Datasets:

sia-precision-education

/

pile_python

like 3

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)

https://github.com/keiffster/program-y/blob/8c99b56f8c32f01a7b9887b5daae9465619d0385/src/utils/botflow/src/botflow.py#L181-L186

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 get_config_vars().get(name)

def get_config_var(name): """Return the value of a single variable using the dictionary returned by 'get_config_vars()'. Equivalent to get_config_vars().get(name) """ return get_config_vars().get(name)

https://github.com/exodrifter/unity-python/blob/bef6e4e9ddfbbf1eaf7acbbb973e9aa3dd64a20d/Lib/distutils/sysconfig.py#L478-L483

Komodo/KomodoEdit

61edab75dce2bdb03943b387b0608ea36f548e8e

src/lint/koLintService.py

python

RequestQueue.remove_uid

(self, uid)

Remove all current requests with the given uid. Does not return anything.

Remove all current requests with the given uid.

def remove_uid(self, uid): """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 try: 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") self.mutex.release()

https://github.com/Komodo/KomodoEdit/blob/61edab75dce2bdb03943b387b0608ea36f548e8e/src/lint/koLintService.py#L130-L155

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.

def 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. """ 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)

https://github.com/bruderstein/PythonScript/blob/df9f7071ddf3a079e3a301b9b53a6dc78cf1208f/PythonLib/min/bisect.py#L4-L16

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.

def console_command(self, *command_str: str) -> None: """ Prompt the user to edit a command with a (possibly empty) starting value. """ quoted = " ".join(command_lexer.quote(x) for x in command_str) if quoted: quoted += " " signals.status_prompt_command.send(partial=quoted)

https://github.com/mitmproxy/mitmproxy/blob/1abb8f69217910c8623bd1339da2502aed98ff0d/mitmproxy/tools/console/consoleaddons.py#L262-L269

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.

def isRelevant(cls): """Check whether plugin might be required. Returns: True if this is a standalone compilation. """ return isStandaloneMode()

https://github.com/Nuitka/Nuitka/blob/39262276993757fa4e299f497654065600453fc9/nuitka/plugins/standard/MatplotlibPlugin.py#L53-L59

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.

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 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. """ 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) else: raise ValueError("Harmonic mean only defined if all elements greater than zero")

https://github.com/chribsen/simple-machine-learning-examples/blob/dc94e52a4cebdc8bb959ff88b81ff8cfeca25022/venv/lib/python2.7/site-packages/scipy/stats/stats.py#L318-L372

freqtrade/freqtrade

13651fd3be8d5ce8dcd7c94b920bda4e00b75aca

scripts/rest_client.py

python

FtRestClient.locks

(self)

return self._get("locks")

Return current locks :return: json object

Return current locks

def locks(self): """Return current locks :return: json object """ return self._get("locks")

https://github.com/freqtrade/freqtrade/blob/13651fd3be8d5ce8dcd7c94b920bda4e00b75aca/scripts/rest_client.py#L114-L119

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.

def props(self): """Return a copy of (1) the resource properties (from the template), combined with (2) the current deployment state properties of the resource.""" result = dict(self.properties) result.update(self.state or {}) return result

https://github.com/localstack/localstack/blob/ec8b72d5c926ae8495ca50ce168494247aef54be/localstack/services/cloudformation/service_models.py#L154-L159

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.

def backward(self): """ Compute the gradient of the loss wrt the current network parameters. """ dX_emb = self.loss.grad(retain_grads=True, update_params=False) self.embeddings.backward(dX_emb)

https://github.com/ddbourgin/numpy-ml/blob/b0359af5285fbf9699d64fd5ec059493228af03e/numpy_ml/neural_nets/models/w2v.py#L235-L240

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.

def is_last_step(self): """Check if last step.""" return self.index1 == self.count

https://github.com/barseghyanartur/django-fobi/blob/a998feae007d7fe3637429a80e42952ec7cda79f/src/fobi/wizard/views/dynamic.py#L123-L125

timkpaine/paperboy

6c0854b2c0dad139c25153e520ca79ffed820fa4

paperboy/resources/autocomplete.py

python

AutocompleteResource.__init__

(self, *args, **kwargs)

def __init__(self, *args, **kwargs): super(AutocompleteResource, self).__init__(*args, **kwargs)

https://github.com/timkpaine/paperboy/blob/6c0854b2c0dad139c25153e520ca79ffed820fa4/paperboy/resources/autocomplete.py#L8-L9

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.

def __call__(self, policy: str): """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

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.

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

https://github.com/dropbox/changes/blob/37e23c3141b75e4785cf398d015e3dbca41bdd56/changes/backends/jenkins/builder.py#L344-L383

nopernik/mpDNS

b17dc39e7068406df82cb3431b3042e74e520cf9

dnslib/fixedresolver.py

python

FixedResolver.__init__

(self,zone)

def __init__(self,zone): # Parse RRs self.rrs = RR.fromZone(zone)

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.

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) )

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.

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

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.

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

https://github.com/schematics/schematics/blob/3a144be0aa50f68a4da917e8d957b924dedf9a52/schematics/types/base.py#L1132-L1175

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.

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

https://github.com/IronLanguages/ironpython2/blob/51fdedeeda15727717fb8268a805f71b06c0b9f1/Src/StdLib/Lib/site-packages/win32/Demos/security/security_enums.py#L27-L35

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.

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)

https://github.com/complexdb/zincbase/blob/0c8ce46bc392dfa8ee99414877adb3b41648451e/zincbase/web/__init__.py#L81-L153

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.

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))

https://github.com/toxygen-project/toxygen/blob/0a54012cf5ee72434b923bcde7d8f1a4e575ce2f/toxygen/tox.py#L987-L1002

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.

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

https://github.com/EasyIME/PIME/blob/0f1eee10169c1cb2eaa0b59a77fa6f931ecb33b3/python/python3/tornado/websocket.py#L489-L543

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.

def setField(self,name,typ=None,label=None): """ 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.""" f=self.getField(name) if f: return f return self.addChild(node=DataField(name,None,typ,0,label))

https://github.com/kuri65536/python-for-android/blob/26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891/python-build/python-libs/xmpppy/xmpp/protocol.py#L695-L700

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

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

https://github.com/cgre-aachen/gempy/blob/6ad16c46fc6616c9f452fba85d31ce32decd8b10/gempy/core/theano_modules/theano_graph_pro.py#L1244-L1325

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 :class:`HTTPAdapter <requests.adapters.HTTPAdapter>`.

def 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. """ if url.lower().startswith('https') and verify: cert_loc = None # Allow self-specified cert location. if verify is not True: cert_loc = verify if not cert_loc: cert_loc = extract_zipped_paths(DEFAULT_CA_BUNDLE_PATH) if not cert_loc or not os.path.exists(cert_loc): raise IOError("Could not find a suitable TLS CA certificate bundle, " "invalid path: {}".format(cert_loc)) conn.cert_reqs = 'CERT_REQUIRED' if not os.path.isdir(cert_loc): conn.ca_certs = cert_loc else: conn.ca_cert_dir = cert_loc else: conn.cert_reqs = 'CERT_NONE' conn.ca_certs = None conn.ca_cert_dir = None if cert: if not isinstance(cert, basestring): conn.cert_file = cert[0] conn.key_file = cert[1] else: conn.cert_file = cert conn.key_file = None if conn.cert_file and not os.path.exists(conn.cert_file): raise IOError("Could not find the TLS certificate file, " "invalid path: {}".format(conn.cert_file)) if conn.key_file and not os.path.exists(conn.key_file): raise IOError("Could not find the TLS key file, " "invalid path: {}".format(conn.key_file))

https://github.com/pantsbuild/pex/blob/473c6ac732ed4bc338b4b20a9ec930d1d722c9b4/pex/vendor/_vendored/pip/pip/_vendor/requests/adapters.py#L203-L253

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.

def records(self): """ Return an iterator of :class:`~Record` instances. """ # Ignore the "DeletionFlag" field which always comes first fields = self._reader.fields[1:] for shape_record in self._reader.iterShapeRecords(): attributes = shape_record.record.as_dict() yield Record(shape_record.shape, attributes, fields)

https://github.com/SciTools/cartopy/blob/591fb5450e11b42b6de1cebe4f240112f915bd52/lib/cartopy/io/shapereader.py#L157-L166

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 self.ui.path_lineedit.setValidator(QRegExpValidator(QRegExp("m(/[0-9]+['Hh]?)+"), None)) self.ui.path_lineedit.setFocus() self.ui.go_button.clicked.connect(self.go_button_clicked) self.ui.buttonBox.clicked.connect(self.accept)

https://github.com/bitcoin-core/HWI/blob/6871946c2176f2f9777b6ac8f0614d96d99bfa0e/hwilib/_gui.py#L158-L169

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.

def get_cookie_header(jar, request): """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')

https://github.com/fooying/3102/blob/0faee38c30b2e24154f41e68457cfd8f7a61c040/thirdparty/requests/cookies.py#L131-L135

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.

def user_log_list(self, userid, cur_p=''): ''' 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, } if self.is_p: self.render('admin/log_ajax/user_log_list.html', kwd=kwd, infos=MLog.query_pager_by_user( userid, current_page_num=current_page_number), format_date=tools.format_date, userinfo=self.userinfo) else: self.render('misc/log/user_log_list.html', kwd=kwd, infos=MLog.query_pager_by_user( userid, current_page_num=current_page_number), format_date=tools.format_date, userinfo=self.userinfo)

https://github.com/bukun/TorCMS/blob/f7b44e8650aa54774f6b57e7b178edebbbf57e8e/torcms/handlers/log_handler.py#L112-L145

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.

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

https://github.com/MobSF/Mobile-Security-Framework-MobSF/blob/33abc69b54689fb535c72c720b593dc7ed21a4cf/mobsf/StaticAnalyzer/views/android/network_security.py#L10-L34

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.

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

https://github.com/neuropsychology/NeuroKit/blob/d01111b9b82364d28da01c002e6cbfc45d9493d9/neurokit2/signal/signal_recompose.py#L165-L173

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

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.

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!")

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]

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)

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

def reset_active_choice(self, active_choice='weighted'): """ 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

https://github.com/david-cortes/contextualbandits/blob/293b47ce80d8330a238cca0614147256ffea5529/contextualbandits/online.py#L1675-L1696

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') )

Determine if given path appears to be an unpacked egg.

Determine if given path appears to be an unpacked egg.

def _is_unpacked_egg(path): """ Determine if given path appears to be an unpacked egg. """ return ( path.lower().endswith('.egg') )

https://github.com/realpython/book2-exercises/blob/cde325eac8e6d8cff2316601c2e5b36bb46af7d0/web2py/venv/lib/python2.7/site-packages/pip/_vendor/pkg_resources/__init__.py#L2210-L2216

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): return self.validation_end(outputs)

https://github.com/huggingface/transformers/blob/623b4f7c63f60cce917677ee704d6c93ee960b4b/examples/research_projects/rag/lightning_base.py#L161-L162

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 """ return (writeUniversalTag(Tag.BER_TAG_OCTET_STRING, False), writeLength(len(value)), String(value))

https://github.com/citronneur/rdpy/blob/cef16a9f64d836a3221a344ca7d571644280d829/rdpy/protocol/rdp/t125/ber.py#L232-L238

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) return

https://github.com/phantomcyber/playbooks/blob/9e850ecc44cb98c5dde53784744213a1ed5799bd/risk_notable_investigate.py#L80-L96

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.

def numpy_pad_and_concatenate(array1, array2, padding_index=-100): """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 return result

https://github.com/huggingface/transformers/blob/623b4f7c63f60cce917677ee704d6c93ee960b4b/src/transformers/trainer_pt_utils.py#L82-L94

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.

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

https://github.com/sympy/sympy/blob/d822fcba181155b85ff2b29fe525adbafb22b448/sympy/series/sequences.py#L158-L172

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

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 """ PeriodicSenderThread.__init__(self, sock, pkt, interval)

https://github.com/4shadoww/hakkuframework/blob/409a11fc3819d251f86faa3473439f8c19066a21/lib/scapy/contrib/automotive/uds.py#L1201-L1209

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, or None if canceled.

def _askDirectory(self, message=_('Choose a directory.'), defaultPath=''): """Show a modal directory dialog and return the resulting path, or None if canceled.""" return DirOpen.display_dialog(self.window, message, defaultPath, create_dir=True)

https://github.com/wrye-bash/wrye-bash/blob/d495c47cfdb44475befa523438a40c4419cb386f/Mopy/bash/balt.py#L1705-L1709

Instagram/MonkeyType

d582ee3914f9eee1fdfb76a57bb9f4206e017ceb

monkeytype/typing.py

python

field_annotations

(typed_dict)

return (typed_dict.__annotations__["required_fields"].__annotations__, typed_dict.__annotations__["optional_fields"].__annotations__)

Return the required and optional fields in the TypedDict.

Return the required and optional fields in the TypedDict.

def field_annotations(typed_dict) -> Tuple[Dict[str, type], Dict[str, type]]: """Return the required and optional fields in the TypedDict.""" return (typed_dict.__annotations__["required_fields"].__annotations__, typed_dict.__annotations__["optional_fields"].__annotations__)

https://github.com/Instagram/MonkeyType/blob/d582ee3914f9eee1fdfb76a57bb9f4206e017ceb/monkeytype/typing.py#L67-L70

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. :param obj: :param matches: :param mt: :param lt: :param eq: :return: Boolean

Returns True if object is aligned to the criteria.

def __criteria(self, obj, matches=None, mt=None, lt=None, eq=None): """ Returns True if object is aligned to the criteria. :param obj: :param matches: :param mt: :param lt: :param eq: :return: Boolean """ # Fail matcher if "less than" for field, value in (mt or {}).items(): if getattr(obj, field) <= value: return False # Fail matcher if "more than" for field, value in (lt or {}).items(): if getattr(obj, field) >= value: return False # Fail matcher if "not equal" for field, value in (eq or {}).items(): if getattr(obj, field) != value: return False # Fail matcher if "doesn't match" for field, value in (matches or {}).items(): if not re.search(value, str(getattr(obj, field))): return False return True

https://github.com/saltstack/salt/blob/fae5bc757ad0f1716483ce7ae180b451545c2058/salt/modules/inspectlib/fsdb.py#L276-L307

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)

def markDst(self): """Mark current position as 'destination' (called by UI)""" self.linkDestination = self.c.p.copy() self.showMessage('Dest. marked')

https://github.com/leo-editor/leo-editor/blob/383d6776d135ef17d73d935a2f0ecb3ac0e99494/leo/plugins/backlink.py#L482-L485

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

def check_value(self, value, path, **kwargs): """Check Value""" matches = [] primitive_type = kwargs.get('primitive_type', {}) item_type = kwargs.get('item_type', {}) strict_check = kwargs.get('non_strict', self.config['strict']) if item_type in ['Map']: if isinstance(value, dict): for map_key, map_value in value.items(): if not isinstance(map_value, dict): matches.extend(self.check_primitive_type( map_value, primitive_type, path + [map_key], strict_check)) else: # 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)) return matches

https://github.com/aws-cloudformation/cfn-lint/blob/16df5d0ca0d8ebcf9330ebea701e83d883b47217/src/cfnlint/rules/resources/properties/ValuePrimitiveType.py#L123-L142

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.

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, ['_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))), )

https://github.com/tensorflow/lingvo/blob/ce10019243d954c3c3ebe739f7589b5eebfdf907/lingvo/core/gshard_builder.py#L888-L969

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.

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)

https://github.com/mypaint/mypaint/blob/90b36dbc7b8bd2f323383f7edf608a5e0a3a1a33/lib/floodfill.py#L56-L76

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.

def write_text(self, text): """Writes re-indented text into the buffer. This rewraps and preserves paragraphs. """ 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, ) ) self.write("\n")

https://github.com/general03/flask-autoindex/blob/424246242c9f40aeb9ac2c8c63f4d2234024256e/.eggs/click-7.1.1-py3.7.egg/click/formatting.py#L173-L188

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/

def _get_channel_bindings_value(server_certificate_hash): """ 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

https://github.com/RedTeamOperations/PivotSuite/blob/9078d1ede1f076d30b6d72ca14e05ddf991f51f4/pivot_suite/ntlm_auth/compute_response.py#L360-L392

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

https://github.com/ethereum/trinity/blob/6383280c5044feb06695ac2f7bc1100b7bcf4fe0/p2p/kademlia.py#L72-L73

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.

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

https://github.com/cagbal/ros_people_object_detection_tensorflow/blob/982ffd4a54b8059638f5cd4aa167299c7fc9e61f/src/object_detection/create_pascal_tf_record.py#L58-L142

facebookresearch/pysparnn

c299c825fd99f263f3957e9b31197daf23a1e7a3

pysparnn/matrix_distance.py

python

DenseCosineDistance._distance

(self, a_matrix)

return 1 - (dprod * magnitude)

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)

https://github.com/facebookresearch/pysparnn/blob/c299c825fd99f263f3957e9b31197daf23a1e7a3/pysparnn/matrix_distance.py#L331-L342

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.

def connection_from_host(self, host, port=None, scheme='http'): """ 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``. """ if not host: raise LocationValueError("No host specified.") 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)

https://github.com/nosmokingbandit/Watcher3/blob/0217e75158b563bdefc8e01c3be7620008cf3977/lib/requests/packages/urllib3/poolmanager.py#L158-L176

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.

def tracks(self): """ 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'> """ if not hasattr(self, "_tracks"): self._populate_tracks() return self._tracks

https://github.com/pymedusa/Medusa/blob/1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38/lib/pymediainfo/__init__.py#L285-L299

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. :returns: A generator of objects that are of type :class:`~openstack.clustering.v1.policy_type.PolicyType`

Get a generator of policy types.

def policy_types(self, **query): """Get a generator of policy types. :returns: A generator of objects that are of type :class:`~openstack.clustering.v1.policy_type.PolicyType` """ return self._list(_policy_type.PolicyType, **query)

https://github.com/openstack/openstacksdk/blob/58384268487fa854f21c470b101641ab382c9897/openstack/clustering/v1/_proxy.py#L60-L66

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: parameter: The asked parameter after getting the current mode. parser: A function that parses the input buffer read. Returns: Any: the parsed ask command. The parser determines the return data-type.

Reads the current mode of the multimeter and ask for the given parameter.

def _get_mode_param(self, parameter: str, parser: Callable[[str], T]) -> T: """ Reads the current mode of the multimeter and ask for the given parameter. Args: parameter: The asked parameter after getting the current mode. parser: A function that parses the input buffer read. Returns: Any: the parsed ask command. The parser determines the return data-type. """ mode = _parse_output_string(self._mode_map[self.mode()]) cmd = f'{mode}:{parameter}?' return parser(self.ask(cmd))

https://github.com/QCoDeS/Qcodes/blob/3cda2cef44812e2aa4672781f2423bf5f816f9f9/qcodes/instrument_drivers/tektronix/Keithley_6500.py#L203-L215

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.

def get_reports(self, file_path: str) -> List[Report]: """ Get reports from the given analyzer result. """ return Parser().get_reports(file_path)

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.

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, )

https://github.com/saltstack/salt/blob/fae5bc757ad0f1716483ce7ae180b451545c2058/salt/states/file.py#L7458-L7844

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.

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()

https://github.com/vertexproject/synapse/blob/8173f43cb5fba5ca2648d12a659afb432139b0a7/synapse/lib/cell.py#L246-L253

pfalcon/pycopy-lib

56ebf2110f3caa63a3785d439ce49b11e13c75c0

email.internal/email/_policybase.py

python

_PolicyBase.__init__

(self, **kw)

Create new Policy, possibly overriding some defaults. See class docstring for a list of overridable attributes.

Create new Policy, possibly overriding some defaults.

def __init__(self, **kw): """Create new Policy, possibly overriding some defaults. See class docstring for a list of overridable attributes. """ for name, value in kw.items(): if hasattr(self, name): super(_PolicyBase,self).__setattr__(name, value) else: raise TypeError( "{!r} is an invalid keyword argument for {}".format( name, self.__class__.__name__))

https://github.com/pfalcon/pycopy-lib/blob/56ebf2110f3caa63a3785d439ce49b11e13c75c0/email.internal/email/_policybase.py#L41-L53

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.

def showwarning(message, category, filename, lineno, file=None, line=None): """Hook to write a warning to a file; replace if you like.""" msg = WarningMessage(message, category, filename, lineno, file, line) _showwarnmsg_impl(msg)

https://github.com/XX-net/XX-Net/blob/a9898cfcf0084195fb7e69b6bc834e59aecdf14f/python3.8.2/Lib/warnings.py#L10-L13

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.

def get(optimizer, learning_rate=None, decay=None): """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.")

https://github.com/lululxvi/deepxde/blob/730c97282636e86c845ce2ba3253482f2178469e/deepxde/optimizers/tensorflow/optimizers.py#L11-L31

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']]

def 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: """ 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

https://github.com/cltk/cltk/blob/1a8c2f5ef72389e2579dfce1fa5af8e59ebc9ec1/src/cltk/prosody/non.py#L388-L413

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

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)

https://github.com/r9y9/wavenet_vocoder/blob/a35fff76ea3687b05e1a10023cad3f7f64fa25a3/lrschedule.py#L20-L35

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.

def add_ratings(self, *args): ''' Add ratings, rating type and votes. ''' self.cursor.execute(QU.add_rating, args)

https://github.com/jellyfin/jellyfin-kodi/blob/e21e059e000f06890b33e2794a7e57959fdf19a3/jellyfin_kodi/objects/kodi/movies.py#L68-L72

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.

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)

https://github.com/jgagneastro/coffeegrindsize/blob/22661ebd21831dba4cf32bfc6ba59fe3d49f879c/App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/pandas/core/arrays/categorical.py#L772-L783

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`

def __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` """ self.File = None

https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/ie/v20200304/models.py#L1955-L1961

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.

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)

https://github.com/openhatch/oh-mainline/blob/ce29352a034e1223141dcc2f317030bbc3359a51/vendor/packages/gdata/src/gdata/tlslite/integration/HTTPTLSConnection.py#L59-L166

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.

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

https://github.com/natashamjaques/neural_chat/blob/ddb977bb4602a67c460d02231e7bbf7b2cb49a97/ParlAI/parlai/core/torch_agent.py#L1486-L1525

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.

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

https://github.com/tensorflow/tfx/blob/b4a6b83269815ed12ba9df9e9154c7376fef2ea0/tfx/dsl/compiler/compiler.py#L621-L630

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.

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)

https://github.com/inspurer/WorkAttendanceSystem/blob/1221e2d67bdf5bb15fe99517cc3ded58ccb066df/V2.0/venv/Lib/site-packages/pip-9.0.1-py3.5.egg/pip/_vendor/requests/utils.py#L462-L481

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.

def __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 """ 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. # which methods get an "if-match:" etag header added to them. self.optimistic_concurrency_methods = ["PUT"] # If 'follow_redirects' is True, and this is set to True then # all redirecs are followed, including unsafe ones. self.follow_all_redirects = False self.ignore_etag = False self.force_exception_to_status_code = False self.timeout = timeout

https://github.com/twilio/stashboard/blob/3e4b18a8168c102d1e1d7f88fec22bcbfc530d23/stashboard/contrib/httplib2/__init__.py#L831-L878

samuelclay/NewsBlur

2c45209df01a1566ea105e04d499367f32ac9ad2

vendor/mms-agent/confPull.py

python

ConfPullThread._pullRemoteConf

( self )

Pull the remote configuration data

Pull the remote configuration data

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 ) )

https://github.com/samuelclay/NewsBlur/blob/2c45209df01a1566ea105e04d499367f32ac9ad2/vendor/mms-agent/confPull.py#L39-L108

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.

def cancel(self): """Cancels any documents/deletions added by this object and unlocks the index. """ pass

https://github.com/whoosh-community/whoosh/blob/5421f1ab3bb802114105b3181b7ce4f44ad7d0bb/src/whoosh/writing.py#L496-L500

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

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)

https://github.com/zwczou/weixin-python/blob/4d0964b1fbaad270abb2a64037b173894e66019d/weixin/pay.py#L212-L222

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') 8405007

r"""Converts a list of bytes or an 8-bit string to an integer.

def bytes2int(raw_bytes): 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') 8405007 """ return int(binascii.hexlify(raw_bytes), 16)

https://github.com/aws-samples/aws-kube-codesuite/blob/ab4e5ce45416b83bffb947ab8d234df5437f4fca/src/rsa/transform.py#L40-L52

replit-archive/empythoned

977ec10ced29a3541a4973dc2b59910805695752

cpython/Lib/lib-tk/Tkinter.py

python

Misc._options

(self, cnf, kw = None)

return res

Internal function.

Internal function.

def _options(self, cnf, kw = None): """Internal function.""" if kw: cnf = _cnfmerge((cnf, kw)) else: cnf = _cnfmerge(cnf) res = () for k, v in cnf.items(): if v is not None: if k[-1] == '_': k = k[:-1] if hasattr(v, '__call__'): v = self._register(v) elif isinstance(v, (tuple, list)): 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

https://github.com/replit-archive/empythoned/blob/977ec10ced29a3541a4973dc2b59910805695752/cpython/Lib/lib-tk/Tkinter.py#L1040-L1065

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.

def __str__(self): """Return a string representation of the object.""" return "Density matrix of %s" % (self.data)

https://github.com/cclib/cclib/blob/81cd4a81cc4a3bbed7016b3e417ca9bff8ad3a92/cclib/method/density.py#L26-L28

SteveDoyle2/pyNastran

eda651ac2d4883d95a34951f8a002ff94f642a1a

pyNastran/dev/bdf_vectorized/bdf.py

python

BDF._parse_darea

(self, card_name, cards)

adds dareas

adds dareas

def _parse_darea(self, card_name, cards): """adds dareas""" self._parse_multi(card_name, cards, self.darea, [5])

https://github.com/SteveDoyle2/pyNastran/blob/eda651ac2d4883d95a34951f8a002ff94f642a1a/pyNastran/dev/bdf_vectorized/bdf.py#L3103-L3105

zhl2008/awd-platform

0416b31abea29743387b10b3914581fbe8e7da5e

web_flaskbb/lib/python2.7/site-packages/speaklater.py

python

_LazyString.__copy__

(self)

return self

def __copy__(self): return self

https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/speaklater.py#L188-L189

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.

def __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] """ 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) else: raise NotImplementedError( "Distribution %s not supported" % distribution) self.distribution = distribution

https://github.com/ray-project/ray/blob/703c1610348615dcb8c2d141a0c46675084660f5/python/ray/tune/automl/search_space.py#L70-L91

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.

def from_string(self): 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

https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/databases/findstat.py#L4359-L4382

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

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")

https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_hxb2/lib/python3.5/site-packages/redis/connection.py#L504-L542

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

def __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 ''' 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()

https://github.com/OptMLGroup/VRP-RL/blob/b794fb1e4c4bb70a62cfa54504ee7a247adbc2a0/VRP/vrp_utils.py#L57-L80

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

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

https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_openshift/library/oc_serviceaccount_secret.py#L671-L697

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

def list_basic(self, context, request, items): """Non-detailed list of items""" return [self.show_basic(context, request, i) for i in items]

https://github.com/openstack/designate/blob/bff3d5f6e31fe595a77143ec4ac779c187bf72a8/designate/api/admin/views/base.py#L69-L71

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.

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"

https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/google/appengine/api/datastore_distributed.py#L329-L333

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.

def invalid_input(self, match=None, context=None, next_state=None): """Not a compound element member. Abort this state machine.""" self.state_machine.previous_line() # back up so parent SM can reassess raise EOFError

https://github.com/openhatch/oh-mainline/blob/ce29352a034e1223141dcc2f317030bbc3359a51/vendor/packages/docutils/docutils/parsers/rst/states.py#L2450-L2453

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 spin-up (alpha) modes and odd indices correspond to spin-down (beta) modes.

r"""Return the s^{2} operator.

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 n_spatial_orbitals. Note: The indexing convention used is that even indices correspond to spin-up (alpha) modes and odd indices correspond to spin-down (beta) 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

https://github.com/quantumlib/OpenFermion/blob/6187085f2a7707012b68370b625acaeed547e62b/src/openfermion/hamiltonians/special_operators.py#L182-L211

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.

def _draw_number(screen, x_offset, y_offset, number, style='class:clock', transparent=False): " Write number at position. " fg = Char(' ', 'class:clock') bg = Char(' ', '') for y, row in enumerate(_numbers[number]): screen_row = screen.data_buffer[y + y_offset] for x, n in enumerate(row): if n == '#': screen_row[x + x_offset] = fg elif not transparent: screen_row[x + x_offset] = bg

https://github.com/prompt-toolkit/pymux/blob/3f66e62b9de4b2251c7f9afad6c516dc5a30ec67/pymux/layout.py#L102-L114

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", ]

https://github.com/tp4a/teleport/blob/1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad/server/www/packages/packages-darwin/x64/cryptography/hazmat/bindings/openssl/_conditional.py#L8-L13

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

https://github.com/JacquesLucke/animation_nodes/blob/b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1/animation_nodes/nodes/mesh/mesh_object_input.py#L113-L117

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`.

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

https://github.com/timonwong/OmniMarkupPreviewer/blob/21921ac7a99d2b5924a2219b33679a5b53621392/OmniMarkupLib/Renderers/libs/python2/docutils/parsers/rst/states.py#L441-L461

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)

https://github.com/scrapy/scrapy/blob/b04cfa48328d5d5749dca6f50fa34e0cfc664c89/scrapy/linkextractors/lxmlhtml.py#L61-L82

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

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)

https://github.com/sahana/eden/blob/1696fa50e90ce967df69f66b571af45356cc18da/modules/s3db/project.py#L5996-L6025

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.

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]

https://github.com/linxid/Machine_Learning_Study_Path/blob/558e82d13237114bbb8152483977806fc0c222af/Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/random.py#L252-L258

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.

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)

https://github.com/jgagneastro/coffeegrindsize/blob/22661ebd21831dba4cf32bfc6ba59fe3d49f879c/App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/scipy/interpolate/fitpack.py#L293-L368

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.

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)

https://github.com/xmengli/H-DenseUNet/blob/06cc436a43196310fe933d114a353839907cc176/Keras-2.0.8/keras/backend/tensorflow_backend.py#L1323-L1333