715 lines
24 KiB
Python
715 lines
24 KiB
Python
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import numpy as np
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from scipy.special import factorial
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from scipy._lib._util import _asarray_validated, float_factorial
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__all__ = ["KroghInterpolator", "krogh_interpolate", "BarycentricInterpolator",
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"barycentric_interpolate", "approximate_taylor_polynomial"]
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def _isscalar(x):
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"""Check whether x is if a scalar type, or 0-dim"""
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return np.isscalar(x) or hasattr(x, 'shape') and x.shape == ()
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class _Interpolator1D(object):
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"""
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Common features in univariate interpolation
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Deal with input data type and interpolation axis rolling. The
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actual interpolator can assume the y-data is of shape (n, r) where
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`n` is the number of x-points, and `r` the number of variables,
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and use self.dtype as the y-data type.
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Attributes
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----------
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_y_axis
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Axis along which the interpolation goes in the original array
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_y_extra_shape
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Additional trailing shape of the input arrays, excluding
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the interpolation axis.
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dtype
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Dtype of the y-data arrays. Can be set via _set_dtype, which
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forces it to be float or complex.
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Methods
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-------
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__call__
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_prepare_x
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_finish_y
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_reshape_yi
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_set_yi
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_set_dtype
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_evaluate
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"""
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__slots__ = ('_y_axis', '_y_extra_shape', 'dtype')
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def __init__(self, xi=None, yi=None, axis=None):
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self._y_axis = axis
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self._y_extra_shape = None
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self.dtype = None
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if yi is not None:
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self._set_yi(yi, xi=xi, axis=axis)
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def __call__(self, x):
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"""
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Evaluate the interpolant
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Parameters
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----------
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x : array_like
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Points to evaluate the interpolant at.
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Returns
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-------
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y : array_like
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Interpolated values. Shape is determined by replacing
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the interpolation axis in the original array with the shape of x.
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Notes
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-----
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Input values `x` must be convertible to `float` values like `int`
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or `float`.
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"""
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x, x_shape = self._prepare_x(x)
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y = self._evaluate(x)
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return self._finish_y(y, x_shape)
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def _evaluate(self, x):
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"""
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Actually evaluate the value of the interpolator.
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"""
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raise NotImplementedError()
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def _prepare_x(self, x):
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"""Reshape input x array to 1-D"""
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x = _asarray_validated(x, check_finite=False, as_inexact=True)
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x_shape = x.shape
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return x.ravel(), x_shape
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def _finish_y(self, y, x_shape):
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"""Reshape interpolated y back to an N-D array similar to initial y"""
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y = y.reshape(x_shape + self._y_extra_shape)
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if self._y_axis != 0 and x_shape != ():
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nx = len(x_shape)
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ny = len(self._y_extra_shape)
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s = (list(range(nx, nx + self._y_axis))
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+ list(range(nx)) + list(range(nx+self._y_axis, nx+ny)))
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y = y.transpose(s)
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return y
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def _reshape_yi(self, yi, check=False):
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yi = np.rollaxis(np.asarray(yi), self._y_axis)
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if check and yi.shape[1:] != self._y_extra_shape:
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ok_shape = "%r + (N,) + %r" % (self._y_extra_shape[-self._y_axis:],
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self._y_extra_shape[:-self._y_axis])
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raise ValueError("Data must be of shape %s" % ok_shape)
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return yi.reshape((yi.shape[0], -1))
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def _set_yi(self, yi, xi=None, axis=None):
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if axis is None:
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axis = self._y_axis
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if axis is None:
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raise ValueError("no interpolation axis specified")
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yi = np.asarray(yi)
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shape = yi.shape
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if shape == ():
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shape = (1,)
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if xi is not None and shape[axis] != len(xi):
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raise ValueError("x and y arrays must be equal in length along "
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"interpolation axis.")
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self._y_axis = (axis % yi.ndim)
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self._y_extra_shape = yi.shape[:self._y_axis]+yi.shape[self._y_axis+1:]
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self.dtype = None
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self._set_dtype(yi.dtype)
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def _set_dtype(self, dtype, union=False):
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if np.issubdtype(dtype, np.complexfloating) \
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or np.issubdtype(self.dtype, np.complexfloating):
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self.dtype = np.complex_
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else:
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if not union or self.dtype != np.complex_:
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self.dtype = np.float_
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class _Interpolator1DWithDerivatives(_Interpolator1D):
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def derivatives(self, x, der=None):
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"""
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Evaluate many derivatives of the polynomial at the point x
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Produce an array of all derivative values at the point x.
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Parameters
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----------
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x : array_like
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Point or points at which to evaluate the derivatives
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der : int or None, optional
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How many derivatives to extract; None for all potentially
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nonzero derivatives (that is a number equal to the number
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of points). This number includes the function value as 0th
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derivative.
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Returns
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-------
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d : ndarray
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Array with derivatives; d[j] contains the jth derivative.
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Shape of d[j] is determined by replacing the interpolation
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axis in the original array with the shape of x.
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Examples
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--------
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>>> from scipy.interpolate import KroghInterpolator
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>>> KroghInterpolator([0,0,0],[1,2,3]).derivatives(0)
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array([1.0,2.0,3.0])
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>>> KroghInterpolator([0,0,0],[1,2,3]).derivatives([0,0])
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array([[1.0,1.0],
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[2.0,2.0],
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[3.0,3.0]])
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"""
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x, x_shape = self._prepare_x(x)
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y = self._evaluate_derivatives(x, der)
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y = y.reshape((y.shape[0],) + x_shape + self._y_extra_shape)
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if self._y_axis != 0 and x_shape != ():
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nx = len(x_shape)
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ny = len(self._y_extra_shape)
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s = ([0] + list(range(nx+1, nx + self._y_axis+1))
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+ list(range(1, nx+1)) +
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list(range(nx+1+self._y_axis, nx+ny+1)))
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y = y.transpose(s)
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return y
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def derivative(self, x, der=1):
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"""
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Evaluate one derivative of the polynomial at the point x
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Parameters
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----------
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x : array_like
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Point or points at which to evaluate the derivatives
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der : integer, optional
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Which derivative to extract. This number includes the
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function value as 0th derivative.
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Returns
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-------
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d : ndarray
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Derivative interpolated at the x-points. Shape of d is
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determined by replacing the interpolation axis in the
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original array with the shape of x.
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Notes
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-----
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This is computed by evaluating all derivatives up to the desired
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one (using self.derivatives()) and then discarding the rest.
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"""
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x, x_shape = self._prepare_x(x)
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y = self._evaluate_derivatives(x, der+1)
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return self._finish_y(y[der], x_shape)
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class KroghInterpolator(_Interpolator1DWithDerivatives):
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"""
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Interpolating polynomial for a set of points.
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The polynomial passes through all the pairs (xi,yi). One may
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additionally specify a number of derivatives at each point xi;
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this is done by repeating the value xi and specifying the
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derivatives as successive yi values.
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Allows evaluation of the polynomial and all its derivatives.
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For reasons of numerical stability, this function does not compute
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the coefficients of the polynomial, although they can be obtained
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by evaluating all the derivatives.
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Parameters
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----------
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xi : array_like, length N
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Known x-coordinates. Must be sorted in increasing order.
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yi : array_like
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Known y-coordinates. When an xi occurs two or more times in
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a row, the corresponding yi's represent derivative values.
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axis : int, optional
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Axis in the yi array corresponding to the x-coordinate values.
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Notes
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-----
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Be aware that the algorithms implemented here are not necessarily
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the most numerically stable known. Moreover, even in a world of
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exact computation, unless the x coordinates are chosen very
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carefully - Chebyshev zeros (e.g., cos(i*pi/n)) are a good choice -
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polynomial interpolation itself is a very ill-conditioned process
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due to the Runge phenomenon. In general, even with well-chosen
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x values, degrees higher than about thirty cause problems with
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numerical instability in this code.
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Based on [1]_.
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References
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----------
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.. [1] Krogh, "Efficient Algorithms for Polynomial Interpolation
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and Numerical Differentiation", 1970.
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Examples
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--------
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To produce a polynomial that is zero at 0 and 1 and has
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derivative 2 at 0, call
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>>> from scipy.interpolate import KroghInterpolator
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>>> KroghInterpolator([0,0,1],[0,2,0])
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This constructs the quadratic 2*X**2-2*X. The derivative condition
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is indicated by the repeated zero in the xi array; the corresponding
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yi values are 0, the function value, and 2, the derivative value.
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For another example, given xi, yi, and a derivative ypi for each
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point, appropriate arrays can be constructed as:
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>>> xi = np.linspace(0, 1, 5)
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>>> yi, ypi = np.random.rand(2, 5)
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>>> xi_k, yi_k = np.repeat(xi, 2), np.ravel(np.dstack((yi,ypi)))
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>>> KroghInterpolator(xi_k, yi_k)
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To produce a vector-valued polynomial, supply a higher-dimensional
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array for yi:
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>>> KroghInterpolator([0,1],[[2,3],[4,5]])
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This constructs a linear polynomial giving (2,3) at 0 and (4,5) at 1.
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"""
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def __init__(self, xi, yi, axis=0):
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_Interpolator1DWithDerivatives.__init__(self, xi, yi, axis)
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self.xi = np.asarray(xi)
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self.yi = self._reshape_yi(yi)
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self.n, self.r = self.yi.shape
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c = np.zeros((self.n+1, self.r), dtype=self.dtype)
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c[0] = self.yi[0]
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Vk = np.zeros((self.n, self.r), dtype=self.dtype)
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for k in range(1, self.n):
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s = 0
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while s <= k and xi[k-s] == xi[k]:
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s += 1
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s -= 1
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Vk[0] = self.yi[k]/float_factorial(s)
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for i in range(k-s):
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if xi[i] == xi[k]:
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raise ValueError("Elements if `xi` can't be equal.")
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if s == 0:
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Vk[i+1] = (c[i]-Vk[i])/(xi[i]-xi[k])
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else:
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Vk[i+1] = (Vk[i+1]-Vk[i])/(xi[i]-xi[k])
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c[k] = Vk[k-s]
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self.c = c
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def _evaluate(self, x):
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pi = 1
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p = np.zeros((len(x), self.r), dtype=self.dtype)
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p += self.c[0,np.newaxis,:]
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for k in range(1, self.n):
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w = x - self.xi[k-1]
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pi = w*pi
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p += pi[:,np.newaxis] * self.c[k]
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return p
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def _evaluate_derivatives(self, x, der=None):
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n = self.n
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r = self.r
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if der is None:
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der = self.n
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pi = np.zeros((n, len(x)))
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w = np.zeros((n, len(x)))
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pi[0] = 1
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p = np.zeros((len(x), self.r), dtype=self.dtype)
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p += self.c[0, np.newaxis, :]
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for k in range(1, n):
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w[k-1] = x - self.xi[k-1]
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pi[k] = w[k-1] * pi[k-1]
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p += pi[k, :, np.newaxis] * self.c[k]
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cn = np.zeros((max(der, n+1), len(x), r), dtype=self.dtype)
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cn[:n+1, :, :] += self.c[:n+1, np.newaxis, :]
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cn[0] = p
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for k in range(1, n):
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for i in range(1, n-k+1):
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pi[i] = w[k+i-1]*pi[i-1] + pi[i]
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cn[k] = cn[k] + pi[i, :, np.newaxis]*cn[k+i]
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cn[k] *= float_factorial(k)
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cn[n, :, :] = 0
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return cn[:der]
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def krogh_interpolate(xi, yi, x, der=0, axis=0):
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"""
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Convenience function for polynomial interpolation.
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See `KroghInterpolator` for more details.
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Parameters
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----------
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xi : array_like
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Known x-coordinates.
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yi : array_like
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Known y-coordinates, of shape ``(xi.size, R)``. Interpreted as
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|
vectors of length R, or scalars if R=1.
|
||
|
|
x : array_like
|
||
|
|
Point or points at which to evaluate the derivatives.
|
||
|
|
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.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
d : ndarray
|
||
|
|
If the interpolator's values are R-D then the
|
||
|
|
returned array will be the number of derivatives by N by R.
|
||
|
|
If `x` is a scalar, the middle dimension will be dropped; if
|
||
|
|
the `yi` are scalars then the last dimension will be dropped.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
KroghInterpolator : Krogh interpolator
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Construction of the interpolating polynomial is a relatively expensive
|
||
|
|
process. If you want to evaluate it repeatedly consider using the class
|
||
|
|
KroghInterpolator (which is what this function uses).
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
We can interpolate 2D observed data using krogh interpolation:
|
||
|
|
|
||
|
|
>>> import matplotlib.pyplot as plt
|
||
|
|
>>> from scipy.interpolate import krogh_interpolate
|
||
|
|
>>> x_observed = np.linspace(0.0, 10.0, 11)
|
||
|
|
>>> y_observed = np.sin(x_observed)
|
||
|
|
>>> x = np.linspace(min(x_observed), max(x_observed), num=100)
|
||
|
|
>>> y = krogh_interpolate(x_observed, y_observed, x)
|
||
|
|
>>> plt.plot(x_observed, y_observed, "o", label="observation")
|
||
|
|
>>> plt.plot(x, y, label="krogh interpolation")
|
||
|
|
>>> plt.legend()
|
||
|
|
>>> plt.show()
|
||
|
|
|
||
|
|
"""
|
||
|
|
P = KroghInterpolator(xi, yi, axis=axis)
|
||
|
|
if der == 0:
|
||
|
|
return P(x)
|
||
|
|
elif _isscalar(der):
|
||
|
|
return P.derivative(x,der=der)
|
||
|
|
else:
|
||
|
|
return P.derivatives(x,der=np.amax(der)+1)[der]
|
||
|
|
|
||
|
|
|
||
|
|
def approximate_taylor_polynomial(f,x,degree,scale,order=None):
|
||
|
|
"""
|
||
|
|
Estimate the Taylor polynomial of f at x by polynomial fitting.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
f : callable
|
||
|
|
The function whose Taylor polynomial is sought. Should accept
|
||
|
|
a vector of `x` values.
|
||
|
|
x : scalar
|
||
|
|
The point at which the polynomial is to be evaluated.
|
||
|
|
degree : int
|
||
|
|
The degree of the Taylor polynomial
|
||
|
|
scale : scalar
|
||
|
|
The width of the interval to use to evaluate the Taylor polynomial.
|
||
|
|
Function values spread over a range this wide are used to fit the
|
||
|
|
polynomial. Must be chosen carefully.
|
||
|
|
order : int or None, optional
|
||
|
|
The order of the polynomial to be used in the fitting; `f` will be
|
||
|
|
evaluated ``order+1`` times. If None, use `degree`.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
p : poly1d instance
|
||
|
|
The Taylor polynomial (translated to the origin, so that
|
||
|
|
for example p(0)=f(x)).
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The appropriate choice of "scale" is a trade-off; too large and the
|
||
|
|
function differs from its Taylor polynomial too much to get a good
|
||
|
|
answer, too small and round-off errors overwhelm the higher-order terms.
|
||
|
|
The algorithm used becomes numerically unstable around order 30 even
|
||
|
|
under ideal circumstances.
|
||
|
|
|
||
|
|
Choosing order somewhat larger than degree may improve the higher-order
|
||
|
|
terms.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
We can calculate Taylor approximation polynomials of sin function with
|
||
|
|
various degrees:
|
||
|
|
|
||
|
|
>>> import matplotlib.pyplot as plt
|
||
|
|
>>> from scipy.interpolate import approximate_taylor_polynomial
|
||
|
|
>>> x = np.linspace(-10.0, 10.0, num=100)
|
||
|
|
>>> plt.plot(x, np.sin(x), label="sin curve")
|
||
|
|
>>> for degree in np.arange(1, 15, step=2):
|
||
|
|
... sin_taylor = approximate_taylor_polynomial(np.sin, 0, degree, 1,
|
||
|
|
... order=degree + 2)
|
||
|
|
... plt.plot(x, sin_taylor(x), label=f"degree={degree}")
|
||
|
|
>>> plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left',
|
||
|
|
... borderaxespad=0.0, shadow=True)
|
||
|
|
>>> plt.tight_layout()
|
||
|
|
>>> plt.axis([-10, 10, -10, 10])
|
||
|
|
>>> plt.show()
|
||
|
|
|
||
|
|
"""
|
||
|
|
if order is None:
|
||
|
|
order = degree
|
||
|
|
|
||
|
|
n = order+1
|
||
|
|
# Choose n points that cluster near the endpoints of the interval in
|
||
|
|
# a way that avoids the Runge phenomenon. Ensure, by including the
|
||
|
|
# endpoint or not as appropriate, that one point always falls at x
|
||
|
|
# exactly.
|
||
|
|
xs = scale*np.cos(np.linspace(0,np.pi,n,endpoint=n % 1)) + x
|
||
|
|
|
||
|
|
P = KroghInterpolator(xs, f(xs))
|
||
|
|
d = P.derivatives(x,der=degree+1)
|
||
|
|
|
||
|
|
return np.poly1d((d/factorial(np.arange(degree+1)))[::-1])
|
||
|
|
|
||
|
|
|
||
|
|
class BarycentricInterpolator(_Interpolator1D):
|
||
|
|
"""The interpolating polynomial for a set of points
|
||
|
|
|
||
|
|
Constructs a polynomial that passes through a given set of points.
|
||
|
|
Allows evaluation of the polynomial, efficient changing of the y
|
||
|
|
values to be interpolated, and updating by adding more x values.
|
||
|
|
For reasons of numerical stability, this function does not compute
|
||
|
|
the coefficients of the polynomial.
|
||
|
|
|
||
|
|
The values yi need to be provided before the function is
|
||
|
|
evaluated, but none of the preprocessing depends on them, so rapid
|
||
|
|
updates are possible.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
xi : array_like
|
||
|
|
1-D array of x coordinates of the points the polynomial
|
||
|
|
should pass through
|
||
|
|
yi : array_like, optional
|
||
|
|
The y coordinates of the points the polynomial should pass through.
|
||
|
|
If None, the y values will be supplied later via the `set_y` method.
|
||
|
|
axis : int, optional
|
||
|
|
Axis in the yi array corresponding to the x-coordinate values.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
This class uses a "barycentric interpolation" method that treats
|
||
|
|
the problem as a special case of rational function interpolation.
|
||
|
|
This algorithm is quite stable, numerically, but even in a world of
|
||
|
|
exact computation, unless the x coordinates are chosen very
|
||
|
|
carefully - Chebyshev zeros (e.g., cos(i*pi/n)) are a good choice -
|
||
|
|
polynomial interpolation itself is a very ill-conditioned process
|
||
|
|
due to the Runge phenomenon.
|
||
|
|
|
||
|
|
Based on Berrut and Trefethen 2004, "Barycentric Lagrange Interpolation".
|
||
|
|
|
||
|
|
"""
|
||
|
|
def __init__(self, xi, yi=None, axis=0):
|
||
|
|
_Interpolator1D.__init__(self, xi, yi, axis)
|
||
|
|
|
||
|
|
self.xi = np.asfarray(xi)
|
||
|
|
self.set_yi(yi)
|
||
|
|
self.n = len(self.xi)
|
||
|
|
|
||
|
|
self.wi = np.zeros(self.n)
|
||
|
|
self.wi[0] = 1
|
||
|
|
for j in range(1, self.n):
|
||
|
|
self.wi[:j] *= (self.xi[j]-self.xi[:j])
|
||
|
|
self.wi[j] = np.multiply.reduce(self.xi[:j]-self.xi[j])
|
||
|
|
self.wi **= -1
|
||
|
|
|
||
|
|
def set_yi(self, yi, axis=None):
|
||
|
|
"""
|
||
|
|
Update the y values to be interpolated
|
||
|
|
|
||
|
|
The barycentric interpolation algorithm requires the calculation
|
||
|
|
of weights, but these depend only on the xi. The yi can be changed
|
||
|
|
at any time.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
yi : array_like
|
||
|
|
The y coordinates of the points the polynomial should pass through.
|
||
|
|
If None, the y values will be supplied later.
|
||
|
|
axis : int, optional
|
||
|
|
Axis in the yi array corresponding to the x-coordinate values.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if yi is None:
|
||
|
|
self.yi = None
|
||
|
|
return
|
||
|
|
self._set_yi(yi, xi=self.xi, axis=axis)
|
||
|
|
self.yi = self._reshape_yi(yi)
|
||
|
|
self.n, self.r = self.yi.shape
|
||
|
|
|
||
|
|
def add_xi(self, xi, yi=None):
|
||
|
|
"""
|
||
|
|
Add more x values to the set to be interpolated
|
||
|
|
|
||
|
|
The barycentric interpolation algorithm allows easy updating by
|
||
|
|
adding more points for the polynomial to pass through.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
xi : array_like
|
||
|
|
The x coordinates of the points that the polynomial should pass
|
||
|
|
through.
|
||
|
|
yi : array_like, optional
|
||
|
|
The y coordinates of the points the polynomial should pass through.
|
||
|
|
Should have shape ``(xi.size, R)``; if R > 1 then the polynomial is
|
||
|
|
vector-valued.
|
||
|
|
If `yi` is not given, the y values will be supplied later. `yi` should
|
||
|
|
be given if and only if the interpolator has y values specified.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if yi is not None:
|
||
|
|
if self.yi is None:
|
||
|
|
raise ValueError("No previous yi value to update!")
|
||
|
|
yi = self._reshape_yi(yi, check=True)
|
||
|
|
self.yi = np.vstack((self.yi,yi))
|
||
|
|
else:
|
||
|
|
if self.yi is not None:
|
||
|
|
raise ValueError("No update to yi provided!")
|
||
|
|
old_n = self.n
|
||
|
|
self.xi = np.concatenate((self.xi,xi))
|
||
|
|
self.n = len(self.xi)
|
||
|
|
self.wi **= -1
|
||
|
|
old_wi = self.wi
|
||
|
|
self.wi = np.zeros(self.n)
|
||
|
|
self.wi[:old_n] = old_wi
|
||
|
|
for j in range(old_n, self.n):
|
||
|
|
self.wi[:j] *= (self.xi[j]-self.xi[:j])
|
||
|
|
self.wi[j] = np.multiply.reduce(self.xi[:j]-self.xi[j])
|
||
|
|
self.wi **= -1
|
||
|
|
|
||
|
|
def __call__(self, x):
|
||
|
|
"""Evaluate the interpolating polynomial at the points x
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
x : array_like
|
||
|
|
Points to evaluate the interpolant at.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y : array_like
|
||
|
|
Interpolated values. Shape is determined by replacing
|
||
|
|
the interpolation axis in the original array with the shape of x.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Currently the code computes an outer product between x and the
|
||
|
|
weights, that is, it constructs an intermediate array of size
|
||
|
|
N by len(x), where N is the degree of the polynomial.
|
||
|
|
"""
|
||
|
|
return _Interpolator1D.__call__(self, x)
|
||
|
|
|
||
|
|
def _evaluate(self, x):
|
||
|
|
if x.size == 0:
|
||
|
|
p = np.zeros((0, self.r), dtype=self.dtype)
|
||
|
|
else:
|
||
|
|
c = x[...,np.newaxis]-self.xi
|
||
|
|
z = c == 0
|
||
|
|
c[z] = 1
|
||
|
|
c = self.wi/c
|
||
|
|
p = np.dot(c,self.yi)/np.sum(c,axis=-1)[...,np.newaxis]
|
||
|
|
# Now fix where x==some xi
|
||
|
|
r = np.nonzero(z)
|
||
|
|
if len(r) == 1: # evaluation at a scalar
|
||
|
|
if len(r[0]) > 0: # equals one of the points
|
||
|
|
p = self.yi[r[0][0]]
|
||
|
|
else:
|
||
|
|
p[r[:-1]] = self.yi[r[-1]]
|
||
|
|
return p
|
||
|
|
|
||
|
|
|
||
|
|
def barycentric_interpolate(xi, yi, x, axis=0):
|
||
|
|
"""
|
||
|
|
Convenience function for polynomial interpolation.
|
||
|
|
|
||
|
|
Constructs a polynomial that passes through a given set of points,
|
||
|
|
then evaluates the polynomial. For reasons of numerical stability,
|
||
|
|
this function does not compute the coefficients of the polynomial.
|
||
|
|
|
||
|
|
This function uses a "barycentric interpolation" method that treats
|
||
|
|
the problem as a special case of rational function interpolation.
|
||
|
|
This algorithm is quite stable, numerically, but even in a world of
|
||
|
|
exact computation, unless the `x` coordinates are chosen very
|
||
|
|
carefully - Chebyshev zeros (e.g., cos(i*pi/n)) are a good choice -
|
||
|
|
polynomial interpolation itself is a very ill-conditioned process
|
||
|
|
due to the Runge phenomenon.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
xi : array_like
|
||
|
|
1-D array of x coordinates of the points the polynomial should
|
||
|
|
pass through
|
||
|
|
yi : array_like
|
||
|
|
The y coordinates of the points the polynomial should pass through.
|
||
|
|
x : scalar or array_like
|
||
|
|
Points to evaluate the interpolator at.
|
||
|
|
axis : int, optional
|
||
|
|
Axis in the yi array corresponding to the x-coordinate values.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y : scalar or array_like
|
||
|
|
Interpolated values. Shape is determined by replacing
|
||
|
|
the interpolation axis in the original array with the shape of x.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
BarycentricInterpolator : Bary centric interpolator
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Construction of the interpolation weights is a relatively slow process.
|
||
|
|
If you want to call this many times with the same xi (but possibly
|
||
|
|
varying yi or x) you should use the class `BarycentricInterpolator`.
|
||
|
|
This is what this function uses internally.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
We can interpolate 2D observed data using barycentric interpolation:
|
||
|
|
|
||
|
|
>>> import matplotlib.pyplot as plt
|
||
|
|
>>> from scipy.interpolate import barycentric_interpolate
|
||
|
|
>>> x_observed = np.linspace(0.0, 10.0, 11)
|
||
|
|
>>> y_observed = np.sin(x_observed)
|
||
|
|
>>> x = np.linspace(min(x_observed), max(x_observed), num=100)
|
||
|
|
>>> y = barycentric_interpolate(x_observed, y_observed, x)
|
||
|
|
>>> plt.plot(x_observed, y_observed, "o", label="observation")
|
||
|
|
>>> plt.plot(x, y, label="barycentric interpolation")
|
||
|
|
>>> plt.legend()
|
||
|
|
>>> plt.show()
|
||
|
|
|
||
|
|
"""
|
||
|
|
return BarycentricInterpolator(xi, yi, axis=axis)(x)
|