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tony-landis/datomic-py | datomic/datomic.py | https://github.com/tony-landis/datomic-py/blob/54f713d29ad85ba86d53d5115c9b312ff14b7846/datomic/datomic.py#L129-L137 | def retract(self, e, a, v):
""" redact the value of an attribute
"""
ta = datetime.datetime.now()
ret = u"[:db/retract %i :%s %s]" % (e, a, dump_edn_val(v))
rs = self.tx(ret)
tb = datetime.datetime.now() - ta
print cl('<<< retracted %s,%s,%s in %sms' % (e,a,v, tb.microseconds/1000.0), 'cyan')
return rs | [
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| python | train |
f3at/feat | src/feat/agencies/agency.py | https://github.com/f3at/feat/blob/15da93fc9d6ec8154f52a9172824e25821195ef8/src/feat/agencies/agency.py#L1477-L1526 | def _start_host_agent(self):
'''
This method starts saves the host agent descriptor and runs it.
To make this happen following conditions needs to be fulfilled:
- it is a master agency,
- we are not starting a host agent already,
- we are not terminating,
- and last but not least, we dont have a host agent running.
'''
def set_flag(value):
self._starting_host = value
if not self._can_start_host_agent():
return
def handle_error_on_get(fail, connection, doc_id):
fail.trap(NotFoundError)
factory = serialization.lookup('host_agent')
desc = factory(shard=u'lobby', doc_id=doc_id,
agency_id=self.agency_id)
self.info("Host Agent descriptor not found in database, "
"creating a brand new instance.")
return connection.save_document(desc)
def handle_success_on_get(desc):
msg = ("Descriptor of host agent has been found in "
"database (hostname: %s). I will wait patiently "
"until in disappears. It should happened any minute now "
"providing there is still a monitor agent in the "
"cluster who will take care of it. In other case "
"you would have to cleanup the database. In development "
"the --force-host-restart feat script option is your "
"only friend. " % desc.doc_id)
raise error.FeatError(msg)
set_flag(True)
self.info('Starting host agent.')
conn = self._database.get_connection()
doc_id = self._get_host_agent_id()
d = defer.Deferred()
d.addCallback(defer.drop_param, self._database.wait_connected)
d.addCallback(defer.drop_param, conn.get_document, doc_id)
d.addCallbacks(handle_success_on_get, handle_error_on_get,
errbackArgs=(conn, doc_id, ))
d.addCallback(self.start_agent, hostdef=self._hostdef)
d.addBoth(defer.bridge_param, set_flag, False)
d.addErrback(self._host_restart_failed)
time.callLater(0, d.callback, None) | [
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| python | train |
nicferrier/md | src/mdlib/pull.py | https://github.com/nicferrier/md/blob/302ca8882dae060fb15bd5ae470d8e661fb67ec4/src/mdlib/pull.py#L144-L152 | def _filter(msgdata, mailparser, mdfolder, mailfilters):
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if mailfilters:
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msg = mailparser.parse(StringIO(msgdata))
rule = f(msg, folder=mdfolder)
if rule:
yield rule
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| python | train |
CZ-NIC/yangson | yangson/statement.py | https://github.com/CZ-NIC/yangson/blob/a4b9464041fa8b28f6020a420ababf18fddf5d4a/yangson/statement.py#L291-L320 | def argument(self) -> bool:
"""Parse statement argument.
Return ``True`` if the argument is followed by block of substatements.
"""
next = self.peek()
if next == "'":
quoted = True
self.sq_argument()
elif next == '"':
quoted = True
self.dq_argument()
elif self._arg == "":
quoted = False
self.unq_argument()
else:
raise UnexpectedInput(self, "single or double quote")
self.opt_separator()
next = self.peek()
if next == ";":
return False
if next == "{":
return True
elif quoted and next == "+":
self.offset += 1
self.opt_separator()
return self.argument()
else:
raise UnexpectedInput(self, "';', '{'" +
(" or '+'" if quoted else "")) | [
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| python | train |
ganguli-lab/proxalgs | proxalgs/operators.py | https://github.com/ganguli-lab/proxalgs/blob/74f54467ad072d3229edea93fa84ddd98dd77c67/proxalgs/operators.py#L214-L244 | def nucnorm(x0, rho, gamma):
"""
Proximal operator for the nuclear norm (sum of the singular values of a matrix)
Parameters
----------
x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
gamma : float
A constant that weights how strongly to enforce the constraint
Returns
-------
theta : array_like
The parameter vector found after running the proximal update step
"""
# compute SVD
u, s, v = np.linalg.svd(x0, full_matrices=False)
# soft threshold the singular values
sthr = np.maximum(s - (gamma / float(rho)), 0)
# reconstruct
x_out = (u.dot(np.diag(sthr)).dot(v))
return x_out | [
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| python | train |
cloudera/impyla | impala/_thrift_gen/hive_metastore/ThriftHiveMetastore.py | https://github.com/cloudera/impyla/blob/547fa2ba3b6151e2a98b3544301471a643212dc3/impala/_thrift_gen/hive_metastore/ThriftHiveMetastore.py#L3481-L3490 | def get_partitions_by_filter(self, db_name, tbl_name, filter, max_parts):
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- max_parts
"""
self.send_get_partitions_by_filter(db_name, tbl_name, filter, max_parts)
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| python | train |
xtuml/pyxtuml | xtuml/meta.py | https://github.com/xtuml/pyxtuml/blob/7dd9343b9a0191d1db1887ab9288d0a026608d9a/xtuml/meta.py#L632-L644 | def clone(self, instance):
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Create a shallow clone of an *instance*.
**Note:** the clone and the original instance **does not** have to be
part of the same metaclass.
'''
args = list()
for name, _ in get_metaclass(instance).attributes:
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| python | test |
log2timeline/dfvfs | dfvfs/lib/sqlite_database.py | https://github.com/log2timeline/dfvfs/blob/2b3ccd115f9901d89f383397d4a1376a873c83c4/dfvfs/lib/sqlite_database.py#L61-L93 | def GetNumberOfRows(self, table_name):
"""Retrieves the number of rows in the table.
Args:
table_name (str): name of the table.
Returns:
int: number of rows.
Raises:
IOError: if the file-like object has not been opened.
OSError: if the file-like object has not been opened.
"""
if not self._connection:
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self._cursor.execute(self._NUMBER_OF_ROWS_QUERY.format(table_name))
row = self._cursor.fetchone()
if not row:
raise IOError(
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number_of_rows = row[0]
if isinstance(number_of_rows, py2to3.STRING_TYPES):
try:
number_of_rows = int(number_of_rows, 10)
except ValueError as exception:
raise IOError((
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| python | train |
summa-tx/riemann | riemann/tx/tx.py | https://github.com/summa-tx/riemann/blob/04ae336dfd4007ceaed748daadc91cc32fa278ec/riemann/tx/tx.py#L604-L614 | def _sighash_final_hashing(self, copy_tx, sighash_type):
'''
Tx, int -> bytes
Returns the hash that should be signed
https://en.bitcoin.it/wiki/OP_CHECKSIG#Procedure_for_Hashtype_SIGHASH_ANYONECANPAY
'''
sighash = ByteData()
sighash += copy_tx.to_bytes()
sighash += utils.i2le_padded(sighash_type, 4)
return utils.hash256(sighash.to_bytes()) | [
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| python | train |
numenta/nupic | src/nupic/database/client_jobs_dao.py | https://github.com/numenta/nupic/blob/5922fafffdccc8812e72b3324965ad2f7d4bbdad/src/nupic/database/client_jobs_dao.py#L2782-L2825 | def modelSetCompleted(self, modelID, completionReason, completionMsg,
cpuTime=0, useConnectionID=True):
""" Mark a model as completed, with the given completionReason and
completionMsg. This will fail if the model does not currently belong to this
client (connection_id doesn't match).
Parameters:
----------------------------------------------------------------
modelID: model ID of model to modify
completionReason: completionReason string
completionMsg: completionMsg string
cpuTime: amount of CPU time spent on this model
useConnectionID: True if the connection id of the calling function
must be the same as the connection that created the
job. Set to True for hypersearch workers, which use
this mechanism for orphaned model detection.
"""
if completionMsg is None:
completionMsg = ''
query = 'UPDATE %s SET status=%%s, ' \
' completion_reason=%%s, ' \
' completion_msg=%%s, ' \
' end_time=UTC_TIMESTAMP(), ' \
' cpu_time=%%s, ' \
' _eng_last_update_time=UTC_TIMESTAMP(), ' \
' update_counter=update_counter+1 ' \
' WHERE model_id=%%s' \
% (self.modelsTableName,)
sqlParams = [self.STATUS_COMPLETED, completionReason, completionMsg,
cpuTime, modelID]
if useConnectionID:
query += " AND _eng_worker_conn_id=%s"
sqlParams.append(self._connectionID)
with ConnectionFactory.get() as conn:
numRowsAffected = conn.cursor.execute(query, sqlParams)
if numRowsAffected != 1:
raise InvalidConnectionException(
("Tried to set modelID=%r using connectionID=%r, but this model "
"belongs to some other worker or modelID not found; "
"numRowsAffected=%r") % (modelID, self._connectionID, numRowsAffected)) | [
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modelID: model ID of model to modify
completionReason: completionReason string
completionMsg: completionMsg string
cpuTime: amount of CPU time spent on this model
useConnectionID: True if the connection id of the calling function
must be the same as the connection that created the
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| python | valid |
apache/incubator-heron | heronpy/streamlet/streamlet.py | https://github.com/apache/incubator-heron/blob/ad10325a0febe89ad337e561ebcbe37ec5d9a5ac/heronpy/streamlet/streamlet.py#L233-L244 | def _default_stage_name_calculator(self, prefix, existing_stage_names):
"""This is the method that's implemented by the operators to get the name of the Streamlet
:return: The name of the operator
"""
index = 1
calculated_name = ""
while True:
calculated_name = prefix + "-" + str(index)
if calculated_name not in existing_stage_names:
return calculated_name
index = index + 1
return "Should Never Got Here" | [
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| python | valid |
ZeitOnline/briefkasten | application/briefkasten/__init__.py | https://github.com/ZeitOnline/briefkasten/blob/ce6b6eeb89196014fe21d68614c20059d02daa11/application/briefkasten/__init__.py#L51-L62 | def is_equal(a, b):
""" a constant time comparison implementation taken from
http://codahale.com/a-lesson-in-timing-attacks/ and
Django's `util` module https://github.com/django/django/blob/master/django/utils/crypto.py#L82
"""
if len(a) != len(b):
return False
result = 0
for x, y in zip(a, b):
result |= ord(x) ^ ord(y)
return result == 0 | [
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| python | valid |
sporsh/carnifex | fabfile.py | https://github.com/sporsh/carnifex/blob/82dd3bd2bc134dfb69a78f43171e227f2127060b/fabfile.py#L22-L30 | def trial(path=TESTS_PATH, coverage=False):
"""Run tests using trial
"""
args = ['trial']
if coverage:
args.append('--coverage')
args.append(path)
print args
local(' '.join(args)) | [
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| python | train |
kxxoling/flask-decorators | flask_decorators/__init__.py | https://github.com/kxxoling/flask-decorators/blob/e0bf4fc1a5260548063ef8b8adbb782151cd72cc/flask_decorators/__init__.py#L46-L64 | def gen(mimetype):
"""``gen`` is a decorator factory function, you just need to set
a mimetype before using::
@app.route('/')
@gen('')
def index():
pass
A full demo for creating a image stream is available on
`GitHub <https://github.com/kxxoling/flask-video-streaming>`__ .
"""
def streaming(func, *args, **kwargs):
@wraps(func)
def _():
return Response(func(*args, **kwargs),
mimetype=mimetype)
return _
return streaming | [
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| ``gen`` is a decorator factory function, you just need to set
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@app.route('/')
@gen('')
def index():
pass
A full demo for creating a image stream is available on
`GitHub <https://github.com/kxxoling/flask-video-streaming>`__ . | [
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| python | train |
indico/indico-plugins | piwik/indico_piwik/queries/metrics.py | https://github.com/indico/indico-plugins/blob/fe50085cc63be9b8161b09539e662e7b04e4b38e/piwik/indico_piwik/queries/metrics.py#L69-L81 | def _get_cumulative_results(self, results):
"""
Returns a dictionary of {'total': x, 'unique': y} for the
date range.
"""
hits = {'total': 0, 'unique': 0}
day_hits = list(hits[0] for hits in results.values() if hits)
for metrics in day_hits:
hits['total'] += metrics['nb_hits']
hits['unique'] += metrics['nb_uniq_visitors']
return hits | [
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| python | train |
django-danceschool/django-danceschool | danceschool/core/forms.py | https://github.com/django-danceschool/django-danceschool/blob/bb08cbf39017a812a5a94bdb4ea34170bf1a30ba/danceschool/core/forms.py#L942-L947 | def save(self, commit=True):
''' If the staff member is an instructor, also update the availableForPrivates field on the Instructor record. '''
if getattr(self.instance,'instructor',None):
self.instance.instructor.availableForPrivates = self.cleaned_data.pop('availableForPrivates',self.instance.instructor.availableForPrivates)
self.instance.instructor.save(update_fields=['availableForPrivates',])
super(StaffMemberBioChangeForm,self).save(commit=True) | [
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| python | train |
Kane610/deconz | pydeconz/group.py | https://github.com/Kane610/deconz/blob/8a9498dbbc8c168d4a081173ad6c3b1e17fffdf6/pydeconz/group.py#L46-L61 | async def async_set_state(self, data):
"""Set state of light group.
{
"on": true,
"bri": 180,
"hue": 43680,
"sat": 255,
"transitiontime": 10
}
Also update local values of group since websockets doesn't.
"""
field = self.deconz_id + '/action'
await self._async_set_state_callback(field, data)
self.async_update({'state': data}) | [
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| python | train |
Qiskit/qiskit-terra | qiskit/visualization/counts_visualization.py | https://github.com/Qiskit/qiskit-terra/blob/d4f58d903bc96341b816f7c35df936d6421267d1/qiskit/visualization/counts_visualization.py#L45-L184 | def plot_histogram(data, figsize=(7, 5), color=None, number_to_keep=None,
sort='asc', target_string=None,
legend=None, bar_labels=True, title=None):
"""Plot a histogram of data.
Args:
data (list or dict): This is either a list of dictionaries or a single
dict containing the values to represent (ex {'001': 130})
figsize (tuple): Figure size in inches.
color (list or str): String or list of strings for histogram bar colors.
number_to_keep (int): The number of terms to plot and rest
is made into a single bar called 'rest'.
sort (string): Could be 'asc', 'desc', or 'hamming'.
target_string (str): Target string if 'sort' is a distance measure.
legend(list): A list of strings to use for labels of the data.
The number of entries must match the length of data (if data is a
list or 1 if it's a dict)
bar_labels (bool): Label each bar in histogram with probability value.
title (str): A string to use for the plot title
Returns:
matplotlib.Figure: A figure for the rendered histogram.
Raises:
ImportError: Matplotlib not available.
VisualizationError: When legend is provided and the length doesn't
match the input data.
"""
if not HAS_MATPLOTLIB:
raise ImportError('Must have Matplotlib installed.')
if sort not in VALID_SORTS:
raise VisualizationError("Value of sort option, %s, isn't a "
"valid choice. Must be 'asc', "
"'desc', or 'hamming'")
elif sort in DIST_MEAS.keys() and target_string is None:
err_msg = 'Must define target_string when using distance measure.'
raise VisualizationError(err_msg)
if isinstance(data, dict):
data = [data]
if legend and len(legend) != len(data):
raise VisualizationError("Length of legendL (%s) doesn't match "
"number of input executions: %s" %
(len(legend), len(data)))
fig, ax = plt.subplots(figsize=figsize)
labels = list(sorted(
functools.reduce(lambda x, y: x.union(y.keys()), data, set())))
if number_to_keep is not None:
labels.append('rest')
if sort in DIST_MEAS.keys():
dist = []
for item in labels:
dist.append(DIST_MEAS[sort](item, target_string))
labels = [list(x) for x in zip(*sorted(zip(dist, labels),
key=lambda pair: pair[0]))][1]
labels_dict = OrderedDict()
# Set bar colors
if color is None:
color = ['#648fff', '#dc267f', '#785ef0', '#ffb000', '#fe6100']
elif isinstance(color, str):
color = [color]
all_pvalues = []
length = len(data)
for item, execution in enumerate(data):
if number_to_keep is not None:
data_temp = dict(Counter(execution).most_common(number_to_keep))
data_temp["rest"] = sum(execution.values()) - sum(data_temp.values())
execution = data_temp
values = []
for key in labels:
if key not in execution:
if number_to_keep is None:
labels_dict[key] = 1
values.append(0)
else:
values.append(-1)
else:
labels_dict[key] = 1
values.append(execution[key])
values = np.array(values, dtype=float)
where_idx = np.where(values >= 0)[0]
pvalues = values[where_idx] / sum(values[where_idx])
for value in pvalues:
all_pvalues.append(value)
numelem = len(values[where_idx])
ind = np.arange(numelem) # the x locations for the groups
width = 1/(len(data)+1) # the width of the bars
rects = []
for idx, val in enumerate(pvalues):
label = None
if not idx and legend:
label = legend[item]
if val >= 0:
rects.append(ax.bar(idx+item*width, val, width, label=label,
color=color[item % len(color)],
zorder=2))
bar_center = (width / 2) * (length - 1)
ax.set_xticks(ind + bar_center)
ax.set_xticklabels(labels_dict.keys(), fontsize=14, rotation=70)
# attach some text labels
if bar_labels:
for rect in rects:
for rec in rect:
height = rec.get_height()
if height >= 1e-3:
ax.text(rec.get_x() + rec.get_width() / 2., 1.05 * height,
'%.3f' % float(height),
ha='center', va='bottom', zorder=3)
else:
ax.text(rec.get_x() + rec.get_width() / 2., 1.05 * height,
'0',
ha='center', va='bottom', zorder=3)
# add some text for labels, title, and axes ticks
ax.set_ylabel('Probabilities', fontsize=14)
ax.set_ylim([0., min([1.2, max([1.2 * val for val in all_pvalues])])])
if sort == 'desc':
ax.invert_xaxis()
ax.yaxis.set_major_locator(MaxNLocator(5))
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(14)
ax.set_facecolor('#eeeeee')
plt.grid(which='major', axis='y', zorder=0, linestyle='--')
if title:
plt.title(title)
if legend:
ax.legend(loc='upper left', bbox_to_anchor=(1.01, 1.0), ncol=1,
borderaxespad=0, frameon=True, fontsize=12)
if fig:
plt.close(fig)
return fig | [
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Args:
data (list or dict): This is either a list of dictionaries or a single
dict containing the values to represent (ex {'001': 130})
figsize (tuple): Figure size in inches.
color (list or str): String or list of strings for histogram bar colors.
number_to_keep (int): The number of terms to plot and rest
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sort (string): Could be 'asc', 'desc', or 'hamming'.
target_string (str): Target string if 'sort' is a distance measure.
legend(list): A list of strings to use for labels of the data.
The number of entries must match the length of data (if data is a
list or 1 if it's a dict)
bar_labels (bool): Label each bar in histogram with probability value.
title (str): A string to use for the plot title
Returns:
matplotlib.Figure: A figure for the rendered histogram.
Raises:
ImportError: Matplotlib not available.
VisualizationError: When legend is provided and the length doesn't
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| python | test |
basecrm/basecrm-python | basecrm/services.py | https://github.com/basecrm/basecrm-python/blob/7c1cf97dbaba8aeb9ff89f8a54f945a8702349f6/basecrm/services.py#L528-L541 | def list(self, **params):
"""
Retrieve all deal unqualified reasons
Returns all deal unqualified reasons available to the user according to the parameters provided
:calls: ``get /deal_unqualified_reasons``
:param dict params: (optional) Search options.
:return: List of dictionaries that support attriubte-style access, which represent collection of DealUnqualifiedReasons.
:rtype: list
"""
_, _, deal_unqualified_reasons = self.http_client.get("/deal_unqualified_reasons", params=params)
return deal_unqualified_reasons | [
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Returns all deal unqualified reasons available to the user according to the parameters provided
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| python | train |
AltSchool/dynamic-rest | dynamic_rest/routers.py | https://github.com/AltSchool/dynamic-rest/blob/5b0338c3dd8bc638d60c3bb92645857c5b89c920/dynamic_rest/routers.py#L108-L154 | def register(self, prefix, viewset, base_name=None):
"""Add any registered route into a global API directory.
If the prefix includes a path separator,
store the URL in the directory under the first path segment.
Otherwise, store it as-is.
For example, if there are two registered prefixes,
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{
'v1': {
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'_url': 'users-list'
'_viewset': <class 'UserViewSet'>
},
}
'groups': {
'_url': 'groups-list'
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"""
if base_name is None:
base_name = prefix
super(DynamicRouter, self).register(prefix, viewset, base_name)
prefix_parts = prefix.split('/')
if len(prefix_parts) > 1:
prefix = prefix_parts[0]
endpoint = '/'.join(prefix_parts[1:])
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endpoint = prefix
prefix = None
if prefix and prefix not in directory:
current = directory[prefix] = {}
else:
current = directory.get(prefix, directory)
list_name = self.routes[0].name
url_name = list_name.format(basename=base_name)
if endpoint not in current:
current[endpoint] = {}
current[endpoint]['_url'] = url_name
current[endpoint]['_viewset'] = viewset | [
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| python | train |
twilio/twilio-python | twilio/rest/ip_messaging/v2/service/channel/message.py | https://github.com/twilio/twilio-python/blob/c867895f55dcc29f522e6e8b8868d0d18483132f/twilio/rest/ip_messaging/v2/service/channel/message.py#L230-L244 | def get_instance(self, payload):
"""
Build an instance of MessageInstance
:param dict payload: Payload response from the API
:returns: twilio.rest.chat.v2.service.channel.message.MessageInstance
:rtype: twilio.rest.chat.v2.service.channel.message.MessageInstance
"""
return MessageInstance(
self._version,
payload,
service_sid=self._solution['service_sid'],
channel_sid=self._solution['channel_sid'],
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| python | train |
dillonhicks/rekt | rekt/service.py | https://github.com/dillonhicks/rekt/blob/3848b272726c78214cb96b906f9b9f289497f27e/rekt/service.py#L227-L249 | def create_rest_client_class(name, apis, BaseClass=RestClient):
"""
Generate the api call functions and attach them to the generated
RestClient subclass with the name <Service>Client.
"""
apis_with_actions = list(itertools.chain.from_iterable([ zip([api] * len(api.actions), api.actions) for api in apis]))
api_funcs = [create_api_call_func(api, verb) for api, verb in apis_with_actions]
api_funcs.extend([create_async_api_call_func(api, verb) for api, verb in apis_with_actions])
api_mapper = dict([ (f.__name__, f) for f in api_funcs ])
# Adapted from :
# http://stackoverflow.com/questions/15247075/how-can-i-dynamically-create-derived-classes-from-a-base-class
def __init__(self, thread_count=_ASYNC_WORKER_THREAD_COUNT, **reqargs):
BaseClass.__init__(self)
setattr(self, 'reqargs', read_only_dict(reqargs))
self._executor = concurrent.futures.ThreadPoolExecutor(thread_count)
api_mapper['__init__'] = __init__
ClientClass = type(_CLIENT_NAME_FMT.format(name), (BaseClass,), api_mapper)
return ClientClass | [
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| python | train |
cuihantao/andes | andes/utils/time.py | https://github.com/cuihantao/andes/blob/7067898d4f26ce7534e968b8486c4aa8fe3a511a/andes/utils/time.py#L5-L19 | def elapsed(t0=0.0):
"""get elapsed time from the give time
Returns:
now: the absolute time now
dt_str: elapsed time in string
"""
now = time()
dt = now - t0
dt_sec = Decimal(str(dt)).quantize(Decimal('.0001'), rounding=ROUND_DOWN)
if dt_sec <= 1:
dt_str = str(dt_sec) + ' second'
else:
dt_str = str(dt_sec) + ' seconds'
return now, dt_str | [
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| python | train |
senaite/senaite.core | bika/lims/browser/analysisrequest/analysisrequests.py | https://github.com/senaite/senaite.core/blob/7602ce2ea2f9e81eb34e20ce17b98a3e70713f85/bika/lims/browser/analysisrequest/analysisrequests.py#L434-L517 | def update(self):
"""Called before the listing renders
"""
super(AnalysisRequestsView, self).update()
self.workflow = api.get_tool("portal_workflow")
self.member = self.mtool.getAuthenticatedMember()
self.roles = self.member.getRoles()
setup = api.get_bika_setup()
# remove `to_be_sampled` filter
if not setup.getSamplingWorkflowEnabled():
self.review_states = filter(
lambda x: x.get("id") != "to_be_sampled", self.review_states)
# remove `scheduled_sampling` filter
if not setup.getScheduleSamplingEnabled():
self.review_states = filter(
lambda x: x.get("id") != "scheduled_sampling",
self.review_states)
# remove `to_be_preserved` filter
if not setup.getSamplePreservationEnabled():
self.review_states = filter(
lambda x: x.get("id") != "to_be_preserved", self.review_states)
# remove `rejected` filter
if not setup.getRejectionReasons():
self.review_states = filter(
lambda x: x.get("id") != "rejected", self.review_states)
self.hideclientlink = "RegulatoryInspector" in self.roles \
and "Manager" not in self.roles \
and "LabManager" not in self.roles \
and "LabClerk" not in self.roles
if self.context.portal_type == "AnalysisRequestsFolder" and \
(self.mtool.checkPermission(AddAnalysisRequest, self.context)):
self.context_actions[_("Add")] = \
{"url": "ar_add?ar_count=1",
'permission': 'Add portal content',
"icon": "++resource++bika.lims.images/add.png"}
self.editresults = -1
self.clients = {}
# self.user_is_preserver = "Preserver" in self.roles
# Printing workflow enabled?
# If not, remove the Column
self.printwfenabled = \
self.context.bika_setup.getPrintingWorkflowEnabled()
printed_colname = "Printed"
if not self.printwfenabled and printed_colname in self.columns:
# Remove "Printed" columns
del self.columns[printed_colname]
tmprvs = []
for rs in self.review_states:
tmprs = rs
tmprs["columns"] = [c for c in rs.get("columns", []) if
c != printed_colname]
tmprvs.append(tmprs)
self.review_states = tmprvs
elif self.printwfenabled:
# Print button to choose multiple ARs and print them.
review_states = []
for review_state in self.review_states:
review_state.get("custom_transitions", []).extend(
[{"id": "print_sample",
"title": _("Print"),
"url": "workflow_action?action=print_sample"}, ])
review_states.append(review_state)
self.review_states = review_states
# Only "senaite.core: ManageAnalysisRequests" may see the copy to new button.
# elsewhere it is hacked in where required.
if self.copy_to_new_allowed:
review_states = []
for review_state in self.review_states:
review_state.get("custom_transitions", []).extend(
[{"id": "copy_to_new",
"title": _("Copy to new"),
"url": "workflow_action?action=copy_to_new"}, ])
review_states.append(review_state)
self.review_states = review_states | [
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| python | train |
pypa/pipenv | pipenv/vendor/click/utils.py | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/click/utils.py#L105-L121 | def open(self):
"""Opens the file if it's not yet open. This call might fail with
a :exc:`FileError`. Not handling this error will produce an error
that Click shows.
"""
if self._f is not None:
return self._f
try:
rv, self.should_close = open_stream(self.name, self.mode,
self.encoding,
self.errors,
atomic=self.atomic)
except (IOError, OSError) as e:
from .exceptions import FileError
raise FileError(self.name, hint=get_streerror(e))
self._f = rv
return rv | [
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| python | train |
mixmastamyk/fr | fr/utils.py | https://github.com/mixmastamyk/fr/blob/f96df8ed7210a033b9e711bbed768d4116213bfb/fr/utils.py#L67-L74 | def run(cmd, shell=False, debug=False):
'Run a command and return the output.'
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=shell)
(out, _) = proc.communicate() # no need for stderr
if debug:
print(cmd)
print(out)
return out | [
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| python | train |
scottrice/pysteam | pysteam/_crc_algorithms.py | https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L190-L212 | def gen_table(self):
"""
This function generates the CRC table used for the table_driven CRC
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instead.
"""
table_length = 1 << self.TableIdxWidth
tbl = [0] * table_length
for i in range(table_length):
register = i
if self.ReflectIn:
register = self.reflect(register, self.TableIdxWidth)
register = register << (self.Width - self.TableIdxWidth + self.CrcShift)
for j in range(self.TableIdxWidth):
if register & (self.MSB_Mask << self.CrcShift) != 0:
register = (register << 1) ^ (self.Poly << self.CrcShift)
else:
register = (register << 1)
if self.ReflectIn:
register = self.reflect(register >> self.CrcShift, self.Width) << self.CrcShift
tbl[i] = register & (self.Mask << self.CrcShift)
return tbl | [
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| python | train |
tyarkoni/pliers | pliers/external/tensorflow/classify_image.py | https://github.com/tyarkoni/pliers/blob/5b3385960ebd8c6ef1e86dd5e1be0080b2cb7f2b/pliers/external/tensorflow/classify_image.py#L130-L167 | def run_inference_on_image(image):
"""Runs inference on an image.
Args:
image: Image file name.
Returns:
Nothing
"""
if not tf.gfile.Exists(image):
tf.logging.fatal('File does not exist %s', image)
image_data = tf.gfile.FastGFile(image, 'rb').read()
# Creates graph from saved GraphDef.
create_graph()
with tf.Session() as sess:
# Some useful tensors:
# 'softmax:0': A tensor containing the normalized prediction across
# 1000 labels.
# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
# float description of the image.
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
# encoding of the image.
# Runs the softmax tensor by feeding the image_data as input to the graph.
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = NodeLookup()
top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score)) | [
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| python | train |
MagicStack/asyncpg | asyncpg/pool.py | https://github.com/MagicStack/asyncpg/blob/92c2d81256a1efd8cab12c0118d74ccd1c18131b/asyncpg/pool.py#L276-L293 | def _release(self):
"""Release this connection holder."""
if self._in_use is None:
# The holder is not checked out.
return
if not self._in_use.done():
self._in_use.set_result(None)
self._in_use = None
# Deinitialize the connection proxy. All subsequent
# operations on it will fail.
if self._proxy is not None:
self._proxy._detach()
self._proxy = None
# Put ourselves back to the pool queue.
self._pool._queue.put_nowait(self) | [
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| python | train |
pletzer/pnumpy | src/pnGhostedDistArray.py | https://github.com/pletzer/pnumpy/blob/9e6d308be94a42637466b91ab1a7b4d64b4c29ae/src/pnGhostedDistArray.py#L161-L172 | def gmdaZeros(shape, dtype, mask=None, numGhosts=1):
"""
ghosted distributed array zero constructor
@param shape the shape of the array
@param dtype the numpy data type
@param numGhosts the number of ghosts (>= 0)
"""
res = GhostedMaskedDistArray(shape, dtype)
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res[:] = 0
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| python | train |
outini/python-pylls | pylls/cachet.py | https://github.com/outini/python-pylls/blob/f9fa220594bc1974469097d9bad690a42d0d0f0f/pylls/cachet.py#L303-L334 | def update(self, incident_id, name=None, message=None, status=None,
visible=None, component_id=None, component_status=None,
notify=None, created_at=None, template=None, tpl_vars=None):
"""Update an Incident
:param int incident_id: Incident ID
:param str name: Name of the incident
:param str message: Incident explanation message
:param int status: Status of the incident
:param int visible: Whether the incident is publicly visible
:param int component_id: Component to update
:param int component_status: The status to update the given component
:param bool notify: Whether to notify subscribers
:param str created_at: When the incident was created
:param str template: The template slug to use
:param list tpl_vars: The variables to pass to the template
:return: Created incident data (:class:`dict`)
.. seealso:: https://docs.cachethq.io/reference#update-an-incident
"""
data = ApiParams()
data['name'] = name
data['message'] = message
data['status'] = status
data['visible'] = visible
data['component_id'] = component_id
data['component_status'] = component_status
data['notify'] = notify
data['created_at'] = created_at
data['template'] = template
data['vars'] = tpl_vars
return self._put('incidents/%s' % incident_id, data=data)['data'] | [
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:return: Created incident data (:class:`dict`)
.. seealso:: https://docs.cachethq.io/reference#update-an-incident | [
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| python | train |
liampauling/betfair | betfairlightweight/resources/baseresource.py | https://github.com/liampauling/betfair/blob/8479392eb4849c525d78d43497c32c0bb108e977/betfairlightweight/resources/baseresource.py#L26-L39 | def strip_datetime(value):
"""
Converts value to datetime if string or int.
"""
if isinstance(value, basestring):
try:
return parse_datetime(value)
except ValueError:
return
elif isinstance(value, integer_types):
try:
return datetime.datetime.utcfromtimestamp(value / 1e3)
except (ValueError, OverflowError, OSError):
return | [
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| python | train |
maxharp3r/archive-rotator | archive_rotator/rotator.py | https://github.com/maxharp3r/archive-rotator/blob/40b8e571461c54717cee7daead04dbc9751062c8/archive_rotator/rotator.py#L85-L92 | def _locate_files_to_delete(algorithm, rotated_files, next_rotation_id):
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"""
rotation_slot = algorithm.id_to_slot(next_rotation_id)
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| python | train |
fhs/pyhdf | pyhdf/V.py | https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/V.py#L1039-L1059 | def delete(self, tag, ref):
"""Delete from the vgroup the member identified by its tag
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Args::
tag tag of the member to delete
ref reference number of the member to delete
Returns::
None
Only the link of the member with the vgroup is deleted.
The member object is not deleted.
C library equivalent : Vdeletatagref
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ref reference number of the member to delete
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Only the link of the member with the vgroup is deleted.
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| python | train |
fedora-infra/fedora-messaging | fedora_messaging/config.py | https://github.com/fedora-infra/fedora-messaging/blob/be3e88534e2b15d579bcd24f9c4b7e795cb7e0b7/fedora_messaging/config.py#L391-L418 | def validate_queues(queues):
"""
Validate the queues configuration.
Raises:
exceptions.ConfigurationException: If the configuration provided is of an
invalid format.
"""
if not isinstance(queues, dict):
raise exceptions.ConfigurationException(
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| python | train |
ouroboroscoding/format-oc-python | FormatOC/__init__.py | https://github.com/ouroboroscoding/format-oc-python/blob/c160b46fe4ff2c92333c776991c712de23991225/FormatOC/__init__.py#L2217-L2228 | def keys(self):
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Returns a list of the node names in the parent
Returns:
list
"""
if hasattr(self._nodes, 'iterkeys'):
return self._nodes.keys()
else:
return tuple(self._nodes.keys()) | [
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| python | train |
alimanfoo/csvvalidator | csvvalidator.py | https://github.com/alimanfoo/csvvalidator/blob/50a86eefdc549c48f65a91a5c0a66099010ee65d/csvvalidator.py#L883-L905 | def _apply_skips(self, i, r,
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for skip in self._skips:
try:
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p['exception'] = e
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| python | valid |
poppy-project/pypot | pypot/primitive/primitive.py | https://github.com/poppy-project/pypot/blob/d9c6551bbc87d45d9d1f0bc15e35b616d0002afd/pypot/primitive/primitive.py#L116-L124 | def start(self):
""" Start or restart (the :meth:`~pypot.primitive.primitive.Primitive.stop` method will automatically be called) the primitive. """
if not self.robot._primitive_manager.running:
raise RuntimeError('Cannot run a primitive when the sync is stopped!')
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self.wait_to_start()
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| python | train |
pypa/pipenv | pipenv/vendor/pyparsing.py | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/pyparsing.py#L3067-L3094 | def sub(self, repl):
"""
Return Regex with an attached parse action to transform the parsed
result as if called using `re.sub(expr, repl, string) <https://docs.python.org/3/library/re.html#re.sub>`_.
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make_html = Regex(r"(\w+):(.*?):").sub(r"<\1>\2</\1>")
print(make_html.transformString("h1:main title:"))
# prints "<h1>main title</h1>"
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raise SyntaxError()
if self.asMatch:
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| Return Regex with an attached parse action to transform the parsed
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Example::
make_html = Regex(r"(\w+):(.*?):").sub(r"<\1>\2</\1>")
print(make_html.transformString("h1:main title:"))
# prints "<h1>main title</h1>" | [
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| python | train |
paolodragone/pymzn | pymzn/mzn/minizinc.py | https://github.com/paolodragone/pymzn/blob/35b04cfb244918551649b9bb8a0ab65d37c31fe4/pymzn/mzn/minizinc.py#L502-L658 | def minizinc(
mzn, *dzn_files, args=None, data=None, include=None, stdlib_dir=None,
globals_dir=None, declare_enums=True, allow_multiple_assignments=False,
keep=False, output_vars=None, output_base=None, output_mode='dict',
solver=None, timeout=None, two_pass=None, pre_passes=None,
output_objective=False, non_unique=False, all_solutions=False,
num_solutions=None, free_search=False, parallel=None, seed=None,
rebase_arrays=True, keep_solutions=True, return_enums=False, **kwargs
):
"""Implements the workflow for solving a CSP problem encoded with MiniZinc.
Parameters
----------
mzn : str
The minizinc model. This can be either the path to the ``.mzn`` file or
the content of the model itself.
*dzn_files
A list of paths to dzn files to attach to the minizinc execution,
provided as positional arguments; by default no data file is attached.
args : dict
Arguments for the template engine.
data : dict
Additional data as a dictionary of variables assignments to supply to
the minizinc executable. The dictionary is automatically converted to
dzn format by the ``pymzn.dict2dzn`` function.
include : str or list
One or more additional paths to search for included ``.mzn`` files.
stdlib_dir : str
The path to the MiniZinc standard library. Provide it only if it is
different from the default one.
globals_dir : str
The path to the MiniZinc globals directory. Provide it only if it is
different from the default one.
declare_enums : bool
Whether to declare enum types when converting inline data into dzn
format. If the enum types are declared elsewhere this option should be
False. Default is ``True``.
allow_multiple_assignments : bool
Whether to allow multiple assignments of variables. Sometimes is
convenient to simply let the data file override the value already
assigned in the minizinc file. Default is ``False``.
keep : bool
Whether to keep the generated ``.mzn``, ``.dzn``, ``.fzn`` and ``.ozn``
files or not. If False, the generated files are created as temporary
files which will be deleted right after the problem is solved. Though
files generated by PyMzn are not intended to be kept, this property can
be used for debugging purpose. Note that in case of error the files are
not deleted even if this parameter is ``False``. Default is ``False``.
output_vars : list of str
A list of output variables. These variables will be the ones included in
the output dictionary. Only available if ``ouptut_mode='dict'``.
output_base : str
Output directory for the files generated by PyMzn. The default
(``None``) is the temporary directory of your OS (if ``keep=False``) or
the current working directory (if ``keep=True``).
output_mode : {'dict', 'item', 'dzn', 'json', 'raw'}
The desired output format. The default is ``'dict'`` which returns a
stream of solutions decoded as python dictionaries. The ``'item'``
format outputs a stream of strings as returned by the ``solns2out``
tool, formatted according to the output statement of the MiniZinc model.
The ``'dzn'`` and ``'json'`` formats output a stream of strings
formatted in dzn of json respectively. The ``'raw'`` format, instead
returns the whole solution stream, without parsing.
solver : Solver
The ``Solver`` instance to use. The default solver is ``gecode``.
timeout : int
The timeout in seconds for the flattening + solving process.
two_pass : bool or int
If ``two_pass`` is True, then it is equivalent to the ``--two-pass``
option for the ``minizinc`` executable. If ``two_pass`` is an integer
``<n>``, instead, it is equivalent to the ``-O<n>`` option for the
``minizinc`` executable.
pre_passes : int
Equivalent to the ``--pre-passes`` option for the ``minizinc``
executable.
output_objective : bool
Equivalent to the ``--output-objective`` option for the ``minizinc``
executable. Adds a field ``_objective`` to all solutions.
non_unique : bool
Equivalent to the ``--non-unique`` option for the ``minizinc``
executable.
all_solutions : bool
Whether all the solutions must be returned. This option might not work
if the solver does not support it. Default is ``False``.
num_solutions : int
The upper bound on the number of solutions to be returned. This option
might not work if the solver does not support it. Default is ``1``.
free_search : bool
If ``True``, instruct the solver to perform free search.
parallel : int
The number of parallel threads the solver can utilize for the solving.
seed : int
The random number generator seed to pass to the solver.
rebase_arrays : bool
Whether to "rebase" parsed arrays (see the `Dzn files
<http://paolodragone.com/pymzn/reference/dzn>`__ section). Default is
True.
keep_solutions : bool
Whether to store the solutions in memory after solving is done. If
``keep_solutions`` is ``False``, the returned solution stream can only
be iterated once and cannot be addressed as a list.
return_enums : bool
Wheter to return enum types along with the variable assignments in the
solutions. Only used if ``output_mode='dict'``. Default is ``False``.
**kwargs
Additional arguments to pass to the solver, provided as additional
keyword arguments to this function. Check the solver documentation for
the available arguments.
Returns
-------
Solutions or str
If ``output_mode`` is not ``'raw'``, returns a list-like object
containing the solutions found by the solver. The format of the solution
depends on the specified ``output_mode``. If ``keep_solutions=False``,
the returned object cannot be addressed as a list and can only be
iterated once. If ``output_mode='raw'``, the function returns the whole
solution stream as a single string.
"""
mzn_file, dzn_files, data_file, data, keep, _output_mode, types = \
_minizinc_preliminaries(
mzn, *dzn_files, args=args, data=data, include=include,
stdlib_dir=stdlib_dir, globals_dir=globals_dir,
output_vars=output_vars, keep=keep, output_base=output_base,
output_mode=output_mode, declare_enums=declare_enums,
allow_multiple_assignments=allow_multiple_assignments
)
if not solver:
solver = config.get('solver', gecode)
solver_args = {**kwargs, **config.get('solver_args', {})}
proc = solve(
solver, mzn_file, *dzn_files, data=data, include=include,
stdlib_dir=stdlib_dir, globals_dir=globals_dir,
output_mode=_output_mode, timeout=timeout, two_pass=two_pass,
pre_passes=pre_passes, output_objective=output_objective,
non_unique=non_unique, all_solutions=all_solutions,
num_solutions=num_solutions, free_search=free_search, parallel=parallel,
seed=seed, allow_multiple_assignments=allow_multiple_assignments,
**solver_args
)
if not keep:
_cleanup([mzn_file, data_file])
if output_mode == 'raw':
return proc.stdout_data
parser = SolutionParser(
solver, output_mode=output_mode, rebase_arrays=rebase_arrays,
types=types, keep_solutions=keep_solutions, return_enums=return_enums
)
solns = parser.parse(proc)
return solns | [
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Parameters
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mzn : str
The minizinc model. This can be either the path to the ``.mzn`` file or
the content of the model itself.
*dzn_files
A list of paths to dzn files to attach to the minizinc execution,
provided as positional arguments; by default no data file is attached.
args : dict
Arguments for the template engine.
data : dict
Additional data as a dictionary of variables assignments to supply to
the minizinc executable. The dictionary is automatically converted to
dzn format by the ``pymzn.dict2dzn`` function.
include : str or list
One or more additional paths to search for included ``.mzn`` files.
stdlib_dir : str
The path to the MiniZinc standard library. Provide it only if it is
different from the default one.
globals_dir : str
The path to the MiniZinc globals directory. Provide it only if it is
different from the default one.
declare_enums : bool
Whether to declare enum types when converting inline data into dzn
format. If the enum types are declared elsewhere this option should be
False. Default is ``True``.
allow_multiple_assignments : bool
Whether to allow multiple assignments of variables. Sometimes is
convenient to simply let the data file override the value already
assigned in the minizinc file. Default is ``False``.
keep : bool
Whether to keep the generated ``.mzn``, ``.dzn``, ``.fzn`` and ``.ozn``
files or not. If False, the generated files are created as temporary
files which will be deleted right after the problem is solved. Though
files generated by PyMzn are not intended to be kept, this property can
be used for debugging purpose. Note that in case of error the files are
not deleted even if this parameter is ``False``. Default is ``False``.
output_vars : list of str
A list of output variables. These variables will be the ones included in
the output dictionary. Only available if ``ouptut_mode='dict'``.
output_base : str
Output directory for the files generated by PyMzn. The default
(``None``) is the temporary directory of your OS (if ``keep=False``) or
the current working directory (if ``keep=True``).
output_mode : {'dict', 'item', 'dzn', 'json', 'raw'}
The desired output format. The default is ``'dict'`` which returns a
stream of solutions decoded as python dictionaries. The ``'item'``
format outputs a stream of strings as returned by the ``solns2out``
tool, formatted according to the output statement of the MiniZinc model.
The ``'dzn'`` and ``'json'`` formats output a stream of strings
formatted in dzn of json respectively. The ``'raw'`` format, instead
returns the whole solution stream, without parsing.
solver : Solver
The ``Solver`` instance to use. The default solver is ``gecode``.
timeout : int
The timeout in seconds for the flattening + solving process.
two_pass : bool or int
If ``two_pass`` is True, then it is equivalent to the ``--two-pass``
option for the ``minizinc`` executable. If ``two_pass`` is an integer
``<n>``, instead, it is equivalent to the ``-O<n>`` option for the
``minizinc`` executable.
pre_passes : int
Equivalent to the ``--pre-passes`` option for the ``minizinc``
executable.
output_objective : bool
Equivalent to the ``--output-objective`` option for the ``minizinc``
executable. Adds a field ``_objective`` to all solutions.
non_unique : bool
Equivalent to the ``--non-unique`` option for the ``minizinc``
executable.
all_solutions : bool
Whether all the solutions must be returned. This option might not work
if the solver does not support it. Default is ``False``.
num_solutions : int
The upper bound on the number of solutions to be returned. This option
might not work if the solver does not support it. Default is ``1``.
free_search : bool
If ``True``, instruct the solver to perform free search.
parallel : int
The number of parallel threads the solver can utilize for the solving.
seed : int
The random number generator seed to pass to the solver.
rebase_arrays : bool
Whether to "rebase" parsed arrays (see the `Dzn files
<http://paolodragone.com/pymzn/reference/dzn>`__ section). Default is
True.
keep_solutions : bool
Whether to store the solutions in memory after solving is done. If
``keep_solutions`` is ``False``, the returned solution stream can only
be iterated once and cannot be addressed as a list.
return_enums : bool
Wheter to return enum types along with the variable assignments in the
solutions. Only used if ``output_mode='dict'``. Default is ``False``.
**kwargs
Additional arguments to pass to the solver, provided as additional
keyword arguments to this function. Check the solver documentation for
the available arguments.
Returns
-------
Solutions or str
If ``output_mode`` is not ``'raw'``, returns a list-like object
containing the solutions found by the solver. The format of the solution
depends on the specified ``output_mode``. If ``keep_solutions=False``,
the returned object cannot be addressed as a list and can only be
iterated once. If ``output_mode='raw'``, the function returns the whole
solution stream as a single string. | [
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| python | train |
pywbem/pywbem | pywbem/cim_obj.py | https://github.com/pywbem/pywbem/blob/e54ecb82c2211e289a268567443d60fdd489f1e4/pywbem/cim_obj.py#L4063-L4074 | def path(self, path):
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| python | train |
mozilla-services/python-dockerflow | src/dockerflow/flask/app.py | https://github.com/mozilla-services/python-dockerflow/blob/43703c5e8934ba6901b0a1520d6da4ed6457208c/src/dockerflow/flask/app.py#L354-L391 | def check(self, func=None, name=None):
"""
A decorator to register a new Dockerflow check to be run
when the /__heartbeat__ endpoint is called., e.g.::
from dockerflow.flask import checks
@dockerflow.check
def storage_reachable():
try:
acme.storage.ping()
except SlowConnectionException as exc:
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except StorageException as exc:
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or using a custom name::
@dockerflow.check(name='acme-storage-check)
def storage_reachable():
# ...
"""
if func is None:
return functools.partial(self.check, name=name)
if name is None:
name = func.__name__
self.logger.info('Registered Dockerflow check %s', name)
@functools.wraps(func)
def decorated_function(*args, **kwargs):
self.logger.info('Called Dockerflow check %s', name)
return func(*args, **kwargs)
self.checks[name] = decorated_function
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def storage_reachable():
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| python | train |
llazzaro/analyzerstrategies | analyzerstrategies/periodStrategy.py | https://github.com/llazzaro/analyzerstrategies/blob/3c647802f582bf2f06c6793f282bee0d26514cd6/analyzerstrategies/periodStrategy.py#L27-L34 | def increase_and_check_counter(self):
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| python | train |
jwodder/javaproperties | javaproperties/propclass.py | https://github.com/jwodder/javaproperties/blob/8b48f040305217ebeb80c98c4354691bbb01429b/javaproperties/propclass.py#L155-L167 | def store(self, out, comments=None):
"""
Write the `Properties` object's entries (in unspecified order) in
``.properties`` format to ``out``, including the current timestamp.
:param out: A file-like object to write the properties to. It must
have been opened as a text file with a Latin-1-compatible encoding.
:param comments: If non-`None`, ``comments`` will be written to ``out``
as a comment before any other content
:type comments: text string or `None`
:return: `None`
"""
dump(self.data, out, comments=comments) | [
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| python | train |
ionelmc/python-hunter | src/hunter/event.py | https://github.com/ionelmc/python-hunter/blob/b3a1310b0593d2c6b6ef430883843896e17d6a81/src/hunter/event.py#L247-L258 | def source(self, getline=linecache.getline):
"""
A string with the sourcecode for the current line (from ``linecache`` - failures are ignored).
Fast but sometimes incomplete.
:type: str
"""
try:
return getline(self.filename, self.lineno)
except Exception as exc:
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| python | train |
Rapptz/discord.py | discord/ext/commands/help.py | https://github.com/Rapptz/discord.py/blob/05d4f7f9620ef33635d6ac965b26528e09cdaf5b/discord/ext/commands/help.py#L865-L869 | def shorten_text(self, text):
"""Shortens text to fit into the :attr:`width`."""
if len(text) > self.width:
return text[:self.width - 3] + '...'
return text | [
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| python | train |
tempodb/tempodb-python | tempodb/protocol/objects.py | https://github.com/tempodb/tempodb-python/blob/8ce45231bd728c6c97ef799cf0f1513ea3a9a7d3/tempodb/protocol/objects.py#L475-L502 | def from_json(self, json_text):
"""Deserialize a JSON object into this object. This method will
check that the JSON object has the required keys and will set each
of the keys in that JSON object as an instance attribute of this
object.
:param json_text: the JSON text or object to deserialize from
:type json_text: dict or string
:raises ValueError: if the JSON object lacks an expected key
:rtype: None"""
if type(json_text) in [str, unicode]:
j = json.loads(json_text)
else:
j = json_text
try:
for p in self.properties:
if p == 't':
t = convert_iso_stamp(j[p], self.tz)
setattr(self, 't', t)
else:
setattr(self, p, j[p])
#overriding this exception allows us to handle optional values like
#id and key which are only present during particular API calls like
#multi writes
except KeyError:
pass | [
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| python | train |
pyblish/pyblish-qml | pyblish_qml/vendor/mock.py | https://github.com/pyblish/pyblish-qml/blob/6095d18b2ec0afd0409a9b1a17e53b0658887283/pyblish_qml/vendor/mock.py#L1879-L1886 | def mock_add_spec(self, spec, spec_set=False):
"""Add a spec to a mock. `spec` can either be an object or a
list of strings. Only attributes on the `spec` can be fetched as
attributes from the mock.
If `spec_set` is True then only attributes on the spec can be set."""
self._mock_add_spec(spec, spec_set)
self._mock_set_magics() | [
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| python | train |
data-8/datascience | datascience/tables.py | https://github.com/data-8/datascience/blob/4cee38266903ca169cea4a53b8cc39502d85c464/datascience/tables.py#L2765-L2776 | def _is_array_integer(arr):
"""Returns True if an array contains integers (integer type or near-int
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>>> _is_array_integer(np.arange(10))
True
>>> _is_array_integer(np.arange(7.0, 20.0, 1.0))
True
>>> _is_array_integer(np.arange(0, 1, 0.1))
False
"""
return issubclass(arr.dtype.type, np.integer) or np.allclose(arr, np.round(arr)) | [
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| python | train |
Erotemic/utool | utool/util_latex.py | https://github.com/Erotemic/utool/blob/3b27e1f4e6e6fb23cd8744af7b7195b57d99e03a/utool/util_latex.py#L727-L875 | def get_latex_figure_str(fpath_list, caption_str=None, label_str=None,
width_str=r'\textwidth', height_str=None, nCols=None,
dpath=None, colpos_sep=' ', nlsep='',
use_sublbls=None, use_frame=False):
r"""
Args:
fpath_list (list):
dpath (str): directory relative to main tex file
Returns:
str: figure_str
CommandLine:
python -m utool.util_latex --test-get_latex_figure_str
Example:
>>> # DISABLE_DOCTEST
>>> from utool.util_latex import * # NOQA
>>> fpath_list = ['figures/foo.png']
>>> figure_str = get_latex_figure_str(fpath_list)
>>> result = str(figure_str)
>>> print(result)
"""
import utool as ut
if nCols is None:
nCols = len(fpath_list)
USE_SUBFIGURE = True
if width_str is not None:
colwidth = (1.0 / nCols)
if USE_SUBFIGURE:
colwidth *= .95
graphics_sizestr = ('%.2f' % (colwidth,)) + width_str
else:
graphics_sizestr = '[width=%.1f%s]' % (colwidth, width_str)
elif height_str is not None:
graphics_sizestr = '[height=%s]' % (height_str)
else:
graphics_sizestr = ''
if dpath is not None:
fpath_list = [ut.relpath_unix(fpath_, dpath) for fpath_ in fpath_list]
if USE_SUBFIGURE:
# References: https://en.wikibooks.org/wiki/LaTeX/Floats,_Figures_and_Captions#Subfloats
# TODO ? http://tex.stackexchange.com/questions/159290/how-can-i-place-a-vertical-rule-between-subfigures
# Use subfigures
graphics_list = []
sublbl_prefix = label_str if label_str is not None else ''
for count, fpath in enumerate(fpath_list):
"""
print(', '.join([str(x) + ':' + chr(x) for x in range(65, 123)]))
print(', '.join([str(x) + ':' + chr(x) for x in range(97, 123)]))
"""
CHRLBLS = True
if CHRLBLS:
#subchar = chr(97 + count)
subchar = chr(65 + count)
else:
subchar = str(count)
parts = []
subfigure_str = ''
if len(fpath_list) > 1:
parts.append('\\begin{subfigure}[h]{' + graphics_sizestr + '}')
parts.append('\\centering')
graphics_part = '\\includegraphics[width=%s]{%s}' % (width_str, fpath,)
if use_frame:
parts.append('\\fbox{%s}' % (graphics_part,))
else:
parts.append(graphics_part)
if use_sublbls is True or use_sublbls is None and len(fpath_list) > 1:
parts.append('\\caption{}\\label{sub:' + sublbl_prefix + subchar + '}')
if len(fpath_list) > 1:
parts.append('\\end{subfigure}')
subfigure_str = ''.join(parts)
graphics_list.append(subfigure_str)
else:
if True:
graphics_list = [
r'\includegraphics%s{%s}\captionof{figure}{%s}' % (
graphics_sizestr, fpath, 'fd',
#'(' + str(count) + ')'
#'(' + chr(97 + count) + ')'
)
for count, fpath in enumerate(fpath_list)]
else:
graphics_list = [r'\includegraphics%s{%s}' % (graphics_sizestr, fpath,) for fpath in fpath_list]
#graphics_list = [r'\includegraphics%s{%s}' % (graphics_sizestr, fpath,) ]
#nRows = len(graphics_list) // nCols
# Add separators
NL = '\n'
if USE_SUBFIGURE:
col_spacer_mid = NL + '~~' + '% --' + NL
col_spacer_end = NL + r'\\' + '% --' + NL
else:
col_spacer_mid = NL + '&' + NL
col_spacer_end = NL + r'\\' + nlsep + NL
sep_list = [
col_spacer_mid if count % nCols > 0 else col_spacer_end
for count in range(1, len(graphics_list) + 1)
]
if len(sep_list) > 0:
sep_list[-1] = ''
graphics_list_ = [graphstr + sep for graphstr, sep in zip(graphics_list, sep_list)]
#graphics_body = '\n&\n'.join(graphics_list)
graphics_body = ''.join(graphics_list_)
header_str = colpos_sep.join(['c'] * nCols)
if USE_SUBFIGURE:
figure_body = graphics_body
else:
figure_body = ut.codeblock(
r'''
\begin{tabular}{%s}
%s
\end{tabular}
'''
) % (header_str, graphics_body)
if caption_str is not None:
#tabular_body += '\n\caption{\\footnotesize{%s}}' % (caption_str,)
if label_str is not None:
figure_body += '\n\caption[%s]{%s}' % (label_str, caption_str,)
else:
figure_body += '\n\caption{%s}' % (caption_str,)
if label_str is not None:
figure_body += '\n\label{fig:%s}' % (label_str,)
#figure_fmtstr = ut.codeblock(
# r'''
# \begin{figure*}
# \begin{center}
# %s
# \end{center}
# \end{figure*}
# '''
#)
figure_fmtstr = ut.codeblock(
r'''
\begin{figure}[ht!]
\centering
%s
\end{figure}
'''
)
figure_str = figure_fmtstr % (figure_body)
return figure_str | [
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Args:
fpath_list (list):
dpath (str): directory relative to main tex file
Returns:
str: figure_str
CommandLine:
python -m utool.util_latex --test-get_latex_figure_str
Example:
>>> # DISABLE_DOCTEST
>>> from utool.util_latex import * # NOQA
>>> fpath_list = ['figures/foo.png']
>>> figure_str = get_latex_figure_str(fpath_list)
>>> result = str(figure_str)
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| python | train |
xtuml/pyxtuml | xtuml/load.py | https://github.com/xtuml/pyxtuml/blob/7dd9343b9a0191d1db1887ab9288d0a026608d9a/xtuml/load.py#L274-L282 | def populate_unique_identifiers(self, metamodel):
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| python | test |
ThreshingFloor/libtf | libtf/logparsers/tf_log_base.py | https://github.com/ThreshingFloor/libtf/blob/f1a8710f750639c9b9e2a468ece0d2923bf8c3df/libtf/logparsers/tf_log_base.py#L80-L92 | def reduce(self, show_noisy=False):
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Yield the reduced log lines
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"""
if not show_noisy:
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yield log['raw'].strip()
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| python | train |
mitsei/dlkit | dlkit/json_/hierarchy/sessions.py | https://github.com/mitsei/dlkit/blob/445f968a175d61c8d92c0f617a3c17dc1dc7c584/dlkit/json_/hierarchy/sessions.py#L555-L570 | def remove_root(self, id_):
"""Removes a root node.
arg: id (osid.id.Id): the ``Id`` of the node
raise: NotFound - ``id`` was not found or not in hierarchy
raise: NullArgument - ``id`` is ``null``
raise: OperationFailed - unable to complete request
raise: PermissionDenied - authorization failure
*compliance: mandatory -- This method must be implemented.*
"""
result = self._rls.get_relationships_by_genus_type_for_peers(self._phantom_root_id, id_, self._relationship_type)
if not bool(result.available()):
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self._ras.delete_relationship(result.get_next_relationship().get_id())
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| python | train |
uber/doubles | doubles/class_double.py | https://github.com/uber/doubles/blob/15e68dcf98f709b19a581915fa6af5ef49ebdd8a/doubles/class_double.py#L7-L20 | def patch_class(input_class):
"""Create a new class based on the input_class.
:param class input_class: The class to patch.
:rtype class:
"""
class Instantiator(object):
@classmethod
def _doubles__new__(self, *args, **kwargs):
pass
new_class = type(input_class.__name__, (input_class, Instantiator), {})
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| python | train |
titusjan/argos | argos/repo/rtiplugins/pillowio.py | https://github.com/titusjan/argos/blob/20d0a3cae26c36ea789a5d219c02ca7df21279dd/argos/repo/rtiplugins/pillowio.py#L135-L153 | def dimensionNames(self):
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"""
if self._array is None:
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if self._array.ndim == 2:
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# Defensive programming: fall back on default names
msg = "Expected 3D image. Got: {}".format(self._array.ndim)
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logger.warn(msg)
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| python | train |
pallets/werkzeug | examples/simplewiki/actions.py | https://github.com/pallets/werkzeug/blob/a220671d66755a94630a212378754bb432811158/examples/simplewiki/actions.py#L45-L85 | def on_edit(request, page_name):
"""Edit the current revision of a page."""
change_note = error = ""
revision = (
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page = None
else:
page = revision.page
if request.method == "POST":
text = request.form.get("text")
if request.form.get("cancel") or revision and revision.text == text:
return redirect(href(page.name))
elif not text:
error = "You cannot save empty revisions."
else:
change_note = request.form.get("change_note", "")
if page is None:
page = Page(page_name)
session.add(page)
session.add(Revision(page, text, change_note))
session.commit()
return redirect(href(page.name))
return Response(
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page=page,
new=page is None,
page_name=page_name,
change_note=change_note,
error=error,
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| python | train |
cloud-custodian/cloud-custodian | tools/sandbox/c7n_sphere11/c7n_sphere11/controller.py | https://github.com/cloud-custodian/cloud-custodian/blob/52ef732eb3d7bc939d1579faf519314814695c08/tools/sandbox/c7n_sphere11/c7n_sphere11/controller.py#L50-L60 | def get_session(self, account_id):
"""Get an active session in the target account."""
if account_id not in self.account_sessions:
if account_id not in self.config['accounts']:
raise AccountNotFound("account:%s is unknown" % account_id)
self.account_sessions[account_id] = s = assumed_session(
self.config['accounts'][account_id]['role'], "Sphere11")
s._session.user_agent_name = "Sphere11"
s._session.user_agent_version = "0.07"
return self.account_sessions[account_id] | [
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| python | train |
tensorflow/tensor2tensor | tensor2tensor/trax/models/transformer.py | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/models/transformer.py#L123-L151 | def DecoderLayer(feature_depth,
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Args:
feature_depth: int: depth of embedding
feedforward_depth: int: depth of feed-forward layer
num_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer.
"""
return layers.Serial(
layers.Residual( # Self-attention block.
layers.LayerNorm(),
layers.Branch(),
layers.Parallel(layers.Identity(), # activation for (q, k, v)
layers.CausalMask(axis=-2)), # attention mask
layers.MultiHeadedAttention(feature_depth, num_heads=num_heads,
dropout=dropout, mode=mode),
layers.Dropout(rate=dropout, mode=mode)
),
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| python | train |
Shapeways/coyote_framework | coyote_framework/util/apps/objects.py | https://github.com/Shapeways/coyote_framework/blob/cb29899b984a21d56bf65d0b1d907073948fe16c/coyote_framework/util/apps/objects.py#L6-L12 | def objectify(dictionary, name='Object'):
"""Converts a dictionary into a named tuple (shallow)
"""
o = namedtuple(name, dictionary.keys())(*dictionary.values())
return o | [
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| python | train |
rq/Flask-RQ2 | src/flask_rq2/functions.py | https://github.com/rq/Flask-RQ2/blob/58eedf6f0cd7bcde4ccd787074762ea08f531337/src/flask_rq2/functions.py#L223-L279 | def cron(self, pattern, name, *args, **kwargs):
"""
A function to setup a RQ job as a cronjob::
@rq.job('low', timeout=60)
def add(x, y):
return x + y
add.cron('* * * * *', 'add-some-numbers', 1, 2, timeout=10)
:param \\*args: The positional arguments to pass to the queued job.
:param \\*\\*kwargs: The keyword arguments to pass to the queued job.
:param pattern: A Crontab pattern.
:type pattern: str
:param name: The name of the cronjob.
:type name: str
:param queue: Name of the queue to queue in, defaults to
queue of of job or :attr:`~flask_rq2.RQ.default_queue`.
:type queue: str
:param timeout: The job timeout in seconds.
If not provided uses the job's timeout or
:attr:`~flask_rq2.RQ.default_timeout`.
:type timeout: int
:param description: Description of the job.
:type description: str
:param repeat: The number of times the job needs to be repeatedly
queued via the cronjob. Take care only using this for
cronjob that don't already repeat themselves natively
due to their crontab.
:type repeat: int
:return: An RQ job instance.
:rtype: ~flask_rq2.job.FlaskJob
"""
queue_name = kwargs.pop('queue', self.queue_name)
timeout = kwargs.pop('timeout', self.timeout)
description = kwargs.pop('description', None)
repeat = kwargs.pop('repeat', None)
return self.rq.get_scheduler().cron(
pattern,
self.wrapped,
args=args,
kwargs=kwargs,
repeat=repeat,
queue_name=queue_name,
id='cron-%s' % name,
timeout=timeout,
description=description,
) | [
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:param timeout: The job timeout in seconds.
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:type timeout: int
:param description: Description of the job.
:type description: str
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:type repeat: int
:return: An RQ job instance.
:rtype: ~flask_rq2.job.FlaskJob | [
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| python | train |
3DLIRIOUS/MeshLabXML | meshlabxml/transform.py | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/transform.py#L623-L655 | def wrap2cylinder(script, radius=1, pitch=0, taper=0, pitch_func=None,
taper_func=None):
"""Deform mesh around cylinder of radius and axis z
y = 0 will be on the surface of radius "radius"
pitch != 0 will create a helix, with distance "pitch" traveled in z for each rotation
taper = change in r over z. E.g. a value of 0.5 will shrink r by 0.5 for every z length of 1
"""
"""vert_function(s=s, x='(%s+y-taper)*sin(x/(%s+y))' % (radius, radius),
y='(%s+y)*cos(x/(%s+y))' % (radius, radius),
z='z-%s*x/(2*%s*(%s+y))' % (pitch, pi, radius))"""
if pitch_func is None:
pitch_func = '-(pitch)*x/(2*pi*(radius))'
pitch_func = pitch_func.replace(
'pitch', str(pitch)).replace(
'pi', str(math.pi)).replace(
'radius', str(radius))
if taper_func is None:
taper_func = '-(taper)*(pitch_func)'
taper_func = taper_func.replace(
'taper', str(taper)).replace(
'pitch_func', str(pitch_func)).replace(
'pi', str(math.pi))
x_func = '(y+(radius)+(taper_func))*sin(x/(radius))'.replace(
'radius', str(radius)).replace('taper_func', str(taper_func))
y_func = '(y+(radius)+(taper_func))*cos(x/(radius))'.replace(
'radius', str(radius)).replace('taper_func', str(taper_func))
z_func = 'z+(pitch_func)'.replace('pitch_func', str(pitch_func))
vert_function(script, x_func, y_func, z_func)
return None | [
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| python | test |
cloud9ers/gurumate | environment/lib/python2.7/site-packages/nose/plugins/prof.py | https://github.com/cloud9ers/gurumate/blob/075dc74d1ee62a8c6b7a8bf2b271364f01629d1e/environment/lib/python2.7/site-packages/nose/plugins/prof.py#L33-L54 | def options(self, parser, env):
"""Register commandline options.
"""
if not self.available():
return
Plugin.options(self, parser, env)
parser.add_option('--profile-sort', action='store', dest='profile_sort',
default=env.get('NOSE_PROFILE_SORT', 'cumulative'),
metavar="SORT",
help="Set sort order for profiler output")
parser.add_option('--profile-stats-file', action='store',
dest='profile_stats_file',
metavar="FILE",
default=env.get('NOSE_PROFILE_STATS_FILE'),
help='Profiler stats file; default is a new '
'temp file on each run')
parser.add_option('--profile-restrict', action='append',
dest='profile_restrict',
metavar="RESTRICT",
default=env.get('NOSE_PROFILE_RESTRICT'),
help="Restrict profiler output. See help for "
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| python | test |
polyaxon/rhea | rhea/manager.py | https://github.com/polyaxon/rhea/blob/f47b59777cd996d834a0497a1ab442541aaa8a62/rhea/manager.py#L231-L285 | def get_dict(self,
key,
is_list=False,
is_optional=False,
is_secret=False,
is_local=False,
default=None,
options=None):
"""
Get a the value corresponding to the key and converts it to `dict`.
Args:
key: the dict key.
is_list: If this is one element or a list of elements.
is_optional: To raise an error if key was not found.
is_secret: If the key is a secret.
is_local: If the key is a local to this service.
default: default value if is_optional is True.
options: list/tuple if provided, the value must be one of these values.
Returns:
`str`: value corresponding to the key.
"""
def convert_to_dict(x):
x = json.loads(x)
if not isinstance(x, Mapping):
raise RheaError("Cannot convert value `{}` (key: `{}`) to `dict`".format(x, key))
return x
if is_list:
return self._get_typed_list_value(key=key,
target_type=Mapping,
type_convert=convert_to_dict,
is_optional=is_optional,
is_secret=is_secret,
is_local=is_local,
default=default,
options=options)
value = self._get_typed_value(key=key,
target_type=Mapping,
type_convert=convert_to_dict,
is_optional=is_optional,
is_secret=is_secret,
is_local=is_local,
default=default,
options=options)
if not value:
return default
if not isinstance(value, Mapping):
raise RheaError("Cannot convert value `{}` (key: `{}`) "
"to `dict`".format(value, key))
return value | [
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is_secret: If the key is a secret.
is_local: If the key is a local to this service.
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| python | train |
google-research/batch-ppo | agents/algorithms/ppo/ppo.py | https://github.com/google-research/batch-ppo/blob/3d09705977bae4e7c3eb20339a3b384d2a5531e4/agents/algorithms/ppo/ppo.py#L382-L415 | def _update_step(self, sequence):
"""Compute the current combined loss and perform a gradient update step.
The sequences must be a dict containing the keys `length` and `sequence`,
where the latter is a tuple containing observations, actions, parameters of
the behavioral policy, rewards, and advantages.
Args:
sequence: Sequences of episodes or chunks of episodes.
Returns:
Tuple of value loss, policy loss, and summary tensor.
"""
observ, action, old_policy_params, reward, advantage = sequence['sequence']
length = sequence['length']
old_policy = self._policy_type(**old_policy_params)
value_loss, value_summary = self._value_loss(observ, reward, length)
network = self._network(observ, length)
policy_loss, policy_summary = self._policy_loss(
old_policy, network.policy, action, advantage, length)
network_loss = network.get('loss', 0.0)
loss = policy_loss + value_loss + tf.reduce_mean(network_loss)
gradients, variables = (
zip(*self._optimizer.compute_gradients(loss)))
optimize = self._optimizer.apply_gradients(
zip(gradients, variables))
summary = tf.summary.merge([
value_summary, policy_summary,
tf.summary.histogram('network_loss', network_loss),
tf.summary.scalar('avg_network_loss', tf.reduce_mean(network_loss)),
tf.summary.scalar('gradient_norm', tf.global_norm(gradients)),
utility.gradient_summaries(zip(gradients, variables))])
with tf.control_dependencies([optimize]):
return [tf.identity(x) for x in (value_loss, policy_loss, summary)] | [
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The sequences must be a dict containing the keys `length` and `sequence`,
where the latter is a tuple containing observations, actions, parameters of
the behavioral policy, rewards, and advantages.
Args:
sequence: Sequences of episodes or chunks of episodes.
Returns:
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| python | train |
9wfox/tornadoweb | tornadoweb/utility.py | https://github.com/9wfox/tornadoweb/blob/2286b66fbe10e4d9f212b979664c15fa17adf378/tornadoweb/utility.py#L28-L40 | def get_modules(pkg_name, module_filter = None):
"""
返回包中所有符合条件的模块。
参数:
pkg_name 包名称
module_filter 模块名过滤器 def (module_name)
"""
path = app_path(pkg_name)
#py_filter = lambda f: all((fnmatch(f, "*.py"), not f.startswith("__"), module_filter and module_filter(f) or True))
py_filter = lambda f: all((fnmatch(f, "*.pyc") or fnmatch(f, "*.py"), not f.startswith("__"), module_filter and module_filter(f) or True))
names = [splitext(n)[0] for n in os.listdir(path) if py_filter(n)]
return [__import__("{0}.{1}".format(pkg_name, n)).__dict__[n] for n in names] | [
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| python | train |
SuryaSankar/flask-sqlalchemy-booster | flask_sqlalchemy_booster/model_booster/queryable_mixin.py | https://github.com/SuryaSankar/flask-sqlalchemy-booster/blob/444048d167ab7718f758e943665ef32d101423a5/flask_sqlalchemy_booster/model_booster/queryable_mixin.py#L203-L235 | def update(self, **kwargs):
"""Updates an instance.
Args:
**kwargs : Arbitrary keyword arguments. Column names are
keywords and their new values are the values.
Examples:
>>> customer.update(email="[email protected]", name="new")
"""
kwargs = self._preprocess_params(kwargs)
kwargs = self.preprocess_kwargs_before_update(kwargs)
for key, value in kwargs.iteritems():
cls = type(self)
if not hasattr(cls, key) or isinstance(getattr(cls, key), property):
continue
if key not in self._no_overwrite_:
setattr(self, key, value)
if isinstance(getattr(self, key), OrderingList):
getattr(self, key).reorder()
elif isinstance(getattr(cls, key), AssociationProxyInstance):
target_name = getattr(cls, key).target_collection
target_rel = getattr(self, target_name)
if isinstance(target_rel, OrderingList):
target_rel.reorder()
try:
self.session.commit()
return self
except Exception as e:
self.session.rollback()
raise e | [
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| python | train |
LEMS/pylems | lems/model/simulation.py | https://github.com/LEMS/pylems/blob/4eeb719d2f23650fe16c38626663b69b5c83818b/lems/model/simulation.py#L367-L397 | def toxml(self):
"""
Exports this object into a LEMS XML object
"""
chxmlstr = ''
for run in self.runs:
chxmlstr += run.toxml()
for record in self.records:
chxmlstr += record.toxml()
for event_record in self.event_records:
chxmlstr += event_record.toxml()
for data_display in self.data_displays:
chxmlstr += data_display.toxml()
for data_writer in self.data_writers:
chxmlstr += data_writer.toxml()
for event_writer in self.event_writers:
chxmlstr += event_writer.toxml()
if chxmlstr:
xmlstr = '<Simulation>' + chxmlstr + '</Simulation>'
else:
xmlstr = ''
return xmlstr | [
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| python | train |
fermiPy/fermipy | fermipy/jobs/batch.py | https://github.com/fermiPy/fermipy/blob/9df5e7e3728307fd58c5bba36fd86783c39fbad4/fermipy/jobs/batch.py#L17-L41 | def get_batch_job_args(job_time=1500):
""" Get the correct set of batch jobs arguments.
Parameters
----------
job_time : int
Expected max length of the job, in seconds.
This is used to select the batch queue and set the
job_check_sleep parameter that sets how often
we check for job completion.
Returns
-------
job_args : dict
Dictionary of arguments used to submit a batch job
"""
if DEFAULT_JOB_TYPE == 'slac':
from fermipy.jobs.slac_impl import get_slac_default_args
return get_slac_default_args(job_time)
elif DEFAULT_JOB_TYPE == 'native':
from fermipy.jobs.native_impl import get_native_default_args
return get_native_default_args()
return None | [
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Expected max length of the job, in seconds.
This is used to select the batch queue and set the
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job_args : dict
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| python | train |
tnkteja/myhelp | virtualEnvironment/lib/python2.7/site-packages/coverage/control.py | https://github.com/tnkteja/myhelp/blob/fb3a4809d448ad14d5b2e6ddf2e7e89ad52b71cb/virtualEnvironment/lib/python2.7/site-packages/coverage/control.py#L495-L510 | def combine(self):
"""Combine together a number of similarly-named coverage data files.
All coverage data files whose name starts with `data_file` (from the
coverage() constructor) will be read, and combined together into the
current measurements.
"""
aliases = None
if self.config.paths:
aliases = PathAliases(self.file_locator)
for paths in self.config.paths.values():
result = paths[0]
for pattern in paths[1:]:
aliases.add(pattern, result)
self.data.combine_parallel_data(aliases=aliases) | [
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| python | test |
rueckstiess/mtools | mtools/util/logevent.py | https://github.com/rueckstiess/mtools/blob/a6a22910c3569c0c8a3908660ca218a4557e4249/mtools/util/logevent.py#L618-L624 | def w(self):
"""Extract write lock (w) counter if available (lazy)."""
if not self._counters_calculated:
self._counters_calculated = True
self._extract_counters()
return self._w | [
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| python | train |
StagPython/StagPy | stagpy/args.py | https://github.com/StagPython/StagPy/blob/18c4416cc4a1011db2fd736ee8b0ec29aa6e4fd4/stagpy/args.py#L65-L103 | def parse_args(arglist=None):
"""Parse cmd line arguments.
Update :attr:`stagpy.conf` accordingly.
Args:
arglist (list of str): the list of cmd line arguments. If set to
None, the arguments are taken from :attr:`sys.argv`.
Returns:
function: the function implementing the sub command to be executed.
"""
climan = CLIManager(conf, **SUB_CMDS)
create_complete_files(climan, CONFIG_DIR, 'stagpy', 'stagpy-git',
zsh_sourceable=True)
cmd_args, all_subs = climan.parse_args(arglist)
sub_cmd = cmd_args.loam_sub_name
if sub_cmd is None:
return cmd_args.func
if sub_cmd != 'config':
commands.report_parsing_problems(PARSING_OUT)
if conf.common.set:
set_conf_str(conf, conf.common.set)
if conf.common.config:
commands.config_pp(all_subs)
load_mplstyle()
try:
_steps_to_slices()
except AttributeError:
pass
return cmd_args.func | [
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| python | train |
jonathf/chaospy | chaospy/distributions/operators/trunkation.py | https://github.com/jonathf/chaospy/blob/25ecfa7bf5608dc10c0b31d142ded0e3755f5d74/chaospy/distributions/operators/trunkation.py#L163-L194 | def _ppf(self, q, left, right, cache):
"""
Point percentile function.
Example:
>>> print(chaospy.Uniform().inv([0.1, 0.2, 0.9]))
[0.1 0.2 0.9]
>>> print(chaospy.Trunc(chaospy.Uniform(), 0.4).inv([0.1, 0.2, 0.9]))
[0.04 0.08 0.36]
>>> print(chaospy.Trunc(0.6, chaospy.Uniform()).inv([0.1, 0.2, 0.9]))
[0.64 0.68 0.96]
"""
if isinstance(left, Dist) and left in cache:
left = cache[left]
if isinstance(right, Dist) and right in cache:
right = cache[right]
if isinstance(left, Dist):
if isinstance(right, Dist):
raise StochasticallyDependentError(
"under-defined distribution {} or {}".format(left, right))
elif not isinstance(right, Dist):
raise StochasticallyDependentError(
"truncated variable indirectly depends on underlying variable")
else:
left = (numpy.array(left).T*numpy.ones(q.shape).T).T
uloc = evaluation.evaluate_forward(right, left)
return evaluation.evaluate_inverse(right, q*(1-uloc)+uloc, cache=cache)
right = (numpy.array(right).T*numpy.ones(q.shape).T).T
uloc = evaluation.evaluate_forward(left, right, cache=cache.copy())
return evaluation.evaluate_inverse(left, q*uloc, cache=cache) | [
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>>> print(chaospy.Uniform().inv([0.1, 0.2, 0.9]))
[0.1 0.2 0.9]
>>> print(chaospy.Trunc(chaospy.Uniform(), 0.4).inv([0.1, 0.2, 0.9]))
[0.04 0.08 0.36]
>>> print(chaospy.Trunc(0.6, chaospy.Uniform()).inv([0.1, 0.2, 0.9]))
[0.64 0.68 0.96] | [
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| python | train |
user-cont/conu | conu/utils/probes.py | https://github.com/user-cont/conu/blob/08caae7bb6bdd265b55bb106c3da6a7946a5a352/conu/utils/probes.py#L92-L116 | def _wrapper(self, q, start):
"""
_wrapper checks return status of Probe.fnc and provides the result for process managing
:param q: Queue for function results
:param start: Time of function run (used for logging)
:return: Return value or Exception
"""
try:
func_name = self.fnc.__name__
except AttributeError:
func_name = str(self.fnc)
logger.debug("Running \"%s\" with parameters: \"%s\":\t%s/%s"
% (func_name, str(self.kwargs), round(time.time() - start), self.timeout))
try:
result = self.fnc(**self.kwargs)
# let's log only first 50 characters of the response
logger.debug("callback result = %s", str(result)[:50])
q.put(result)
except self.expected_exceptions as ex:
logger.debug("expected exception was caught: %s", ex)
q.put(False)
except Exception as ex:
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| python | train |
pydata/xarray | xarray/core/dataset.py | https://github.com/pydata/xarray/blob/6d93a95d05bdbfc33fff24064f67d29dd891ab58/xarray/core/dataset.py#L923-L933 | def _level_coords(self):
"""Return a mapping of all MultiIndex levels and their corresponding
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"""
level_coords = OrderedDict()
for name, index in self.indexes.items():
if isinstance(index, pd.MultiIndex):
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level_coords.update({lname: dim for lname in level_names})
return level_coords | [
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| python | train |
cltk/cltk | cltk/inflection/old_norse/phonemic_rules.py | https://github.com/cltk/cltk/blob/ed9c025b7ec43c949481173251b70e05e4dffd27/cltk/inflection/old_norse/phonemic_rules.py#L122-L213 | def add_r_ending_to_syllable(last_syllable: str, is_first=True) -> str:
"""
Adds an the -r ending to the last syllable of an Old Norse word.
In some cases, it really adds an -r. In other cases, it on doubles the last character or left the syllable
unchanged.
>>> add_r_ending_to_syllable("arm", True)
'armr'
>>> add_r_ending_to_syllable("ás", True)
'áss'
>>> add_r_ending_to_syllable("stól", True)
'stóll'
>>> "jö"+add_r_ending_to_syllable("kul", False)
'jökull'
>>> add_r_ending_to_syllable("stein", True)
'steinn'
>>> 'mi'+add_r_ending_to_syllable('kil', False)
'mikill'
>>> add_r_ending_to_syllable('sæl', True)
'sæll'
>>> 'li'+add_r_ending_to_syllable('til', False)
'litill'
>>> add_r_ending_to_syllable('vænn', True)
'vænn'
>>> add_r_ending_to_syllable('lauss', True)
'lauss'
>>> add_r_ending_to_syllable("vin", True)
'vinr'
>>> add_r_ending_to_syllable("sel", True)
'selr'
>>> add_r_ending_to_syllable('fagr', True)
'fagr'
>>> add_r_ending_to_syllable('vitr', True)
'vitr'
>>> add_r_ending_to_syllable('vetr', True)
'vetr'
>>> add_r_ending_to_syllable('akr', True)
'akr'
>>> add_r_ending_to_syllable('Björn', True)
'Björn'
>>> add_r_ending_to_syllable('þurs', True)
'þurs'
>>> add_r_ending_to_syllable('karl', True)
'karl'
>>> add_r_ending_to_syllable('hrafn', True)
'hrafn'
:param last_syllable: last syllable of the word
:param is_first: is it the first syllable of the word?
:return: inflected syllable
"""
if len(last_syllable) >= 2:
if last_syllable[-1] in ['l', 'n', 's', 'r']:
if last_syllable[-2] in CONSONANTS:
# Apocope of r
return last_syllable
else:
# Assimilation of r
if len(last_syllable) >= 3 and last_syllable[-3:-1] in DIPHTHONGS:
return apply_raw_r_assimilation(last_syllable)
elif last_syllable[-2] in SHORT_VOWELS and is_first:
# No assimilation when r is supposed to be added to a stressed syllable
# whose last letter is l, n or s and the penultimate letter is a short vowel
return last_syllable + "r"
elif last_syllable[-2] in SHORT_VOWELS:
return apply_raw_r_assimilation(last_syllable)
elif last_syllable[-2] in LONG_VOWELS:
return apply_raw_r_assimilation(last_syllable)
return apply_raw_r_assimilation(last_syllable)
else:
return last_syllable + "r"
else:
return last_syllable + "r" | [
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In some cases, it really adds an -r. In other cases, it on doubles the last character or left the syllable
unchanged.
>>> add_r_ending_to_syllable("arm", True)
'armr'
>>> add_r_ending_to_syllable("ás", True)
'áss'
>>> add_r_ending_to_syllable("stól", True)
'stóll'
>>> "jö"+add_r_ending_to_syllable("kul", False)
'jökull'
>>> add_r_ending_to_syllable("stein", True)
'steinn'
>>> 'mi'+add_r_ending_to_syllable('kil', False)
'mikill'
>>> add_r_ending_to_syllable('sæl', True)
'sæll'
>>> 'li'+add_r_ending_to_syllable('til', False)
'litill'
>>> add_r_ending_to_syllable('vænn', True)
'vænn'
>>> add_r_ending_to_syllable('lauss', True)
'lauss'
>>> add_r_ending_to_syllable("vin", True)
'vinr'
>>> add_r_ending_to_syllable("sel", True)
'selr'
>>> add_r_ending_to_syllable('fagr', True)
'fagr'
>>> add_r_ending_to_syllable('vitr', True)
'vitr'
>>> add_r_ending_to_syllable('vetr', True)
'vetr'
>>> add_r_ending_to_syllable('akr', True)
'akr'
>>> add_r_ending_to_syllable('Björn', True)
'Björn'
>>> add_r_ending_to_syllable('þurs', True)
'þurs'
>>> add_r_ending_to_syllable('karl', True)
'karl'
>>> add_r_ending_to_syllable('hrafn', True)
'hrafn'
:param last_syllable: last syllable of the word
:param is_first: is it the first syllable of the word?
:return: inflected syllable | [
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| python | train |
mitsei/dlkit | dlkit/json_/grading/sessions.py | https://github.com/mitsei/dlkit/blob/445f968a175d61c8d92c0f617a3c17dc1dc7c584/dlkit/json_/grading/sessions.py#L705-L747 | def create_grade_system(self, grade_system_form):
"""Creates a new ``GradeSystem``.
arg: grade_system_form (osid.grading.GradeSystemForm): the
form for this ``GradeSystem``
return: (osid.grading.GradeSystem) - the new ``GradeSystem``
raise: IllegalState - ``grade_system_form`` already used in a
create transaction
raise: InvalidArgument - one or more of the form elements is
invalid
raise: NullArgument - ``grade_system_form`` is ``null``
raise: OperationFailed - unable to complete request
raise: PermissionDenied - authorization failure
raise: Unsupported - ``grade_system_form`` did not originate
from ``get_grade_system_form_for_create()``
*compliance: mandatory -- This method must be implemented.*
"""
# Implemented from template for
# osid.resource.ResourceAdminSession.create_resource_template
collection = JSONClientValidated('grading',
collection='GradeSystem',
runtime=self._runtime)
if not isinstance(grade_system_form, ABCGradeSystemForm):
raise errors.InvalidArgument('argument type is not an GradeSystemForm')
if grade_system_form.is_for_update():
raise errors.InvalidArgument('the GradeSystemForm is for update only, not create')
try:
if self._forms[grade_system_form.get_id().get_identifier()] == CREATED:
raise errors.IllegalState('grade_system_form already used in a create transaction')
except KeyError:
raise errors.Unsupported('grade_system_form did not originate from this session')
if not grade_system_form.is_valid():
raise errors.InvalidArgument('one or more of the form elements is invalid')
insert_result = collection.insert_one(grade_system_form._my_map)
self._forms[grade_system_form.get_id().get_identifier()] = CREATED
result = objects.GradeSystem(
osid_object_map=collection.find_one({'_id': insert_result.inserted_id}),
runtime=self._runtime,
proxy=self._proxy)
return result | [
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raise: IllegalState - ``grade_system_form`` already used in a
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raise: NullArgument - ``grade_system_form`` is ``null``
raise: OperationFailed - unable to complete request
raise: PermissionDenied - authorization failure
raise: Unsupported - ``grade_system_form`` did not originate
from ``get_grade_system_form_for_create()``
*compliance: mandatory -- This method must be implemented.* | [
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| python | train |
GoogleCloudPlatform/appengine-gcs-client | python/src/cloudstorage/storage_api.py | https://github.com/GoogleCloudPlatform/appengine-gcs-client/blob/d11078331ecd915d753c886e96a80133599f3f98/python/src/cloudstorage/storage_api.py#L332-L372 | def readline(self, size=-1):
"""Read one line delimited by '\n' from the file.
A trailing newline character is kept in the string. It may be absent when a
file ends with an incomplete line. If the size argument is non-negative,
it specifies the maximum string size (counting the newline) to return.
A negative size is the same as unspecified. Empty string is returned
only when EOF is encountered immediately.
Args:
size: Maximum number of bytes to read. If not specified, readline stops
only on '\n' or EOF.
Returns:
The data read as a string.
Raises:
IOError: When this buffer is closed.
"""
self._check_open()
if size == 0 or not self._remaining():
return ''
data_list = []
newline_offset = self._buffer.find_newline(size)
while newline_offset < 0:
data = self._buffer.read(size)
size -= len(data)
self._offset += len(data)
data_list.append(data)
if size == 0 or not self._remaining():
return ''.join(data_list)
self._buffer.reset(self._buffer_future.get_result())
self._request_next_buffer()
newline_offset = self._buffer.find_newline(size)
data = self._buffer.read_to_offset(newline_offset + 1)
self._offset += len(data)
data_list.append(data)
return ''.join(data_list) | [
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| python | train |
jic-dtool/dtoolcore | dtoolcore/storagebroker.py | https://github.com/jic-dtool/dtoolcore/blob/eeb9a924dc8fcf543340653748a7877be1f98e0f/dtoolcore/storagebroker.py#L372-L400 | def list_dataset_uris(cls, base_uri, config_path):
"""Return list containing URIs in location given by base_uri."""
parsed_uri = generous_parse_uri(base_uri)
uri_list = []
path = parsed_uri.path
if IS_WINDOWS:
path = unix_to_windows_path(parsed_uri.path, parsed_uri.netloc)
for d in os.listdir(path):
dir_path = os.path.join(path, d)
if not os.path.isdir(dir_path):
continue
storage_broker = cls(dir_path, config_path)
if not storage_broker.has_admin_metadata():
continue
uri = storage_broker.generate_uri(
name=d,
uuid=None,
base_uri=base_uri
)
uri_list.append(uri)
return uri_list | [
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| python | train |
xgfs/NetLSD | netlsd/kernels.py | https://github.com/xgfs/NetLSD/blob/54820b3669a94852bd9653be23b09e126e901ab3/netlsd/kernels.py#L208-L247 | def _wkt(eivals, timescales, normalization, normalized_laplacian):
"""
Computes wave kernel trace from given eigenvalues, timescales, and normalization.
For precise definition, please refer to "NetLSD: Hearing the Shape of a Graph" by A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, E. Müller. Published at KDD'18.
Parameters
----------
eivals : numpy.ndarray
Eigenvalue vector
timescales : numpy.ndarray
Vector of discrete timesteps for the kernel computation
normalization : str or numpy.ndarray
Either 'empty', 'complete' or None.
If None or any ther value, return unnormalized wave kernel trace.
For the details how 'empty' and 'complete' are computed, please refer to the paper.
If np.ndarray, they are treated as exact normalization constants
normalized_laplacian: bool
Defines whether the eigenvalues came from the normalized Laplacian. It only affects 'complete' normalization.
Returns
-------
numpy.ndarray
Wave kernel trace signature
"""
nv = eivals.shape[0]
wkt = np.zeros(timescales.shape)
for idx, t in enumerate(timescales):
wkt[idx] = np.sum(np.exp(-1j * t * eivals))
if isinstance(normalization, np.ndarray):
return hkt / normalization
if normalization == 'empty' or normalization == True:
return wkt / nv
if normalization == 'complete':
if normalized_laplacian:
return wkt / (1 + (nv - 1) * np.cos(timescales))
else:
return wkt / (1 + (nv - 1) * np.cos(nv * timescales))
return wkt | [
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Parameters
----------
eivals : numpy.ndarray
Eigenvalue vector
timescales : numpy.ndarray
Vector of discrete timesteps for the kernel computation
normalization : str or numpy.ndarray
Either 'empty', 'complete' or None.
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normalized_laplacian: bool
Defines whether the eigenvalues came from the normalized Laplacian. It only affects 'complete' normalization.
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-------
numpy.ndarray
Wave kernel trace signature | [
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| python | train |
bfontaine/p7magma | magma/courses.py | https://github.com/bfontaine/p7magma/blob/713647aa9e3187c93c2577ef812f33ec42ae5494/magma/courses.py#L105-L125 | def _populate_large_table(self, trs):
"""
Populate the list, given that ``trs`` is a ``BeautifulSoup`` elements
list from a large table (8 columns).
"""
for tr in trs:
tds = tr.select('td')
cs = Course(
code=coursecode(tds[0]),
title=text(tds[1]),
semester=parseint(tds[2]),
status=text(tds[3]),
ects=parsefloat(tds[4]),
followed=parsebool(tds[5]),
)
followed = cs['followed']
cs['session'] = text(tds[7]) if followed else None
cs['result'] = parseresult(tds[6]) if followed else None
self.append(cs) | [
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| python | train |
PyPSA/PyPSA | pypsa/pf.py | https://github.com/PyPSA/PyPSA/blob/46954b1b3c21460550f7104681517065279a53b7/pypsa/pf.py#L155-L185 | def newton_raphson_sparse(f, guess, dfdx, x_tol=1e-10, lim_iter=100):
"""Solve f(x) = 0 with initial guess for x and dfdx(x). dfdx(x) should
return a sparse Jacobian. Terminate if error on norm of f(x) is <
x_tol or there were more than lim_iter iterations.
"""
converged = False
n_iter = 0
F = f(guess)
diff = norm(F,np.Inf)
logger.debug("Error at iteration %d: %f", n_iter, diff)
while diff > x_tol and n_iter < lim_iter:
n_iter +=1
guess = guess - spsolve(dfdx(guess),F)
F = f(guess)
diff = norm(F,np.Inf)
logger.debug("Error at iteration %d: %f", n_iter, diff)
if diff > x_tol:
logger.warning("Warning, we didn't reach the required tolerance within %d iterations, error is at %f. See the section \"Troubleshooting\" in the documentation for tips to fix this. ", n_iter, diff)
elif not np.isnan(diff):
converged = True
return guess, n_iter, diff, converged | [
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| python | train |
ciena/afkak | afkak/producer.py | https://github.com/ciena/afkak/blob/6f5e05ba6f135ea3c29cdb80efda009f7845569a/afkak/producer.py#L235-L252 | def stop(self):
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# Do we have to worry about our looping call?
if self.batch_every_t is not None:
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if self._sendLooper is not None:
self._sendLooper.stop()
# Make sure requests that wasn't cancelled above are now
self._cancel_outstanding()
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| python | train |
log2timeline/plaso | plaso/analysis/windows_services.py | https://github.com/log2timeline/plaso/blob/9c564698d2da3ffbe23607a3c54c0582ea18a6cc/plaso/analysis/windows_services.py#L230-L253 | def CompileReport(self, mediator):
"""Compiles an analysis report.
Args:
mediator (AnalysisMediator): mediates interactions between analysis
plugins and other components, such as storage and dfvfs.
Returns:
AnalysisReport: report.
"""
# TODO: move YAML representation out of plugin and into serialization.
lines_of_text = []
if self._output_format == 'yaml':
lines_of_text.append(
yaml.safe_dump_all(self._service_collection.services))
else:
lines_of_text.append('Listing Windows Services')
for service in self._service_collection.services:
lines_of_text.append(self._FormatServiceText(service))
lines_of_text.append('')
lines_of_text.append('')
report_text = '\n'.join(lines_of_text)
return reports.AnalysisReport(plugin_name=self.NAME, text=report_text) | [
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| python | train |
sorgerlab/indra | rest_api/api.py | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/rest_api/api.py#L534-L543 | def map_grounding():
"""Map grounding on a list of INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
stmts_out = ac.map_grounding(stmts)
return _return_stmts(stmts_out) | [
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| python | train |
scizzorz/bumpy | bumpy.py | https://github.com/scizzorz/bumpy/blob/99ed5c5ccaa61842cafe9faf8b082de44bdf01f9/bumpy.py#L270-L293 | def require(*reqs):
'''Require tasks or files at runtime.'''
for req in reqs:
if type(req) is str:
# does not exist and unknown generator
if not os.path.exists(req) and req not in GENERATES:
abort(LOCALE['abort_bad_file'].format(req))
# exists but unknown generator
if req not in GENERATES:
return
# exists and known generator
if req in GENERATES:
req = GENERATES[req]
if req.valid is None:
if len(req.args):
abort(LOCALE['abort_bad_args'], req, len(req.args))
req()
if req.valid is False:
abort(LOCALE['abort_bad_task'], req) | [
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| python | train |
phoebe-project/phoebe2 | phoebe/frontend/bundle.py | https://github.com/phoebe-project/phoebe2/blob/e64b8be683977064e2d55dd1b3ac400f64c3e379/phoebe/frontend/bundle.py#L3320-L3330 | def remove_model(self, model, **kwargs):
"""
Remove a 'model' from the bundle
:parameter str twig: twig to filter for the model
:parameter **kwargs: any other tags to do the filter
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"""
kwargs['model'] = model
kwargs['context'] = 'model'
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| python | train |
waqasbhatti/astrobase | astrobase/lcmodels/eclipses.py | https://github.com/waqasbhatti/astrobase/blob/2922a14619d183fb28005fa7d02027ac436f2265/astrobase/lcmodels/eclipses.py#L19-L46 | def _gaussian(x, amp, loc, std):
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----------
x : np.array
The items at which the Gaussian is evaluated.
amp : float
The amplitude of the Gaussian.
loc : float
The central value of the Gaussian.
std : float
The standard deviation of the Gaussian.
Returns
-------
np.array
Returns the Gaussian evaluated at the items in `x`, using the provided
parameters of `amp`, `loc`, and `std`.
'''
return amp * np.exp(-((x - loc)*(x - loc))/(2.0*std*std)) | [
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The items at which the Gaussian is evaluated.
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The amplitude of the Gaussian.
loc : float
The central value of the Gaussian.
std : float
The standard deviation of the Gaussian.
Returns
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Returns the Gaussian evaluated at the items in `x`, using the provided
parameters of `amp`, `loc`, and `std`. | [
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| python | valid |
spyder-ide/spyder | spyder/utils/introspection/rope_patch.py | https://github.com/spyder-ide/spyder/blob/f76836ce1b924bcc4efd3f74f2960d26a4e528e0/spyder/utils/introspection/rope_patch.py#L30-L211 | def apply():
"""Monkey patching rope
See [1], [2], [3], [4] and [5] in module docstring."""
from spyder.utils.programs import is_module_installed
if is_module_installed('rope', '<0.9.4'):
import rope
raise ImportError("rope %s can't be patched" % rope.VERSION)
# [1] Patching project.Project for compatibility with py2exe/cx_Freeze
# distributions
from spyder.config.base import is_py2exe_or_cx_Freeze
if is_py2exe_or_cx_Freeze():
from rope.base import project
class PatchedProject(project.Project):
def _default_config(self):
# py2exe/cx_Freeze distribution
from spyder.config.base import get_module_source_path
fname = get_module_source_path('spyder',
'default_config.py')
return open(fname, 'rb').read()
project.Project = PatchedProject
# Patching pycore.PyCore...
from rope.base import pycore
class PatchedPyCore(pycore.PyCore):
# [2] ...so that forced builtin modules (i.e. modules that were
# declared as 'extension_modules' in rope preferences) will be indeed
# recognized as builtins by rope, as expected
#
# This patch is included in rope 0.9.4+ but applying it anyway is ok
def get_module(self, name, folder=None):
"""Returns a `PyObject` if the module was found."""
# check if this is a builtin module
pymod = self._builtin_module(name)
if pymod is not None:
return pymod
module = self.find_module(name, folder)
if module is None:
raise pycore.ModuleNotFoundError(
'Module %s not found' % name)
return self.resource_to_pyobject(module)
# [3] ...to avoid considering folders without __init__.py as Python
# packages
def _find_module_in_folder(self, folder, modname):
module = folder
packages = modname.split('.')
for pkg in packages[:-1]:
if module.is_folder() and module.has_child(pkg):
module = module.get_child(pkg)
else:
return None
if module.is_folder():
if module.has_child(packages[-1]) and \
module.get_child(packages[-1]).is_folder() and \
module.get_child(packages[-1]).has_child('__init__.py'):
return module.get_child(packages[-1])
elif module.has_child(packages[-1] + '.py') and \
not module.get_child(packages[-1] + '.py').is_folder():
return module.get_child(packages[-1] + '.py')
pycore.PyCore = PatchedPyCore
# [2] Patching BuiltinName for the go to definition feature to simply work
# with forced builtins
from rope.base import builtins, libutils, pyobjects
import inspect
import os.path as osp
class PatchedBuiltinName(builtins.BuiltinName):
def _pycore(self):
p = self.pyobject
while p.parent is not None:
p = p.parent
if isinstance(p, builtins.BuiltinModule) and p.pycore is not None:
return p.pycore
def get_definition_location(self):
if not inspect.isbuiltin(self.pyobject):
_lines, lineno = inspect.getsourcelines(self.pyobject.builtin)
path = inspect.getfile(self.pyobject.builtin)
if path.endswith('pyc') and osp.isfile(path[:-1]):
path = path[:-1]
pycore = self._pycore()
if pycore and pycore.project:
resource = libutils.path_to_resource(pycore.project, path)
module = pyobjects.PyModule(pycore, None, resource)
return (module, lineno)
return (None, None)
builtins.BuiltinName = PatchedBuiltinName
# [4] Patching several PyDocExtractor methods:
# 1. get_doc:
# To force rope to return the docstring of any object which has one, even
# if it's not an instance of AbstractFunction, AbstractClass, or
# AbstractModule.
# Also, to use utils.dochelpers.getdoc to get docs from forced builtins.
#
# 2. _get_class_docstring and _get_single_function_docstring:
# To not let rope add a 2 spaces indentation to every docstring, which was
# breaking our rich text mode. The only value that we are modifying is the
# 'indents' keyword of those methods, from 2 to 0.
#
# 3. get_calltip
# To easily get calltips of forced builtins
from rope.contrib import codeassist
from spyder_kernels.utils.dochelpers import getdoc
from rope.base import exceptions
class PatchedPyDocExtractor(codeassist.PyDocExtractor):
def get_builtin_doc(self, pyobject):
buitin = pyobject.builtin
return getdoc(buitin)
def get_doc(self, pyobject):
if hasattr(pyobject, 'builtin'):
doc = self.get_builtin_doc(pyobject)
return doc
elif isinstance(pyobject, builtins.BuiltinModule):
docstring = pyobject.get_doc()
if docstring is not None:
docstring = self._trim_docstring(docstring)
else:
docstring = ''
# TODO: Add a module_name key, so that the name could appear
# on the OI text filed but not be used by sphinx to render
# the page
doc = {'name': '',
'argspec': '',
'note': '',
'docstring': docstring
}
return doc
elif isinstance(pyobject, pyobjects.AbstractFunction):
return self._get_function_docstring(pyobject)
elif isinstance(pyobject, pyobjects.AbstractClass):
return self._get_class_docstring(pyobject)
elif isinstance(pyobject, pyobjects.AbstractModule):
return self._trim_docstring(pyobject.get_doc())
elif pyobject.get_doc() is not None: # Spyder patch
return self._trim_docstring(pyobject.get_doc())
return None
def get_calltip(self, pyobject, ignore_unknown=False, remove_self=False):
if hasattr(pyobject, 'builtin'):
doc = self.get_builtin_doc(pyobject)
return doc['name'] + doc['argspec']
try:
if isinstance(pyobject, pyobjects.AbstractClass):
pyobject = pyobject['__init__'].get_object()
if not isinstance(pyobject, pyobjects.AbstractFunction):
pyobject = pyobject['__call__'].get_object()
except exceptions.AttributeNotFoundError:
return None
if ignore_unknown and not isinstance(pyobject, pyobjects.PyFunction):
return
if isinstance(pyobject, pyobjects.AbstractFunction):
result = self._get_function_signature(pyobject, add_module=True)
if remove_self and self._is_method(pyobject):
return result.replace('(self)', '()').replace('(self, ', '(')
return result
def _get_class_docstring(self, pyclass):
contents = self._trim_docstring(pyclass.get_doc(), indents=0)
supers = [super.get_name() for super in pyclass.get_superclasses()]
doc = 'class %s(%s):\n\n' % (pyclass.get_name(), ', '.join(supers)) + contents
if '__init__' in pyclass:
init = pyclass['__init__'].get_object()
if isinstance(init, pyobjects.AbstractFunction):
doc += '\n\n' + self._get_single_function_docstring(init)
return doc
def _get_single_function_docstring(self, pyfunction):
docs = pyfunction.get_doc()
docs = self._trim_docstring(docs, indents=0)
return docs
codeassist.PyDocExtractor = PatchedPyDocExtractor
# [5] Get the right matplotlib docstrings for Help
try:
import matplotlib as mpl
mpl.rcParams['docstring.hardcopy'] = True
except:
pass | [
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| python | train |
pdkit/pdkit | pdkit/finger_tapping_processor.py | https://github.com/pdkit/pdkit/blob/c7120263da2071bb139815fbdb56ca77b544f340/pdkit/finger_tapping_processor.py#L209-L229 | def extract_features(self, data_frame, pre=''):
"""
This method extracts all the features available to the Finger Tapping Processor class.
:param data_frame: the data frame
:type data_frame: pandas.DataFrame
:return: 'frequency', 'moving_frequency','continuous_frequency','mean_moving_time','incoordination_score', \
'mean_alnt_target_distance','kinesia_scores', 'akinesia_times','dysmetria_score'
:rtype: list
"""
try:
return {pre+'frequency': self.frequency(data_frame)[0],
pre+'mean_moving_time': self.mean_moving_time(data_frame)[0],
pre+'incoordination_score': self.incoordination_score(data_frame)[0],
pre+'mean_alnt_target_distance': self.mean_alnt_target_distance(data_frame)[0],
pre+'kinesia_scores': self.kinesia_scores(data_frame)[0],
pre+'akinesia_times': self.akinesia_times(data_frame)[0],
pre+'dysmetria_score': self.dysmetria_score(data_frame)[0]}
except:
logging.error("Error on FingerTappingProcessor process, extract features: %s", sys.exc_info()[0]) | [
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| python | train |
dropbox/stone | stone/ir/data_types.py | https://github.com/dropbox/stone/blob/2e95cbcd1c48e05cca68c919fd8d24adec6b0f58/stone/ir/data_types.py#L1979-L2001 | def unwrap(data_type):
"""
Convenience method to unwrap all Aliases and Nullables from around a
DataType. This checks for nullable wrapping aliases, as well as aliases
wrapping nullables.
Args:
data_type (DataType): The target to unwrap.
Return:
Tuple[DataType, bool, bool]: The underlying data type; a bool that is
set if a nullable was present; a bool that is set if an alias was
present.
"""
unwrapped_nullable = False
unwrapped_alias = False
while is_alias(data_type) or is_nullable_type(data_type):
if is_nullable_type(data_type):
unwrapped_nullable = True
if is_alias(data_type):
unwrapped_alias = True
data_type = data_type.data_type
return data_type, unwrapped_nullable, unwrapped_alias | [
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| python | train |
rpcope1/HackerNewsAPI-Py | HackerNewsAPI/API.py | https://github.com/rpcope1/HackerNewsAPI-Py/blob/b231aed24ec59fc32af320bbef27d48cc4b69914/HackerNewsAPI/API.py#L108-L120 | def get_top_stories(self):
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Get the item numbers for the current top stories.
Will raise an requests.HTTPError if we got a non-200 response back.
:return: A list with the top story item numbers.
"""
suburl = "v0/topstories.json"
try:
top_stories = self._make_request(suburl)
except requests.HTTPError as e:
hn_logger.exception('Faulted on getting top stories, with status {}'.format(e.errno))
raise e
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| python | train |
brocade/pynos | pynos/versions/ver_6/ver_6_0_1/yang/brocade_threshold_monitor.py | https://github.com/brocade/pynos/blob/bd8a34e98f322de3fc06750827d8bbc3a0c00380/pynos/versions/ver_6/ver_6_0_1/yang/brocade_threshold_monitor.py#L263-L278 | def threshold_monitor_hidden_threshold_monitor_security_policy_area_sec_area_value(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
threshold_monitor_hidden = ET.SubElement(config, "threshold-monitor-hidden", xmlns="urn:brocade.com:mgmt:brocade-threshold-monitor")
threshold_monitor = ET.SubElement(threshold_monitor_hidden, "threshold-monitor")
security = ET.SubElement(threshold_monitor, "security")
policy = ET.SubElement(security, "policy")
sec_policy_name_key = ET.SubElement(policy, "sec_policy_name")
sec_policy_name_key.text = kwargs.pop('sec_policy_name')
area = ET.SubElement(policy, "area")
sec_area_value = ET.SubElement(area, "sec_area_value")
sec_area_value.text = kwargs.pop('sec_area_value')
callback = kwargs.pop('callback', self._callback)
return callback(config) | [
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| python | train |
RetailMeNotSandbox/acky | acky/ec2.py | https://github.com/RetailMeNotSandbox/acky/blob/fcd4d092c42892ede7c924cafc41e9cf4be3fb9f/acky/ec2.py#L485-L508 | def create(self, az, size_or_snap, volume_type=None, iops=None,
encrypted=True):
"""Create an EBS Volume using an availability-zone and size_or_snap
parameter, encrypted by default.
If the volume is crated from a snapshot, (str)size_or_snap denotes
the snapshot id. Otherwise, (int)size_or_snap denotes the amount of
GiB's to allocate. iops must be set if the volume type is io1.
"""
kwargs = {}
kwargs['encrypted'] = encrypted
if volume_type:
kwargs['VolumeType'] = volume_type
if iops:
kwargs['Iops'] = iops
is_snapshot_id = False
try:
size_or_snap = int(size_or_snap)
except ValueError:
is_snapshot_id = True
if is_snapshot_id:
return self.call("CreateVolume", AvailabilityZone=az,
SnapshotId=size_or_snap, **kwargs)
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| python | train |
xolox/python-update-dotdee | update_dotdee/__init__.py | https://github.com/xolox/python-update-dotdee/blob/04d5836f0d217e32778745b533beeb8159d80c32/update_dotdee/__init__.py#L251-L265 | def available_files(self):
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matches = []
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| python | train |
yinkaisheng/Python-UIAutomation-for-Windows | uiautomation/uiautomation.py | https://github.com/yinkaisheng/Python-UIAutomation-for-Windows/blob/2cc91060982cc8b777152e698d677cc2989bf263/uiautomation/uiautomation.py#L7703-L7719 | def WaitHotKeyReleased(hotkey: tuple) -> None:
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mod = {ModifierKey.Alt: Keys.VK_MENU,
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while True:
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| python | valid |
mpg-age-bioinformatics/AGEpy | AGEpy/kegg.py | https://github.com/mpg-age-bioinformatics/AGEpy/blob/887808a7a2c1504f39ce8d8cb36c15c1721cd29f/AGEpy/kegg.py#L83-L105 | def ensembl_to_kegg(organism,kegg_db):
"""
Looks up KEGG mappings of KEGG ids to ensembl ids
:param organism: an organisms as listed in organismsKEGG()
:param kegg_db: a matching KEGG db as reported in databasesKEGG
:returns: a Pandas dataframe of with 'KEGGid' and 'ENSid'.
"""
print("KEGG API: http://rest.genome.jp/link/"+kegg_db+"/"+organism)
sys.stdout.flush()
kegg_ens=urlopen("http://rest.genome.jp/link/"+kegg_db+"/"+organism).read()
kegg_ens=kegg_ens.split("\n")
final=[]
for i in kegg_ens:
final.append(i.split("\t"))
df=pd.DataFrame(final[0:len(final)-1])[[0,1]]
ens_id=pd.DataFrame(df[1].str.split(":").tolist())[1]
df=pd.concat([df,ens_id],axis=1)
df.columns=['KEGGid','ensDB','ENSid']
df=df[['KEGGid','ENSid']]
return df | [
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| Looks up KEGG mappings of KEGG ids to ensembl ids
:param organism: an organisms as listed in organismsKEGG()
:param kegg_db: a matching KEGG db as reported in databasesKEGG
:returns: a Pandas dataframe of with 'KEGGid' and 'ENSid'. | [
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| python | train |
sorgerlab/indra | indra/sources/bel/rdf_processor.py | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/sources/bel/rdf_processor.py#L21-L35 | def namespace_from_uri(uri):
"""Return the entity namespace from the URI. Examples:
http://www.openbel.org/bel/p_HGNC_RAF1 -> HGNC
http://www.openbel.org/bel/p_RGD_Raf1 -> RGD
http://www.openbel.org/bel/p_PFH_MEK1/2_Family -> PFH
"""
patterns = ['http://www.openbel.org/bel/[pragm]_([A-Za-z]+)_.*',
'http://www.openbel.org/bel/[a-z]+_[pr]_([A-Za-z]+)_.*',
'http://www.openbel.org/bel/[a-z]+_complex_([A-Za-z]+)_.*',
'http://www.openbel.org/bel/complex_([A-Za-z]+)_.*']
for pr in patterns:
match = re.match(pr, uri)
if match is not None:
return match.groups()[0]
return None | [
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| Return the entity namespace from the URI. Examples:
http://www.openbel.org/bel/p_HGNC_RAF1 -> HGNC
http://www.openbel.org/bel/p_RGD_Raf1 -> RGD
http://www.openbel.org/bel/p_PFH_MEK1/2_Family -> PFH | [
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| python | train |
ContextLab/hypertools | hypertools/tools/reduce.py | https://github.com/ContextLab/hypertools/blob/b76c7ac8061998b560e969ff8e4f4c915088e7a0/hypertools/tools/reduce.py#L36-L158 | def reduce(x, reduce='IncrementalPCA', ndims=None, normalize=None, align=None,
model=None, model_params=None, internal=False, format_data=True):
"""
Reduces dimensionality of an array, or list of arrays
Parameters
----------
x : Numpy array or list of arrays
Dimensionality reduction using PCA is performed on this array.
reduce : str or dict
Decomposition/manifold learning model to use. Models supported: PCA,
IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA,
FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning,
TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, MDS and UMAP.
Can be passed as a string, but for finer control of the model
parameters, pass as a dictionary, e.g. reduce={'model' : 'PCA',
'params' : {'whiten' : True}}. See scikit-learn specific model docs
for details on parameters supported for each model.
ndims : int
Number of dimensions to reduce
format_data : bool
Whether or not to first call the format_data function (default: True).
model : None
Deprecated argument. Please use reduce.
model_params : None
Deprecated argument. Please use reduce.
align : None
Deprecated argument. Please use new analyze function to perform
combinations of transformations
normalize : None
Deprecated argument. Please use new analyze function to perform
combinations of transformations
Returns
----------
x_reduced : Numpy array or list of arrays
The reduced data with ndims dimensionality is returned. If the input
is a list, a list is returned.
"""
# deprecated warning
if (model is not None) or (model_params is not None):
warnings.warn('Model and model params will be deprecated. Please use the \
reduce keyword. See API docs for more info: http://hypertools.readthedocs.io/en/latest/hypertools.tools.reduce.html#hypertools.tools.reduce')
reduce = {}
reduce['model'] = model
reduce['params'] = model_params
# if model is None, just return data
if reduce is None:
return x
else:
# common format
if format_data:
x = formatter(x, ppca=True)
if np.vstack([i for i in x]).shape[0]==1:
warnings.warn('Cannot reduce the dimensionality of a single row of'
' data. Return zeros length of ndims')
return [np.zeros((1, ndims))]
if ndims:
if np.vstack([i for i in x]).shape[0]<ndims:
warnings.warn('The number of rows in your data is less than ndims.'
' The data will be reduced to the number of rows.')
# deprecation warnings
if normalize is not None:
warnings.warn('The normalize argument will be deprecated for this function. Please use the \
analyze function to perform combinations of these transformations. See API docs for more info: http://hypertools.readthedocs.io/en/latest/hypertools.analyze.html#hypertools.analyze')
x = normalizer(x, normalize=normalize)
if align is not None:
warnings.warn('The align argument will be deprecated for this function. Please use the \
analyze function to perform combinations of these transformations. See API docs for more info: http://hypertools.readthedocs.io/en/latest/hypertools.analyze.html#hypertools.analyze')
x = aligner(x, align=align)
# if the shape of the data is already less than ndims, just return it
if ndims is None:
return x
elif all([i.shape[1]<=ndims for i in x]):
return x
# if reduce is a string, find the corresponding model
if type(reduce) in [str, np.string_]:
model = models[reduce]
model_params = {
'n_components' : ndims
}
# if its a dict, use custom params
elif type(reduce) is dict:
if isinstance((reduce['model']), six.string_types):
model = models[reduce['model']]
if reduce['params'] is None:
model_params = {
'n_components' : ndims
}
else:
model_params = reduce['params']
if ndims:
model_params = {
'n_components' : ndims
}
# initialize model
model = model(**model_params)
# reduce data
x_reduced = reduce_list(x, model)
# return data
if internal or len(x_reduced)>1:
return x_reduced
else:
return x_reduced[0] | [
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| Reduces dimensionality of an array, or list of arrays
Parameters
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x : Numpy array or list of arrays
Dimensionality reduction using PCA is performed on this array.
reduce : str or dict
Decomposition/manifold learning model to use. Models supported: PCA,
IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA,
FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning,
TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, MDS and UMAP.
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for details on parameters supported for each model.
ndims : int
Number of dimensions to reduce
format_data : bool
Whether or not to first call the format_data function (default: True).
model : None
Deprecated argument. Please use reduce.
model_params : None
Deprecated argument. Please use reduce.
align : None
Deprecated argument. Please use new analyze function to perform
combinations of transformations
normalize : None
Deprecated argument. Please use new analyze function to perform
combinations of transformations
Returns
----------
x_reduced : Numpy array or list of arrays
The reduced data with ndims dimensionality is returned. If the input
is a list, a list is returned. | [
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| python | train |
jobovy/galpy | galpy/orbit/Orbit.py | https://github.com/jobovy/galpy/blob/9c5b9fe65d58835624dffe432be282060918ee08/galpy/orbit/Orbit.py#L794-L825 | def E(self,*args,**kwargs):
"""
NAME:
E
PURPOSE:
calculate the energy
INPUT:
t - (optional) time at which to get the energy (can be Quantity)
pot= Potential instance or list of such instances
vo= (Object-wide default) physical scale for velocities to use to convert (can be Quantity)
use_physical= use to override Object-wide default for using a physical scale for output
OUTPUT:
energy
HISTORY:
2010-09-15 - Written - Bovy (NYU)
"""
if not kwargs.get('pot',None) is None: kwargs['pot']= flatten_potential(kwargs.get('pot'))
_check_consistent_units(self,kwargs.get('pot',None))
return self._orb.E(*args,**kwargs) | [
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OUTPUT:
energy
HISTORY:
2010-09-15 - Written - Bovy (NYU) | [
"NAME",
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| python | train |
pavlin-policar/openTSNE | openTSNE/affinity.py | https://github.com/pavlin-policar/openTSNE/blob/28513a0d669f2f20e7b971c0c6373dc375f72771/openTSNE/affinity.py#L139-L178 | def set_perplexity(self, new_perplexity):
"""Change the perplexity of the affinity matrix.
Note that we only allow lowering the perplexity or restoring it to its
original value. This restriction exists because setting a higher
perplexity value requires recomputing all the nearest neighbors, which
can take a long time. To avoid potential confusion as to why execution
time is slow, this is not allowed. If you would like to increase the
perplexity above the initial value, simply create a new instance.
Parameters
----------
new_perplexity: float
The new perplexity.
"""
# If the value hasn't changed, there's nothing to do
if new_perplexity == self.perplexity:
return
# Verify that the perplexity isn't too large
new_perplexity = self.check_perplexity(new_perplexity)
# Recompute the affinity matrix
k_neighbors = min(self.n_samples - 1, int(3 * new_perplexity))
if k_neighbors > self.__neighbors.shape[1]:
raise RuntimeError(
"The desired perplexity `%.2f` is larger than the initial one "
"used. This would need to recompute the nearest neighbors, "
"which is not efficient. Please create a new `%s` instance "
"with the increased perplexity."
% (new_perplexity, self.__class__.__name__)
)
self.perplexity = new_perplexity
self.P = joint_probabilities_nn(
self.__neighbors[:, :k_neighbors],
self.__distances[:, :k_neighbors],
[self.perplexity],
symmetrize=True,
n_jobs=self.n_jobs,
) | [
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perplexity value requires recomputing all the nearest neighbors, which
can take a long time. To avoid potential confusion as to why execution
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perplexity above the initial value, simply create a new instance.
Parameters
----------
new_perplexity: float
The new perplexity. | [
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| python | train |
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