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quantopian/zipline | zipline/algorithm.py | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/algorithm.py#L1811-L1870 | def order_target_percent(self, asset, target,
limit_price=None, stop_price=None, style=None):
"""Place an order to adjust a position to a target percent of the
current portfolio value. If the position doesn't already exist, this is
equivalent to placing a new order. If the position does exist, this is
equivalent to placing an order for the difference between the target
percent and the current percent.
Parameters
----------
asset : Asset
The asset that this order is for.
target : float
The desired percentage of the portfolio value to allocate to
``asset``. This is specified as a decimal, for example:
0.50 means 50%.
limit_price : float, optional
The limit price for the order.
stop_price : float, optional
The stop price for the order.
style : ExecutionStyle
The execution style for the order.
Returns
-------
order_id : str
The unique identifier for this order.
Notes
-----
``order_target_value`` does not take into account any open orders. For
example:
.. code-block:: python
order_target_percent(sid(0), 10)
order_target_percent(sid(0), 10)
This code will result in 20% of the portfolio being allocated to sid(0)
because the first call to ``order_target_percent`` will not have been
filled when the second ``order_target_percent`` call is made.
See :func:`zipline.api.order` for more information about
``limit_price``, ``stop_price``, and ``style``
See Also
--------
:class:`zipline.finance.execution.ExecutionStyle`
:func:`zipline.api.order`
:func:`zipline.api.order_target`
:func:`zipline.api.order_target_value`
"""
if not self._can_order_asset(asset):
return None
amount = self._calculate_order_target_percent_amount(asset, target)
return self.order(asset, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style) | [
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equivalent to placing an order for the difference between the target
percent and the current percent.
Parameters
----------
asset : Asset
The asset that this order is for.
target : float
The desired percentage of the portfolio value to allocate to
``asset``. This is specified as a decimal, for example:
0.50 means 50%.
limit_price : float, optional
The limit price for the order.
stop_price : float, optional
The stop price for the order.
style : ExecutionStyle
The execution style for the order.
Returns
-------
order_id : str
The unique identifier for this order.
Notes
-----
``order_target_value`` does not take into account any open orders. For
example:
.. code-block:: python
order_target_percent(sid(0), 10)
order_target_percent(sid(0), 10)
This code will result in 20% of the portfolio being allocated to sid(0)
because the first call to ``order_target_percent`` will not have been
filled when the second ``order_target_percent`` call is made.
See :func:`zipline.api.order` for more information about
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See Also
--------
:class:`zipline.finance.execution.ExecutionStyle`
:func:`zipline.api.order`
:func:`zipline.api.order_target`
:func:`zipline.api.order_target_value` | [
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alantygel/ckanext-semantictags | ckanext/semantictags/db.py | https://github.com/alantygel/ckanext-semantictags/blob/10bb31d29f34b2b5a6feae693961842f93007ce1/ckanext/semantictags/db.py#L99-L117 | def get(cls, tag_id_or_URI, label=None):
'''Return the tag with the given id or URI, or None.
:param tag_id_or_name: the id or name of the tag to return
:type tag_id_or_name: string
:returns: the tag object with the given id or name, or None if there is
no tag with that id or name
:rtype: ckan.model.tag.Tag
'''
# First try to get the tag by ID.
semantictag = SemanticTag.by_id(tag_id_or_URI)
if semantictag:
return semantictag
else:
semantictag = SemanticTag.by_URI(tag_id_or_URI)
return semantictag | [
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biosignalsnotebooks/biosignalsnotebooks | biosignalsnotebooks/build/lib/biosignalsnotebooks/__notebook_support__.py | https://github.com/biosignalsnotebooks/biosignalsnotebooks/blob/aaa01d4125180b3a34f1e26e0d3ff08c23f666d3/biosignalsnotebooks/build/lib/biosignalsnotebooks/__notebook_support__.py#L796-L877 | def plot_median_freq_evol(time_signal, signal, time_median_freq, median_freq, activations_begin,
activations_end, sample_rate, file_name=None):
"""
-----
Brief
-----
Graphical representation of the EMG median power frequency evolution time series.
-----------
Description
-----------
Function intended to generate a Bokeh figure with 2x1 format, where each muscular activation
period is identified through a colored box and the plot that shows the median frequency
evolution is also presented.
In the first cell is presented the EMG signal, highlighting each muscular activation.
The second cell has the same time scale as the first one (the two plots are synchronized), being
plotted the evolution time series of EMG median frequency.
Per muscular activation period is extracted a Median Power Frequency value (sample), so, our
window is a muscular activation period.
Median power frequency is a commonly used parameter for evaluating muscular fatigue.
It is widely accepted that this parameter decreases as fatigue sets in.
Applied in the Notebook "Fatigue Evaluation - Evolution of Median Power Frequency".
----------
Parameters
----------
time_signal : list
List with the time axis samples of EMG signal.
signal : list
List with EMG signal to present.
time_median_freq : list
List with the time axis samples of the median frequency evolution time-series.
median_freq : list
List with the Median Frequency samples.
activations_begin : list
List with the samples where each muscular activation period starts.
activations_end : list
List with the samples where each muscular activation period ends.
sample_rate : int
Sampling rate of acquisition.
file_name : str
Path containing the destination folder where the Bokeh figure will be stored.
"""
# Generation of the HTML file where the plot will be stored.
#file_name = _generate_bokeh_file(file_name)
list_figures_1 = plot([list(time_signal), list(time_median_freq)],
[list(signal), list(median_freq)],
title=["EMG Acquisition highlighting muscular activations",
"Median Frequency Evolution"], grid_plot=True,
grid_lines=2, grid_columns=1, open_signals_style=True,
x_axis_label="Time (s)",
yAxisLabel=["Raw Data", "Median Frequency (Hz)"],
x_range=[0, 125], get_fig_list=True, show_plot=False)
# Highlighting of each processing window
for activation in range(0, len(activations_begin)):
color = opensignals_color_pallet()
box_annotation = BoxAnnotation(left=activations_begin[activation] / sample_rate,
right=activations_end[activation] / sample_rate,
fill_color=color, fill_alpha=0.1)
box_annotation_copy = BoxAnnotation(left=activations_begin[activation] / sample_rate,
right=activations_end[activation] / sample_rate,
fill_color=color, fill_alpha=0.1)
list_figures_1[0].add_layout(box_annotation)
list_figures_1[1].add_layout(box_annotation_copy)
gridplot_1 = gridplot([[list_figures_1[0]], [list_figures_1[1]]],
**opensignals_kwargs("gridplot"))
show(gridplot_1) | [
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Graphical representation of the EMG median power frequency evolution time series.
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Description
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Function intended to generate a Bokeh figure with 2x1 format, where each muscular activation
period is identified through a colored box and the plot that shows the median frequency
evolution is also presented.
In the first cell is presented the EMG signal, highlighting each muscular activation.
The second cell has the same time scale as the first one (the two plots are synchronized), being
plotted the evolution time series of EMG median frequency.
Per muscular activation period is extracted a Median Power Frequency value (sample), so, our
window is a muscular activation period.
Median power frequency is a commonly used parameter for evaluating muscular fatigue.
It is widely accepted that this parameter decreases as fatigue sets in.
Applied in the Notebook "Fatigue Evaluation - Evolution of Median Power Frequency".
----------
Parameters
----------
time_signal : list
List with the time axis samples of EMG signal.
signal : list
List with EMG signal to present.
time_median_freq : list
List with the time axis samples of the median frequency evolution time-series.
median_freq : list
List with the Median Frequency samples.
activations_begin : list
List with the samples where each muscular activation period starts.
activations_end : list
List with the samples where each muscular activation period ends.
sample_rate : int
Sampling rate of acquisition.
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Path containing the destination folder where the Bokeh figure will be stored. | [
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tanghaibao/jcvi | jcvi/formats/posmap.py | https://github.com/tanghaibao/jcvi/blob/d2e31a77b6ade7f41f3b321febc2b4744d1cdeca/jcvi/formats/posmap.py#L155-L180 | def index(args):
"""
%prog index frgscf.sorted
Compress frgscffile.sorted and index it using `tabix`.
"""
p = OptionParser(index.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(p.print_help())
frgscffile, = args
gzfile = frgscffile + ".gz"
cmd = "bgzip -c {0}".format(frgscffile)
if not op.exists(gzfile):
sh(cmd, outfile=gzfile)
tbifile = gzfile + ".tbi"
# Sequence, begin, end in 2, 3, 4-th column, respectively
cmd = "tabix -s 2 -b 3 -e 4 {0}".format(gzfile)
if not op.exists(tbifile):
sh(cmd) | [
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galaxy-genome-annotation/python-apollo | apollo/client.py | https://github.com/galaxy-genome-annotation/python-apollo/blob/2bc9991302abe4402ec2885dcaac35915475b387/apollo/client.py#L29-L65 | def post(self, client_method, data, post_params=None, is_json=True):
"""Make a POST request"""
url = self._wa.apollo_url + self.CLIENT_BASE + client_method
if post_params is None:
post_params = {}
headers = {
'Content-Type': 'application/json'
}
data.update({
'username': self._wa.username,
'password': self._wa.password,
})
curl_command = ['curl', url]
for (k, v) in headers.items():
curl_command += ['-H', quote('%s: %s' % (k, v))]
curl_command += ['-d', quote(json.dumps(data))]
log.info(' '.join(curl_command))
resp = requests.post(url, data=json.dumps(data),
headers=headers, verify=self.__verify,
params=post_params, allow_redirects=False,
**self._request_args)
if resp.status_code == 200 or resp.status_code == 302:
if is_json:
data = resp.json()
return self._scrub_data(data)
else:
return resp.text
# @see self.body for HTTP response body
raise Exception("Unexpected response from apollo %s: %s" %
(resp.status_code, resp.text)) | [
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72squared/redpipe | redpipe/keyspaces.py | https://github.com/72squared/redpipe/blob/e6ee518bc9f3e2fee323c8c53d08997799bd9b1b/redpipe/keyspaces.py#L629-L638 | def getbit(self, name, offset):
"""
Returns a boolean indicating the value of ``offset`` in key
:param name: str the name of the redis key
:param offset: int
:return: Future()
"""
with self.pipe as pipe:
return pipe.getbit(self.redis_key(name), offset) | [
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saltstack/salt | salt/utils/decorators/state.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/utils/decorators/state.py#L75-L95 | def unify(self, result):
'''
While comments as a list are allowed,
comments needs to be strings for backward compatibility.
See such claim here: https://github.com/saltstack/salt/pull/43070
Rules applied:
- 'comment' is joined into a multi-line string, in case the value is a list.
- 'result' should be always either True, False or None.
:param result:
:return:
'''
if isinstance(result.get('comment'), list):
result['comment'] = u'\n'.join([
salt.utils.stringutils.to_unicode(elm) for elm in result['comment']
])
if result.get('result') is not None:
result['result'] = bool(result['result'])
return result | [
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keon/algorithms | algorithms/strings/decode_string.py | https://github.com/keon/algorithms/blob/4d6569464a62a75c1357acc97e2dd32ee2f9f4a3/algorithms/strings/decode_string.py#L20-L38 | def decode_string(s):
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:rtype: str
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TeamHG-Memex/eli5 | eli5/sklearn/utils.py | https://github.com/TeamHG-Memex/eli5/blob/371b402a0676295c05e582a2dd591f7af476b86b/eli5/sklearn/utils.py#L196-L213 | def get_num_features(estimator):
""" Return size of a feature vector estimator expects as an input. """
if hasattr(estimator, 'coef_'): # linear models
if len(estimator.coef_.shape) == 0:
return 1
return estimator.coef_.shape[-1]
elif hasattr(estimator, 'feature_importances_'): # ensembles
return estimator.feature_importances_.shape[-1]
elif hasattr(estimator, 'feature_count_'): # naive bayes
return estimator.feature_count_.shape[-1]
elif hasattr(estimator, 'theta_'):
return estimator.theta_.shape[-1]
elif hasattr(estimator, 'estimators_') and len(estimator.estimators_):
# OvR
return get_num_features(estimator.estimators_[0])
else:
raise ValueError("Can't figure out feature vector size for %s" %
estimator) | [
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qacafe/cdrouter.py | cdrouter/system.py | https://github.com/qacafe/cdrouter.py/blob/aacf2c6ab0b987250f7b1892f4bba14bb2b7dbe5/cdrouter/system.py#L425-L433 | def space(self):
"""Get system disk space usage.
:return: :class:`system.Space <system.Space>` object
:rtype: system.Space
"""
schema = SpaceSchema()
resp = self.service.get(self.base+'space/')
return self.service.decode(schema, resp) | [
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cggh/scikit-allel | allel/model/ndarray.py | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3429-L3470 | def locate_key(self, key):
"""Get index location for the requested key.
Parameters
----------
key : object
Value to locate.
Returns
-------
loc : int or slice
Location of `key` (will be slice if there are duplicate entries).
Examples
--------
>>> import allel
>>> idx = allel.SortedIndex([3, 6, 6, 11])
>>> idx.locate_key(3)
0
>>> idx.locate_key(11)
3
>>> idx.locate_key(6)
slice(1, 3, None)
>>> try:
... idx.locate_key(2)
... except KeyError as e:
... print(e)
...
2
"""
left = bisect.bisect_left(self, key)
right = bisect.bisect_right(self, key)
diff = right - left
if diff == 0:
raise KeyError(key)
elif diff == 1:
return left
else:
return slice(left, right) | [
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Qiskit/qiskit-terra | qiskit/transpiler/passes/mapping/dense_layout.py | https://github.com/Qiskit/qiskit-terra/blob/d4f58d903bc96341b816f7c35df936d6421267d1/qiskit/transpiler/passes/mapping/dense_layout.py#L45-L66 | def run(self, dag):
"""
Pick a convenient layout depending on the best matching
qubit connectivity, and set the property `layout`.
Args:
dag (DAGCircuit): DAG to find layout for.
Raises:
TranspilerError: if dag wider than self.coupling_map
"""
num_dag_qubits = sum([qreg.size for qreg in dag.qregs.values()])
if num_dag_qubits > self.coupling_map.size():
raise TranspilerError('Number of qubits greater than device.')
best_sub = self._best_subset(num_dag_qubits)
layout = Layout()
map_iter = 0
for qreg in dag.qregs.values():
for i in range(qreg.size):
layout[(qreg, i)] = int(best_sub[map_iter])
map_iter += 1
self.property_set['layout'] = layout | [
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chrisrink10/basilisp | src/basilisp/lang/runtime.py | https://github.com/chrisrink10/basilisp/blob/3d82670ee218ec64eb066289c82766d14d18cc92/src/basilisp/lang/runtime.py#L567-L570 | def get(cls, name: sym.Symbol) -> "Optional[Namespace]":
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return cls._NAMESPACES.deref().entry(name, None) | [
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ttm/participationLegacy | participation/triplification/participaTriplification.py | https://github.com/ttm/participationLegacy/blob/d78975038a64ea018120889d019a559409dae631/participation/triplification/participaTriplification.py#L157-L170 | def triplifyOverallStructures(self):
"""Insert into RDF graph the textual and network structures.
Ideally, one should be able to make bag of words related to
each item (communities, users, posts, comments, tags, etc).
Interaction and friendship networks should be made.
Human networks mediated by co-ocurrance (time os posts,
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if self.compute_networks:
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self.computeBows() | [
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idlesign/django-sitecats | sitecats/utils.py | https://github.com/idlesign/django-sitecats/blob/9b45e91fc0dcb63a0011780437fe28145e3ecce9/sitecats/utils.py#L57-L85 | def _cache_init(self):
"""Initializes local cache from Django cache if required."""
cache_ = cache.get(self.CACHE_ENTRY_NAME)
if cache_ is None:
categories = get_category_model().objects.order_by('sort_order')
ids = {category.id: category for category in categories}
aliases = {category.alias: category for category in categories if category.alias}
parent_to_children = OrderedDict() # Preserve aliases order.
for category in categories:
parent_category = ids.get(category.parent_id, False)
parent_alias = None
if parent_category:
parent_alias = parent_category.alias
if parent_alias not in parent_to_children:
parent_to_children[parent_alias] = []
parent_to_children[parent_alias].append(category.id)
cache_ = {
self.CACHE_NAME_IDS: ids,
self.CACHE_NAME_PARENTS: parent_to_children,
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cache.set(self.CACHE_ENTRY_NAME, cache_, self.CACHE_TIMEOUT)
self._cache = cache_ | [
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waqasbhatti/astrobase | astrobase/checkplot/pkl_io.py | https://github.com/waqasbhatti/astrobase/blob/2922a14619d183fb28005fa7d02027ac436f2265/astrobase/checkplot/pkl_io.py#L130-L179 | def _read_checkplot_picklefile(checkplotpickle):
'''This reads a checkplot gzipped pickle file back into a dict.
NOTE: the try-except is for Python 2 pickles that have numpy arrays in
them. Apparently, these aren't compatible with Python 3. See here:
http://stackoverflow.com/q/11305790
The workaround is noted in this answer:
http://stackoverflow.com/a/41366785
Parameters
----------
checkplotpickle : str
The path to a checkplot pickle file. This can be a gzipped file (in
which case the file extension should end in '.gz')
Returns
-------
dict
This returns a checkplotdict.
'''
if checkplotpickle.endswith('.gz'):
try:
with gzip.open(checkplotpickle,'rb') as infd:
cpdict = pickle.load(infd)
except UnicodeDecodeError:
with gzip.open(checkplotpickle,'rb') as infd:
cpdict = pickle.load(infd, encoding='latin1')
else:
try:
with open(checkplotpickle,'rb') as infd:
cpdict = pickle.load(infd)
except UnicodeDecodeError:
with open(checkplotpickle,'rb') as infd:
cpdict = pickle.load(infd, encoding='latin1')
return cpdict | [
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NOTE: the try-except is for Python 2 pickles that have numpy arrays in
them. Apparently, these aren't compatible with Python 3. See here:
http://stackoverflow.com/q/11305790
The workaround is noted in this answer:
http://stackoverflow.com/a/41366785
Parameters
----------
checkplotpickle : str
The path to a checkplot pickle file. This can be a gzipped file (in
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Returns
-------
dict
This returns a checkplotdict. | [
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acutesoftware/AIKIF | aikif/toolbox/cls_grid.py | https://github.com/acutesoftware/AIKIF/blob/fcf1582dc5f884b9a4fa7c6e20e9de9d94d21d03/aikif/toolbox/cls_grid.py#L52-L58 | def save(self, fname):
""" saves a grid to file as ASCII text """
try:
with open(fname, "w") as f:
f.write(str(self))
except Exception as ex:
print('ERROR = cant save grid results to ' + fname + str(ex)) | [
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craffel/mir_eval | mir_eval/segment.py | https://github.com/craffel/mir_eval/blob/f41c8dafaea04b411252a516d1965af43c7d531b/mir_eval/segment.py#L663-L701 | def _mutual_info_score(reference_indices, estimated_indices, contingency=None):
"""Compute the mutual information between two sequence labelings.
Parameters
----------
reference_indices : np.ndarray
Array of reference indices
estimated_indices : np.ndarray
Array of estimated indices
contingency : np.ndarray
Pre-computed contingency matrix. If None, one will be computed.
(Default value = None)
Returns
-------
mi : float
Mutual information
.. note:: Based on sklearn.metrics.cluster.mutual_info_score
"""
if contingency is None:
contingency = _contingency_matrix(reference_indices,
estimated_indices).astype(float)
contingency_sum = np.sum(contingency)
pi = np.sum(contingency, axis=1)
pj = np.sum(contingency, axis=0)
outer = np.outer(pi, pj)
nnz = contingency != 0.0
# normalized contingency
contingency_nm = contingency[nnz]
log_contingency_nm = np.log(contingency_nm)
contingency_nm /= contingency_sum
# log(a / b) should be calculated as log(a) - log(b) for
# possible loss of precision
log_outer = -np.log(outer[nnz]) + np.log(pi.sum()) + np.log(pj.sum())
mi = (contingency_nm * (log_contingency_nm - np.log(contingency_sum)) +
contingency_nm * log_outer)
return mi.sum() | [
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Pre-computed contingency matrix. If None, one will be computed.
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KyleJamesWalker/yamlsettings | yamlsettings/extensions/registry.py | https://github.com/KyleJamesWalker/yamlsettings/blob/ddd7df2ca995ddf191b24c4d35e9dd28186e4535/yamlsettings/extensions/registry.py#L39-L45 | def _discover(self):
"""Find and install all extensions"""
for ep in pkg_resources.iter_entry_points('yamlsettings10'):
ext = ep.load()
if callable(ext):
ext = ext()
self.add(ext) | [
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saltstack/salt | salt/states/selinux.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/states/selinux.py#L292-L315 | def module_remove(name):
'''
Removes SELinux module
name
The name of the module to remove
.. versionadded:: 2016.11.6
'''
ret = {'name': name,
'result': True,
'comment': '',
'changes': {}}
modules = __salt__['selinux.list_semod']()
if name not in modules:
ret['comment'] = 'Module {0} is not available'.format(name)
ret['result'] = False
return ret
if __salt__['selinux.remove_semod'](name):
ret['comment'] = 'Module {0} has been removed'.format(name)
return ret
ret['result'] = False
ret['comment'] = 'Failed to remove module {0}'.format(name)
return ret | [
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odlgroup/odl | odl/discr/lp_discr.py | https://github.com/odlgroup/odl/blob/b8443f6aca90e191ba36c91d32253c5a36249a6c/odl/discr/lp_discr.py#L697-L714 | def imag(self, newimag):
"""Set the imaginary part of this element to ``newimag``.
This method is invoked by ``x.imag = other``.
Parameters
----------
newimag : array-like or scalar
Values to be assigned to the imaginary part of this element.
Raises
------
ValueError
If the space is real, i.e., no imagninary part can be set.
"""
if self.space.is_real:
raise ValueError('cannot set imaginary part in real spaces')
self.tensor.imag = newimag | [
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ThreatConnect-Inc/tcex | tcex/tcex_bin_run.py | https://github.com/ThreatConnect-Inc/tcex/blob/dd4d7a1ef723af1561687120191886b9a2fd4b47/tcex/tcex_bin_run.py#L1453-L1466 | def stage_tc_create_tag(self, tag, resource):
"""Add a tag to a resource.
Args:
tag (str): The tag to be added to the resource.
resource (obj): An instance of tcex resource class.
"""
tag_resource = resource.tags(self.tcex.safetag(tag))
tag_resource.http_method = 'POST'
t_response = tag_resource.request()
if t_response.get('status') != 'Success':
self.log.warning(
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Julius2342/pyvlx | pyvlx/frames/frame_get_version.py | https://github.com/Julius2342/pyvlx/blob/ee78e1324bcb1be5b8d1a9d05ab5496b72eae848/pyvlx/frames/frame_get_version.py#L56-L61 | def from_payload(self, payload):
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self._software_version = payload[0:6]
self.hardware_version = payload[6]
self.product_group = payload[7]
self.product_type = payload[8] | [
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Kozea/cairocffi | cairocffi/fonts.py | https://github.com/Kozea/cairocffi/blob/450853add7e32eea20985b6aa5f54d9cb3cd04fe/cairocffi/fonts.py#L465-L472 | def set_hint_style(self, hint_style):
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and if so, whether to optimize for fidelity or contrast.
"""
cairo.cairo_font_options_set_hint_style(self._pointer, hint_style)
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titusjan/argos | argos/config/abstractcti.py | https://github.com/titusjan/argos/blob/20d0a3cae26c36ea789a5d219c02ca7df21279dd/argos/config/abstractcti.py#L590-L606 | def addSubEditor(self, subEditor, isFocusProxy=False):
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self.hBoxLayout.insertWidget(len(self._subEditors), subEditor)
self._subEditors.append(subEditor)
subEditor.installEventFilter(self)
subEditor.setFocusPolicy(Qt.StrongFocus)
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krukas/Trionyx | trionyx/trionyx/views/core.py | https://github.com/krukas/Trionyx/blob/edac132cc0797190153f2e60bc7e88cb50e80da6/trionyx/trionyx/views/core.py#L304-L320 | def items(self):
"""Get all list items"""
query = self.get_queryset()
fields = self.get_model_config().get_list_fields()
for item in query.iterator():
row = OrderedDict()
for field_name in self.get_current_fields():
field = fields.get(field_name)
if not field_name:
row[field_name] = ''
if hasattr(item, field['field']):
row[field_name] = getattr(item, field['field'])
else:
row[field_name] = '' # TODO Maybe render field ans strip html?
yield row | [
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eddieantonio/perfection | perfection/forest.py | https://github.com/eddieantonio/perfection/blob/69b7a06b31a15bd9534c69d4bdcc2e48e8ddfc43/perfection/forest.py#L104-L113 | def edges(self):
"""
Edges of this graph, in canonical order.
"""
canonical_edges = set()
for v1, neighbours in self._vertices.items():
for v2 in neighbours:
edge = self.canonical_order((v1, v2))
canonical_edges.add(edge)
return canonical_edges | [
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vnmabus/dcor | dcor/_utils.py | https://github.com/vnmabus/dcor/blob/b0ff1273c0a52efdabdfdadefc7ff2a49def7e8d/dcor/_utils.py#L66-L78 | def _can_be_double(x):
"""
Return if the array can be safely converted to double.
That happens when the dtype is a float with the same size of
a double or narrower, or when is an integer that can be safely
converted to double (if the roundtrip conversion works).
"""
return ((np.issubdtype(x.dtype, np.floating) and
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KelSolaar/Umbra | umbra/ui/widgets/basic_QPlainTextEdit.py | https://github.com/KelSolaar/Umbra/blob/66f45f08d9d723787f1191989f8b0dda84b412ce/umbra/ui/widgets/basic_QPlainTextEdit.py#L566-L583 | def set_content(self, content):
"""
Sets document with given content while providing undo capability.
:param content: Content to set.
:type content: list
:return: Method success.
:rtype: bool
"""
cursor = self.textCursor()
cursor.movePosition(QTextCursor.Start, QTextCursor.MoveAnchor)
cursor.movePosition(QTextCursor.End, QTextCursor.KeepAnchor)
cursor.removeSelectedText()
for line in content:
self.moveCursor(QTextCursor.End)
self.insertPlainText(line)
return True | [
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apple/turicreate | deps/src/libxml2-2.9.1/python/libxml2.py | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/deps/src/libxml2-2.9.1/python/libxml2.py#L3540-L3545 | def setContentLen(self, content, len):
"""Replace the content of a node. NOTE: @content is supposed
to be a piece of XML CDATA, so it allows entity references,
but XML special chars need to be escaped first by using
xmlEncodeEntitiesReentrant() resp. xmlEncodeSpecialChars(). """
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Becksteinlab/GromacsWrapper | gromacs/fileformats/xvg.py | https://github.com/Becksteinlab/GromacsWrapper/blob/d4f9a8cb6f48292732cf7c7e4ef4a6d2ccbc51b9/gromacs/fileformats/xvg.py#L997-L1015 | def decimate_rms(self, a, maxpoints, **kwargs):
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Histograms each column into *maxpoints* bins and calculates
the root mean square sum in each bin as the decimated data,
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If *a* contains <= *maxpoints* then *a* is simply returned;
otherwise a new array of the same dimensions but with a
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.. Note::
Assumes that the first column is time.
"""
return self._decimate(numkit.timeseries.rms_histogrammed_function, a, maxpoints, **kwargs) | [
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wummel/linkchecker | linkcheck/url.py | https://github.com/wummel/linkchecker/blob/c2ce810c3fb00b895a841a7be6b2e78c64e7b042/linkcheck/url.py#L558-L572 | def is_duplicate_content_url(url1, url2):
"""Check if both URLs are allowed to point to the same content."""
if url1 == url2:
return True
if url2 in url1:
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CartoDB/carto-python | carto/maps.py | https://github.com/CartoDB/carto-python/blob/f6ac3d17ed08e5bc3f99edd2bde4fb7dba3eee16/carto/maps.py#L39-L106 | def get_tile_url(self, x, y, z, layer_id=None, feature_id=None,
filter=None, extension="png"):
"""
Prepares a URL to get data (raster or vector) from a NamedMap or
AnonymousMap
:param x: The x tile
:param y: The y tile
:param z: The zoom level
:param layer_id: Can be a number (referring to the # layer of your \
map), all layers of your map, or a list of layers.
To show just the basemap layer, enter the value 0
To show the first layer, enter the value 1
To show all layers, enter the value 'all'
To show a list of layers, enter the comma separated \
layer value as '0,1,2'
:param feature_id: The id of the feature
:param filter: The filter to be applied to the layer
:param extension: The format of the data to be retrieved: png, mvt, ...
:type x: int
:type y: int
:type z: int
:type layer_id: str
:type feature_id: str
:type filter: str
:type extension: str
:return: A URL to download data
:rtype: str
:raise: CartoException
"""
base_url = self.client.base_url + self.Meta.collection_endpoint
template_id = self.template_id if hasattr(self, 'template_id') \
else self.layergroupid
if layer_id is not None and feature_id is not None:
url = urljoin(base_url,
"{template_id}/{layer}/attributes/{feature_id}"). \
format(template_id=template_id,
layer=layer_id,
feature_id=feature_id)
elif layer_id is not None and filter is not None:
url = urljoin(base_url,
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format(template_id=template_id,
filter=filter,
z=z, x=x, y=y,
extension=extension)
elif layer_id is not None:
url = urljoin(base_url,
"{template_id}/{layer}/{z}/{x}/{y}.{extension}"). \
format(template_id=template_id,
layer=layer_id,
z=z, x=x, y=y,
extension=extension)
else:
url = urljoin(base_url, "{template_id}/{z}/{x}/{y}.{extension}"). \
format(
template_id=template_id,
z=z, x=x, y=y,
extension=extension)
if hasattr(self, 'auth') and self.auth is not None \
and len(self.auth['valid_tokens']) > 0:
url = urljoin(url, "?auth_token={auth_token}"). \
format(auth_token=self.auth['valid_tokens'][0])
return url | [
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StanfordBioinformatics/loom | server/loomengine_server/api/models/base.py | https://github.com/StanfordBioinformatics/loom/blob/db2031a1a87124fee1aeb7414a668c03d774a698/server/loomengine_server/api/models/base.py#L126-L147 | def filter_by_name_or_id_or_tag(self, query_string, queryset = None):
"""Find objects that match the identifier of form {name}@{ID}, {name},
or @{ID}, where ID may be truncated
"""
assert self.Model.NAME_FIELD, \
'NAME_FIELD is missing on model %s' % self.Model.__name__
assert self.Model.ID_FIELD, \
'ID_FIELD is missing on model %s' % self.Model.__name__
assert self.Model.TAG_FIELD, \
'TAG_FIELD is missing on model %s' % self.Model.__name__
filter_args = {}
name, uuid, tag = self._parse_as_name_or_id_or_tag(query_string)
if name is not None:
filter_args[self.Model.NAME_FIELD] = name
if uuid is not None:
filter_args[self.Model.ID_FIELD+'__startswith'] = uuid
if tag is not None:
filter_args[self.Model.TAG_FIELD] = tag
if queryset is None:
queryset = self.Model.objects.all()
return queryset.filter(**filter_args) | [
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proycon/pynlpl | pynlpl/algorithms.py | https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/algorithms.py#L19-L31 | def sum_to_n(n, size, limit=None): #from http://stackoverflow.com/questions/2065553/python-get-all-numbers-that-add-up-to-a-number
"""Produce all lists of `size` positive integers in decreasing order
that add up to `n`."""
if size == 1:
yield [n]
return
if limit is None:
limit = n
start = (n + size - 1) // size
stop = min(limit, n - size + 1) + 1
for i in range(start, stop):
for tail in sum_to_n(n - i, size - 1, i):
yield [i] + tail | [
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pdkit/pdkit | pdkit/utils.py | https://github.com/pdkit/pdkit/blob/c7120263da2071bb139815fbdb56ca77b544f340/pdkit/utils.py#L753-L802 | def separate_walks_turns(data, window=[1, 1, 1]):
""" Will separate peaks into the clusters by following the trend in the clusters array.
This is usedful because scipy's k-mean clustering will give us a continous clusters
array.
:param clusters array: A continous array representing different classes.
:param peaks array: The peaks that we want to separate into the classes from the custers.
:return walks arrays: An array of arrays that will have all the peaks corresponding to every
individual walk.
:return turns arraays: Array of array which has all the indices of the peaks that correspond
to turning.
"""
clusters, peaks, promi = cluster_walk_turn(data, window=window)
group_one = []
group_two = []
start = 0
for i in range(1, len(clusters)):
if clusters[i-1] != clusters[i]:
assert np.all(clusters[start: i] == clusters[start]), 'Some values are mixed up, please check!'
add = group_one if clusters[start] == 0 else group_two
add.append(peaks[start: i])
start = i
# hacky fix for the last part of the signal ...
# I need to change this ...
if i == len(clusters)-1:
if not peaks[start] in add[-1]:
add = group_one if clusters[start] == 0 else group_two
add.append(peaks[start: ])
maxes_one = [np.max(data[c]) for c in group_one]
maxes_two = [np.max(data[c]) for c in group_two]
walks, turns = group_two, group_one
if np.max(maxes_one) > np.max(maxes_two):
walks, turns = group_one, group_two
# let's drop any turns at the end of the signal
# if len(turns[-1]) > len(walks[-1]):
# turns.pop()
return walks, turns | [
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individual walk.
:return turns arraays: Array of array which has all the indices of the peaks that correspond
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gwastro/pycbc | pycbc/workflow/minifollowups.py | https://github.com/gwastro/pycbc/blob/7a64cdd104d263f1b6ea0b01e6841837d05a4cb3/pycbc/workflow/minifollowups.py#L591-L682 | def make_qscan_plot(workflow, ifo, trig_time, out_dir, injection_file=None,
data_segments=None, time_window=100, tags=None):
""" Generate a make_qscan node and add it to workflow.
This function generates a single node of the singles_timefreq executable
and adds it to the current workflow. Parent/child relationships are set by
the input/output files automatically.
Parameters
-----------
workflow: pycbc.workflow.core.Workflow
The workflow class that stores the jobs that will be run.
ifo: str
Which interferometer are we using?
trig_time: int
The time of the trigger being followed up.
out_dir: str
Location of directory to output to
injection_file: pycbc.workflow.File (optional, default=None)
If given, add the injections in the file to strain before making the
plot.
data_segments: ligo.segments.segmentlist (optional, default=None)
The list of segments for which data exists and can be read in. If given
the start/end times given to singles_timefreq will be adjusted if
[trig_time - time_window, trig_time + time_window] does not completely
lie within a valid data segment. A ValueError will be raised if the
trig_time is not within a valid segment, or if it is not possible to
find 2*time_window (plus the padding) of continuous data around the
trigger. This **must** be coalesced.
time_window: int (optional, default=None)
The amount of data (not including padding) that will be read in by the
singles_timefreq job. The default value of 100s should be fine for most
cases.
tags: list (optional, default=None)
List of tags to add to the created nodes, which determine file naming.
"""
tags = [] if tags is None else tags
makedir(out_dir)
name = 'plot_qscan'
curr_exe = PlotQScanExecutable(workflow.cp, name, ifos=[ifo],
out_dir=out_dir, tags=tags)
node = curr_exe.create_node()
# Determine start/end times, using data segments if needed.
# Begin by choosing "optimal" times
start = trig_time - time_window
end = trig_time + time_window
# Then if data_segments is available, check against that, and move if
# needed
if data_segments is not None:
# Assumes coalesced, so trig_time can only be within one segment
for seg in data_segments:
if trig_time in seg:
data_seg = seg
break
elif trig_time == -1.0:
node.add_opt('--gps-start-time', int(trig_time))
node.add_opt('--gps-end-time', int(trig_time))
node.add_opt('--center-time', trig_time)
caption_string = "'No trigger in %s'" % ifo
node.add_opt('--plot-caption', caption_string)
node.new_output_file_opt(workflow.analysis_time, '.png', '--output-file')
workflow += node
return node.output_files
else:
err_msg = "Trig time {} ".format(trig_time)
err_msg += "does not seem to lie within any data segments. "
err_msg += "This shouldn't be possible, please ask for help!"
raise ValueError(err_msg)
# Check for pad-data
if curr_exe.has_opt('pad-data'):
pad_data = int(curr_exe.get_opt('pad-data'))
else:
pad_data = 0
# We only read data that's available. The code must handle the case
# of not much data being available.
if end > (data_seg[1] - pad_data):
end = data_seg[1] - pad_data
if start < (data_seg[0] + pad_data):
start = data_seg[0] + pad_data
node.add_opt('--gps-start-time', int(start))
node.add_opt('--gps-end-time', int(end))
node.add_opt('--center-time', trig_time)
if injection_file is not None:
node.add_input_opt('--injection-file', injection_file)
node.new_output_file_opt(workflow.analysis_time, '.png', '--output-file')
workflow += node
return node.output_files | [
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workflow: pycbc.workflow.core.Workflow
The workflow class that stores the jobs that will be run.
ifo: str
Which interferometer are we using?
trig_time: int
The time of the trigger being followed up.
out_dir: str
Location of directory to output to
injection_file: pycbc.workflow.File (optional, default=None)
If given, add the injections in the file to strain before making the
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data_segments: ligo.segments.segmentlist (optional, default=None)
The list of segments for which data exists and can be read in. If given
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time_window: int (optional, default=None)
The amount of data (not including padding) that will be read in by the
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tags: list (optional, default=None)
List of tags to add to the created nodes, which determine file naming. | [
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wishtack/pysynthetic | synthetic/synthetic_meta_data.py | https://github.com/wishtack/pysynthetic/blob/f37a4a2f1e0313b8c544f60d37c93726bc806ec6/synthetic/synthetic_meta_data.py#L64-L73 | def insertSyntheticMemberAtBegin(self, synthesizedMember):
"""
:type synthesizedMember: SyntheticMember
:raises DuplicateMemberNameError
"""
memberName = synthesizedMember.memberName()
if memberName in [m.memberName() for m in self._syntheticMemberList]:
raise DuplicateMemberNameError(memberName, self._class.__name__)
self._syntheticMemberList.insert(0, synthesizedMember) | [
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stephenmcd/django-socketio | django_socketio/example_project/chat/events.py | https://github.com/stephenmcd/django-socketio/blob/b704f912551829a3bcf15872ba0e1baf81dea106/django_socketio/example_project/chat/events.py#L10-L37 | def message(request, socket, context, message):
"""
Event handler for a room receiving a message. First validates a
joining user's name and sends them the list of users.
"""
room = get_object_or_404(ChatRoom, id=message["room"])
if message["action"] == "start":
name = strip_tags(message["name"])
user, created = room.users.get_or_create(name=name)
if not created:
socket.send({"action": "in-use"})
else:
context["user"] = user
users = [u.name for u in room.users.exclude(id=user.id)]
socket.send({"action": "started", "users": users})
user.session = socket.session.session_id
user.save()
joined = {"action": "join", "name": user.name, "id": user.id}
socket.send_and_broadcast_channel(joined)
else:
try:
user = context["user"]
except KeyError:
return
if message["action"] == "message":
message["message"] = strip_tags(message["message"])
message["name"] = user.name
socket.send_and_broadcast_channel(message) | [
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OCR-D/core | ocrd/ocrd/processor/base.py | https://github.com/OCR-D/core/blob/57e68c578526cb955fd2e368207f5386c459d91d/ocrd/ocrd/processor/base.py#L76-L105 | def run_cli(
executable,
mets_url=None,
resolver=None,
workspace=None,
page_id=None,
log_level=None,
input_file_grp=None,
output_file_grp=None,
parameter=None,
working_dir=None,
):
"""
Create a workspace for mets_url and run MP CLI through it
"""
workspace = _get_workspace(workspace, resolver, mets_url, working_dir)
args = [executable, '--working-dir', workspace.directory]
args += ['--mets', mets_url]
if log_level:
args += ['--log-level', log_level]
if page_id:
args += ['--page-id', page_id]
if input_file_grp:
args += ['--input-file-grp', input_file_grp]
if output_file_grp:
args += ['--output-file-grp', output_file_grp]
if parameter:
args += ['--parameter', parameter]
log.debug("Running subprocess '%s'", ' '.join(args))
return subprocess.call(args) | [
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GNS3/gns3-server | gns3server/controller/project.py | https://github.com/GNS3/gns3-server/blob/a221678448fb5d24e977ef562f81d56aacc89ab1/gns3server/controller/project.py#L902-L909 | def start_all(self):
"""
Start all nodes
"""
pool = Pool(concurrency=3)
for node in self.nodes.values():
pool.append(node.start)
yield from pool.join() | [
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spyder-ide/spyder | spyder/utils/encoding.py | https://github.com/spyder-ide/spyder/blob/f76836ce1b924bcc4efd3f74f2960d26a4e528e0/spyder/utils/encoding.py#L63-L77 | def to_unicode_from_fs(string):
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openstates/billy | billy/importers/names.py | https://github.com/openstates/billy/blob/5fc795347f12a949e410a8cfad0c911ea6bced67/billy/importers/names.py#L120-L131 | def _normalize(self, name):
"""
Normalizes a legislator name by stripping titles from the front,
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"""
name = re.sub(
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name)
return name.strip().lower().replace('.', '') | [
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sorgerlab/indra | indra/sources/geneways/symbols_parser.py | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/sources/geneways/symbols_parser.py#L50-L57 | def symbol_to_id(self, symbol):
"""Returns the list of Entrez IDs for a given Geneways symbol
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if symbol not in self.symbols_to_ids:
m = 'Could not look up Entrez ID for Geneways symbol ' + symbol
raise Exception(m)
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skyfielders/python-skyfield | skyfield/positionlib.py | https://github.com/skyfielders/python-skyfield/blob/51d9e042e06457f6b1f2415296d50a38cb3a300f/skyfield/positionlib.py#L277-L304 | def from_altaz(self, alt=None, az=None, alt_degrees=None, az_degrees=None,
distance=Distance(au=0.1)):
"""Generate an Apparent position from an altitude and azimuth.
The altitude and azimuth can each be provided as an `Angle`
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alt=Angle(...), az=Angle(...)
alt_degrees=23.2289, az_degrees=142.1161
alt_degrees=(23, 13, 44.1), az_degrees=(142, 6, 58.1)
The distance should be a :class:`~skyfield.units.Distance`
object, if provided; otherwise a default of 0.1 au is used.
"""
# TODO: should this method live on another class?
R = self.observer_data.altaz_rotation if self.observer_data else None
if R is None:
raise ValueError('only a position generated by a topos() call'
' knows the orientation of the horizon'
' and can understand altitude and azimuth')
alt = _interpret_angle('alt', alt, alt_degrees)
az = _interpret_angle('az', az, az_degrees)
r = distance.au
p = from_polar(r, alt, az)
p = einsum('ji...,j...->i...', R, p)
return Apparent(p) | [
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chrislim2888/IP2Location-Python | IP2Location.py | https://github.com/chrislim2888/IP2Location-Python/blob/6b2a7d3a5e61c9f8efda5ae96c7064f9a7714621/IP2Location.py#L177-L180 | def get_area_code(self, ip):
''' Get area_code '''
rec = self.get_all(ip)
return rec and rec.area_code | [
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horazont/aiosasl | aiosasl/__init__.py | https://github.com/horazont/aiosasl/blob/af58bf30f688757e58af6e87892d35a8ce798482/aiosasl/__init__.py#L464-L509 | def response(self, payload):
"""
Send a response to the previously received challenge, with the given
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Return the next state of the state machine as tuple (see
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next_state, payload = yield from self.interface.respond(payload)
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self._state = SASLState.FAILURE
raise
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# unfold the (SASLState.SUCCESS, payload) to a sequence of
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if next_state == SASLState.SUCCESS and payload is not None:
self._state = SASLState.SUCCESS_SIMULATE_CHALLENGE
return SASLState.CHALLENGE, payload
self._state = next_state
return next_state, payload | [
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wavycloud/pyboto3 | pyboto3/ssm.py | https://github.com/wavycloud/pyboto3/blob/924957ccf994303713a4eed90b775ff2ab95b2e5/pyboto3/ssm.py#L3774-L3883 | def register_task_with_maintenance_window(WindowId=None, Targets=None, TaskArn=None, ServiceRoleArn=None, TaskType=None, TaskParameters=None, Priority=None, MaxConcurrency=None, MaxErrors=None, LoggingInfo=None, ClientToken=None):
"""
Adds a new task to a Maintenance Window.
See also: AWS API Documentation
:example: response = client.register_task_with_maintenance_window(
WindowId='string',
Targets=[
{
'Key': 'string',
'Values': [
'string',
]
},
],
TaskArn='string',
ServiceRoleArn='string',
TaskType='RUN_COMMAND',
TaskParameters={
'string': {
'Values': [
'string',
]
}
},
Priority=123,
MaxConcurrency='string',
MaxErrors='string',
LoggingInfo={
'S3BucketName': 'string',
'S3KeyPrefix': 'string',
'S3Region': 'string'
},
ClientToken='string'
)
:type WindowId: string
:param WindowId: [REQUIRED]
The id of the Maintenance Window the task should be added to.
:type Targets: list
:param Targets: [REQUIRED]
The targets (either instances or tags). Instances are specified using Key=instanceids,Values=instanceid1,instanceid2. Tags are specified using Key=tag name,Values=tag value.
(dict) --An array of search criteria that targets instances using a Key,Value combination that you specify. Targets is required if you don't provide one or more instance IDs in the call.
Key (string) --User-defined criteria for sending commands that target instances that meet the criteria. Key can be tag:Amazon EC2 tagor InstanceIds. For more information about how to send commands that target instances using Key,Value parameters, see Executing a Command Using Systems Manager Run Command .
Values (list) --User-defined criteria that maps to Key. For example, if you specified tag:ServerRole, you could specify value:WebServer to execute a command on instances that include Amazon EC2 tags of ServerRole,WebServer. For more information about how to send commands that target instances using Key,Value parameters, see Executing a Command Using Systems Manager Run Command .
(string) --
:type TaskArn: string
:param TaskArn: [REQUIRED]
The ARN of the task to execute
:type ServiceRoleArn: string
:param ServiceRoleArn: [REQUIRED]
The role that should be assumed when executing the task.
:type TaskType: string
:param TaskType: [REQUIRED]
The type of task being registered.
:type TaskParameters: dict
:param TaskParameters: The parameters that should be passed to the task when it is executed.
(string) --
(dict) --Defines the values for a task parameter.
Values (list) --This field contains an array of 0 or more strings, each 1 to 255 characters in length.
(string) --
:type Priority: integer
:param Priority: The priority of the task in the Maintenance Window, the lower the number the higher the priority. Tasks in a Maintenance Window are scheduled in priority order with tasks that have the same priority scheduled in parallel.
:type MaxConcurrency: string
:param MaxConcurrency: [REQUIRED]
The maximum number of targets this task can be run for in parallel.
:type MaxErrors: string
:param MaxErrors: [REQUIRED]
The maximum number of errors allowed before this task stops being scheduled.
:type LoggingInfo: dict
:param LoggingInfo: A structure containing information about an Amazon S3 bucket to write instance-level logs to.
S3BucketName (string) -- [REQUIRED]The name of an Amazon S3 bucket where execution logs are stored .
S3KeyPrefix (string) --(Optional) The Amazon S3 bucket subfolder.
S3Region (string) -- [REQUIRED]The region where the Amazon S3 bucket is located.
:type ClientToken: string
:param ClientToken: User-provided idempotency token.
This field is autopopulated if not provided.
:rtype: dict
:return: {
'WindowTaskId': 'string'
}
"""
pass | [
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The role that should be assumed when executing the task.
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:type MaxConcurrency: string
:param MaxConcurrency: [REQUIRED]
The maximum number of targets this task can be run for in parallel.
:type MaxErrors: string
:param MaxErrors: [REQUIRED]
The maximum number of errors allowed before this task stops being scheduled.
:type LoggingInfo: dict
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S3KeyPrefix (string) --(Optional) The Amazon S3 bucket subfolder.
S3Region (string) -- [REQUIRED]The region where the Amazon S3 bucket is located.
:type ClientToken: string
:param ClientToken: User-provided idempotency token.
This field is autopopulated if not provided.
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'WindowTaskId': 'string'
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Kozea/cairocffi | cairocffi/context.py | https://github.com/Kozea/cairocffi/blob/450853add7e32eea20985b6aa5f54d9cb3cd04fe/cairocffi/context.py#L1754-L1764 | def set_font_face(self, font_face):
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or :obj:`None` to restore the default font.
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thriftrw/thriftrw-python | thriftrw/idl/parser.py | https://github.com/thriftrw/thriftrw-python/blob/4f2f71acd7a0ac716c9ea5cdcea2162aa561304a/thriftrw/idl/parser.py#L206-L210 | def p_struct(self, p):
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wavycloud/pyboto3 | pyboto3/cloudformation.py | https://github.com/wavycloud/pyboto3/blob/924957ccf994303713a4eed90b775ff2ab95b2e5/pyboto3/cloudformation.py#L123-L253 | def create_change_set(StackName=None, TemplateBody=None, TemplateURL=None, UsePreviousTemplate=None, Parameters=None, Capabilities=None, ResourceTypes=None, RoleARN=None, NotificationARNs=None, Tags=None, ChangeSetName=None, ClientToken=None, Description=None, ChangeSetType=None):
"""
Creates a list of changes that will be applied to a stack so that you can review the changes before executing them. You can create a change set for a stack that doesn't exist or an existing stack. If you create a change set for a stack that doesn't exist, the change set shows all of the resources that AWS CloudFormation will create. If you create a change set for an existing stack, AWS CloudFormation compares the stack's information with the information that you submit in the change set and lists the differences. Use change sets to understand which resources AWS CloudFormation will create or change, and how it will change resources in an existing stack, before you create or update a stack.
To create a change set for a stack that doesn't exist, for the ChangeSetType parameter, specify CREATE . To create a change set for an existing stack, specify UPDATE for the ChangeSetType parameter. After the CreateChangeSet call successfully completes, AWS CloudFormation starts creating the change set. To check the status of the change set or to review it, use the DescribeChangeSet action.
When you are satisfied with the changes the change set will make, execute the change set by using the ExecuteChangeSet action. AWS CloudFormation doesn't make changes until you execute the change set.
See also: AWS API Documentation
:example: response = client.create_change_set(
StackName='string',
TemplateBody='string',
TemplateURL='string',
UsePreviousTemplate=True|False,
Parameters=[
{
'ParameterKey': 'string',
'ParameterValue': 'string',
'UsePreviousValue': True|False
},
],
Capabilities=[
'CAPABILITY_IAM'|'CAPABILITY_NAMED_IAM',
],
ResourceTypes=[
'string',
],
RoleARN='string',
NotificationARNs=[
'string',
],
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ChangeSetName='string',
ClientToken='string',
Description='string',
ChangeSetType='CREATE'|'UPDATE'
)
:type StackName: string
:param StackName: [REQUIRED]
The name or the unique ID of the stack for which you are creating a change set. AWS CloudFormation generates the change set by comparing this stack's information with the information that you submit, such as a modified template or different parameter input values.
:type TemplateBody: string
:param TemplateBody: A structure that contains the body of the revised template, with a minimum length of 1 byte and a maximum length of 51,200 bytes. AWS CloudFormation generates the change set by comparing this template with the template of the stack that you specified.
Conditional: You must specify only TemplateBody or TemplateURL .
:type TemplateURL: string
:param TemplateURL: The location of the file that contains the revised template. The URL must point to a template (max size: 460,800 bytes) that is located in an S3 bucket. AWS CloudFormation generates the change set by comparing this template with the stack that you specified.
Conditional: You must specify only TemplateBody or TemplateURL .
:type UsePreviousTemplate: boolean
:param UsePreviousTemplate: Whether to reuse the template that is associated with the stack to create the change set.
:type Parameters: list
:param Parameters: A list of Parameter structures that specify input parameters for the change set. For more information, see the Parameter data type.
(dict) --The Parameter data type.
ParameterKey (string) --The key associated with the parameter. If you don't specify a key and value for a particular parameter, AWS CloudFormation uses the default value that is specified in your template.
ParameterValue (string) --The value associated with the parameter.
UsePreviousValue (boolean) --During a stack update, use the existing parameter value that the stack is using for a given parameter key. If you specify true , do not specify a parameter value.
:type Capabilities: list
:param Capabilities: A list of values that you must specify before AWS CloudFormation can update certain stacks. Some stack templates might include resources that can affect permissions in your AWS account, for example, by creating new AWS Identity and Access Management (IAM) users. For those stacks, you must explicitly acknowledge their capabilities by specifying this parameter.
The only valid values are CAPABILITY_IAM and CAPABILITY_NAMED_IAM . The following resources require you to specify this parameter: AWS::IAM::AccessKey , AWS::IAM::Group , AWS::IAM::InstanceProfile , AWS::IAM::Policy , AWS::IAM::Role , AWS::IAM::User , and AWS::IAM::UserToGroupAddition . If your stack template contains these resources, we recommend that you review all permissions associated with them and edit their permissions if necessary.
If you have IAM resources, you can specify either capability. If you have IAM resources with custom names, you must specify CAPABILITY_NAMED_IAM . If you don't specify this parameter, this action returns an InsufficientCapabilities error.
For more information, see Acknowledging IAM Resources in AWS CloudFormation Templates .
(string) --
:type ResourceTypes: list
:param ResourceTypes: The template resource types that you have permissions to work with if you execute this change set, such as AWS::EC2::Instance , AWS::EC2::* , or Custom::MyCustomInstance .
If the list of resource types doesn't include a resource type that you're updating, the stack update fails. By default, AWS CloudFormation grants permissions to all resource types. AWS Identity and Access Management (IAM) uses this parameter for condition keys in IAM policies for AWS CloudFormation. For more information, see Controlling Access with AWS Identity and Access Management in the AWS CloudFormation User Guide.
(string) --
:type RoleARN: string
:param RoleARN: The Amazon Resource Name (ARN) of an AWS Identity and Access Management (IAM) role that AWS CloudFormation assumes when executing the change set. AWS CloudFormation uses the role's credentials to make calls on your behalf. AWS CloudFormation uses this role for all future operations on the stack. As long as users have permission to operate on the stack, AWS CloudFormation uses this role even if the users don't have permission to pass it. Ensure that the role grants least privilege.
If you don't specify a value, AWS CloudFormation uses the role that was previously associated with the stack. If no role is available, AWS CloudFormation uses a temporary session that is generated from your user credentials.
:type NotificationARNs: list
:param NotificationARNs: The Amazon Resource Names (ARNs) of Amazon Simple Notification Service (Amazon SNS) topics that AWS CloudFormation associates with the stack. To remove all associated notification topics, specify an empty list.
(string) --
:type Tags: list
:param Tags: Key-value pairs to associate with this stack. AWS CloudFormation also propagates these tags to resources in the stack. You can specify a maximum of 10 tags.
(dict) --The Tag type enables you to specify a key-value pair that can be used to store information about an AWS CloudFormation stack.
Key (string) --
Required . A string used to identify this tag. You can specify a maximum of 128 characters for a tag key. Tags owned by Amazon Web Services (AWS) have the reserved prefix: aws: .
Value (string) --
Required . A string containing the value for this tag. You can specify a maximum of 256 characters for a tag value.
:type ChangeSetName: string
:param ChangeSetName: [REQUIRED]
The name of the change set. The name must be unique among all change sets that are associated with the specified stack.
A change set name can contain only alphanumeric, case sensitive characters and hyphens. It must start with an alphabetic character and cannot exceed 128 characters.
:type ClientToken: string
:param ClientToken: A unique identifier for this CreateChangeSet request. Specify this token if you plan to retry requests so that AWS CloudFormation knows that you're not attempting to create another change set with the same name. You might retry CreateChangeSet requests to ensure that AWS CloudFormation successfully received them.
:type Description: string
:param Description: A description to help you identify this change set.
:type ChangeSetType: string
:param ChangeSetType: The type of change set operation. To create a change set for a new stack, specify CREATE . To create a change set for an existing stack, specify UPDATE .
If you create a change set for a new stack, AWS Cloudformation creates a stack with a unique stack ID, but no template or resources. The stack will be in the ` REVIEW_IN_PROGRESS http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-describing-stacks.html#d0e11995`_ state until you execute the change set.
By default, AWS CloudFormation specifies UPDATE . You can't use the UPDATE type to create a change set for a new stack or the CREATE type to create a change set for an existing stack.
:rtype: dict
:return: {
'Id': 'string',
'StackId': 'string'
}
"""
pass | [
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To create a change set for a stack that doesn't exist, for the ChangeSetType parameter, specify CREATE . To create a change set for an existing stack, specify UPDATE for the ChangeSetType parameter. After the CreateChangeSet call successfully completes, AWS CloudFormation starts creating the change set. To check the status of the change set or to review it, use the DescribeChangeSet action.
When you are satisfied with the changes the change set will make, execute the change set by using the ExecuteChangeSet action. AWS CloudFormation doesn't make changes until you execute the change set.
See also: AWS API Documentation
:example: response = client.create_change_set(
StackName='string',
TemplateBody='string',
TemplateURL='string',
UsePreviousTemplate=True|False,
Parameters=[
{
'ParameterKey': 'string',
'ParameterValue': 'string',
'UsePreviousValue': True|False
},
],
Capabilities=[
'CAPABILITY_IAM'|'CAPABILITY_NAMED_IAM',
],
ResourceTypes=[
'string',
],
RoleARN='string',
NotificationARNs=[
'string',
],
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ChangeSetName='string',
ClientToken='string',
Description='string',
ChangeSetType='CREATE'|'UPDATE'
)
:type StackName: string
:param StackName: [REQUIRED]
The name or the unique ID of the stack for which you are creating a change set. AWS CloudFormation generates the change set by comparing this stack's information with the information that you submit, such as a modified template or different parameter input values.
:type TemplateBody: string
:param TemplateBody: A structure that contains the body of the revised template, with a minimum length of 1 byte and a maximum length of 51,200 bytes. AWS CloudFormation generates the change set by comparing this template with the template of the stack that you specified.
Conditional: You must specify only TemplateBody or TemplateURL .
:type TemplateURL: string
:param TemplateURL: The location of the file that contains the revised template. The URL must point to a template (max size: 460,800 bytes) that is located in an S3 bucket. AWS CloudFormation generates the change set by comparing this template with the stack that you specified.
Conditional: You must specify only TemplateBody or TemplateURL .
:type UsePreviousTemplate: boolean
:param UsePreviousTemplate: Whether to reuse the template that is associated with the stack to create the change set.
:type Parameters: list
:param Parameters: A list of Parameter structures that specify input parameters for the change set. For more information, see the Parameter data type.
(dict) --The Parameter data type.
ParameterKey (string) --The key associated with the parameter. If you don't specify a key and value for a particular parameter, AWS CloudFormation uses the default value that is specified in your template.
ParameterValue (string) --The value associated with the parameter.
UsePreviousValue (boolean) --During a stack update, use the existing parameter value that the stack is using for a given parameter key. If you specify true , do not specify a parameter value.
:type Capabilities: list
:param Capabilities: A list of values that you must specify before AWS CloudFormation can update certain stacks. Some stack templates might include resources that can affect permissions in your AWS account, for example, by creating new AWS Identity and Access Management (IAM) users. For those stacks, you must explicitly acknowledge their capabilities by specifying this parameter.
The only valid values are CAPABILITY_IAM and CAPABILITY_NAMED_IAM . The following resources require you to specify this parameter: AWS::IAM::AccessKey , AWS::IAM::Group , AWS::IAM::InstanceProfile , AWS::IAM::Policy , AWS::IAM::Role , AWS::IAM::User , and AWS::IAM::UserToGroupAddition . If your stack template contains these resources, we recommend that you review all permissions associated with them and edit their permissions if necessary.
If you have IAM resources, you can specify either capability. If you have IAM resources with custom names, you must specify CAPABILITY_NAMED_IAM . If you don't specify this parameter, this action returns an InsufficientCapabilities error.
For more information, see Acknowledging IAM Resources in AWS CloudFormation Templates .
(string) --
:type ResourceTypes: list
:param ResourceTypes: The template resource types that you have permissions to work with if you execute this change set, such as AWS::EC2::Instance , AWS::EC2::* , or Custom::MyCustomInstance .
If the list of resource types doesn't include a resource type that you're updating, the stack update fails. By default, AWS CloudFormation grants permissions to all resource types. AWS Identity and Access Management (IAM) uses this parameter for condition keys in IAM policies for AWS CloudFormation. For more information, see Controlling Access with AWS Identity and Access Management in the AWS CloudFormation User Guide.
(string) --
:type RoleARN: string
:param RoleARN: The Amazon Resource Name (ARN) of an AWS Identity and Access Management (IAM) role that AWS CloudFormation assumes when executing the change set. AWS CloudFormation uses the role's credentials to make calls on your behalf. AWS CloudFormation uses this role for all future operations on the stack. As long as users have permission to operate on the stack, AWS CloudFormation uses this role even if the users don't have permission to pass it. Ensure that the role grants least privilege.
If you don't specify a value, AWS CloudFormation uses the role that was previously associated with the stack. If no role is available, AWS CloudFormation uses a temporary session that is generated from your user credentials.
:type NotificationARNs: list
:param NotificationARNs: The Amazon Resource Names (ARNs) of Amazon Simple Notification Service (Amazon SNS) topics that AWS CloudFormation associates with the stack. To remove all associated notification topics, specify an empty list.
(string) --
:type Tags: list
:param Tags: Key-value pairs to associate with this stack. AWS CloudFormation also propagates these tags to resources in the stack. You can specify a maximum of 10 tags.
(dict) --The Tag type enables you to specify a key-value pair that can be used to store information about an AWS CloudFormation stack.
Key (string) --
Required . A string used to identify this tag. You can specify a maximum of 128 characters for a tag key. Tags owned by Amazon Web Services (AWS) have the reserved prefix: aws: .
Value (string) --
Required . A string containing the value for this tag. You can specify a maximum of 256 characters for a tag value.
:type ChangeSetName: string
:param ChangeSetName: [REQUIRED]
The name of the change set. The name must be unique among all change sets that are associated with the specified stack.
A change set name can contain only alphanumeric, case sensitive characters and hyphens. It must start with an alphabetic character and cannot exceed 128 characters.
:type ClientToken: string
:param ClientToken: A unique identifier for this CreateChangeSet request. Specify this token if you plan to retry requests so that AWS CloudFormation knows that you're not attempting to create another change set with the same name. You might retry CreateChangeSet requests to ensure that AWS CloudFormation successfully received them.
:type Description: string
:param Description: A description to help you identify this change set.
:type ChangeSetType: string
:param ChangeSetType: The type of change set operation. To create a change set for a new stack, specify CREATE . To create a change set for an existing stack, specify UPDATE .
If you create a change set for a new stack, AWS Cloudformation creates a stack with a unique stack ID, but no template or resources. The stack will be in the ` REVIEW_IN_PROGRESS http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-describing-stacks.html#d0e11995`_ state until you execute the change set.
By default, AWS CloudFormation specifies UPDATE . You can't use the UPDATE type to create a change set for a new stack or the CREATE type to create a change set for an existing stack.
:rtype: dict
:return: {
'Id': 'string',
'StackId': 'string'
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waleedka/hiddenlayer | hiddenlayer/canvas.py | https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/canvas.py#L191-L204 | def draw_image(self, metric, limit=5):
"""Display a series of images at different time steps."""
rows = 1
cols = limit
self.ax.axis("off")
# Take the Axes gridspec and divide it into a grid
gs = matplotlib.gridspec.GridSpecFromSubplotSpec(
rows, cols, subplot_spec=self.gs)
# Loop through images in last few steps
for i, image in enumerate(metric.data[-cols:]):
ax = self.figure.add_subplot(gs[0, i])
ax.axis('off')
ax.set_title(metric.formatted_steps[-cols:][i])
ax.imshow(norm(image)) | [
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googledatalab/pydatalab | google/datalab/bigquery/_query.py | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/bigquery/_query.py#L324-L339 | def execute(self, output_options=None, sampling=None, context=None, query_params=None):
""" Initiate the query and return a QueryJob.
Args:
output_options: a QueryOutput object describing how to execute the query
sampling: sampling function to use. No sampling is done if None. See bigquery.Sampling
context: an optional Context object providing project_id and credentials. If a specific
project id or credentials are unspecified, the default ones configured at the global
level are used.
Returns:
A Job object that can be used to get the query results, or export to a file or dataframe
Raises:
Exception if query could not be executed.
"""
return self.execute_async(output_options, sampling=sampling, context=context,
query_params=query_params).wait() | [
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limix/glimix-core | glimix_core/cov/_free.py | https://github.com/limix/glimix-core/blob/cddd0994591d100499cc41c1f480ddd575e7a980/glimix_core/cov/_free.py#L170-L189 | def logdet(self):
"""
Log of |K|.
Returns
-------
float
Log-determinant of K.
"""
from numpy.linalg import slogdet
K = self.value()
sign, logdet = slogdet(K)
if sign != 1.0:
msg = "The estimated determinant of K is not positive: "
msg += f" ({sign}, {logdet})."
raise RuntimeError(msg)
return logdet | [
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oz123/blogit | blogit/blogit.py | https://github.com/oz123/blogit/blob/15b94969fa43aaf8dc677a8184b144ae8c0f7700/blogit/blogit.py#L467-L480 | def preview(): # pragma: no coverage
"""launch an HTTP to preview the website"""
Handler = http.server.SimpleHTTPRequestHandler
socketserver.TCPServer.allow_reuse_address = True
port = CONFIG['http_port']
httpd = socketserver.TCPServer(("", port), Handler)
os.chdir(CONFIG['output_to'])
try:
logger.info("and ready to test at "
"http://127.0.0.1:%d" % CONFIG['http_port'])
logger.info("Hit Ctrl+C to exit")
httpd.serve_forever()
except KeyboardInterrupt:
httpd.shutdown() | [
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pypa/pipenv | pipenv/vendor/click/termui.py | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/click/termui.py#L232-L258 | def echo_via_pager(text_or_generator, color=None):
"""This function takes a text and shows it via an environment specific
pager on stdout.
.. versionchanged:: 3.0
Added the `color` flag.
:param text_or_generator: the text to page, or alternatively, a
generator emitting the text to page.
:param color: controls if the pager supports ANSI colors or not. The
default is autodetection.
"""
color = resolve_color_default(color)
if inspect.isgeneratorfunction(text_or_generator):
i = text_or_generator()
elif isinstance(text_or_generator, string_types):
i = [text_or_generator]
else:
i = iter(text_or_generator)
# convert every element of i to a text type if necessary
text_generator = (el if isinstance(el, string_types) else text_type(el)
for el in i)
from ._termui_impl import pager
return pager(itertools.chain(text_generator, "\n"), color) | [
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studionow/pybrightcove | pybrightcove/playlist.py | https://github.com/studionow/pybrightcove/blob/19c946b689a80156e070fe9bc35589c4b768e614/pybrightcove/playlist.py#L171-L177 | def find_all(connection=None, page_size=100, page_number=0,
sort_by=DEFAULT_SORT_BY, sort_order=DEFAULT_SORT_ORDER):
"""
List all playlists.
"""
return pybrightcove.connection.ItemResultSet("find_all_playlists",
Playlist, connection, page_size, page_number, sort_by, sort_order) | [
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saltstack/salt | salt/states/boto_vpc.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/states/boto_vpc.py#L674-L691 | def _verify_subnet_association(route_table_desc, subnet_id):
'''
Helper function verify a subnet's route table association
route_table_desc
the description of a route table, as returned from boto_vpc.describe_route_table
subnet_id
the subnet id to verify
.. versionadded:: 2016.11.0
'''
if route_table_desc:
if 'associations' in route_table_desc:
for association in route_table_desc['associations']:
if association['subnet_id'] == subnet_id:
return True
return False | [
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observermedia/django-wordpress-rest | wordpress/loading.py | https://github.com/observermedia/django-wordpress-rest/blob/f0d96891d8ac5a69c8ba90e044876e756fad1bfe/wordpress/loading.py#L500-L519 | def set_posts_param_modified_after(self, params, post_type, status):
"""
Set modified_after date to "continue where we left off" if appropriate
:param params: the GET params dict, which may be updated to include the "modified_after" key
:param post_type: post, page, attachment, or any custom post type set up in the WP API
:param status: publish, private, draft, etc.
:return: None
"""
if not self.purge_first and not self.full and not self.modified_after:
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else:
latest = Post.objects.filter(post_type=post_type, status=status).order_by("-modified").first()
if latest:
self.modified_after = latest.modified
if self.modified_after:
params["modified_after"] = self.modified_after.isoformat()
logger.info("getting posts after: %s", params["modified_after"]) | [
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mdsol/rwslib | rwslib/builders/admindata.py | https://github.com/mdsol/rwslib/blob/1a86bc072d408c009ed1de8bf6e98a1769f54d18/rwslib/builders/admindata.py#L236-L255 | def build(self, builder):
"""Build XML by appending to builder"""
params = dict(OID=self.oid)
if self.user_type:
params.update(dict(UserType=self.user_type.value))
builder.start(self.__class__.__name__, params)
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odlgroup/odl | odl/solvers/nonsmooth/proximal_operators.py | https://github.com/odlgroup/odl/blob/b8443f6aca90e191ba36c91d32253c5a36249a6c/odl/solvers/nonsmooth/proximal_operators.py#L1352-L1448 | def proximal_l1_l2(space, lam=1, g=None):
r"""Proximal operator factory of the group-L1-L2 norm/distance.
Implements the proximal operator of the functional ::
F(x) = lam || |x - g|_2 ||_1
with ``x`` and ``g`` elements in ``space``, and scaling factor ``lam``.
Here, ``|.|_2`` is the pointwise Euclidean norm of a vector-valued
function.
Parameters
----------
space : `LinearSpace` or `ProductSpace`
Domain of the functional.
lam : positive float, optional
Scaling factor or regularization parameter.
g : ``space`` element, optional
Element to which the L1-L2 distance is taken.
Default: ``space.zero``.
Returns
-------
prox_factory : function
Factory for the proximal operator to be initialized
Notes
-----
For the functional
.. math::
F(x) = \lambda \| |x - g|_2 \|_1,
and a step size :math:`\sigma`, the proximal operator of
:math:`\sigma F` is given as the "soft-shrinkage" operator
.. math::
\mathrm{prox}_{\sigma F}(x) =
\begin{cases}
g, & \text{where } |x - g|_2 \leq \sigma\lambda, \\
x - \sigma\lambda \frac{x - g}{|x - g|_2}, & \text{elsewhere.}
\end{cases}
Here, all operations are to be read pointwise.
See Also
--------
proximal_l1 : Scalar or non-isotropic vectorial variant
"""
lam = float(lam)
if g is not None and g not in space:
raise TypeError('{!r} is not an element of {!r}'.format(g, space))
class ProximalL1L2(Operator):
"""Proximal operator of the group-L1-L2 norm/distance."""
def __init__(self, sigma):
"""Initialize a new instance.
Parameters
----------
sigma : positive float
Step size parameter.
"""
super(ProximalL1L2, self).__init__(
domain=space, range=space, linear=False)
self.sigma = float(sigma)
def _call(self, x, out):
"""Return ``self(x, out=out)``."""
# diff = x - g
if g is not None:
diff = x - g
else:
if x is out:
# Handle aliased `x` and `out` (original `x` needed later)
diff = x.copy()
else:
diff = x
# We write the operator as
# x - (x - g) / max(|x - g|_2 / sig*lam, 1)
pwnorm = PointwiseNorm(self.domain, exponent=2)
denom = pwnorm(diff)
denom /= self.sigma * lam
denom.ufuncs.maximum(1, out=denom)
# out = (x - g) / denom
for out_i, diff_i in zip(out, diff):
diff_i.divide(denom, out=out_i)
# out = x - ...
out.lincomb(1, x, -1, out)
return ProximalL1L2 | [
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] | r"""Proximal operator factory of the group-L1-L2 norm/distance.
Implements the proximal operator of the functional ::
F(x) = lam || |x - g|_2 ||_1
with ``x`` and ``g`` elements in ``space``, and scaling factor ``lam``.
Here, ``|.|_2`` is the pointwise Euclidean norm of a vector-valued
function.
Parameters
----------
space : `LinearSpace` or `ProductSpace`
Domain of the functional.
lam : positive float, optional
Scaling factor or regularization parameter.
g : ``space`` element, optional
Element to which the L1-L2 distance is taken.
Default: ``space.zero``.
Returns
-------
prox_factory : function
Factory for the proximal operator to be initialized
Notes
-----
For the functional
.. math::
F(x) = \lambda \| |x - g|_2 \|_1,
and a step size :math:`\sigma`, the proximal operator of
:math:`\sigma F` is given as the "soft-shrinkage" operator
.. math::
\mathrm{prox}_{\sigma F}(x) =
\begin{cases}
g, & \text{where } |x - g|_2 \leq \sigma\lambda, \\
x - \sigma\lambda \frac{x - g}{|x - g|_2}, & \text{elsewhere.}
\end{cases}
Here, all operations are to be read pointwise.
See Also
--------
proximal_l1 : Scalar or non-isotropic vectorial variant | [
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NyashniyVladya/MarkovTextGenerator | MarkovTextGenerator/markov_text_generator.py | https://github.com/NyashniyVladya/MarkovTextGenerator/blob/3d90e02a507939709773ef01c7ff3ec68b2b8d4b/MarkovTextGenerator/markov_text_generator.py#L382-L395 | def _parse_from_file(self, file_path):
"""
см. описание _parse_from_text.
Только на вход подаётся не текст, а путь к файлу.
"""
file_path = abspath(file_path)
if not isfile(file_path):
raise MarkovTextExcept("Передан не файл.")
with open(file_path, "rb") as txt_file:
for line in txt_file:
text = line.decode("utf-8", "ignore").strip()
if not text:
continue
yield from self._parse_from_text(text) | [
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LogicalDash/LiSE | ELiDE/ELiDE/board/board.py | https://github.com/LogicalDash/LiSE/blob/fe6fd4f0a7c1780e065f4c9babb9bc443af6bb84/ELiDE/ELiDE/board/board.py#L846-L850 | def arrows(self):
"""Iterate over all my arrows."""
for o in self.arrow.values():
for arro in o.values():
yield arro | [
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fossasia/AYABInterface | AYABInterface/communication/__init__.py | https://github.com/fossasia/AYABInterface/blob/e2065eed8daf17b2936f6ca5e488c9bfb850914e/AYABInterface/communication/__init__.py#L79-L84 | def _message_received(self, message):
"""Notify the observers about the received message."""
with self.lock:
self._state.receive_message(message)
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jsommers/switchyard | switchyard/lib/packet/common.py | https://github.com/jsommers/switchyard/blob/fdcb3869c937dcedbd6ea7a7822ebd412bf1e2b0/switchyard/lib/packet/common.py#L317-L349 | def checksum (data, start = 0, skip_word = None):
"""
Calculate standard internet checksum over data starting at start'th byte
skip_word: If specified, it's the word offset of a word in data to "skip"
(as if it were zero). The purpose is when data is received
data which contains a computed checksum that you are trying to
verify -- you want to skip that word since it was zero when
the checksum was initially calculated.
"""
if len(data) % 2 != 0:
arr = array.array('H', data[:-1])
else:
arr = array.array('H', data)
if skip_word is not None:
for i in range(0, len(arr)):
if i == skip_word:
continue
start += arr[i]
else:
for i in range(0, len(arr)):
start += arr[i]
if len(data) % 2 != 0:
start += struct.unpack('H', data[-1:]+b'\x00')[0] # Specify order?
start = (start >> 16) + (start & 0xffff)
start += (start >> 16)
#while start >> 16:
# start = (start >> 16) + (start & 0xffff)
return ntohs(~start & 0xffff) | [
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(as if it were zero). The purpose is when data is received
data which contains a computed checksum that you are trying to
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the checksum was initially calculated. | [
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JarryShaw/PyPCAPKit | src/const/hip/parameter.py | https://github.com/JarryShaw/PyPCAPKit/blob/c7f0da9aebc2cf210bf8f8b912f7d3cbb98ca10e/src/const/hip/parameter.py#L71-L77 | def get(key, default=-1):
"""Backport support for original codes."""
if isinstance(key, int):
return Parameter(key)
if key not in Parameter._member_map_:
extend_enum(Parameter, key, default)
return Parameter[key] | [
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paramiko/paramiko | paramiko/server.py | https://github.com/paramiko/paramiko/blob/cf7d49d66f3b1fbc8b0853518a54050182b3b5eb/paramiko/server.py#L239-L267 | def check_auth_gssapi_with_mic(
self, username, gss_authenticated=AUTH_FAILED, cc_file=None
):
"""
Authenticate the given user to the server if he is a valid krb5
principal.
:param str username: The username of the authenticating client
:param int gss_authenticated: The result of the krb5 authentication
:param str cc_filename: The krb5 client credentials cache filename
:return: ``AUTH_FAILED`` if the user is not authenticated otherwise
``AUTH_SUCCESSFUL``
:rtype: int
:note: Kerberos credential delegation is not supported.
:see: `.ssh_gss`
:note: : We are just checking in L{AuthHandler} that the given user is
a valid krb5 principal!
We don't check if the krb5 principal is allowed to log in on
the server, because there is no way to do that in python. So
if you develop your own SSH server with paramiko for a cetain
plattform like Linux, you should call C{krb5_kuserok()} in
your local kerberos library to make sure that the
krb5_principal has an account on the server and is allowed to
log in as a user.
:see: http://www.unix.com/man-page/all/3/krb5_kuserok/
"""
if gss_authenticated == AUTH_SUCCESSFUL:
return AUTH_SUCCESSFUL
return AUTH_FAILED | [
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if you develop your own SSH server with paramiko for a cetain
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schneiderfelipe/pyrrole | pyrrole/drawing.py | https://github.com/schneiderfelipe/pyrrole/blob/13e26accc9a059f0ab69773648b24292fe1fbfd6/pyrrole/drawing.py#L257-L371 | def draw_diagram_nodes(graph, pos=None, nodelist=None, node_size=.7,
node_color='k', style='solid', alpha=1.0, cmap=None,
vmin=None, vmax=None, ax=None, label=None):
"""
Draw nodes of graph.
This draws only the nodes of graph as horizontal lines at each
``y = pos[1]`` from ``x - node_size/2`` to ``x + node_size/2``, where
``x = pos[0]``.
Parameters
----------
graph : `networkx.Graph`
A NetworkX graph.
pos : mapping, optional
A mapping with nodes as keys and positions as values. Positions should
be sequences of length 2. If not specified (default), a diagram layout
positioning will be computed. See `networkx.layout` and
`pyrrole.drawing` for functions that compute node positions.
nodelist : `list`, optional
Draw only specified nodes (default is ``graph.nodes()``).
node_size : scalar or array
Size of nodes (default is ``.7``). If an array is specified it must be
the same length as nodelist.
node_color : color `str`, or array of `float`
Node color. Can be a single color format `str` (default is ``'k'``), or
a sequence of colors with the same length as nodelist. If numeric
values are specified they will be mapped to colors using the `cmap` and
`vmin`, `vmax` parameters. See `matplotlib.hlines` for more details.
style : `str` (``'solid'``, ``'dashed'``, ``'dotted'``, ``'dashdot'``)
Edge line style (default is ``'solid'``). See `matplotlib.hlines` for
more details.
alpha : `float` or array of `float`, optional
The node transparency. This can be a single alpha value (default is
``'1.0'``), in which case it will be applied to all the nodes of color.
Otherwise, if it is an array, the elements of alpha will be applied to
the colors in order (cycling through alpha multiple times if
necessary).
cmap : Matplotlib colormap, optional
Colormap name or Colormap instance for mapping intensities of nodes.
vmin : `float`, optional
Minimum for node colormap scaling.
vmax : `float`, optional
Maximum for node colormap scaling.
ax : `matplotlib.axes.Axes`, optional
Draw the graph in the specified Matplotlib axes.
label : `str`, optional
Label for legend.
Returns
-------
`matplotlib.collections.LineCollection`
`LineCollection` of the nodes.
Raises
------
networkx.NetworkXError
Raised if a node has no position or one with bad value.
Examples
--------
>>> import pandas as pd
>>> from pyrrole import ChemicalSystem
>>> from pyrrole.drawing import draw_diagram_nodes
>>> data = pd.DataFrame(
... [{"name": "Separated_Reactants", "freeenergy": 0.},
... {"name": "mlC1", "freeenergy": -5.4},
... {"name": "mlC2", "freeenergy": -15.6},
... {"name": "mTS1", "freeenergy": 28.5, "color": "g"},
... {"name": "mCARB1", "freeenergy": -9.7},
... {"name": "mCARB2", "freeenergy": -19.8},
... {"name": "mCARBX", "freeenergy": 20}]).set_index("name")
>>> system = ChemicalSystem(
... ["Separated_Reactants -> mlC1 -> mTS1",
... "Separated_Reactants -> mlC2 -> mTS1",
... "mCARB2 <- mTS1 -> mCARB1",
... "Separated_Reactants -> mCARBX"], data)
>>> digraph = system.to_digraph()
>>> nodes = draw_diagram_nodes(digraph)
"""
if ax is None:
ax = _plt.gca()
if nodelist is None:
nodelist = list(graph.nodes())
if not nodelist or len(nodelist) == 0: # empty nodelist, no drawing
return None
if pos is None:
pos = diagram_layout(graph)
try:
xy = _np.asarray([pos[v] for v in nodelist])
except KeyError as e:
raise _nx.NetworkXError('Node {} has no position.'.format(e))
except ValueError:
raise _nx.NetworkXError('Bad value in node positions.')
if isinstance(alpha, _collections.Iterable):
node_color = _nx.drawing.apply_alpha(node_color, alpha, nodelist, cmap,
vmin, vmax)
alpha = None
node_collection = ax.hlines(xy[:, 1],
xy[:, 0] - node_size/2.,
xy[:, 0] + node_size/2.,
colors=node_color,
linestyles=style,
label=label,
cmap=cmap)
node_collection.set_zorder(2)
return node_collection | [
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This draws only the nodes of graph as horizontal lines at each
``y = pos[1]`` from ``x - node_size/2`` to ``x + node_size/2``, where
``x = pos[0]``.
Parameters
----------
graph : `networkx.Graph`
A NetworkX graph.
pos : mapping, optional
A mapping with nodes as keys and positions as values. Positions should
be sequences of length 2. If not specified (default), a diagram layout
positioning will be computed. See `networkx.layout` and
`pyrrole.drawing` for functions that compute node positions.
nodelist : `list`, optional
Draw only specified nodes (default is ``graph.nodes()``).
node_size : scalar or array
Size of nodes (default is ``.7``). If an array is specified it must be
the same length as nodelist.
node_color : color `str`, or array of `float`
Node color. Can be a single color format `str` (default is ``'k'``), or
a sequence of colors with the same length as nodelist. If numeric
values are specified they will be mapped to colors using the `cmap` and
`vmin`, `vmax` parameters. See `matplotlib.hlines` for more details.
style : `str` (``'solid'``, ``'dashed'``, ``'dotted'``, ``'dashdot'``)
Edge line style (default is ``'solid'``). See `matplotlib.hlines` for
more details.
alpha : `float` or array of `float`, optional
The node transparency. This can be a single alpha value (default is
``'1.0'``), in which case it will be applied to all the nodes of color.
Otherwise, if it is an array, the elements of alpha will be applied to
the colors in order (cycling through alpha multiple times if
necessary).
cmap : Matplotlib colormap, optional
Colormap name or Colormap instance for mapping intensities of nodes.
vmin : `float`, optional
Minimum for node colormap scaling.
vmax : `float`, optional
Maximum for node colormap scaling.
ax : `matplotlib.axes.Axes`, optional
Draw the graph in the specified Matplotlib axes.
label : `str`, optional
Label for legend.
Returns
-------
`matplotlib.collections.LineCollection`
`LineCollection` of the nodes.
Raises
------
networkx.NetworkXError
Raised if a node has no position or one with bad value.
Examples
--------
>>> import pandas as pd
>>> from pyrrole import ChemicalSystem
>>> from pyrrole.drawing import draw_diagram_nodes
>>> data = pd.DataFrame(
... [{"name": "Separated_Reactants", "freeenergy": 0.},
... {"name": "mlC1", "freeenergy": -5.4},
... {"name": "mlC2", "freeenergy": -15.6},
... {"name": "mTS1", "freeenergy": 28.5, "color": "g"},
... {"name": "mCARB1", "freeenergy": -9.7},
... {"name": "mCARB2", "freeenergy": -19.8},
... {"name": "mCARBX", "freeenergy": 20}]).set_index("name")
>>> system = ChemicalSystem(
... ["Separated_Reactants -> mlC1 -> mTS1",
... "Separated_Reactants -> mlC2 -> mTS1",
... "mCARB2 <- mTS1 -> mCARB1",
... "Separated_Reactants -> mCARBX"], data)
>>> digraph = system.to_digraph()
>>> nodes = draw_diagram_nodes(digraph) | [
"Draw",
"nodes",
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"graph",
"."
] | python | train |
rigetti/quantumflow | quantumflow/backend/numpybk.py | https://github.com/rigetti/quantumflow/blob/13a66cabbe8aabf6e023cc675f4a4ebe6ccda8fb/quantumflow/backend/numpybk.py#L98-L102 | def astensor(array: TensorLike) -> BKTensor:
"""Converts a numpy array to the backend's tensor object
"""
array = np.asarray(array, dtype=CTYPE)
return array | [
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tobgu/pyrsistent | pyrsistent/_plist.py | https://github.com/tobgu/pyrsistent/blob/c84dab0daaa44973cbe83830d14888827b307632/pyrsistent/_plist.py#L73-L86 | def mcons(self, iterable):
"""
Return a new list with all elements of iterable repeatedly cons:ed to the current list.
NB! The elements will be inserted in the reverse order of the iterable.
Runs in O(len(iterable)).
>>> plist([1, 2]).mcons([3, 4])
plist([4, 3, 1, 2])
"""
head = self
for elem in iterable:
head = head.cons(elem)
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wbond/oscrypto | oscrypto/_openssl/asymmetric.py | https://github.com/wbond/oscrypto/blob/af778bf1c88bf6c4a7342f5353b130686a5bbe1c/oscrypto/_openssl/asymmetric.py#L208-L218 | def public_key(self):
"""
:return:
The PublicKey object for the public key this certificate contains
"""
if not self._public_key and self.x509:
evp_pkey = libcrypto.X509_get_pubkey(self.x509)
self._public_key = PublicKey(evp_pkey, self.asn1['tbs_certificate']['subject_public_key_info'])
return self._public_key | [
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rameshg87/pyremotevbox | pyremotevbox/ZSI/wstools/XMLSchema.py | https://github.com/rameshg87/pyremotevbox/blob/123dffff27da57c8faa3ac1dd4c68b1cf4558b1a/pyremotevbox/ZSI/wstools/XMLSchema.py#L594-L646 | def getSchemaItem(self, collection, namespace, name):
"""returns object instance representing namespace, name,
or if does not exist return None if built-in, else
raise SchemaError.
namespace -- namespace item defined in.
name -- name of item.
collection -- collection in parent Schema instance to search.
"""
parent = GetSchema(self)
if parent.targetNamespace == namespace:
try:
obj = getattr(parent, collection)[name]
except KeyError, ex:
raise KeyError, 'targetNamespace(%s) collection(%s) has no item(%s)'\
%(namespace, collection, name)
return obj
if not parent.imports.has_key(namespace):
if namespace in BUILT_IN_NAMESPACES:
# built-in just return
# WARNING: expecting import if "redefine" or add to built-in namespace.
return
raise SchemaError, 'schema "%s" does not import namespace "%s"' %(
parent.targetNamespace, namespace)
# Lazy Eval
schema = parent.imports[namespace]
if not isinstance(schema, XMLSchema):
schema = schema.getSchema()
if schema is not None:
parent.imports[namespace] = schema
if schema is None:
if namespace in BUILT_IN_NAMESPACES:
# built-in just return
return
raise SchemaError, 'no schema instance for imported namespace (%s).'\
%(namespace)
if not isinstance(schema, XMLSchema):
raise TypeError, 'expecting XMLSchema instance not "%r"' %schema
try:
obj = getattr(schema, collection)[name]
except KeyError, ex:
raise KeyError, 'targetNamespace(%s) collection(%s) has no item(%s)'\
%(namespace, collection, name)
return obj | [
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foxx/peewee-extras | peewee_extras.py | https://github.com/foxx/peewee-extras/blob/327e7e63465b3f6e1afc0e6a651f4cb5c8c60889/peewee_extras.py#L322-L357 | def list(self, filters, cursor, count):
"""
List items from query
"""
assert isinstance(filters, dict), "expected filters type 'dict'"
assert isinstance(cursor, dict), "expected cursor type 'dict'"
# start with our base query
query = self.get_query()
assert isinstance(query, peewee.Query)
# XXX: convert and apply user specified filters
#filters = {field.name: cursor[field.name] for field in fields}
#query.where(
paginator = self.get_paginator()
assert isinstance(paginator, Pagination)
# always include an extra row for next cursor position
count += 1
# apply pagination to query
pquery = paginator.filter_query(query, cursor, count)
items = [ item for item in pquery ]
# determine next cursor position
next_item = items.pop(1)
next_cursor = next_item.to_cursor_ref()
'''
# is this field allowed for sort?
if field not in self.sort_fields:
raise ValueError("Cannot sort on field '{}'".format(field))
'''
return items, next_cursor | [
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floydhub/floyd-cli | floyd/client/files.py | https://github.com/floydhub/floyd-cli/blob/ea6b9521119cbde2dfc71ce0cc87c0d9c143fc6c/floyd/client/files.py#L77-L88 | def matches_glob_list(path, glob_list):
"""
Given a list of glob patterns, returns a boolean
indicating if a path matches any glob in the list
"""
for glob in glob_list:
try:
if PurePath(path).match(glob):
return True
except TypeError:
pass
return False | [
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inveniosoftware/invenio-records-files | invenio_records_files/utils.py | https://github.com/inveniosoftware/invenio-records-files/blob/c410eba986ea43be7e97082d5dcbbdc19ccec39c/invenio_records_files/utils.py#L54-L101 | def file_download_ui(pid, record, _record_file_factory=None, **kwargs):
"""File download view for a given record.
Plug this method into your ``RECORDS_UI_ENDPOINTS`` configuration:
.. code-block:: python
RECORDS_UI_ENDPOINTS = dict(
recid=dict(
# ...
route='/records/<pid_value/files/<filename>',
view_imp='invenio_records_files.utils:file_download_ui',
record_class='invenio_records_files.api:Record',
)
)
If ``download`` is passed as a querystring argument, the file is sent as an
attachment.
:param pid: The :class:`invenio_pidstore.models.PersistentIdentifier`
instance.
:param record: The record metadata.
"""
_record_file_factory = _record_file_factory or record_file_factory
# Extract file from record.
fileobj = _record_file_factory(
pid, record, kwargs.get('filename')
)
if not fileobj:
abort(404)
obj = fileobj.obj
# Check permissions
ObjectResource.check_object_permission(obj)
# Send file.
return ObjectResource.send_object(
obj.bucket, obj,
expected_chksum=fileobj.get('checksum'),
logger_data={
'bucket_id': obj.bucket_id,
'pid_type': pid.pid_type,
'pid_value': pid.pid_value,
},
as_attachment=('download' in request.args)
) | [
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.. code-block:: python
RECORDS_UI_ENDPOINTS = dict(
recid=dict(
# ...
route='/records/<pid_value/files/<filename>',
view_imp='invenio_records_files.utils:file_download_ui',
record_class='invenio_records_files.api:Record',
)
)
If ``download`` is passed as a querystring argument, the file is sent as an
attachment.
:param pid: The :class:`invenio_pidstore.models.PersistentIdentifier`
instance.
:param record: The record metadata. | [
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tensorflow/datasets | tensorflow_datasets/core/features/image_feature.py | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/features/image_feature.py#L135-L144 | def encode_example(self, image_or_path_or_fobj):
"""Convert the given image into a dict convertible to tf example."""
if isinstance(image_or_path_or_fobj, np.ndarray):
encoded_image = self._encode_image(image_or_path_or_fobj)
elif isinstance(image_or_path_or_fobj, six.string_types):
with tf.io.gfile.GFile(image_or_path_or_fobj, 'rb') as image_f:
encoded_image = image_f.read()
else:
encoded_image = image_or_path_or_fobj.read()
return encoded_image | [
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brocade/pynos | pynos/versions/ver_6/ver_6_0_1/yang/brocade_qos.py | https://github.com/brocade/pynos/blob/bd8a34e98f322de3fc06750827d8bbc3a0c00380/pynos/versions/ver_6/ver_6_0_1/yang/brocade_qos.py#L135-L151 | def qos_map_dscp_cos_mark_to(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
qos = ET.SubElement(config, "qos", xmlns="urn:brocade.com:mgmt:brocade-qos")
map = ET.SubElement(qos, "map")
dscp_cos = ET.SubElement(map, "dscp-cos")
dscp_cos_map_name_key = ET.SubElement(dscp_cos, "dscp-cos-map-name")
dscp_cos_map_name_key.text = kwargs.pop('dscp_cos_map_name')
mark = ET.SubElement(dscp_cos, "mark")
dscp_in_values_key = ET.SubElement(mark, "dscp-in-values")
dscp_in_values_key.text = kwargs.pop('dscp_in_values')
to = ET.SubElement(mark, "to")
to.text = kwargs.pop('to')
callback = kwargs.pop('callback', self._callback)
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pylast/pylast | src/pylast/__init__.py | https://github.com/pylast/pylast/blob/a52f66d316797fc819b5f1d186d77f18ba97b4ff/src/pylast/__init__.py#L1855-L1861 | def get_top_tracks(self, limit=None, cacheable=True):
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params = self._get_params()
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params["limit"] = limit
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Yubico/yubikey-manager | ykman/cli/piv.py | https://github.com/Yubico/yubikey-manager/blob/3ac27bc59ae76a59db9d09a530494add2edbbabf/ykman/cli/piv.py#L869-L894 | def write_object(ctx, pin, management_key, object_id, data):
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Yubico writable PIV objects are available in
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UDST/urbansim | urbansim/models/util.py | https://github.com/UDST/urbansim/blob/79f815a6503e109f50be270cee92d0f4a34f49ef/urbansim/models/util.py#L307-L347 | def columns_in_formula(formula):
"""
Returns the names of all the columns used in a patsy formula.
Parameters
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formula : str, iterable, or dict
Any formula construction supported by ``str_model_expression``.
Returns
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columns : list of str
"""
if formula is None:
return []
formula = str_model_expression(formula, add_constant=False)
columns = []
tokens = map(
lambda x: x.extra,
tz.remove(
lambda x: x.extra is None,
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for tok in tokens:
# if there are parentheses in the expression we
# want to drop them and everything outside
# and start again from the top
if '(' in tok:
start = tok.find('(') + 1
fin = tok.rfind(')')
columns.extend(columns_in_formula(tok[start:fin]))
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columns.append(tokval)
return list(tz.unique(columns)) | [
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timstaley/voeventdb | voeventdb/server/database/models.py | https://github.com/timstaley/voeventdb/blob/e37b176d65fced4ca4f059109a95d6974bb8a091/voeventdb/server/database/models.py#L62-L70 | def to_odict(self, exclude=None):
"""
Returns an OrderedDict representation of the SQLalchemy table row.
"""
if exclude is None:
exclude = tuple()
colnames = [c.name for c in self.__table__.columns
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return OrderedDict(((col, getattr(self, col)) for col in colnames)) | [
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Qiskit/qiskit-terra | qiskit/quantum_info/operators/channel/kraus.py | https://github.com/Qiskit/qiskit-terra/blob/d4f58d903bc96341b816f7c35df936d6421267d1/qiskit/quantum_info/operators/channel/kraus.py#L361-L397 | def _evolve(self, state, qargs=None):
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state (QuantumState): The input statevector or density matrix.
qargs (list): a list of QuantumState subsystem positions to apply
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# If subsystem evolution we use the SuperOp representation
if qargs is not None:
return SuperOp(self)._evolve(state, qargs)
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state = self._format_state(state)
if state.shape[0] != self._input_dim:
raise QiskitError(
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if state.ndim == 1 and self._data[1] is None and len(
self._data[0]) == 1:
# If we only have a single Kraus operator we can implement unitary-type
# evolution of a state vector psi -> K[0].psi
return np.dot(self._data[0][0], state)
# Otherwise we always return a density matrix
state = self._format_state(state, density_matrix=True)
kraus_l, kraus_r = self._data
if kraus_r is None:
kraus_r = kraus_l
return np.einsum('AiB,BC,AjC->ij', kraus_l, state,
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cloud9ers/gurumate | environment/lib/python2.7/site-packages/IPython/external/path/_path.py | https://github.com/cloud9ers/gurumate/blob/075dc74d1ee62a8c6b7a8bf2b271364f01629d1e/environment/lib/python2.7/site-packages/IPython/external/path/_path.py#L646-L670 | def lines(self, encoding=None, errors='strict', retain=True):
r""" Open this file, read all lines, return them in a list.
Optional arguments:
encoding - The Unicode encoding (or character set) of
the file. The default is None, meaning the content
of the file is read as 8-bit characters and returned
as a list of (non-Unicode) str objects.
errors - How to handle Unicode errors; see help(str.decode)
for the options. Default is 'strict'
retain - If true, retain newline characters; but all newline
character combinations ('\r', '\n', '\r\n') are
translated to '\n'. If false, newline characters are
stripped off. Default is True.
This uses 'U' mode in Python 2.3 and later.
"""
if encoding is None and retain:
f = self.open('U')
try:
return f.readlines()
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f.close()
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return self.text(encoding, errors).splitlines(retain) | [
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fossasia/knittingpattern | knittingpattern/Parser.py | https://github.com/fossasia/knittingpattern/blob/8e608896b0ab82fea1ca9fbfa2b4ee023d8c8027/knittingpattern/Parser.py#L184-L192 | def new_pattern(self, id_, name, rows=None):
"""Create a new knitting pattern.
If rows is :obj:`None` it is replaced with the
:meth:`new_row_collection`.
"""
if rows is None:
rows = self.new_row_collection()
return self._spec.new_pattern(id_, name, rows, self) | [
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lepture/flask-oauthlib | flask_oauthlib/utils.py | https://github.com/lepture/flask-oauthlib/blob/9e6f152a5bb360e7496210da21561c3e6d41b0e1/flask_oauthlib/utils.py#L40-L46 | def to_bytes(text, encoding='utf-8'):
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text = text.encode(encoding)
return text | [
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senaite/senaite.core | bika/lims/browser/analysisrequest/add2.py | https://github.com/senaite/senaite.core/blob/7602ce2ea2f9e81eb34e20ce17b98a3e70713f85/bika/lims/browser/analysisrequest/add2.py#L1443-L1520 | def ajax_recalculate_prices(self):
"""Recalculate prices for all ARs
"""
# When the option "Include and display pricing information" in
# Bika Setup Accounting tab is not selected
if not self.show_recalculate_prices():
return {}
# The sorted records from the request
records = self.get_records()
client = self.get_client()
bika_setup = api.get_bika_setup()
member_discount = float(bika_setup.getMemberDiscount())
member_discount_applies = False
if client:
member_discount_applies = client.getMemberDiscountApplies()
prices = {}
for n, record in enumerate(records):
ardiscount_amount = 0.00
arservices_price = 0.00
arprofiles_price = 0.00
arprofiles_vat_amount = 0.00
arservice_vat_amount = 0.00
services_from_priced_profile = []
profile_uids = record.get("Profiles_uid", "").split(",")
profile_uids = filter(lambda x: x, profile_uids)
profiles = map(self.get_object_by_uid, profile_uids)
services = map(self.get_object_by_uid, record.get("Analyses", []))
# ANALYSIS PROFILES PRICE
for profile in profiles:
use_profile_price = profile.getUseAnalysisProfilePrice()
if not use_profile_price:
continue
profile_price = float(profile.getAnalysisProfilePrice())
arprofiles_price += profile_price
arprofiles_vat_amount += profile.getVATAmount()
profile_services = profile.getService()
services_from_priced_profile.extend(profile_services)
# ANALYSIS SERVICES PRICE
for service in services:
if service in services_from_priced_profile:
continue
service_price = float(service.getPrice())
# service_vat = float(service.getVAT())
service_vat_amount = float(service.getVATAmount())
arservice_vat_amount += service_vat_amount
arservices_price += service_price
base_price = arservices_price + arprofiles_price
# Calculate the member discount if it applies
if member_discount and member_discount_applies:
logger.info("Member discount applies with {}%".format(
member_discount))
ardiscount_amount = base_price * member_discount / 100
subtotal = base_price - ardiscount_amount
vat_amount = arprofiles_vat_amount + arservice_vat_amount
total = subtotal + vat_amount
prices[n] = {
"discount": "{0:.2f}".format(ardiscount_amount),
"subtotal": "{0:.2f}".format(subtotal),
"vat": "{0:.2f}".format(vat_amount),
"total": "{0:.2f}".format(total),
}
logger.info("Prices for AR {}: Discount={discount} "
"VAT={vat} Subtotal={subtotal} total={total}"
.format(n, **prices[n]))
return prices | [
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RedFantom/ttkwidgets | ttkwidgets/table.py | https://github.com/RedFantom/ttkwidgets/blob/02150322060f867b6e59a175522ef84b09168019/ttkwidgets/table.py#L616-L627 | def set_children(self, item, *newchildren):
"""
Replaces item’s children with newchildren.
Children present in item that are not present in newchildren are detached
from tree. No items in newchildren may be an ancestor of item.
:param newchildren: new item's children (list of item identifiers)
:type newchildren: sequence[str]
"""
self._visual_drag.set_children(item, *newchildren)
ttk.Treeview.set_children(self, item, *newchildren) | [
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erget/StereoVision | stereovision/calibration.py | https://github.com/erget/StereoVision/blob/1adff45e291362f52188e0fd0211265845a4461a/stereovision/calibration.py#L124-L128 | def export(self, output_folder):
"""Export matrices as ``*.npy`` files to an output folder."""
if not os.path.exists(output_folder):
os.makedirs(output_folder)
self._interact_with_folder(output_folder, 'w') | [
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pypa/pipenv | pipenv/vendor/pyparsing.py | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/pyparsing.py#L1300-L1327 | def copy( self ):
"""
Make a copy of this :class:`ParserElement`. Useful for defining
different parse actions for the same parsing pattern, using copies of
the original parse element.
Example::
integer = Word(nums).setParseAction(lambda toks: int(toks[0]))
integerK = integer.copy().addParseAction(lambda toks: toks[0]*1024) + Suppress("K")
integerM = integer.copy().addParseAction(lambda toks: toks[0]*1024*1024) + Suppress("M")
print(OneOrMore(integerK | integerM | integer).parseString("5K 100 640K 256M"))
prints::
[5120, 100, 655360, 268435456]
Equivalent form of ``expr.copy()`` is just ``expr()``::
integerM = integer().addParseAction(lambda toks: toks[0]*1024*1024) + Suppress("M")
"""
cpy = copy.copy( self )
cpy.parseAction = self.parseAction[:]
cpy.ignoreExprs = self.ignoreExprs[:]
if self.copyDefaultWhiteChars:
cpy.whiteChars = ParserElement.DEFAULT_WHITE_CHARS
return cpy | [
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integerK = integer.copy().addParseAction(lambda toks: toks[0]*1024) + Suppress("K")
integerM = integer.copy().addParseAction(lambda toks: toks[0]*1024*1024) + Suppress("M")
print(OneOrMore(integerK | integerM | integer).parseString("5K 100 640K 256M"))
prints::
[5120, 100, 655360, 268435456]
Equivalent form of ``expr.copy()`` is just ``expr()``::
integerM = integer().addParseAction(lambda toks: toks[0]*1024*1024) + Suppress("M") | [
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PyCQA/pylint | pylint/checkers/raw_metrics.py | https://github.com/PyCQA/pylint/blob/2bf5c61a3ff6ae90613b81679de42c0f19aea600/pylint/checkers/raw_metrics.py#L77-L84 | def process_tokens(self, tokens):
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aiogram/aiogram | aiogram/dispatcher/filters/filters.py | https://github.com/aiogram/aiogram/blob/2af930149ce2482547721e2c8755c10307295e48/aiogram/dispatcher/filters/filters.py#L214-L225 | def validate(cls, full_config: typing.Dict[str, typing.Any]) -> typing.Dict[str, typing.Any]:
"""
If ``cls.key`` is not :obj:`None` and that is in config returns config with that argument.
:param full_config:
:return:
"""
if cls.key is not None:
if cls.key in full_config:
return {cls.key: full_config[cls.key]}
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marshallward/f90nml | f90nml/parser.py | https://github.com/marshallward/f90nml/blob/4932cabc5221afc844ee6a5b4a05ceb8bd4a2711/f90nml/parser.py#L127-L131 | def sparse_arrays(self, value):
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IdentityPython/fedoidcmsg | src/fedoidcmsg/entity.py | https://github.com/IdentityPython/fedoidcmsg/blob/d30107be02521fa6cdfe285da3b6b0cdd153c8cc/src/fedoidcmsg/entity.py#L209-L232 | def update_metadata_statement(self, metadata_statement, receiver='',
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:return: An augmented metadata statement
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self.add_sms_spec_to_request(metadata_statement, federation=federation,
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self.add_signing_keys(metadata_statement)
metadata_statement = self.self_sign(metadata_statement, receiver)
# These are unprotected here so can as well be removed
del metadata_statement['signing_keys']
return metadata_statement | [
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architv/harvey | harvey/harvey.py | https://github.com/architv/harvey/blob/2b96d57b7a1e0dd706f1f00aba3d92a7ae702960/harvey/harvey.py#L53-L58 | def _get_config_name():
'''Get git config user name'''
p = subprocess.Popen('git config --get user.name', shell=True,
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output = p.stdout.readlines()
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cthoyt/onto2nx | src/onto2nx/ontospy/core/loader.py | https://github.com/cthoyt/onto2nx/blob/94c86e5e187cca67534afe0260097177b66e02c8/src/onto2nx/ontospy/core/loader.py#L116-L141 | def load_uri(self, uri, verbose):
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:param uri:
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:param verbose:
:return:
"""
if verbose: printDebug("----------")
if verbose: printDebug("Reading: <%s>" % uri)
success = False
for f in self.rdf_format_opts:
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self.rdfgraph.parse(uri, format=f)
if verbose: printDebug("..... success!", bold=True)
success = True
self.sources_valid += [uri]
break
except:
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if not success == True:
self.loading_failed(self.rdf_format_opts)
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JdeRobot/base | src/drivers/MAVLinkServer/MAVProxy/modules/mavproxy_map/srtm.py | https://github.com/JdeRobot/base/blob/303b18992785b2fe802212f2d758a60873007f1f/src/drivers/MAVLinkServer/MAVProxy/modules/mavproxy_map/srtm.py#L305-L313 | def _avg(value1, value2, weight):
"""Returns the weighted average of two values and handles the case where
one value is None. If both values are None, None is returned.
"""
if value1 is None:
return value2
if value2 is None:
return value1
return value2 * weight + value1 * (1 - weight) | [
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pazz/urwidtrees | urwidtrees/lru_cache.py | https://github.com/pazz/urwidtrees/blob/d1fa38ce4f37db00bdfc574b856023b5db4c7ead/urwidtrees/lru_cache.py#L10-L141 | def lru_cache(maxsize=100, typed=False):
"""Least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and the cache
can grow without bound.
If *typed* is True, arguments of different types will be cached separately.
For example, f(3.0) and f(3) will be treated as distinct calls with
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Arguments to the cached function must be hashable.
View the cache statistics named tuple (hits, misses, maxsize, currsize) with
f.cache_info(). Clear the cache and statistics with f.cache_clear().
Access the underlying function with f.__wrapped__.
See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
"""
# Users should only access the lru_cache through its public API:
# cache_info, cache_clear, and f.__wrapped__
# The internals of the lru_cache are encapsulated for thread safety and
# to allow the implementation to change (including a possible C version).
def decorating_function(user_function):
cache = dict()
stats = [0, 0] # make statistics updateable non-locally
HITS, MISSES = 0, 1 # names for the stats fields
kwd_mark = (object(),) # separate positional and keyword args
cache_get = cache.get # bound method to lookup key or return None
_len = len # localize the global len() function
lock = Lock() # because linkedlist updates aren't threadsafe
root = [] # root of the circular doubly linked list
nonlocal_root = [root] # make updateable non-locally
root[:] = [root, root, None, None] # initialize by pointing to self
PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
def make_key(args, kwds, typed, tuple=tuple, sorted=sorted, type=type):
# helper function to build a cache key from positional and keyword args
key = args
if kwds:
sorted_items = tuple(sorted(kwds.items()))
key += kwd_mark + sorted_items
if typed:
key += tuple(type(v) for v in args)
if kwds:
key += tuple(type(v) for k, v in sorted_items)
return key
if maxsize == 0:
def wrapper(*args, **kwds):
# no caching, just do a statistics update after a successful call
result = user_function(*args, **kwds)
stats[MISSES] += 1
return result
elif maxsize is None:
def wrapper(*args, **kwds):
# simple caching without ordering or size limit
key = make_key(args, kwds, typed) if kwds or typed else args
result = cache_get(key, root) # root used here as a unique not-found sentinel
if result is not root:
stats[HITS] += 1
return result
result = user_function(*args, **kwds)
cache[key] = result
stats[MISSES] += 1
return result
else:
def wrapper(*args, **kwds):
# size limited caching that tracks accesses by recency
key = make_key(args, kwds, typed) if kwds or typed else args
with lock:
link = cache_get(key)
if link is not None:
# record recent use of the key by moving it to the front of the list
root, = nonlocal_root
link_prev, link_next, key, result = link
link_prev[NEXT] = link_next
link_next[PREV] = link_prev
last = root[PREV]
last[NEXT] = root[PREV] = link
link[PREV] = last
link[NEXT] = root
stats[HITS] += 1
return result
result = user_function(*args, **kwds)
with lock:
root = nonlocal_root[0]
if _len(cache) < maxsize:
# put result in a new link at the front of the list
last = root[PREV]
link = [last, root, key, result]
cache[key] = last[NEXT] = root[PREV] = link
else:
# use root to store the new key and result
root[KEY] = key
root[RESULT] = result
cache[key] = root
# empty the oldest link and make it the new root
root = nonlocal_root[0] = root[NEXT]
del cache[root[KEY]]
root[KEY] = None
root[RESULT] = None
stats[MISSES] += 1
return result
def cache_info():
"""Report cache statistics"""
with lock:
return _CacheInfo(stats[HITS], stats[MISSES], maxsize, len(cache))
def cache_clear():
"""Clear the cache and cache statistics"""
with lock:
cache.clear()
root = nonlocal_root[0]
root[:] = [root, root, None, None]
stats[:] = [0, 0]
wrapper.__wrapped__ = user_function
wrapper.cache_info = cache_info
wrapper.cache_clear = cache_clear
return update_wrapper(wrapper, user_function)
return decorating_function | [
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If *typed* is True, arguments of different types will be cached separately.
For example, f(3.0) and f(3) will be treated as distinct calls with
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Arguments to the cached function must be hashable.
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See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used | [
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osrg/ryu | ryu/lib/packet/bgp.py | https://github.com/osrg/ryu/blob/6f906e72c92e10bd0264c9b91a2f7bb85b97780c/ryu/lib/packet/bgp.py#L3731-L3740 | def has_matching_leftmost(self, remote_as):
"""Check if leftmost AS matches *remote_as*."""
if not self.value or not remote_as:
return False
leftmost_seg = self.path_seg_list[0]
if leftmost_seg and leftmost_seg[0] == remote_as:
return True
return False | [
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ToucanToco/toucan-data-sdk | toucan_data_sdk/utils/generic/clean.py | https://github.com/ToucanToco/toucan-data-sdk/blob/c3ca874e1b64f4bdcc2edda750a72d45d1561d8a/toucan_data_sdk/utils/generic/clean.py#L14-L33 | def clean_dataframe(df, is_slugify=True, threshold=50, rename_cols=None):
"""
This method is used to:
- slugify the column names (if slugify is set to True)
- convert columns to 'category' (if len(unique) < threshold) or 'int'
- clean the dataframe and rename if necessary
"""
if is_slugify:
df = df.rename(columns=slugify)
df = df.dropna(axis=1, how='all')
for column in get_category_cols(df, threshold=threshold):
df[column] = df[column].astype('category')
for column in get_int_cols(df):
df[column] = df[column].astype(int)
if rename_cols is not None:
df = df.rename(columns=rename_cols)
return df | [
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bsolomon1124/pyfinance | pyfinance/general.py | https://github.com/bsolomon1124/pyfinance/blob/c95925209a809b4e648e79cbeaf7711d8e5ff1a6/pyfinance/general.py#L986-L988 | def opt_weights(self):
"""Optimal weights (period-end)."""
return pd.DataFrame(self._xs, index=self.newidx, columns=self.cols) | [
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