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scot-dev/scot | scot/utils.py | cuthill_mckee | def cuthill_mckee(matrix):
"""Implementation of the Cuthill-McKee algorithm.
Permute a symmetric binary matrix into a band matrix form with a small bandwidth.
Parameters
----------
matrix : ndarray, dtype=bool, shape = [n, n]
The matrix is internally converted to a symmetric matrix by sett... | python | def cuthill_mckee(matrix):
"""Implementation of the Cuthill-McKee algorithm.
Permute a symmetric binary matrix into a band matrix form with a small bandwidth.
Parameters
----------
matrix : ndarray, dtype=bool, shape = [n, n]
The matrix is internally converted to a symmetric matrix by sett... | [
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Permute a symmetric binary matrix into a band matrix form with a small bandwidth.
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matrix : ndarray, dtype=bool, shape = [n, n]
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scot-dev/scot | scot/connectivity.py | connectivity | def connectivity(measure_names, b, c=None, nfft=512):
"""Calculate connectivity measures.
Parameters
----------
measure_names : str or list of str
Name(s) of the connectivity measure(s) to calculate. See
:class:`Connectivity` for supported measures.
b : array, shape (n_channels, n_c... | python | def connectivity(measure_names, b, c=None, nfft=512):
"""Calculate connectivity measures.
Parameters
----------
measure_names : str or list of str
Name(s) of the connectivity measure(s) to calculate. See
:class:`Connectivity` for supported measures.
b : array, shape (n_channels, n_c... | [
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Parameters
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measure_names : str or list of str
Name(s) of the connectivity measure(s) to calculate. See
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b : array, shape (n_channels, n_channels * model_order)
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scot-dev/scot | scot/connectivity.py | Connectivity.Cinv | def Cinv(self):
"""Inverse of the noise covariance."""
try:
return np.linalg.inv(self.c)
except np.linalg.linalg.LinAlgError:
print('Warning: non-invertible noise covariance matrix c.')
return np.eye(self.c.shape[0]) | python | def Cinv(self):
"""Inverse of the noise covariance."""
try:
return np.linalg.inv(self.c)
except np.linalg.linalg.LinAlgError:
print('Warning: non-invertible noise covariance matrix c.')
return np.eye(self.c.shape[0]) | [
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scot-dev/scot | scot/connectivity.py | Connectivity.A | def A(self):
"""Spectral VAR coefficients.
.. math:: \mathbf{A}(f) = \mathbf{I} - \sum_{k=1}^{p} \mathbf{a}^{(k)}
\mathrm{e}^{-2\pi f}
"""
return fft(np.dstack([np.eye(self.m), -self.b]),
self.nfft * 2 - 1)[:, :, :self.nfft] | python | def A(self):
"""Spectral VAR coefficients.
.. math:: \mathbf{A}(f) = \mathbf{I} - \sum_{k=1}^{p} \mathbf{a}^{(k)}
\mathrm{e}^{-2\pi f}
"""
return fft(np.dstack([np.eye(self.m), -self.b]),
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scot-dev/scot | scot/connectivity.py | Connectivity.S | def S(self):
"""Cross-spectral density.
.. math:: \mathbf{S}(f) = \mathbf{H}(f) \mathbf{C} \mathbf{H}'(f)
"""
if self.c is None:
raise RuntimeError('Cross-spectral density requires noise '
'covariance matrix c.')
H = self.H()
# ... | python | def S(self):
"""Cross-spectral density.
.. math:: \mathbf{S}(f) = \mathbf{H}(f) \mathbf{C} \mathbf{H}'(f)
"""
if self.c is None:
raise RuntimeError('Cross-spectral density requires noise '
'covariance matrix c.')
H = self.H()
# ... | [
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.. math:: \mathbf{S}(f) = \mathbf{H}(f) \mathbf{C} \mathbf{H}'(f) | [
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scot-dev/scot | scot/connectivity.py | Connectivity.G | def G(self):
"""Inverse cross-spectral density.
.. math:: \mathbf{G}(f) = \mathbf{A}(f) \mathbf{C}^{-1} \mathbf{A}'(f)
"""
if self.c is None:
raise RuntimeError('Inverse cross spectral density requires '
'invertible noise covariance matrix c.')... | python | def G(self):
"""Inverse cross-spectral density.
.. math:: \mathbf{G}(f) = \mathbf{A}(f) \mathbf{C}^{-1} \mathbf{A}'(f)
"""
if self.c is None:
raise RuntimeError('Inverse cross spectral density requires '
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scot-dev/scot | scot/connectivity.py | Connectivity.pCOH | def pCOH(self):
"""Partial coherence.
.. math:: \mathrm{pCOH}_{ij}(f) = \\frac{G_{ij}(f)}
{\sqrt{G_{ii}(f) G_{jj}(f)}}
References
----------
P. J. Franaszczuk, K. J. Blinowska, M. Kowalczyk. The application of
parametric m... | python | def pCOH(self):
"""Partial coherence.
.. math:: \mathrm{pCOH}_{ij}(f) = \\frac{G_{ij}(f)}
{\sqrt{G_{ii}(f) G_{jj}(f)}}
References
----------
P. J. Franaszczuk, K. J. Blinowska, M. Kowalczyk. The application of
parametric m... | [
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.. math:: \mathrm{pCOH}_{ij}(f) = \\frac{G_{ij}(f)}
{\sqrt{G_{ii}(f) G_{jj}(f)}}
References
----------
P. J. Franaszczuk, K. J. Blinowska, M. Kowalczyk. The application of
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scot-dev/scot | scot/connectivity.py | Connectivity.PDC | def PDC(self):
"""Partial directed coherence.
.. math:: \mathrm{PDC}_{ij}(f) = \\frac{A_{ij}(f)}
{\sqrt{A_{:j}'(f) A_{:j}(f)}}
References
----------
L. A. Baccalá, K. Sameshima. Partial directed coherence: a new concept
in ... | python | def PDC(self):
"""Partial directed coherence.
.. math:: \mathrm{PDC}_{ij}(f) = \\frac{A_{ij}(f)}
{\sqrt{A_{:j}'(f) A_{:j}(f)}}
References
----------
L. A. Baccalá, K. Sameshima. Partial directed coherence: a new concept
in ... | [
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.. math:: \mathrm{PDC}_{ij}(f) = \\frac{A_{ij}(f)}
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L. A. Baccalá, K. Sameshima. Partial directed coherence: a new concept
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scot-dev/scot | scot/connectivity.py | Connectivity.ffPDC | def ffPDC(self):
"""Full frequency partial directed coherence.
.. math:: \mathrm{ffPDC}_{ij}(f) =
\\frac{A_{ij}(f)}{\sqrt{\sum_f A_{:j}'(f) A_{:j}(f)}}
"""
A = self.A()
return np.abs(A * self.nfft / np.sqrt(np.sum(A.conj() * A, axis=(0, 2),
... | python | def ffPDC(self):
"""Full frequency partial directed coherence.
.. math:: \mathrm{ffPDC}_{ij}(f) =
\\frac{A_{ij}(f)}{\sqrt{\sum_f A_{:j}'(f) A_{:j}(f)}}
"""
A = self.A()
return np.abs(A * self.nfft / np.sqrt(np.sum(A.conj() * A, axis=(0, 2),
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.. math:: \mathrm{ffPDC}_{ij}(f) =
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scot-dev/scot | scot/connectivity.py | Connectivity.PDCF | def PDCF(self):
"""Partial directed coherence factor.
.. math:: \mathrm{PDCF}_{ij}(f) =
\\frac{A_{ij}(f)}{\sqrt{A_{:j}'(f) \mathbf{C}^{-1} A_{:j}(f)}}
References
----------
L. A. Baccalá, K. Sameshima. Partial directed coherence: a new concept
in neural structur... | python | def PDCF(self):
"""Partial directed coherence factor.
.. math:: \mathrm{PDCF}_{ij}(f) =
\\frac{A_{ij}(f)}{\sqrt{A_{:j}'(f) \mathbf{C}^{-1} A_{:j}(f)}}
References
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.. math:: \mathrm{PDCF}_{ij}(f) =
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L. A. Baccalá, K. Sameshima. Partial directed coherence: a new concept
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scot-dev/scot | scot/connectivity.py | Connectivity.GPDC | def GPDC(self):
"""Generalized partial directed coherence.
.. math:: \mathrm{GPDC}_{ij}(f) = \\frac{|A_{ij}(f)|}
{\sigma_i \sqrt{A_{:j}'(f) \mathrm{diag}(\mathbf{C})^{-1} A_{:j}(f)}}
References
----------
L. Faes, S. Erla, G. Nollo. Measuring connectivity in linear
... | python | def GPDC(self):
"""Generalized partial directed coherence.
.. math:: \mathrm{GPDC}_{ij}(f) = \\frac{|A_{ij}(f)|}
{\sigma_i \sqrt{A_{:j}'(f) \mathrm{diag}(\mathbf{C})^{-1} A_{:j}(f)}}
References
----------
L. Faes, S. Erla, G. Nollo. Measuring connectivity in linear
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.. math:: \mathrm{GPDC}_{ij}(f) = \\frac{|A_{ij}(f)|}
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scot-dev/scot | scot/connectivity.py | Connectivity.DTF | def DTF(self):
"""Directed transfer function.
.. math:: \mathrm{DTF}_{ij}(f) = \\frac{H_{ij}(f)}
{\sqrt{H_{i:}(f) H_{i:}'(f)}}
References
----------
M. J. Kaminski, K. J. Blinowska. A new method of the description of the
in... | python | def DTF(self):
"""Directed transfer function.
.. math:: \mathrm{DTF}_{ij}(f) = \\frac{H_{ij}(f)}
{\sqrt{H_{i:}(f) H_{i:}'(f)}}
References
----------
M. J. Kaminski, K. J. Blinowska. A new method of the description of the
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.. math:: \mathrm{DTF}_{ij}(f) = \\frac{H_{ij}(f)}
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scot-dev/scot | scot/connectivity.py | Connectivity.ffDTF | def ffDTF(self):
"""Full frequency directed transfer function.
.. math:: \mathrm{ffDTF}_{ij}(f) =
\\frac{H_{ij}(f)}{\sqrt{\sum_f H_{i:}(f) H_{i:}'(f)}}
References
----------
A. Korzeniewska, M. Mańczak, M. Kaminski, K. J. Blinowska, S. Kasicki.
Determi... | python | def ffDTF(self):
"""Full frequency directed transfer function.
.. math:: \mathrm{ffDTF}_{ij}(f) =
\\frac{H_{ij}(f)}{\sqrt{\sum_f H_{i:}(f) H_{i:}'(f)}}
References
----------
A. Korzeniewska, M. Mańczak, M. Kaminski, K. J. Blinowska, S. Kasicki.
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.. math:: \mathrm{ffDTF}_{ij}(f) =
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scot-dev/scot | scot/connectivity.py | Connectivity.GDTF | def GDTF(self):
"""Generalized directed transfer function.
.. math:: \mathrm{GPDC}_{ij}(f) = \\frac{\sigma_j |H_{ij}(f)|}
{\sqrt{H_{i:}(f) \mathrm{diag}(\mathbf{C}) H_{i:}'(f)}}
References
----------
L. Faes, S. Erla, G. Nollo. Measuring connectivity in linear
... | python | def GDTF(self):
"""Generalized directed transfer function.
.. math:: \mathrm{GPDC}_{ij}(f) = \\frac{\sigma_j |H_{ij}(f)|}
{\sqrt{H_{i:}(f) \mathrm{diag}(\mathbf{C}) H_{i:}'(f)}}
References
----------
L. Faes, S. Erla, G. Nollo. Measuring connectivity in linear
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.. math:: \mathrm{GPDC}_{ij}(f) = \\frac{\sigma_j |H_{ij}(f)|}
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kolypto/py-good | good/schema/errors.py | Invalid.enrich | def enrich(self, expected=None, provided=None, path=None, validator=None):
""" Enrich this error with additional information.
This works with both Invalid and MultipleInvalid (thanks to `Invalid` being iterable):
in the latter case, the defaults are applied to all collected errors.
The... | python | def enrich(self, expected=None, provided=None, path=None, validator=None):
""" Enrich this error with additional information.
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kolypto/py-good | good/schema/errors.py | MultipleInvalid.flatten | def flatten(cls, errors):
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:type errors: list[Invalid|MultipleInvalid]
:rtype: list[Invalid]
"""
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scot-dev/scot | scot/eegtopo/warp_layout.py | warp_locations | def warp_locations(locations, y_center=None, return_ellipsoid=False, verbose=False):
""" Warp EEG electrode locations to spherical layout.
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1. An ellipsoid is least-squares-fitted to the electrode locations.
2. Electrodes are displa... | python | def warp_locations(locations, y_center=None, return_ellipsoid=False, verbose=False):
""" Warp EEG electrode locations to spherical layout.
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scot-dev/scot | scot/eegtopo/warp_layout.py | _project_on_ellipsoid | def _project_on_ellipsoid(c, r, locations):
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p0 = locations - c # original locations
l2 = 1 / np.sum(p0**2 / r**2, axis=1, keepdims=True)
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... | python | def _project_on_ellipsoid(c, r, locations):
"""displace locations to the nearest point on ellipsoid surface"""
p0 = locations - c # original locations
l2 = 1 / np.sum(p0**2 / r**2, axis=1, keepdims=True)
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scot-dev/scot | scot/datatools.py | cut_segments | def cut_segments(x2d, tr, start, stop):
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x2d : array, shape (m, n)
Input data with m signals and n samples.
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Trigger positions.
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x2d : array, shape (m, n)
Input data with m signals and n samples.
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Window start (offset relative to trigger).
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scot-dev/scot | scot/datatools.py | cat_trials | def cat_trials(x3d):
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x3d : array, shape (t, m, n)
Segmented input data with t trials, m signals, and n samples.
Returns
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x2d : array, shape (m, t * n)
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"""Concatenate trials along time axis.
Parameters
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x3d : array, shape (t, m, n)
Segmented input data with t trials, m signals, and n samples.
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scot-dev/scot | scot/datatools.py | dot_special | def dot_special(x2d, x3d):
"""Segment-wise dot product.
This function calculates the dot product of x2d with each trial of x3d.
Parameters
----------
x2d : array, shape (p, m)
Input argument.
x3d : array, shape (t, m, n)
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"""Segment-wise dot product.
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x2d : array, shape (p, m)
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scot-dev/scot | scot/datatools.py | randomize_phase | def randomize_phase(data, random_state=None):
"""Phase randomization.
This function randomizes the spectral phase of the input data along the
last dimension.
Parameters
----------
data : array
Input array.
Returns
-------
out : array
Array of same shape as data.
... | python | def randomize_phase(data, random_state=None):
"""Phase randomization.
This function randomizes the spectral phase of the input data along the
last dimension.
Parameters
----------
data : array
Input array.
Returns
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out : array
Array of same shape as data.
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scot-dev/scot | scot/datatools.py | acm | def acm(x, l):
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Parameters
----------
x : array, shape (n_trials, n_channels, n_samples)
Signal data (2D or 3D for multiple trials)
l : int
Lag
Returns
-----... | python | def acm(x, l):
"""Compute autocovariance matrix at lag l.
This function calculates the autocovariance matrix of `x` at lag `l`.
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----------
x : array, shape (n_trials, n_channels, n_samples)
Signal data (2D or 3D for multiple trials)
l : int
Lag
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scot-dev/scot | scot/connectivity_statistics.py | jackknife_connectivity | def jackknife_connectivity(measures, data, var, nfft=512, leaveout=1, n_jobs=1,
verbose=0):
"""Calculate jackknife estimates of connectivity.
For each jackknife estimate a block of trials is left out. This is repeated
until each trial was left out exactly once. The number of esti... | python | def jackknife_connectivity(measures, data, var, nfft=512, leaveout=1, n_jobs=1,
verbose=0):
"""Calculate jackknife estimates of connectivity.
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scot-dev/scot | scot/connectivity_statistics.py | bootstrap_connectivity | def bootstrap_connectivity(measures, data, var, nfft=512, repeats=100,
num_samples=None, n_jobs=1, verbose=0,
random_state=None):
"""Calculate bootstrap estimates of connectivity.
To obtain a bootstrap estimate trials are sampled randomly with replacement
... | python | def bootstrap_connectivity(measures, data, var, nfft=512, repeats=100,
num_samples=None, n_jobs=1, verbose=0,
random_state=None):
"""Calculate bootstrap estimates of connectivity.
To obtain a bootstrap estimate trials are sampled randomly with replacement
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scot-dev/scot | scot/connectivity_statistics.py | significance_fdr | def significance_fdr(p, alpha):
"""Calculate significance by controlling for the false discovery rate.
This function determines which of the p-values in `p` can be considered
significant. Correction for multiple comparisons is performed by
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kolypto/py-good | good/schema/util.py | register_type_name | def register_type_name(t, name):
""" Register a human-friendly name for the given type. This will be used in Invalid errors
:param t: The type to register
:type t: type
:param name: Name for the type
:type name: unicode
"""
assert isinstance(t, type)
assert isinstance(name, unicode)
... | python | def register_type_name(t, name):
""" Register a human-friendly name for the given type. This will be used in Invalid errors
:param t: The type to register
:type t: type
:param name: Name for the type
:type name: unicode
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kolypto/py-good | good/schema/util.py | get_type_name | def get_type_name(t):
""" Get a human-friendly name for the given type.
:type t: type|None
:rtype: unicode
"""
# Lookup in the mapping
try:
return __type_names[t]
except KeyError:
# Specific types
if issubclass(t, six.integer_types):
return _(u'Integer nu... | python | def get_type_name(t):
""" Get a human-friendly name for the given type.
:type t: type|None
:rtype: unicode
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try:
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kolypto/py-good | good/schema/util.py | get_callable_name | def get_callable_name(c):
""" Get a human-friendly name for the given callable.
:param c: The callable to get the name for
:type c: callable
:rtype: unicode
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if hasattr(c, 'name'):
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return six.text_type(c.__name__) ... | python | def get_callable_name(c):
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:param c: The callable to get the name for
:type c: callable
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kolypto/py-good | good/schema/util.py | get_primitive_name | def get_primitive_name(schema):
""" Get a human-friendly name for the given primitive.
:param schema: Schema
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""" Get a human-friendly name for the given primitive.
:param schema: Schema
:type schema: *
:rtype: unicode
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kolypto/py-good | good/schema/util.py | primitive_type | def primitive_type(schema):
""" Get schema type for the primitive argument.
Note: it does treats markers & schemas as callables!
:param schema: Value of a primitive type
:type schema: *
:return: const.COMPILED_TYPE.*
:rtype: str|None
"""
schema_type = type(schema)
# Literal
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""" Get schema type for the primitive argument.
Note: it does treats markers & schemas as callables!
:param schema: Value of a primitive type
:type schema: *
:return: const.COMPILED_TYPE.*
:rtype: str|None
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kolypto/py-good | good/schema/util.py | commajoin_as_strings | def commajoin_as_strings(iterable):
""" Join the given iterable with ',' """
return _(u',').join((six.text_type(i) for i in iterable)) | python | def commajoin_as_strings(iterable):
""" Join the given iterable with ',' """
return _(u',').join((six.text_type(i) for i in iterable)) | [
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scot-dev/scot | scot/plotting.py | prepare_topoplots | def prepare_topoplots(topo, values):
"""Prepare multiple topo maps for cached plotting.
.. note:: Parameter `topo` is modified by the function by calling :func:`~eegtopo.topoplot.Topoplot.set_values`.
Parameters
----------
topo : :class:`~eegtopo.topoplot.Topoplot`
Scalp maps are created w... | python | def prepare_topoplots(topo, values):
"""Prepare multiple topo maps for cached plotting.
.. note:: Parameter `topo` is modified by the function by calling :func:`~eegtopo.topoplot.Topoplot.set_values`.
Parameters
----------
topo : :class:`~eegtopo.topoplot.Topoplot`
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scot-dev/scot | scot/plotting.py | plot_topo | def plot_topo(axis, topo, topomap, crange=None, offset=(0,0),
plot_locations=True, plot_head=True):
"""Draw a topoplot in given axis.
.. note:: Parameter `topo` is modified by the function by calling :func:`~eegtopo.topoplot.Topoplot.set_map`.
Parameters
----------
axis : axis
... | python | def plot_topo(axis, topo, topomap, crange=None, offset=(0,0),
plot_locations=True, plot_head=True):
"""Draw a topoplot in given axis.
.. note:: Parameter `topo` is modified by the function by calling :func:`~eegtopo.topoplot.Topoplot.set_map`.
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axis : axis
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scot-dev/scot | scot/plotting.py | plot_sources | def plot_sources(topo, mixmaps, unmixmaps, global_scale=None, fig=None):
"""Plot all scalp projections of mixing- and unmixing-maps.
.. note:: Parameter `topo` is modified by the function by calling :func:`~eegtopo.topoplot.Topoplot.set_map`.
Parameters
----------
topo : :class:`~eegtopo.topoplot.... | python | def plot_sources(topo, mixmaps, unmixmaps, global_scale=None, fig=None):
"""Plot all scalp projections of mixing- and unmixing-maps.
.. note:: Parameter `topo` is modified by the function by calling :func:`~eegtopo.topoplot.Topoplot.set_map`.
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----------
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scot-dev/scot | scot/plotting.py | plot_connectivity_topos | def plot_connectivity_topos(layout='diagonal', topo=None, topomaps=None, fig=None):
"""Place topo plots in a figure suitable for connectivity visualization.
.. note:: Parameter `topo` is modified by the function by calling :func:`~eegtopo.topoplot.Topoplot.set_map`.
Parameters
----------
layout : ... | python | def plot_connectivity_topos(layout='diagonal', topo=None, topomaps=None, fig=None):
"""Place topo plots in a figure suitable for connectivity visualization.
.. note:: Parameter `topo` is modified by the function by calling :func:`~eegtopo.topoplot.Topoplot.set_map`.
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----------
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scot-dev/scot | scot/plotting.py | plot_connectivity_significance | def plot_connectivity_significance(s, fs=2, freq_range=(-np.inf, np.inf), diagonal=0, border=False, fig=None):
"""Plot significance.
Significance is drawn as a background image where dark vertical stripes indicate freuquencies where a evaluates to
True.
Parameters
----------
a : array, shape (... | python | def plot_connectivity_significance(s, fs=2, freq_range=(-np.inf, np.inf), diagonal=0, border=False, fig=None):
"""Plot significance.
Significance is drawn as a background image where dark vertical stripes indicate freuquencies where a evaluates to
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----------
a : array, shape (... | [
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scot-dev/scot | scot/plotting.py | plot_whiteness | def plot_whiteness(var, h, repeats=1000, axis=None):
""" Draw distribution of the Portmanteu whiteness test.
Parameters
----------
var : :class:`~scot.var.VARBase`-like object
Vector autoregressive model (VAR) object whose residuals are tested for whiteness.
h : int
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""" Draw distribution of the Portmanteu whiteness test.
Parameters
----------
var : :class:`~scot.var.VARBase`-like object
Vector autoregressive model (VAR) object whose residuals are tested for whiteness.
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scot-dev/scot | scot/xvschema.py | singletrial | def singletrial(num_trials, skipstep=1):
""" Single-trial cross-validation schema
Use one trial for training, all others for testing.
Parameters
----------
num_trials : int
Total number of trials
skipstep : int
only use every `skipstep` trial for training
Returns
-----... | python | def singletrial(num_trials, skipstep=1):
""" Single-trial cross-validation schema
Use one trial for training, all others for testing.
Parameters
----------
num_trials : int
Total number of trials
skipstep : int
only use every `skipstep` trial for training
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scot-dev/scot | scot/xvschema.py | splitset | def splitset(num_trials, skipstep=None):
""" Split-set cross validation
Use half the trials for training, and the other half for testing. Then
repeat the other way round.
Parameters
----------
num_trials : int
Total number of trials
skipstep : int
unused
Returns
--... | python | def splitset(num_trials, skipstep=None):
""" Split-set cross validation
Use half the trials for training, and the other half for testing. Then
repeat the other way round.
Parameters
----------
num_trials : int
Total number of trials
skipstep : int
unused
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scot-dev/scot | scot/ooapi.py | Workspace.set_data | def set_data(self, data, cl=None, time_offset=0):
""" Assign data to the workspace.
This function assigns a new data set to the workspace. Doing so invalidates currently fitted VAR models,
connectivity estimates, and activations.
Parameters
----------
data : array-like,... | python | def set_data(self, data, cl=None, time_offset=0):
""" Assign data to the workspace.
This function assigns a new data set to the workspace. Doing so invalidates currently fitted VAR models,
connectivity estimates, and activations.
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----------
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scot-dev/scot | scot/ooapi.py | Workspace.set_used_labels | def set_used_labels(self, labels):
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----------
labels : list of class labels
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This function masks trials based on their class labels.
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----------
labels : list of class labels
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scot-dev/scot | scot/ooapi.py | Workspace.remove_sources | def remove_sources(self, sources):
""" Remove sources from the decomposition.
This function removes sources from the decomposition. Doing so invalidates currently fitted VAR models and
connectivity estimates.
Parameters
----------
sources : {slice, int, array of ints}
... | python | def remove_sources(self, sources):
""" Remove sources from the decomposition.
This function removes sources from the decomposition. Doing so invalidates currently fitted VAR models and
connectivity estimates.
Parameters
----------
sources : {slice, int, array of ints}
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Parameters
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sources : {slice, int, array of ints}
Indices of components to remove.
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scot-dev/scot | scot/ooapi.py | Workspace.keep_sources | def keep_sources(self, keep):
"""Keep only the specified sources in the decomposition.
"""
if self.unmixing_ is None or self.mixing_ is None:
raise RuntimeError("No sources available (run do_mvarica first)")
n_sources = self.mixing_.shape[0]
self.remove_sources(np.set... | python | def keep_sources(self, keep):
"""Keep only the specified sources in the decomposition.
"""
if self.unmixing_ is None or self.mixing_ is None:
raise RuntimeError("No sources available (run do_mvarica first)")
n_sources = self.mixing_.shape[0]
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scot-dev/scot | scot/ooapi.py | Workspace.fit_var | def fit_var(self):
""" Fit a VAR model to the source activations.
Returns
-------
self : Workspace
The Workspace object.
Raises
------
RuntimeError
If the :class:`Workspace` instance does not contain source activations.
"""
... | python | def fit_var(self):
""" Fit a VAR model to the source activations.
Returns
-------
self : Workspace
The Workspace object.
Raises
------
RuntimeError
If the :class:`Workspace` instance does not contain source activations.
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scot-dev/scot | scot/ooapi.py | Workspace.get_connectivity | def get_connectivity(self, measure_name, plot=False):
""" Calculate spectral connectivity measure.
Parameters
----------
measure_name : str
Name of the connectivity measure to calculate. See :class:`Connectivity` for supported measures.
plot : {False, None, Figure ob... | python | def get_connectivity(self, measure_name, plot=False):
""" Calculate spectral connectivity measure.
Parameters
----------
measure_name : str
Name of the connectivity measure to calculate. See :class:`Connectivity` for supported measures.
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scot-dev/scot | scot/ooapi.py | Workspace.get_surrogate_connectivity | def get_surrogate_connectivity(self, measure_name, repeats=100, plot=False, random_state=None):
""" Calculate spectral connectivity measure under the assumption of no actual connectivity.
Repeatedly samples connectivity from phase-randomized data. This provides estimates of the connectivity
dis... | python | def get_surrogate_connectivity(self, measure_name, repeats=100, plot=False, random_state=None):
""" Calculate spectral connectivity measure under the assumption of no actual connectivity.
Repeatedly samples connectivity from phase-randomized data. This provides estimates of the connectivity
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scot-dev/scot | scot/ooapi.py | Workspace.get_bootstrap_connectivity | def get_bootstrap_connectivity(self, measure_names, repeats=100, num_samples=None, plot=False, random_state=None):
""" Calculate bootstrap estimates of spectral connectivity measures.
Bootstrapping is performed on trial level.
Parameters
----------
measure_names : {str, list of... | python | def get_bootstrap_connectivity(self, measure_names, repeats=100, num_samples=None, plot=False, random_state=None):
""" Calculate bootstrap estimates of spectral connectivity measures.
Bootstrapping is performed on trial level.
Parameters
----------
measure_names : {str, list of... | [
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scot-dev/scot | scot/ooapi.py | Workspace.plot_source_topos | def plot_source_topos(self, common_scale=None):
""" Plot topography of the Source decomposition.
Parameters
----------
common_scale : float, optional
If set to None, each topoplot's color axis is scaled individually. Otherwise specifies the percentile
(1-99) of v... | python | def plot_source_topos(self, common_scale=None):
""" Plot topography of the Source decomposition.
Parameters
----------
common_scale : float, optional
If set to None, each topoplot's color axis is scaled individually. Otherwise specifies the percentile
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scot-dev/scot | scot/ooapi.py | Workspace.plot_connectivity_topos | def plot_connectivity_topos(self, fig=None):
""" Plot scalp projections of the sources.
This function only plots the topos. Use in combination with connectivity plotting.
Parameters
----------
fig : {None, Figure object}, optional
Where to plot the topos. f set to *... | python | def plot_connectivity_topos(self, fig=None):
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This function only plots the topos. Use in combination with connectivity plotting.
Parameters
----------
fig : {None, Figure object}, optional
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scot-dev/scot | scot/ooapi.py | Workspace.plot_connectivity_surrogate | def plot_connectivity_surrogate(self, measure_name, repeats=100, fig=None):
""" Plot spectral connectivity measure under the assumption of no actual connectivity.
Repeatedly samples connectivity from phase-randomized data. This provides estimates of the connectivity
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scot-dev/scot | scot/parallel.py | parallel_loop | def parallel_loop(func, n_jobs=1, verbose=1):
"""run loops in parallel, if joblib is available.
Parameters
----------
func : function
function to be executed in parallel
n_jobs : int | None
Number of jobs. If set to None, do not attempt to use joblib.
verbose : int
verbo... | python | def parallel_loop(func, n_jobs=1, verbose=1):
"""run loops in parallel, if joblib is available.
Parameters
----------
func : function
function to be executed in parallel
n_jobs : int | None
Number of jobs. If set to None, do not attempt to use joblib.
verbose : int
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kolypto/py-good | good/voluptuous.py | _convert_errors | def _convert_errors(func):
""" Decorator to convert throws errors to Voluptuous format."""
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e.path,
... | python | def _convert_errors(func):
""" Decorator to convert throws errors to Voluptuous format."""
cast_Invalid = lambda e: Invalid(
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message=e.message,
expected=e.expected)
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kolypto/py-good | good/schema/markers.py | Marker.on_compiled | def on_compiled(self, name=None, key_schema=None, value_schema=None, as_mapping_key=None):
""" When CompiledSchema compiles this marker, it sets informational values onto it.
Note that arguments may be provided in two incomplete sets,
e.g. (name, key_schema, None) and then (None, None, value_sc... | python | def on_compiled(self, name=None, key_schema=None, value_schema=None, as_mapping_key=None):
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openearth/bmi-python | bmi/runner.py | colorlogs | def colorlogs(format="short"):
"""Append a rainbow logging handler and a formatter to the root logger"""
try:
from rainbow_logging_handler import RainbowLoggingHandler
import sys
# setup `RainbowLoggingHandler`
logger = logging.root
# same as default
if format == ... | python | def colorlogs(format="short"):
"""Append a rainbow logging handler and a formatter to the root logger"""
try:
from rainbow_logging_handler import RainbowLoggingHandler
import sys
# setup `RainbowLoggingHandler`
logger = logging.root
# same as default
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openearth/bmi-python | bmi/runner.py | main | def main():
"""main bmi runner program"""
arguments = docopt.docopt(__doc__, version=__version__)
colorlogs()
# Read input file file
wrapper = BMIWrapper(
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configfile=arguments['<config>'] or ''
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# add logger if required
if not arguments[... | python | def main():
"""main bmi runner program"""
arguments = docopt.docopt(__doc__, version=__version__)
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# Read input file file
wrapper = BMIWrapper(
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# add logger if required
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insomnia-lab/libreant | conf/defaults.py | get_def_conf | def get_def_conf():
'''return default configurations as simple dict'''
ret = dict()
for k,v in defConf.items():
ret[k] = v[0]
return ret | python | def get_def_conf():
'''return default configurations as simple dict'''
ret = dict()
for k,v in defConf.items():
ret[k] = v[0]
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iamjarret/pystockfish | pystockfish.py | Match.move | def move(self):
"""
Advance game by single move, if possible.
@return: logical indicator if move was performed.
"""
if len(self.moves) == MAX_MOVES:
return False
elif len(self.moves) % 2:
active_engine = self.black_engine
active_engine... | python | def move(self):
"""
Advance game by single move, if possible.
@return: logical indicator if move was performed.
"""
if len(self.moves) == MAX_MOVES:
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iamjarret/pystockfish | pystockfish.py | Engine.bestmove | def bestmove(self):
"""
Get proposed best move for current position.
@return: dictionary with 'move', 'ponder', 'info' containing best move's UCI notation,
ponder value and info dictionary.
"""
self.go()
last_info = ""
while True:
text = self.... | python | def bestmove(self):
"""
Get proposed best move for current position.
@return: dictionary with 'move', 'ponder', 'info' containing best move's UCI notation,
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"""
self.go()
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iamjarret/pystockfish | pystockfish.py | Engine._bestmove_get_info | def _bestmove_get_info(text):
"""
Parse stockfish evaluation output as dictionary.
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iamjarret/pystockfish | pystockfish.py | Engine.isready | def isready(self):
"""
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"""
self.put('isready')
while True:
text = self.stdout.readline().strip()
if text == 'readyok':
return t... | python | def isready(self):
"""
Used to synchronize the python engine object with the back-end engine. Sends 'isready' and waits for 'readyok.'
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self.put('isready')
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chaoss/grimoirelab-manuscripts | manuscripts2/metrics/github_prs.py | project_activity | def project_activity(index, start, end):
"""Compute the metrics for the project activity section of the enriched
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Returns a dictionary containing a "metric" key. This key contains the
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:param index: index object
:param start: start date to ge... | python | def project_activity(index, start, end):
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chaoss/grimoirelab-manuscripts | manuscripts2/metrics/github_prs.py | DaysToClosePRMedian.aggregations | def aggregations(self):
"""Get the single valued aggregations with respect to the
previous time interval."""
prev_month_start = get_prev_month(self.end, self.query.interval_)
self.query.since(prev_month_start)
agg = super().aggregations()
if agg is None:
agg ... | python | def aggregations(self):
"""Get the single valued aggregations with respect to the
previous time interval."""
prev_month_start = get_prev_month(self.end, self.query.interval_)
self.query.since(prev_month_start)
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chaoss/grimoirelab-manuscripts | manuscripts2/metrics/github_prs.py | BMIPR.timeseries | def timeseries(self, dataframe=False):
"""Get BMIPR as a time series."""
closed_timeseries = self.closed.timeseries(dataframe=dataframe)
opened_timeseries = self.opened.timeseries(dataframe=dataframe)
return calculate_bmi(closed_timeseries, opened_timeseries) | python | def timeseries(self, dataframe=False):
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closed_timeseries = self.closed.timeseries(dataframe=dataframe)
opened_timeseries = self.opened.timeseries(dataframe=dataframe)
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chaoss/grimoirelab-manuscripts | manuscripts/metrics/metrics.py | Metrics.get_query | def get_query(self, evolutionary=False):
"""
Basic query to get the metric values
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:return: the DSL query to be sent to Elasticsearch
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chaoss/grimoirelab-manuscripts | manuscripts/metrics/metrics.py | Metrics.get_list | def get_list(self):
"""
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:return: a list with the values in a DSL aggregated response
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"""
Extract from a DSL aggregated response the values for each bucket
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field = self.FIELD_NAME
query = ElasticQuery.get_agg(field=field,
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chaoss/grimoirelab-manuscripts | manuscripts/metrics/metrics.py | Metrics.get_metrics_data | def get_metrics_data(self, query):
"""
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:param query: query to be sent to Elasticsearch
:return: a dict with the results of executing the query
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url = self.es_url
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Get the metrics data from Elasticsearch given a DSL query
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chaoss/grimoirelab-manuscripts | manuscripts/metrics/metrics.py | Metrics.get_ts | def get_ts(self):
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"""
Returns a time series of a specific class
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chaoss/grimoirelab-manuscripts | manuscripts/metrics/metrics.py | Metrics.get_agg | def get_agg(self):
"""
Returns the aggregated value for the metric
:return: the value of the metric
"""
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query = self.get_query(False)
res = self.get_metrics_data(query)
# We need to extract the data from the JSON res
... | python | def get_agg(self):
"""
Returns the aggregated value for the metric
:return: the value of the metric
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res = self.get_metrics_data(query)
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chaoss/grimoirelab-manuscripts | manuscripts/metrics/metrics.py | Metrics.get_trend | def get_trend(self):
"""
Get the trend for the last two metric values using the interval defined in the metric
:return: a tuple with the metric value for the last interval and the
trend percentage between the last two intervals
"""
""" """
# TODO: We j... | python | def get_trend(self):
"""
Get the trend for the last two metric values using the interval defined in the metric
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insomnia-lab/libreant | presets/presetManager.py | PresetManager._load_preset | def _load_preset(self, path):
''' load, validate and store a single preset file'''
try:
with open(path, 'r') as f:
presetBody = json.load(f)
except IOError as e:
raise PresetException("IOError: " + e.strerror)
except ValueError as e:
r... | python | def _load_preset(self, path):
''' load, validate and store a single preset file'''
try:
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presetBody = json.load(f)
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insomnia-lab/libreant | presets/presetManager.py | Preset.validate | def validate(self, data):
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'''
for prop in self.properties:
if prop.id in data:
... | python | def validate(self, data):
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Checks if `data` respects this preset specification
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insomnia-lab/libreant | webant/util.py | requestedFormat | def requestedFormat(request,acceptedFormat):
"""Return the response format requested by client
Client could specify requested format using:
(options are processed in this order)
- `format` field in http request
- `Accept` header in http request
Example:
... | python | def requestedFormat(request,acceptedFormat):
"""Return the response format requested by client
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- `format` field in http request
- `Accept` header in http request
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insomnia-lab/libreant | webant/util.py | routes_collector | def routes_collector(gatherer):
"""Decorator utility to collect flask routes in a dictionary.
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:param gatherer: dict in which will be collected routes
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"""Decorator utility to collect flask routes in a dictionary.
This function together with :func:`add_routes` provides an
easy way to split flask routes declaration in multiple modules.
:param gatherer: dict in which will be collected routes
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insomnia-lab/libreant | webant/util.py | add_routes | def add_routes(fapp, routes, prefix=""):
"""Batch routes registering
Register routes to a blueprint/flask_app previously collected
with :func:`routes_collector`.
:param fapp: bluprint or flask_app to whom attach new routes.
:param routes: dict of routes collected by :func:`routes_collector`
:p... | python | def add_routes(fapp, routes, prefix=""):
"""Batch routes registering
Register routes to a blueprint/flask_app previously collected
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:param fapp: bluprint or flask_app to whom attach new routes.
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insomnia-lab/libreant | webant/util.py | get_centered_pagination | def get_centered_pagination(current, total, visible=5):
''' Return the range of pages to render in a pagination menu.
The current page is always kept in the middle except
for the edge cases.
Reeturns a dict
{ prev, first, current, last, next }
:param current: the curre... | python | def get_centered_pagination(current, total, visible=5):
''' Return the range of pages to render in a pagination menu.
The current page is always kept in the middle except
for the edge cases.
Reeturns a dict
{ prev, first, current, last, next }
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SiLab-Bonn/pylandau | examples/mpv_fwhm.py | fwhm | def fwhm(x, y, k=10): # http://stackoverflow.com/questions/10582795/finding-the-full-width-half-maximum-of-a-peak
"""
Determine full-with-half-maximum of a peaked set of points, x and y.
Assumes that there is only one peak present in the datasset. The function
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... | python | def fwhm(x, y, k=10): # http://stackoverflow.com/questions/10582795/finding-the-full-width-half-maximum-of-a-peak
"""
Determine full-with-half-maximum of a peaked set of points, x and y.
Assumes that there is only one peak present in the datasset. The function
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snowblink14/smatch | smatch.py | main | def main(arguments):
"""
Main function of smatch score calculation
"""
global verbose
global veryVerbose
global iteration_num
global single_score
global pr_flag
global match_triple_dict
# set the iteration number
# total iteration number = restart number + 1
iteration_num... | python | def main(arguments):
"""
Main function of smatch score calculation
"""
global verbose
global veryVerbose
global iteration_num
global single_score
global pr_flag
global match_triple_dict
# set the iteration number
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insomnia-lab/libreant | archivant/archivant.py | Archivant.normalize_volume | def normalize_volume(volume):
'''convert volume metadata from es to archivant format
This function makes side effect on input volume
output example::
{
'id': 'AU0paPZOMZchuDv1iDv8',
'type': 'volume',
'metadata': {'_language': '... | python | def normalize_volume(volume):
'''convert volume metadata from es to archivant format
This function makes side effect on input volume
output example::
{
'id': 'AU0paPZOMZchuDv1iDv8',
'type': 'volume',
'metadata': {'_language': '... | [
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insomnia-lab/libreant | archivant/archivant.py | Archivant.normalize_attachment | def normalize_attachment(attachment):
''' Convert attachment metadata from es to archivant format
This function makes side effect on input attachment
'''
res = dict()
res['type'] = 'attachment'
res['id'] = attachment['id']
del(attachment['id'])
res['u... | python | def normalize_attachment(attachment):
''' Convert attachment metadata from es to archivant format
This function makes side effect on input attachment
'''
res = dict()
res['type'] = 'attachment'
res['id'] = attachment['id']
del(attachment['id'])
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insomnia-lab/libreant | archivant/archivant.py | Archivant.denormalize_volume | def denormalize_volume(volume):
'''convert volume metadata from archivant to es format'''
id = volume.get('id', None)
res = dict()
res.update(volume['metadata'])
denorm_attachments = list()
for a in volume['attachments']:
denorm_attachments.append(Archivant.de... | python | def denormalize_volume(volume):
'''convert volume metadata from archivant to es format'''
id = volume.get('id', None)
res = dict()
res.update(volume['metadata'])
denorm_attachments = list()
for a in volume['attachments']:
denorm_attachments.append(Archivant.de... | [
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insomnia-lab/libreant | archivant/archivant.py | Archivant.denormalize_attachment | def denormalize_attachment(attachment):
'''convert attachment metadata from archivant to es format'''
res = dict()
ext = ['id', 'url']
for k in ext:
if k in attachment['metadata']:
raise ValueError("metadata section could not contain special key '{}'".format(k... | python | def denormalize_attachment(attachment):
'''convert attachment metadata from archivant to es format'''
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insomnia-lab/libreant | archivant/archivant.py | Archivant.iter_all_volumes | def iter_all_volumes(self):
'''iterate over all stored volumes'''
for raw_volume in self._db.iterate_all():
v = self.normalize_volume(raw_volume)
del v['score']
yield v | python | def iter_all_volumes(self):
'''iterate over all stored volumes'''
for raw_volume in self._db.iterate_all():
v = self.normalize_volume(raw_volume)
del v['score']
yield v | [
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insomnia-lab/libreant | archivant/archivant.py | Archivant.delete_attachments | def delete_attachments(self, volumeID, attachmentsID):
''' delete attachments from a volume '''
log.debug("deleting attachments from volume '{}': {}".format(volumeID, attachmentsID))
rawVolume = self._req_raw_volume(volumeID)
insID = [a['id'] for a in rawVolume['_source']['_attachments']... | python | def delete_attachments(self, volumeID, attachmentsID):
''' delete attachments from a volume '''
log.debug("deleting attachments from volume '{}': {}".format(volumeID, attachmentsID))
rawVolume = self._req_raw_volume(volumeID)
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insomnia-lab/libreant | archivant/archivant.py | Archivant.insert_attachments | def insert_attachments(self, volumeID, attachments):
''' add attachments to an already existing volume '''
log.debug("adding new attachments to volume '{}': {}".format(volumeID, attachments))
if not attachments:
return
rawVolume = self._req_raw_volume(volumeID)
attsID... | python | def insert_attachments(self, volumeID, attachments):
''' add attachments to an already existing volume '''
log.debug("adding new attachments to volume '{}': {}".format(volumeID, attachments))
if not attachments:
return
rawVolume = self._req_raw_volume(volumeID)
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insomnia-lab/libreant | archivant/archivant.py | Archivant.insert_volume | def insert_volume(self, metadata, attachments=[]):
'''Insert a new volume
Returns the ID of the added volume
`metadata` must be a dict containg metadata of the volume::
{
"_language" : "it", # language of the metadata
"key1" : "value1", # attribute
... | python | def insert_volume(self, metadata, attachments=[]):
'''Insert a new volume
Returns the ID of the added volume
`metadata` must be a dict containg metadata of the volume::
{
"_language" : "it", # language of the metadata
"key1" : "value1", # attribute
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insomnia-lab/libreant | archivant/archivant.py | Archivant._assemble_attachment | def _assemble_attachment(self, file, metadata):
''' store file and return a dict containing assembled metadata
param `file` must be a path or a File Object
param `metadata` must be a dict:
{
"name" : "nome_buffo.ext" # name of the file (extensi... | python | def _assemble_attachment(self, file, metadata):
''' store file and return a dict containing assembled metadata
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insomnia-lab/libreant | archivant/archivant.py | Archivant.update_volume | def update_volume(self, volumeID, metadata):
'''update existing volume metadata
the given metadata will substitute the old one
'''
log.debug('updating volume metadata: {}'.format(volumeID))
rawVolume = self._req_raw_volume(volumeID)
normalized = self.normalize_volume(r... | python | def update_volume(self, volumeID, metadata):
'''update existing volume metadata
the given metadata will substitute the old one
'''
log.debug('updating volume metadata: {}'.format(volumeID))
rawVolume = self._req_raw_volume(volumeID)
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insomnia-lab/libreant | archivant/archivant.py | Archivant.update_attachment | def update_attachment(self, volumeID, attachmentID, metadata):
'''update an existing attachment
the given metadata dict will be merged with the old one.
only the following fields could be updated:
[name, mime, notes, download_count]
'''
log.debug('updating metadata of at... | python | def update_attachment(self, volumeID, attachmentID, metadata):
'''update an existing attachment
the given metadata dict will be merged with the old one.
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[name, mime, notes, download_count]
'''
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insomnia-lab/libreant | archivant/archivant.py | Archivant.dangling_files | def dangling_files(self):
'''iterate over fsdb files no more attached to any volume'''
for fid in self._fsdb:
if not self._db.file_is_attached('fsdb:///' + fid):
yield fid | python | def dangling_files(self):
'''iterate over fsdb files no more attached to any volume'''
for fid in self._fsdb:
if not self._db.file_is_attached('fsdb:///' + fid):
yield fid | [
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pricingassistant/mongokat | mongokat/_bson/__init__.py | _get_string | def _get_string(data, position, obj_end, dummy):
"""Decode a BSON string to python unicode string."""
length = _UNPACK_INT(data[position:position + 4])[0]
position += 4
if length < 1 or obj_end - position < length:
raise InvalidBSON("invalid string length")
end = position + length - 1
if... | python | def _get_string(data, position, obj_end, dummy):
"""Decode a BSON string to python unicode string."""
length = _UNPACK_INT(data[position:position + 4])[0]
position += 4
if length < 1 or obj_end - position < length:
raise InvalidBSON("invalid string length")
end = position + length - 1
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pricingassistant/mongokat | mongokat/_bson/__init__.py | _get_regex | def _get_regex(data, position, dummy0, dummy1):
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pattern, position = _get_c_string(data, position)
bson_flags, position = _get_c_string(data, position)
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return bson_re, position | python | def _get_regex(data, position, dummy0, dummy1):
"""Decode a BSON regex to bson.regex.Regex or a python pattern object."""
pattern, position = _get_c_string(data, position)
bson_flags, position = _get_c_string(data, position)
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pricingassistant/mongokat | mongokat/_bson/__init__.py | _encode_mapping | def _encode_mapping(name, value, check_keys, opts):
"""Encode a mapping type."""
data = b"".join([_element_to_bson(key, val, check_keys, opts)
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return b"\x03" + name + _PACK_INT(len(data) + 5) + data + b"\x00" | python | def _encode_mapping(name, value, check_keys, opts):
"""Encode a mapping type."""
data = b"".join([_element_to_bson(key, val, check_keys, opts)
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pricingassistant/mongokat | mongokat/_bson/__init__.py | _encode_code | def _encode_code(name, value, dummy, opts):
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cstrlen = len(cstring)
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"""Encode bson.code.Code."""
cstring = _make_c_string(value)
cstrlen = len(cstring)
if not value.scope:
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insomnia-lab/libreant | users/models.py | Capability.simToReg | def simToReg(self, sim):
"""Convert simplified domain expression to regular expression"""
# remove initial slash if present
res = re.sub('^/', '', sim)
res = re.sub('/$', '', res)
return '^/?' + re.sub('\*', '[^/]+', res) + '/?$' | python | def simToReg(self, sim):
"""Convert simplified domain expression to regular expression"""
# remove initial slash if present
res = re.sub('^/', '', sim)
res = re.sub('/$', '', res)
return '^/?' + re.sub('\*', '[^/]+', res) + '/?$' | [
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insomnia-lab/libreant | users/models.py | Capability.match | def match(self, dom, act):
"""
Check if the given `domain` and `act` are allowed
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"""
return self.match_domain(dom) and self.match_action(act) | python | def match(self, dom, act):
"""
Check if the given `domain` and `act` are allowed
by this capability
"""
return self.match_domain(dom) and self.match_action(act) | [
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insomnia-lab/libreant | users/models.py | Action.to_list | def to_list(self):
'''convert an actions bitmask into a list of action strings'''
res = []
for a in self.__class__.ACTIONS:
aBit = self.__class__.action_bitmask(a)
if ((self & aBit) == aBit):
res.append(a)
return res | python | def to_list(self):
'''convert an actions bitmask into a list of action strings'''
res = []
for a in self.__class__.ACTIONS:
aBit = self.__class__.action_bitmask(a)
if ((self & aBit) == aBit):
res.append(a)
return res | [
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insomnia-lab/libreant | users/models.py | Action.from_list | def from_list(cls, actions):
'''convert list of actions into the corresponding bitmask'''
bitmask = 0
for a in actions:
bitmask |= cls.action_bitmask(a)
return Action(bitmask) | python | def from_list(cls, actions):
'''convert list of actions into the corresponding bitmask'''
bitmask = 0
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bitmask |= cls.action_bitmask(a)
return Action(bitmask) | [
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chaoss/grimoirelab-manuscripts | manuscripts2/utils.py | str_val | def str_val(val):
"""
Format the value of a metric value to a string
:param val: number to be formatted
:return: a string with the formatted value
"""
str_val = val
if val is None:
str_val = "NA"
elif type(val) == float:
str_val = '%0.2f' % val
else:
str_val ... | python | def str_val(val):
"""
Format the value of a metric value to a string
:param val: number to be formatted
:return: a string with the formatted value
"""
str_val = val
if val is None:
str_val = "NA"
elif type(val) == float:
str_val = '%0.2f' % val
else:
str_val ... | [
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insomnia-lab/libreant | cli/__init__.py | load_cfg | def load_cfg(path, envvar_prefix='LIBREANT_', debug=False):
'''wrapper of config_utils.load_configs'''
try:
return load_configs(envvar_prefix, path=path)
except Exception as e:
if debug:
raise
else:
die(str(e)) | python | def load_cfg(path, envvar_prefix='LIBREANT_', debug=False):
'''wrapper of config_utils.load_configs'''
try:
return load_configs(envvar_prefix, path=path)
except Exception as e:
if debug:
raise
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die(str(e)) | [
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