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awacha/sastool | sastool/io/credo_saxsctrl/header.py | Header.date | def date(self) -> datetime.datetime:
"""Date of the experiment (start of exposure)"""
return self._data['Date'] - datetime.timedelta(0, float(self.exposuretime), 0) | python | def date(self) -> datetime.datetime:
"""Date of the experiment (start of exposure)"""
return self._data['Date'] - datetime.timedelta(0, float(self.exposuretime), 0) | [
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awacha/sastool | sastool/io/credo_saxsctrl/header.py | Header.flux | def flux(self) -> ErrorValue:
"""X-ray flux in photons/sec."""
try:
return ErrorValue(self._data['Flux'], self._data.setdefault('FluxError',0.0))
except KeyError:
return 1 / self.pixelsizex / self.pixelsizey / ErrorValue(self._data['NormFactor'],
... | python | def flux(self) -> ErrorValue:
"""X-ray flux in photons/sec."""
try:
return ErrorValue(self._data['Flux'], self._data.setdefault('FluxError',0.0))
except KeyError:
return 1 / self.pixelsizex / self.pixelsizey / ErrorValue(self._data['NormFactor'],
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awacha/sastool | sastool/misc/easylsq.py | nonlinear_leastsquares | def nonlinear_leastsquares(x: np.ndarray, y: np.ndarray, dy: np.ndarray, func: Callable, params_init: np.ndarray,
verbose: bool = False, **kwargs):
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x: one-dimensional numpy ar... | python | def nonlinear_leastsquares(x: np.ndarray, y: np.ndarray, dy: np.ndarray, func: Callable, params_init: np.ndarray,
verbose: bool = False, **kwargs):
"""Perform a non-linear least squares fit, return the results as
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awacha/sastool | sastool/misc/easylsq.py | nonlinear_odr | def nonlinear_odr(x, y, dx, dy, func, params_init, **kwargs):
"""Perform a non-linear orthogonal distance regression, return the results as
ErrorValue() instances.
Inputs:
x: one-dimensional numpy array of the independent variable
y: one-dimensional numpy array of the dependent variable
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"""Perform a non-linear orthogonal distance regression, return the results as
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Inputs:
x: one-dimensional numpy array of the independent variable
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awacha/sastool | sastool/misc/easylsq.py | simultaneous_nonlinear_leastsquares | def simultaneous_nonlinear_leastsquares(xs, ys, dys, func, params_inits, verbose=False, **kwargs):
"""Do a simultaneous nonlinear least-squares fit and return the fitted
parameters as instances of ErrorValue.
Input:
------
`xs`: tuple of abscissa vectors (1d numpy ndarrays)
`ys`: tuple of ordin... | python | def simultaneous_nonlinear_leastsquares(xs, ys, dys, func, params_inits, verbose=False, **kwargs):
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awacha/sastool | sastool/misc/easylsq.py | nlsq_fit | def nlsq_fit(x, y, dy, func, params_init, verbose=False, **kwargs):
"""Perform a non-linear least squares fit
Inputs:
x: one-dimensional numpy array of the independent variable
y: one-dimensional numpy array of the dependent variable
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x: one-dimensional numpy array of the independent variable
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awacha/sastool | sastool/misc/easylsq.py | simultaneous_nlsq_fit | def simultaneous_nlsq_fit(xs, ys, dys, func, params_inits, verbose=False,
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"""Do a simultaneous nonlinear least-squares fit
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`xs`: tuple of abscissa vectors (1d numpy ndarrays)
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`xs`: tuple of abscissa vectors (1d numpy ndarrays)
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awacha/sastool | sastool/misc/errorvalue.py | ErrorValue.tostring | def tostring(self: 'ErrorValue', extra_digits: int = 0, plusminus: str = ' +/- ', fmt: str = None) -> str:
"""Make a string representation of the value and its uncertainty.
Inputs:
-------
``extra_digits``: integer
how many extra digits should be shown (plus or minus... | python | def tostring(self: 'ErrorValue', extra_digits: int = 0, plusminus: str = ' +/- ', fmt: str = None) -> str:
"""Make a string representation of the value and its uncertainty.
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``extra_digits``: integer
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awacha/sastool | sastool/misc/errorvalue.py | ErrorValue.random | def random(self: 'ErrorValue') -> np.ndarray:
"""Sample a random number (array) of the distribution defined by
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"""
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"""Sample a random number (array) of the distribution defined by
mean=`self.val` and variance=`self.err`^2.
"""
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awacha/sastool | sastool/misc/errorvalue.py | ErrorValue.evalfunc | def evalfunc(cls, func, *args, **kwargs):
"""Evaluate a function with error propagation.
Inputs:
-------
``func``: callable
this is the function to be evaluated. Should return either a
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``*args``: other positional ar... | python | def evalfunc(cls, func, *args, **kwargs):
"""Evaluate a function with error propagation.
Inputs:
-------
``func``: callable
this is the function to be evaluated. Should return either a
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | Fsphere | def Fsphere(q, R):
"""Scattering form-factor amplitude of a sphere normalized to F(q=0)=V
Inputs:
-------
``q``: independent variable
``R``: sphere radius
Formula:
--------
``4*pi/q^3 * (sin(qR) - qR*cos(qR))``
"""
return 4 * np.pi / q ** 3 * (np.sin(q * R) - q * R ... | python | def Fsphere(q, R):
"""Scattering form-factor amplitude of a sphere normalized to F(q=0)=V
Inputs:
-------
``q``: independent variable
``R``: sphere radius
Formula:
--------
``4*pi/q^3 * (sin(qR) - qR*cos(qR))``
"""
return 4 * np.pi / q ** 3 * (np.sin(q * R) - q * R ... | [
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | GeneralGuinier | def GeneralGuinier(q, G, Rg, s):
"""Generalized Guinier scattering
Inputs:
-------
``q``: independent variable
``G``: factor
``Rg``: radius of gyration
``s``: dimensionality parameter (can be 1, 2, 3)
Formula:
--------
``G/q**(3-s)*exp(-(q^2*Rg^2)/s)``
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"""Generalized Guinier scattering
Inputs:
-------
``q``: independent variable
``G``: factor
``Rg``: radius of gyration
``s``: dimensionality parameter (can be 1, 2, 3)
Formula:
--------
``G/q**(3-s)*exp(-(q^2*Rg^2)/s)``
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | GuinierPorod | def GuinierPorod(q, G, Rg, alpha):
"""Empirical Guinier-Porod scattering
Inputs:
-------
``q``: independent variable
``G``: factor of the Guinier-branch
``Rg``: radius of gyration
``alpha``: power-law exponent
Formula:
--------
``G * exp(-q^2*Rg^2/3)`` if ``... | python | def GuinierPorod(q, G, Rg, alpha):
"""Empirical Guinier-Porod scattering
Inputs:
-------
``q``: independent variable
``G``: factor of the Guinier-branch
``Rg``: radius of gyration
``alpha``: power-law exponent
Formula:
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | PorodGuinier | def PorodGuinier(q, a, alpha, Rg):
"""Empirical Porod-Guinier scattering
Inputs:
-------
``q``: independent variable
``a``: factor of the power-law branch
``alpha``: power-law exponent
``Rg``: radius of gyration
Formula:
--------
``G * exp(-q^2*Rg^2/3)`` if ... | python | def PorodGuinier(q, a, alpha, Rg):
"""Empirical Porod-Guinier scattering
Inputs:
-------
``q``: independent variable
``a``: factor of the power-law branch
``alpha``: power-law exponent
``Rg``: radius of gyration
Formula:
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | PorodGuinierPorod | def PorodGuinierPorod(q, a, alpha, Rg, beta):
"""Empirical Porod-Guinier-Porod scattering
Inputs:
-------
``q``: independent variable
``a``: factor of the first power-law branch
``alpha``: exponent of the first power-law branch
``Rg``: radius of gyration
``beta``: ex... | python | def PorodGuinierPorod(q, a, alpha, Rg, beta):
"""Empirical Porod-Guinier-Porod scattering
Inputs:
-------
``q``: independent variable
``a``: factor of the first power-law branch
``alpha``: exponent of the first power-law branch
``Rg``: radius of gyration
``beta``: ex... | [
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``a``: factor of the first power-law branch
``alpha``: exponent of the first power-law branch
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``beta``: exponent of the second power-law branch
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | GuinierPorodGuinier | def GuinierPorodGuinier(q, G, Rg1, alpha, Rg2):
"""Empirical Guinier-Porod-Guinier scattering
Inputs:
-------
``q``: independent variable
``G``: factor for the first Guinier-branch
``Rg1``: the first radius of gyration
``alpha``: the power-law exponent
``Rg2``: the s... | python | def GuinierPorodGuinier(q, G, Rg1, alpha, Rg2):
"""Empirical Guinier-Porod-Guinier scattering
Inputs:
-------
``q``: independent variable
``G``: factor for the first Guinier-branch
``Rg1``: the first radius of gyration
``alpha``: the power-law exponent
``Rg2``: the s... | [
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | DampedPowerlaw | def DampedPowerlaw(q, a, alpha, sigma):
"""Damped power-law
Inputs:
-------
``q``: independent variable
``a``: factor
``alpha``: exponent
``sigma``: hwhm of the damping Gaussian
Formula:
--------
``a*q^alpha*exp(-q^2/(2*sigma^2))``
"""
return a * q *... | python | def DampedPowerlaw(q, a, alpha, sigma):
"""Damped power-law
Inputs:
-------
``q``: independent variable
``a``: factor
``alpha``: exponent
``sigma``: hwhm of the damping Gaussian
Formula:
--------
``a*q^alpha*exp(-q^2/(2*sigma^2))``
"""
return a * q *... | [
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | LogNormSpheres | def LogNormSpheres(q, A, mu, sigma, N=1000):
"""Scattering of a population of non-correlated spheres (radii from a log-normal distribution)
Inputs:
-------
``q``: independent variable
``A``: scaling factor
``mu``: expectation of ``ln(R)``
``sigma``: hwhm of ``ln(R)``
No... | python | def LogNormSpheres(q, A, mu, sigma, N=1000):
"""Scattering of a population of non-correlated spheres (radii from a log-normal distribution)
Inputs:
-------
``q``: independent variable
``A``: scaling factor
``mu``: expectation of ``ln(R)``
``sigma``: hwhm of ``ln(R)``
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | GaussSpheres | def GaussSpheres(q, A, R0, sigma, N=1000, weighting='intensity'):
"""Scattering of a population of non-correlated spheres (radii from a gaussian distribution)
Inputs:
-------
``q``: independent variable
``A``: scaling factor
``R0``: expectation of ``R``
``sigma``: hwhm of ``... | python | def GaussSpheres(q, A, R0, sigma, N=1000, weighting='intensity'):
"""Scattering of a population of non-correlated spheres (radii from a gaussian distribution)
Inputs:
-------
``q``: independent variable
``A``: scaling factor
``R0``: expectation of ``R``
``sigma``: hwhm of ``... | [
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | PowerlawGuinierPorodConst | def PowerlawGuinierPorodConst(q, A, alpha, G, Rg, beta, C):
"""Sum of a Power-law, a Guinier-Porod curve and a constant.
Inputs:
-------
``q``: independent variable (momentum transfer)
``A``: scaling factor of the power-law
``alpha``: power-law exponent
``G``: scaling factor... | python | def PowerlawGuinierPorodConst(q, A, alpha, G, Rg, beta, C):
"""Sum of a Power-law, a Guinier-Porod curve and a constant.
Inputs:
-------
``q``: independent variable (momentum transfer)
``A``: scaling factor of the power-law
``alpha``: power-law exponent
``G``: scaling factor... | [
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | GuinierPorodMulti | def GuinierPorodMulti(q, G, *Rgsalphas):
"""Empirical multi-part Guinier-Porod scattering
Inputs:
-------
``q``: independent variable
``G``: factor for the first Guinier-branch
other arguments: [Rg1, alpha1, Rg2, alpha2, Rg3 ...] the radii of
gyration and power-law exponents... | python | def GuinierPorodMulti(q, G, *Rgsalphas):
"""Empirical multi-part Guinier-Porod scattering
Inputs:
-------
``q``: independent variable
``G``: factor for the first Guinier-branch
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | PorodGuinierMulti | def PorodGuinierMulti(q, A, *alphasRgs):
"""Empirical multi-part Porod-Guinier scattering
Inputs:
-------
``q``: independent variable
``A``: factor for the first Power-law-branch
other arguments: [alpha1, Rg1, alpha2, Rg2, alpha3 ...] the radii of
gyration and power-law expo... | python | def PorodGuinierMulti(q, A, *alphasRgs):
"""Empirical multi-part Porod-Guinier scattering
Inputs:
-------
``q``: independent variable
``A``: factor for the first Power-law-branch
other arguments: [alpha1, Rg1, alpha2, Rg2, alpha3 ...] the radii of
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awacha/sastool | sastool/fitting/fitfunctions/sasbasic.py | GeneralGuinierPorod | def GeneralGuinierPorod(q, factor, *args, **kwargs):
"""Empirical generalized multi-part Guinier-Porod scattering
Inputs:
-------
``q``: independent variable
``factor``: factor for the first branch
other arguments (*args): the defining arguments of the consecutive
parts... | python | def GeneralGuinierPorod(q, factor, *args, **kwargs):
"""Empirical generalized multi-part Guinier-Porod scattering
Inputs:
-------
``q``: independent variable
``factor``: factor for the first branch
other arguments (*args): the defining arguments of the consecutive
parts... | [
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awacha/sastool | sastool/fitting/fitfunctions/saspolymer.py | DebyeChain | def DebyeChain(q, Rg):
"""Scattering form-factor intensity of a Gaussian chain (Debye)
Inputs:
-------
``q``: independent variable
``Rg``: radius of gyration
Formula:
--------
``2*(exp(-a)-1+a)/a^2`` where ``a=(q*Rg)^2``
"""
a = (q * Rg) ** 2
return 2 * (np.exp(... | python | def DebyeChain(q, Rg):
"""Scattering form-factor intensity of a Gaussian chain (Debye)
Inputs:
-------
``q``: independent variable
``Rg``: radius of gyration
Formula:
--------
``2*(exp(-a)-1+a)/a^2`` where ``a=(q*Rg)^2``
"""
a = (q * Rg) ** 2
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``q``: independent variable
``Rg``: radius of gyration
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``2*(exp(-a)-1+a)/a^2`` where ``a=(q*Rg)^2`` | [
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awacha/sastool | sastool/fitting/fitfunctions/saspolymer.py | ExcludedVolumeChain | def ExcludedVolumeChain(q, Rg, nu):
"""Scattering intensity of a generalized excluded-volume Gaussian chain
Inputs:
-------
``q``: independent variable
``Rg``: radius of gyration
``nu``: excluded volume exponent
Formula:
--------
``(u^(1/nu)*gamma(0.5/nu)*gammainc_l... | python | def ExcludedVolumeChain(q, Rg, nu):
"""Scattering intensity of a generalized excluded-volume Gaussian chain
Inputs:
-------
``q``: independent variable
``Rg``: radius of gyration
``nu``: excluded volume exponent
Formula:
--------
``(u^(1/nu)*gamma(0.5/nu)*gammainc_l... | [
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awacha/sastool | sastool/fitting/fitfunctions/saspolymer.py | BorueErukhimovich | def BorueErukhimovich(q, C, r0, s, t):
"""Borue-Erukhimovich model of microphase separation in polyelectrolytes
Inputs:
-------
``q``: independent variable
``C``: scaling factor
``r0``: typical el.stat. screening length
``s``: dimensionless charge concentration
``t``... | python | def BorueErukhimovich(q, C, r0, s, t):
"""Borue-Erukhimovich model of microphase separation in polyelectrolytes
Inputs:
-------
``q``: independent variable
``C``: scaling factor
``r0``: typical el.stat. screening length
``s``: dimensionless charge concentration
``t``... | [
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awacha/sastool | sastool/fitting/fitfunctions/saspolymer.py | BorueErukhimovich_Powerlaw | def BorueErukhimovich_Powerlaw(q, C, r0, s, t, nu):
"""Borue-Erukhimovich model ending in a power-law.
Inputs:
-------
``q``: independent variable
``C``: scaling factor
``r0``: typical el.stat. screening length
``s``: dimensionless charge concentration
``t``: dimensi... | python | def BorueErukhimovich_Powerlaw(q, C, r0, s, t, nu):
"""Borue-Erukhimovich model ending in a power-law.
Inputs:
-------
``q``: independent variable
``C``: scaling factor
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``s``: dimensionless charge concentration
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bmcfee/pumpp | pumpp/sampler.py | Sampler.sample | def sample(self, data, interval):
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Parameters
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data : dict
A data dict as produced by pumpp.Pump.transform
interval : slice
The time interval to sample
Returns
-------
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data : dict
A data dict as produced by pumpp.Pump.transform
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bmcfee/pumpp | pumpp/sampler.py | Sampler.indices | def indices(self, data):
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The start index of a sample patch
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bmcfee/pumpp | pumpp/sampler.py | SequentialSampler.indices | def indices(self, data):
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The start index of a sample patch
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bmcfee/pumpp | pumpp/sampler.py | VariableLengthSampler.indices | def indices(self, data):
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bmcfee/pumpp | pumpp/base.py | Scope.scope | def scope(self, key):
'''Apply the name scope to a key
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key : string
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`name/key` if `name` is not `None`;
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'''Apply the name scope to a key
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key : string
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bmcfee/pumpp | pumpp/base.py | Scope.register | def register(self, field, shape, dtype):
'''Register a field as a tensor with specified shape and type.
A `Tensor` of the given shape and type will be registered in this
object's `fields` dict.
Parameters
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field : str
The name of the field
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'''Register a field as a tensor with specified shape and type.
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data : list of dict
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bmcfee/pumpp | pumpp/base.py | Slicer.add | def add(self, operator):
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The new operator to add
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'''Add an operator to the Slicer
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bmcfee/pumpp | pumpp/base.py | Slicer.data_duration | def data_duration(self, data):
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data : dict
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Returns
-------
length : int
The minimum temporal extent of a dynamic observation in data
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bmcfee/pumpp | pumpp/base.py | Slicer.crop | def crop(self, data):
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data : dict
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-------
data_cropped : dict
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bmcfee/pumpp | pumpp/feature/mel.py | Mel.transform_audio | def transform_audio(self, y):
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y : np.ndarray
The audio buffer
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-------
data : dict
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'''Compute the Mel spectrogram
Parameters
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y : np.ndarray
The audio buffer
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data : dict
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bmcfee/pumpp | pumpp/task/regression.py | VectorTransformer.empty | def empty(self, duration):
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----------
duration : number >0
Length of the track
Returns
-------
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'''Empty vector annotations.
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bmcfee/pumpp | pumpp/task/regression.py | VectorTransformer.transform_annotation | def transform_annotation(self, ann, duration):
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ann : jams.Annotation
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The duration of the track
Returns
-------
data : dict
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bmcfee/pumpp | pumpp/task/regression.py | VectorTransformer.inverse | def inverse(self, vector, duration=None):
'''Inverse vector transformer'''
ann = jams.Annotation(namespace=self.namespace, duration=duration)
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duration = 0
ann.append(time=0, duration=duration, value=vector)
return ann | python | def inverse(self, vector, duration=None):
'''Inverse vector transformer'''
ann = jams.Annotation(namespace=self.namespace, duration=duration)
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duration = 0
ann.append(time=0, duration=duration, value=vector)
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bmcfee/pumpp | pumpp/task/tags.py | DynamicLabelTransformer.set_transition | def set_transition(self, p_self):
'''Set the transition matrix according to self-loop probabilities.
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----------
p_self : None, float in (0, 1), or np.ndarray [shape=(n_labels,)]
Optional self-loop probability(ies), used for Viterbi decoding
'''
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'''Set the transition matrix according to self-loop probabilities.
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p_self : None, float in (0, 1), or np.ndarray [shape=(n_labels,)]
Optional self-loop probability(ies), used for Viterbi decoding
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bmcfee/pumpp | pumpp/task/tags.py | DynamicLabelTransformer.empty | def empty(self, duration):
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----------
duration : number > 0
The duration of the annotation
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duration : number > 0
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bmcfee/pumpp | pumpp/task/tags.py | DynamicLabelTransformer.transform_annotation | def transform_annotation(self, ann, duration):
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----------
ann : jams.Annotation
The annotation to convert
duration : number > 0
The duration of the track
Returns
-------
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ann : jams.Annotation
The annotation to convert
duration : number > 0
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bmcfee/pumpp | pumpp/task/tags.py | DynamicLabelTransformer.inverse | def inverse(self, encoded, duration=None):
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bmcfee/pumpp | pumpp/feature/time.py | TimePosition.transform_audio | def transform_audio(self, y):
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bmcfee/pumpp | pumpp/core.py | Pump.add | def add(self, operator):
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Parameters
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The operation to add
Raises
------
ParameterError
if `op` is not of a correct type
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'''Add an operation to this pump.
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The operation to add
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bmcfee/pumpp | pumpp/core.py | Pump.transform | def transform(self, audio_f=None, jam=None, y=None, sr=None, crop=False):
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audio_f : str
Path to audio file
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bmcfee/pumpp | pumpp/core.py | Pump.sampler | def sampler(self, n_samples, duration, random_state=None):
'''Construct a sampler object for this pump's operators.
Parameters
----------
n_samples : None or int > 0
The number of samples to generate
duration : int > 0
The duration (in frames) of each sa... | python | def sampler(self, n_samples, duration, random_state=None):
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n_samples : None or int > 0
The number of samples to generate
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bmcfee/pumpp | pumpp/core.py | Pump.fields | def fields(self):
'''A dictionary of fields constructed by this pump'''
out = dict()
for operator in self.ops:
out.update(**operator.fields)
return out | python | def fields(self):
'''A dictionary of fields constructed by this pump'''
out = dict()
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out.update(**operator.fields)
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bmcfee/pumpp | pumpp/core.py | Pump.layers | def layers(self):
'''Construct Keras input layers for all feature transformers
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Returns
-------
layers : {field: keras.layers.Input}
A dictionary of keras input layers, keyed by the corresponding
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'''
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... | python | def layers(self):
'''Construct Keras input layers for all feature transformers
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Returns
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layers : {field: keras.layers.Input}
A dictionary of keras input layers, keyed by the corresponding
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bmcfee/pumpp | pumpp/task/beat.py | BeatTransformer.set_transition_beat | def set_transition_beat(self, p_self):
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Parameters
----------
p_self : None, float in (0, 1), or np.ndarray [shape=(2,)]
Optional self-loop probability(ies), used for Viterbi decoding
... | python | def set_transition_beat(self, p_self):
'''Set the beat-tracking transition matrix according to
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Parameters
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Optional self-loop probability(ies), used for Viterbi decoding
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bmcfee/pumpp | pumpp/task/beat.py | BeatTransformer.set_transition_down | def set_transition_down(self, p_self):
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bmcfee/pumpp | pumpp/task/beat.py | BeatTransformer.transform_annotation | def transform_annotation(self, ann, duration):
'''Apply the beat transformer
Parameters
----------
ann : jams.Annotation
The input annotation
duration : number > 0
The duration of the audio
Returns
-------
data : dict
... | python | def transform_annotation(self, ann, duration):
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ann : jams.Annotation
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bmcfee/pumpp | pumpp/task/beat.py | BeatTransformer.inverse | def inverse(self, encoded, downbeat=None, duration=None):
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bmcfee/pumpp | pumpp/task/beat.py | BeatPositionTransformer.transform_annotation | def transform_annotation(self, ann, duration):
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----------
ann : jams.Annotation
The annotation to convert
duration : number > 0
The duration of the track
Returns
-------... | python | def transform_annotation(self, ann, duration):
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bmcfee/pumpp | pumpp/feature/rhythm.py | Tempogram.transform_audio | def transform_audio(self, y):
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y : np.ndarray
Audio buffer
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-------
data : dict
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bmcfee/pumpp | pumpp/feature/rhythm.py | TempoScale.transform_audio | def transform_audio(self, y):
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y : np.ndarray
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The segment annotation
duration : number > 0
The target duration
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data : d... | python | def transform_annotation(self, ann, duration):
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y : np.ndarray
the audio buffer
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bmcfee/pumpp | pumpp/feature/fft.py | STFTMag.transform_audio | def transform_audio(self, y):
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bmcfee/pumpp | pumpp/task/chord.py | _pad_nochord | def _pad_nochord(target, axis=-1):
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the input data
axis : int
the axis along which to pad
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the axis along which to pad
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bmcfee/pumpp | pumpp/task/chord.py | ChordTransformer.empty | def empty(self, duration):
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duration : number
The length (in seconds) of the empty annotation
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duration : number
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bmcfee/pumpp | pumpp/task/chord.py | ChordTransformer.transform_annotation | def transform_annotation(self, ann, duration):
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bmcfee/pumpp | pumpp/task/chord.py | SimpleChordTransformer.transform_annotation | def transform_annotation(self, ann, duration):
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bmcfee/pumpp | pumpp/task/chord.py | ChordTagTransformer.set_transition | def set_transition(self, p_self):
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Optional self-loop probability(ies), used for Viterbi decoding
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bmcfee/pumpp | pumpp/task/chord.py | ChordTagTransformer.simplify | def simplify(self, chord):
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'''Simplify a chord string down to the vocabulary space'''
# Drop inversions
chord = re.sub(r'/.*$', r'', chord)
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bmcfee/pumpp | pumpp/task/chord.py | ChordTagTransformer.transform_annotation | def transform_annotation(self, ann, duration):
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The annotation to convert
duration : number > 0
The duration of the track
Returns
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bmcfee/pumpp | pumpp/feature/cqt.py | CQT.transform_audio | def transform_audio(self, y):
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----------
y : np.ndarray
The audio buffer
Returns
-------
data : dict
data['mag'] : np.ndarray, shape = (n_frames, n_bins)
The CQT magnitude
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bmcfee/pumpp | pumpp/feature/cqt.py | CQTMag.transform_audio | def transform_audio(self, y):
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y : np.ndarray
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bmcfee/pumpp | pumpp/feature/cqt.py | CQTPhaseDiff.transform_audio | def transform_audio(self, y):
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y : np.ndarray
The audio buffer
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-------
data : dict
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CQT magnitude
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bmcfee/pumpp | pumpp/feature/cqt.py | HCQT.transform_audio | def transform_audio(self, y):
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y : np.ndarray
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y : np.ndarray
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bmcfee/pumpp | pumpp/feature/cqt.py | HCQT._index | def _index(self, value):
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bmcfee/pumpp | pumpp/feature/cqt.py | HCQTMag.transform_audio | def transform_audio(self, y):
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Parameters
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y : np.ndarray
the audio buffer
Returns
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data : dict
data['mag'] : np.ndarray, shape=(n_frames, n_bins)
The CQT magnitude
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'''Compute HCQT magnitude.
Parameters
----------
y : np.ndarray
the audio buffer
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data : dict
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bmcfee/pumpp | pumpp/feature/cqt.py | HCQTPhaseDiff.transform_audio | def transform_audio(self, y):
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Parameters
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bmcfee/pumpp | pumpp/task/base.py | fill_value | def fill_value(dtype):
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dtype : type
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`np.nan` if `dtype` is real or complex
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'''
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'''Get a fill-value for a given dtype
Parameters
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dtype : type
Returns
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`np.nan` if `dtype` is real or complex
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bmcfee/pumpp | pumpp/task/base.py | BaseTaskTransformer.empty | def empty(self, duration):
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This method should be overridden by derived classes.
Parameters
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duration : int >= 0
Duration of the annotation
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This method should be overridden by derived classes.
Parameters
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duration : int >= 0
Duration of the annotation
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bmcfee/pumpp | pumpp/task/base.py | BaseTaskTransformer.transform | def transform(self, jam, query=None):
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Parameters
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The jams container object
query : string, dict, or callable [optional]
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The jams container object
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bmcfee/pumpp | pumpp/task/base.py | BaseTaskTransformer.encode_events | def encode_events(self, duration, events, values, dtype=np.bool):
'''Encode labeled events as a time-series matrix.
Parameters
----------
duration : number
The duration of the track
events : ndarray, shape=(n,)
Time index of the events
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duration : number
The duration of the track
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Time index of the events
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bmcfee/pumpp | pumpp/task/base.py | BaseTaskTransformer.encode_intervals | def encode_intervals(self, duration, intervals, values, dtype=np.bool,
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----------
duration : number
The duration (in frames) of the track
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bmcfee/pumpp | pumpp/task/base.py | BaseTaskTransformer.decode_events | def decode_events(self, encoded, transition=None, p_state=None, p_init=None):
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bmcfee/pumpp | pumpp/task/base.py | BaseTaskTransformer.decode_intervals | def decode_intervals(self, encoded, duration=None, multi=True, sparse=False,
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bmcfee/pumpp | pumpp/feature/base.py | FeatureExtractor.transform | def transform(self, y, sr):
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----------
y : np.ndarray
The audio signal
sr : number > 0
The native sampling rate of y
Returns
-------
dict
Data dictionary containing features ext... | python | def transform(self, y, sr):
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y : np.ndarray
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The native sampling rate of y
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bmcfee/pumpp | pumpp/feature/base.py | FeatureExtractor.phase_diff | def phase_diff(self, phase):
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Parameters
----------
phase : np.ndarray
Input phase (in radians)
Returns
-------
dphase : np.ndarray like `phase`
The phase differential.
'''
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phase : np.ndarray
Input phase (in radians)
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dphase : np.ndarray like `phase`
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bmcfee/pumpp | pumpp/feature/base.py | FeatureExtractor.layers | def layers(self):
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Returns
-------
layers : {field: keras.layers.Input}
A dictionary of keras input layers, keyed by the corresponding
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'''
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bmcfee/pumpp | pumpp/feature/base.py | FeatureExtractor.n_frames | def n_frames(self, duration):
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duration : number >= 0
The duration, in seconds
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-------
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novopl/peltak | src/peltak/extra/gitflow/logic/release.py | start | def start(component, exact):
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Version component to bump when creating the release. Can be *major*,
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novopl/peltak | src/peltak/extra/gitflow/logic/release.py | tag | def tag(message):
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novopl/peltak | src/peltak/logic/lint.py | lint | def lint(exclude, skip_untracked, commit_only):
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""" Lint python files.
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exclude (list[str]):
A list of glob string patterns to test against. If the file/path
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comm... | [
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novopl/peltak | src/peltak/logic/lint.py | tool | def tool(name):
# type: (str) -> FunctionType
""" Decorator for defining lint tools.
Args:
name (str):
The name of the tool. This name will be used to identify the tool
in `pelconf.yaml`.
"""
global g_tools
def decorator(fn): # pylint: disable=missing-docstring... | python | def tool(name):
# type: (str) -> FunctionType
""" Decorator for defining lint tools.
Args:
name (str):
The name of the tool. This name will be used to identify the tool
in `pelconf.yaml`.
"""
global g_tools
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novopl/peltak | src/peltak/logic/lint.py | pep8_check | def pep8_check(files):
# type: (List[str]) -> int
""" Run code checks using pep8.
Args:
files (list[str]):
A list of files to check
Returns:
bool: **True** if all files passed the checks, **False** otherwise.
pep8 tool is **very** fast. Especially compared to pylint an... | python | def pep8_check(files):
# type: (List[str]) -> int
""" Run code checks using pep8.
Args:
files (list[str]):
A list of files to check
Returns:
bool: **True** if all files passed the checks, **False** otherwise.
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novopl/peltak | src/peltak/logic/lint.py | pylint_check | def pylint_check(files):
# type: (List[str]) -> int
""" Run code checks using pylint.
Args:
files (list[str]):
A list of files to check
Returns:
bool: **True** if all files passed the checks, **False** otherwise.
"""
files = fs.wrap_paths(files)
cfg_path = conf.... | python | def pylint_check(files):
# type: (List[str]) -> int
""" Run code checks using pylint.
Args:
files (list[str]):
A list of files to check
Returns:
bool: **True** if all files passed the checks, **False** otherwise.
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files = fs.wrap_paths(files)
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novopl/peltak | src/peltak/logic/lint.py | LintRunner.run | def run(self):
# type: () -> bool
""" Run all linters and report results.
Returns:
bool: **True** if all checks were successful, **False** otherwise.
"""
with util.timed_block() as t:
files = self._collect_files()
log.info("Collected <33>{} <32>f... | python | def run(self):
# type: () -> bool
""" Run all linters and report results.
Returns:
bool: **True** if all checks were successful, **False** otherwise.
"""
with util.timed_block() as t:
files = self._collect_files()
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dacker-team/pyzure | pyzure/send/send.py | send_to_azure | def send_to_azure(instance, data, replace=True, types=None, primary_key=(), sub_commit=True):
"""
data = {
"table_name" : 'name_of_the_azure_schema' + '.' + 'name_of_the_azure_table' #Must already exist,
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"row... | python | def send_to_azure(instance, data, replace=True, types=None, primary_key=(), sub_commit=True):
"""
data = {
"table_name" : 'name_of_the_azure_schema' + '.' + 'name_of_the_azure_table' #Must already exist,
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cons3rt/pycons3rt | pycons3rt/dyndict.py | getdict | def getdict(source):
"""Returns a standard python Dict with computed values
from the DynDict
:param source: (DynDict) input
:return: (dict) Containing computed values
"""
std_dict = {}
for var, val in source.iteritems():
std_dict[var] = source[var]
return std_dict | python | def getdict(source):
"""Returns a standard python Dict with computed values
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:param source: (DynDict) input
:return: (dict) Containing computed values
"""
std_dict = {}
for var, val in source.iteritems():
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Varkal/chuda | chuda/plugins.py | Plugin.enrich_app | def enrich_app(self, name, value):
'''
Add a new property to the app (with setattr)
Args:
name (str): the name of the new property
value (any): the value of the new property
'''
#Method shouldn't be added: https://stackoverflow.com/a/28060251/3042398
... | python | def enrich_app(self, name, value):
'''
Add a new property to the app (with setattr)
Args:
name (str): the name of the new property
value (any): the value of the new property
'''
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Vital-Fernandez/dazer | bin/lib/Math_Libraries/fitting_methods.py | linfit | def linfit(x_true, y, sigmay=None, relsigma=True, cov=False, chisq=False, residuals=False):
"""
Least squares linear fit.
Fit a straight line `f(x_true) = a + bx` to points `(x_true, y)`. Returns
coefficients `a` and `b` that minimize the squared error.
Parameters
----------
x_t... | python | def linfit(x_true, y, sigmay=None, relsigma=True, cov=False, chisq=False, residuals=False):
"""
Least squares linear fit.
Fit a straight line `f(x_true) = a + bx` to points `(x_true, y)`. Returns
coefficients `a` and `b` that minimize the squared error.
Parameters
----------
x_t... | [
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