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723 lines
25 KiB
Python
723 lines
25 KiB
Python
6 years ago
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from __future__ import print_function, division, absolute_import
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__all__ = ['splrep', 'splprep', 'splev', 'splint', 'sproot', 'spalde',
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'bisplrep', 'bisplev', 'insert', 'splder', 'splantider']
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import warnings
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import numpy as np
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from ._fitpack_impl import bisplrep, bisplev, dblint
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from . import _fitpack_impl as _impl
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from ._bsplines import BSpline
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def splprep(x, w=None, u=None, ub=None, ue=None, k=3, task=0, s=None, t=None,
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full_output=0, nest=None, per=0, quiet=1):
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"""
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Find the B-spline representation of an N-dimensional curve.
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Given a list of N rank-1 arrays, `x`, which represent a curve in
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N-dimensional space parametrized by `u`, find a smooth approximating
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spline curve g(`u`). Uses the FORTRAN routine parcur from FITPACK.
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Parameters
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----------
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x : array_like
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A list of sample vector arrays representing the curve.
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w : array_like, optional
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Strictly positive rank-1 array of weights the same length as `x[0]`.
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The weights are used in computing the weighted least-squares spline
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fit. If the errors in the `x` values have standard-deviation given by
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the vector d, then `w` should be 1/d. Default is ``ones(len(x[0]))``.
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u : array_like, optional
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An array of parameter values. If not given, these values are
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calculated automatically as ``M = len(x[0])``, where
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v[0] = 0
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v[i] = v[i-1] + distance(`x[i]`, `x[i-1]`)
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u[i] = v[i] / v[M-1]
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ub, ue : int, optional
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The end-points of the parameters interval. Defaults to
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u[0] and u[-1].
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k : int, optional
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Degree of the spline. Cubic splines are recommended.
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Even values of `k` should be avoided especially with a small s-value.
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``1 <= k <= 5``, default is 3.
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task : int, optional
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If task==0 (default), find t and c for a given smoothing factor, s.
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If task==1, find t and c for another value of the smoothing factor, s.
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There must have been a previous call with task=0 or task=1
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for the same set of data.
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If task=-1 find the weighted least square spline for a given set of
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knots, t.
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s : float, optional
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A smoothing condition. The amount of smoothness is determined by
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satisfying the conditions: ``sum((w * (y - g))**2,axis=0) <= s``,
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where g(x) is the smoothed interpolation of (x,y). The user can
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use `s` to control the trade-off between closeness and smoothness
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of fit. Larger `s` means more smoothing while smaller values of `s`
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indicate less smoothing. Recommended values of `s` depend on the
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weights, w. If the weights represent the inverse of the
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standard-deviation of y, then a good `s` value should be found in
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the range ``(m-sqrt(2*m),m+sqrt(2*m))``, where m is the number of
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data points in x, y, and w.
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t : int, optional
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The knots needed for task=-1.
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full_output : int, optional
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If non-zero, then return optional outputs.
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nest : int, optional
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An over-estimate of the total number of knots of the spline to
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help in determining the storage space. By default nest=m/2.
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Always large enough is nest=m+k+1.
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per : int, optional
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If non-zero, data points are considered periodic with period
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``x[m-1] - x[0]`` and a smooth periodic spline approximation is
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returned. Values of ``y[m-1]`` and ``w[m-1]`` are not used.
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quiet : int, optional
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Non-zero to suppress messages.
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This parameter is deprecated; use standard Python warning filters
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instead.
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Returns
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-------
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tck : tuple
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(t,c,k) a tuple containing the vector of knots, the B-spline
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coefficients, and the degree of the spline.
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u : array
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An array of the values of the parameter.
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fp : float
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The weighted sum of squared residuals of the spline approximation.
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ier : int
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An integer flag about splrep success. Success is indicated
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if ier<=0. If ier in [1,2,3] an error occurred but was not raised.
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Otherwise an error is raised.
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msg : str
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A message corresponding to the integer flag, ier.
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See Also
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--------
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splrep, splev, sproot, spalde, splint,
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bisplrep, bisplev
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UnivariateSpline, BivariateSpline
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BSpline
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make_interp_spline
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Notes
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-----
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See `splev` for evaluation of the spline and its derivatives.
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The number of dimensions N must be smaller than 11.
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The number of coefficients in the `c` array is ``k+1`` less then the number
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of knots, ``len(t)``. This is in contrast with `splrep`, which zero-pads
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the array of coefficients to have the same length as the array of knots.
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These additional coefficients are ignored by evaluation routines, `splev`
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and `BSpline`.
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References
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----------
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.. [1] P. Dierckx, "Algorithms for smoothing data with periodic and
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parametric splines, Computer Graphics and Image Processing",
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20 (1982) 171-184.
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.. [2] P. Dierckx, "Algorithms for smoothing data with periodic and
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parametric splines", report tw55, Dept. Computer Science,
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K.U.Leuven, 1981.
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.. [3] P. Dierckx, "Curve and surface fitting with splines", Monographs on
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Numerical Analysis, Oxford University Press, 1993.
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Examples
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--------
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Generate a discretization of a limacon curve in the polar coordinates:
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>>> phi = np.linspace(0, 2.*np.pi, 40)
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>>> r = 0.5 + np.cos(phi) # polar coords
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>>> x, y = r * np.cos(phi), r * np.sin(phi) # convert to cartesian
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And interpolate:
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>>> from scipy.interpolate import splprep, splev
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>>> tck, u = splprep([x, y], s=0)
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>>> new_points = splev(u, tck)
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Notice that (i) we force interpolation by using `s=0`,
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(ii) the parameterization, ``u``, is generated automatically.
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Now plot the result:
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>>> import matplotlib.pyplot as plt
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>>> fig, ax = plt.subplots()
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>>> ax.plot(x, y, 'ro')
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>>> ax.plot(new_points[0], new_points[1], 'r-')
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>>> plt.show()
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"""
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res = _impl.splprep(x, w, u, ub, ue, k, task, s, t, full_output, nest, per,
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quiet)
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return res
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def splrep(x, y, w=None, xb=None, xe=None, k=3, task=0, s=None, t=None,
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full_output=0, per=0, quiet=1):
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"""
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Find the B-spline representation of 1-D curve.
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Given the set of data points ``(x[i], y[i])`` determine a smooth spline
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approximation of degree k on the interval ``xb <= x <= xe``.
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Parameters
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----------
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x, y : array_like
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The data points defining a curve y = f(x).
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w : array_like, optional
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Strictly positive rank-1 array of weights the same length as x and y.
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The weights are used in computing the weighted least-squares spline
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fit. If the errors in the y values have standard-deviation given by the
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vector d, then w should be 1/d. Default is ones(len(x)).
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xb, xe : float, optional
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The interval to fit. If None, these default to x[0] and x[-1]
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respectively.
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k : int, optional
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The degree of the spline fit. It is recommended to use cubic splines.
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Even values of k should be avoided especially with small s values.
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1 <= k <= 5
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task : {1, 0, -1}, optional
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If task==0 find t and c for a given smoothing factor, s.
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If task==1 find t and c for another value of the smoothing factor, s.
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There must have been a previous call with task=0 or task=1 for the same
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set of data (t will be stored an used internally)
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If task=-1 find the weighted least square spline for a given set of
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knots, t. These should be interior knots as knots on the ends will be
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added automatically.
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s : float, optional
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A smoothing condition. The amount of smoothness is determined by
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satisfying the conditions: sum((w * (y - g))**2,axis=0) <= s where g(x)
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is the smoothed interpolation of (x,y). The user can use s to control
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the tradeoff between closeness and smoothness of fit. Larger s means
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more smoothing while smaller values of s indicate less smoothing.
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Recommended values of s depend on the weights, w. If the weights
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represent the inverse of the standard-deviation of y, then a good s
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value should be found in the range (m-sqrt(2*m),m+sqrt(2*m)) where m is
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the number of datapoints in x, y, and w. default : s=m-sqrt(2*m) if
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weights are supplied. s = 0.0 (interpolating) if no weights are
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supplied.
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t : array_like, optional
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The knots needed for task=-1. If given then task is automatically set
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to -1.
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full_output : bool, optional
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If non-zero, then return optional outputs.
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per : bool, optional
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If non-zero, data points are considered periodic with period x[m-1] -
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x[0] and a smooth periodic spline approximation is returned. Values of
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y[m-1] and w[m-1] are not used.
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quiet : bool, optional
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Non-zero to suppress messages.
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This parameter is deprecated; use standard Python warning filters
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instead.
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Returns
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-------
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tck : tuple
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A tuple (t,c,k) containing the vector of knots, the B-spline
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coefficients, and the degree of the spline.
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fp : array, optional
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The weighted sum of squared residuals of the spline approximation.
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ier : int, optional
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An integer flag about splrep success. Success is indicated if ier<=0.
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If ier in [1,2,3] an error occurred but was not raised. Otherwise an
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error is raised.
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msg : str, optional
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A message corresponding to the integer flag, ier.
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See Also
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--------
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UnivariateSpline, BivariateSpline
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splprep, splev, sproot, spalde, splint
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bisplrep, bisplev
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BSpline
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make_interp_spline
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Notes
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-----
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See `splev` for evaluation of the spline and its derivatives. Uses the
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FORTRAN routine ``curfit`` from FITPACK.
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The user is responsible for assuring that the values of `x` are unique.
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Otherwise, `splrep` will not return sensible results.
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If provided, knots `t` must satisfy the Schoenberg-Whitney conditions,
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i.e., there must be a subset of data points ``x[j]`` such that
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``t[j] < x[j] < t[j+k+1]``, for ``j=0, 1,...,n-k-2``.
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This routine zero-pads the coefficients array ``c`` to have the same length
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as the array of knots ``t`` (the trailing ``k + 1`` coefficients are ignored
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by the evaluation routines, `splev` and `BSpline`.) This is in contrast with
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`splprep`, which does not zero-pad the coefficients.
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References
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----------
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Based on algorithms described in [1]_, [2]_, [3]_, and [4]_:
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.. [1] P. Dierckx, "An algorithm for smoothing, differentiation and
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integration of experimental data using spline functions",
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J.Comp.Appl.Maths 1 (1975) 165-184.
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.. [2] P. Dierckx, "A fast algorithm for smoothing data on a rectangular
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grid while using spline functions", SIAM J.Numer.Anal. 19 (1982)
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1286-1304.
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.. [3] P. Dierckx, "An improved algorithm for curve fitting with spline
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functions", report tw54, Dept. Computer Science,K.U. Leuven, 1981.
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.. [4] P. Dierckx, "Curve and surface fitting with splines", Monographs on
|
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|
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Numerical Analysis, Oxford University Press, 1993.
|
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|
|
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|
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Examples
|
||
|
|
--------
|
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>>> import matplotlib.pyplot as plt
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>>> from scipy.interpolate import splev, splrep
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>>> x = np.linspace(0, 10, 10)
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>>> y = np.sin(x)
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>>> spl = splrep(x, y)
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>>> x2 = np.linspace(0, 10, 200)
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>>> y2 = splev(x2, spl)
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>>> plt.plot(x, y, 'o', x2, y2)
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>>> plt.show()
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"""
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res = _impl.splrep(x, y, w, xb, xe, k, task, s, t, full_output, per, quiet)
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return res
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def splev(x, tck, der=0, ext=0):
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"""
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Evaluate a B-spline or its derivatives.
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Given the knots and coefficients of a B-spline representation, evaluate
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the value of the smoothing polynomial and its derivatives. This is a
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wrapper around the FORTRAN routines splev and splder of FITPACK.
|
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Parameters
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||
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----------
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x : array_like
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An array of points at which to return the value of the smoothed
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spline or its derivatives. If `tck` was returned from `splprep`,
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then the parameter values, u should be given.
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tck : 3-tuple or a BSpline object
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If a tuple, then it should be a sequence of length 3 returned by
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`splrep` or `splprep` containing the knots, coefficients, and degree
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of the spline. (Also see Notes.)
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der : int, optional
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The order of derivative of the spline to compute (must be less than
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or equal to k).
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ext : int, optional
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Controls the value returned for elements of ``x`` not in the
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interval defined by the knot sequence.
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* if ext=0, return the extrapolated value.
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* if ext=1, return 0
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* if ext=2, raise a ValueError
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* if ext=3, return the boundary value.
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The default value is 0.
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Returns
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-------
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y : ndarray or list of ndarrays
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An array of values representing the spline function evaluated at
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the points in `x`. If `tck` was returned from `splprep`, then this
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is a list of arrays representing the curve in N-dimensional space.
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Notes
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||
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-----
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Manipulating the tck-tuples directly is not recommended. In new code,
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prefer using `BSpline` objects.
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||
|
|
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See Also
|
||
|
|
--------
|
||
|
|
splprep, splrep, sproot, spalde, splint
|
||
|
|
bisplrep, bisplev
|
||
|
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BSpline
|
||
|
|
|
||
|
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References
|
||
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|
----------
|
||
|
|
.. [1] C. de Boor, "On calculating with b-splines", J. Approximation
|
||
|
|
Theory, 6, p.50-62, 1972.
|
||
|
|
.. [2] M. G. Cox, "The numerical evaluation of b-splines", J. Inst. Maths
|
||
|
|
Applics, 10, p.134-149, 1972.
|
||
|
|
.. [3] P. Dierckx, "Curve and surface fitting with splines", Monographs
|
||
|
|
on Numerical Analysis, Oxford University Press, 1993.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if isinstance(tck, BSpline):
|
||
|
|
if tck.c.ndim > 1:
|
||
|
|
mesg = ("Calling splev() with BSpline objects with c.ndim > 1 is "
|
||
|
|
"not recommended. Use BSpline.__call__(x) instead.")
|
||
|
|
warnings.warn(mesg, DeprecationWarning)
|
||
|
|
|
||
|
|
# remap the out-of-bounds behavior
|
||
|
|
try:
|
||
|
|
extrapolate = {0: True, }[ext]
|
||
|
|
except KeyError:
|
||
|
|
raise ValueError("Extrapolation mode %s is not supported "
|
||
|
|
"by BSpline." % ext)
|
||
|
|
|
||
|
|
return tck(x, der, extrapolate=extrapolate)
|
||
|
|
else:
|
||
|
|
return _impl.splev(x, tck, der, ext)
|
||
|
|
|
||
|
|
|
||
|
|
def splint(a, b, tck, full_output=0):
|
||
|
|
"""
|
||
|
|
Evaluate the definite integral of a B-spline between two given points.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
a, b : float
|
||
|
|
The end-points of the integration interval.
|
||
|
|
tck : tuple or a BSpline instance
|
||
|
|
If a tuple, then it should be a sequence of length 3, containing the
|
||
|
|
vector of knots, the B-spline coefficients, and the degree of the
|
||
|
|
spline (see `splev`).
|
||
|
|
full_output : int, optional
|
||
|
|
Non-zero to return optional output.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
integral : float
|
||
|
|
The resulting integral.
|
||
|
|
wrk : ndarray
|
||
|
|
An array containing the integrals of the normalized B-splines
|
||
|
|
defined on the set of knots.
|
||
|
|
(Only returned if `full_output` is non-zero)
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
`splint` silently assumes that the spline function is zero outside the data
|
||
|
|
interval (`a`, `b`).
|
||
|
|
|
||
|
|
Manipulating the tck-tuples directly is not recommended. In new code,
|
||
|
|
prefer using the `BSpline` objects.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
splprep, splrep, sproot, spalde, splev
|
||
|
|
bisplrep, bisplev
|
||
|
|
BSpline
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] P.W. Gaffney, The calculation of indefinite integrals of b-splines",
|
||
|
|
J. Inst. Maths Applics, 17, p.37-41, 1976.
|
||
|
|
.. [2] P. Dierckx, "Curve and surface fitting with splines", Monographs
|
||
|
|
on Numerical Analysis, Oxford University Press, 1993.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if isinstance(tck, BSpline):
|
||
|
|
if tck.c.ndim > 1:
|
||
|
|
mesg = ("Calling splint() with BSpline objects with c.ndim > 1 is "
|
||
|
|
"not recommended. Use BSpline.integrate() instead.")
|
||
|
|
warnings.warn(mesg, DeprecationWarning)
|
||
|
|
|
||
|
|
if full_output != 0:
|
||
|
|
mesg = ("full_output = %s is not supported. Proceeding as if "
|
||
|
|
"full_output = 0" % full_output)
|
||
|
|
|
||
|
|
return tck.integrate(a, b, extrapolate=False)
|
||
|
|
else:
|
||
|
|
return _impl.splint(a, b, tck, full_output)
|
||
|
|
|
||
|
|
|
||
|
|
def sproot(tck, mest=10):
|
||
|
|
"""
|
||
|
|
Find the roots of a cubic B-spline.
|
||
|
|
|
||
|
|
Given the knots (>=8) and coefficients of a cubic B-spline return the
|
||
|
|
roots of the spline.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
tck : tuple or a BSpline object
|
||
|
|
If a tuple, then it should be a sequence of length 3, containing the
|
||
|
|
vector of knots, the B-spline coefficients, and the degree of the
|
||
|
|
spline.
|
||
|
|
The number of knots must be >= 8, and the degree must be 3.
|
||
|
|
The knots must be a montonically increasing sequence.
|
||
|
|
mest : int, optional
|
||
|
|
An estimate of the number of zeros (Default is 10).
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
zeros : ndarray
|
||
|
|
An array giving the roots of the spline.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Manipulating the tck-tuples directly is not recommended. In new code,
|
||
|
|
prefer using the `BSpline` objects.
|
||
|
|
|
||
|
|
See also
|
||
|
|
--------
|
||
|
|
splprep, splrep, splint, spalde, splev
|
||
|
|
bisplrep, bisplev
|
||
|
|
BSpline
|
||
|
|
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] C. de Boor, "On calculating with b-splines", J. Approximation
|
||
|
|
Theory, 6, p.50-62, 1972.
|
||
|
|
.. [2] M. G. Cox, "The numerical evaluation of b-splines", J. Inst. Maths
|
||
|
|
Applics, 10, p.134-149, 1972.
|
||
|
|
.. [3] P. Dierckx, "Curve and surface fitting with splines", Monographs
|
||
|
|
on Numerical Analysis, Oxford University Press, 1993.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if isinstance(tck, BSpline):
|
||
|
|
if tck.c.ndim > 1:
|
||
|
|
mesg = ("Calling sproot() with BSpline objects with c.ndim > 1 is "
|
||
|
|
"not recommended.")
|
||
|
|
warnings.warn(mesg, DeprecationWarning)
|
||
|
|
|
||
|
|
t, c, k = tck.tck
|
||
|
|
|
||
|
|
# _impl.sproot expects the interpolation axis to be last, so roll it.
|
||
|
|
# NB: This transpose is a no-op if c is 1D.
|
||
|
|
sh = tuple(range(c.ndim))
|
||
|
|
c = c.transpose(sh[1:] + (0,))
|
||
|
|
return _impl.sproot((t, c, k), mest)
|
||
|
|
else:
|
||
|
|
return _impl.sproot(tck, mest)
|
||
|
|
|
||
|
|
|
||
|
|
def spalde(x, tck):
|
||
|
|
"""
|
||
|
|
Evaluate all derivatives of a B-spline.
|
||
|
|
|
||
|
|
Given the knots and coefficients of a cubic B-spline compute all
|
||
|
|
derivatives up to order k at a point (or set of points).
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
x : array_like
|
||
|
|
A point or a set of points at which to evaluate the derivatives.
|
||
|
|
Note that ``t(k) <= x <= t(n-k+1)`` must hold for each `x`.
|
||
|
|
tck : tuple
|
||
|
|
A tuple ``(t, c, k)``, containing the vector of knots, the B-spline
|
||
|
|
coefficients, and the degree of the spline (see `splev`).
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
results : {ndarray, list of ndarrays}
|
||
|
|
An array (or a list of arrays) containing all derivatives
|
||
|
|
up to order k inclusive for each point `x`.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
splprep, splrep, splint, sproot, splev, bisplrep, bisplev,
|
||
|
|
BSpline
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] C. de Boor: On calculating with b-splines, J. Approximation Theory
|
||
|
|
6 (1972) 50-62.
|
||
|
|
.. [2] M. G. Cox : The numerical evaluation of b-splines, J. Inst. Maths
|
||
|
|
applics 10 (1972) 134-149.
|
||
|
|
.. [3] P. Dierckx : Curve and surface fitting with splines, Monographs on
|
||
|
|
Numerical Analysis, Oxford University Press, 1993.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if isinstance(tck, BSpline):
|
||
|
|
raise TypeError("spalde does not accept BSpline instances.")
|
||
|
|
else:
|
||
|
|
return _impl.spalde(x, tck)
|
||
|
|
|
||
|
|
|
||
|
|
def insert(x, tck, m=1, per=0):
|
||
|
|
"""
|
||
|
|
Insert knots into a B-spline.
|
||
|
|
|
||
|
|
Given the knots and coefficients of a B-spline representation, create a
|
||
|
|
new B-spline with a knot inserted `m` times at point `x`.
|
||
|
|
This is a wrapper around the FORTRAN routine insert of FITPACK.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
x (u) : array_like
|
||
|
|
A 1-D point at which to insert a new knot(s). If `tck` was returned
|
||
|
|
from ``splprep``, then the parameter values, u should be given.
|
||
|
|
tck : a `BSpline` instance or a tuple
|
||
|
|
If tuple, then it is expected to be a tuple (t,c,k) containing
|
||
|
|
the vector of knots, the B-spline coefficients, and the degree of
|
||
|
|
the spline.
|
||
|
|
m : int, optional
|
||
|
|
The number of times to insert the given knot (its multiplicity).
|
||
|
|
Default is 1.
|
||
|
|
per : int, optional
|
||
|
|
If non-zero, the input spline is considered periodic.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
BSpline instance or a tuple
|
||
|
|
A new B-spline with knots t, coefficients c, and degree k.
|
||
|
|
``t(k+1) <= x <= t(n-k)``, where k is the degree of the spline.
|
||
|
|
In case of a periodic spline (``per != 0``) there must be
|
||
|
|
either at least k interior knots t(j) satisfying ``t(k+1)<t(j)<=x``
|
||
|
|
or at least k interior knots t(j) satisfying ``x<=t(j)<t(n-k)``.
|
||
|
|
A tuple is returned iff the input argument `tck` is a tuple, otherwise
|
||
|
|
a BSpline object is constructed and returned.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Based on algorithms from [1]_ and [2]_.
|
||
|
|
|
||
|
|
Manipulating the tck-tuples directly is not recommended. In new code,
|
||
|
|
prefer using the `BSpline` objects.
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] W. Boehm, "Inserting new knots into b-spline curves.",
|
||
|
|
Computer Aided Design, 12, p.199-201, 1980.
|
||
|
|
.. [2] P. Dierckx, "Curve and surface fitting with splines, Monographs on
|
||
|
|
Numerical Analysis", Oxford University Press, 1993.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if isinstance(tck, BSpline):
|
||
|
|
|
||
|
|
t, c, k = tck.tck
|
||
|
|
|
||
|
|
# FITPACK expects the interpolation axis to be last, so roll it over
|
||
|
|
# NB: if c array is 1D, transposes are no-ops
|
||
|
|
sh = tuple(range(c.ndim))
|
||
|
|
c = c.transpose(sh[1:] + (0,))
|
||
|
|
t_, c_, k_ = _impl.insert(x, (t, c, k), m, per)
|
||
|
|
|
||
|
|
# and roll the last axis back
|
||
|
|
c_ = np.asarray(c_)
|
||
|
|
c_ = c_.transpose((sh[-1],) + sh[:-1])
|
||
|
|
return BSpline(t_, c_, k_)
|
||
|
|
else:
|
||
|
|
return _impl.insert(x, tck, m, per)
|
||
|
|
|
||
|
|
|
||
|
|
def splder(tck, n=1):
|
||
|
|
"""
|
||
|
|
Compute the spline representation of the derivative of a given spline
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
tck : BSpline instance or a tuple of (t, c, k)
|
||
|
|
Spline whose derivative to compute
|
||
|
|
n : int, optional
|
||
|
|
Order of derivative to evaluate. Default: 1
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
`BSpline` instance or tuple
|
||
|
|
Spline of order k2=k-n representing the derivative
|
||
|
|
of the input spline.
|
||
|
|
A tuple is returned iff the input argument `tck` is a tuple, otherwise
|
||
|
|
a BSpline object is constructed and returned.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
|
||
|
|
.. versionadded:: 0.13.0
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
splantider, splev, spalde
|
||
|
|
BSpline
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
This can be used for finding maxima of a curve:
|
||
|
|
|
||
|
|
>>> from scipy.interpolate import splrep, splder, sproot
|
||
|
|
>>> x = np.linspace(0, 10, 70)
|
||
|
|
>>> y = np.sin(x)
|
||
|
|
>>> spl = splrep(x, y, k=4)
|
||
|
|
|
||
|
|
Now, differentiate the spline and find the zeros of the
|
||
|
|
derivative. (NB: `sproot` only works for order 3 splines, so we
|
||
|
|
fit an order 4 spline):
|
||
|
|
|
||
|
|
>>> dspl = splder(spl)
|
||
|
|
>>> sproot(dspl) / np.pi
|
||
|
|
array([ 0.50000001, 1.5 , 2.49999998])
|
||
|
|
|
||
|
|
This agrees well with roots :math:`\\pi/2 + n\\pi` of
|
||
|
|
:math:`\\cos(x) = \\sin'(x)`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if isinstance(tck, BSpline):
|
||
|
|
return tck.derivative(n)
|
||
|
|
else:
|
||
|
|
return _impl.splder(tck, n)
|
||
|
|
|
||
|
|
|
||
|
|
def splantider(tck, n=1):
|
||
|
|
"""
|
||
|
|
Compute the spline for the antiderivative (integral) of a given spline.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
tck : BSpline instance or a tuple of (t, c, k)
|
||
|
|
Spline whose antiderivative to compute
|
||
|
|
n : int, optional
|
||
|
|
Order of antiderivative to evaluate. Default: 1
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
BSpline instance or a tuple of (t2, c2, k2)
|
||
|
|
Spline of order k2=k+n representing the antiderivative of the input
|
||
|
|
spline.
|
||
|
|
A tuple is returned iff the input argument `tck` is a tuple, otherwise
|
||
|
|
a BSpline object is constructed and returned.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
splder, splev, spalde
|
||
|
|
BSpline
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The `splder` function is the inverse operation of this function.
|
||
|
|
Namely, ``splder(splantider(tck))`` is identical to `tck`, modulo
|
||
|
|
rounding error.
|
||
|
|
|
||
|
|
.. versionadded:: 0.13.0
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.interpolate import splrep, splder, splantider, splev
|
||
|
|
>>> x = np.linspace(0, np.pi/2, 70)
|
||
|
|
>>> y = 1 / np.sqrt(1 - 0.8*np.sin(x)**2)
|
||
|
|
>>> spl = splrep(x, y)
|
||
|
|
|
||
|
|
The derivative is the inverse operation of the antiderivative,
|
||
|
|
although some floating point error accumulates:
|
||
|
|
|
||
|
|
>>> splev(1.7, spl), splev(1.7, splder(splantider(spl)))
|
||
|
|
(array(2.1565429877197317), array(2.1565429877201865))
|
||
|
|
|
||
|
|
Antiderivative can be used to evaluate definite integrals:
|
||
|
|
|
||
|
|
>>> ispl = splantider(spl)
|
||
|
|
>>> splev(np.pi/2, ispl) - splev(0, ispl)
|
||
|
|
2.2572053588768486
|
||
|
|
|
||
|
|
This is indeed an approximation to the complete elliptic integral
|
||
|
|
:math:`K(m) = \\int_0^{\\pi/2} [1 - m\\sin^2 x]^{-1/2} dx`:
|
||
|
|
|
||
|
|
>>> from scipy.special import ellipk
|
||
|
|
>>> ellipk(0.8)
|
||
|
|
2.2572053268208538
|
||
|
|
|
||
|
|
"""
|
||
|
|
if isinstance(tck, BSpline):
|
||
|
|
return tck.antiderivative(n)
|
||
|
|
else:
|
||
|
|
return _impl.splantider(tck, n)
|