (float, optional): scale to apply to input for activation
gradient bprop. Defaults to 1.0
beta (float, optional): scale to apply to output activation
gradient bprop. Defaults to 0.0
Returns:
Tensor: deltas to propagate to the adjacent lower layer | Branch nodes should be skipped in bprop, since their deltas are shared | [
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"should",
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] | def bprop(self, error, alpha=1.0, beta=0.0):
"""
Branch nodes should be skipped in bprop, since their deltas are shared
Arguments:
error (Tensor): deltas back propagated from the adjacent higher layer
alpha (float, optional): scale to apply to input for activation
gradient bprop. Defaults to 1.0
beta (float, optional): scale to apply to output activation
gradient bprop. Defaults to 0.0
Returns:
Tensor: deltas to propagate to the adjacent lower layer
"""
pass | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/layout/_shape.py | python | Shape.yanchor | (self) | return self["yanchor"] | Only relevant in conjunction with `ysizemode` set to "pixel".
Specifies the anchor point on the y axis to which `y0`, `y1`
and y coordinates within `path` are relative to. E.g. useful to
attach a pixel sized shape to a certain data value. No effect
when `ysizemode` not set to "pixel".
The 'yanchor' property accepts values of any type
Returns
-------
Any | Only relevant in conjunction with `ysizemode` set to "pixel".
Specifies the anchor point on the y axis to which `y0`, `y1`
and y coordinates within `path` are relative to. E.g. useful to
attach a pixel sized shape to a certain data value. No effect
when `ysizemode` not set to "pixel".
The 'yanchor' property accepts values of any type | [
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"""
Only relevant in conjunction with `ysizemode` set to "pixel".
Specifies the anchor point on the y axis to which `y0`, `y1`
and y coordinates within `path` are relative to. E.g. useful to
attach a pixel sized shape to a certain data value. No effect
when `ysizemode` not set to "pixel".
The 'yanchor' property accepts values of any type
Returns
-------
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"""
return self["yanchor"] | [
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nchammas/flintrock | fad4e15877a5bf7058395ecbc7be717b21ef6b85 | flintrock/flintrock.py | python | format_message | (*, message: str, indent: int=4, wrap: int=70) | return textwrap.indent(
textwrap.fill(
textwrap.dedent(text=message),
width=wrap),
prefix=' ' * indent) | Format a lengthy message for printing to screen. | Format a lengthy message for printing to screen. | [
"Format",
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"lengthy",
"message",
"for",
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"to",
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"."
] | def format_message(*, message: str, indent: int=4, wrap: int=70):
"""
Format a lengthy message for printing to screen.
"""
return textwrap.indent(
textwrap.fill(
textwrap.dedent(text=message),
width=wrap),
prefix=' ' * indent) | [
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openstack/nova | b49b7663e1c3073917d5844b81d38db8e86d05c4 | nova/compute/manager.py | python | ComputeManager._confirm_snapshot_based_resize_delete_port_bindings | (
self, ctxt, instance) | Delete port bindings for the source host when confirming
snapshot-based resize on the source host."
:param ctxt: nova auth RequestContext
:param instance: Instance object that was resized/cold migrated | Delete port bindings for the source host when confirming
snapshot-based resize on the source host." | [
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"."
] | def _confirm_snapshot_based_resize_delete_port_bindings(
self, ctxt, instance):
"""Delete port bindings for the source host when confirming
snapshot-based resize on the source host."
:param ctxt: nova auth RequestContext
:param instance: Instance object that was resized/cold migrated
"""
LOG.debug('Deleting port bindings for source host.',
instance=instance)
try:
self.network_api.cleanup_instance_network_on_host(
ctxt, instance, self.host)
except exception.PortBindingDeletionFailed as e:
# Do not let this stop us from cleaning up since the guest
# is already gone.
LOG.error('Failed to delete port bindings from source host. '
'Error: %s', str(e), instance=instance) | [
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"... | https://github.com/openstack/nova/blob/b49b7663e1c3073917d5844b81d38db8e86d05c4/nova/compute/manager.py#L4562-L4579 | ||
assertpy/assertpy | c970c6612a80aa10769dc612324630d27019e1b5 | assertpy/string.py | python | StringMixin.contains_ignoring_case | (self, *items) | return self | Asserts that val is string and contains the given item or items.
Walks val and checks for item or items using the ``==`` operator and ``str.lower()``.
Args:
*items: the item or items expected to be contained
Examples:
Usage::
assert_that('foo').contains_ignoring_case('F', 'oO')
assert_that(['a', 'B']).contains_ignoring_case('A', 'b')
assert_that({'a': 1, 'B': 2}).contains_ignoring_case('A', 'b')
assert_that({'a', 'B'}).contains_ignoring_case('A', 'b')
Returns:
AssertionBuilder: returns this instance to chain to the next assertion
Raises:
AssertionError: if val does **not** contain the case-insensitive item or items | Asserts that val is string and contains the given item or items. | [
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"is",
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] | def contains_ignoring_case(self, *items):
"""Asserts that val is string and contains the given item or items.
Walks val and checks for item or items using the ``==`` operator and ``str.lower()``.
Args:
*items: the item or items expected to be contained
Examples:
Usage::
assert_that('foo').contains_ignoring_case('F', 'oO')
assert_that(['a', 'B']).contains_ignoring_case('A', 'b')
assert_that({'a': 1, 'B': 2}).contains_ignoring_case('A', 'b')
assert_that({'a', 'B'}).contains_ignoring_case('A', 'b')
Returns:
AssertionBuilder: returns this instance to chain to the next assertion
Raises:
AssertionError: if val does **not** contain the case-insensitive item or items
"""
if len(items) == 0:
raise ValueError('one or more args must be given')
if isinstance(self.val, str_types):
if len(items) == 1:
if not isinstance(items[0], str_types):
raise TypeError('given arg must be a string')
if items[0].lower() not in self.val.lower():
return self.error('Expected <%s> to case-insensitive contain item <%s>, but did not.' % (self.val, items[0]))
else:
missing = []
for i in items:
if not isinstance(i, str_types):
raise TypeError('given args must all be strings')
if i.lower() not in self.val.lower():
missing.append(i)
if missing:
return self.error('Expected <%s> to case-insensitive contain items %s, but did not contain %s.' % (
self.val, self._fmt_items(items), self._fmt_items(missing)))
elif isinstance(self.val, Iterable):
missing = []
for i in items:
if not isinstance(i, str_types):
raise TypeError('given args must all be strings')
found = False
for v in self.val:
if not isinstance(v, str_types):
raise TypeError('val items must all be strings')
if i.lower() == v.lower():
found = True
break
if not found:
missing.append(i)
if missing:
return self.error('Expected <%s> to case-insensitive contain items %s, but did not contain %s.' % (
self.val, self._fmt_items(items), self._fmt_items(missing)))
else:
raise TypeError('val is not a string or iterable')
return self | [
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"... | https://github.com/assertpy/assertpy/blob/c970c6612a80aa10769dc612324630d27019e1b5/assertpy/string.py#L77-L136 | |
oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/urllib2.py | python | FileHandler.file_open | (self, req) | [] | def file_open(self, req):
url = req.get_selector()
if url[:2] == '//' and url[2:3] != '/' and (req.host and
req.host != 'localhost'):
req.type = 'ftp'
return self.parent.open(req)
else:
return self.open_local_file(req) | [
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blurstudio/cross3d | 277968d1227de740fc87ef61005c75034420eadf | cross3d/studiomax/studiomaxscene.py | python | StudiomaxScene.renderOutputPath | (self) | return mxs.rendOutputFilename | \remarks return the render output file path for the scene
\return <str> | \remarks return the render output file path for the scene
\return <str> | [
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] | def renderOutputPath(self):
"""
\remarks return the render output file path for the scene
\return <str>
"""
return mxs.rendOutputFilename | [
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] | https://github.com/blurstudio/cross3d/blob/277968d1227de740fc87ef61005c75034420eadf/cross3d/studiomax/studiomaxscene.py#L2836-L2841 | |
golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | golismero/api/data/vulnerability/suspicious/header.py | python | SuspiciousHeader.name | (self) | return self.__name | :returns: Header name.
:rtype: str | :returns: Header name.
:rtype: str | [
":",
"returns",
":",
"Header",
"name",
".",
":",
"rtype",
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] | def name(self):
"""
:returns: Header name.
:rtype: str
"""
return self.__name | [
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] | https://github.com/golismero/golismero/blob/7d605b937e241f51c1ca4f47b20f755eeefb9d76/golismero/api/data/vulnerability/suspicious/header.py#L79-L84 | |
ceph/ceph-ansible | 583e60af84180f0414d67ee52c3ec7cd64ddb4dd | library/ceph_key.py | python | get_key | (cluster, user, user_key, name, dest, container_image=None) | return cmd_list | Get a CephX key (write on the filesystem) | Get a CephX key (write on the filesystem) | [
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] | def get_key(cluster, user, user_key, name, dest, container_image=None):
'''
Get a CephX key (write on the filesystem)
'''
cmd_list = []
args = [
'get',
name,
'-o',
dest,
]
cmd_list.append(generate_ceph_cmd(sub_cmd=['auth'],
args=args,
cluster=cluster,
user=user,
user_key=user_key,
container_image=container_image))
return cmd_list | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/tkinter/__init__.py | python | Wm.wm_overrideredirect | (self, boolean=None) | return self._getboolean(self.tk.call(
'wm', 'overrideredirect', self._w, boolean)) | Instruct the window manager to ignore this widget
if BOOLEAN is given with 1. Return the current value if None
is given. | Instruct the window manager to ignore this widget
if BOOLEAN is given with 1. Return the current value if None
is given. | [
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] | def wm_overrideredirect(self, boolean=None):
"""Instruct the window manager to ignore this widget
if BOOLEAN is given with 1. Return the current value if None
is given."""
return self._getboolean(self.tk.call(
'wm', 'overrideredirect', self._w, boolean)) | [
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aleju/imgaug | 0101108d4fed06bc5056c4a03e2bcb0216dac326 | imgaug/augmenters/color.py | python | RemoveSaturation.__init__ | (self, mul=1, from_colorspace=CSPACE_RGB,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated") | [] | def __init__(self, mul=1, from_colorspace=CSPACE_RGB,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
mul = iap.Subtract(
1.0,
iap.handle_continuous_param(mul, "mul",
value_range=(0.0, 1.0),
tuple_to_uniform=True,
list_to_choice=True),
elementwise=True
)
super(RemoveSaturation, self).__init__(
mul, from_colorspace=from_colorspace,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic) | [
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... | https://github.com/aleju/imgaug/blob/0101108d4fed06bc5056c4a03e2bcb0216dac326/imgaug/augmenters/color.py#L2180-L2194 |
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