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rocky/python3-trepan | celery/ctrepan.py | https://github.com/rocky/python3-trepan/blob/14e91bc0acce090d67be145b1ac040cab92ac5f3/celery/ctrepan.py#L104-L110 | def debugger():
"""Return the current debugger instance (if any),
or creates a new one."""
dbg = _current[0]
if dbg is None or not dbg.active:
dbg = _current[0] = RemoteCeleryTrepan()
return dbg | [
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saltstack/salt | salt/modules/temp.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/temp.py#L33-L46 | def file(suffix='', prefix='tmp', parent=None):
'''
Create a temporary file
CLI Example:
.. code-block:: bash
salt '*' temp.file
salt '*' temp.file prefix='mytemp-' parent='/var/run/'
'''
fh_, tmp_ = tempfile.mkstemp(suffix, prefix, parent)
os.close(fh_)
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zhmcclient/python-zhmcclient | zhmcclient_mock/_hmc.py | https://github.com/zhmcclient/python-zhmcclient/blob/9657563e5d9184c51d3c903442a58b9725fdf335/zhmcclient_mock/_hmc.py#L2995-L3020 | def get_metric_group_definitions(self):
"""
Get the faked metric group definitions for this context object
that are to be returned from its create operation.
If a 'metric-groups' property had been specified for this context,
only those faked metric group definitions of its manager object that
are in that list, are included in the result. Otherwise, all metric
group definitions of its manager are included in the result.
Returns:
iterable of :class:~zhmcclient.FakedMetricGroupDefinition`: The faked
metric group definitions, in the order they had been added.
"""
group_names = self.properties.get('metric-groups', None)
if not group_names:
group_names = self.manager.get_metric_group_definition_names()
mg_defs = []
for group_name in group_names:
try:
mg_def = self.manager.get_metric_group_definition(group_name)
mg_defs.append(mg_def)
except ValueError:
pass # ignore metric groups without metric group defs
return mg_defs | [
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viveksck/changepoint | changepoint/mean_shift_model.py | https://github.com/viveksck/changepoint/blob/001792cb148c991ec704463d3213997ebb7171af/changepoint/mean_shift_model.py#L27-L31 | def generate_null_timeseries(self, ts, mu, sigma):
""" Generate a time series with a given mu and sigma. This serves as the
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l = len(ts)
return np.random.normal(mu, sigma, l) | [
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graphql-python/graphql-core | scripts/casing.py | https://github.com/graphql-python/graphql-core/blob/d8e9d3abe7c209eb2f51cf001402783bfd480596/scripts/casing.py#L19-L29 | def snake(s):
"""Convert from title or camelCase to snake_case."""
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return s.lower()
out = s[0].lower()
for c in s[1:]:
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sony/nnabla | python/src/nnabla/parametric_functions.py | https://github.com/sony/nnabla/blob/aaf3d33b7cbb38f2a03aa754178ba8f7c8481320/python/src/nnabla/parametric_functions.py#L26-L125 | def parametric_function_api(scope_name=None, param_desc=None):
"""Decorator for parametric functions.
The decorated function is always called under
a parameter scope ``scope_name``.
Also, the decorator adds an additional argument ``name`` (:obj:`str`,
default is ``None``) at the end. If ``name`` is specified, the
scope ``scope_name`` comes under a scope ``name``. This feature
could reduce vertical space usage of the source code.
Any parametric function should be decorated by this.
Args:
scope_name (str, optional): The original function will be called
under a parameter scope named by ``scope_name``.
param_desc (list, optional):
Descriptions of parameters will be automatically included into docstring.
This must be a list of tuples with 4 elements composed of
(name (str), description (str), shape info (str), need_grad (bool)).
Returns:
function: A decorated parametric function.
"""
if scope_name is None:
scope_name = name
def parametric_function_api_inside(func):
from nnabla.utils.py23_compatible import getargspec
import inspect
name = func.__name__
doc = func.__doc__
if param_desc:
indent = 8
try:
desc = map(lambda d: ' ' * indent +
'* {} (``need_grad={}``) : {}. (shape: ``{}``)'.format(d[0], d[3], d[1], d[2]), param_desc)
except:
ValueError(
'param_desc argument of parametric_function_api must be '
'None or a list of tuple with three elements composed of '
'(name(str), description(str), need_grad(bool)).')
doc += '''
Parameters to be registered
The following variables are registered in a parameter scope ``"{}"``;
{}
'''.format(scope_name, '\n'.join(desc))
doc += """
Note:
If the ``name`` option is passed, the parameters become wrapped inside the parameter scope
with the specified name, yielding the same results as the following code.
This can be used to simplify the code.
.. code-block:: python
with parametric_scope(name):
output = {name}(<args>)
""".format(name=name)
spec = getargspec(func)
defaults = spec.defaults
if defaults is None:
defaults = tuple() # None will be appended later
signature = inspect.formatargspec(
spec.args + ['name'],
spec.varargs, spec.keywords,
defaults + (None,))
shortsignature = inspect.formatargspec(
spec.args, spec.varargs, spec.keywords, None)
# Check required argument
assert 'fix_parameters' in spec.args, \
"A parametric function must take `fix_parameters` as an argument." \
" `{}{}` doesn't have it.".format(name, signature)
code = """
def {name}{signature}:
if name is None:
with parameter_scope(scope_name):
return func{shortsignature}
with parameter_scope(name):
with parameter_scope(scope_name):
return func{shortsignature}
""".format(**locals())
execdict = dict(
func=func, parameter_scope=nn.parameter_scope, scope_name=scope_name)
exec_(code, execdict)
newfunc = execdict[name]
newfunc.__doc__ = doc
newfunc.__parametric_function_api_base__ = func
newfunc.__scope_name__ = scope_name
newfunc.__module__ = __name__
return newfunc
return parametric_function_api_inside | [
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ronniedada/tabula | tabula/painter.py | https://github.com/ronniedada/tabula/blob/ba18bb2f7db75972256b950711415031dc5421c7/tabula/painter.py#L17-L23 | def enter_fullscreen(self):
"""
Invoke before printing out anything.
This method should be replaced by or merged to blessings package
"""
self.term.stream.write(self.term.enter_fullscreen)
self.term.stream.write(self.term.hide_cursor) | [
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JdeRobot/base | src/drivers/MAVLinkServer/MAVProxy/modules/mavproxy_nsh.py | https://github.com/JdeRobot/base/blob/303b18992785b2fe802212f2d758a60873007f1f/src/drivers/MAVLinkServer/MAVProxy/modules/mavproxy_nsh.py#L26-L36 | def mavlink_packet(self, m):
'''handle an incoming mavlink packet'''
if m.get_type() == 'SERIAL_CONTROL':
data = m.data[:m.count]
if m.count > 0:
s = ''.join(str(chr(x)) for x in data)
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s = s.replace("\033[K","")
sys.stdout.write(s)
self.last_packet = time.time() | [
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jsvine/markovify | markovify/chain.py | https://github.com/jsvine/markovify/blob/6968649a4c5d80f8a1b2279734417348013789e5/markovify/chain.py#L85-L97 | def move(self, state):
"""
Given a state, choose the next item at random.
"""
if state == tuple([ BEGIN ] * self.state_size):
choices = self.begin_choices
cumdist = self.begin_cumdist
else:
choices, weights = zip(*self.model[state].items())
cumdist = list(accumulate(weights))
r = random.random() * cumdist[-1]
selection = choices[bisect.bisect(cumdist, r)]
return selection | [
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F5Networks/f5-common-python | f5/bigip/tm/vcmp/virtual_disk.py | https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/bigip/tm/vcmp/virtual_disk.py#L53-L66 | def load(self, **kwargs):
"""Loads a given resource
Loads a given resource provided a 'name' and an optional 'slot'
parameter. The 'slot' parameter is not a required load parameter
because it is provided as an optional way of constructing the
correct 'name' of the vCMP resource.
:param kwargs:
:return:
"""
kwargs['transform_name'] = True
kwargs = self._mutate_name(kwargs)
return self._load(**kwargs) | [
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Nic30/ipCorePackager | ipCorePackager/intfIpMeta.py | https://github.com/Nic30/ipCorePackager/blob/0af4e56ebfdc3749fffa40d50d9ccbf8b5445881/ipCorePackager/intfIpMeta.py#L30-L53 | def _asQuartusTcl(self, buff: List[str], version: str, intfName: str,
component: "Component", packager: "IpPackager",
thisIf: 'Interface', intfMapOrName: Dict[str, Union[Dict, str]]):
"""
Add interface to Quartus tcl by specified name map
:param buff: line buffer for output
:param version: Quartus version
:param intfName: name of top interface
:param component: component object from ipcore generator
:param packager: instance of IpPackager which is packagin current design
:param thisIf: interface to add into Quartus TCL
:param intfMapOrName: Quartus name string for this interface
or dictionary to map subinterfaces
"""
if isinstance(intfMapOrName, str):
self.quartus_add_interface_port(
buff, intfName, thisIf, intfMapOrName, packager)
else:
for thisIf_ in thisIf._interfaces:
v = intfMapOrName[thisIf_._name]
self._asQuartusTcl(buff, version, intfName, component,
packager, thisIf_, v) | [
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ardydedase/pycouchbase | pycouchbase/fields.py | https://github.com/ardydedase/pycouchbase/blob/6f010b4d2ef41aead2366878d0cf0b1284c0db0e/pycouchbase/fields.py#L189-L199 | def check_password(self, raw_password):
"""Validates the given raw password against the intance's encrypted one.
:param raw_password: Raw password to be checked against.
:type raw_password: unicode
:returns: True if comparison was successful, False otherwise.
:rtype: bool
:raises: :exc:`ImportError` if `py-bcrypt` was not found.
"""
bcrypt = self.get_bcrypt()
return bcrypt.hashpw(raw_password, self.value)==self.value | [
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jaumebonet/libconfig | libconfig/config.py | https://github.com/jaumebonet/libconfig/blob/9b34cefcbaf9a326e3f3cd517896c2933cf61a3b/libconfig/config.py#L123-L144 | def get_option_default(self, key, subkey):
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LogicalDash/LiSE | ELiDE/ELiDE/charmenu.py | https://github.com/LogicalDash/LiSE/blob/fe6fd4f0a7c1780e065f4c9babb9bc443af6bb84/ELiDE/ELiDE/charmenu.py#L120-L138 | def toggle_pawn_cfg(self):
"""Show or hide the pop-over where you can configure the dummy pawn"""
if self.app.manager.current == 'pawncfg':
dummything = self.app.dummything
self.ids.thingtab.remove_widget(dummything)
dummything.clear()
if self.app.pawncfg.prefix:
dummything.prefix = self.app.pawncfg.prefix
dummything.num = dummynum(
self.app.character, dummything.prefix
) + 1
if self.app.pawncfg.imgpaths:
dummything.paths = self.app.pawncfg.imgpaths
else:
dummything.paths = ['atlas://rltiles/base/unseen']
self.ids.thingtab.add_widget(dummything)
else:
self.app.pawncfg.prefix = self.ids.dummything.prefix
self.app.pawncfg.toggle() | [
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coleifer/walrus | walrus/graph.py | https://github.com/coleifer/walrus/blob/82bf15a6613487b5b5fefeb488f186d7e0106547/walrus/graph.py#L90-L98 | def store_many(self, items):
"""
Store multiple subject-predicate-object triples in the database.
:param items: A list of (subj, pred, obj) 3-tuples.
"""
with self.walrus.atomic():
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gem/oq-engine | openquake/hazardlib/gsim/campbell_bozorgnia_2014.py | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L227-L232 | def _get_f2rx(self, C, r_x, r_1, r_2):
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myint/autoflake | autoflake.py | https://github.com/myint/autoflake/blob/68fea68646922b920d55975f9f2adaeafd84df4f/autoflake.py#L274-L304 | def filter_from_import(line, unused_module):
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"""
(indentation, imports) = re.split(pattern=r'\bimport\b',
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dhermes/bezier | src/bezier/surface.py | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L303-L319 | def _get_edges(self):
"""Get the edges for the current surface.
If they haven't been computed yet, first compute and store them.
This is provided as a means for internal calls to get the edges
without copying (since :attr:`.edges` copies before giving to
a user to keep the stored data immutable).
Returns:
Tuple[~bezier.curve.Curve, ~bezier.curve.Curve, \
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if self._edges is None:
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openstack/horizon | horizon/base.py | https://github.com/openstack/horizon/blob/5601ea9477323e599d9b766fcac1f8be742935b2/horizon/base.py#L185-L200 | def _register(self, cls):
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Yubico/python-yubico | yubico/yubikey_usb_hid.py | https://github.com/Yubico/python-yubico/blob/a72e8eddb90da6ee96e29f60912ca1f2872c9aea/yubico/yubikey_usb_hid.py#L578-L587 | def valid_configs(self):
""" Return a list of slots having a valid configurtion. Requires firmware 2.1. """
if self.ykver() < (2,1,0):
raise YubiKeyUSBHIDError('Valid configs unsupported in firmware %s' % (self.version()))
res = []
if self.touch_level & self.CONFIG1_VALID == self.CONFIG1_VALID:
res.append(1)
if self.touch_level & self.CONFIG2_VALID == self.CONFIG2_VALID:
res.append(2)
return res | [
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redhat-cip/dci-control-server | dci/api/v1/jobs.py | https://github.com/redhat-cip/dci-control-server/blob/b416cf935ec93e4fdd5741f61a21cabecf8454d2/dci/api/v1/jobs.py#L521-L551 | def get_all_results_from_jobs(user, j_id):
"""Get all results from job.
"""
job = v1_utils.verify_existence_and_get(j_id, _TABLE)
if not user.is_in_team(job['team_id']) and not user.is_read_only_user():
raise dci_exc.Unauthorized()
# get testscases from tests_results
query = sql.select([models.TESTS_RESULTS]). \
where(models.TESTS_RESULTS.c.job_id == job['id'])
all_tests_results = flask.g.db_conn.execute(query).fetchall()
results = []
for test_result in all_tests_results:
test_result = dict(test_result)
results.append({'filename': test_result['name'],
'name': test_result['name'],
'total': test_result['total'],
'failures': test_result['failures'],
'errors': test_result['errors'],
'skips': test_result['skips'],
'time': test_result['time'],
'regressions': test_result['regressions'],
'successfixes': test_result['successfixes'],
'success': test_result['success'],
'file_id': test_result['file_id']})
return flask.jsonify({'results': results,
'_meta': {'count': len(results)}}) | [
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pywbem/pywbem | pywbem_mock/_wbemconnection_mock.py | https://github.com/pywbem/pywbem/blob/e54ecb82c2211e289a268567443d60fdd489f1e4/pywbem_mock/_wbemconnection_mock.py#L2882-L2918 | def _fake_references(self, namespace, **params):
"""
Implements a mock WBEM server responder for
:meth:`~pywbem.WBEMConnection.References`
"""
rc = None if params['ResultClass'] is None else \
params['ResultClass'].classname
role = params['Role']
obj_name = params['ObjectName']
classname = obj_name.classname
pl = params['PropertyList']
ico = params['IncludeClassOrigin']
iq = params['IncludeQualifiers']
if isinstance(obj_name, CIMClassName):
rtn_classnames = self._get_reference_classnames(
classname, namespace, rc, role)
# returns list of tuples of (CIMClassname, CIMClass)
return self._return_assoc_class_tuples(rtn_classnames, namespace,
iq, ico, pl)
assert isinstance(obj_name, CIMInstanceName)
ref_paths = self._get_reference_instnames(obj_name, namespace, rc,
role)
rtn_insts = []
for path in ref_paths:
rtn_insts.append(self._get_instance(
path, namespace, None,
params['PropertyList'],
params['IncludeClassOrigin'],
params['IncludeQualifiers']))
for inst in rtn_insts:
if inst.path.host is None:
inst.path.host = self.host
return self._return_assoc_tuple(rtn_insts) | [
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web-push-libs/pywebpush | pywebpush/__init__.py | https://github.com/web-push-libs/pywebpush/blob/2a23f45b7819e31bd030de9fe1357a1cf7dcfdc4/pywebpush/__init__.py#L226-L254 | def as_curl(self, endpoint, encoded_data, headers):
"""Return the send as a curl command.
Useful for debugging. This will write out the encoded data to a local
file named `encrypted.data`
:param endpoint: Push service endpoint URL
:type endpoint: basestring
:param encoded_data: byte array of encoded data
:type encoded_data: bytearray
:param headers: Additional headers for the send
:type headers: dict
:returns string
"""
header_list = [
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key.lower(), val) for key, val in headers.items()
]
data = ""
if encoded_data:
with open("encrypted.data", "wb") as f:
f.write(encoded_data)
data = "--data-binary @encrypted.data"
if 'content-length' not in headers:
header_list.append(
'-H "content-length: {}" \\ \n'.format(len(encoded_data)))
return ("""curl -vX POST {url} \\\n{headers}{data}""".format(
url=endpoint, headers="".join(header_list), data=data)) | [
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mrcagney/gtfstk | gtfstk/shapes.py | https://github.com/mrcagney/gtfstk/blob/c91494e6fefc02523889655a0dc92d1c0eee8d03/gtfstk/shapes.py#L158-L210 | def append_dist_to_shapes(feed: "Feed") -> "Feed":
"""
Calculate and append the optional ``shape_dist_traveled`` field in
``feed.shapes`` in terms of the distance units ``feed.dist_units``.
Return the resulting Feed.
Notes
-----
- As a benchmark, using this function on `this Portland feed
<https://transitfeeds.com/p/trimet/43/1400947517>`_
produces a ``shape_dist_traveled`` column that differs by at most
0.016 km in absolute value from of the original values
- Assume the following feed attributes are not ``None``:
* ``feed.shapes``
"""
if feed.shapes is None:
raise ValueError(
"This function requires the feed to have a shapes.txt file"
)
feed = feed.copy()
f = feed.shapes
m_to_dist = hp.get_convert_dist("m", feed.dist_units)
def compute_dist(group):
# Compute the distances of the stops along this trip
group = group.sort_values("shape_pt_sequence")
shape = group["shape_id"].iat[0]
if not isinstance(shape, str):
group["shape_dist_traveled"] = np.nan
return group
points = [
sg.Point(utm.from_latlon(lat, lon)[:2])
for lon, lat in group[["shape_pt_lon", "shape_pt_lat"]].values
]
p_prev = points[0]
d = 0
distances = [0]
for p in points[1:]:
d += p.distance(p_prev)
distances.append(d)
p_prev = p
group["shape_dist_traveled"] = distances
return group
g = f.groupby("shape_id", group_keys=False).apply(compute_dist)
# Convert from meters
g["shape_dist_traveled"] = g["shape_dist_traveled"].map(m_to_dist)
feed.shapes = g
return feed | [
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Notes
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- As a benchmark, using this function on `this Portland feed
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mcocdawc/chemcoord | version.py | https://github.com/mcocdawc/chemcoord/blob/95561ce387c142227c38fb14a1d182179aef8f5f/version.py#L208-L216 | def call_git_branch():
"""return the string output of git desribe"""
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arguments = [GIT_COMMAND, 'rev-parse', '--abbrev-ref', 'HEAD']
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tensorlayer/tensorlayer | tensorlayer/activation.py | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/activation.py#L303-L333 | def pixel_wise_softmax(x, name='pixel_wise_softmax'):
"""Return the softmax outputs of images, every pixels have multiple label, the sum of a pixel is 1.
Usually be used for image segmentation.
Parameters
----------
x : Tensor
input.
- For 2d image, 4D tensor (batch_size, height, weight, channel), where channel >= 2.
- For 3d image, 5D tensor (batch_size, depth, height, weight, channel), where channel >= 2.
name : str
function name (optional)
Returns
-------
Tensor
A ``Tensor`` in the same type as ``x``.
Examples
--------
>>> outputs = pixel_wise_softmax(network.outputs)
>>> dice_loss = 1 - dice_coe(outputs, y_, epsilon=1e-5)
References
----------
- `tf.reverse <https://www.tensorflow.org/versions/master/api_docs/python/array_ops.html#reverse>`__
"""
with tf.name_scope(name):
return tf.nn.softmax(x) | [
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data-8/datascience | datascience/tables.py | https://github.com/data-8/datascience/blob/4cee38266903ca169cea4a53b8cc39502d85c464/datascience/tables.py#L1461-L1505 | def split(self, k):
"""Return a tuple of two tables where the first table contains
``k`` rows randomly sampled and the second contains the remaining rows.
Args:
``k`` (int): The number of rows randomly sampled into the first
table. ``k`` must be between 1 and ``num_rows - 1``.
Raises:
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Returns:
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>>> jobs = Table().with_columns(
... 'job', make_array('a', 'b', 'c', 'd'),
... 'wage', make_array(10, 20, 15, 8))
>>> jobs
job | wage
a | 10
b | 20
c | 15
d | 8
>>> sample, rest = jobs.split(3)
>>> sample # doctest: +SKIP
job | wage
c | 15
a | 10
b | 20
>>> rest # doctest: +SKIP
job | wage
d | 8
"""
if not 1 <= k <= self.num_rows - 1:
raise ValueError("Invalid value of k. k must be between 1 and the"
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rows = np.random.permutation(self.num_rows)
first = self.take(rows[:k])
rest = self.take(rows[k:])
for column_label in self._formats:
first._formats[column_label] = self._formats[column_label]
rest._formats[column_label] = self._formats[column_label]
return first, rest | [
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table. ``k`` must be between 1 and ``num_rows - 1``.
Raises:
``ValueError``: ``k`` is not between 1 and ``num_rows - 1``.
Returns:
A tuple containing two instances of ``Table``.
>>> jobs = Table().with_columns(
... 'job', make_array('a', 'b', 'c', 'd'),
... 'wage', make_array(10, 20, 15, 8))
>>> jobs
job | wage
a | 10
b | 20
c | 15
d | 8
>>> sample, rest = jobs.split(3)
>>> sample # doctest: +SKIP
job | wage
c | 15
a | 10
b | 20
>>> rest # doctest: +SKIP
job | wage
d | 8 | [
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numenta/nupic | src/nupic/swarming/hypersearch/permutation_helpers.py | https://github.com/numenta/nupic/blob/5922fafffdccc8812e72b3324965ad2f7d4bbdad/src/nupic/swarming/hypersearch/permutation_helpers.py#L436-L446 | def pushAwayFrom(self, otherPositions, rng):
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# Get the count of how many in each position
positions = [self.choices.index(x) for x in otherPositions]
positionCounts = [0] * len(self.choices)
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sveetch/py-css-styleguide | py_css_styleguide/serializer.py | https://github.com/sveetch/py-css-styleguide/blob/5acc693f71b2fa7d944d7fed561ae0a7699ccd0f/py_css_styleguide/serializer.py#L436-L456 | def get_enabled_references(self, datas, meta_references):
"""
Get enabled manifest references declarations.
Enabled references are defined through meta references declaration,
every other references are ignored.
Arguments:
datas (dict): Data where to search for reference declarations.
This is commonly the fully parsed manifest.
meta_references (list): List of enabled reference names.
Returns:
collections.OrderedDict: Serialized enabled references datas.
"""
references = OrderedDict()
for section in meta_references:
references[section] = self.get_reference(datas, section)
return references | [
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Ex-Mente/auxi.0 | auxi/modelling/business/basic.py | https://github.com/Ex-Mente/auxi.0/blob/2dcdae74154f136f8ca58289fe5b20772f215046/auxi/modelling/business/basic.py#L223-L236 | def prepare_to_run(self, clock, period_count):
"""
Prepare the activity for execution.
:param clock: The clock containing the execution start time and
execution period information.
:param period_count: The total amount of periods this activity will be
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"""
super(BasicLoanActivity, self).prepare_to_run(clock, period_count)
self._months_executed = 0
self._amount_left = self.amount | [
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ionelmc/python-cogen | cogen/web/wsgi.py | https://github.com/ionelmc/python-cogen/blob/83b0edb88425eba6e5bfda9f1dcd34642517e2a8/cogen/web/wsgi.py#L723-L729 | def bind(self, family, type, proto=0):
"""Create (or recreate) the actual socket object."""
self.socket = sockets.Socket(family, type, proto)
self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
self.socket.setblocking(0)
#~ self.socket.setsockopt(socket.SOL_SOCKET, socket.TCP_NODELAY, 1)
self.socket.bind(self.bind_addr) | [
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DiamondLightSource/python-workflows | workflows/transport/common_transport.py | https://github.com/DiamondLightSource/python-workflows/blob/7ef47b457655b96f4d2ef7ee9863cf1b6d20e023/workflows/transport/common_transport.py#L231-L258 | def ack(self, message, subscription_id=None, **kwargs):
"""Acknowledge receipt of a message. This only makes sense when the
'acknowledgement' flag was set for the relevant subscription.
:param message: ID of the message to be acknowledged, OR a dictionary
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:param subscription_id: ID of the associated subscription. Optional when
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that dictionary contains field 'subscription'.
:param **kwargs: Further parameters for the transport layer. For example
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"""
if isinstance(message, dict):
message_id = message.get("message-id")
if not subscription_id:
subscription_id = message.get("subscription")
else:
message_id = message
if not message_id:
raise workflows.Error("Cannot acknowledge message without " + "message ID")
if not subscription_id:
raise workflows.Error(
"Cannot acknowledge message without " + "subscription ID"
)
self.log.debug(
"Acknowledging message %s on subscription %s", message_id, subscription_id
)
self._ack(message_id, subscription_id, **kwargs) | [
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shad7/tvrenamer | tvrenamer/core/parser.py | https://github.com/shad7/tvrenamer/blob/7fb59cb02669357e73b7acb92dcb6d74fdff4654/tvrenamer/core/parser.py#L29-L54 | def parse_filename(filename):
"""Parse media filename for metadata.
:param str filename: the name of media file
:returns: dict of metadata attributes found in filename
or None if no matching expression.
:rtype: dict
"""
_patterns = patterns.get_expressions()
result = {}
for cmatcher in _patterns:
match = cmatcher.match(filename)
if match:
namedgroups = match.groupdict().keys()
result['pattern'] = cmatcher.pattern
result['series_name'] = match.group('seriesname')
result['season_number'] = _get_season_no(match, namedgroups)
result['episode_numbers'] = _get_episodes(match, namedgroups)
break
else:
result = None
return result | [
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rbuffat/pyepw | pyepw/epw.py | https://github.com/rbuffat/pyepw/blob/373d4d3c8386c8d35789f086ac5f6018c2711745/pyepw/epw.py#L1550-L1581 | def design_stat_cooling(self, value="Cooling"):
"""Corresponds to IDD Field `design_stat_cooling`
Args:
value (str): value for IDD Field `design_stat_cooling`
Accepted values are:
- Cooling
Default value: Cooling
if `value` is None it will not be checked against the
specification and is assumed to be a missing value
Raises:
ValueError: if `value` is not a valid value
"""
if value is not None:
try:
value = str(value)
except ValueError:
raise ValueError(
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if ',' in value:
raise ValueError('value should not contain a comma '
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vals = set()
vals.add("Cooling")
if value not in vals:
raise ValueError('value {} is not an accepted value for '
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self._design_stat_cooling = value | [
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paramiko/paramiko | paramiko/transport.py | https://github.com/paramiko/paramiko/blob/cf7d49d66f3b1fbc8b0853518a54050182b3b5eb/paramiko/transport.py#L1634-L1647 | def set_log_channel(self, name):
"""
Set the channel for this transport's logging. The default is
``"paramiko.transport"`` but it can be set to anything you want. (See
the `.logging` module for more info.) SSH Channels will log to a
sub-channel of the one specified.
:param str name: new channel name for logging
.. versionadded:: 1.1
"""
self.log_name = name
self.logger = util.get_logger(name)
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openstax/cnx-archive | cnxarchive/views/xpath.py | https://github.com/openstax/cnx-archive/blob/d31d34aa8bbc8a9fde6cd4227a0df92726e8daf4/cnxarchive/views/xpath.py#L123-L144 | def execute_xpath(xpath_string, sql_function, uuid, version):
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params."""
settings = get_current_registry().settings
with db_connect() as db_connection:
with db_connection.cursor() as cursor:
try:
cursor.execute(SQL[sql_function],
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'document_version': version,
'xpath_string': xpath_string})
except psycopg2.Error as e:
exc = httpexceptions.HTTPBadRequest()
exc.explanation = e.pgerror
raise exc
for res in cursor.fetchall():
yield {'name': res[0],
'uuid': res[1],
'version': res[2],
'xpath_results': res[3]} | [
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IntegralDefense/splunklib | splunklib/__init__.py | https://github.com/IntegralDefense/splunklib/blob/c3a02c83daad20cf24838f52b22cd2476f062eed/splunklib/__init__.py#L336-L348 | def json(self):
"""Returns the search results as a list of JSON objects."""
if self.search_results is None:
return None
result = []
for row in self.search_results['rows']:
obj = {}
for index in range(0, len(self.search_results['fields'])):
obj[self.search_results['fields'][index]] = row[index]
result.append(obj)
return result | [
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sorgerlab/indra | indra/sources/isi/processor.py | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/sources/isi/processor.py#L38-L43 | def get_statements(self):
"""Process reader output to produce INDRA Statements."""
for k, v in self.reader_output.items():
for interaction in v['interactions']:
self._process_interaction(k, interaction, v['text'], self.pmid,
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klen/makesite | makesite/modules/django/main/utils/cache.py | https://github.com/klen/makesite/blob/f6f77a43a04a256189e8fffbeac1ffd63f35a10c/makesite/modules/django/main/utils/cache.py#L15-L22 | def cached_instance(model, timeout=None, **filters):
""" Auto cached model instance.
"""
if isinstance(model, basestring):
model = _str_to_model(model)
cache_key = generate_cache_key(model, **filters)
return get_cached(cache_key, model.objects.select_related().get, kwargs=filters) | [
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onelogin/python-saml | src/onelogin/saml2/idp_metadata_parser.py | https://github.com/onelogin/python-saml/blob/9fe7a72da5b4caa1529c1640b52d2649447ce49b/src/onelogin/saml2/idp_metadata_parser.py#L28-L63 | def get_metadata(url, validate_cert=True):
"""
Gets the metadata XML from the provided URL
:param url: Url where the XML of the Identity Provider Metadata is published.
:type url: string
:param validate_cert: If the url uses https schema, that flag enables or not the verification of the associated certificate.
:type validate_cert: bool
:returns: metadata XML
:rtype: string
"""
valid = False
if validate_cert:
response = urllib2.urlopen(url)
else:
ctx = ssl.create_default_context()
ctx.check_hostname = False
ctx.verify_mode = ssl.CERT_NONE
response = urllib2.urlopen(url, context=ctx)
xml = response.read()
if xml:
try:
dom = fromstring(xml, forbid_dtd=True)
idp_descriptor_nodes = OneLogin_Saml2_Utils.query(dom, '//md:IDPSSODescriptor')
if idp_descriptor_nodes:
valid = True
except Exception:
pass
if not valid:
raise Exception('Not valid IdP XML found from URL: %s' % (url))
return xml | [
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:type validate_cert: bool
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theislab/scanpy | scanpy/_exporting.py | https://github.com/theislab/scanpy/blob/9e4e5ee02e04cf618872d9b098e24f0542e8b227/scanpy/_exporting.py#L409-L514 | def cellbrowser(
adata, data_dir, data_name,
embedding_keys = None,
annot_keys = ["louvain", "percent_mito", "n_genes", "n_counts"],
cluster_field = "louvain",
nb_marker = 50,
skip_matrix = False,
html_dir = None,
port = None,
do_debug = False
):
"""
Export adata to a UCSC Cell Browser project directory. If `html_dir` is
set, subsequently build the html files from the project directory into
`html_dir`. If `port` is set, start an HTTP server in the background and
serve `html_dir` on `port`.
By default, export all gene expression data from `adata.raw`, the
annotations `louvain`, `percent_mito`, `n_genes` and `n_counts` and the top
`nb_marker` cluster markers. All existing files in data_dir are
overwritten, except cellbrowser.conf.
See `UCSC Cellbrowser <https://github.com/maximilianh/cellBrowser>`__ for
details.
Parameters
----------
adata : :class:`~anndata.AnnData`
Annotated data matrix
data_dir : `str`
Path to directory for exported Cell Browser files.
Usually these are the files `exprMatrix.tsv.gz`, `meta.tsv`,
coordinate files like `tsne.coords.tsv`,
and cluster marker gene lists like `markers.tsv`.
A file `cellbrowser.conf` is also created with pointers to these files.
As a result, each adata object should have its own project_dir.
data_name : `str`
Name of dataset in Cell Browser, a string without special characters.
This is written to `data_dir`/cellbrowser.conf.
Ideally this is a short unique name for the dataset,
like "pbmc3k" or "tabulamuris".
embedding_keys: `list` of `str` or `dict` of `key (str)`->`display label (str)`
2-D embeddings in `adata.obsm` to export.
The prefix "`X_`" or "`X_draw_graph_`" is not necessary.
Coordinates missing from `adata` are skipped.
By default, these keys are tried: ["tsne", "umap", "pagaFa", "pagaFr",
"pagaUmap", "phate", "fa", "fr", "kk", "drl", "rt"].
For these, default display labels are automatically used.
For other values, you can specify a dictionary instead of a list,
the values of the dictionary are then the display labels for the
coordinates, e.g. `{'tsne' : "t-SNE by Scanpy"}`
annot_keys: `list` of `str` or `dict` of `key (str)`->`display label (str)`
Annotations in `adata.obsm` to export.
Can be a dictionary with key -> display label.
skip_matrix: `boolean`
Do not export the matrix.
If you had previously exported this adata into the same `data_dir`,
then there is no need to export the whole matrix again.
This option will make the export a lot faster,
e.g. when only coordinates or meta data were changed.
html_dir: `str`
If this variable is set, the export will build html
files from `data_dir` to `html_dir`, creating html/js/json files.
Usually there is one global html output directory for all datasets.
Often, `html_dir` is located under a webserver's (like Apache)
htdocs directory or is copied to one.
A directory `html_dir`/`project_name` will be created and
an index.html will be created under `html_dir` for all subdirectories.
Existing files will be overwritten.
If do not to use html_dir,
you can use the command line tool `cbBuild` to build the html directory.
port: `int`
If this variable and `html_dir` are set,
Python's built-in web server will be spawned as a daemon in the
background and serve the files under `html_dir`.
To kill the process, call `cellbrowser.cellbrowser.stop()`.
do_debug: `boolean`
Activate debugging output
Examples
--------
See this
`tutorial <https://github.com/theislab/scanpy_usage/tree/master/181126_Cellbrowser_exports>`__.
"""
try:
import cellbrowser.cellbrowser as cb
except ImportError:
print("The package cellbrowser is not installed. Install with 'pip "
"install cellbrowser' and retry.")
cb.setDebug(do_debug)
cb.scanpyToCellbrowser(adata, data_dir, data_name,
coordFields=embedding_keys,
metaFields=annot_keys,
clusterField=cluster_field,
nb_marker=nb_marker,
skipMatrix=skip_matrix,
doDebug = None
)
if html_dir:
cb.build(data_dir, html_dir, doDebug=None)
if port:
cb.serve(html_dir, port) | [
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By default, export all gene expression data from `adata.raw`, the
annotations `louvain`, `percent_mito`, `n_genes` and `n_counts` and the top
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overwritten, except cellbrowser.conf.
See `UCSC Cellbrowser <https://github.com/maximilianh/cellBrowser>`__ for
details.
Parameters
----------
adata : :class:`~anndata.AnnData`
Annotated data matrix
data_dir : `str`
Path to directory for exported Cell Browser files.
Usually these are the files `exprMatrix.tsv.gz`, `meta.tsv`,
coordinate files like `tsne.coords.tsv`,
and cluster marker gene lists like `markers.tsv`.
A file `cellbrowser.conf` is also created with pointers to these files.
As a result, each adata object should have its own project_dir.
data_name : `str`
Name of dataset in Cell Browser, a string without special characters.
This is written to `data_dir`/cellbrowser.conf.
Ideally this is a short unique name for the dataset,
like "pbmc3k" or "tabulamuris".
embedding_keys: `list` of `str` or `dict` of `key (str)`->`display label (str)`
2-D embeddings in `adata.obsm` to export.
The prefix "`X_`" or "`X_draw_graph_`" is not necessary.
Coordinates missing from `adata` are skipped.
By default, these keys are tried: ["tsne", "umap", "pagaFa", "pagaFr",
"pagaUmap", "phate", "fa", "fr", "kk", "drl", "rt"].
For these, default display labels are automatically used.
For other values, you can specify a dictionary instead of a list,
the values of the dictionary are then the display labels for the
coordinates, e.g. `{'tsne' : "t-SNE by Scanpy"}`
annot_keys: `list` of `str` or `dict` of `key (str)`->`display label (str)`
Annotations in `adata.obsm` to export.
Can be a dictionary with key -> display label.
skip_matrix: `boolean`
Do not export the matrix.
If you had previously exported this adata into the same `data_dir`,
then there is no need to export the whole matrix again.
This option will make the export a lot faster,
e.g. when only coordinates or meta data were changed.
html_dir: `str`
If this variable is set, the export will build html
files from `data_dir` to `html_dir`, creating html/js/json files.
Usually there is one global html output directory for all datasets.
Often, `html_dir` is located under a webserver's (like Apache)
htdocs directory or is copied to one.
A directory `html_dir`/`project_name` will be created and
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Existing files will be overwritten.
If do not to use html_dir,
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port: `int`
If this variable and `html_dir` are set,
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background and serve the files under `html_dir`.
To kill the process, call `cellbrowser.cellbrowser.stop()`.
do_debug: `boolean`
Activate debugging output
Examples
--------
See this
`tutorial <https://github.com/theislab/scanpy_usage/tree/master/181126_Cellbrowser_exports>`__. | [
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roclark/sportsreference | sportsreference/nba/roster.py | https://github.com/roclark/sportsreference/blob/ea0bae432be76450e137671d2998eb38f962dffd/sportsreference/nba/roster.py#L389-L411 | def _parse_contract_wages(self, table):
"""
Parse the wages on the contract.
The wages are listed as the data points in the contract table. Any
values that don't have a value which starts with a '$' sign are likely
not valid and should be dropped.
Parameters
----------
table : PyQuery object
A PyQuery object containing the contract table.
Returns
-------
list
Returns a list of all wages where each element is a string denoting
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"""
wages = [i.text() if i.text().startswith('$') else ''
for i in table('td').items()]
wages.remove('')
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google-research/batch-ppo | agents/algorithms/ppo/ppo.py | https://github.com/google-research/batch-ppo/blob/3d09705977bae4e7c3eb20339a3b384d2a5531e4/agents/algorithms/ppo/ppo.py#L334-L380 | def _perform_update_steps(
self, observ, action, old_policy_params, reward, length):
"""Perform multiple update steps of value function and policy.
The advantage is computed once at the beginning and shared across
iterations. We need to decide for the summary of one iteration, and thus
choose the one after half of the iterations.
Args:
observ: Sequences of observations.
action: Sequences of actions.
old_policy_params: Parameters of the behavioral policy.
reward: Sequences of rewards.
length: Batch of sequence lengths.
Returns:
Summary tensor.
"""
return_ = utility.discounted_return(
reward, length, self._config.discount)
value = self._network(observ, length).value
if self._config.gae_lambda:
advantage = utility.lambda_advantage(
reward, value, length, self._config.discount,
self._config.gae_lambda)
else:
advantage = return_ - value
mean, variance = tf.nn.moments(advantage, axes=[0, 1], keep_dims=True)
advantage = (advantage - mean) / (tf.sqrt(variance) + 1e-8)
advantage = tf.Print(
advantage, [tf.reduce_mean(return_), tf.reduce_mean(value)],
'return and value: ')
advantage = tf.Print(
advantage, [tf.reduce_mean(advantage)],
'normalized advantage: ')
episodes = (observ, action, old_policy_params, reward, advantage)
value_loss, policy_loss, summary = parts.iterate_sequences(
self._update_step, [0., 0., ''], episodes, length,
self._config.chunk_length,
self._config.batch_size,
self._config.update_epochs,
padding_value=1)
print_losses = tf.group(
tf.Print(0, [tf.reduce_mean(value_loss)], 'value loss: '),
tf.Print(0, [tf.reduce_mean(policy_loss)], 'policy loss: '))
with tf.control_dependencies([value_loss, policy_loss, print_losses]):
return summary[self._config.update_epochs // 2] | [
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The advantage is computed once at the beginning and shared across
iterations. We need to decide for the summary of one iteration, and thus
choose the one after half of the iterations.
Args:
observ: Sequences of observations.
action: Sequences of actions.
old_policy_params: Parameters of the behavioral policy.
reward: Sequences of rewards.
length: Batch of sequence lengths.
Returns:
Summary tensor. | [
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] | python | train |
openid/JWTConnect-Python-CryptoJWT | src/cryptojwt/key_bundle.py | https://github.com/openid/JWTConnect-Python-CryptoJWT/blob/8863cfbfe77ca885084870b234a66b55bd52930c/src/cryptojwt/key_bundle.py#L56-L85 | def rsa_init(spec):
"""
Initiates a :py:class:`oidcmsg.keybundle.KeyBundle` instance
containing newly minted RSA keys according to a spec.
Example of specification::
{'size':2048, 'use': ['enc', 'sig'] }
Using the spec above 2 RSA keys would be minted, one for
encryption and one for signing.
:param spec:
:return: KeyBundle
"""
try:
size = spec['size']
except KeyError:
size = 2048
kb = KeyBundle(keytype="RSA")
if 'use' in spec:
for use in harmonize_usage(spec["use"]):
_key = new_rsa_key(use=use, key_size=size)
kb.append(_key)
else:
_key = new_rsa_key(key_size=size)
kb.append(_key)
return kb | [
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spyder-ide/spyder | spyder/plugins/plots/widgets/figurebrowser.py | https://github.com/spyder-ide/spyder/blob/f76836ce1b924bcc4efd3f74f2960d26a4e528e0/spyder/plugins/plots/widgets/figurebrowser.py#L284-L291 | def show_fig_outline_in_viewer(self, state):
"""Draw a frame around the figure viewer if state is True."""
if state is True:
self.figviewer.figcanvas.setStyleSheet(
"FigureCanvas{border: 1px solid lightgrey;}")
else:
self.figviewer.figcanvas.setStyleSheet("FigureCanvas{}")
self.option_changed('show_plot_outline', state) | [
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materialsproject/pymatgen | pymatgen/analysis/pourbaix_diagram.py | https://github.com/materialsproject/pymatgen/blob/4ca558cf72f8d5f8a1f21dfdfc0181a971c186da/pymatgen/analysis/pourbaix_diagram.py#L683-L714 | def get_decomposition_energy(self, entry, pH, V):
"""
Finds decomposition to most stable entry
Args:
entry (PourbaixEntry): PourbaixEntry corresponding to
compound to find the decomposition for
pH (float): pH at which to find the decomposition
V (float): voltage at which to find the decomposition
Returns:
reaction corresponding to the decomposition
"""
# Find representative multientry
if self._multielement and not isinstance(entry, MultiEntry):
possible_entries = self._generate_multielement_entries(
self._filtered_entries, forced_include=[entry])
# Filter to only include materials where the entry is only solid
if entry.phase_type == "solid":
possible_entries = [e for e in possible_entries
if e.phase_type.count("Solid") == 1]
possible_energies = [e.normalized_energy_at_conditions(pH, V)
for e in possible_entries]
else:
possible_energies = [entry.normalized_energy_at_conditions(pH, V)]
min_energy = np.min(possible_energies, axis=0)
# Find entry and take the difference
hull = self.get_hull_energy(pH, V)
return min_energy - hull | [
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Args:
entry (PourbaixEntry): PourbaixEntry corresponding to
compound to find the decomposition for
pH (float): pH at which to find the decomposition
V (float): voltage at which to find the decomposition
Returns:
reaction corresponding to the decomposition | [
"Finds",
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] | python | train |
pyblish/pyblish-qml | pyblish_qml/host.py | https://github.com/pyblish/pyblish-qml/blob/6095d18b2ec0afd0409a9b1a17e53b0658887283/pyblish_qml/host.py#L393-L428 | def install(self, host):
"""Setup common to all Qt-based hosts"""
print("Installing..")
if self._state["installed"]:
return
if self.is_headless():
log.info("Headless host")
return
print("aboutToQuit..")
self.app.aboutToQuit.connect(self._on_application_quit)
if host == "Maya":
print("Maya host..")
window = {
widget.objectName(): widget
for widget in self.app.topLevelWidgets()
}["MayaWindow"]
else:
window = self.find_window()
# Install event filter
print("event filter..")
event_filter = self.EventFilter(window)
window.installEventFilter(event_filter)
for signal in SIGNALS_TO_REMOVE_EVENT_FILTER:
pyblish.api.register_callback(signal, self.uninstall)
log.info("Installed event filter")
self.window = window
self._state["installed"] = True
self._state["eventFilter"] = event_filter | [
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autokey/autokey | lib/autokey/iomediator/_iomediator.py | https://github.com/autokey/autokey/blob/35decb72f286ce68cd2a1f09ace8891a520b58d1/lib/autokey/iomediator/_iomediator.py#L218-L223 | def send_backspace(self, count):
"""
Sends the given number of backspace key presses.
"""
for i in range(count):
self.interface.send_key(Key.BACKSPACE) | [
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rbarrois/aionotify | aionotify/base.py | https://github.com/rbarrois/aionotify/blob/6cfa35b26a2660f77f29a92d3efb7d1dde685b43/aionotify/base.py#L50-L59 | def watch(self, path, flags, *, alias=None):
"""Add a new watching rule."""
if alias is None:
alias = path
if alias in self.requests:
raise ValueError("A watch request is already scheduled for alias %s" % alias)
self.requests[alias] = (path, flags)
if self._fd is not None:
# We've started, register the watch immediately.
self._setup_watch(alias, path, flags) | [
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] | python | test |
saltstack/salt | salt/states/grafana.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/states/grafana.py#L205-L219 | def _rows_differ(row, _row):
'''
Check if grafana dashboard row and _row differ
'''
row_copy = copy.deepcopy(row)
_row_copy = copy.deepcopy(_row)
# Strip id from all panels in both rows, since they are always generated.
for panel in row_copy['panels']:
if 'id' in panel:
del panel['id']
for _panel in _row_copy['panels']:
if 'id' in _panel:
del _panel['id']
diff = DictDiffer(row_copy, _row_copy)
return diff.changed() or diff.added() or diff.removed() | [
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phaethon/kamene | kamene/arch/windows/__init__.py | https://github.com/phaethon/kamene/blob/11d4064844f4f68ac5d7546f5633ac7d02082914/kamene/arch/windows/__init__.py#L200-L209 | def show(self, resolve_mac=True):
"""Print list of available network interfaces in human readable form"""
print("%s %s %s %s" % ("INDEX".ljust(5), "IFACE".ljust(35), "IP".ljust(15), "MAC"))
for iface_name in sorted(self.data.keys()):
dev = self.data[iface_name]
mac = dev.mac
if resolve_mac and iface_name != LOOPBACK_NAME:
mac = conf.manufdb._resolve_MAC(mac)
print("%s %s %s %s" % (str(dev.win_index).ljust(5), str(dev.name).ljust(35), str(dev.ip).ljust(15), mac) ) | [
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blockstack/blockstack-core | blockstack/lib/nameset/db.py | https://github.com/blockstack/blockstack-core/blob/1dcfdd39b152d29ce13e736a6a1a0981401a0505/blockstack/lib/nameset/db.py#L2546-L2559 | def namedb_get_all_namespace_ids( cur ):
"""
Get a list of all READY namespace IDs.
"""
query = "SELECT namespace_id FROM namespaces WHERE op = ?;"
args = (NAMESPACE_READY,)
namespace_rows = namedb_query_execute( cur, query, args )
ret = []
for namespace_row in namespace_rows:
ret.append( namespace_row['namespace_id'] )
return ret | [
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romanvm/django-tinymce4-lite | tinymce/widgets.py | https://github.com/romanvm/django-tinymce4-lite/blob/3b9221db5f0327e1e08c79b7b8cdbdcb1848a390/tinymce/widgets.py#L49-L60 | def language_file_exists(language_code):
"""
Check if TinyMCE has a language file for the specified lang code
:param language_code: language code
:type language_code: str
:return: check result
:rtype: bool
"""
filename = '{0}.js'.format(language_code)
path = os.path.join('tinymce', 'js', 'tinymce', 'langs', filename)
return finders.find(path) is not None | [
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ionelmc/python-hunter | src/hunter/__init__.py | https://github.com/ionelmc/python-hunter/blob/b3a1310b0593d2c6b6ef430883843896e17d6a81/src/hunter/__init__.py#L120-L126 | def And(*predicates, **kwargs):
"""
`And` predicate. Returns ``False`` at the first sub-predicate that returns ``False``.
"""
if kwargs:
predicates += Query(**kwargs),
return _flatten(_And, *predicates) | [
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oscarlazoarjona/fast | fast/angular_momentum.py | https://github.com/oscarlazoarjona/fast/blob/3e5400672af2a7b7cc616e7f4aa10d7672720222/fast/angular_momentum.py#L510-L538 | def wigner_d(J, alpha, beta, gamma):
u"""Return the Wigner D matrix for angular momentum J.
We use the general formula from [Edmonds74]_, equation 4.1.12.
The simplest possible example:
>>> from sympy import Integer, symbols, pprint
>>> half = 1/Integer(2)
>>> alpha, beta, gamma = symbols("alpha, beta, gamma", real=True)
>>> pprint(wigner_d(half, alpha, beta, gamma), use_unicode=True)
⎡ ⅈ⋅α ⅈ⋅γ ⅈ⋅α -ⅈ⋅γ ⎤
⎢ ─── ─── ─── ───── ⎥
⎢ 2 2 ⎛β⎞ 2 2 ⎛β⎞ ⎥
⎢ ℯ ⋅ℯ ⋅cos⎜─⎟ ℯ ⋅ℯ ⋅sin⎜─⎟ ⎥
⎢ ⎝2⎠ ⎝2⎠ ⎥
⎢ ⎥
⎢ -ⅈ⋅α ⅈ⋅γ -ⅈ⋅α -ⅈ⋅γ ⎥
⎢ ───── ─── ───── ───── ⎥
⎢ 2 2 ⎛β⎞ 2 2 ⎛β⎞⎥
⎢-ℯ ⋅ℯ ⋅sin⎜─⎟ ℯ ⋅ℯ ⋅cos⎜─⎟⎥
⎣ ⎝2⎠ ⎝2⎠⎦
"""
d = wigner_d_small(J, beta)
M = [J-i for i in range(2*J+1)]
D = [[exp(I*Mi*alpha)*d[i, j]*exp(I*Mj*gamma)
for j, Mj in enumerate(M)] for i, Mi in enumerate(M)]
return Matrix(D) | [
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>>> half = 1/Integer(2)
>>> alpha, beta, gamma = symbols("alpha, beta, gamma", real=True)
>>> pprint(wigner_d(half, alpha, beta, gamma), use_unicode=True)
⎡ ⅈ⋅α ⅈ⋅γ ⅈ⋅α -ⅈ⋅γ ⎤
⎢ ─── ─── ─── ───── ⎥
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⎢-ℯ ⋅ℯ ⋅sin⎜─⎟ ℯ ⋅ℯ ⋅cos⎜─⎟⎥
⎣ ⎝2⎠ ⎝2⎠⎦ | [
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theislab/scanpy | scanpy/plotting/_utils.py | https://github.com/theislab/scanpy/blob/9e4e5ee02e04cf618872d9b098e24f0542e8b227/scanpy/plotting/_utils.py#L51-L111 | def timeseries_subplot(X,
time=None,
color=None,
var_names=(),
highlightsX=(),
xlabel='',
ylabel='gene expression',
yticks=None,
xlim=None,
legend=True,
palette=None,
color_map='viridis'):
"""Plot X.
Parameters
----------
X : np.ndarray
Call this with:
X with one column, color categorical.
X with one column, color continuous.
X with n columns, color is of length n.
"""
if color is not None:
use_color_map = isinstance(color[0], float) or isinstance(color[0], np.float32)
palette = default_palette(palette)
x_range = np.arange(X.shape[0]) if time is None else time
if X.ndim == 1: X = X[:, None]
if X.shape[1] > 1:
colors = palette[:X.shape[1]].by_key()['color']
subsets = [(x_range, X[:, i]) for i in range(X.shape[1])]
elif use_color_map:
colors = [color]
subsets = [(x_range, X[:, 0])]
else:
levels, _ = np.unique(color, return_inverse=True)
colors = np.array(palette[:len(levels)].by_key()['color'])
subsets = [(x_range[color == l], X[color == l, :]) for l in levels]
for i, (x, y) in enumerate(subsets):
pl.scatter(
x, y,
marker='.',
edgecolor='face',
s=rcParams['lines.markersize'],
c=colors[i],
label=var_names[i] if len(var_names) > 0 else '',
cmap=color_map,
rasterized=settings._vector_friendly)
ylim = pl.ylim()
for ih, h in enumerate(highlightsX):
pl.plot([h, h], [ylim[0], ylim[1]], '--', color='black')
pl.ylim(ylim)
if xlim is not None:
pl.xlim(xlim)
pl.xlabel(xlabel)
pl.ylabel(ylabel)
if yticks is not None:
pl.yticks(yticks)
if len(var_names) > 0 and legend:
pl.legend(frameon=False) | [
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bukun/TorCMS | torcms/handlers/link_handler.py | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/link_handler.py#L212-L229 | def delete_by_id(self, del_id):
'''
Delete a link by id.
'''
if self.check_post_role()['DELETE']:
pass
else:
return False
if self.is_p:
if MLink.delete(del_id):
output = {'del_link': 1}
else:
output = {'del_link': 0}
return json.dump(output, self)
else:
is_deleted = MLink.delete(del_id)
if is_deleted:
self.redirect('/link/list') | [
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LudovicRousseau/pyscard | smartcard/pcsc/PCSCReaderGroups.py | https://github.com/LudovicRousseau/pyscard/blob/62e675028086c75656444cc21d563d9f08ebf8e7/smartcard/pcsc/PCSCReaderGroups.py#L43-L56 | def getreadergroups(self):
""" Returns the list of smartcard reader groups."""
innerreadergroups.getreadergroups(self)
hresult, hcontext = SCardEstablishContext(SCARD_SCOPE_USER)
if hresult != 0:
raise EstablishContextException(hresult)
hresult, readers = SCardListReaderGroups(hcontext)
if hresult != 0:
raise ListReadersException(hresult)
hresult = SCardReleaseContext(hcontext)
if hresult != 0:
raise ReleaseContextException(hresult)
return readers | [
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tjcsl/ion | intranet/apps/events/models.py | https://github.com/tjcsl/ion/blob/5d722b0725d572039bb0929fd5715a4070c82c72/intranet/apps/events/models.py#L36-L45 | def visible_to_user(self, user):
"""Get a list of visible events for a given user (usually request.user).
These visible events will be those that either have no groups
assigned to them (and are therefore public) or those in which
the user is a member.
"""
return (Event.objects.filter(approved=True).filter(Q(groups__in=user.groups.all()) | Q(groups__isnull=True) | Q(user=user))) | [
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sernst/cauldron | cauldron/environ/logger.py | https://github.com/sernst/cauldron/blob/4086aec9c038c402ea212c79fe8bd0d27104f9cf/cauldron/environ/logger.py#L14-L27 | def add_output_path(path: str = None) -> str:
"""
Adds the specified path to the output logging paths if it is not
already in the listed paths.
:param path:
The path to add to the logging output paths. If the path is empty
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"""
cleaned = paths.clean(path or os.getcwd())
if cleaned not in _logging_paths:
_logging_paths.append(cleaned)
return cleaned | [
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materialsproject/pymatgen | pymatgen/io/zeopp.py | https://github.com/materialsproject/pymatgen/blob/4ca558cf72f8d5f8a1f21dfdfc0181a971c186da/pymatgen/io/zeopp.py#L262-L336 | def get_voronoi_nodes(structure, rad_dict=None, probe_rad=0.1):
"""
Analyze the void space in the input structure using voronoi decomposition
Calls Zeo++ for Voronoi decomposition.
Args:
structure: pymatgen.core.structure.Structure
rad_dict (optional): Dictionary of radii of elements in structure.
If not given, Zeo++ default values are used.
Note: Zeo++ uses atomic radii of elements.
For ionic structures, pass rad_dict with ionic radii
probe_rad (optional): Sampling probe radius in Angstroms. Default is
0.1 A
Returns:
voronoi nodes as pymatgen.core.structure.Strucutre within the
unit cell defined by the lattice of input structure
voronoi face centers as pymatgen.core.structure.Strucutre within the
unit cell defined by the lattice of input structure
"""
with ScratchDir('.'):
name = "temp_zeo1"
zeo_inp_filename = name + ".cssr"
ZeoCssr(structure).write_file(zeo_inp_filename)
rad_file = None
rad_flag = False
if rad_dict:
rad_file = name + ".rad"
rad_flag = True
with open(rad_file, 'w+') as fp:
for el in rad_dict.keys():
fp.write("{} {}\n".format(el, rad_dict[el].real))
atmnet = AtomNetwork.read_from_CSSR(
zeo_inp_filename, rad_flag=rad_flag, rad_file=rad_file)
vornet, vor_edge_centers, vor_face_centers = \
atmnet.perform_voronoi_decomposition()
vornet.analyze_writeto_XYZ(name, probe_rad, atmnet)
voro_out_filename = name + '_voro.xyz'
voro_node_mol = ZeoVoronoiXYZ.from_file(voro_out_filename).molecule
species = ["X"] * len(voro_node_mol.sites)
coords = []
prop = []
for site in voro_node_mol.sites:
coords.append(list(site.coords))
prop.append(site.properties['voronoi_radius'])
lattice = Lattice.from_lengths_and_angles(
structure.lattice.abc, structure.lattice.angles)
vor_node_struct = Structure(
lattice, species, coords, coords_are_cartesian=True,
to_unit_cell=True, site_properties={"voronoi_radius": prop})
# PMG-Zeo c<->a transformation for voronoi face centers
rot_face_centers = [(center[1], center[2], center[0]) for center in
vor_face_centers]
rot_edge_centers = [(center[1], center[2], center[0]) for center in
vor_edge_centers]
species = ["X"] * len(rot_face_centers)
prop = [0.0] * len(rot_face_centers) # Vor radius not evaluated for fc
vor_facecenter_struct = Structure(
lattice, species, rot_face_centers, coords_are_cartesian=True,
to_unit_cell=True, site_properties={"voronoi_radius": prop})
species = ["X"] * len(rot_edge_centers)
prop = [0.0] * len(rot_edge_centers) # Vor radius not evaluated for fc
vor_edgecenter_struct = Structure(
lattice, species, rot_edge_centers, coords_are_cartesian=True,
to_unit_cell=True, site_properties={"voronoi_radius": prop})
return vor_node_struct, vor_edgecenter_struct, vor_facecenter_struct | [
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] | Analyze the void space in the input structure using voronoi decomposition
Calls Zeo++ for Voronoi decomposition.
Args:
structure: pymatgen.core.structure.Structure
rad_dict (optional): Dictionary of radii of elements in structure.
If not given, Zeo++ default values are used.
Note: Zeo++ uses atomic radii of elements.
For ionic structures, pass rad_dict with ionic radii
probe_rad (optional): Sampling probe radius in Angstroms. Default is
0.1 A
Returns:
voronoi nodes as pymatgen.core.structure.Strucutre within the
unit cell defined by the lattice of input structure
voronoi face centers as pymatgen.core.structure.Strucutre within the
unit cell defined by the lattice of input structure | [
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"."
] | python | train |
jbasko/configmanager | configmanager/items.py | https://github.com/jbasko/configmanager/blob/1d7229ce367143c7210d8e5f0782de03945a1721/configmanager/items.py#L283-L295 | def is_default(self):
"""
``True`` if the item's value is its default value or if no value and no default value are set.
If the item is backed by an environment variable, this will be ``True`` only
if the environment variable is set and is different to the
default value of the item.
"""
envvar_value = self._get_envvar_value()
if envvar_value is not not_set:
return envvar_value == self.default
else:
return self._value is not_set or self._value == self.default | [
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monarch-initiative/dipper | dipper/sources/KEGG.py | https://github.com/monarch-initiative/dipper/blob/24cc80db355bbe15776edc5c7b41e0886959ba41/dipper/sources/KEGG.py#L354-L423 | def _process_ortholog_classes(self, limit=None):
"""
This method add the KEGG orthology classes to the graph.
If there's an embedded enzyme commission number,
that is added as an xref.
Triples created:
<orthology_class_id> is a class
<orthology_class_id> has label <orthology_symbols>
<orthology_class_id> has description <orthology_description>
:param limit:
:return:
"""
LOG.info("Processing ortholog classes")
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
line_counter = 0
raw = '/'.join((self.rawdir, self.files['ortholog_classes']['file']))
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
line_counter += 1
(orthology_class_id, orthology_class_name) = row
if self.test_mode and orthology_class_id \
not in self.test_ids['orthology_classes']:
continue
# The orthology class is essentially a KEGG gene ID
# that is species agnostic.
# Add the ID and label as a gene family class
other_labels = re.split(r'[;,]', orthology_class_name)
# the first one is the label we'll use
orthology_label = other_labels[0]
orthology_class_id = 'KEGG-'+orthology_class_id.strip()
orthology_type = self.globaltt['gene_family']
model.addClassToGraph(
orthology_class_id, orthology_label, orthology_type)
if len(other_labels) > 1:
# add the rest as synonyms
# todo skip the first
for s in other_labels:
model.addSynonym(orthology_class_id, s.strip())
# add the last one as the description
d = other_labels[len(other_labels)-1]
model.addDescription(orthology_class_id, d)
# add the enzyme commission number (EC:1.2.99.5)as an xref
# sometimes there's two, like [EC:1.3.5.1 1.3.5.4]
# can also have a dash, like EC:1.10.3.-
ec_matches = re.findall(r'((?:\d+|\.|-){5,7})', d)
if ec_matches is not None:
for ecm in ec_matches:
model.addXref(orthology_class_id, 'EC:' + ecm)
if not self.test_mode and limit is not None and line_counter > limit:
break
LOG.info("Done with ortholog classes")
return | [
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worldcompany/djangoembed | oembed/fields.py | https://github.com/worldcompany/djangoembed/blob/f3f2be283441d91d1f89db780444dc75f7b51902/oembed/fields.py#L66-L75 | def register_field(cls, field):
"""
Handles registering the fields with the FieldRegistry and creating a
post-save signal for the model.
"""
FieldRegistry.add_field(cls, field)
signals.post_save.connect(handle_save_embeds, sender=cls,
dispatch_uid='%s.%s.%s' % \
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google/apitools | apitools/base/protorpclite/protojson.py | https://github.com/google/apitools/blob/f3745a7ea535aa0e88b0650c16479b696d6fd446/apitools/base/protorpclite/protojson.py#L195-L216 | def decode_message(self, message_type, encoded_message):
"""Merge JSON structure to Message instance.
Args:
message_type: Message to decode data to.
encoded_message: JSON encoded version of message.
Returns:
Decoded instance of message_type.
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"""
encoded_message = six.ensure_str(encoded_message)
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LordGaav/python-chaos | chaos/arguments.py | https://github.com/LordGaav/python-chaos/blob/52cd29a6fd15693ee1e53786b93bcb23fbf84ddd/chaos/arguments.py#L82-L119 | def get_default_config_file(argparser, suppress=None, default_override=None):
"""
Turn an ArgumentParser into a ConfigObj compatible configuration file.
This method will take the given argparser, and loop over all options contained. The configuration file is formatted
as follows:
# <option help info>
<option destination variable>=<option default value>
Arguments
---------
argparser: ArgumentParser
suppress: list of strings
All options specified will be suppressed from the config file. Useful to avoid adding stuff like version or help.
default_override: dict
This method will use the defaults from the given ArgumentParser, unless the option is specified here. If specified,
the default from this dict will be used instead. The format is { "option": <new default value>, ... } .
"""
if not suppress:
suppress = []
if not default_override:
default_override = {}
lines = []
seen_arguments = []
for arg in argparser._actions:
if arg.dest in suppress:
continue
if arg.dest in seen_arguments:
continue
default = arg.default
if arg.dest in default_override.keys():
default = default_override[arg.dest]
lines.append("# {0}\n{1}={2}\n".format(arg.help, arg.dest, default))
seen_arguments.append(arg.dest)
return "".join(lines) | [
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<option destination variable>=<option default value>
Arguments
---------
argparser: ArgumentParser
suppress: list of strings
All options specified will be suppressed from the config file. Useful to avoid adding stuff like version or help.
default_override: dict
This method will use the defaults from the given ArgumentParser, unless the option is specified here. If specified,
the default from this dict will be used instead. The format is { "option": <new default value>, ... } . | [
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] | python | train |
ivilata/pymultihash | multihash/funcs.py | https://github.com/ivilata/pymultihash/blob/093365f20f6d8627c1fae13e0f4e0b35e9b39ad2/multihash/funcs.py#L247-L259 | def hash_from_func(cls, func):
"""Return a hashlib-compatible object for the multihash `func`.
If the `func` is registered but no hashlib-compatible constructor is
available for it, `None` is returned. If the `func` is not
registered, a `KeyError` is raised.
>>> h = FuncReg.hash_from_func(Func.sha2_256)
>>> h.name
'sha256'
"""
new = cls._func_hash[func].new
return new() if new else None | [
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>>> h = FuncReg.hash_from_func(Func.sha2_256)
>>> h.name
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StorjOld/pyp2p | pyp2p/unl.py | https://github.com/StorjOld/pyp2p/blob/7024208c3af20511496a652ff212f54c420e0464/pyp2p/unl.py#L239-L373 | def connect_handler(self, their_unl, events, force_master, hairpin, nonce):
# Figure out who should make the connection.
our_unl = self.value.encode("ascii")
their_unl = their_unl.encode("ascii")
master = self.is_master(their_unl)
"""
Master defines who connects if either side can. It's used to
eliminate having multiple connections with the same host.
"""
if force_master:
master = 1
# Deconstruct binary UNLs into dicts.
our_unl = self.deconstruct(our_unl)
their_unl = self.deconstruct(their_unl)
if our_unl is None:
raise Exception("Unable to deconstruct our UNL.")
if their_unl is None:
raise Exception("Unable to deconstruct their UNL.")
# This means the nodes are behind the same router.
if our_unl["wan_ip"] == their_unl["wan_ip"]:
# Connect to LAN IP.
our_unl["wan_ip"] = our_unl["lan_ip"]
their_unl["wan_ip"] = their_unl["lan_ip"]
# Already behind NAT so no forwarding needed.
if hairpin:
our_unl["node_type"] = "passive"
their_unl["node_type"] = "passive"
# Generate con ID.
if nonce != "0" * 64:
# Convert nonce to bytes.
if sys.version_info >= (3, 0, 0):
if type(nonce) == str:
nonce.encode("ascii")
else:
if type(nonce) == unicode:
nonce = str(nonce)
# Check nonce length.
assert(len(nonce) == 64)
# Create con ID.
con_id = self.net.generate_con_id(
nonce,
our_unl["wan_ip"],
their_unl["wan_ip"]
)
else:
con_id = None
# Acquire mutex.
self.mutex.acquire()
# Wait for other UNLs to finish.
end_time = time.time()
end_time += len(self.pending_unls) * 60
self.debug_print("Waiting for other unls to finish")
while their_unl in self.pending_unls and time.time() < end_time:
# This is an undifferentiated duplicate.
if events is None:
self.mutex.release()
return
time.sleep(1)
self.debug_print("Other unl finished")
is_exception = 0
try:
# Wait for any other hole punches to finish.
if (their_unl["node_type"] == "simultaneous" and
our_unl["node_type"] != "passive"):
self.pending_sim_open.append(their_unl["value"])
end_time = time.time()
end_time += len(self.pending_unls) * 60
self.debug_print("wait for other hole punches to finish")
while len(self.pending_sim_open) and time.time() < end_time:
if self.pending_sim_open[0] == their_unl["value"]:
break
time.sleep(1)
self.debug_print("other hole punches finished")
# Set pending UNL.
self.pending_unls.append(their_unl)
# Release mutex.
self.mutex.release()
# Get connection.
con = self.get_connection(
our_unl,
their_unl,
master,
nonce,
force_master,
con_id
)
except Exception as e:
is_exception = 1
print(e)
print("EXCEPTION IN UNL.GET_CONNECTION")
log_exception("error.log", parse_exception(e))
finally:
# Release mutex.
if self.mutex.locked() and is_exception:
self.mutex.release()
# Undo pending connect state.
if their_unl in self.pending_unls:
self.pending_unls.remove(their_unl)
# Undo pending sim open.
if len(self.pending_sim_open):
if self.pending_sim_open[0] == their_unl["value"]:
self.pending_sim_open = self.pending_sim_open[1:]
# Only execute events if this function was called manually.
if events is not None:
# Success.
if con is not None:
if "success" in events:
events["success"](con)
# Failure.
if con is None:
if "failure" in events:
events["failure"](con) | [
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collectiveacuity/labPack | labpack/platforms/aws/ec2.py | https://github.com/collectiveacuity/labPack/blob/52949ece35e72e3cc308f54d9ffa6bfbd96805b8/labpack/platforms/aws/ec2.py#L1239-L1298 | def delete_image(self, image_id):
'''
method for removing an image from AWS EC2
:param image_id: string with AWS id of instance
:return: string with AWS response from snapshot delete
'''
title = '%s.delete_image' % self.__class__.__name__
# validate inputs
input_fields = {
'image_id': image_id
}
for key, value in input_fields.items():
object_title = '%s(%s=%s)' % (title, key, str(value))
self.fields.validate(value, '.%s' % key, object_title)
# report query
self.iam.printer('Removing image %s from AWS region %s.' % (image_id, self.iam.region_name))
# retrieve state
old_state = self.check_image_state(image_id)
# discover snapshot id and tags associated with instance id
image_details = self.read_image(image_id)
tag_list = image_details['tags']
snapshot_id = image_details['snapshot_id']
# remove tags from instance
try:
delete_kwargs = {
'Resources': [ image_id ],
'Tags': self.iam.prepare(tag_list)
}
self.connection.delete_tags(**delete_kwargs)
self.iam.printer('Tags have been deleted from %s.' % image_id)
except:
raise AWSConnectionError(title)
# deregister image
try:
self.connection.deregister_image(
ImageId=image_id
)
except:
raise AWSConnectionError(title)
self.iam.printer('Image %s has been deregistered.' % image_id)
# delete snapshot
try:
response = self.connection.delete_snapshot(
SnapshotId=snapshot_id
)
except:
raise AWSConnectionError(title)
self.iam.printer('Snapshot %s associated with image %s has been deleted.' % (snapshot_id, image_id))
return response | [
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pgmpy/pgmpy | pgmpy/models/ClusterGraph.py | https://github.com/pgmpy/pgmpy/blob/9381a66aba3c3871d3ccd00672b148d17d63239e/pgmpy/models/ClusterGraph.py#L211-L255 | def get_cardinality(self, node=None):
"""
Returns the cardinality of the node
Parameters
----------
node: any hashable python object (optional)
The node whose cardinality we want. If node is not specified returns a
dictionary with the given variable as keys and their respective cardinality
as values.
Returns
-------
int or dict : If node is specified returns the cardinality of the node.
If node is not specified returns a dictionary with the given
variable as keys and their respective cardinality as values.
Examples
--------
>>> from pgmpy.models import ClusterGraph
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> student = ClusterGraph()
>>> factor = DiscreteFactor(['Alice', 'Bob'], cardinality=[2, 2],
... values=np.random.rand(4))
>>> student.add_node(('Alice', 'Bob'))
>>> student.add_factors(factor)
>>> student.get_cardinality()
defaultdict(<class 'int'>, {'Bob': 2, 'Alice': 2})
>>> student.get_cardinality(node='Alice')
2
"""
if node:
for factor in self.factors:
for variable, cardinality in zip(factor.scope(), factor.cardinality):
if node == variable:
return cardinality
else:
cardinalities = defaultdict(int)
for factor in self.factors:
for variable, cardinality in zip(factor.scope(), factor.cardinality):
cardinalities[variable] = cardinality
return cardinalities | [
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Parameters
----------
node: any hashable python object (optional)
The node whose cardinality we want. If node is not specified returns a
dictionary with the given variable as keys and their respective cardinality
as values.
Returns
-------
int or dict : If node is specified returns the cardinality of the node.
If node is not specified returns a dictionary with the given
variable as keys and their respective cardinality as values.
Examples
--------
>>> from pgmpy.models import ClusterGraph
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> student = ClusterGraph()
>>> factor = DiscreteFactor(['Alice', 'Bob'], cardinality=[2, 2],
... values=np.random.rand(4))
>>> student.add_node(('Alice', 'Bob'))
>>> student.add_factors(factor)
>>> student.get_cardinality()
defaultdict(<class 'int'>, {'Bob': 2, 'Alice': 2})
>>> student.get_cardinality(node='Alice')
2 | [
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"node"
] | python | train |
Pelagicore/qface | qface/idl/domain.py | https://github.com/Pelagicore/qface/blob/7f60e91e3a91a7cb04cfacbc9ce80f43df444853/qface/idl/domain.py#L146-L149 | def add_tag(self, tag):
""" add a tag to the tag list """
if tag not in self._tags:
self._tags[tag] = dict() | [
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klen/graphite-beacon | graphite_beacon/core.py | https://github.com/klen/graphite-beacon/blob/c1f071e9f557693bc90f6acbc314994985dc3b77/graphite_beacon/core.py#L148-L162 | def start(self, start_loop=True):
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:param start_loop bool: whether to start the ioloop. should be False if
the IOLoop is managed externally
"""
self.start_alerts()
if self.options.get('pidfile'):
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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L3705-L3759 | def concatenate(arrays, axis=0, always_copy=True):
"""DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
The axis along which to concatenate.
always_copy : bool
Default `True`. When not `True`, if the arrays only contain one
`NDArray`, that element will be returned directly, avoid copying.
Returns
-------
NDArray
An `NDArray` that lives on the same context as `arrays[0].context`.
"""
assert isinstance(arrays, list)
assert len(arrays) > 0
assert isinstance(arrays[0], NDArray)
if not always_copy and len(arrays) == 1:
return arrays[0]
shape_axis = arrays[0].shape[axis]
shape_rest1 = arrays[0].shape[0:axis]
shape_rest2 = arrays[0].shape[axis+1:]
dtype = arrays[0].dtype
for arr in arrays[1:]:
shape_axis += arr.shape[axis]
assert shape_rest1 == arr.shape[0:axis]
assert shape_rest2 == arr.shape[axis+1:]
assert dtype == arr.dtype
ret_shape = shape_rest1 + (shape_axis,) + shape_rest2
ret = empty(ret_shape, ctx=arrays[0].context, dtype=dtype)
idx = 0
begin = [0 for _ in ret_shape]
end = list(ret_shape)
for arr in arrays:
if axis == 0:
ret[idx:idx+arr.shape[0]] = arr
else:
begin[axis] = idx
end[axis] = idx+arr.shape[axis]
# pylint: disable=no-member,protected-access
_internal._crop_assign(ret, arr, out=ret,
begin=tuple(begin),
end=tuple(end))
# pylint: enable=no-member,protected-access
idx += arr.shape[axis]
return ret | [
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axis : int
The axis along which to concatenate.
always_copy : bool
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klmitch/turnstile | turnstile/control.py | https://github.com/klmitch/turnstile/blob/8fe9a359b45e505d3192ab193ecf9be177ab1a17/turnstile/control.py#L82-L98 | def get_limits(self, limit_sum=None):
"""
Gets the current limit data if it is different from the data
indicated by limit_sum. The db argument is used for hydrating
the limit objects. Raises a NoChangeException if the
limit_sum represents no change, otherwise returns a tuple
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"""
with self.limit_lock:
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newville/wxmplot | examples/tifffile.py | https://github.com/newville/wxmplot/blob/8e0dc037453e5cdf18c968dc5a3d29efd761edee/examples/tifffile.py#L1249-L1255 | def _fromdata(self, code, dtype, count, value, name=None):
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self.code = int(code)
self.name = name if name else str(code)
self.dtype = TIFF_DATA_TYPES[dtype]
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dpgaspar/Flask-AppBuilder | flask_appbuilder/api/__init__.py | https://github.com/dpgaspar/Flask-AppBuilder/blob/c293734c1b86e176a3ba57ee2deab6676d125576/flask_appbuilder/api/__init__.py#L1496-L1523 | def _get_fields_info(self, cols, model_schema, filter_rel_fields, **kwargs):
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:param cols: list of columns to show info for
:param model_schema: Marshmallow model schema
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:return: dict with all fields details
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lpantano/seqcluster | seqcluster/make_predictions.py | https://github.com/lpantano/seqcluster/blob/774e23add8cd4fdc83d626cea3bd1f458e7d060d/seqcluster/make_predictions.py#L11-L29 | def predictions(args):
"""
Create predictions of clusters
"""
logger.info(args)
logger.info("reading sequeces")
out_file = os.path.abspath(os.path.splitext(args.json)[0] + "_prediction.json")
data = load_data(args.json)
out_dir = os.path.abspath(safe_dirs(os.path.join(args.out, "predictions")))
logger.info("make predictions")
data = is_tRNA(data, out_dir, args)
if args.coral:
logger.info("make CoRaL predictions")
run_coral(data, out_dir, args)
write_data(data[0], out_file)
logger.info("Done") | [
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androguard/androguard | androguard/core/bytecodes/apk.py | https://github.com/androguard/androguard/blob/984c0d981be2950cf0451e484f7b0d4d53bc4911/androguard/core/bytecodes/apk.py#L785-L794 | def get_dex_names(self):
"""
Return the names of all DEX files found in the APK.
This method only accounts for "offical" dex files, i.e. all files
in the root directory of the APK named classes.dex or classes[0-9]+.dex
:rtype: a list of str
"""
dexre = re.compile(r"classes(\d*).dex")
return filter(lambda x: dexre.match(x), self.get_files()) | [
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Ouranosinc/xclim | xclim/utils.py | https://github.com/Ouranosinc/xclim/blob/2080d139188bd8de2aeca097a025c2d89d6e0e09/xclim/utils.py#L116-L143 | def pint2cfunits(value):
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Parameters
----------
value : pint.Unit
Input unit.
Returns
-------
out : str
Units following CF-Convention.
"""
# Print units using abbreviations (millimeter -> mm)
s = "{:~}".format(value)
# Search and replace patterns
pat = r'(?P<inverse>/ )?(?P<unit>\w+)(?: \*\* (?P<pow>\d))?'
def repl(m):
i, u, p = m.groups()
p = p or (1 if i else '')
neg = '-' if i else ('^' if p else '')
return "{}{}{}".format(u, neg, p)
out, n = re.subn(pat, repl, s)
return out | [
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Parameters
----------
value : pint.Unit
Input unit.
Returns
-------
out : str
Units following CF-Convention. | [
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cuihantao/andes | andes/routines/tds.py | https://github.com/cuihantao/andes/blob/7067898d4f26ce7534e968b8486c4aa8fe3a511a/andes/routines/tds.py#L343-L371 | def restore_values(self):
"""
Restore x, y, and f values if not converged
Returns
-------
None
"""
if self.convergence is True:
return
dae = self.system.dae
system = self.system
inc_g = self.inc[dae.n:dae.m + dae.n]
max_g_err_sign = 1 if abs(max(inc_g)) > abs(min(inc_g)) else -1
if max_g_err_sign == 1:
max_g_err_idx = list(inc_g).index(max(inc_g))
else:
max_g_err_idx = list(inc_g).index(min(inc_g))
logger.debug(
'Maximum mismatch = {:.4g} at equation <{}>'.format(
max(abs(inc_g)), system.varname.unamey[max_g_err_idx]))
logger.debug(
'Reducing time step h={:.4g}s for t={:.4g}'.format(self.h, self.t))
# restore initial variable data
dae.x = matrix(self.x0)
dae.y = matrix(self.y0)
dae.f = matrix(self.f0) | [
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cloud9ers/gurumate | environment/lib/python2.7/site-packages/IPython/core/completer.py | https://github.com/cloud9ers/gurumate/blob/075dc74d1ee62a8c6b7a8bf2b271364f01629d1e/environment/lib/python2.7/site-packages/IPython/core/completer.py#L132-L169 | def expand_user(path):
"""Expand '~'-style usernames in strings.
This is similar to :func:`os.path.expanduser`, but it computes and returns
extra information that will be useful if the input was being used in
computing completions, and you wish to return the completions with the
original '~' instead of its expanded value.
Parameters
----------
path : str
String to be expanded. If no ~ is present, the output is the same as the
input.
Returns
-------
newpath : str
Result of ~ expansion in the input path.
tilde_expand : bool
Whether any expansion was performed or not.
tilde_val : str
The value that ~ was replaced with.
"""
# Default values
tilde_expand = False
tilde_val = ''
newpath = path
if path.startswith('~'):
tilde_expand = True
rest = len(path)-1
newpath = os.path.expanduser(path)
if rest:
tilde_val = newpath[:-rest]
else:
tilde_val = newpath
return newpath, tilde_expand, tilde_val | [
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ocaballeror/LyricFetch | lyricfetch/song.py | https://github.com/ocaballeror/LyricFetch/blob/86e62fb39c4c413ad7e1acf5bf0d28c9ed7c8fcb/lyricfetch/song.py#L65-L103 | def from_filename(cls, filename):
"""
Class constructor using the path to the corresponding mp3 file. The
metadata will be read from this file to create the song object, so it
must at least contain valid ID3 tags for artist and title.
"""
if not filename:
logger.error('No filename specified')
return None
if not os.path.exists(filename):
logger.error("Err: File '%s' does not exist", filename)
return None
if os.path.isdir(filename):
logger.error("Err: File '%s' is a directory", filename)
return None
try:
audiofile = eyed3.load(filename)
except Exception as error:
print(type(error), error)
return None
# Sometimes eyed3 may return a null object and not raise any exceptions
if audiofile is None:
return None
tags = audiofile.tag
album = tags.album
title = tags.title
lyrics = ''.join([l.text for l in tags.lyrics])
artist = tags.album_artist
if not artist:
artist = tags.artist
song = cls(artist, title, album, lyrics)
song.filename = filename
return song | [
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pywbem/pywbem | pywbem/_valuemapping.py | https://github.com/pywbem/pywbem/blob/e54ecb82c2211e289a268567443d60fdd489f1e4/pywbem/_valuemapping.py#L468-L476 | def _to_int(self, val_str):
"""Conver val_str to an integer or raise ValueError"""
val = _integerValue_to_int(val_str)
if val is None:
raise ValueError(
_format("The value-mapped {0} has an invalid integer "
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CivicSpleen/ambry | ambry/library/__init__.py | https://github.com/CivicSpleen/ambry/blob/d7f2be4bf1f7ffd086f3fadd4fcae60c32473e42/ambry/library/__init__.py#L825-L847 | def _find_remote_bundle(self, ref, remote_service_type='s3'):
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Locate a bundle, by any reference, among the configured remotes. The routine will
only look in the cache directory lists stored in the remotes, which must
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for r in self.remotes:
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if 'list' not in r.data:
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return None, None | [
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jcrist/skein | skein/core.py | https://github.com/jcrist/skein/blob/16f8b1d3b3d9f79f36e2f152e45893339a1793e8/skein/core.py#L801-L811 | def move_application(self, app_id, queue):
"""Move an application to a different queue.
Parameters
----------
app_id : str
The id of the application to move.
queue : str
The queue to move the application to.
"""
self._call('moveApplication', proto.MoveRequest(id=app_id, queue=queue)) | [
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ianepperson/pyredminews | redmine/redmine_rest.py | https://github.com/ianepperson/pyredminews/blob/b2b0581483632738a3acca3b4e093c181847b813/redmine/redmine_rest.py#L769-L783 | def find_all_item_classes(self):
'''Finds and stores a reference to all Redmine_Item subclasses for later use.'''
# This is a circular import, but performed after the class is defined and an object is instatiated.
# We do this in order to get references to any objects definitions in the redmine.py file
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import redmine as public_classes
item_class = {}
for key, value in public_classes.__dict__.items():
try:
if issubclass(value, Redmine_Item):
item_class[key.lower()] = value
except:
continue
self.item_class = item_class | [
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codenerix/django-codenerix | codenerix/djng/angular_base.py | https://github.com/codenerix/django-codenerix/blob/1f5527b352141caaee902b37b2648791a06bd57d/codenerix/djng/angular_base.py#L291-L303 | def convert_widgets(self):
"""
During form initialization, some widgets have to be replaced by a counterpart suitable to
be rendered the AngularJS way.
"""
for field in self.base_fields.values():
try:
new_widget = field.get_converted_widget()
except AttributeError:
pass
else:
if new_widget:
field.widget = new_widget | [
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materialsproject/pymatgen | pymatgen/io/vasp/outputs.py | https://github.com/materialsproject/pymatgen/blob/4ca558cf72f8d5f8a1f21dfdfc0181a971c186da/pymatgen/io/vasp/outputs.py#L1784-L1806 | def read_electrostatic_potential(self):
"""
Parses the eletrostatic potential for the last ionic step
"""
pattern = {"ngf": r"\s+dimension x,y,z NGXF=\s+([\.\-\d]+)\sNGYF=\s+([\.\-\d]+)\sNGZF=\s+([\.\-\d]+)"}
self.read_pattern(pattern, postprocess=int)
self.ngf = self.data.get("ngf", [[]])[0]
pattern = {"radii": r"the test charge radii are((?:\s+[\.\-\d]+)+)"}
self.read_pattern(pattern, reverse=True, terminate_on_match=True, postprocess=str)
self.sampling_radii = [float(f) for f in self.data["radii"][0][0].split()]
header_pattern = r"\(the norm of the test charge is\s+[\.\-\d]+\)"
table_pattern = r"((?:\s+\d+\s*[\.\-\d]+)+)"
footer_pattern = r"\s+E-fermi :"
pots = self.read_table_pattern(header_pattern, table_pattern, footer_pattern)
pots = "".join(itertools.chain.from_iterable(pots))
pots = re.findall(r"\s+\d+\s?([\.\-\d]+)+", pots)
pots = [float(f) for f in pots]
self.electrostatic_potential = pots | [
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datamachine/twx | twx/mtproto/rpc.py | https://github.com/datamachine/twx/blob/d9633f12f3647b1e54ba87b70b39df3b7e02b4eb/twx/mtproto/rpc.py#L265-L275 | def get_bytes(self):
"""client_DH_inner_data#6643b654 nonce:int128 server_nonce:int128 retry_id:long g_b:string = Client_DH_Inner_Data"""
ret = struct.pack("<I16s16sQ", client_DH_inner_data.constructor, self.nonce, self.server_nonce,
self.retry_id)
bytes_io = BytesIO()
bytes_io.write(ret)
serialize_string(bytes_io, self.g_b)
return bytes_io.getvalue() | [
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saltant-org/saltant-py | saltant/models/task_queue.py | https://github.com/saltant-org/saltant-py/blob/bf3bdbc4ec9c772c7f621f8bd6a76c5932af68be/saltant/models/task_queue.py#L336-L397 | def put(
self,
id,
name,
description,
private,
runs_executable_tasks,
runs_docker_container_tasks,
runs_singularity_container_tasks,
active,
whitelists,
):
"""Updates a task queue on the saltant server.
Args:
id (int): The ID of the task queue.
name (str): The name of the task queue.
description (str): The description of the task queue.
private (bool): A Booleon signalling whether the queue can
only be used by its associated user.
runs_executable_tasks (bool): A Boolean specifying whether
the queue runs executable tasks.
runs_docker_container_tasks (bool): A Boolean specifying
whether the queue runs container tasks that run in
Docker containers.
runs_singularity_container_tasks (bool): A Boolean
specifying whether the queue runs container tasks that
run in Singularity containers.
active (bool): A Booleon signalling whether the queue is
active.
whitelists (list): A list of task whitelist IDs.
Returns:
:class:`saltant.models.task_queue.TaskQueue`:
A task queue model instance representing the task queue
just updated.
"""
# Update the object
request_url = self._client.base_api_url + self.detail_url.format(id=id)
data_to_put = {
"name": name,
"description": description,
"private": private,
"runs_executable_tasks": runs_executable_tasks,
"runs_docker_container_tasks": runs_docker_container_tasks,
"runs_singularity_container_tasks": runs_singularity_container_tasks,
"active": active,
"whitelists": whitelists,
}
response = self._client.session.put(request_url, data=data_to_put)
# Validate that the request was successful
self.validate_request_success(
response_text=response.text,
request_url=request_url,
status_code=response.status_code,
expected_status_code=HTTP_200_OK,
)
# Return a model instance representing the task instance
return self.response_data_to_model_instance(response.json()) | [
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name (str): The name of the task queue.
description (str): The description of the task queue.
private (bool): A Booleon signalling whether the queue can
only be used by its associated user.
runs_executable_tasks (bool): A Boolean specifying whether
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theislab/scanpy | scanpy/tools/_phenograph.py | https://github.com/theislab/scanpy/blob/9e4e5ee02e04cf618872d9b098e24f0542e8b227/scanpy/tools/_phenograph.py#L7-L145 | def phenograph( adata,
k=30,
directed=False,
prune=False,
min_cluster_size=10,
jaccard=True,
primary_metric='euclidean',
n_jobs=-1,
q_tol=1e-3,
louvain_time_limit=2000,
nn_method='kdtree'):
"""
PhenoGraph clustering [Levine15]_.
:param adata: Numpy ndarray of data to cluster,
or sparse matrix of k-nearest neighbor graph
If ndarray, n-by-d array of n cells in d dimensions
If sparse matrix, n-by-n adjacency matrix
:param k: Number of nearest neighbors to use in first
step of graph construction
:param directed: Whether to use a symmetric (default) or
asymmetric ("directed") graph
The graph construction process produces a directed graph,
which is symmetrized by one of two methods (see below)
:param prune: Whether to symmetrize by taking the average (prune=False) or
product (prune=True) between the graph and its transpose
:param min_cluster_size:
Cells that end up in a cluster smaller than min_cluster_size
are considered outliers and are assigned to -1 in the cluster labels
:param jaccard: If True, use Jaccard metric between k-neighborhoods to build graph
If False, use a Gaussian kernel
:param primary_metric:
Distance metric to define nearest neighbors
Options include: {'euclidean','manhattan','correlation','cosine'}.
Note that performance will be slower for correlation and cosine
:param n_jobs: Nearest Neighbors and Jaccard coefficients will be computed
in parallel using n_jobs. If n_jobs=-1, the number of jobs is
determined automatically
:param q_tol: Tolerance (i.e., precision) for monitoring modularity optimization
:param louvain_time_limit:
Maximum number of seconds to run modularity optimization.
If exceeded the best result so far is returned
:param nn_method:
Whether to use brute force or kdtree for nearest neighbor
search. For very large high-dimensional data sets, brute force
(with parallel computation) performs faster than kdtree
:return communities:
numpy integer array of community assignments for each
row in data
:return graph: numpy sparse array of the graph that was used for clustering
:return Q: the modularity score for communities on graph
Example
-------
>>> import scanpy.external as sce
>>> import scanpy.api as sc
>>> import numpy as np
>>> import pandas as pd
Assume adata is your annotated data which has the normalized data.
Then do PCA:
>>> sc.tl.pca(adata, n_comps = 100)
Compute phenograph clusters:
>>> result = sce.tl.phenograph(adata.obsm['X_pca'], k = 30)
Embed the phenograph result into adata as a *categorical* variable (this helps in plotting):
>>> adata.obs['pheno'] = pd.Categorical(result[0])
Check by typing "adata" and you should see under obs a key called 'pheno'.
Now to show phenograph on tSNE (for example):
Compute tSNE:
>>> sc.tl.tsne(adata, random_state = 7)
Plot phenograph clusters on tSNE:
>>> sc.pl.tsne(adata, color = ['pheno'], s = 100, palette = sc.pl.palettes.vega_20_scanpy, legend_fontsize = 10)
Cluster and cluster centroids for input Numpy ndarray
>>> df = np.random.rand(1000,40)
>>> df.shape
(1000, 40)
>>> result = sce.tl.phenograph(df, k=50)
Finding 50 nearest neighbors using minkowski metric and 'auto' algorithm
Neighbors computed in 0.16141605377197266 seconds
Jaccard graph constructed in 0.7866239547729492 seconds
Wrote graph to binary file in 0.42542195320129395 seconds
Running Louvain modularity optimization
After 1 runs, maximum modularity is Q = 0.223536
After 2 runs, maximum modularity is Q = 0.235874
Louvain completed 22 runs in 1.5609488487243652 seconds
PhenoGraph complete in 2.9466471672058105 seconds
New results can be pushed into adata object:
>>> dframe = pd.DataFrame(data=df, columns=range(df.shape[1]),index=range(df.shape[0]) )
>>> adata = sc.AnnData( X=dframe, obs=dframe, var=dframe)
>>> adata.obs['pheno'] = pd.Categorical(result[0])
"""
logg.info('PhenoGraph clustering', r=True)
try:
import phenograph
except ImportError:
raise ImportError(
'please install phenograph: '
'pip3 install git+https://github.com/jacoblevine/phenograph.git')
communities, graph, Q = phenograph.cluster(
data=adata,
k=k,
directed=directed,
prune=prune,
min_cluster_size=min_cluster_size,
jaccard=jaccard,
primary_metric=primary_metric,
n_jobs=n_jobs,
q_tol=q_tol,
louvain_time_limit=louvain_time_limit,
nn_method=nn_method
)
logg.info(' finished', time=True)
return communities, graph, Q | [
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If sparse matrix, n-by-n adjacency matrix
:param k: Number of nearest neighbors to use in first
step of graph construction
:param directed: Whether to use a symmetric (default) or
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The graph construction process produces a directed graph,
which is symmetrized by one of two methods (see below)
:param prune: Whether to symmetrize by taking the average (prune=False) or
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:param min_cluster_size:
Cells that end up in a cluster smaller than min_cluster_size
are considered outliers and are assigned to -1 in the cluster labels
:param jaccard: If True, use Jaccard metric between k-neighborhoods to build graph
If False, use a Gaussian kernel
:param primary_metric:
Distance metric to define nearest neighbors
Options include: {'euclidean','manhattan','correlation','cosine'}.
Note that performance will be slower for correlation and cosine
:param n_jobs: Nearest Neighbors and Jaccard coefficients will be computed
in parallel using n_jobs. If n_jobs=-1, the number of jobs is
determined automatically
:param q_tol: Tolerance (i.e., precision) for monitoring modularity optimization
:param louvain_time_limit:
Maximum number of seconds to run modularity optimization.
If exceeded the best result so far is returned
:param nn_method:
Whether to use brute force or kdtree for nearest neighbor
search. For very large high-dimensional data sets, brute force
(with parallel computation) performs faster than kdtree
:return communities:
numpy integer array of community assignments for each
row in data
:return graph: numpy sparse array of the graph that was used for clustering
:return Q: the modularity score for communities on graph
Example
-------
>>> import scanpy.external as sce
>>> import scanpy.api as sc
>>> import numpy as np
>>> import pandas as pd
Assume adata is your annotated data which has the normalized data.
Then do PCA:
>>> sc.tl.pca(adata, n_comps = 100)
Compute phenograph clusters:
>>> result = sce.tl.phenograph(adata.obsm['X_pca'], k = 30)
Embed the phenograph result into adata as a *categorical* variable (this helps in plotting):
>>> adata.obs['pheno'] = pd.Categorical(result[0])
Check by typing "adata" and you should see under obs a key called 'pheno'.
Now to show phenograph on tSNE (for example):
Compute tSNE:
>>> sc.tl.tsne(adata, random_state = 7)
Plot phenograph clusters on tSNE:
>>> sc.pl.tsne(adata, color = ['pheno'], s = 100, palette = sc.pl.palettes.vega_20_scanpy, legend_fontsize = 10)
Cluster and cluster centroids for input Numpy ndarray
>>> df = np.random.rand(1000,40)
>>> df.shape
(1000, 40)
>>> result = sce.tl.phenograph(df, k=50)
Finding 50 nearest neighbors using minkowski metric and 'auto' algorithm
Neighbors computed in 0.16141605377197266 seconds
Jaccard graph constructed in 0.7866239547729492 seconds
Wrote graph to binary file in 0.42542195320129395 seconds
Running Louvain modularity optimization
After 1 runs, maximum modularity is Q = 0.223536
After 2 runs, maximum modularity is Q = 0.235874
Louvain completed 22 runs in 1.5609488487243652 seconds
PhenoGraph complete in 2.9466471672058105 seconds
New results can be pushed into adata object:
>>> dframe = pd.DataFrame(data=df, columns=range(df.shape[1]),index=range(df.shape[0]) )
>>> adata = sc.AnnData( X=dframe, obs=dframe, var=dframe)
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LuminosoInsight/python-ftfy | ftfy/bad_codecs/__init__.py | https://github.com/LuminosoInsight/python-ftfy/blob/476acc6ad270bffe07f97d4f7cf2139acdc69633/ftfy/bad_codecs/__init__.py#L47-L76 | def search_function(encoding):
"""
Register our "bad codecs" with Python's codecs API. This involves adding
a search function that takes in an encoding name, and returns a codec
for that encoding if it knows one, or None if it doesn't.
The encodings this will match are:
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- The 'utf-8-variants' encoding, which has the several aliases seen
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"""
if encoding in _CACHE:
return _CACHE[encoding]
norm_encoding = normalize_encoding(encoding)
codec = None
if norm_encoding in UTF8_VAR_NAMES:
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codec = CODEC_INFO
elif norm_encoding.startswith('sloppy_'):
from ftfy.bad_codecs.sloppy import CODECS
codec = CODECS.get(norm_encoding)
if codec is not None:
_CACHE[encoding] = codec
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poppy-project/pypot | pypot/vrep/remoteApiBindings/vrep.py | https://github.com/poppy-project/pypot/blob/d9c6551bbc87d45d9d1f0bc15e35b616d0002afd/pypot/vrep/remoteApiBindings/vrep.py#L487-L511 | def simxAuxiliaryConsoleOpen(clientID, title, maxLines, mode, position, size, textColor, backgroundColor, operationMode):
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Please have a look at the function description/documentation in the V-REP user manual
'''
consoleHandle = ct.c_int()
if (sys.version_info[0] == 3) and (type(title) is str):
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if position != None:
c_position = (ct.c_int*2)(*position)
else:
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else:
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if textColor != None:
c_textColor = (ct.c_float*3)(*textColor)
else:
c_textColor = None
if backgroundColor != None:
c_backgroundColor = (ct.c_float*3)(*backgroundColor)
else:
c_backgroundColor = None
return c_AuxiliaryConsoleOpen(clientID, title, maxLines, mode, c_position, c_size, c_textColor, c_backgroundColor, ct.byref(consoleHandle), operationMode), consoleHandle.value | [
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openatx/facebook-wda | wda/__init__.py | https://github.com/openatx/facebook-wda/blob/aa644204620c6d5c7705a9c7452d8c0cc39330d5/wda/__init__.py#L882-L902 | def wait_gone(self, timeout=None, raise_error=True):
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Args:
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raise_error (bool): return bool or raise error
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bool: works when raise_error is False
Raises:
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klen/adrest | adrest/utils/emitter.py | https://github.com/klen/adrest/blob/8b75c67123cffabe5ed98c222bb7ab43c904d89c/adrest/utils/emitter.py#L135-L143 | def serialize(self, content):
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shmir/PyTrafficGenerator | trafficgenerator/tgn_object.py | https://github.com/shmir/PyTrafficGenerator/blob/382e5d549c83404af2a6571fe19c9e71df8bac14/trafficgenerator/tgn_object.py#L177-L187 | def get_objects_or_children_by_type(self, *types):
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objects = self.get_objects_by_type(*types)
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rigetti/pyquil | pyquil/quilatom.py | https://github.com/rigetti/pyquil/blob/ec98e453084b0037d69d8c3245f6822a5422593d/pyquil/quilatom.py#L124-L146 | def unpack_classical_reg(c):
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Get the address for a classical register.
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awslabs/sockeye | sockeye/decoder.py | https://github.com/awslabs/sockeye/blob/5d64a1ee1ef3cbba17c6d1d94bc061020c43f6ab/sockeye/decoder.py#L116-L133 | def decode_step(self,
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tensorflow/tensor2tensor | tensor2tensor/rl/player_utils.py | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/rl/player_utils.py#L267-L298 | def create_simulated_env(
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random_starts=True, which_epoch_data="last", **other_hparams
):
""""Create SimulatedEnv with minimal subset of hparams."""
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# no effect on player.
a_bit_risky_defaults = {
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"max_num_noops": 0
}
for key in a_bit_risky_defaults:
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other_hparams[key] = a_bit_risky_defaults[key]
hparams = hparam.HParams(
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resize_height_factor=resize_height_factor,
frame_stack_size=frame_stack_size,
generative_model=generative_model,
generative_model_params=generative_model_params,
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)
return load_data_and_make_simulated_env(
output_dir, wm_dir=None, hparams=hparams,
which_epoch_data=which_epoch_data,
random_starts=random_starts) | [
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bkjones/pyrabbit | pyrabbit/api.py | https://github.com/bkjones/pyrabbit/blob/e8a9f74ed5c6bba958994fb9a72c396e6a99ea0f/pyrabbit/api.py#L258-L264 | def get_permissions(self):
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