Python scipy.interpolate.piecewise_polynomial_interpolate() Examples
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code examples of scipy.interpolate.piecewise_polynomial_interpolate().
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Example #1
| Source File: missing.py From recruit with Apache License 2.0 | 4 votes |
|
def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
"""
Convenience function for interpolate.BPoly.from_derivatives
Construct a piecewise polynomial in the Bernstein basis, compatible
with the specified values and derivatives at breakpoints.
Parameters
----------
xi : array_like
sorted 1D array of x-coordinates
yi : array_like or list of array-likes
yi[i][j] is the j-th derivative known at xi[i]
orders : None or int or array_like of ints. Default: None.
Specifies the degree of local polynomials. If not None, some
derivatives are ignored.
der : int or list
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 numberincludes the function
value as 0th derivative.
extrapolate : bool, optional
Whether to extrapolate to ouf-of-bounds points based on first and last
intervals, or to return NaNs. Default: True.
See Also
--------
scipy.interpolate.BPoly.from_derivatives
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
import scipy
from scipy import interpolate
if LooseVersion(scipy.__version__) < LooseVersion('0.18.0'):
try:
method = interpolate.piecewise_polynomial_interpolate
return method(xi, yi.reshape(-1, 1), x,
orders=order, der=der)
except AttributeError:
pass
# return the method for compat with scipy version & backwards compat
method = interpolate.BPoly.from_derivatives
m = method(xi, yi.reshape(-1, 1),
orders=order, extrapolate=extrapolate)
return m(x)
Example #2
| Source File: missing.py From vnpy_crypto with MIT License | 4 votes |
|
def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
"""
Convenience function for interpolate.BPoly.from_derivatives
Construct a piecewise polynomial in the Bernstein basis, compatible
with the specified values and derivatives at breakpoints.
Parameters
----------
xi : array_like
sorted 1D array of x-coordinates
yi : array_like or list of array-likes
yi[i][j] is the j-th derivative known at xi[i]
orders : None or int or array_like of ints. Default: None.
Specifies the degree of local polynomials. If not None, some
derivatives are ignored.
der : int or list
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 numberincludes the function
value as 0th derivative.
extrapolate : bool, optional
Whether to extrapolate to ouf-of-bounds points based on first and last
intervals, or to return NaNs. Default: True.
See Also
--------
scipy.interpolate.BPoly.from_derivatives
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
import scipy
from scipy import interpolate
if LooseVersion(scipy.__version__) < LooseVersion('0.18.0'):
try:
method = interpolate.piecewise_polynomial_interpolate
return method(xi, yi.reshape(-1, 1), x,
orders=order, der=der)
except AttributeError:
pass
# return the method for compat with scipy version & backwards compat
method = interpolate.BPoly.from_derivatives
m = method(xi, yi.reshape(-1, 1),
orders=order, extrapolate=extrapolate)
return m(x)
Example #3
| Source File: common.py From Computable with MIT License | 4 votes |
|
def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None,
bounds_error=False, order=None, **kwargs):
"""
passed off to scipy.interpolate.interp1d. method is scipy's kind.
Returns an array interpolated at new_x. Add any new methods to
the list in _clean_interp_method
"""
try:
from scipy import interpolate
from pandas import DatetimeIndex
except ImportError:
raise ImportError('{0} interpolation requires Scipy'.format(method))
new_x = np.asarray(new_x)
# ignores some kwargs that could be passed along.
alt_methods = {
'barycentric': interpolate.barycentric_interpolate,
'krogh': interpolate.krogh_interpolate,
'piecewise_polynomial': interpolate.piecewise_polynomial_interpolate,
}
if getattr(x, 'is_all_dates', False):
# GH 5975, scipy.interp1d can't hande datetime64s
x, new_x = x.values.astype('i8'), new_x.astype('i8')
try:
alt_methods['pchip'] = interpolate.pchip_interpolate
except AttributeError:
if method == 'pchip':
raise ImportError("Your version of scipy does not support "
"PCHIP interpolation.")
interp1d_methods = ['nearest', 'zero', 'slinear', 'quadratic', 'cubic',
'polynomial']
if method in interp1d_methods:
if method == 'polynomial':
method = order
terp = interpolate.interp1d(x, y, kind=method, fill_value=fill_value,
bounds_error=bounds_error)
new_y = terp(new_x)
elif method == 'spline':
terp = interpolate.UnivariateSpline(x, y, k=order)
new_y = terp(new_x)
else:
method = alt_methods[method]
new_y = method(x, y, new_x)
return new_y
Example #4
| Source File: missing.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
"""
Convenience function for interpolate.BPoly.from_derivatives
Construct a piecewise polynomial in the Bernstein basis, compatible
with the specified values and derivatives at breakpoints.
Parameters
----------
xi : array_like
sorted 1D array of x-coordinates
yi : array_like or list of array-likes
yi[i][j] is the j-th derivative known at xi[i]
orders : None or int or array_like of ints. Default: None.
Specifies the degree of local polynomials. If not None, some
derivatives are ignored.
der : int or list
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 numberincludes the function
value as 0th derivative.
extrapolate : bool, optional
Whether to extrapolate to ouf-of-bounds points based on first and last
intervals, or to return NaNs. Default: True.
See Also
--------
scipy.interpolate.BPoly.from_derivatives
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
import scipy
from scipy import interpolate
if LooseVersion(scipy.__version__) < LooseVersion('0.18.0'):
try:
method = interpolate.piecewise_polynomial_interpolate
return method(xi, yi.reshape(-1, 1), x,
orders=order, der=der)
except AttributeError:
pass
# return the method for compat with scipy version & backwards compat
method = interpolate.BPoly.from_derivatives
m = method(xi, yi.reshape(-1, 1),
orders=order, extrapolate=extrapolate)
return m(x)
Example #5
| Source File: missing.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
"""
Convenience function for interpolate.BPoly.from_derivatives
Construct a piecewise polynomial in the Bernstein basis, compatible
with the specified values and derivatives at breakpoints.
Parameters
----------
xi : array_like
sorted 1D array of x-coordinates
yi : array_like or list of array-likes
yi[i][j] is the j-th derivative known at xi[i]
orders : None or int or array_like of ints. Default: None.
Specifies the degree of local polynomials. If not None, some
derivatives are ignored.
der : int or list
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 numberincludes the function
value as 0th derivative.
extrapolate : bool, optional
Whether to extrapolate to ouf-of-bounds points based on first and last
intervals, or to return NaNs. Default: True.
See Also
--------
scipy.interpolate.BPoly.from_derivatives
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
import scipy
from scipy import interpolate
if LooseVersion(scipy.__version__) < '0.18.0':
try:
method = interpolate.piecewise_polynomial_interpolate
return method(xi, yi.reshape(-1, 1), x,
orders=order, der=der)
except AttributeError:
pass
# return the method for compat with scipy version & backwards compat
method = interpolate.BPoly.from_derivatives
m = method(xi, yi.reshape(-1, 1),
orders=order, extrapolate=extrapolate)
return m(x)
Example #6
| Source File: missing.py From elasticintel with GNU General Public License v3.0 | 4 votes |
|
def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
"""
Convenience function for interpolate.BPoly.from_derivatives
Construct a piecewise polynomial in the Bernstein basis, compatible
with the specified values and derivatives at breakpoints.
Parameters
----------
xi : array_like
sorted 1D array of x-coordinates
yi : array_like or list of array-likes
yi[i][j] is the j-th derivative known at xi[i]
orders : None or int or array_like of ints. Default: None.
Specifies the degree of local polynomials. If not None, some
derivatives are ignored.
der : int or list
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 numberincludes the function
value as 0th derivative.
extrapolate : bool, optional
Whether to extrapolate to ouf-of-bounds points based on first and last
intervals, or to return NaNs. Default: True.
See Also
--------
scipy.interpolate.BPoly.from_derivatives
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
import scipy
from scipy import interpolate
if LooseVersion(scipy.__version__) < '0.18.0':
try:
method = interpolate.piecewise_polynomial_interpolate
return method(xi, yi.reshape(-1, 1), x,
orders=order, der=der)
except AttributeError:
pass
# return the method for compat with scipy version & backwards compat
method = interpolate.BPoly.from_derivatives
m = method(xi, yi.reshape(-1, 1),
orders=order, extrapolate=extrapolate)
return m(x)
