Python scipy.interpolate.Akima1DInterpolator() Examples
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code examples of scipy.interpolate.Akima1DInterpolator().
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Example #1
| Source File: fitting.py From grizli with MIT License | 6 votes |
|
def compute_cdf_percentiles(fit, cdf_sigmas=CDF_SIGMAS):
"""
Compute tabulated percentiles of the PDF
"""
from scipy.interpolate import Akima1DInterpolator
from scipy.integrate import cumtrapz
import scipy.stats
if cdf_sigmas is None:
cdf_sigmas = CDF_SIGMAS
cdf_y = scipy.stats.norm.cdf(cdf_sigmas)
if len(fit['zgrid']) == 1:
return np.ones_like(cdf_y)*fit['zgrid'][0], cdf_y
spl = Akima1DInterpolator(fit['zgrid'], np.log(fit['pdf']), axis=1)
zrfine = [fit['zgrid'].min(), fit['zgrid'].max()]
zfine = utils.log_zgrid(zr=zrfine, dz=0.0001)
ok = np.isfinite(spl(zfine))
pz_fine = np.exp(spl(zfine))
pz_fine[~ok] = 0
cdf_fine = cumtrapz(pz_fine, x=zfine)
cdf_x = np.interp(cdf_y, cdf_fine/cdf_fine[-1], zfine[1:])
return cdf_x, cdf_y
Example #2
| Source File: missing.py From recruit with Apache License 2.0 | 4 votes |
|
def _akima_interpolate(xi, yi, x, der=0, axis=0):
"""
Convenience function for akima interpolation.
xi and yi are arrays of values used to approximate some function f,
with ``yi = f(xi)``.
See `Akima1DInterpolator` for details.
Parameters
----------
xi : array_like
A sorted list of x-coordinates, of length N.
yi : array_like
A 1-D array of real values. `yi`'s length along the interpolation
axis must be equal to the length of `xi`. If N-D array, use axis
parameter to select correct axis.
x : scalar or array_like
Of length M.
der : int or list, optional
How many derivatives to extract; None for all potentially
nonzero derivatives (that is a number equal to the number
of points), or a list of derivatives to extract. This number
includes the function value as 0th derivative.
axis : int, optional
Axis in the yi array corresponding to the x-coordinate values.
See Also
--------
scipy.interpolate.Akima1DInterpolator
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
from scipy import interpolate
try:
P = interpolate.Akima1DInterpolator(xi, yi, axis=axis)
except TypeError:
# Scipy earlier than 0.17.0 missing axis
P = interpolate.Akima1DInterpolator(xi, yi)
if der == 0:
return P(x)
elif interpolate._isscalar(der):
return P(x, der=der)
else:
return [P(x, nu) for nu in der]
Example #3
| Source File: missing.py From vnpy_crypto with MIT License | 4 votes |
|
def _akima_interpolate(xi, yi, x, der=0, axis=0):
"""
Convenience function for akima interpolation.
xi and yi are arrays of values used to approximate some function f,
with ``yi = f(xi)``.
See `Akima1DInterpolator` for details.
Parameters
----------
xi : array_like
A sorted list of x-coordinates, of length N.
yi : array_like
A 1-D array of real values. `yi`'s length along the interpolation
axis must be equal to the length of `xi`. If N-D array, use axis
parameter to select correct axis.
x : scalar or array_like
Of length M.
der : int or list, optional
How many derivatives to extract; None for all potentially
nonzero derivatives (that is a number equal to the number
of points), or a list of derivatives to extract. This number
includes the function value as 0th derivative.
axis : int, optional
Axis in the yi array corresponding to the x-coordinate values.
See Also
--------
scipy.interpolate.Akima1DInterpolator
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
from scipy import interpolate
try:
P = interpolate.Akima1DInterpolator(xi, yi, axis=axis)
except TypeError:
# Scipy earlier than 0.17.0 missing axis
P = interpolate.Akima1DInterpolator(xi, yi)
if der == 0:
return P(x)
elif interpolate._isscalar(der):
return P(x, der=der)
else:
return [P(x, nu) for nu in der]
Example #4
| Source File: missing.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def _akima_interpolate(xi, yi, x, der=0, axis=0):
"""
Convenience function for akima interpolation.
xi and yi are arrays of values used to approximate some function f,
with ``yi = f(xi)``.
See `Akima1DInterpolator` for details.
Parameters
----------
xi : array_like
A sorted list of x-coordinates, of length N.
yi : array_like
A 1-D array of real values. `yi`'s length along the interpolation
axis must be equal to the length of `xi`. If N-D array, use axis
parameter to select correct axis.
x : scalar or array_like
Of length M.
der : int or list, optional
How many derivatives to extract; None for all potentially
nonzero derivatives (that is a number equal to the number
of points), or a list of derivatives to extract. This number
includes the function value as 0th derivative.
axis : int, optional
Axis in the yi array corresponding to the x-coordinate values.
See Also
--------
scipy.interpolate.Akima1DInterpolator
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
from scipy import interpolate
try:
P = interpolate.Akima1DInterpolator(xi, yi, axis=axis)
except TypeError:
# Scipy earlier than 0.17.0 missing axis
P = interpolate.Akima1DInterpolator(xi, yi)
if der == 0:
return P(x)
elif interpolate._isscalar(der):
return P(x, der=der)
else:
return [P(x, nu) for nu in der]
Example #5
| Source File: missing.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
def _akima_interpolate(xi, yi, x, der=0, axis=0):
"""
Convenience function for akima interpolation.
xi and yi are arrays of values used to approximate some function f,
with ``yi = f(xi)``.
See `Akima1DInterpolator` for details.
Parameters
----------
xi : array_like
A sorted list of x-coordinates, of length N.
yi : array_like
A 1-D array of real values. `yi`'s length along the interpolation
axis must be equal to the length of `xi`. If N-D array, use axis
parameter to select correct axis.
x : scalar or array_like
Of length M.
der : int or list, optional
How many derivatives to extract; None for all potentially
nonzero derivatives (that is a number equal to the number
of points), or a list of derivatives to extract. This number
includes the function value as 0th derivative.
axis : int, optional
Axis in the yi array corresponding to the x-coordinate values.
See Also
--------
scipy.interpolate.Akima1DInterpolator
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
from scipy import interpolate
try:
P = interpolate.Akima1DInterpolator(xi, yi, axis=axis)
except TypeError:
# Scipy earlier than 0.17.0 missing axis
P = interpolate.Akima1DInterpolator(xi, yi)
if der == 0:
return P(x)
elif interpolate._isscalar(der):
return P(x, der=der)
else:
return [P(x, nu) for nu in der]
Example #6
| Source File: missing.py From elasticintel with GNU General Public License v3.0 | 4 votes |
|
def _akima_interpolate(xi, yi, x, der=0, axis=0):
"""
Convenience function for akima interpolation.
xi and yi are arrays of values used to approximate some function f,
with ``yi = f(xi)``.
See `Akima1DInterpolator` for details.
Parameters
----------
xi : array_like
A sorted list of x-coordinates, of length N.
yi : array_like
A 1-D array of real values. `yi`'s length along the interpolation
axis must be equal to the length of `xi`. If N-D array, use axis
parameter to select correct axis.
x : scalar or array_like
Of length M.
der : int or list, optional
How many derivatives to extract; None for all potentially
nonzero derivatives (that is a number equal to the number
of points), or a list of derivatives to extract. This number
includes the function value as 0th derivative.
axis : int, optional
Axis in the yi array corresponding to the x-coordinate values.
See Also
--------
scipy.interpolate.Akima1DInterpolator
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
from scipy import interpolate
try:
P = interpolate.Akima1DInterpolator(xi, yi, axis=axis)
except TypeError:
# Scipy earlier than 0.17.0 missing axis
P = interpolate.Akima1DInterpolator(xi, yi)
if der == 0:
return P(x)
elif interpolate._isscalar(der):
return P(x, der=der)
else:
return [P(x, nu) for nu in der]
Example #7
| Source File: batteries.py From dymos with Apache License 2.0 | 4 votes |
|
def setup(self):
num_nodes = self.options['num_nodes']
# Inputs
self.add_input('I_Li', val=np.ones(num_nodes), units='A',
desc='Current demanded per cell')
# State Variables
self.add_input('SOC', val=np.ones(num_nodes), units=None, desc='State of charge')
# Outputs
self.add_output('V_L',
val=np.ones(num_nodes),
units='V',
desc='Terminal voltage of the battery')
self.add_output('dXdt:SOC',
val=np.ones(num_nodes),
units='1/s',
desc='Time derivative of state of charge')
self.add_output('V_oc', val=np.ones(num_nodes), units='V',
desc='Open Circuit Voltage')
self.add_output('I_pack', val=0.1*np.ones(num_nodes), units='A',
desc='Total Pack Current')
self.add_output('V_pack', val=9.0*np.ones(num_nodes), units='V',
desc='Total Pack Voltage')
self.add_output('P_pack', val=1.0*np.ones(num_nodes), units='W',
desc='Total Pack Power')
# Derivatives
row_col = np.arange(num_nodes)
self.declare_partials(of='V_oc', wrt=['SOC'], rows=row_col, cols=row_col)
self.declare_partials(of='V_L', wrt=['SOC'], rows=row_col, cols=row_col)
self.declare_partials(of='V_L', wrt=['I_Li'], rows=row_col, cols=row_col)
self.declare_partials(of='dXdt:SOC', wrt=['I_Li'], rows=row_col, cols=row_col)
self.declare_partials(of='I_pack', wrt=['I_Li'], rows=row_col, cols=row_col)
self.declare_partials(of='V_pack', wrt=['SOC', 'I_Li'], rows=row_col, cols=row_col)
self.declare_partials(of='P_pack', wrt=['SOC', 'I_Li'], rows=row_col, cols=row_col)
self.voltage_model = Akima1DInterpolator(train_SOC, train_V_oc)
self.voltage_model_derivative = self.voltage_model.derivative()
