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keiffster/program-y | 8c99b56f8c32f01a7b9887b5daae9465619d0385 | src/utils/botflow/src/botflow.py | python | Step.__str__ | (self) | return "[%s] - %s -> %s" % (self._step, self._prompt, next_steps) | [] | def __str__(self):
if self._conditions:
next_steps = ", ".join("%s %s"(x._next_step, x.condition) for x in self._conditions)
else:
next_steps = "EXECUTE"
return "[%s] - %s -> %s" % (self._step, self._prompt, next_steps) | [
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|||
exodrifter/unity-python | bef6e4e9ddfbbf1eaf7acbbb973e9aa3dd64a20d | Lib/distutils/sysconfig.py | python | get_config_var | (name) | return get_config_vars().get(name) | Return the value of a single variable using the dictionary
returned by 'get_config_vars()'. Equivalent to
get_config_vars().get(name) | Return the value of a single variable using the dictionary
returned by 'get_config_vars()'. Equivalent to
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Komodo/KomodoEdit | 61edab75dce2bdb03943b387b0608ea36f548e8e | src/lint/koLintService.py | python | RequestQueue.remove_uid | (self, uid) | Remove all current requests with the given uid.
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"""Remove all current requests with the given uid.
Does not return anything.
"""
log.debug("in RequestQueue.remove_uid, acquiring esema")
if not self.esema.acquire(0): # do not block to acquire lock
# return if could not acquire: means queue is empty and
# therefore do not have any items to remove
log.debug("in RequestQueue.remove_uid, did not acquire esema")
return
log.debug("in RequestQueue.remove_uid, acquired mutex")
log.debug("in RequestQueue.remove_uid, acquiring mutex")
self.mutex.acquire()
release_esema = 1
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self._remove_uid(uid)
# Failure means empty state also unchanged - release_esema
# remains true.
release_esema = not self._empty()
finally:
if release_esema:
log.debug("in RequestQueue.remove_uid, releasing esema")
self.esema.release()
log.debug("in RequestQueue.remove_uid, releasing mutex")
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/min/bisect.py | python | insort_right | (a, x, lo=0, hi=None, *, key=None) | Insert item x in list a, and keep it sorted assuming a is sorted.
If x is already in a, insert it to the right of the rightmost x.
Optional args lo (default 0) and hi (default len(a)) bound the
slice of a to be searched. | Insert item x in list a, and keep it sorted assuming a is sorted. | [
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] | def insort_right(a, x, lo=0, hi=None, *, key=None):
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If x is already in a, insert it to the right of the rightmost x.
Optional args lo (default 0) and hi (default len(a)) bound the
slice of a to be searched.
"""
if key is None:
lo = bisect_right(a, x, lo, hi)
else:
lo = bisect_right(a, key(x), lo, hi, key=key)
a.insert(lo, x) | [
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||
mitmproxy/mitmproxy | 1abb8f69217910c8623bd1339da2502aed98ff0d | mitmproxy/tools/console/consoleaddons.py | python | ConsoleAddon.console_command | (self, *command_str: str) | Prompt the user to edit a command with a (possibly empty) starting value. | Prompt the user to edit a command with a (possibly empty) starting value. | [
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"""
Prompt the user to edit a command with a (possibly empty) starting value.
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quoted = " ".join(command_lexer.quote(x) for x in command_str)
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quoted += " "
signals.status_prompt_command.send(partial=quoted) | [
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||
Nuitka/Nuitka | 39262276993757fa4e299f497654065600453fc9 | nuitka/plugins/standard/MatplotlibPlugin.py | python | NuitkaPluginMatplotlib.isRelevant | (cls) | return isStandaloneMode() | Check whether plugin might be required.
Returns:
True if this is a standalone compilation. | Check whether plugin might be required. | [
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"""Check whether plugin might be required.
Returns:
True if this is a standalone compilation.
"""
return isStandaloneMode() | [
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|
chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/scipy/stats/stats.py | python | hmean | (a, axis=0, dtype=None) | Calculates the harmonic mean along the specified axis.
That is: n / (1/x1 + 1/x2 + ... + 1/xn)
Parameters
----------
a : array_like
Input array, masked array or object that can be converted to an array.
axis : int or None, optional
Axis along which the harmonic mean is computed. Default is 0.
If None, compute over the whole array `a`.
dtype : dtype, optional
Type of the returned array and of the accumulator in which the
elements are summed. If `dtype` is not specified, it defaults to the
dtype of `a`, unless `a` has an integer `dtype` with a precision less
than that of the default platform integer. In that case, the default
platform integer is used.
Returns
-------
hmean : ndarray
see `dtype` parameter above
See Also
--------
numpy.mean : Arithmetic average
numpy.average : Weighted average
gmean : Geometric mean
Notes
-----
The harmonic mean is computed over a single dimension of the input
array, axis=0 by default, or all values in the array if axis=None.
float64 intermediate and return values are used for integer inputs.
Use masked arrays to ignore any non-finite values in the input or that
arise in the calculations such as Not a Number and infinity. | Calculates the harmonic mean along the specified axis. | [
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] | def hmean(a, axis=0, dtype=None):
"""
Calculates the harmonic mean along the specified axis.
That is: n / (1/x1 + 1/x2 + ... + 1/xn)
Parameters
----------
a : array_like
Input array, masked array or object that can be converted to an array.
axis : int or None, optional
Axis along which the harmonic mean is computed. Default is 0.
If None, compute over the whole array `a`.
dtype : dtype, optional
Type of the returned array and of the accumulator in which the
elements are summed. If `dtype` is not specified, it defaults to the
dtype of `a`, unless `a` has an integer `dtype` with a precision less
than that of the default platform integer. In that case, the default
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Returns
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gmean : Geometric mean
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if not isinstance(a, np.ndarray):
a = np.array(a, dtype=dtype)
if np.all(a > 0): # Harmonic mean only defined if greater than zero
if isinstance(a, np.ma.MaskedArray):
size = a.count(axis)
else:
if axis is None:
a = a.ravel()
size = a.shape[0]
else:
size = a.shape[axis]
return size / np.sum(1.0/a, axis=axis, dtype=dtype)
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raise ValueError("Harmonic mean only defined if all elements greater than zero") | [
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freqtrade/freqtrade | 13651fd3be8d5ce8dcd7c94b920bda4e00b75aca | scripts/rest_client.py | python | FtRestClient.locks | (self) | return self._get("locks") | Return current locks
:return: json object | Return current locks | [
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"locks"
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"""Return current locks
:return: json object
"""
return self._get("locks") | [
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|
localstack/localstack | ec8b72d5c926ae8495ca50ce168494247aef54be | localstack/services/cloudformation/service_models.py | python | GenericBaseModel.props | (self) | return result | Return a copy of (1) the resource properties (from the template), combined with
(2) the current deployment state properties of the resource. | Return a copy of (1) the resource properties (from the template), combined with
(2) the current deployment state properties of the resource. | [
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"""Return a copy of (1) the resource properties (from the template), combined with
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result = dict(self.properties)
result.update(self.state or {})
return result | [
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ddbourgin/numpy-ml | b0359af5285fbf9699d64fd5ec059493228af03e | numpy_ml/neural_nets/models/w2v.py | python | Word2Vec.backward | (self) | Compute the gradient of the loss wrt the current network parameters. | Compute the gradient of the loss wrt the current network parameters. | [
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Compute the gradient of the loss wrt the current network parameters.
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dX_emb = self.loss.grad(retain_grads=True, update_params=False)
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barseghyanartur/django-fobi | a998feae007d7fe3637429a80e42952ec7cda79f | src/fobi/wizard/views/dynamic.py | python | StepsHelper.is_last_step | (self) | return self.index1 == self.count | Check if last step. | Check if last step. | [
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timkpaine/paperboy | 6c0854b2c0dad139c25153e520ca79ffed820fa4 | paperboy/resources/autocomplete.py | python | AutocompleteResource.__init__ | (self, *args, **kwargs) | [] | def __init__(self, *args, **kwargs):
super(AutocompleteResource, self).__init__(*args, **kwargs) | [
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||||
Neoteroi/BlackSheep | 2936cdd3ba6fceacd230a02c99241bde1d06b265 | blacksheep/server/cors.py | python | CORSStrategy.__call__ | (self, policy: str) | return decorator | Decorates a request handler to bind it to a specific policy by name. | Decorates a request handler to bind it to a specific policy by name. | [
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"""Decorates a request handler to bind it to a specific policy by name."""
def decorator(fn):
is_match = False
policy_object = self.policies.get(policy)
if not policy_object:
raise CORSPolicyNotConfiguredError(policy)
for route in self.router:
if route.handler is fn:
self._policies_by_route[route] = policy_object
is_match = True
if not is_match:
raise NotRequestHandlerError()
return fn
return decorator | [
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"return",
"decorator"
] | https://github.com/Neoteroi/BlackSheep/blob/2936cdd3ba6fceacd230a02c99241bde1d06b265/blacksheep/server/cors.py#L183-L202 |
|
dropbox/changes | 37e23c3141b75e4785cf398d015e3dbca41bdd56 | changes/backends/jenkins/builder.py | python | JenkinsBuilder._pick_master | (self, job_name, is_diff=False) | return best | Identify a master to run the given job on.
The master with the lowest queue for the given job is chosen. By random
sorting the first empty queue will be prioritized. | Identify a master to run the given job on. | [
"Identify",
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"to",
"run",
"the",
"given",
"job",
"on",
"."
] | def _pick_master(self, job_name, is_diff=False):
"""
Identify a master to run the given job on.
The master with the lowest queue for the given job is chosen. By random
sorting the first empty queue will be prioritized.
"""
candidate_urls = self.master_urls
if is_diff and self.diff_urls:
candidate_urls = self.diff_urls
blacklist = redis.smembers(MASTER_BLACKLIST_KEY)
master_urls = [c for c in candidate_urls if c not in blacklist]
if len(master_urls) == 0:
raise ValueError("No masters to pick from.")
if len(master_urls) == 1:
return master_urls[0]
random.shuffle(master_urls)
best_match = (sys.maxint, None)
for url in master_urls:
try:
queued_jobs = self._count_queued_jobs(url, job_name)
except:
self.logger.exception("Couldn't count queued jobs on master %s", url)
continue
if queued_jobs == 0:
return url
if best_match[0] > queued_jobs:
best_match = (queued_jobs, url)
best = best_match[1]
if not best:
raise Exception("Unable to successfully pick a master from {}.".format(master_urls))
return best | [
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|
nopernik/mpDNS | b17dc39e7068406df82cb3431b3042e74e520cf9 | dnslib/fixedresolver.py | python | FixedResolver.__init__ | (self,zone) | [] | def __init__(self,zone):
# Parse RRs
self.rrs = RR.fromZone(zone) | [
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] | https://github.com/nopernik/mpDNS/blob/b17dc39e7068406df82cb3431b3042e74e520cf9/dnslib/fixedresolver.py#L19-L21 |
||||
polakowo/vectorbt | 6638735c131655760474d72b9f045d1dbdbd8fe9 | vectorbt/signals/nb.py | python | generate_rand_enex_by_prob_nb | (shape: tp.Shape,
entry_prob: tp.MaybeArray[float],
exit_prob: tp.MaybeArray[float],
entry_wait: int,
exit_wait: int,
entry_pick_first: bool,
exit_pick_first: bool,
flex_2d: bool,
seed: tp.Optional[int] = None) | return generate_enex_nb(
shape,
entry_wait,
exit_wait,
entry_pick_first,
exit_pick_first,
rand_by_prob_choice_nb, (entry_prob, entry_pick_first, temp_idx_arr, flex_2d),
rand_by_prob_choice_nb, (exit_prob, exit_pick_first, temp_idx_arr, flex_2d)
) | Pick entries by probability `entry_prob` and exits by probability `exit_prob` one after another.
`entry_prob` and `exit_prob` should be 2-dim arrays of shape `shape`.
Specify `seed` to make output deterministic. | Pick entries by probability `entry_prob` and exits by probability `exit_prob` one after another. | [
"Pick",
"entries",
"by",
"probability",
"entry_prob",
"and",
"exits",
"by",
"probability",
"exit_prob",
"one",
"after",
"another",
"."
] | def generate_rand_enex_by_prob_nb(shape: tp.Shape,
entry_prob: tp.MaybeArray[float],
exit_prob: tp.MaybeArray[float],
entry_wait: int,
exit_wait: int,
entry_pick_first: bool,
exit_pick_first: bool,
flex_2d: bool,
seed: tp.Optional[int] = None) -> tp.Tuple[tp.Array2d, tp.Array2d]:
"""Pick entries by probability `entry_prob` and exits by probability `exit_prob` one after another.
`entry_prob` and `exit_prob` should be 2-dim arrays of shape `shape`.
Specify `seed` to make output deterministic."""
if seed is not None:
np.random.seed(seed)
temp_idx_arr = np.empty((shape[0],), dtype=np.int_)
return generate_enex_nb(
shape,
entry_wait,
exit_wait,
entry_pick_first,
exit_pick_first,
rand_by_prob_choice_nb, (entry_prob, entry_pick_first, temp_idx_arr, flex_2d),
rand_by_prob_choice_nb, (exit_prob, exit_pick_first, temp_idx_arr, flex_2d)
) | [
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] | https://github.com/polakowo/vectorbt/blob/6638735c131655760474d72b9f045d1dbdbd8fe9/vectorbt/signals/nb.py#L524-L548 |
|
kamalgill/flask-appengine-template | 11760f83faccbb0d0afe416fc58e67ecfb4643c2 | src/lib/flask/app.py | python | Flask.create_jinja_environment | (self) | return rv | Creates the Jinja2 environment based on :attr:`jinja_options`
and :meth:`select_jinja_autoescape`. Since 0.7 this also adds
the Jinja2 globals and filters after initialization. Override
this function to customize the behavior.
.. versionadded:: 0.5
.. versionchanged:: 0.11
``Environment.auto_reload`` set in accordance with
``TEMPLATES_AUTO_RELOAD`` configuration option. | Creates the Jinja2 environment based on :attr:`jinja_options`
and :meth:`select_jinja_autoescape`. Since 0.7 this also adds
the Jinja2 globals and filters after initialization. Override
this function to customize the behavior. | [
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] | def create_jinja_environment(self):
"""Creates the Jinja2 environment based on :attr:`jinja_options`
and :meth:`select_jinja_autoescape`. Since 0.7 this also adds
the Jinja2 globals and filters after initialization. Override
this function to customize the behavior.
.. versionadded:: 0.5
.. versionchanged:: 0.11
``Environment.auto_reload`` set in accordance with
``TEMPLATES_AUTO_RELOAD`` configuration option.
"""
options = dict(self.jinja_options)
if 'autoescape' not in options:
options['autoescape'] = self.select_jinja_autoescape
if 'auto_reload' not in options:
if self.config['TEMPLATES_AUTO_RELOAD'] is not None:
options['auto_reload'] = self.config['TEMPLATES_AUTO_RELOAD']
else:
options['auto_reload'] = self.debug
rv = self.jinja_environment(self, **options)
rv.globals.update(
url_for=url_for,
get_flashed_messages=get_flashed_messages,
config=self.config,
# request, session and g are normally added with the
# context processor for efficiency reasons but for imported
# templates we also want the proxies in there.
request=request,
session=session,
g=g
)
rv.filters['tojson'] = json.tojson_filter
return rv | [
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] | https://github.com/kamalgill/flask-appengine-template/blob/11760f83faccbb0d0afe416fc58e67ecfb4643c2/src/lib/flask/app.py#L679-L711 |
|
schematics/schematics | 3a144be0aa50f68a4da917e8d957b924dedf9a52 | schematics/types/base.py | python | MultilingualStringType.to_primitive | (self, value, context=None) | return localized | Use a combination of ``default_locale`` and ``context.app_data['locale']`` to return
the best localized string. | Use a combination of ``default_locale`` and ``context.app_data['locale']`` to return
the best localized string. | [
"Use",
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"[",
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"]",
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"best",
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"string",
"."
] | def to_primitive(self, value, context=None):
"""
Use a combination of ``default_locale`` and ``context.app_data['locale']`` to return
the best localized string.
"""
if value is None:
return None
context_locale = None
if context and 'locale' in context.app_data:
context_locale = context.app_data['locale']
# Build a list of all possible locales to try
possible_locales = []
for locale in (context_locale, self.default_locale):
if not locale:
continue
if isinstance(locale, string_type):
possible_locales.append(locale)
else:
possible_locales.extend(locale)
if not possible_locales:
raise ConversionError(self.messages['no_locale'])
for locale in possible_locales:
if locale in value:
localized = value[locale]
break
else:
raise ConversionError(self.messages['locale_not_found'])
if not isinstance(localized, str):
if isinstance(localized, self.allow_casts):
if isinstance(localized, bytes):
localized = str(localized, 'utf-8')
else:
localized = str(localized)
else:
raise ConversionError(self.messages['convert'])
return localized | [
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|
IronLanguages/ironpython2 | 51fdedeeda15727717fb8268a805f71b06c0b9f1 | Src/StdLib/Lib/site-packages/win32/Demos/security/security_enums.py | python | Enum.lookup_flags | (self, flags) | return flag_names, unknown_flags | Returns the names of all recognized flags in input, and any flags not found in the enum. | Returns the names of all recognized flags in input, and any flags not found in the enum. | [
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"input",
"and",
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"in",
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"enum",
"."
] | def lookup_flags(self, flags):
"""Returns the names of all recognized flags in input, and any flags not found in the enum."""
flag_names=[]
unknown_flags=flags
for k,v in self.__dict__.iteritems():
if flags & v == v:
flag_names.append(k)
unknown_flags = unknown_flags & ~v
return flag_names, unknown_flags | [
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|
complexdb/zincbase | 0c8ce46bc392dfa8ee99414877adb3b41648451e | zincbase/web/__init__.py | python | GraphCaster.render | (self, node_color=0x11bb88, node_size=10, node_opacity=0.9,
node_label='id', node_visibility=True, edge_label='pred',
edge_opacity=1, edge_color=0x333333, edge_size=0,
edge_visibility=True, arrow_size=0, arrow_color=0x000001,
label_node=False, label_node_color='black', label_node_height=3,
label_node_offset=1, label_edge=False, label_edge_color='black',
label_edge_height=3, label_edge_offset=1,
bg_color=0xffffff, engine='d3') | Perform the initial setup/rendering of the current graph.
:param node_color: Either a 24bit RGB int (such as 0xFF001A) or a string
containing a Javascript function which takes `node` as an argument, for
example `node => node.color`
:param node_size: Either a number >= 0 or a string containing a Javascript
function, for example `node => Math.log(node.enormity)`
:param node_label: Either a string representing a property of the node
to display (on hover) as its label, or a Javascript function returning a string.
All nodes have a property called `id` which is their name/string repr.
:param node_visibility: Either a string representing a property of the node
which evaluates truthy/falsy (in Javascript) to determine whether to display
the node, or a JS function that returns true or false, or True/False.
:param label_node: If True, nodes will be labeled with `node_label`. Unlike
`node_label`, which only displays on hover, this is a permanent text. Note
that the value updates when the value of `node[node_label]` changes (in Python).
:param label_node_color: RGB value for the color of a node's permanent label
:param label_node_height: Text height for the node's permanent label
:param label_node_offset: Integer specifying how far out from the node the
label should appear. Default is 1 unit on the z-axis.
:param edge_visibility: Either a string representing a property of the edge
which evaluates truthy/falsy (in Javascript) to determine whether to display
the edge, or a JS function that returns true or false, or True/False.
:param edge_label: Either a string representing a property of an edge
to display (on hover) as its label, or a Javascript function returning a string.
Defaults to the predicate.
:param float edge_opacity: Opacity of the edges, from 0-1
:param edge_color: Either a 24bit RGB int or a string containing a Javascript
function which takes `edge` as an argument, for example `edge => edge.color`.
:param edge_size: The width of an edge. Either a number >= 0 (where 0 means 1px)
or a string containing a Javascript function.
:param label_edge: If True, nodes will be labeled with `edge_label`. Unlike
`edge_label`, which only displays on hover, this is a permanent text. Note
that the value updates when the value of `edge[edge_label]` changes (in Python).
:param label_edge_color: RGB value for the color of a edge's permanent label
:param label_edge_height: Text height for the edge's permanent label
:param label_edge_offset: Integer specifying how far out from the edge the
label should appear. Default is 1 unit on the z-axis.
:param int arrow_size: If >0, display directional arrows on edges of that size.
:param int arrow_color: Color of arrows (if arrow_size > 0)
:param int bg_color: Hex background color for the graph, e.g. 0xFF0000 is red.
:param str engine: Specify d3 or ngraph. ngraph is faster but can be buggy, and
is only really suitable for static graphs. The layouts can look different also. | Perform the initial setup/rendering of the current graph. | [
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"the",
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] | def render(self, node_color=0x11bb88, node_size=10, node_opacity=0.9,
node_label='id', node_visibility=True, edge_label='pred',
edge_opacity=1, edge_color=0x333333, edge_size=0,
edge_visibility=True, arrow_size=0, arrow_color=0x000001,
label_node=False, label_node_color='black', label_node_height=3,
label_node_offset=1, label_edge=False, label_edge_color='black',
label_edge_height=3, label_edge_offset=1,
bg_color=0xffffff, engine='d3'):
"""Perform the initial setup/rendering of the current graph.
:param node_color: Either a 24bit RGB int (such as 0xFF001A) or a string
containing a Javascript function which takes `node` as an argument, for
example `node => node.color`
:param node_size: Either a number >= 0 or a string containing a Javascript
function, for example `node => Math.log(node.enormity)`
:param node_label: Either a string representing a property of the node
to display (on hover) as its label, or a Javascript function returning a string.
All nodes have a property called `id` which is their name/string repr.
:param node_visibility: Either a string representing a property of the node
which evaluates truthy/falsy (in Javascript) to determine whether to display
the node, or a JS function that returns true or false, or True/False.
:param label_node: If True, nodes will be labeled with `node_label`. Unlike
`node_label`, which only displays on hover, this is a permanent text. Note
that the value updates when the value of `node[node_label]` changes (in Python).
:param label_node_color: RGB value for the color of a node's permanent label
:param label_node_height: Text height for the node's permanent label
:param label_node_offset: Integer specifying how far out from the node the
label should appear. Default is 1 unit on the z-axis.
:param edge_visibility: Either a string representing a property of the edge
which evaluates truthy/falsy (in Javascript) to determine whether to display
the edge, or a JS function that returns true or false, or True/False.
:param edge_label: Either a string representing a property of an edge
to display (on hover) as its label, or a Javascript function returning a string.
Defaults to the predicate.
:param float edge_opacity: Opacity of the edges, from 0-1
:param edge_color: Either a 24bit RGB int or a string containing a Javascript
function which takes `edge` as an argument, for example `edge => edge.color`.
:param edge_size: The width of an edge. Either a number >= 0 (where 0 means 1px)
or a string containing a Javascript function.
:param label_edge: If True, nodes will be labeled with `edge_label`. Unlike
`edge_label`, which only displays on hover, this is a permanent text. Note
that the value updates when the value of `edge[edge_label]` changes (in Python).
:param label_edge_color: RGB value for the color of a edge's permanent label
:param label_edge_height: Text height for the edge's permanent label
:param label_edge_offset: Integer specifying how far out from the edge the
label should appear. Default is 1 unit on the z-axis.
:param int arrow_size: If >0, display directional arrows on edges of that size.
:param int arrow_color: Color of arrows (if arrow_size > 0)
:param int bg_color: Hex background color for the graph, e.g. 0xFF0000 is red.
:param str engine: Specify d3 or ngraph. ngraph is faster but can be buggy, and
is only really suitable for static graphs. The layouts can look different also.
"""
if label_node:
label_node = {
'color': 'black',
'height': 3,
'offset': node_size + label_node_offset
}
if label_edge:
label_edge = {
'color': 'black',
'height': 3,
'offset': edge_size + label_edge_offset
}
attributes = { 'node_color': node_color, 'node_size': node_size,
'node_opacity': node_opacity, 'node_label': node_label,
'node_visibility': node_visibility, 'edge_visibility': edge_visibility,
'edge_opacity': edge_opacity, 'edge_color': edge_color,
'edge_size': edge_size, 'edge_label': edge_label,
'arrow_size': arrow_size, 'arrow_color': arrow_color,
'label_node': label_node, 'label_edge': label_edge,
'engine': engine, 'bg_color': bg_color }
self.socketio.emit('render', attributes, json=True) | [
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||
toxygen-project/toxygen | 0a54012cf5ee72434b923bcde7d8f1a4e575ce2f | toxygen/tox.py | python | Tox.callback_friend_request | (self, callback, user_data) | Set the callback for the `friend_request` event. Pass None to unset.
This event is triggered when a friend request is received.
:param callback: Python function. Should take pointer (c_void_p) to Tox object,
The Public Key (c_uint8 array) of the user who sent the friend request,
The message (c_char_p) they sent along with the request,
The size (c_size_t) of the message byte array,
pointer (c_void_p) to user_data
:param user_data: pointer (c_void_p) to user data | Set the callback for the `friend_request` event. Pass None to unset. | [
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] | def callback_friend_request(self, callback, user_data):
"""
Set the callback for the `friend_request` event. Pass None to unset.
This event is triggered when a friend request is received.
:param callback: Python function. Should take pointer (c_void_p) to Tox object,
The Public Key (c_uint8 array) of the user who sent the friend request,
The message (c_char_p) they sent along with the request,
The size (c_size_t) of the message byte array,
pointer (c_void_p) to user_data
:param user_data: pointer (c_void_p) to user data
"""
c_callback = CFUNCTYPE(None, c_void_p, POINTER(c_uint8), c_char_p, c_size_t, c_void_p)
self.friend_request_cb = c_callback(callback)
Tox.libtoxcore.tox_callback_friend_request(self._tox_pointer, self.friend_request_cb, c_void_p(user_data)) | [
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||
EasyIME/PIME | 0f1eee10169c1cb2eaa0b59a77fa6f931ecb33b3 | python/python3/tornado/websocket.py | python | WebSocketHandler.check_origin | (self, origin: str) | return origin == host | Override to enable support for allowing alternate origins.
The ``origin`` argument is the value of the ``Origin`` HTTP
header, the url responsible for initiating this request. This
method is not called for clients that do not send this header;
such requests are always allowed (because all browsers that
implement WebSockets support this header, and non-browser
clients do not have the same cross-site security concerns).
Should return ``True`` to accept the request or ``False`` to
reject it. By default, rejects all requests with an origin on
a host other than this one.
This is a security protection against cross site scripting attacks on
browsers, since WebSockets are allowed to bypass the usual same-origin
policies and don't use CORS headers.
.. warning::
This is an important security measure; don't disable it
without understanding the security implications. In
particular, if your authentication is cookie-based, you
must either restrict the origins allowed by
``check_origin()`` or implement your own XSRF-like
protection for websocket connections. See `these
<https://www.christian-schneider.net/CrossSiteWebSocketHijacking.html>`_
`articles
<https://devcenter.heroku.com/articles/websocket-security>`_
for more.
To accept all cross-origin traffic (which was the default prior to
Tornado 4.0), simply override this method to always return ``True``::
def check_origin(self, origin):
return True
To allow connections from any subdomain of your site, you might
do something like::
def check_origin(self, origin):
parsed_origin = urllib.parse.urlparse(origin)
return parsed_origin.netloc.endswith(".mydomain.com")
.. versionadded:: 4.0 | Override to enable support for allowing alternate origins. | [
"Override",
"to",
"enable",
"support",
"for",
"allowing",
"alternate",
"origins",
"."
] | def check_origin(self, origin: str) -> bool:
"""Override to enable support for allowing alternate origins.
The ``origin`` argument is the value of the ``Origin`` HTTP
header, the url responsible for initiating this request. This
method is not called for clients that do not send this header;
such requests are always allowed (because all browsers that
implement WebSockets support this header, and non-browser
clients do not have the same cross-site security concerns).
Should return ``True`` to accept the request or ``False`` to
reject it. By default, rejects all requests with an origin on
a host other than this one.
This is a security protection against cross site scripting attacks on
browsers, since WebSockets are allowed to bypass the usual same-origin
policies and don't use CORS headers.
.. warning::
This is an important security measure; don't disable it
without understanding the security implications. In
particular, if your authentication is cookie-based, you
must either restrict the origins allowed by
``check_origin()`` or implement your own XSRF-like
protection for websocket connections. See `these
<https://www.christian-schneider.net/CrossSiteWebSocketHijacking.html>`_
`articles
<https://devcenter.heroku.com/articles/websocket-security>`_
for more.
To accept all cross-origin traffic (which was the default prior to
Tornado 4.0), simply override this method to always return ``True``::
def check_origin(self, origin):
return True
To allow connections from any subdomain of your site, you might
do something like::
def check_origin(self, origin):
parsed_origin = urllib.parse.urlparse(origin)
return parsed_origin.netloc.endswith(".mydomain.com")
.. versionadded:: 4.0
"""
parsed_origin = urlparse(origin)
origin = parsed_origin.netloc
origin = origin.lower()
host = self.request.headers.get("Host")
# Check to see that origin matches host directly, including ports
return origin == host | [
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|
kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python-build/python-libs/xmpppy/xmpp/protocol.py | python | DataReported.setField | (self,name,typ=None,label=None) | return self.addChild(node=DataField(name,None,typ,0,label)) | Create if nessessary or get the existing datafield object with name 'name' and return it.
If created, attributes 'type' and 'label' are applied to new datafield. | Create if nessessary or get the existing datafield object with name 'name' and return it.
If created, attributes 'type' and 'label' are applied to new datafield. | [
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If created, attributes 'type' and 'label' are applied to new datafield."""
f=self.getField(name)
if f: return f
return self.addChild(node=DataField(name,None,typ,0,label)) | [
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|
cgre-aachen/gempy | 6ad16c46fc6616c9f452fba85d31ce32decd8b10 | gempy/core/theano_modules/theano_graph_pro.py | python | TheanoGraphPro.covariance_matrix | (self) | return C_matrix | Set all the previous covariances together in the universal cokriging matrix
Returns:
theano.tensor.matrix: Multivariate covariance | Set all the previous covariances together in the universal cokriging matrix | [
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] | def covariance_matrix(self):
"""
Set all the previous covariances together in the universal cokriging matrix
Returns:
theano.tensor.matrix: Multivariate covariance
"""
# Lengths
length_of_CG, length_of_CGI, length_of_U_I, length_of_faults, length_of_C = self.matrices_shapes()
# Individual matrices
C_G = self.cov_gradients()
C_I = self.cov_surface_points()
C_GI = self.cov_interface_gradients()
U_I, U_G = self.universal_matrix()
F_I, F_G = self.faults_matrix()
# =================================
# Creation of the Covariance Matrix
# =================================
C_matrix = T.zeros((length_of_C, length_of_C))
# First row of matrices
# Set C_G
C_matrix = T.set_subtensor(C_matrix[0:length_of_CG, 0:length_of_CG], C_G)
# Set CGI
C_matrix = T.set_subtensor(
C_matrix[0:length_of_CG, length_of_CG:length_of_CG + length_of_CGI],
C_GI.T)
# Set UG
C_matrix = T.set_subtensor(C_matrix[0:length_of_CG,
length_of_CG + length_of_CGI:length_of_CG + length_of_CGI + length_of_U_I],
U_G)
# Set FG. I cannot use -index because when is -0 is equivalent to 0
C_matrix = T.set_subtensor(
C_matrix[0:length_of_CG, length_of_CG + length_of_CGI + length_of_U_I:],
F_G.T)
# Second row of matrices
# Set C_IG
C_matrix = T.set_subtensor(
C_matrix[length_of_CG:length_of_CG + length_of_CGI, 0:length_of_CG],
C_GI)
# Set C_I
C_matrix = T.set_subtensor(
C_matrix[length_of_CG:length_of_CG + length_of_CGI,
length_of_CG:length_of_CG + length_of_CGI], C_I)
# Set U_I
# if not self.u_grade_T.get_value() == 0:
C_matrix = T.set_subtensor(
C_matrix[length_of_CG:length_of_CG + length_of_CGI,
length_of_CG + length_of_CGI:length_of_CG + length_of_CGI + length_of_U_I],
U_I)
# Set F_I
C_matrix = T.set_subtensor(
C_matrix[length_of_CG:length_of_CG + length_of_CGI,
length_of_CG + length_of_CGI + length_of_U_I:], F_I.T)
# Third row of matrices
# Set U_G
C_matrix = T.set_subtensor(
C_matrix[
length_of_CG + length_of_CGI:length_of_CG + length_of_CGI + length_of_U_I,
0:length_of_CG], U_G.T)
# Set U_I
C_matrix = T.set_subtensor(C_matrix[
length_of_CG + length_of_CGI:length_of_CG + length_of_CGI + length_of_U_I,
length_of_CG:length_of_CG + length_of_CGI], U_I.T)
# Fourth row of matrices
# Set F_G
C_matrix = T.set_subtensor(
C_matrix[length_of_CG + length_of_CGI + length_of_U_I:, 0:length_of_CG],
F_G)
# Set F_I
C_matrix = T.set_subtensor(
C_matrix[length_of_CG + length_of_CGI + length_of_U_I:,
length_of_CG:length_of_CG + length_of_CGI], F_I)
# Add name to the theano node
C_matrix.name = 'Block Covariance Matrix'
if str(sys._getframe().f_code.co_name) in self.verbose:
C_matrix = theano.printing.Print('cov_function')(C_matrix)
return C_matrix | [
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|
pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/pip/pip/_vendor/requests/adapters.py | python | HTTPAdapter.cert_verify | (self, conn, url, verify, cert) | Verify a SSL certificate. This method should not be called from user
code, and is only exposed for use when subclassing the
:class:`HTTPAdapter <requests.adapters.HTTPAdapter>`.
:param conn: The urllib3 connection object associated with the cert.
:param url: The requested URL.
:param verify: Either a boolean, in which case it controls whether we verify
the server's TLS certificate, or a string, in which case it must be a path
to a CA bundle to use
:param cert: The SSL certificate to verify. | Verify a SSL certificate. This method should not be called from user
code, and is only exposed for use when subclassing the
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||
SciTools/cartopy | 591fb5450e11b42b6de1cebe4f240112f915bd52 | lib/cartopy/io/shapereader.py | python | BasicReader.records | (self) | Return an iterator of :class:`~Record` instances. | Return an iterator of :class:`~Record` instances. | [
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Return an iterator of :class:`~Record` instances.
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# Ignore the "DeletionFlag" field which always comes first
fields = self._reader.fields[1:]
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bitcoin-core/HWI | 6871946c2176f2f9777b6ac8f0614d96d99bfa0e | hwilib/_gui.py | python | DisplayAddressDialog.__init__ | (self, client) | [] | def __init__(self, client):
super(DisplayAddressDialog, self).__init__()
self.ui = Ui_DisplayAddressDialog()
self.ui.setupUi(self)
self.setWindowTitle('Display Address')
self.client = client
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||||
fooying/3102 | 0faee38c30b2e24154f41e68457cfd8f7a61c040 | thirdparty/requests/cookies.py | python | get_cookie_header | (jar, request) | return r.get_new_headers().get('Cookie') | Produce an appropriate Cookie header string to be sent with `request`, or None. | Produce an appropriate Cookie header string to be sent with `request`, or None. | [
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"""Produce an appropriate Cookie header string to be sent with `request`, or None."""
r = MockRequest(request)
jar.add_cookie_header(r)
return r.get_new_headers().get('Cookie') | [
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|
bukun/TorCMS | f7b44e8650aa54774f6b57e7b178edebbbf57e8e | torcms/handlers/log_handler.py | python | LogHandler.user_log_list | (self, userid, cur_p='') | View the list of the Log. | View the list of the Log. | [
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'''
View the list of the Log.
'''
if cur_p == '':
current_page_number = 1
else:
current_page_number = int(cur_p)
current_page_number = 1 if current_page_number < 1 else current_page_number
pager_num = int(MLog.total_number() / CMS_CFG['list_num'])
kwd = {
'pager': '',
'title': '',
'current_page': current_page_number,
'user_id': userid,
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if self.is_p:
self.render('admin/log_ajax/user_log_list.html',
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infos=MLog.query_pager_by_user(
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format_date=tools.format_date,
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kwd=kwd,
infos=MLog.query_pager_by_user(
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||
MobSF/Mobile-Security-Framework-MobSF | 33abc69b54689fb535c72c720b593dc7ed21a4cf | mobsf/StaticAnalyzer/views/android/network_security.py | python | read_netsec_config | (app_dir, config, src_type) | return None | Read the manifest file. | Read the manifest file. | [
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] | def read_netsec_config(app_dir, config, src_type):
"""Read the manifest file."""
msg = 'Reading Network Security Config'
try:
config_file = None
config = config.replace('@xml/', '', 1)
base = Path(app_dir)
if src_type:
# Support only android studio source files
xml_dir = base / 'app' / 'src' / 'main' / 'res' / 'xml'
else:
# APK
xml_dir = base / 'apktool_out' / 'res' / 'xml'
xmls = Path(xml_dir).glob('*.xml')
for xml in xmls:
if xml.stem in [config, 'network_security_config']:
config_file = xml
break
if not config_file:
return None
logger.info(msg)
return config_file.read_text('utf8', 'ignore')
except Exception:
logger.exception(msg)
return None | [
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|
neuropsychology/NeuroKit | d01111b9b82364d28da01c002e6cbfc45d9493d9 | neurokit2/signal/signal_recompose.py | python | _signal_recompose_meanfreq | (components, sampling_rate=1000) | Get the mean frequency of components. | Get the mean frequency of components. | [
"Get",
"the",
"mean",
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"of",
"components",
"."
] | def _signal_recompose_meanfreq(components, sampling_rate=1000):
"""Get the mean frequency of components."""
duration = components.shape[1] / sampling_rate
n = len(components)
freqs = np.zeros(n)
for i in range(n):
c = components[i, :] - np.mean(components[i, :])
freqs[i] = len(signal_zerocrossings(c)) / duration | [
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||
huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/models/electra/modeling_electra.py | python | ElectraSelfAttention.forward | (
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
) | return outputs | [] | def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in ElectraModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs | [
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] | https://github.com/huggingface/transformers/blob/623b4f7c63f60cce917677ee704d6c93ee960b4b/src/transformers/models/electra/modeling_electra.py#L251-L343 |
|||
sopel-irc/sopel | 787baa6e39f9dad57d94600c92e10761c41b21ef | sopel/modules/url.py | python | title_command | (bot, trigger) | Show the title or URL information for the given URL, or the last URL seen
in this channel. | Show the title or URL information for the given URL, or the last URL seen
in this channel. | [
"Show",
"the",
"title",
"or",
"URL",
"information",
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"the",
"given",
"URL",
"or",
"the",
"last",
"URL",
"seen",
"in",
"this",
"channel",
"."
] | def title_command(bot, trigger):
"""
Show the title or URL information for the given URL, or the last URL seen
in this channel.
"""
if not trigger.group(2):
if trigger.sender not in bot.memory['last_seen_url']:
return
matched = check_callbacks(
bot, bot.memory['last_seen_url'][trigger.sender])
if matched:
return
else:
urls = [bot.memory['last_seen_url'][trigger.sender]]
else:
urls = list( # needs to be a list so len() can be checked later
web.search_urls(
trigger,
exclusion_char=bot.config.url.exclusion_char
)
)
result_count = 0
for url, title, domain, tinyurl in process_urls(bot, trigger, urls):
message = '%s | %s' % (title, domain)
if tinyurl:
message += ' ( %s )' % tinyurl
bot.reply(message)
bot.memory['last_seen_url'][trigger.sender] = url
result_count += 1
expected_count = len(urls)
if result_count < expected_count:
if expected_count == 1:
bot.reply("Sorry, fetching that title failed. Make sure the site is working.")
elif result_count == 0:
bot.reply("Sorry, I couldn't fetch titles for any of those.")
else:
bot.reply("I couldn't get all of the titles, but I fetched what I could!") | [
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] | https://github.com/sopel-irc/sopel/blob/787baa6e39f9dad57d94600c92e10761c41b21ef/sopel/modules/url.py#L247-L285 |
||
lensacom/sparkit-learn | 0498502107c1f7dcf33cda0cdb6f5ba4b42524b7 | splearn/preprocessing/label.py | python | SparkLabelEncoder.transform | (self, y) | return y.transform(mapper) | Transform labels to normalized encoding.
Parameters
----------
y : ArrayRDD [n_samples]
Target values.
Returns
-------
y : ArrayRDD [n_samples] | Transform labels to normalized encoding.
Parameters
----------
y : ArrayRDD [n_samples]
Target values.
Returns
-------
y : ArrayRDD [n_samples] | [
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] | def transform(self, y):
"""Transform labels to normalized encoding.
Parameters
----------
y : ArrayRDD [n_samples]
Target values.
Returns
-------
y : ArrayRDD [n_samples]
"""
mapper = super(SparkLabelEncoder, self).transform
mapper = self.broadcast(mapper, y.context)
return y.transform(mapper) | [
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] | https://github.com/lensacom/sparkit-learn/blob/0498502107c1f7dcf33cda0cdb6f5ba4b42524b7/splearn/preprocessing/label.py#L84-L96 |
|
david-cortes/contextualbandits | 293b47ce80d8330a238cca0614147256ffea5529 | contextualbandits/online.py | python | _ActivePolicy.reset_active_choice | (self, active_choice='weighted') | return self | Set the active gradient criteria to a custom form
Parameters
----------
active_choice : str in {'min', 'max', 'weighted'}
How to calculate the gradient that an observation would have on the loss
function for each classifier, given that it could be either class (positive or negative)
for the classifier that predicts each arm. If weighted, they are weighted by the same
probability estimates from the base algorithm.
Returns
-------
self : obj
This object | Set the active gradient criteria to a custom form | [
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"""
Set the active gradient criteria to a custom form
Parameters
----------
active_choice : str in {'min', 'max', 'weighted'}
How to calculate the gradient that an observation would have on the loss
function for each classifier, given that it could be either class (positive or negative)
for the classifier that predicts each arm. If weighted, they are weighted by the same
probability estimates from the base algorithm.
Returns
-------
self : obj
This object
"""
if self.active_choice is None: ### AdaptiveGreedy
raise ValueError("Cannot change active choice for non-active policy.")
assert active_choice in ['min', 'max', 'weighted']
self.active_choice = active_choice
return self | [
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realpython/book2-exercises | cde325eac8e6d8cff2316601c2e5b36bb46af7d0 | web2py/venv/lib/python2.7/site-packages/pip/_vendor/pkg_resources/__init__.py | python | _is_unpacked_egg | (path) | return (
path.lower().endswith('.egg')
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | examples/research_projects/rag/lightning_base.py | python | BaseTransformer.test_epoch_end | (self, outputs) | return self.validation_end(outputs) | [] | def test_epoch_end(self, outputs):
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citronneur/rdpy | cef16a9f64d836a3221a344ca7d571644280d829 | rdpy/protocol/rdp/t125/ber.py | python | writeOctetstring | (value) | return (writeUniversalTag(Tag.BER_TAG_OCTET_STRING, False), writeLength(len(value)), String(value)) | @summary: Write string in BER representation
@param value: string
@return: BER octet string block | [] | def writeOctetstring(value):
"""
@summary: Write string in BER representation
@param value: string
@return: BER octet string block
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return (writeUniversalTag(Tag.BER_TAG_OCTET_STRING, False), writeLength(len(value)), String(value)) | [
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||
phantomcyber/playbooks | 9e850ecc44cb98c5dde53784744213a1ed5799bd | risk_notable_investigate.py | python | risk_notable_enrich | (action=None, success=None, container=None, results=None, handle=None, filtered_artifacts=None, filtered_results=None, custom_function=None, **kwargs) | return | [] | def risk_notable_enrich(action=None, success=None, container=None, results=None, handle=None, filtered_artifacts=None, filtered_results=None, custom_function=None, **kwargs):
phantom.debug("risk_notable_enrich() called")
################################################################################
## Custom Code Start
################################################################################
# Write your custom code here...
################################################################################
## Custom Code End
################################################################################
# call playbook "community/risk_notable_enrich", returns the playbook_run_id
playbook_run_id = phantom.playbook("community/risk_notable_enrich", container=container, name="risk_notable_enrich", callback=note_decision_2)
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|||
huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/trainer_pt_utils.py | python | numpy_pad_and_concatenate | (array1, array2, padding_index=-100) | return result | Concatenates `array1` and `array2` on first axis, applying padding on the second if necessary. | Concatenates `array1` and `array2` on first axis, applying padding on the second if necessary. | [
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"""Concatenates `array1` and `array2` on first axis, applying padding on the second if necessary."""
if len(array1.shape) == 1 or array1.shape[1] == array2.shape[1]:
return np.concatenate((array1, array2), axis=0)
# Let's figure out the new shape
new_shape = (array1.shape[0] + array2.shape[0], max(array1.shape[1], array2.shape[1])) + array1.shape[2:]
# Now let's fill the result tensor
result = np.full_like(array1, padding_index, shape=new_shape)
result[: array1.shape[0], : array1.shape[1]] = array1
result[array1.shape[0] :, : array2.shape[1]] = array2
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|
sympy/sympy | d822fcba181155b85ff2b29fe525adbafb22b448 | sympy/series/sequences.py | python | SeqBase._add | (self, other) | return None | Should only be used internally.
Explanation
===========
self._add(other) returns a new, term-wise added sequence if self
knows how to add with other, otherwise it returns ``None``.
``other`` should only be a sequence object.
Used within :class:`SeqAdd` class. | Should only be used internally. | [
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] | def _add(self, other):
"""
Should only be used internally.
Explanation
===========
self._add(other) returns a new, term-wise added sequence if self
knows how to add with other, otherwise it returns ``None``.
``other`` should only be a sequence object.
Used within :class:`SeqAdd` class.
"""
return None | [
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|
4shadoww/hakkuframework | 409a11fc3819d251f86faa3473439f8c19066a21 | lib/scapy/contrib/automotive/uds.py | python | UDS_TesterPresentSender.__init__ | (self, sock, pkt=UDS() / UDS_TP(), interval=2) | Thread to send TesterPresent messages packets periodically
Args:
sock: socket where packet is sent periodically
pkt: packet to send
interval: interval between two packets | Thread to send TesterPresent messages packets periodically | [
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] | def __init__(self, sock, pkt=UDS() / UDS_TP(), interval=2):
""" Thread to send TesterPresent messages packets periodically
Args:
sock: socket where packet is sent periodically
pkt: packet to send
interval: interval between two packets
"""
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||
wrye-bash/wrye-bash | d495c47cfdb44475befa523438a40c4419cb386f | Mopy/bash/balt.py | python | Link._askDirectory | (self, message=_('Choose a directory.'), defaultPath='') | return DirOpen.display_dialog(self.window, message, defaultPath,
create_dir=True) | Show a modal directory dialog and return the resulting path,
or None if canceled. | Show a modal directory dialog and return the resulting path,
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Instagram/MonkeyType | d582ee3914f9eee1fdfb76a57bb9f4206e017ceb | monkeytype/typing.py | python | field_annotations | (typed_dict) | return (typed_dict.__annotations__["required_fields"].__annotations__,
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/inspectlib/fsdb.py | python | CsvDB.__criteria | (self, obj, matches=None, mt=None, lt=None, eq=None) | return True | Returns True if object is aligned to the criteria.
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Returns True if object is aligned to the criteria.
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# Fail matcher if "not equal"
for field, value in (eq or {}).items():
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# Fail matcher if "doesn't match"
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leo-editor/leo-editor | 383d6776d135ef17d73d935a2f0ecb3ac0e99494 | leo/plugins/backlink.py | python | backlinkController.markDst | (self) | Mark current position as 'destination' (called by UI) | Mark current position as 'destination' (called by UI) | [
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self.linkDestination = self.c.p.copy()
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aws-cloudformation/cfn-lint | 16df5d0ca0d8ebcf9330ebea701e83d883b47217 | src/cfnlint/rules/resources/properties/ValuePrimitiveType.py | python | ValuePrimitiveType.check_value | (self, value, path, **kwargs) | return matches | Check Value | Check Value | [
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"""Check Value"""
matches = []
primitive_type = kwargs.get('primitive_type', {})
item_type = kwargs.get('item_type', {})
strict_check = kwargs.get('non_strict', self.config['strict'])
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# some properties support primitive types and objects
# skip in the case it could be an object and the value is a object
if (item_type or primitive_type) and isinstance(value, dict):
return matches
matches.extend(self.check_primitive_type(value, primitive_type, path, strict_check))
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|
tensorflow/lingvo | ce10019243d954c3c3ebe739f7589b5eebfdf907 | lingvo/core/gshard_builder.py | python | MoEBuilder.Attention | (self, name) | return self._Graph(
name,
['_q', '_k', '_v', 'bias'],
['outputs'],
('_q->q', self.Split('_q')),
('_k->k', self.Split('_k')),
('_v->v', self.Split('_v')),
('q,k->l', self._Fn('logits', fn=_LogitsFnF32)),
('l,bias->logits', self._Fn('bias', fn=_AddBiasF32)),
('logits->w', self._Fn('weights', _SoftmaxF32)),
('w->weights',
self._Dropout('dropout', 1 - self.params.attention_dropout_prob)),
('weights,v->outputs',
self._Fn(
'outputs',
fn=lambda weights, v: tf.einsum('BLHM,BMHD->BLHD', weights, v))),
) | Attention with multiple attention heads.
Keys, values share same dimensionality
params.self.params.attention_key_value_dim.
Args:
name: name of the layer
Returns:
The Attention layer params. | Attention with multiple attention heads. | [
"Attention",
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] | def Attention(self, name):
"""Attention with multiple attention heads.
Keys, values share same dimensionality
params.self.params.attention_key_value_dim.
Args:
name: name of the layer
Returns:
The Attention layer params.
"""
p = self.params
def _AddBiasF32(logits, bias):
# logits: BLHM [batch, length, heads, memory_length]
# bias: BLHM [batch, length, heads, memory_length]
# (in case of attention with relative bias) OR
#
# BLM [batch, length, memory_length]
# (default masking bias with very negative logits).
bias = tf.cast(bias, logits.dtype)
if bias.shape.ndims == 3:
# Expanding the 'heads' dimension
retval = logits + tf.expand_dims(bias, 2)
else:
assert bias.shape.ndims == 4
retval = logits + bias
return retval
def _ReduceLogsumexp(x):
max_logit = tf.math.reduce_max(
tf.stop_gradient(x), axis=-1, keepdims=True)
extra_logit = p.attention_extra_logit
if extra_logit is not None:
extra_logit = tf.convert_to_tensor(extra_logit, max_logit.dtype)
max_logit = tf.math.maximum(max_logit, extra_logit)
x -= max_logit
exp_x = tf.math.exp(x)
sum_exp_x = tf.math.reduce_sum(exp_x, axis=-1, keepdims=True)
if extra_logit is not None:
sum_exp_x += tf.math.exp(extra_logit - max_logit)
return tf.math.log(sum_exp_x) + max_logit
def _LogSoftmax(x):
return x - _ReduceLogsumexp(x)
def _LogitsFnF32(q, k):
# logits.dtype == tf.float32 leads to better training stability
if p.attention_logits_dtype is not None:
q = tf.cast(q, p.attention_logits_dtype)
k = tf.cast(k, p.attention_logits_dtype)
return tf.einsum('BLHD,BMHD->BLHM', q, k)
def _SoftmaxF32(x):
# expecting x.dtype == tf.float32
#
# TODO(lepikhin): consider
# if p.attention_extra_logit is None:
# return tf.nn.softmax(x)
softmax = tf.math.exp(_LogSoftmax(x))
softmax = tf.cast(softmax, py_utils.FPropDtype(self.params))
return softmax
return self._Graph(
name,
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['outputs'],
('_q->q', self.Split('_q')),
('_k->k', self.Split('_k')),
('_v->v', self.Split('_v')),
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('l,bias->logits', self._Fn('bias', fn=_AddBiasF32)),
('logits->w', self._Fn('weights', _SoftmaxF32)),
('w->weights',
self._Dropout('dropout', 1 - self.params.attention_dropout_prob)),
('weights,v->outputs',
self._Fn(
'outputs',
fn=lambda weights, v: tf.einsum('BLHM,BMHD->BLHD', weights, v))),
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|
mypaint/mypaint | 90b36dbc7b8bd2f323383f7edf608a5e0a3a1a33 | lib/floodfill.py | python | enqueue_overflows | (queue, tile_coord, seeds, tiles_bbox, *p) | Conditionally add (coordinate, seed list, data...) tuples to a queue.
:param queue: the queue which may be appended
:type queue: list
:param tile_coord: the 2d coordinate in the middle of the seed coordinates
:type tile_coord: (int, int)
:param seeds: 4-tuple of seed lists for n, e, s, w, relative to tile_coord
:type seeds: (list, list, list, list)
:param tiles_bbox: the bounding box of the fill operation
:type tiles_bbox: lib.fill_common.TileBoundingBox
:param p: tuples of length >= 4, items added to queue items w. same index
NOTE: This function improves readability significantly in exchange for a
small performance hit. Replace with explicit queueing if too slow. | Conditionally add (coordinate, seed list, data...) tuples to a queue. | [
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] | def enqueue_overflows(queue, tile_coord, seeds, tiles_bbox, *p):
""" Conditionally add (coordinate, seed list, data...) tuples to a queue.
:param queue: the queue which may be appended
:type queue: list
:param tile_coord: the 2d coordinate in the middle of the seed coordinates
:type tile_coord: (int, int)
:param seeds: 4-tuple of seed lists for n, e, s, w, relative to tile_coord
:type seeds: (list, list, list, list)
:param tiles_bbox: the bounding box of the fill operation
:type tiles_bbox: lib.fill_common.TileBoundingBox
:param p: tuples of length >= 4, items added to queue items w. same index
NOTE: This function improves readability significantly in exchange for a
small performance hit. Replace with explicit queueing if too slow.
"""
for edge in zip(*(fc.orthogonal(tile_coord), seeds) + p):
edge_coord = edge[0]
edge_seeds = edge[1]
if edge_seeds and not tiles_bbox.outside(edge_coord):
queue.append(edge) | [
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||
general03/flask-autoindex | 424246242c9f40aeb9ac2c8c63f4d2234024256e | .eggs/click-7.1.1-py3.7.egg/click/formatting.py | python | HelpFormatter.write_text | (self, text) | Writes re-indented text into the buffer. This rewraps and
preserves paragraphs. | Writes re-indented text into the buffer. This rewraps and
preserves paragraphs. | [
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"""Writes re-indented text into the buffer. This rewraps and
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"""
text_width = max(self.width - self.current_indent, 11)
indent = " " * self.current_indent
self.write(
wrap_text(
text,
text_width,
initial_indent=indent,
subsequent_indent=indent,
preserve_paragraphs=True,
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self.write("\n") | [
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||
RedTeamOperations/PivotSuite | 9078d1ede1f076d30b6d72ca14e05ddf991f51f4 | pivot_suite/ntlm_auth/compute_response.py | python | ComputeResponse._get_channel_bindings_value | (server_certificate_hash) | return channel_bindings | https://msdn.microsoft.com/en-us/library/windows/desktop/dd919963%28v=vs.85%29.aspx?f=255&MSPPError=-2147217396
https://blogs.msdn.microsoft.com/openspecification/2013/03/26/ntlm-and-channel-binding-hash-aka-extended-protection-for-authentication/
Get's the MD5 hash of the gss_channel_bindings_struct to add to the AV_PAIR MSV_AV_CHANNEL_BINDINGS.
This method takes in the SHA256 hash (Hash of the DER encoded certificate of the server we are connecting to)
and add's it to the gss_channel_bindings_struct. It then gets the MD5 hash and converts this to a
byte array in preparation of adding it to the AV_PAIR structure.
:param server_certificate_hash: The SHA256 hash of the server certificate (DER encoded) NTLM is authenticated to
:return channel_bindings: An MD5 hash of the gss_channel_bindings_struct to add to the AV_PAIR MsvChannelBindings | https://msdn.microsoft.com/en-us/library/windows/desktop/dd919963%28v=vs.85%29.aspx?f=255&MSPPError=-2147217396
https://blogs.msdn.microsoft.com/openspecification/2013/03/26/ntlm-and-channel-binding-hash-aka-extended-protection-for-authentication/ | [
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"""
https://msdn.microsoft.com/en-us/library/windows/desktop/dd919963%28v=vs.85%29.aspx?f=255&MSPPError=-2147217396
https://blogs.msdn.microsoft.com/openspecification/2013/03/26/ntlm-and-channel-binding-hash-aka-extended-protection-for-authentication/
Get's the MD5 hash of the gss_channel_bindings_struct to add to the AV_PAIR MSV_AV_CHANNEL_BINDINGS.
This method takes in the SHA256 hash (Hash of the DER encoded certificate of the server we are connecting to)
and add's it to the gss_channel_bindings_struct. It then gets the MD5 hash and converts this to a
byte array in preparation of adding it to the AV_PAIR structure.
:param server_certificate_hash: The SHA256 hash of the server certificate (DER encoded) NTLM is authenticated to
:return channel_bindings: An MD5 hash of the gss_channel_bindings_struct to add to the AV_PAIR MsvChannelBindings
"""
# Channel Binding Tokens support, used for NTLMv2
# Decode the SHA256 certificate hash
certificate_digest = base64.b16decode(server_certificate_hash)
# Initialise the GssChannelBindingsStruct and add the certificate_digest to the application_data field
gss_channel_bindings = GssChannelBindingsStruct()
gss_channel_bindings[gss_channel_bindings.APPLICATION_DATA] = 'tls-server-end-point:'.encode() + certificate_digest
# Get the gss_channel_bindings_struct and create an MD5 hash
channel_bindings_struct_data = gss_channel_bindings.get_data()
channel_bindings_hash = hashlib.md5(channel_bindings_struct_data).hexdigest()
try:
cbt_value = bytearray.fromhex(channel_bindings_hash)
except TypeError:
# Work-around for Python 2.6 bug
cbt_value = bytearray.fromhex(unicode(channel_bindings_hash))
channel_bindings = bytes(cbt_value)
return channel_bindings | [
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|
ethereum/trinity | 6383280c5044feb06695ac2f7bc1100b7bcf4fe0 | p2p/kademlia.py | python | Address.is_unspecified | (self) | return self._ip.is_unspecified | [] | def is_unspecified(self) -> bool:
return self._ip.is_unspecified | [
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|||
cagbal/ros_people_object_detection_tensorflow | 982ffd4a54b8059638f5cd4aa167299c7fc9e61f | src/object_detection/create_pascal_tf_record.py | python | dict_to_tf_example | (data,
dataset_directory,
label_map_dict,
ignore_difficult_instances=False,
image_subdirectory='JPEGImages') | return example | Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
dataset_directory: Path to root directory holding PASCAL dataset
label_map_dict: A map from string label names to integers ids.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
image_subdirectory: String specifying subdirectory within the
PASCAL dataset directory holding the actual image data.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG | Convert XML derived dict to tf.Example proto. | [
"Convert",
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] | def dict_to_tf_example(data,
dataset_directory,
label_map_dict,
ignore_difficult_instances=False,
image_subdirectory='JPEGImages'):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
dataset_directory: Path to root directory holding PASCAL dataset
label_map_dict: A map from string label names to integers ids.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
image_subdirectory: String specifying subdirectory within the
PASCAL dataset directory holding the actual image data.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
img_path = os.path.join(data['folder'], image_subdirectory, data['filename'])
full_path = os.path.join(dataset_directory, img_path)
with tf.gfile.GFile(full_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width = int(data['size']['width'])
height = int(data['size']['height'])
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
for obj in data['object']:
difficult = bool(int(obj['difficult']))
if ignore_difficult_instances and difficult:
continue
difficult_obj.append(int(difficult))
xmin.append(float(obj['bndbox']['xmin']) / width)
ymin.append(float(obj['bndbox']['ymin']) / height)
xmax.append(float(obj['bndbox']['xmax']) / width)
ymax.append(float(obj['bndbox']['ymax']) / height)
classes_text.append(obj['name'].encode('utf8'))
classes.append(label_map_dict[obj['name']])
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(
data['filename'].encode('utf8')),
'image/source_id': dataset_util.bytes_feature(
data['filename'].encode('utf8')),
'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses),
}))
return example | [
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|
facebookresearch/pysparnn | c299c825fd99f263f3957e9b31197daf23a1e7a3 | pysparnn/matrix_distance.py | python | DenseCosineDistance._distance | (self, a_matrix) | return 1 - (dprod * magnitude) | Vectorised cosine distance | Vectorised cosine distance | [
"Vectorised",
"cosine",
"distance"
] | def _distance(self, a_matrix):
"""Vectorised cosine distance"""
# what is the implmentation of transpose? can i change the order?
dprod = self.matrix.dot(a_matrix.transpose()).transpose() * 1.0
a_root_sum_square = (a_matrix ** 2).sum(axis=1).reshape(-1)
a_root_sum_square = a_root_sum_square.reshape(len(a_root_sum_square), 1)
a_root_sum_square = _np.sqrt(a_root_sum_square)
magnitude = 1.0 / (a_root_sum_square * self.matrix_root_sum_square)
return 1 - (dprod * magnitude) | [
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|
nosmokingbandit/Watcher3 | 0217e75158b563bdefc8e01c3be7620008cf3977 | lib/requests/packages/urllib3/poolmanager.py | python | PoolManager.connection_from_host | (self, host, port=None, scheme='http') | return self.connection_from_context(request_context) | Get a :class:`ConnectionPool` based on the host, port, and scheme.
If ``port`` isn't given, it will be derived from the ``scheme`` using
``urllib3.connectionpool.port_by_scheme``. | Get a :class:`ConnectionPool` based on the host, port, and scheme. | [
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Get a :class:`ConnectionPool` based on the host, port, and scheme.
If ``port`` isn't given, it will be derived from the ``scheme`` using
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if not host:
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request_context = self.connection_pool_kw.copy()
request_context['scheme'] = scheme or 'http'
if not port:
port = port_by_scheme.get(request_context['scheme'].lower(), 80)
request_context['port'] = port
request_context['host'] = host
return self.connection_from_context(request_context) | [
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | lib/pymediainfo/__init__.py | python | MediaInfo.tracks | (self) | return self._tracks | A list of :py:class:`Track` objects which the media file contains.
For instance:
>>> mi = pymediainfo.MediaInfo.parse("/path/to/file.mp4")
>>> for t in mi.tracks:
... print(t)
<Track track_id='None', track_type='General'>
<Track track_id='1', track_type='Text'> | A list of :py:class:`Track` objects which the media file contains. | [
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>>> for t in mi.tracks:
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|
openstack/openstacksdk | 58384268487fa854f21c470b101641ab382c9897 | openstack/clustering/v1/_proxy.py | python | Proxy.policy_types | (self, **query) | return self._list(_policy_type.PolicyType, **query) | Get a generator of policy types.
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QCoDeS/Qcodes | 3cda2cef44812e2aa4672781f2423bf5f816f9f9 | qcodes/instrument_drivers/tektronix/Keithley_6500.py | python | Keithley_6500._get_mode_param | (self, parameter: str, parser: Callable[[str], T]) | return parser(self.ask(cmd)) | Reads the current mode of the multimeter and ask for the given parameter.
Args:
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Ericsson/codechecker | c4e43f62dc3acbf71d3109b337db7c97f7852f43 | tools/report-converter/codechecker_report_converter/analyzers/sanitizers/thread/analyzer_result.py | python | AnalyzerResult.get_reports | (self, file_path: str) | return Parser().get_reports(file_path) | Get reports from the given analyzer result. | Get reports from the given analyzer result. | [
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] | https://github.com/Ericsson/codechecker/blob/c4e43f62dc3acbf71d3109b337db7c97f7852f43/tools/report-converter/codechecker_report_converter/analyzers/sanitizers/thread/analyzer_result.py#L24-L26 |
|
saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/states/file.py | python | serialize | (
name,
dataset=None,
dataset_pillar=None,
user=None,
group=None,
mode=None,
backup="",
makedirs=False,
show_changes=True,
create=True,
merge_if_exists=False,
encoding=None,
encoding_errors="strict",
serializer=None,
serializer_opts=None,
deserializer_opts=None,
**kwargs
) | return __salt__["file.manage_file"](
name=name,
sfn="",
ret=ret,
source=None,
source_sum={},
user=user,
group=group,
mode=mode,
attrs=None,
saltenv=__env__,
backup=backup,
makedirs=makedirs,
template=None,
show_changes=show_changes,
encoding=encoding,
encoding_errors=encoding_errors,
contents=contents,
) | Serializes dataset and store it into managed file. Useful for sharing
simple configuration files.
name
The location of the file to create
dataset
The dataset that will be serialized
dataset_pillar
Operates like ``dataset``, but draws from a value stored in pillar,
using the pillar path syntax used in :mod:`pillar.get
<salt.modules.pillar.get>`. This is useful when the pillar value
contains newlines, as referencing a pillar variable using a jinja/mako
template can result in YAML formatting issues due to the newlines
causing indentation mismatches.
.. versionadded:: 2015.8.0
serializer (or formatter)
Write the data as this format. See the list of
:ref:`all-salt.serializers` for supported output formats.
.. versionchanged:: 3002
``serializer`` argument added as an alternative to ``formatter``.
Both are accepted, but using both will result in an error.
encoding
If specified, then the specified encoding will be used. Otherwise, the
file will be encoded using the system locale (usually UTF-8). See
https://docs.python.org/3/library/codecs.html#standard-encodings for
the list of available encodings.
.. versionadded:: 2017.7.0
encoding_errors
Error encoding scheme. Default is ```'strict'```.
See https://docs.python.org/2/library/codecs.html#codec-base-classes
for the list of available schemes.
.. versionadded:: 2017.7.0
user
The user to own the directory, this defaults to the user salt is
running as on the minion
group
The group ownership set for the directory, this defaults to the group
salt is running as on the minion
mode
The permissions to set on this file, e.g. ``644``, ``0775``, or
``4664``.
The default mode for new files and directories corresponds umask of salt
process. For existing files and directories it's not enforced.
.. note::
This option is **not** supported on Windows.
backup
Overrides the default backup mode for this specific file.
makedirs
Create parent directories for destination file.
.. versionadded:: 2014.1.3
show_changes
Output a unified diff of the old file and the new file. If ``False``
return a boolean if any changes were made.
create
Default is True, if create is set to False then the file will only be
managed if the file already exists on the system.
merge_if_exists
Default is False, if merge_if_exists is True then the existing file will
be parsed and the dataset passed in will be merged with the existing
content
.. versionadded:: 2014.7.0
serializer_opts
Pass through options to serializer. For example:
.. code-block:: yaml
/etc/dummy/package.yaml
file.serialize:
- serializer: yaml
- serializer_opts:
- explicit_start: True
- default_flow_style: True
- indent: 4
The valid opts are the additional opts (i.e. not the data being
serialized) for the function used to serialize the data. Documentation
for the these functions can be found in the list below:
- For **yaml**: `yaml.dump()`_
- For **json**: `json.dumps()`_
- For **python**: `pprint.pformat()`_
- For **msgpack**: Run ``python -c 'import msgpack; help(msgpack.Packer)'``
to see the available options (``encoding``, ``unicode_errors``, etc.)
.. _`yaml.dump()`: https://pyyaml.org/wiki/PyYAMLDocumentation
.. _`json.dumps()`: https://docs.python.org/2/library/json.html#json.dumps
.. _`pprint.pformat()`: https://docs.python.org/2/library/pprint.html#pprint.pformat
deserializer_opts
Like ``serializer_opts`` above, but only used when merging with an
existing file (i.e. when ``merge_if_exists`` is set to ``True``).
The options specified here will be passed to the deserializer to load
the existing data, before merging with the specified data and
re-serializing.
.. code-block:: yaml
/etc/dummy/package.yaml
file.serialize:
- serializer: yaml
- serializer_opts:
- explicit_start: True
- default_flow_style: True
- indent: 4
- deserializer_opts:
- encoding: latin-1
- merge_if_exists: True
The valid opts are the additional opts (i.e. not the data being
deserialized) for the function used to deserialize the data.
Documentation for the these functions can be found in the list below:
- For **yaml**: `yaml.load()`_
- For **json**: `json.loads()`_
.. _`yaml.load()`: https://pyyaml.org/wiki/PyYAMLDocumentation
.. _`json.loads()`: https://docs.python.org/2/library/json.html#json.loads
However, note that not all arguments are supported. For example, when
deserializing JSON, arguments like ``parse_float`` and ``parse_int``
which accept a callable object cannot be handled in an SLS file.
.. versionadded:: 2019.2.0
For example, this state:
.. code-block:: yaml
/etc/dummy/package.json:
file.serialize:
- dataset:
name: naive
description: A package using naive versioning
author: A confused individual <iam@confused.com>
dependencies:
express: '>= 1.2.0'
optimist: '>= 0.1.0'
engine: node 0.4.1
- serializer: json
will manage the file ``/etc/dummy/package.json``:
.. code-block:: json
{
"author": "A confused individual <iam@confused.com>",
"dependencies": {
"express": ">= 1.2.0",
"optimist": ">= 0.1.0"
},
"description": "A package using naive versioning",
"engine": "node 0.4.1",
"name": "naive"
} | Serializes dataset and store it into managed file. Useful for sharing
simple configuration files. | [
"Serializes",
"dataset",
"and",
"store",
"it",
"into",
"managed",
"file",
".",
"Useful",
"for",
"sharing",
"simple",
"configuration",
"files",
"."
] | def serialize(
name,
dataset=None,
dataset_pillar=None,
user=None,
group=None,
mode=None,
backup="",
makedirs=False,
show_changes=True,
create=True,
merge_if_exists=False,
encoding=None,
encoding_errors="strict",
serializer=None,
serializer_opts=None,
deserializer_opts=None,
**kwargs
):
"""
Serializes dataset and store it into managed file. Useful for sharing
simple configuration files.
name
The location of the file to create
dataset
The dataset that will be serialized
dataset_pillar
Operates like ``dataset``, but draws from a value stored in pillar,
using the pillar path syntax used in :mod:`pillar.get
<salt.modules.pillar.get>`. This is useful when the pillar value
contains newlines, as referencing a pillar variable using a jinja/mako
template can result in YAML formatting issues due to the newlines
causing indentation mismatches.
.. versionadded:: 2015.8.0
serializer (or formatter)
Write the data as this format. See the list of
:ref:`all-salt.serializers` for supported output formats.
.. versionchanged:: 3002
``serializer`` argument added as an alternative to ``formatter``.
Both are accepted, but using both will result in an error.
encoding
If specified, then the specified encoding will be used. Otherwise, the
file will be encoded using the system locale (usually UTF-8). See
https://docs.python.org/3/library/codecs.html#standard-encodings for
the list of available encodings.
.. versionadded:: 2017.7.0
encoding_errors
Error encoding scheme. Default is ```'strict'```.
See https://docs.python.org/2/library/codecs.html#codec-base-classes
for the list of available schemes.
.. versionadded:: 2017.7.0
user
The user to own the directory, this defaults to the user salt is
running as on the minion
group
The group ownership set for the directory, this defaults to the group
salt is running as on the minion
mode
The permissions to set on this file, e.g. ``644``, ``0775``, or
``4664``.
The default mode for new files and directories corresponds umask of salt
process. For existing files and directories it's not enforced.
.. note::
This option is **not** supported on Windows.
backup
Overrides the default backup mode for this specific file.
makedirs
Create parent directories for destination file.
.. versionadded:: 2014.1.3
show_changes
Output a unified diff of the old file and the new file. If ``False``
return a boolean if any changes were made.
create
Default is True, if create is set to False then the file will only be
managed if the file already exists on the system.
merge_if_exists
Default is False, if merge_if_exists is True then the existing file will
be parsed and the dataset passed in will be merged with the existing
content
.. versionadded:: 2014.7.0
serializer_opts
Pass through options to serializer. For example:
.. code-block:: yaml
/etc/dummy/package.yaml
file.serialize:
- serializer: yaml
- serializer_opts:
- explicit_start: True
- default_flow_style: True
- indent: 4
The valid opts are the additional opts (i.e. not the data being
serialized) for the function used to serialize the data. Documentation
for the these functions can be found in the list below:
- For **yaml**: `yaml.dump()`_
- For **json**: `json.dumps()`_
- For **python**: `pprint.pformat()`_
- For **msgpack**: Run ``python -c 'import msgpack; help(msgpack.Packer)'``
to see the available options (``encoding``, ``unicode_errors``, etc.)
.. _`yaml.dump()`: https://pyyaml.org/wiki/PyYAMLDocumentation
.. _`json.dumps()`: https://docs.python.org/2/library/json.html#json.dumps
.. _`pprint.pformat()`: https://docs.python.org/2/library/pprint.html#pprint.pformat
deserializer_opts
Like ``serializer_opts`` above, but only used when merging with an
existing file (i.e. when ``merge_if_exists`` is set to ``True``).
The options specified here will be passed to the deserializer to load
the existing data, before merging with the specified data and
re-serializing.
.. code-block:: yaml
/etc/dummy/package.yaml
file.serialize:
- serializer: yaml
- serializer_opts:
- explicit_start: True
- default_flow_style: True
- indent: 4
- deserializer_opts:
- encoding: latin-1
- merge_if_exists: True
The valid opts are the additional opts (i.e. not the data being
deserialized) for the function used to deserialize the data.
Documentation for the these functions can be found in the list below:
- For **yaml**: `yaml.load()`_
- For **json**: `json.loads()`_
.. _`yaml.load()`: https://pyyaml.org/wiki/PyYAMLDocumentation
.. _`json.loads()`: https://docs.python.org/2/library/json.html#json.loads
However, note that not all arguments are supported. For example, when
deserializing JSON, arguments like ``parse_float`` and ``parse_int``
which accept a callable object cannot be handled in an SLS file.
.. versionadded:: 2019.2.0
For example, this state:
.. code-block:: yaml
/etc/dummy/package.json:
file.serialize:
- dataset:
name: naive
description: A package using naive versioning
author: A confused individual <iam@confused.com>
dependencies:
express: '>= 1.2.0'
optimist: '>= 0.1.0'
engine: node 0.4.1
- serializer: json
will manage the file ``/etc/dummy/package.json``:
.. code-block:: json
{
"author": "A confused individual <iam@confused.com>",
"dependencies": {
"express": ">= 1.2.0",
"optimist": ">= 0.1.0"
},
"description": "A package using naive versioning",
"engine": "node 0.4.1",
"name": "naive"
}
"""
if "env" in kwargs:
# "env" is not supported; Use "saltenv".
kwargs.pop("env")
name = os.path.expanduser(name)
# Set some defaults
serializer_options = {
"yaml.serialize": {"default_flow_style": False},
"json.serialize": {"indent": 2, "separators": (",", ": "), "sort_keys": True},
}
deserializer_options = {
"yaml.deserialize": {},
"json.deserialize": {},
}
if encoding:
serializer_options["yaml.serialize"].update({"allow_unicode": True})
serializer_options["json.serialize"].update({"ensure_ascii": False})
ret = {"changes": {}, "comment": "", "name": name, "result": True}
if not name:
return _error(ret, "Must provide name to file.serialize")
if not create:
if not os.path.isfile(name):
# Don't create a file that is not already present
ret[
"comment"
] = "File {} is not present and is not set for creation".format(name)
return ret
formatter = kwargs.pop("formatter", None)
if serializer and formatter:
return _error(ret, "Only one of serializer and formatter are allowed")
serializer = str(serializer or formatter or "yaml").lower()
if len([x for x in (dataset, dataset_pillar) if x]) > 1:
return _error(ret, "Only one of 'dataset' and 'dataset_pillar' is permitted")
if dataset_pillar:
dataset = __salt__["pillar.get"](dataset_pillar)
if dataset is None:
return _error(ret, "Neither 'dataset' nor 'dataset_pillar' was defined")
if salt.utils.platform.is_windows():
if group is not None:
log.warning(
"The group argument for %s has been ignored as this "
"is a Windows system.",
name,
)
group = user
serializer_name = "{}.serialize".format(serializer)
deserializer_name = "{}.deserialize".format(serializer)
if serializer_name not in __serializers__:
return {
"changes": {},
"comment": (
"The {} serializer could not be found. It either does "
"not exist or its prerequisites are not installed.".format(serializer)
),
"name": name,
"result": False,
}
if serializer_opts:
serializer_options.setdefault(serializer_name, {}).update(
salt.utils.data.repack_dictlist(serializer_opts)
)
if deserializer_opts:
deserializer_options.setdefault(deserializer_name, {}).update(
salt.utils.data.repack_dictlist(deserializer_opts)
)
if merge_if_exists:
if os.path.isfile(name):
if deserializer_name not in __serializers__:
return {
"changes": {},
"comment": (
"merge_if_exists is not supported for the {} serializer".format(
serializer
)
),
"name": name,
"result": False,
}
open_args = "r"
if serializer == "plist":
open_args += "b"
with salt.utils.files.fopen(name, open_args) as fhr:
try:
existing_data = __serializers__[deserializer_name](
fhr, **deserializer_options.get(deserializer_name, {})
)
except (TypeError, DeserializationError) as exc:
ret["result"] = False
ret["comment"] = "Failed to deserialize existing data: {}".format(
exc
)
return False
if existing_data is not None:
merged_data = salt.utils.dictupdate.merge_recurse(
existing_data, dataset
)
if existing_data == merged_data:
ret["result"] = True
ret["comment"] = "The file {} is in the correct state".format(name)
return ret
dataset = merged_data
else:
if deserializer_opts:
ret.setdefault("warnings", []).append(
"The 'deserializer_opts' option is ignored unless "
"merge_if_exists is set to True."
)
contents = __serializers__[serializer_name](
dataset, **serializer_options.get(serializer_name, {})
)
# Insert a newline, but only if the serialized contents are not a
# bytestring. If it's a bytestring, it's almost certainly serialized into a
# binary format that does not take kindly to additional bytes being foisted
# upon it.
try:
contents += "\n"
except TypeError:
pass
# Make sure that any leading zeros stripped by YAML loader are added back
mode = salt.utils.files.normalize_mode(mode)
if __opts__["test"]:
ret["changes"] = __salt__["file.check_managed_changes"](
name=name,
source=None,
source_hash={},
source_hash_name=None,
user=user,
group=group,
mode=mode,
attrs=None,
template=None,
context=None,
defaults=None,
saltenv=__env__,
contents=contents,
skip_verify=False,
**kwargs
)
if ret["changes"]:
ret["result"] = None
ret["comment"] = "Dataset will be serialized and stored into {}".format(
name
)
if not show_changes:
ret["changes"]["diff"] = "<show_changes=False>"
else:
ret["result"] = True
ret["comment"] = "The file {} is in the correct state".format(name)
return ret
return __salt__["file.manage_file"](
name=name,
sfn="",
ret=ret,
source=None,
source_sum={},
user=user,
group=group,
mode=mode,
attrs=None,
saltenv=__env__,
backup=backup,
makedirs=makedirs,
template=None,
show_changes=show_changes,
encoding=encoding,
encoding_errors=encoding_errors,
contents=contents,
) | [
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|
vertexproject/synapse | 8173f43cb5fba5ca2648d12a659afb432139b0a7 | synapse/lib/cell.py | python | CellApi.rotateNexsLog | (self) | return await self.cell.rotateNexsLog() | Rotate the Nexus log at the current offset.
Returns:
int: The starting index of the active Nexus log | Rotate the Nexus log at the current offset. | [
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] | async def rotateNexsLog(self):
'''
Rotate the Nexus log at the current offset.
Returns:
int: The starting index of the active Nexus log
'''
return await self.cell.rotateNexsLog() | [
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|
pfalcon/pycopy-lib | 56ebf2110f3caa63a3785d439ce49b11e13c75c0 | email.internal/email/_policybase.py | python | _PolicyBase.__init__ | (self, **kw) | Create new Policy, possibly overriding some defaults.
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for name, value in kw.items():
if hasattr(self, name):
super(_PolicyBase,self).__setattr__(name, value)
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||
XX-net/XX-Net | a9898cfcf0084195fb7e69b6bc834e59aecdf14f | python3.8.2/Lib/warnings.py | python | showwarning | (message, category, filename, lineno, file=None, line=None) | Hook to write a warning to a file; replace if you like. | Hook to write a warning to a file; replace if you like. | [
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msg = WarningMessage(message, category, filename, lineno, file, line)
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lululxvi/deepxde | 730c97282636e86c845ce2ba3253482f2178469e | deepxde/optimizers/tensorflow/optimizers.py | python | get | (optimizer, learning_rate=None, decay=None) | Retrieves a Keras Optimizer instance. | Retrieves a Keras Optimizer instance. | [
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"""Retrieves a Keras Optimizer instance."""
if isinstance(optimizer, tf.keras.optimizers.Optimizer):
return optimizer
if is_external_optimizer(optimizer):
if learning_rate is not None or decay is not None:
print("Warning: learning rate is ignored for {}".format(optimizer))
return lbfgs_minimize
if learning_rate is None:
raise ValueError("No learning rate for {}.".format(optimizer))
lr_schedule = _get_learningrate(learning_rate, decay)
if optimizer == "adam":
return tf.keras.optimizers.Adam(learning_rate=lr_schedule)
if optimizer == "nadam":
return tf.keras.optimizers.Nadam(learning_rate=lr_schedule)
if optimizer == "sgd":
return tf.keras.optimizers.SGD(learning_rate=lr_schedule)
raise NotImplementedError(f"{optimizer} to be implemented for backend tensorflow.") | [
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cltk/cltk | 1a8c2f5ef72389e2579dfce1fa5af8e59ebc9ec1 | src/cltk/prosody/non.py | python | UnspecifiedStanza.to_phonetics | (self, with_squared_brackets=True) | >>> stanza = "Ein sat hon úti,\\nþá er inn aldni kom\\nyggjungr ása\\nok í augu leit.\\nHvers fregnið mik?\\nHví freistið mín?\\nAllt veit ek, Óðinn,\\nhvar þú auga falt,\\ní inum mæra\\nMímisbrunni.\\nDrekkr mjöð Mímir\\nmorgun hverjan\\naf veði Valföðrs.\\nVituð ér enn - eða hvat?"
>>> us = UnspecifiedStanza()
>>> us.from_short_lines_text(stanza)
>>> us.to_phonetics(False)
>>> us.transcribed_text
[['ɛin', 'sat', 'hɔn', 'uːti'], ['θaː', 'ɛr', 'inː', 'aldni', 'kɔm'], ['ygːjunɣr', 'aːsa'], ['ɔk', 'iː', 'ɒuɣu', 'lɛit'], ['hvɛrs', 'frɛɣnið', 'mik'], ['hviː', 'frɛistið', 'miːn'], ['alːt', 'vɛit', 'ɛk', 'oːðinː'], ['hvar', 'θuː', 'ɒuɣa', 'falt'], ['iː', 'inum', 'mɛːra'], ['miːmisbrunːi'], ['drɛkːr', 'mjœð', 'miːmir'], ['mɔrɣun', 'hvɛrjan'], ['av', 'vɛði', 'valvœðrs'], ['vituð', 'eːr', 'ɛnː', 'ɛða', 'hvat']]
:return: | >>> stanza = "Ein sat hon úti,\\nþá er inn aldni kom\\nyggjungr ása\\nok í augu leit.\\nHvers fregnið mik?\\nHví freistið mín?\\nAllt veit ek, Óðinn,\\nhvar þú auga falt,\\ní inum mæra\\nMímisbrunni.\\nDrekkr mjöð Mímir\\nmorgun hverjan\\naf veði Valföðrs.\\nVituð ér enn - eða hvat?"
>>> us = UnspecifiedStanza()
>>> us.from_short_lines_text(stanza)
>>> us.to_phonetics(False)
>>> us.transcribed_text
[['ɛin', 'sat', 'hɔn', 'uːti'], ['θaː', 'ɛr', 'inː', 'aldni', 'kɔm'], ['ygːjunɣr', 'aːsa'], ['ɔk', 'iː', 'ɒuɣu', 'lɛit'], ['hvɛrs', 'frɛɣnið', 'mik'], ['hviː', 'frɛistið', 'miːn'], ['alːt', 'vɛit', 'ɛk', 'oːðinː'], ['hvar', 'θuː', 'ɒuɣa', 'falt'], ['iː', 'inum', 'mɛːra'], ['miːmisbrunːi'], ['drɛkːr', 'mjœð', 'miːmir'], ['mɔrɣun', 'hvɛrjan'], ['av', 'vɛði', 'valvœðrs'], ['vituð', 'eːr', 'ɛnː', 'ɛða', 'hvat']] | [
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"""
>>> stanza = "Ein sat hon úti,\\nþá er inn aldni kom\\nyggjungr ása\\nok í augu leit.\\nHvers fregnið mik?\\nHví freistið mín?\\nAllt veit ek, Óðinn,\\nhvar þú auga falt,\\ní inum mæra\\nMímisbrunni.\\nDrekkr mjöð Mímir\\nmorgun hverjan\\naf veði Valföðrs.\\nVituð ér enn - eða hvat?"
>>> us = UnspecifiedStanza()
>>> us.from_short_lines_text(stanza)
>>> us.to_phonetics(False)
>>> us.transcribed_text
[['ɛin', 'sat', 'hɔn', 'uːti'], ['θaː', 'ɛr', 'inː', 'aldni', 'kɔm'], ['ygːjunɣr', 'aːsa'], ['ɔk', 'iː', 'ɒuɣu', 'lɛit'], ['hvɛrs', 'frɛɣnið', 'mik'], ['hviː', 'frɛistið', 'miːn'], ['alːt', 'vɛit', 'ɛk', 'oːðinː'], ['hvar', 'θuː', 'ɒuɣa', 'falt'], ['iː', 'inum', 'mɛːra'], ['miːmisbrunːi'], ['drɛkːr', 'mjœð', 'miːmir'], ['mɔrɣun', 'hvɛrjan'], ['av', 'vɛði', 'valvœðrs'], ['vituð', 'eːr', 'ɛnː', 'ɛða', 'hvat']]
:return:
"""
transcriber = Transcriber(
old_norse_transcription.DIPHTHONGS_IPA,
old_norse_transcription.DIPHTHONGS_IPA_class,
old_norse_transcription.IPA_class,
old_norse_transcription.old_norse_rules,
)
transcribed_text = []
phonological_features_text = []
for short_line in self.short_lines:
assert isinstance(short_line, ShortLine) or isinstance(short_line, LongLine)
short_line.to_phonetics(transcriber, with_squared_brackets)
transcribed_text.append(short_line.transcribed)
phonological_features_text.append(short_line.phonological_features_text)
self.transcribed_text = transcribed_text
self.phonological_features_text = phonological_features_text | [
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r9y9/wavenet_vocoder | a35fff76ea3687b05e1a10023cad3f7f64fa25a3 | lrschedule.py | python | cyclic_cosine_annealing | (init_lr, global_step, T, M) | return init_lr / 2.0 * (np.cos(np.pi * ((global_step - 1) % TdivM) / TdivM) + 1.0) | Cyclic cosine annealing
https://arxiv.org/pdf/1704.00109.pdf
Args:
init_lr (float): Initial learning rate
global_step (int): Current iteration number
T (int): Total iteration number (i,e. nepoch)
M (int): Number of ensembles we want
Returns:
float: Annealed learning rate | Cyclic cosine annealing | [
"Cyclic",
"cosine",
"annealing"
] | def cyclic_cosine_annealing(init_lr, global_step, T, M):
"""Cyclic cosine annealing
https://arxiv.org/pdf/1704.00109.pdf
Args:
init_lr (float): Initial learning rate
global_step (int): Current iteration number
T (int): Total iteration number (i,e. nepoch)
M (int): Number of ensembles we want
Returns:
float: Annealed learning rate
"""
TdivM = T // M
return init_lr / 2.0 * (np.cos(np.pi * ((global_step - 1) % TdivM) / TdivM) + 1.0) | [
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jellyfin/jellyfin-kodi | e21e059e000f06890b33e2794a7e57959fdf19a3 | jellyfin_kodi/objects/kodi/movies.py | python | Movies.add_ratings | (self, *args) | Add ratings, rating type and votes. | Add ratings, rating type and votes. | [
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''' Add ratings, rating type and votes.
'''
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/pandas/core/arrays/categorical.py | python | Categorical.as_unordered | (self, inplace=False) | return self.set_ordered(False, inplace=inplace) | Set the Categorical to be unordered.
Parameters
----------
inplace : boolean (default: False)
Whether or not to set the ordered attribute inplace or return a copy
of this categorical with ordered set to False | Set the Categorical to be unordered. | [
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] | def as_unordered(self, inplace=False):
"""
Set the Categorical to be unordered.
Parameters
----------
inplace : boolean (default: False)
Whether or not to set the ordered attribute inplace or return a copy
of this categorical with ordered set to False
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
return self.set_ordered(False, inplace=inplace) | [
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|
TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/ie/v20200304/models.py | python | MediaJoiningTaskResult.__init__ | (self) | r"""
:param File: 拼接结果文件。
注意:此字段可能返回 null,表示取不到有效值。
:type File: :class:`tencentcloud.ie.v20200304.models.TaskResultFile` | r"""
:param File: 拼接结果文件。
注意:此字段可能返回 null,表示取不到有效值。
:type File: :class:`tencentcloud.ie.v20200304.models.TaskResultFile` | [
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:param File: 拼接结果文件。
注意:此字段可能返回 null,表示取不到有效值。
:type File: :class:`tencentcloud.ie.v20200304.models.TaskResultFile`
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||
openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/gdata/src/gdata/tlslite/integration/HTTPTLSConnection.py | python | HTTPTLSConnection.__init__ | (self, host, port=None,
username=None, password=None, sharedKey=None,
certChain=None, privateKey=None,
cryptoID=None, protocol=None,
x509Fingerprint=None,
x509TrustList=None, x509CommonName=None,
settings = None) | Create a new HTTPTLSConnection.
For client authentication, use one of these argument
combinations:
- username, password (SRP)
- username, sharedKey (shared-key)
- certChain, privateKey (certificate)
For server authentication, you can either rely on the
implicit mutual authentication performed by SRP or
shared-keys, or you can do certificate-based server
authentication with one of these argument combinations:
- cryptoID[, protocol] (requires cryptoIDlib)
- x509Fingerprint
- x509TrustList[, x509CommonName] (requires cryptlib_py)
Certificate-based server authentication is compatible with
SRP or certificate-based client authentication. It is
not compatible with shared-keys.
The constructor does not perform the TLS handshake itself, but
simply stores these arguments for later. The handshake is
performed only when this class needs to connect with the
server. Thus you should be prepared to handle TLS-specific
exceptions when calling methods inherited from
L{httplib.HTTPConnection} such as request(), connect(), and
send(). See the client handshake functions in
L{tlslite.TLSConnection.TLSConnection} for details on which
exceptions might be raised.
@type host: str
@param host: Server to connect to.
@type port: int
@param port: Port to connect to.
@type username: str
@param username: SRP or shared-key username. Requires the
'password' or 'sharedKey' argument.
@type password: str
@param password: SRP password for mutual authentication.
Requires the 'username' argument.
@type sharedKey: str
@param sharedKey: Shared key for mutual authentication.
Requires the 'username' argument.
@type certChain: L{tlslite.X509CertChain.X509CertChain} or
L{cryptoIDlib.CertChain.CertChain}
@param certChain: Certificate chain for client authentication.
Requires the 'privateKey' argument. Excludes the SRP or
shared-key related arguments.
@type privateKey: L{tlslite.utils.RSAKey.RSAKey}
@param privateKey: Private key for client authentication.
Requires the 'certChain' argument. Excludes the SRP or
shared-key related arguments.
@type cryptoID: str
@param cryptoID: cryptoID for server authentication. Mutually
exclusive with the 'x509...' arguments.
@type protocol: str
@param protocol: cryptoID protocol URI for server
authentication. Requires the 'cryptoID' argument.
@type x509Fingerprint: str
@param x509Fingerprint: Hex-encoded X.509 fingerprint for
server authentication. Mutually exclusive with the 'cryptoID'
and 'x509TrustList' arguments.
@type x509TrustList: list of L{tlslite.X509.X509}
@param x509TrustList: A list of trusted root certificates. The
other party must present a certificate chain which extends to
one of these root certificates. The cryptlib_py module must be
installed to use this parameter. Mutually exclusive with the
'cryptoID' and 'x509Fingerprint' arguments.
@type x509CommonName: str
@param x509CommonName: The end-entity certificate's 'CN' field
must match this value. For a web server, this is typically a
server name such as 'www.amazon.com'. Mutually exclusive with
the 'cryptoID' and 'x509Fingerprint' arguments. Requires the
'x509TrustList' argument.
@type settings: L{tlslite.HandshakeSettings.HandshakeSettings}
@param settings: Various settings which can be used to control
the ciphersuites, certificate types, and SSL/TLS versions
offered by the client. | Create a new HTTPTLSConnection. | [
"Create",
"a",
"new",
"HTTPTLSConnection",
"."
] | def __init__(self, host, port=None,
username=None, password=None, sharedKey=None,
certChain=None, privateKey=None,
cryptoID=None, protocol=None,
x509Fingerprint=None,
x509TrustList=None, x509CommonName=None,
settings = None):
"""Create a new HTTPTLSConnection.
For client authentication, use one of these argument
combinations:
- username, password (SRP)
- username, sharedKey (shared-key)
- certChain, privateKey (certificate)
For server authentication, you can either rely on the
implicit mutual authentication performed by SRP or
shared-keys, or you can do certificate-based server
authentication with one of these argument combinations:
- cryptoID[, protocol] (requires cryptoIDlib)
- x509Fingerprint
- x509TrustList[, x509CommonName] (requires cryptlib_py)
Certificate-based server authentication is compatible with
SRP or certificate-based client authentication. It is
not compatible with shared-keys.
The constructor does not perform the TLS handshake itself, but
simply stores these arguments for later. The handshake is
performed only when this class needs to connect with the
server. Thus you should be prepared to handle TLS-specific
exceptions when calling methods inherited from
L{httplib.HTTPConnection} such as request(), connect(), and
send(). See the client handshake functions in
L{tlslite.TLSConnection.TLSConnection} for details on which
exceptions might be raised.
@type host: str
@param host: Server to connect to.
@type port: int
@param port: Port to connect to.
@type username: str
@param username: SRP or shared-key username. Requires the
'password' or 'sharedKey' argument.
@type password: str
@param password: SRP password for mutual authentication.
Requires the 'username' argument.
@type sharedKey: str
@param sharedKey: Shared key for mutual authentication.
Requires the 'username' argument.
@type certChain: L{tlslite.X509CertChain.X509CertChain} or
L{cryptoIDlib.CertChain.CertChain}
@param certChain: Certificate chain for client authentication.
Requires the 'privateKey' argument. Excludes the SRP or
shared-key related arguments.
@type privateKey: L{tlslite.utils.RSAKey.RSAKey}
@param privateKey: Private key for client authentication.
Requires the 'certChain' argument. Excludes the SRP or
shared-key related arguments.
@type cryptoID: str
@param cryptoID: cryptoID for server authentication. Mutually
exclusive with the 'x509...' arguments.
@type protocol: str
@param protocol: cryptoID protocol URI for server
authentication. Requires the 'cryptoID' argument.
@type x509Fingerprint: str
@param x509Fingerprint: Hex-encoded X.509 fingerprint for
server authentication. Mutually exclusive with the 'cryptoID'
and 'x509TrustList' arguments.
@type x509TrustList: list of L{tlslite.X509.X509}
@param x509TrustList: A list of trusted root certificates. The
other party must present a certificate chain which extends to
one of these root certificates. The cryptlib_py module must be
installed to use this parameter. Mutually exclusive with the
'cryptoID' and 'x509Fingerprint' arguments.
@type x509CommonName: str
@param x509CommonName: The end-entity certificate's 'CN' field
must match this value. For a web server, this is typically a
server name such as 'www.amazon.com'. Mutually exclusive with
the 'cryptoID' and 'x509Fingerprint' arguments. Requires the
'x509TrustList' argument.
@type settings: L{tlslite.HandshakeSettings.HandshakeSettings}
@param settings: Various settings which can be used to control
the ciphersuites, certificate types, and SSL/TLS versions
offered by the client.
"""
HTTPBaseTLSConnection.__init__(self, host, port)
ClientHelper.__init__(self,
username, password, sharedKey,
certChain, privateKey,
cryptoID, protocol,
x509Fingerprint,
x509TrustList, x509CommonName,
settings) | [
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natashamjaques/neural_chat | ddb977bb4602a67c460d02231e7bbf7b2cb49a97 | ParlAI/parlai/core/torch_agent.py | python | TorchAgent.match_batch | (self, batch_reply, valid_inds, output=None) | return batch_reply | Match sub-batch of predictions to the original batch indices.
Batches may be only partially filled (i.e when completing the remainder
at the end of the validation or test set), or we may want to sort by
e.g the length of the input sequences if using pack_padded_sequence.
This matches rows back with their original row in the batch for
calculating metrics like accuracy.
If output is None (model choosing not to provide any predictions), we
will just return the batch of replies.
Otherwise, output should be a parlai.core.torch_agent.Output object.
This is a namedtuple, which can provide text predictions and/or
text_candidates predictions. If you would like to map additional
fields into the batch_reply, you can override this method as well as
providing your own namedtuple with additional fields.
:param batch_reply:
Full-batchsize list of message dictionaries to put responses into.
:param valid_inds:
Original indices of the predictions.
:param output:
Output namedtuple which contains sub-batchsize list of text outputs
from model. May be None (default) if model chooses not to answer.
This method will check for ``text`` and ``text_candidates`` fields. | Match sub-batch of predictions to the original batch indices. | [
"Match",
"sub",
"-",
"batch",
"of",
"predictions",
"to",
"the",
"original",
"batch",
"indices",
"."
] | def match_batch(self, batch_reply, valid_inds, output=None):
"""
Match sub-batch of predictions to the original batch indices.
Batches may be only partially filled (i.e when completing the remainder
at the end of the validation or test set), or we may want to sort by
e.g the length of the input sequences if using pack_padded_sequence.
This matches rows back with their original row in the batch for
calculating metrics like accuracy.
If output is None (model choosing not to provide any predictions), we
will just return the batch of replies.
Otherwise, output should be a parlai.core.torch_agent.Output object.
This is a namedtuple, which can provide text predictions and/or
text_candidates predictions. If you would like to map additional
fields into the batch_reply, you can override this method as well as
providing your own namedtuple with additional fields.
:param batch_reply:
Full-batchsize list of message dictionaries to put responses into.
:param valid_inds:
Original indices of the predictions.
:param output:
Output namedtuple which contains sub-batchsize list of text outputs
from model. May be None (default) if model chooses not to answer.
This method will check for ``text`` and ``text_candidates`` fields.
"""
if output is None:
return batch_reply
if output.text is not None:
for i, response in zip(valid_inds, output.text):
batch_reply[i]['text'] = response
if output.text_candidates is not None:
for i, cands in zip(valid_inds, output.text_candidates):
batch_reply[i]['text_candidates'] = cands
return batch_reply | [
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tensorflow/tfx | b4a6b83269815ed12ba9df9e9154c7376fef2ea0 | tfx/dsl/compiler/compiler.py | python | _compile_resolver_node | (
resolver_node: base_node.BaseNode,
) | return result | Converts Resolver node to a corresponding ResolverSteps. | Converts Resolver node to a corresponding ResolverSteps. | [
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] | def _compile_resolver_node(
resolver_node: base_node.BaseNode,
) -> List[pipeline_pb2.ResolverConfig.ResolverStep]:
"""Converts Resolver node to a corresponding ResolverSteps."""
assert compiler_utils.is_resolver(resolver_node)
resolver_node = cast(resolver.Resolver, resolver_node)
result = _compile_resolver_function(resolver_node.resolver_function)
for step in result:
step.input_keys.extend(resolver_node.inputs.keys())
return result | [
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inspurer/WorkAttendanceSystem | 1221e2d67bdf5bb15fe99517cc3ded58ccb066df | V2.0/venv/Lib/site-packages/pip-9.0.1-py3.5.egg/pip/_vendor/requests/utils.py | python | requote_uri | (uri) | Re-quote the given URI.
This function passes the given URI through an unquote/quote cycle to
ensure that it is fully and consistently quoted.
:rtype: str | Re-quote the given URI. | [
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] | def requote_uri(uri):
"""Re-quote the given URI.
This function passes the given URI through an unquote/quote cycle to
ensure that it is fully and consistently quoted.
:rtype: str
"""
safe_with_percent = "!#$%&'()*+,/:;=?@[]~"
safe_without_percent = "!#$&'()*+,/:;=?@[]~"
try:
# Unquote only the unreserved characters
# Then quote only illegal characters (do not quote reserved,
# unreserved, or '%')
return quote(unquote_unreserved(uri), safe=safe_with_percent)
except InvalidURL:
# We couldn't unquote the given URI, so let's try quoting it, but
# there may be unquoted '%'s in the URI. We need to make sure they're
# properly quoted so they do not cause issues elsewhere.
return quote(uri, safe=safe_without_percent) | [
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||
twilio/stashboard | 3e4b18a8168c102d1e1d7f88fec22bcbfc530d23 | stashboard/contrib/httplib2/__init__.py | python | Http.__init__ | (self, cache=None, timeout=None, proxy_info=None) | The value of proxy_info is a ProxyInfo instance.
If 'cache' is a string then it is used as a directory name for
a disk cache. Otherwise it must be an object that supports the
same interface as FileCache.
All timeouts are in seconds. If None is passed for timeout
then Python's default timeout for sockets will be used. See
for example the docs of socket.setdefaulttimeout():
http://docs.python.org/library/socket.html#socket.setdefaulttimeout | The value of proxy_info is a ProxyInfo instance. | [
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The value of proxy_info is a ProxyInfo instance.
If 'cache' is a string then it is used as a directory name for
a disk cache. Otherwise it must be an object that supports the
same interface as FileCache.
All timeouts are in seconds. If None is passed for timeout
then Python's default timeout for sockets will be used. See
for example the docs of socket.setdefaulttimeout():
http://docs.python.org/library/socket.html#socket.setdefaulttimeout
"""
self.proxy_info = proxy_info
# Map domain name to an httplib connection
self.connections = {}
# The location of the cache, for now a directory
# where cached responses are held.
if cache and isinstance(cache, str):
self.cache = FileCache(cache)
else:
self.cache = cache
# Name/password
self.credentials = Credentials()
# Key/cert
self.certificates = KeyCerts()
# authorization objects
self.authorizations = []
# If set to False then no redirects are followed, even safe ones.
self.follow_redirects = True
# Which HTTP methods do we apply optimistic concurrency to, i.e.
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self.optimistic_concurrency_methods = ["PUT"]
# If 'follow_redirects' is True, and this is set to True then
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self.follow_all_redirects = False
self.ignore_etag = False
self.force_exception_to_status_code = False
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||
samuelclay/NewsBlur | 2c45209df01a1566ea105e04d499367f32ac9ad2 | vendor/mms-agent/confPull.py | python | ConfPullThread._pullRemoteConf | ( self ) | Pull the remote configuration data | Pull the remote configuration data | [
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] | def _pullRemoteConf( self ):
""" Pull the remote configuration data """
uniqueHostnames = []
res = None
try:
res = urllib.request.urlopen( self.confUrl )
resBson = None
try:
resBson = bson.decode_all( res.read() )
finally:
if res is not None:
res.close()
res = None
if len(resBson) != 1:
return
confResponse = resBson[0]
if 'hosts' not in confResponse:
self.mmsAgent.stopAll()
return
if 'disableDbstats' in confResponse:
self.mmsAgent.disableDbstats = confResponse['disableDbstats']
else:
self.mmsAgent.disableDbstats = False
hosts = confResponse['hosts']
self.mmsAgent.serverHostDefsLock.acquire()
try:
# Extract the host information
if hosts is not None:
for host in hosts:
hostDef, hostDefLast = self.mmsAgent.extractHostDef( host )
hostKey = hostDef['hostKey']
uniqueHostnames.append( hostKey )
if hostKey not in self.mmsAgent.serverHostDefs:
self.mmsAgent.startMonitoringThreads( hostDef )
else:
self.mmsAgent.checkChangedHostDef( hostDef, hostDefLast )
hostDef = None
hostDefLast = None
# Check to see if anything was removed
for hostDef in list(self.mmsAgent.serverHostDefs.values()):
if hostDef['hostKey'] not in uniqueHostnames:
self.mmsAgent.stopAndClearHost( hostDef['hostKey'] )
finally:
self.mmsAgent.serverHostDefsLock.release()
except Exception as e:
if res is not None:
try:
res.close()
res = None
except:
pass
self.logger.warning( "Problem pulling configuration data from MMS (check firewall and network): " + traceback.format_exc( e ) ) | [
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||
whoosh-community/whoosh | 5421f1ab3bb802114105b3181b7ce4f44ad7d0bb | src/whoosh/writing.py | python | IndexWriter.cancel | (self) | Cancels any documents/deletions added by this object
and unlocks the index. | Cancels any documents/deletions added by this object
and unlocks the index. | [
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and unlocks the index.
"""
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||
zwczou/weixin-python | 4d0964b1fbaad270abb2a64037b173894e66019d | weixin/pay.py | python | WeixinPay.close_order | (self, out_trade_no, **data) | return self._fetch(url, data) | 关闭订单
out_trade_no必填
appid, mchid, nonce_str不需要填入 | 关闭订单
out_trade_no必填
appid, mchid, nonce_str不需要填入 | [
"关闭订单",
"out_trade_no必填",
"appid",
"mchid",
"nonce_str不需要填入"
] | def close_order(self, out_trade_no, **data):
"""
关闭订单
out_trade_no必填
appid, mchid, nonce_str不需要填入
"""
url = self.PAY_HOST + '/pay/closeorder'
data.setdefault("out_trade_no", out_trade_no)
return self._fetch(url, data) | [
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|
aws-samples/aws-kube-codesuite | ab4e5ce45416b83bffb947ab8d234df5437f4fca | src/rsa/transform.py | python | bytes2int | (raw_bytes) | return int(binascii.hexlify(raw_bytes), 16) | r"""Converts a list of bytes or an 8-bit string to an integer.
When using unicode strings, encode it to some encoding like UTF8 first.
>>> (((128 * 256) + 64) * 256) + 15
8405007
>>> bytes2int(b'\x80@\x0f')
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When using unicode strings, encode it to some encoding like UTF8 first.
>>> (((128 * 256) + 64) * 256) + 15
8405007
>>> bytes2int(b'\x80@\x0f')
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/lib-tk/Tkinter.py | python | Misc._options | (self, cnf, kw = None) | return res | Internal function. | Internal function. | [
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nv = []
for item in v:
if not isinstance(item, (basestring, int)):
break
elif isinstance(item, int):
nv.append('%d' % item)
else:
# format it to proper Tcl code if it contains space
nv.append(('{%s}' if ' ' in item else '%s') % item)
else:
v = ' '.join(nv)
res = res + ('-'+k, v)
return res | [
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|
cclib/cclib | 81cd4a81cc4a3bbed7016b3e417ca9bff8ad3a92 | cclib/method/density.py | python | Density.__str__ | (self) | return "Density matrix of %s" % (self.data) | Return a string representation of the object. | Return a string representation of the object. | [
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"""Return a string representation of the object."""
return "Density matrix of %s" % (self.data) | [
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|
SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/dev/bdf_vectorized/bdf.py | python | BDF._parse_darea | (self, card_name, cards) | adds dareas | adds dareas | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/speaklater.py | python | _LazyString.__copy__ | (self) | return self | [] | def __copy__(self):
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|||
ray-project/ray | 703c1610348615dcb8c2d141a0c46675084660f5 | python/ray/tune/automl/search_space.py | python | ContinuousSpace.__init__ | (self, name, start, end, num, distribution=LINEAR) | Initialize ContinuousSpace.
Arguments:
name (str): Name of the parameter.
start: Start of the continuous space included.
end: End of the continuous space included.
num: Sampling count if possible.
distribution: Sampling distribution, should be in [LINEAR] | Initialize ContinuousSpace. | [
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Arguments:
name (str): Name of the parameter.
start: Start of the continuous space included.
end: End of the continuous space included.
num: Sampling count if possible.
distribution: Sampling distribution, should be in [LINEAR]
"""
super(ContinuousSpace, self).__init__(name)
self.start = float(start)
self.end = float(end)
self.num = num
if distribution == ContinuousSpace.LINEAR:
self.choices = np.linspace(start, end, num)
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raise NotImplementedError(
"Distribution %s not supported" % distribution)
self.distribution = distribution | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/databases/findstat.py | python | FindStatCollection.from_string | (self) | return self._data["Code"].string_to_element | r"""
Return a function that returns the object given a FindStat
representation.
OUTPUT:
The function that produces the sage object given its FindStat
representation as a string.
EXAMPLES::
sage: from sage.databases.findstat import FindStatCollection
sage: c = FindStatCollection("Posets") # optional -- internet
sage: p = c.from_string()('([(0, 2), (2, 1)], 3)') # optional -- internet
sage: p.cover_relations() # optional -- internet
[[0, 2], [2, 1]]
sage: c = FindStatCollection("Binary Words") # optional -- internet
sage: w = c.from_string()('010101') # optional -- internet
sage: w in c._data["Code"].elements_on_level(6) # optional -- internet
True | r"""
Return a function that returns the object given a FindStat
representation. | [
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r"""
Return a function that returns the object given a FindStat
representation.
OUTPUT:
The function that produces the sage object given its FindStat
representation as a string.
EXAMPLES::
sage: from sage.databases.findstat import FindStatCollection
sage: c = FindStatCollection("Posets") # optional -- internet
sage: p = c.from_string()('([(0, 2), (2, 1)], 3)') # optional -- internet
sage: p.cover_relations() # optional -- internet
[[0, 2], [2, 1]]
sage: c = FindStatCollection("Binary Words") # optional -- internet
sage: w = c.from_string()('010101') # optional -- internet
sage: w in c._data["Code"].elements_on_level(6) # optional -- internet
True
"""
return self._data["Code"].string_to_element | [
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|
zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/redis/connection.py | python | Connection._connect | (self) | Create a TCP socket connection | Create a TCP socket connection | [
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] | def _connect(self):
"Create a TCP socket connection"
# we want to mimic what socket.create_connection does to support
# ipv4/ipv6, but we want to set options prior to calling
# socket.connect()
err = None
for res in socket.getaddrinfo(self.host, self.port, 0,
socket.SOCK_STREAM):
family, socktype, proto, canonname, socket_address = res
sock = None
try:
sock = socket.socket(family, socktype, proto)
# TCP_NODELAY
sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
# TCP_KEEPALIVE
if self.socket_keepalive:
sock.setsockopt(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1)
for k, v in iteritems(self.socket_keepalive_options):
sock.setsockopt(socket.SOL_TCP, k, v)
# set the socket_connect_timeout before we connect
sock.settimeout(self.socket_connect_timeout)
# connect
sock.connect(socket_address)
# set the socket_timeout now that we're connected
sock.settimeout(self.socket_timeout)
return sock
except socket.error as _:
err = _
if sock is not None:
sock.close()
if err is not None:
raise err
raise socket.error("socket.getaddrinfo returned an empty list") | [
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||
OptMLGroup/VRP-RL | b794fb1e4c4bb70a62cfa54504ee7a247adbc2a0 | VRP/vrp_utils.py | python | DataGenerator.__init__ | (self,
args) | This class generates VRP problems for training and test
Inputs:
args: the parameter dictionary. It should include:
args['random_seed']: random seed
args['test_size']: number of problems to test
args['n_nodes']: number of nodes
args['n_cust']: number of customers
args['batch_size']: batchsize for training | This class generates VRP problems for training and test
Inputs:
args: the parameter dictionary. It should include:
args['random_seed']: random seed
args['test_size']: number of problems to test
args['n_nodes']: number of nodes
args['n_cust']: number of customers
args['batch_size']: batchsize for training | [
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args):
'''
This class generates VRP problems for training and test
Inputs:
args: the parameter dictionary. It should include:
args['random_seed']: random seed
args['test_size']: number of problems to test
args['n_nodes']: number of nodes
args['n_cust']: number of customers
args['batch_size']: batchsize for training
'''
self.args = args
self.rnd = np.random.RandomState(seed= args['random_seed'])
print('Created train iterator.')
# create test data
self.n_problems = args['test_size']
self.test_data = create_VRP_dataset(self.n_problems,args['n_cust'],'./data',
seed = args['random_seed']+1,data_type='test')
self.reset() | [
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||
openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_openshift/library/oc_serviceaccount_secret.py | python | Yedit.parse_value | (inc_value, vtype='') | return inc_value | determine value type passed | determine value type passed | [
"determine",
"value",
"type",
"passed"
] | def parse_value(inc_value, vtype=''):
'''determine value type passed'''
true_bools = ['y', 'Y', 'yes', 'Yes', 'YES', 'true', 'True', 'TRUE',
'on', 'On', 'ON', ]
false_bools = ['n', 'N', 'no', 'No', 'NO', 'false', 'False', 'FALSE',
'off', 'Off', 'OFF']
# It came in as a string but you didn't specify value_type as string
# we will convert to bool if it matches any of the above cases
if isinstance(inc_value, str) and 'bool' in vtype:
if inc_value not in true_bools and inc_value not in false_bools:
raise YeditException('Not a boolean type. str=[{}] vtype=[{}]'.format(inc_value, vtype))
elif isinstance(inc_value, bool) and 'str' in vtype:
inc_value = str(inc_value)
# There is a special case where '' will turn into None after yaml loading it so skip
if isinstance(inc_value, str) and inc_value == '':
pass
# If vtype is not str then go ahead and attempt to yaml load it.
elif isinstance(inc_value, str) and 'str' not in vtype:
try:
inc_value = yaml.safe_load(inc_value)
except Exception:
raise YeditException('Could not determine type of incoming value. ' +
'value=[{}] vtype=[{}]'.format(type(inc_value), vtype))
return inc_value | [
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|
openstack/designate | bff3d5f6e31fe595a77143ec4ac779c187bf72a8 | designate/api/admin/views/base.py | python | BaseView.list_basic | (self, context, request, items) | return [self.show_basic(context, request, i) for i in items] | Non-detailed list of items | Non-detailed list of items | [
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] | def list_basic(self, context, request, items):
"""Non-detailed list of items"""
return [self.show_basic(context, request, i) for i in items] | [
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|
AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/google/appengine/api/datastore_distributed.py | python | DatastoreDistributed._maybeSetDefaultAuthDomain | (self) | Sets default auth domain if not set. | Sets default auth domain if not set. | [
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] | def _maybeSetDefaultAuthDomain(self):
""" Sets default auth domain if not set. """
auth_domain = os.environ.get("AUTH_DOMAIN")
if not auth_domain:
os.environ['AUTH_DOMAIN'] = "appscale.com" | [
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||
openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/docutils/docutils/parsers/rst/states.py | python | SpecializedBody.invalid_input | (self, match=None, context=None, next_state=None) | Not a compound element member. Abort this state machine. | Not a compound element member. Abort this state machine. | [
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"""Not a compound element member. Abort this state machine."""
self.state_machine.previous_line() # back up so parent SM can reassess
raise EOFError | [
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||
quantumlib/OpenFermion | 6187085f2a7707012b68370b625acaeed547e62b | src/openfermion/hamiltonians/special_operators.py | python | s_squared_operator | (n_spatial_orbitals: int) | return operator | r"""Return the s^{2} operator.
$$
\begin{align}
S^{2} = S^{-} S^{+} + S^{z}( S^{z} + 1)
\end{align}
$$
Args:
n_spatial_orbitals: number of spatial orbitals (n_qubits + 1 // 2).
Returns:
operator (FermionOperator): corresponding to the s+ operator over
n_spatial_orbitals.
Note:
The indexing convention used is that even indices correspond to
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modes. | r"""Return the s^{2} operator. | [
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] | def s_squared_operator(n_spatial_orbitals: int) -> FermionOperator:
r"""Return the s^{2} operator.
$$
\begin{align}
S^{2} = S^{-} S^{+} + S^{z}( S^{z} + 1)
\end{align}
$$
Args:
n_spatial_orbitals: number of spatial orbitals (n_qubits + 1 // 2).
Returns:
operator (FermionOperator): corresponding to the s+ operator over
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Note:
The indexing convention used is that even indices correspond to
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modes.
"""
if not isinstance(n_spatial_orbitals, int):
raise TypeError("n_orbitals must be specified as an integer")
fermion_identity = FermionOperator(())
operator = (s_minus_operator(n_spatial_orbitals) *
s_plus_operator(n_spatial_orbitals))
operator += (sz_operator(n_spatial_orbitals) *
(sz_operator(n_spatial_orbitals) + fermion_identity))
return operator | [
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|
prompt-toolkit/pymux | 3f66e62b9de4b2251c7f9afad6c516dc5a30ec67 | pymux/layout.py | python | _draw_number | (screen, x_offset, y_offset, number, style='class:clock',
transparent=False) | Write number at position. | Write number at position. | [
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fg = Char(' ', 'class:clock')
bg = Char(' ', '')
for y, row in enumerate(_numbers[number]):
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||
tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-darwin/x64/cryptography/hazmat/bindings/openssl/_conditional.py | python | cryptography_has_ec2m | () | return [
"EC_POINT_set_affine_coordinates_GF2m",
"EC_POINT_get_affine_coordinates_GF2m",
"EC_POINT_set_compressed_coordinates_GF2m",
] | [] | def cryptography_has_ec2m():
return [
"EC_POINT_set_affine_coordinates_GF2m",
"EC_POINT_get_affine_coordinates_GF2m",
"EC_POINT_set_compressed_coordinates_GF2m",
] | [
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|||
JacquesLucke/animation_nodes | b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1 | animation_nodes/nodes/mesh/mesh_object_input.py | python | MeshObjectInputNode.getVertexLocations | (self, mesh, object, useWorldSpace) | return vertices | [] | def getVertexLocations(self, mesh, object, useWorldSpace):
vertices = mesh.an.getVertices()
if useWorldSpace:
vertices.transform(object.matrix_world)
return vertices | [
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|||
timonwong/OmniMarkupPreviewer | 21921ac7a99d2b5924a2219b33679a5b53621392 | OmniMarkupLib/Renderers/libs/python2/docutils/parsers/rst/states.py | python | build_regexp | (definition, compile=True) | Build, compile and return a regular expression based on `definition`.
:Parameter: `definition`: a 4-tuple (group name, prefix, suffix, parts),
where "parts" is a list of regular expressions and/or regular
expression definitions to be joined into an or-group. | Build, compile and return a regular expression based on `definition`. | [
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] | def build_regexp(definition, compile=True):
"""
Build, compile and return a regular expression based on `definition`.
:Parameter: `definition`: a 4-tuple (group name, prefix, suffix, parts),
where "parts" is a list of regular expressions and/or regular
expression definitions to be joined into an or-group.
"""
name, prefix, suffix, parts = definition
part_strings = []
for part in parts:
if type(part) is tuple:
part_strings.append(build_regexp(part, None))
else:
part_strings.append(part)
or_group = '|'.join(part_strings)
regexp = '%(prefix)s(?P<%(name)s>%(or_group)s)%(suffix)s' % locals()
if compile:
return re.compile(regexp, re.UNICODE)
else:
return regexp | [
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||
scrapy/scrapy | b04cfa48328d5d5749dca6f50fa34e0cfc664c89 | scrapy/linkextractors/lxmlhtml.py | python | LxmlParserLinkExtractor._extract_links | (self, selector, response_url, response_encoding, base_url) | return self._deduplicate_if_needed(links) | [] | def _extract_links(self, selector, response_url, response_encoding, base_url):
links = []
# hacky way to get the underlying lxml parsed document
for el, attr, attr_val in self._iter_links(selector.root):
# pseudo lxml.html.HtmlElement.make_links_absolute(base_url)
try:
if self.strip:
attr_val = strip_html5_whitespace(attr_val)
attr_val = urljoin(base_url, attr_val)
except ValueError:
continue # skipping bogus links
else:
url = self.process_attr(attr_val)
if url is None:
continue
url = safe_url_string(url, encoding=response_encoding)
# to fix relative links after process_value
url = urljoin(response_url, url)
link = Link(url, _collect_string_content(el) or '',
nofollow=rel_has_nofollow(el.get('rel')))
links.append(link)
return self._deduplicate_if_needed(links) | [
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|||
sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | modules/s3db/project.py | python | ProjectPlanningModel.project_indicator_activity_create_onaccept | (self, form) | Default all weightings to an even spread | Default all weightings to an even spread | [
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"even",
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] | def project_indicator_activity_create_onaccept(self, form):
"""
Default all weightings to an even spread
"""
db = current.db
record_id = form.vars.id
# Find the indicator_id
table = current.s3db.project_indicator_activity
record = db(table.id == record_id).select(table.indicator_id,
limitby = (0, 1),
).first()
try:
indicator_id = record.indicator_id
except AttributeError:
current.log.error("Cannot find Project Indicator Activity record (no record for this ID), so cannot setup default weightings")
return
# Read the records
query = (table.indicator_id == indicator_id) & \
(table.deleted == False)
records = db(query).select(table.id)
weighting = 1.0 / len(records)
for r in records:
# Set the weighting
r.update_record(weighting = weighting)
# Fire normal onaccept
self.project_indicator_activity_onaccept(form, create=True) | [
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||
linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/random.py | python | Random.choice | (self, seq) | return seq[i] | Choose a random element from a non-empty sequence. | Choose a random element from a non-empty sequence. | [
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] | def choice(self, seq):
"""Choose a random element from a non-empty sequence."""
try:
i = self._randbelow(len(seq))
except ValueError:
raise IndexError('Cannot choose from an empty sequence') from None
return seq[i] | [
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|
jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/scipy/interpolate/fitpack.py | python | splev | (x, tck, der=0, ext=0) | Evaluate a B-spline or its derivatives.
Given the knots and coefficients of a B-spline representation, evaluate
the value of the smoothing polynomial and its derivatives. This is a
wrapper around the FORTRAN routines splev and splder of FITPACK.
Parameters
----------
x : array_like
An array of points at which to return the value of the smoothed
spline or its derivatives. If `tck` was returned from `splprep`,
then the parameter values, u should be given.
tck : 3-tuple or a BSpline object
If a tuple, then it should be a sequence of length 3 returned by
`splrep` or `splprep` containing the knots, coefficients, and degree
of the spline. (Also see Notes.)
der : int, optional
The order of derivative of the spline to compute (must be less than
or equal to k, the degree of the spline).
ext : int, optional
Controls the value returned for elements of ``x`` not in the
interval defined by the knot sequence.
* if ext=0, return the extrapolated value.
* if ext=1, return 0
* if ext=2, raise a ValueError
* if ext=3, return the boundary value.
The default value is 0.
Returns
-------
y : ndarray or list of ndarrays
An array of values representing the spline function evaluated at
the points in `x`. If `tck` was returned from `splprep`, then this
is a list of arrays representing the curve in N-dimensional space.
Notes
-----
Manipulating the tck-tuples directly is not recommended. In new code,
prefer using `BSpline` objects.
See Also
--------
splprep, splrep, sproot, spalde, splint
bisplrep, bisplev
BSpline
References
----------
.. [1] C. de Boor, "On calculating with b-splines", J. Approximation
Theory, 6, p.50-62, 1972.
.. [2] M. G. Cox, "The numerical evaluation of b-splines", J. Inst. Maths
Applics, 10, p.134-149, 1972.
.. [3] P. Dierckx, "Curve and surface fitting with splines", Monographs
on Numerical Analysis, Oxford University Press, 1993. | Evaluate a B-spline or its derivatives. | [
"Evaluate",
"a",
"B",
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"spline",
"or",
"its",
"derivatives",
"."
] | def splev(x, tck, der=0, ext=0):
"""
Evaluate a B-spline or its derivatives.
Given the knots and coefficients of a B-spline representation, evaluate
the value of the smoothing polynomial and its derivatives. This is a
wrapper around the FORTRAN routines splev and splder of FITPACK.
Parameters
----------
x : array_like
An array of points at which to return the value of the smoothed
spline or its derivatives. If `tck` was returned from `splprep`,
then the parameter values, u should be given.
tck : 3-tuple or a BSpline object
If a tuple, then it should be a sequence of length 3 returned by
`splrep` or `splprep` containing the knots, coefficients, and degree
of the spline. (Also see Notes.)
der : int, optional
The order of derivative of the spline to compute (must be less than
or equal to k, the degree of the spline).
ext : int, optional
Controls the value returned for elements of ``x`` not in the
interval defined by the knot sequence.
* if ext=0, return the extrapolated value.
* if ext=1, return 0
* if ext=2, raise a ValueError
* if ext=3, return the boundary value.
The default value is 0.
Returns
-------
y : ndarray or list of ndarrays
An array of values representing the spline function evaluated at
the points in `x`. If `tck` was returned from `splprep`, then this
is a list of arrays representing the curve in N-dimensional space.
Notes
-----
Manipulating the tck-tuples directly is not recommended. In new code,
prefer using `BSpline` objects.
See Also
--------
splprep, splrep, sproot, spalde, splint
bisplrep, bisplev
BSpline
References
----------
.. [1] C. de Boor, "On calculating with b-splines", J. Approximation
Theory, 6, p.50-62, 1972.
.. [2] M. G. Cox, "The numerical evaluation of b-splines", J. Inst. Maths
Applics, 10, p.134-149, 1972.
.. [3] P. Dierckx, "Curve and surface fitting with splines", Monographs
on Numerical Analysis, Oxford University Press, 1993.
"""
if isinstance(tck, BSpline):
if tck.c.ndim > 1:
mesg = ("Calling splev() with BSpline objects with c.ndim > 1 is "
"not recommended. Use BSpline.__call__(x) instead.")
warnings.warn(mesg, DeprecationWarning)
# remap the out-of-bounds behavior
try:
extrapolate = {0: True, }[ext]
except KeyError:
raise ValueError("Extrapolation mode %s is not supported "
"by BSpline." % ext)
return tck(x, der, extrapolate=extrapolate)
else:
return _impl.splev(x, tck, der, ext) | [
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||
xmengli/H-DenseUNet | 06cc436a43196310fe933d114a353839907cc176 | Keras-2.0.8/keras/backend/tensorflow_backend.py | python | argmax | (x, axis=-1) | return tf.argmax(x, axis) | Returns the index of the maximum value along an axis.
# Arguments
x: Tensor or variable.
axis: axis along which to perform the reduction.
# Returns
A tensor. | Returns the index of the maximum value along an axis. | [
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] | def argmax(x, axis=-1):
"""Returns the index of the maximum value along an axis.
# Arguments
x: Tensor or variable.
axis: axis along which to perform the reduction.
# Returns
A tensor.
"""
return tf.argmax(x, axis) | [
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