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771 lines
29 KiB
Python
771 lines
29 KiB
Python
"""Interpolation algorithms using piecewise cubic polynomials."""
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from __future__ import division, print_function, absolute_import
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import numpy as np
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from scipy._lib.six import string_types
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from . import BPoly, PPoly
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from .polyint import _isscalar
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from scipy._lib._util import _asarray_validated
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from scipy.linalg import solve_banded, solve
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__all__ = ["PchipInterpolator", "pchip_interpolate", "pchip",
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"Akima1DInterpolator", "CubicSpline"]
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class PchipInterpolator(BPoly):
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r"""PCHIP 1-d monotonic cubic interpolation.
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`x` and `y` are arrays of values used to approximate some function f,
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with ``y = f(x)``. The interpolant uses monotonic cubic splines
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to find the value of new points. (PCHIP stands for Piecewise Cubic
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Hermite Interpolating Polynomial).
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Parameters
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----------
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x : ndarray
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A 1-D array of monotonically increasing real values. `x` cannot
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include duplicate values (otherwise f is overspecified)
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y : ndarray
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A 1-D array of real values. `y`'s length along the interpolation
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axis must be equal to the length of `x`. If N-D array, use `axis`
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parameter to select correct axis.
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axis : int, optional
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Axis in the y array corresponding to the x-coordinate values.
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extrapolate : bool, optional
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Whether to extrapolate to out-of-bounds points based on first
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and last intervals, or to return NaNs.
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Methods
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-------
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__call__
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derivative
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antiderivative
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roots
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See Also
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--------
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Akima1DInterpolator
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CubicSpline
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BPoly
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Notes
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-----
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The interpolator preserves monotonicity in the interpolation data and does
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not overshoot if the data is not smooth.
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The first derivatives are guaranteed to be continuous, but the second
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derivatives may jump at :math:`x_k`.
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Determines the derivatives at the points :math:`x_k`, :math:`f'_k`,
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by using PCHIP algorithm [1]_.
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Let :math:`h_k = x_{k+1} - x_k`, and :math:`d_k = (y_{k+1} - y_k) / h_k`
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are the slopes at internal points :math:`x_k`.
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If the signs of :math:`d_k` and :math:`d_{k-1}` are different or either of
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them equals zero, then :math:`f'_k = 0`. Otherwise, it is given by the
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weighted harmonic mean
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.. math::
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\frac{w_1 + w_2}{f'_k} = \frac{w_1}{d_{k-1}} + \frac{w_2}{d_k}
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where :math:`w_1 = 2 h_k + h_{k-1}` and :math:`w_2 = h_k + 2 h_{k-1}`.
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The end slopes are set using a one-sided scheme [2]_.
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References
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----------
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.. [1] F. N. Fritsch and R. E. Carlson, Monotone Piecewise Cubic Interpolation,
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SIAM J. Numer. Anal., 17(2), 238 (1980).
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:doi:`10.1137/0717021`.
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.. [2] see, e.g., C. Moler, Numerical Computing with Matlab, 2004.
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:doi:`10.1137/1.9780898717952`
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"""
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def __init__(self, x, y, axis=0, extrapolate=None):
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x = _asarray_validated(x, check_finite=False, as_inexact=True)
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y = _asarray_validated(y, check_finite=False, as_inexact=True)
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axis = axis % y.ndim
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xp = x.reshape((x.shape[0],) + (1,)*(y.ndim-1))
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yp = np.rollaxis(y, axis)
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dk = self._find_derivatives(xp, yp)
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data = np.hstack((yp[:, None, ...], dk[:, None, ...]))
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_b = BPoly.from_derivatives(x, data, orders=None)
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super(PchipInterpolator, self).__init__(_b.c, _b.x,
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extrapolate=extrapolate)
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self.axis = axis
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def roots(self):
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"""
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Return the roots of the interpolated function.
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"""
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return (PPoly.from_bernstein_basis(self)).roots()
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@staticmethod
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def _edge_case(h0, h1, m0, m1):
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# one-sided three-point estimate for the derivative
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d = ((2*h0 + h1)*m0 - h0*m1) / (h0 + h1)
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# try to preserve shape
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mask = np.sign(d) != np.sign(m0)
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mask2 = (np.sign(m0) != np.sign(m1)) & (np.abs(d) > 3.*np.abs(m0))
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mmm = (~mask) & mask2
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d[mask] = 0.
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d[mmm] = 3.*m0[mmm]
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return d
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@staticmethod
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def _find_derivatives(x, y):
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# Determine the derivatives at the points y_k, d_k, by using
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# PCHIP algorithm is:
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# We choose the derivatives at the point x_k by
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# Let m_k be the slope of the kth segment (between k and k+1)
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# If m_k=0 or m_{k-1}=0 or sgn(m_k) != sgn(m_{k-1}) then d_k == 0
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# else use weighted harmonic mean:
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# w_1 = 2h_k + h_{k-1}, w_2 = h_k + 2h_{k-1}
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# 1/d_k = 1/(w_1 + w_2)*(w_1 / m_k + w_2 / m_{k-1})
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# where h_k is the spacing between x_k and x_{k+1}
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y_shape = y.shape
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if y.ndim == 1:
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# So that _edge_case doesn't end up assigning to scalars
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x = x[:, None]
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y = y[:, None]
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hk = x[1:] - x[:-1]
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mk = (y[1:] - y[:-1]) / hk
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if y.shape[0] == 2:
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# edge case: only have two points, use linear interpolation
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dk = np.zeros_like(y)
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dk[0] = mk
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dk[1] = mk
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return dk.reshape(y_shape)
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smk = np.sign(mk)
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condition = (smk[1:] != smk[:-1]) | (mk[1:] == 0) | (mk[:-1] == 0)
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w1 = 2*hk[1:] + hk[:-1]
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w2 = hk[1:] + 2*hk[:-1]
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# values where division by zero occurs will be excluded
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# by 'condition' afterwards
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with np.errstate(divide='ignore'):
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whmean = (w1/mk[:-1] + w2/mk[1:]) / (w1 + w2)
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dk = np.zeros_like(y)
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dk[1:-1][condition] = 0.0
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dk[1:-1][~condition] = 1.0 / whmean[~condition]
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# special case endpoints, as suggested in
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# Cleve Moler, Numerical Computing with MATLAB, Chap 3.4
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dk[0] = PchipInterpolator._edge_case(hk[0], hk[1], mk[0], mk[1])
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dk[-1] = PchipInterpolator._edge_case(hk[-1], hk[-2], mk[-1], mk[-2])
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return dk.reshape(y_shape)
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def pchip_interpolate(xi, yi, x, der=0, axis=0):
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"""
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Convenience function for pchip interpolation.
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xi and yi are arrays of values used to approximate some function f,
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with ``yi = f(xi)``. The interpolant uses monotonic cubic splines
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to find the value of new points x and the derivatives there.
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See `PchipInterpolator` for details.
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Parameters
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----------
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xi : array_like
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A sorted list of x-coordinates, of length N.
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yi : array_like
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A 1-D array of real values. `yi`'s length along the interpolation
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axis must be equal to the length of `xi`. If N-D array, use axis
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parameter to select correct axis.
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x : scalar or array_like
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Of length M.
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der : int or list, optional
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Derivatives to extract. The 0-th derivative can be included to
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return the function value.
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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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See Also
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--------
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PchipInterpolator
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Returns
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-------
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y : scalar or array_like
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The result, of length R or length M or M by R,
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"""
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P = PchipInterpolator(xi, yi, axis=axis)
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if der == 0:
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return P(x)
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elif _isscalar(der):
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return P.derivative(der)(x)
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else:
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return [P.derivative(nu)(x) for nu in der]
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# Backwards compatibility
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pchip = PchipInterpolator
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class Akima1DInterpolator(PPoly):
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"""
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Akima interpolator
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Fit piecewise cubic polynomials, given vectors x and y. The interpolation
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method by Akima uses a continuously differentiable sub-spline built from
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piecewise cubic polynomials. The resultant curve passes through the given
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data points and will appear smooth and natural.
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Parameters
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----------
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x : ndarray, shape (m, )
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1-D array of monotonically increasing real values.
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y : ndarray, shape (m, ...)
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N-D array of real values. The length of `y` along the first axis must
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be equal to the length of `x`.
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axis : int, optional
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Specifies the axis of `y` along which to interpolate. Interpolation
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defaults to the first axis of `y`.
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Methods
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-------
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__call__
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derivative
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antiderivative
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roots
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See Also
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--------
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PchipInterpolator
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CubicSpline
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PPoly
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Notes
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-----
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.. versionadded:: 0.14
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Use only for precise data, as the fitted curve passes through the given
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points exactly. This routine is useful for plotting a pleasingly smooth
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curve through a few given points for purposes of plotting.
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References
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----------
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[1] A new method of interpolation and smooth curve fitting based
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on local procedures. Hiroshi Akima, J. ACM, October 1970, 17(4),
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589-602.
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"""
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def __init__(self, x, y, axis=0):
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# Original implementation in MATLAB by N. Shamsundar (BSD licensed), see
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# https://www.mathworks.com/matlabcentral/fileexchange/1814-akima-interpolation
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x, y = map(np.asarray, (x, y))
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axis = axis % y.ndim
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if np.any(np.diff(x) < 0.):
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raise ValueError("x must be strictly ascending")
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if x.ndim != 1:
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raise ValueError("x must be 1-dimensional")
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if x.size < 2:
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raise ValueError("at least 2 breakpoints are needed")
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if x.size != y.shape[axis]:
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raise ValueError("x.shape must equal y.shape[%s]" % axis)
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# move interpolation axis to front
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y = np.rollaxis(y, axis)
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# determine slopes between breakpoints
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m = np.empty((x.size + 3, ) + y.shape[1:])
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dx = np.diff(x)
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dx = dx[(slice(None), ) + (None, ) * (y.ndim - 1)]
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m[2:-2] = np.diff(y, axis=0) / dx
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# add two additional points on the left ...
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m[1] = 2. * m[2] - m[3]
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m[0] = 2. * m[1] - m[2]
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# ... and on the right
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m[-2] = 2. * m[-3] - m[-4]
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m[-1] = 2. * m[-2] - m[-3]
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# if m1 == m2 != m3 == m4, the slope at the breakpoint is not defined.
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# This is the fill value:
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t = .5 * (m[3:] + m[:-3])
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# get the denominator of the slope t
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dm = np.abs(np.diff(m, axis=0))
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f1 = dm[2:]
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f2 = dm[:-2]
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f12 = f1 + f2
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# These are the mask of where the the slope at breakpoint is defined:
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ind = np.nonzero(f12 > 1e-9 * np.max(f12))
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x_ind, y_ind = ind[0], ind[1:]
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# Set the slope at breakpoint
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t[ind] = (f1[ind] * m[(x_ind + 1,) + y_ind] +
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f2[ind] * m[(x_ind + 2,) + y_ind]) / f12[ind]
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# calculate the higher order coefficients
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c = (3. * m[2:-2] - 2. * t[:-1] - t[1:]) / dx
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d = (t[:-1] + t[1:] - 2. * m[2:-2]) / dx ** 2
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coeff = np.zeros((4, x.size - 1) + y.shape[1:])
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coeff[3] = y[:-1]
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coeff[2] = t[:-1]
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coeff[1] = c
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coeff[0] = d
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super(Akima1DInterpolator, self).__init__(coeff, x, extrapolate=False)
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self.axis = axis
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def extend(self, c, x, right=True):
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raise NotImplementedError("Extending a 1D Akima interpolator is not "
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"yet implemented")
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# These are inherited from PPoly, but they do not produce an Akima
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# interpolator. Hence stub them out.
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@classmethod
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def from_spline(cls, tck, extrapolate=None):
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raise NotImplementedError("This method does not make sense for "
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"an Akima interpolator.")
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@classmethod
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def from_bernstein_basis(cls, bp, extrapolate=None):
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raise NotImplementedError("This method does not make sense for "
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"an Akima interpolator.")
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class CubicSpline(PPoly):
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"""Cubic spline data interpolator.
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Interpolate data with a piecewise cubic polynomial which is twice
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continuously differentiable [1]_. The result is represented as a `PPoly`
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instance with breakpoints matching the given data.
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Parameters
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----------
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x : array_like, shape (n,)
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1-d array containing values of the independent variable.
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Values must be real, finite and in strictly increasing order.
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y : array_like
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Array containing values of the dependent variable. It can have
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arbitrary number of dimensions, but the length along `axis` (see below)
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must match the length of `x`. Values must be finite.
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axis : int, optional
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Axis along which `y` is assumed to be varying. Meaning that for
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``x[i]`` the corresponding values are ``np.take(y, i, axis=axis)``.
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Default is 0.
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bc_type : string or 2-tuple, optional
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Boundary condition type. Two additional equations, given by the
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boundary conditions, are required to determine all coefficients of
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polynomials on each segment [2]_.
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If `bc_type` is a string, then the specified condition will be applied
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at both ends of a spline. Available conditions are:
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* 'not-a-knot' (default): The first and second segment at a curve end
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are the same polynomial. It is a good default when there is no
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information on boundary conditions.
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* 'periodic': The interpolated functions is assumed to be periodic
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of period ``x[-1] - x[0]``. The first and last value of `y` must be
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identical: ``y[0] == y[-1]``. This boundary condition will result in
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``y'[0] == y'[-1]`` and ``y''[0] == y''[-1]``.
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* 'clamped': The first derivative at curves ends are zero. Assuming
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a 1D `y`, ``bc_type=((1, 0.0), (1, 0.0))`` is the same condition.
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* 'natural': The second derivative at curve ends are zero. Assuming
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a 1D `y`, ``bc_type=((2, 0.0), (2, 0.0))`` is the same condition.
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If `bc_type` is a 2-tuple, the first and the second value will be
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applied at the curve start and end respectively. The tuple values can
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be one of the previously mentioned strings (except 'periodic') or a
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tuple `(order, deriv_values)` allowing to specify arbitrary
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derivatives at curve ends:
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* `order`: the derivative order, 1 or 2.
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* `deriv_value`: array_like containing derivative values, shape must
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be the same as `y`, excluding `axis` dimension. For example, if `y`
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is 1D, then `deriv_value` must be a scalar. If `y` is 3D with the
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shape (n0, n1, n2) and axis=2, then `deriv_value` must be 2D
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and have the shape (n0, n1).
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extrapolate : {bool, 'periodic', None}, optional
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If bool, determines whether to extrapolate to out-of-bounds points
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based on first and last intervals, or to return NaNs. If 'periodic',
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periodic extrapolation is used. If None (default), `extrapolate` is
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set to 'periodic' for ``bc_type='periodic'`` and to True otherwise.
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Attributes
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----------
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x : ndarray, shape (n,)
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Breakpoints. The same `x` which was passed to the constructor.
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c : ndarray, shape (4, n-1, ...)
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Coefficients of the polynomials on each segment. The trailing
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dimensions match the dimensions of `y`, excluding `axis`. For example,
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if `y` is 1-d, then ``c[k, i]`` is a coefficient for
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``(x-x[i])**(3-k)`` on the segment between ``x[i]`` and ``x[i+1]``.
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axis : int
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Interpolation axis. The same `axis` which was passed to the
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constructor.
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Methods
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-------
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__call__
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derivative
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antiderivative
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integrate
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roots
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See Also
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--------
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Akima1DInterpolator
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PchipInterpolator
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PPoly
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Notes
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-----
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Parameters `bc_type` and `interpolate` work independently, i.e. the former
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controls only construction of a spline, and the latter only evaluation.
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When a boundary condition is 'not-a-knot' and n = 2, it is replaced by
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a condition that the first derivative is equal to the linear interpolant
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slope. When both boundary conditions are 'not-a-knot' and n = 3, the
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solution is sought as a parabola passing through given points.
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When 'not-a-knot' boundary conditions is applied to both ends, the
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resulting spline will be the same as returned by `splrep` (with ``s=0``)
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and `InterpolatedUnivariateSpline`, but these two methods use a
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representation in B-spline basis.
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.. versionadded:: 0.18.0
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Examples
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--------
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In this example the cubic spline is used to interpolate a sampled sinusoid.
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You can see that the spline continuity property holds for the first and
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second derivatives and violates only for the third derivative.
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>>> from scipy.interpolate import CubicSpline
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>>> import matplotlib.pyplot as plt
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>>> x = np.arange(10)
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>>> y = np.sin(x)
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>>> cs = CubicSpline(x, y)
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>>> xs = np.arange(-0.5, 9.6, 0.1)
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>>> fig, ax = plt.subplots(figsize=(6.5, 4))
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>>> ax.plot(x, y, 'o', label='data')
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>>> ax.plot(xs, np.sin(xs), label='true')
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>>> ax.plot(xs, cs(xs), label="S")
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>>> ax.plot(xs, cs(xs, 1), label="S'")
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>>> ax.plot(xs, cs(xs, 2), label="S''")
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>>> ax.plot(xs, cs(xs, 3), label="S'''")
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>>> ax.set_xlim(-0.5, 9.5)
|
|
>>> ax.legend(loc='lower left', ncol=2)
|
|
>>> plt.show()
|
|
|
|
In the second example, the unit circle is interpolated with a spline. A
|
|
periodic boundary condition is used. You can see that the first derivative
|
|
values, ds/dx=0, ds/dy=1 at the periodic point (1, 0) are correctly
|
|
computed. Note that a circle cannot be exactly represented by a cubic
|
|
spline. To increase precision, more breakpoints would be required.
|
|
|
|
>>> theta = 2 * np.pi * np.linspace(0, 1, 5)
|
|
>>> y = np.c_[np.cos(theta), np.sin(theta)]
|
|
>>> cs = CubicSpline(theta, y, bc_type='periodic')
|
|
>>> print("ds/dx={:.1f} ds/dy={:.1f}".format(cs(0, 1)[0], cs(0, 1)[1]))
|
|
ds/dx=0.0 ds/dy=1.0
|
|
>>> xs = 2 * np.pi * np.linspace(0, 1, 100)
|
|
>>> fig, ax = plt.subplots(figsize=(6.5, 4))
|
|
>>> ax.plot(y[:, 0], y[:, 1], 'o', label='data')
|
|
>>> ax.plot(np.cos(xs), np.sin(xs), label='true')
|
|
>>> ax.plot(cs(xs)[:, 0], cs(xs)[:, 1], label='spline')
|
|
>>> ax.axes.set_aspect('equal')
|
|
>>> ax.legend(loc='center')
|
|
>>> plt.show()
|
|
|
|
The third example is the interpolation of a polynomial y = x**3 on the
|
|
interval 0 <= x<= 1. A cubic spline can represent this function exactly.
|
|
To achieve that we need to specify values and first derivatives at
|
|
endpoints of the interval. Note that y' = 3 * x**2 and thus y'(0) = 0 and
|
|
y'(1) = 3.
|
|
|
|
>>> cs = CubicSpline([0, 1], [0, 1], bc_type=((1, 0), (1, 3)))
|
|
>>> x = np.linspace(0, 1)
|
|
>>> np.allclose(x**3, cs(x))
|
|
True
|
|
|
|
References
|
|
----------
|
|
.. [1] `Cubic Spline Interpolation
|
|
<https://en.wikiversity.org/wiki/Cubic_Spline_Interpolation>`_
|
|
on Wikiversity.
|
|
.. [2] Carl de Boor, "A Practical Guide to Splines", Springer-Verlag, 1978.
|
|
"""
|
|
def __init__(self, x, y, axis=0, bc_type='not-a-knot', extrapolate=None):
|
|
x, y = map(np.asarray, (x, y))
|
|
|
|
if np.issubdtype(x.dtype, np.complexfloating):
|
|
raise ValueError("`x` must contain real values.")
|
|
|
|
if np.issubdtype(y.dtype, np.complexfloating):
|
|
dtype = complex
|
|
else:
|
|
dtype = float
|
|
y = y.astype(dtype, copy=False)
|
|
|
|
axis = axis % y.ndim
|
|
if x.ndim != 1:
|
|
raise ValueError("`x` must be 1-dimensional.")
|
|
if x.shape[0] < 2:
|
|
raise ValueError("`x` must contain at least 2 elements.")
|
|
if x.shape[0] != y.shape[axis]:
|
|
raise ValueError("The length of `y` along `axis`={0} doesn't "
|
|
"match the length of `x`".format(axis))
|
|
|
|
if not np.all(np.isfinite(x)):
|
|
raise ValueError("`x` must contain only finite values.")
|
|
if not np.all(np.isfinite(y)):
|
|
raise ValueError("`y` must contain only finite values.")
|
|
|
|
dx = np.diff(x)
|
|
if np.any(dx <= 0):
|
|
raise ValueError("`x` must be strictly increasing sequence.")
|
|
|
|
n = x.shape[0]
|
|
y = np.rollaxis(y, axis)
|
|
|
|
bc, y = self._validate_bc(bc_type, y, y.shape[1:], axis)
|
|
|
|
if extrapolate is None:
|
|
if bc[0] == 'periodic':
|
|
extrapolate = 'periodic'
|
|
else:
|
|
extrapolate = True
|
|
|
|
dxr = dx.reshape([dx.shape[0]] + [1] * (y.ndim - 1))
|
|
slope = np.diff(y, axis=0) / dxr
|
|
|
|
# If bc is 'not-a-knot' this change is just a convention.
|
|
# If bc is 'periodic' then we already checked that y[0] == y[-1],
|
|
# and the spline is just a constant, we handle this case in the same
|
|
# way by setting the first derivatives to slope, which is 0.
|
|
if n == 2:
|
|
if bc[0] in ['not-a-knot', 'periodic']:
|
|
bc[0] = (1, slope[0])
|
|
if bc[1] in ['not-a-knot', 'periodic']:
|
|
bc[1] = (1, slope[0])
|
|
|
|
# This is a very special case, when both conditions are 'not-a-knot'
|
|
# and n == 3. In this case 'not-a-knot' can't be handled regularly
|
|
# as the both conditions are identical. We handle this case by
|
|
# constructing a parabola passing through given points.
|
|
if n == 3 and bc[0] == 'not-a-knot' and bc[1] == 'not-a-knot':
|
|
A = np.zeros((3, 3)) # This is a standard matrix.
|
|
b = np.empty((3,) + y.shape[1:], dtype=y.dtype)
|
|
|
|
A[0, 0] = 1
|
|
A[0, 1] = 1
|
|
A[1, 0] = dx[1]
|
|
A[1, 1] = 2 * (dx[0] + dx[1])
|
|
A[1, 2] = dx[0]
|
|
A[2, 1] = 1
|
|
A[2, 2] = 1
|
|
|
|
b[0] = 2 * slope[0]
|
|
b[1] = 3 * (dxr[0] * slope[1] + dxr[1] * slope[0])
|
|
b[2] = 2 * slope[1]
|
|
|
|
s = solve(A, b, overwrite_a=True, overwrite_b=True,
|
|
check_finite=False)
|
|
else:
|
|
# Find derivative values at each x[i] by solving a tridiagonal
|
|
# system.
|
|
A = np.zeros((3, n)) # This is a banded matrix representation.
|
|
b = np.empty((n,) + y.shape[1:], dtype=y.dtype)
|
|
|
|
# Filling the system for i=1..n-2
|
|
# (x[i-1] - x[i]) * s[i-1] +\
|
|
# 2 * ((x[i] - x[i-1]) + (x[i+1] - x[i])) * s[i] +\
|
|
# (x[i] - x[i-1]) * s[i+1] =\
|
|
# 3 * ((x[i+1] - x[i])*(y[i] - y[i-1])/(x[i] - x[i-1]) +\
|
|
# (x[i] - x[i-1])*(y[i+1] - y[i])/(x[i+1] - x[i]))
|
|
|
|
A[1, 1:-1] = 2 * (dx[:-1] + dx[1:]) # The diagonal
|
|
A[0, 2:] = dx[:-1] # The upper diagonal
|
|
A[-1, :-2] = dx[1:] # The lower diagonal
|
|
|
|
b[1:-1] = 3 * (dxr[1:] * slope[:-1] + dxr[:-1] * slope[1:])
|
|
|
|
bc_start, bc_end = bc
|
|
|
|
if bc_start == 'periodic':
|
|
# Due to the periodicity, and because y[-1] = y[0], the linear
|
|
# system has (n-1) unknowns/equations instead of n:
|
|
A = A[:, 0:-1]
|
|
A[1, 0] = 2 * (dx[-1] + dx[0])
|
|
A[0, 1] = dx[-1]
|
|
|
|
b = b[:-1]
|
|
|
|
# Also, due to the periodicity, the system is not tri-diagonal.
|
|
# We need to compute a "condensed" matrix of shape (n-2, n-2).
|
|
# See https://web.archive.org/web/20151220180652/http://www.cfm.brown.edu/people/gk/chap6/node14.html
|
|
# for more explanations.
|
|
# The condensed matrix is obtained by removing the last column
|
|
# and last row of the (n-1, n-1) system matrix. The removed
|
|
# values are saved in scalar variables with the (n-1, n-1)
|
|
# system matrix indices forming their names:
|
|
a_m1_0 = dx[-2] # lower left corner value: A[-1, 0]
|
|
a_m1_m2 = dx[-1]
|
|
a_m1_m1 = 2 * (dx[-1] + dx[-2])
|
|
a_m2_m1 = dx[-2]
|
|
a_0_m1 = dx[0]
|
|
|
|
b[0] = 3 * (dxr[0] * slope[-1] + dxr[-1] * slope[0])
|
|
b[-1] = 3 * (dxr[-1] * slope[-2] + dxr[-2] * slope[-1])
|
|
|
|
Ac = A[:, :-1]
|
|
b1 = b[:-1]
|
|
b2 = np.zeros_like(b1)
|
|
b2[0] = -a_0_m1
|
|
b2[-1] = -a_m2_m1
|
|
|
|
# s1 and s2 are the solutions of (n-2, n-2) system
|
|
s1 = solve_banded((1, 1), Ac, b1, overwrite_ab=False,
|
|
overwrite_b=False, check_finite=False)
|
|
|
|
s2 = solve_banded((1, 1), Ac, b2, overwrite_ab=False,
|
|
overwrite_b=False, check_finite=False)
|
|
|
|
# computing the s[n-2] solution:
|
|
s_m1 = ((b[-1] - a_m1_0 * s1[0] - a_m1_m2 * s1[-1]) /
|
|
(a_m1_m1 + a_m1_0 * s2[0] + a_m1_m2 * s2[-1]))
|
|
|
|
# s is the solution of the (n, n) system:
|
|
s = np.empty((n,) + y.shape[1:], dtype=y.dtype)
|
|
s[:-2] = s1 + s_m1 * s2
|
|
s[-2] = s_m1
|
|
s[-1] = s[0]
|
|
else:
|
|
if bc_start == 'not-a-knot':
|
|
A[1, 0] = dx[1]
|
|
A[0, 1] = x[2] - x[0]
|
|
d = x[2] - x[0]
|
|
b[0] = ((dxr[0] + 2*d) * dxr[1] * slope[0] +
|
|
dxr[0]**2 * slope[1]) / d
|
|
elif bc_start[0] == 1:
|
|
A[1, 0] = 1
|
|
A[0, 1] = 0
|
|
b[0] = bc_start[1]
|
|
elif bc_start[0] == 2:
|
|
A[1, 0] = 2 * dx[0]
|
|
A[0, 1] = dx[0]
|
|
b[0] = -0.5 * bc_start[1] * dx[0]**2 + 3 * (y[1] - y[0])
|
|
|
|
if bc_end == 'not-a-knot':
|
|
A[1, -1] = dx[-2]
|
|
A[-1, -2] = x[-1] - x[-3]
|
|
d = x[-1] - x[-3]
|
|
b[-1] = ((dxr[-1]**2*slope[-2] +
|
|
(2*d + dxr[-1])*dxr[-2]*slope[-1]) / d)
|
|
elif bc_end[0] == 1:
|
|
A[1, -1] = 1
|
|
A[-1, -2] = 0
|
|
b[-1] = bc_end[1]
|
|
elif bc_end[0] == 2:
|
|
A[1, -1] = 2 * dx[-1]
|
|
A[-1, -2] = dx[-1]
|
|
b[-1] = 0.5 * bc_end[1] * dx[-1]**2 + 3 * (y[-1] - y[-2])
|
|
|
|
s = solve_banded((1, 1), A, b, overwrite_ab=True,
|
|
overwrite_b=True, check_finite=False)
|
|
|
|
# Compute coefficients in PPoly form.
|
|
t = (s[:-1] + s[1:] - 2 * slope) / dxr
|
|
c = np.empty((4, n - 1) + y.shape[1:], dtype=t.dtype)
|
|
c[0] = t / dxr
|
|
c[1] = (slope - s[:-1]) / dxr - t
|
|
c[2] = s[:-1]
|
|
c[3] = y[:-1]
|
|
|
|
super(CubicSpline, self).__init__(c, x, extrapolate=extrapolate)
|
|
self.axis = axis
|
|
|
|
@staticmethod
|
|
def _validate_bc(bc_type, y, expected_deriv_shape, axis):
|
|
"""Validate and prepare boundary conditions.
|
|
|
|
Returns
|
|
-------
|
|
validated_bc : 2-tuple
|
|
Boundary conditions for a curve start and end.
|
|
y : ndarray
|
|
y casted to complex dtype if one of the boundary conditions has
|
|
complex dtype.
|
|
"""
|
|
if isinstance(bc_type, string_types):
|
|
if bc_type == 'periodic':
|
|
if not np.allclose(y[0], y[-1], rtol=1e-15, atol=1e-15):
|
|
raise ValueError(
|
|
"The first and last `y` point along axis {} must "
|
|
"be identical (within machine precision) when "
|
|
"bc_type='periodic'.".format(axis))
|
|
|
|
bc_type = (bc_type, bc_type)
|
|
|
|
else:
|
|
if len(bc_type) != 2:
|
|
raise ValueError("`bc_type` must contain 2 elements to "
|
|
"specify start and end conditions.")
|
|
|
|
if 'periodic' in bc_type:
|
|
raise ValueError("'periodic' `bc_type` is defined for both "
|
|
"curve ends and cannot be used with other "
|
|
"boundary conditions.")
|
|
|
|
validated_bc = []
|
|
for bc in bc_type:
|
|
if isinstance(bc, string_types):
|
|
if bc == 'clamped':
|
|
validated_bc.append((1, np.zeros(expected_deriv_shape)))
|
|
elif bc == 'natural':
|
|
validated_bc.append((2, np.zeros(expected_deriv_shape)))
|
|
elif bc in ['not-a-knot', 'periodic']:
|
|
validated_bc.append(bc)
|
|
else:
|
|
raise ValueError("bc_type={} is not allowed.".format(bc))
|
|
else:
|
|
try:
|
|
deriv_order, deriv_value = bc
|
|
except Exception:
|
|
raise ValueError("A specified derivative value must be "
|
|
"given in the form (order, value).")
|
|
|
|
if deriv_order not in [1, 2]:
|
|
raise ValueError("The specified derivative order must "
|
|
"be 1 or 2.")
|
|
|
|
deriv_value = np.asarray(deriv_value)
|
|
if deriv_value.shape != expected_deriv_shape:
|
|
raise ValueError(
|
|
"`deriv_value` shape {} is not the expected one {}."
|
|
.format(deriv_value.shape, expected_deriv_shape))
|
|
|
|
if np.issubdtype(deriv_value.dtype, np.complexfloating):
|
|
y = y.astype(complex, copy=False)
|
|
|
|
validated_bc.append((deriv_order, deriv_value))
|
|
|
|
return validated_bc, y
|