From 52411de937ba7615995c6ceeafd8d23a7e9505ce Mon Sep 17 00:00:00 2001 From: Per A Brodtkorb Date: Wed, 25 May 2016 16:06:49 +0200 Subject: [PATCH] Simplified doctests --- wafo/definitions.py | 11 +- wafo/gaussian.py | 3 +- wafo/integrate.py | 119 +- wafo/polynomial.py | 4606 +++++++++++++++++++++---------------------- 4 files changed, 2377 insertions(+), 2362 deletions(-) diff --git a/wafo/definitions.py b/wafo/definitions.py index 52e2993..d75e648 100644 --- a/wafo/definitions.py +++ b/wafo/definitions.py @@ -12,11 +12,12 @@ Examples -------- In order to view the documentation do the following in an ipython window: ->>> import wafo.definitions as wd ->>> wd.crossings() +import wafo.definitions as wd +wd.crossings() or ->>> wd.crossings? + +wd.crossings? """ @@ -303,3 +304,7 @@ def waves(): findcross """ print(waves.__doc__) + +if __name__ == '__main__': + import doctest + doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE) diff --git a/wafo/gaussian.py b/wafo/gaussian.py index 1d72e7a..b9cf985 100644 --- a/wafo/gaussian.py +++ b/wafo/gaussian.py @@ -598,7 +598,8 @@ def prbnormndpc(rho, a, b, abserr=1e-4, relerr=1e-4, usesimpson=True, ''' # Call fortran implementation - val, err, ier = mvnprdmod.prbnormndpc(rho, a, b, abserr, relerr, usebreakpoints, usesimpson) # @UndefinedVariable @IgnorePep8 + val, err, ier = mvnprdmod.prbnormndpc(rho, a, b, abserr, relerr, + usebreakpoints, usesimpson) if ier > 0: warnings.warn('Abnormal termination ier = %d\n\n%s' % diff --git a/wafo/integrate.py b/wafo/integrate.py index 233f3f0..a618a0c 100644 --- a/wafo/integrate.py +++ b/wafo/integrate.py @@ -5,8 +5,9 @@ from numpy import pi, sqrt, ones, zeros # @UnresolvedImport from scipy import integrate as intg import scipy.special.orthogonal as ort from scipy import special as sp -from .plotbackend import plotbackend as plt + from scipy.integrate import simps, trapz +from .plotbackend import plotbackend as plt from .demos import humps from .misc import dea3 from .dctpack import dct @@ -48,9 +49,9 @@ def clencurt(fun, a, b, n0=5, trace=False, args=()): Example ------- >>> import numpy as np - >>> val,err = clencurt(np.exp,0,2) - >>> abs(val-np.expm1(2))< err, err<1e-10 - (array([ True], dtype=bool), array([ True], dtype=bool)) + >>> val, err = clencurt(np.exp, 0, 2) + >>> np.allclose(val, np.expm1(2)), err[0] < 1e-10 + (True, True) See also @@ -103,24 +104,20 @@ def clencurt(fun, a, b, n0=5, trace=False, args=()): f[0, :] = f[0, :] / 2 f[n, :] = f[n, :] / 2 -# % x = cos(pi*0:n/n) -# % f = f(x) -# % -# % N+1 -# % c(k) = (2/N) sum f''(n)*cos(pi*(2*k-2)*(n-1)/N), 1 <= k <= N/2+1. -# % n=1 + # x = cos(pi*0:n/n) + # f = f(x) + # + # N+1 + # c(k) = (2/N) sum f''(n)*cos(pi*(2*k-2)*(n-1)/N), 1 <= k <= N/2+1. + # n=1 fft = np.fft.fft tmp = np.real(fft(f[:n, :], axis=0)) c = 2 / n * (tmp[0:n / 2 + 1, :] + np.cos(np.pi * s2) * f[n, :]) c[0, :] = c[0, :] / 2 c[n / 2, :] = c[n / 2, :] / 2 -# % alternative call - # c2 = dct(f) - c = c[0:n / 2 + 1, :] / ((s2 - 1) * (s2 + 1)) Q = (af - bf) * np.sum(c, axis=0) - # Q = (a-b).*sum( c(1:n/2+1,:)./repmat((s2-1).*(s2+1),1,Na)) abserr = (bf - af) * np.abs(c[n / 2, :]) @@ -238,9 +235,9 @@ def h_roots(n, method='newton'): Example ------- >>> import numpy as np - >>> [x,w] = h_roots(10) - >>> np.sum(x*w) - -5.2516042729766621e-19 + >>> x, w = h_roots(10) + >>> np.allclose(np.sum(x*w), -5.2516042729766621e-19) + True See also -------- @@ -451,7 +448,7 @@ def la_roots(n, alpha=0, method='newton'): >>> import numpy as np >>> [x,w] = h_roots(10) >>> np.sum(x*w) - -5.2516042729766621e-19 + 1.3352627380516791e-17 See also -------- @@ -555,9 +552,9 @@ def p_roots(n, method='newton', a=-1, b=1): ------- Integral of exp(x) from a = 0 to b = 3 is: exp(3)-exp(0)= >>> import numpy as np - >>> [x,w] = p_roots(11,a=0,b=3) - >>> np.sum(np.exp(x)*w) - 19.085536923187668 + >>> x, w = p_roots(11, a=0, b=3) + >>> np.allclose(np.sum(np.exp(x)*w), 19.085536923187668) + True See also -------- @@ -723,15 +720,22 @@ def qrule(n, wfun=1, alpha=0, beta=0): Examples: --------- + >>> import numpy as np + + # integral of x^2 from a = -1 to b = 1 >>> [bp,wf] = qrule(10) - >>> sum(bp**2*wf) # integral of x^2 from a = -1 to b = 1 - 0.66666666666666641 + >>> np.allclose(sum(bp**2*wf), 0.66666666666666641) + True + + # integral of exp(-x.^2)*x.^2 from a = -inf to b = inf >>> [bp,wf] = qrule(10,2) - >>> sum(bp**2*wf) # integral of exp(-x.^2)*x.^2 from a = -inf to b = inf - 0.88622692545275772 + >>> np.allclose(sum(bp**2*wf), 0.88622692545275772) + True + + # integral of (x+1)*(1-x)^2 from a = -1 to b = 1 >>> [bp,wf] = qrule(10,4,1,2) - >>> (bp*wf).sum() # integral of (x+1)*(1-x)^2 from a = -1 to b = 1 - 0.26666666666666755 + >>> np.allclose((bp*wf).sum(), 0.26666666666666755) + True See also -------- @@ -841,23 +845,24 @@ class _Gaussq(object): --------- integration of x**2 from 0 to 2 and from 1 to 4 - >>> from scitools import numpyutils as npt - >>> A = [0, 1]; B = [2,4] - >>> fun = npt.wrap2callable('x**2') - >>> [val1,err1] = gaussq(fun,A,B) - >>> val1 - array([ 2.6666667, 21. ]) - >>> err1 - array([ 1.7763568e-15, 1.0658141e-14]) + >>> import numpy as np + >>> A = [0, 1] + >>> B = [2, 4] + >>> fun = lambda x: x**2 + >>> val1, err1 = gaussq(fun,A,B) + >>> np.allclose(val1, [ 2.6666667, 21. ]) + True + >>> np.allclose(err1, [ 1.7763568e-15, 1.0658141e-14]) + True Integration of x^2*exp(-x) from zero to infinity: - >>> fun2 = npt.wrap2callable('1') - >>> val2, err2 = gaussq(fun2, 0, npt.inf, wfun=3, alpha=2) - >>> val3, err3 = gaussq(lambda x: x**2,0, npt.inf, wfun=3, alpha=0) - >>> val2, err2 - (array([ 2.]), array([ 6.6613381e-15])) - >>> val3, err3 - (array([ 2.]), array([ 1.7763568e-15])) + >>> fun2 = lambda x : np.ones(np.shape(x)) + >>> val2, err2 = gaussq(fun2, 0, np.inf, wfun=3, alpha=2) + >>> val3, err3 = gaussq(lambda x: x**2,0, np.inf, wfun=3, alpha=0) + >>> np.allclose(val2, 2), err2[0] < 1e-14 + (True, True) + >>> np.allclose(val3, 2), err3[0] < 1e-14 + (True, True) Integrate humps from 0 to 2 and from 1 to 4 >>> val4, err4 = gaussq(humps,A,B) @@ -1024,23 +1029,29 @@ class _Quadgr(object): -------- >>> import numpy as np >>> Q, err = quadgr(np.log,0,1) - >>> quadgr(np.exp,0,9999*1j*np.pi) - (-2.0000000000122662, 2.1933237448479304e-09) + >>> q, err = quadgr(np.exp,0,9999*1j*np.pi) + >>> np.allclose(q, -2.0000000000122662), err < 1.0e-08 + (True, True) - >>> quadgr(lambda x: np.sqrt(4-x**2),0,2,1e-12) - (3.1415926535897811, 1.5809575870662229e-13) + >>> q, err = quadgr(lambda x: np.sqrt(4-x**2), 0, 2, abseps=1e-12) + >>> np.allclose(q, 3.1415926535897811), err < 1.0e-12 + (True, True) - >>> quadgr(lambda x: x**-0.75,0,1) - (4.0000000000000266, 5.6843418860808015e-14) + >>> q, err = quadgr(lambda x: x**-0.75, 0, 1) + >>> np.allclose(q, 4), err < 1.e-13 + (True, True) - >>> quadgr(lambda x: 1./np.sqrt(1-x**2),-1,1) - (3.141596056985029, 6.2146261559092864e-06) + >>> q, err = quadgr(lambda x: 1./np.sqrt(1-x**2), -1, 1) + >>> np.allclose(q, 3.141596056985029), err < 1.0e-05 + (True, True) - >>> quadgr(lambda x: np.exp(-x**2),-np.inf,np.inf,1e-9) #% sqrt(pi) - (1.7724538509055152, 1.9722334876348668e-11) + >>> q, err = quadgr(lambda x: np.exp(-x**2), -np.inf, np.inf, 1e-9) + >>> np.allclose(q, np.sqrt(np.pi)), err < 1e-9 + (True, True) - >>> quadgr(lambda x: np.cos(x)*np.exp(-x),0,np.inf,1e-9) - (0.50000000000000044, 7.3296813063450372e-11) + >>> q, err = quadgr(lambda x: np.cos(x)*np.exp(-x), 0, np.inf, 1e-9) + >>> np.allclose(q, 0.5), err < 1e-9 + (True, True) See also -------- diff --git a/wafo/polynomial.py b/wafo/polynomial.py index dd8824a..637a0b2 100644 --- a/wafo/polynomial.py +++ b/wafo/polynomial.py @@ -1,1938 +1,1935 @@ -""" - Extended functions to operate on polynomials -""" -# ------------------------------------------------------------------------- -# Name: polynomial -# Purpose: Functions to operate on polynomials. -# -# Author: pab -# polyXXX functions are based on functions found in the matlab toolbox polyutil -# written by -# Author: Peter J. Acklam -# E-mail: pjacklam@online.no -# WWW URL: http://home.online.no/~pjacklam -# -# Created: 30.12.2008 -# Copyright: (c) pab 2008 -# Licence: LGPL -# ------------------------------------------------------------------------- -# !/usr/bin/env python -from __future__ import absolute_import -import warnings # @UnusedImport -from numpy.polynomial import polyutils as pu -from .plotbackend import plotbackend as plt -import numpy as np -from numpy import (zeros, asarray, newaxis, arange, - logical_or, any, pi, cos, round, diff, all, exp, - where, extract, linalg, sign, concatenate, floor, - linspace, sum, meshgrid) - -from scipy.fftpack import dct, idct as _idct -from numpy.lib.polynomial import * # @UnusedWildImport -from scipy.misc.common import pade # @UnresolvedImport -__all__ = np.lib.polynomial.__all__ -__all__ = __all__ + ['pade', 'padefit', 'polyreloc', 'polyrescl', 'polytrim', - 'poly2hstr', 'poly2str', 'polyshift', 'polyishift', - 'map_from_intervall', 'map_to_intervall', 'cheb2poly', - 'chebextr', 'chebroot', 'chebpoly', 'chebfit', 'chebval', - 'chebder', 'chebint', 'Cheb1d', 'dct', 'idct'] - - -def polyint(p, m=1, k=None): - """ - Return an antiderivative (indefinite integral) of a polynomial. - - The returned order `m` antiderivative `P` of polynomial `p` satisfies - :math:`\\frac{d^m}{dx^m}P(x) = p(x)` and is defined up to `m - 1` - integration constants `k`. The constants determine the low-order - polynomial part - - .. math:: \\frac{k_{m-1}}{0!} x^0 + \\ldots + \\frac{k_0}{(m-1)!}x^{m-1} - - of `P` so that :math:`P^{(j)}(0) = k_{m-j-1}`. - - Parameters - ---------- - p : {array_like, poly1d} - Polynomial to differentiate. - A sequence is interpreted as polynomial coefficients, see `poly1d`. - m : int, optional - Order of the antiderivative. (Default: 1) - k : {None, list of `m` scalars, scalar}, optional - Integration constants. They are given in the order of integration: - those corresponding to highest-order terms come first. - - If ``None`` (default), all constants are assumed to be zero. - If `m = 1`, a single scalar can be given instead of a list. - - See Also - -------- - polyder : derivative of a polynomial - poly1d.integ : equivalent method - - Examples - -------- - The defining property of the antiderivative: - - >>> p = np.poly1d([1,1,1]) - >>> P = np.polyint(p) - >>> P - poly1d([ 0.33333333, 0.5 , 1. , 0. ]) - >>> np.polyder(P) == p - True - - The integration constants default to zero, but can be specified: - - >>> P = np.polyint(p, 3) - >>> P(0) - 0.0 - >>> np.polyder(P)(0) - 0.0 - >>> np.polyder(P, 2)(0) - 0.0 - >>> P = np.polyint(p, 3, k=[6, 5, 3]) - >>> P.coefficients.tolist() - [0.016666666666666666, 0.041666666666666664, 0.16666666666666666, 3.0, - 5.0, 3.0] - - Note that 3 = 6 / 2!, and that the constants are given in the order of - integrations. Constant of the highest-order polynomial term comes first: - - >>> np.polyder(P, 2)(0) - 6.0 - >>> np.polyder(P, 1)(0) - 5.0 - >>> P(0) - 3.0 - - """ - m = int(m) - if m < 0: - raise ValueError("Order of integral must be positive (see polyder)") - if k is None: - k = zeros(m, float) - k = np.atleast_1d(k) - if len(k) == 1 and m > 1: - k = k[0] * np.ones(m, float) - if len(k) < m: - raise ValueError( - "k must be a scalar or a rank-1 array of length 1 or >m.") - truepoly = isinstance(p, poly1d) - p = asarray(p) - if m == 0: - if truepoly: - return poly1d(p) - return p - else: - ix = arange(len(p), 0, -1) - if p.ndim > 1: - ix = ix[..., newaxis] - pieces = p.shape[-1] - k0 = k[0] * np.ones((1, pieces), dtype=int) - else: - k0 = [k[0]] - y = np.concatenate((p.__truediv__(ix), k0), axis=0) - - val = polyint(y, m - 1, k=k[1:]) - if truepoly: - return poly1d(val) - return val - - -def polyder(p, m=1): - """ - Return the derivative of the specified order of a polynomial. - - Parameters - ---------- - p : poly1d or sequence - Polynomial to differentiate. - A sequence is interpreted as polynomial coefficients, see `poly1d`. - m : int, optional - Order of differentiation (default: 1) - - Returns - ------- - der : poly1d - A new polynomial representing the derivative. - - See Also - -------- - polyint : Anti-derivative of a polynomial. - poly1d : Class for one-dimensional polynomials. - - Examples - -------- - The derivative of the polynomial :math:`x^3 + x^2 + x^1 + 1` is: - - >>> p = np.poly1d([1,1,1,1]) - >>> p2 = np.polyder(p) - >>> p2 - poly1d([3, 2, 1]) - - which evaluates to: - - >>> p2(2.) - 17.0 - - We can verify this, approximating the derivative with - ``(f(x + h) - f(x))/h``: - - >>> (p(2. + 0.001) - p(2.)) / 0.001 - 17.007000999997857 - - The fourth-order derivative of a 3rd-order polynomial is zero: - - >>> np.polyder(p, 2) - poly1d([6, 2]) - >>> np.polyder(p, 3) - poly1d([6]) - >>> np.polyder(p, 4) - poly1d([ 0.]) - - """ - m = int(m) - if m < 0: - raise ValueError("Order of derivative must be positive (see polyint)") - truepoly = isinstance(p, poly1d) - p = asarray(p) - if m == 0: - if truepoly: - return poly1d(p) - return p - else: - n = len(p) - 1 - ix = arange(n, 0, -1) - if p.ndim > 1: - ix = ix[..., newaxis] - y = ix * p[:-1] - val = polyder(y, m - 1) - if truepoly: - return poly1d(val) - return val - - -def polydeg(x, y): - ''' - Return optimal degree for polynomial fitting - - - N = POLYDEG(X,Y) finds the optimal degree for polynomial fitting, - according to the Akaike's information criterion. - - Assuming that you want to find the degree N of a polynomial that fits - the data Y(X) best in a least-squares sense, the Akaike's information - criterion is defined by: - 2*(N + 1) + n * (log(2 * pi * RSS / n) + 1) - where n is the number of points and RSS is the residual sum of squares. - The optimal degree N is defined here as that which minimizes AIC: - http://en.wikipedia.org/wiki/Akaike_Information_Criterion - - Notes: - ----- - If the number of data is small, POLYDEG may tend to return: - N = (number of points)-1. - - ORTHOFIT is more appropriate than POLYFIT for polynomial fitting with - relatively high degrees. - - Example: - ------- - >>> x = np.linspace(0,10,300) - >>> y = np.sin(x ** 3 / 100) ** 2 + 0.05 * np.random.randn(x.size) - >>> n = polydeg(x,y) - >>> n - 21 - - ys = orthofit(x,y,n); - plt.plot(x, y, '.', x, ys, 'k') - - See also - -------- - polyfit, orthofit - ''' - x, y = np.atleast_1d(x, y) - x = x.ravel() - y = y.ravel() - N = len(x) - - # Search the optimal degree minimizing the Akaike's information criterion - # y(x) are fitted in a least-squares sense using a polynomial of degree n - # developed in a series of orthogonal polynomials. - ys = np.ones((N,)) * y.mean() - # correction for small sample sizes - logsum2 = (np.log(2 * pi * ((ys - y) ** 2).sum() / N) + 1) - AIC = 2 + N * logsum2 + 4 / (N - 2) - - n = 1 - nit = 0 - - # While-loop is stopped when a minimum is detected. 3 more steps are - # required to take AIC noise into account and to ensure that this minimum - # is a (likely) global minimum. - - while nit < 3: - p = orthofit(x, y, n) - ys = orthoval(p, x) - # -- Akaike's Information Criterion - aic = (2 * (n + 1) * (1 + (n + 2) / (N - n - 2)) + - N * (np.log(2 * pi * sum((ys - y) ** 2) / N) + 1)) - - if aic >= AIC: - nit += 1 - else: - nit = 0 - AIC = aic - - n = n + 1 - - if n >= N: - break - n = n - nit - 1 - return n - - -def orthoval(p, x): - ''' - Evaluation of orthogonal polynomial - - Parameters - ---------- - p : array_like - 2D array of polynomial coefficients (including coefficients equal - to zero) from highest degree to the constant term. - x : array_like - A number or a 1D array of numbers "at" which to evaluate `p`. - - Returns - ------- - values : ndarray - - See Also - -------- - orthofit - ''' - p = np.atleast_2d(p) - n = p.shape[1] - 1 - xi = np.atleast_1d(x) - shape0 = xi.shape - if n == 0: - return np.ones(shape0) * p[0] - xi = xi.ravel() - xn = np.ones((n + 1, len(xi))) - xn[1] = xi - p[1, 1] - for i in range(2, n + 1): - xn[i, :] = (xi - p[1, i]) * xn[i - 1, :] - p[2, i] * xn[i - 2, :] - ys = np.dot(p[0], xn) - return ys.reshape(shape0) - - -def ortho2poly(p): - """ - Converts orthogonal polynomial to ordinary polynomial coefficients - - Parameters - ---------- - p : array-like - orthogonal polynomial coefficients - - Returns - ------- - p : ndarray - ordinary polynomial coefficients - - It is not advised to do this for p.shape[1]>10 due to numerical - cancellations. - - See also - -------- - orthoval - orthofit - - Examples - -------- - >>> import numpy as np - >>> x = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]) - >>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0]) - >>> p = orthofit(x, y, 3) - >>> p - array([[ 0. , -0.30285714, -0.16071429, 0.08703704], - [ 0. , 2.5 , 2.5 , 2.5 ], - [ 0. , 0. , 2.91666667, 2.13333333]]) - >>> ortho2poly(p) - array([ 0.08703704, -0.81349206, 1.69312169, -0.03968254]) - >>> np.polyfit(x, y, 3) - array([ 0.08703704, -0.81349206, 1.69312169, -0.03968254]) - - References - ---------- - """ - p = np.atleast_2d(p) - n = p.shape[1] - 1 - if n == 0: - return p[0] - x = [1, ] * (n + 1) - x[1] = np.array([1, - p[1, 1]]) - for i in range(2, n + 1): - x[i] = polyadd(polymul([1, - p[1, i]], x[i - 1]), - p[2, i] * x[i - 2]) - for i in range(n + 1): - x[i] *= p[0, i] - return reduce(polyadd, x) - - -def orthofit(x, y, n): - ''' - Fit orthogonal polynomial to data. - - Parameters - --------- - x, y : arrays - data Y(X) to fit to a polynomial. - n : integer - degree of fitted polynomial. - - Returns - ------- - p : array - orthogonal polynomial - - Notes: - ----- - Orthofit smooths/fits data using a polynomial of degree N developed in - a sequence of orthogonal polynomials. ORTHOFIT is more appropriate than - polyfit for polynomial fitting and smoothing since this method does not - involve any matrix linear system but a simple recursive procedure. - Degrees much higher than 30 could be used with orthogonal polynomials, - whereas badly conditioned matrices may appear with a classical - polynomial fitting of degree typically higher than 10. - - To avoid using unnecessarily high degrees, you may let the function - POLYDEG choose it for you. POLYDEG finds an optimal polynomial degree - according to the Akaike's information criterion. - - Example: - ------- - >>> x = np.linspace(0,10,300); - >>> y = np.sin(x**3/100)**2 + 0.05*np.random.randn(x.size) - >>> p = orthofit(x, y, 25) - >>> ys = orthoval(p, x) - - plot(x, y,'.',x, ys, 'k') - - See also - -------- - polydeg, polyfit, polyval - - Reference: - --------- - Methodes de calcul numerique 2. JP Nougier. Hermes Science - Publications, 2001. Section 4.7 pp 116-121 - ''' - x, y = np.atleast_1d(x, y) - x = x.ravel() - y = y.ravel() - # Particular case: n=0 - if n == 0: - return y.mean() - - # p = Coefficients of the orthogonal polynomials - p = np.zeros((3, n + 1)) - p[1, 1] = x.mean() - - N = len(x) - PL = np.ones((n + 1, N)) - PL[1] = x - p[1, 1] - - for i in range(2, n + 1): - p[1, i] = np.dot(x, PL[i - 1] ** 2) / sum(PL[i - 1] ** 2) - p[2, i] = np.dot(x, PL[i - 2] * PL[i - 1]) / sum(PL[i - 2] ** 2) - PL[i] = (x - p[1, i]) * PL[i - 1] - p[2, i] * PL[i - 2] - p[0, :] = np.dot(PL, y) / sum(PL ** 2, axis=1) - return p - # ys = np.dot(p[0, :], PL) # smoothed y - - -def polyreloc(p, x, y=0.0): - """ - Relocate polynomial - - The polynomial `p` is relocated by "moving" it `x` - units along the x-axis and `y` units along the y-axis. - So the polynomial `r` is relative to the point (x,y) as - the polynomial `p` is relative to the point (0,0). - - Parameters - ---------- - p : array-like, poly1d - vector or matrix of column vectors of polynomial coefficients to - relocate. (Polynomial coefficients are in decreasing order.) - x : scalar - distance to relocate P along x-axis - y : scalar - distance to relocate P along y-axis (default 0) - - Returns - ------- - r : ndarray, poly1d - vector/matrix/poly1d of relocated polynomial coefficients. - - See also - -------- - polyrescl - - Example - ------- - >>> import numpy as np - >>> p = np.arange(6); p.shape = (2,-1) - >>> np.polyval(p,0) - array([3, 4, 5]) - >>> np.polyval(p,1) - array([3, 5, 7]) - >>> r = polyreloc(p,-1) # move to the left along x-axis - >>> np.polyval(r,-1) # = polyval(p,0) - array([3, 4, 5]) - >>> np.polyval(r,0) # = polyval(p,1) - array([3, 5, 7]) - """ - - truepoly = isinstance(p, poly1d) - r = np.atleast_1d(p).copy() - n = r.shape[0] - - # Relocate polynomial using Horner's algorithm - for ii in range(n, 1, -1): - for i in range(1, ii): - r[i] = r[i] - x * r[i - 1] - r[-1] = r[-1] + y - if r.ndim > 1 and r.shape[-1] == 1: - r.shape = (r.size,) - if truepoly: - r = poly1d(r) - return r - - -def polyrescl(p, x, y=1.0): - """ - Rescale polynomial. - - Parameters - ---------- - p : array-like, poly1d - vector or matrix of column vectors of polynomial coefficients to - rescale. (Polynomial coefficients are in decreasing order.) - x,y : scalars - defining the factors to rescale the polynomial `p` in - x-direction and y-direction, respectively. - - Returns - ------- - r : ndarray, poly1d - vector/matrix/poly1d of rescaled polynomial coefficients. - - See also - -------- - polyreloc - - Example - ------- - >>> import numpy as np - >>> p = np.arange(6); p.shape = (2,-1) - >>> np.polyval(p,0) - array([3, 4, 5]) - >>> np.polyval(p,1) - array([3, 5, 7]) - >>> r = polyrescl(p,2) # scale by 2 along x-axis - >>> np.polyval(r,0) # = polyval(p,0) - array([ 3., 4., 5.]) - >>> np.polyval(r,2) # = polyval(p,1) - array([ 3., 5., 7.]) - """ - - truepoly = isinstance(p, poly1d) - r = np.atleast_1d(p) - n = r.shape[0] - - xscale = (float(x) ** arange(1 - n, 1)) - if r.ndim == 1: - q = y * r * xscale - else: - q = y * r * xscale[:, newaxis] - if truepoly: - q = poly1d(q) - return q - - -def polytrim(p): - """ - Trim polynomial by stripping off leading zeros. - - Parameters - ---------- - p : array-like, poly1d - vector or matrix of column vectors of polynomial coefficients in - decreasing order. - - Returns - ------- - r : ndarray, poly1d - vector/matrix/poly1d of trimmed polynomial coefficients. - - Example - ------- - >>> p = [0,1,2] - >>> polytrim(p) - array([1, 2]) - >>> p1 = [[0,0],[1,2],[3,4]] - >>> polytrim(p1) - array([[1, 2], - [3, 4]]) - """ - - truepoly = isinstance(p, poly1d) - if truepoly: - return p - else: - r = np.atleast_1d(p).copy() - # Remove leading zeros - is_not_lead_zeros = logical_or.accumulate(r != 0, axis=0) - if r.ndim == 1: - r = r[is_not_lead_zeros] - else: - is_not_lead_zeros = any(is_not_lead_zeros, axis=1) - r = r[is_not_lead_zeros, :] - return r - - -def poly2hstr(p, variable='x'): - """ - Return polynomial as a Horner represented string. - - Parameters - ---------- - p : array-like poly1d - vector of polynomial coefficients in decreasing order. - variable : string - display character for variable - - Returns - ------- - p_str : string - consisting of the polynomial coefficients in the vector P multiplied - by powers of the given `variable`. - - Examples - -------- - >>> poly2hstr([1, 1, 2], 's' ) - '(s + 1)*s + 2' - - See also - -------- - poly2str - """ - var = variable - - coefs = polytrim(np.atleast_1d(p)) - order = len(coefs) - 1 # Order of polynomial. - s = '' # Initialize output string. - ix = 1 - for expon in range(order, -1, -1): - coef = coefs[order - expon] - # There is no point in adding a zero term (except if it's the only - # term, but we'll take care of that later). - if coef == 0: - ix += 1 - else: - # Append exponent if necessary. - if ix > 1: - exponstr = '%.0f' % ix - s = '%s**%s' % (s, exponstr) - ix = 1 - # Is it the first term? - isfirst = s == '' - - # We need the coefficient only if it is different from 1 or -1 or - # when it is the constant term. - needcoef = ( - (abs(coef) != 1) | ( - expon == 0) & isfirst) | 1 - isfirst - - # We need the variable except in the constant term. - needvar = (expon != 0) - - # Add sign, but we don't need a leading plus-sign. - if isfirst: - if coef < 0: - s = '-' # % Unary minus. - else: - if coef < 0: - s = '%s - ' % s # % Binary minus (subtraction). - else: - s = '%s + ' % s # % Binary plus (addition). - - # Append the coefficient if it is different from one or when it is - # the constant term. - if needcoef: - coefstr = '%.20g' % abs(coef) - s = '%s%s' % (s, coefstr) - - # Append variable if necessary. - if needvar: - # Append a multiplication sign if necessary. - if needcoef: - if 1 - isfirst: - s = '(%s)' % s - s = '%s*' % s - s = '%s%s' % (s, var) - - # Now treat the special case where the polynomial is zero. - if s == '': - s = '0' - return s - - -def poly2str(p, variable='x'): - """ - Return polynomial as a string. - - Parameters - ---------- - p : array-like poly1d - vector of polynomial coefficients in decreasing order. - variable : string - display character for variable - - Returns - ------- - p_str : string - consisting of the polynomial coefficients in the vector P multiplied - by powers of the given `variable`. - - See also - -------- - poly2hstr - - Examples - -------- - >>> poly2str([1, 1, 2], 's' ) - 's**2 + s + 2' - """ - thestr = "0" - var = variable - - # Remove leading zeros - coeffs = polytrim(np.atleast_1d(p)) - - N = len(coeffs) - 1 - - for k in range(N+1): - coefstr = '%.4g' % abs(coeffs[k]) - if coefstr[-4:] == '0000': - coefstr = coefstr[:-5] - power = (N - k) - if power == 0: - if coefstr != '0': - newstr = '%s' % (coefstr,) - else: - newstr = '0' if k == 0 else '' - elif power == 1: - if coefstr == '0': - newstr = '' - elif coefstr in ['b', '1']: - newstr = var - else: - newstr = '%s*%s' % (coefstr, var) - else: - if coefstr == '0': - newstr = '' - elif coefstr in ['b', '1']: - newstr = '%s**%d' % (var, power,) - else: - newstr = '%s*%s**%d' % (coefstr, var, power) - - if k > 0: - if newstr != '': - if coeffs[k] < 0: - thestr = "%s - %s" % (thestr, newstr) - else: - thestr = "%s + %s" % (thestr, newstr) - elif (k == 0) and (newstr != '') and (coeffs[k] < 0): - thestr = "-%s" % (newstr,) - else: - thestr = newstr - return thestr - - -def polyshift(py, a=-1, b=1): - """ - Polynomial coefficient shift - - Polyshift shift the polynomial coefficients by a variable shift: - - Y = 2*(X-.5*(b+a))/(b-a) - - i.e., the interval -1 <= Y <= 1 is mapped to the interval a <= X <= b - - Parameters - ---------- - py : array-like - polynomial coefficients for the variable y. - a,b : scalars - lower and upper limit. - - Returns - ------- - px : ndarray - polynomial coefficients for the variable x. - - See also - -------- - polyishift - - Example - ------- - >>> py = [1, 0] - >>> px = polyshift(py,0,5) - >>> polyval(px,[0, 2.5, 5]) #% This is the same as the line below - array([-1., 0., 1.]) - >>> polyval(py,[-1, 0, 1 ]) - array([-1, 0, 1]) - """ - - if (a == -1) & (b == 1): - return py - L = b - a - return polyishift(py, -(2. + b + a) / L, (2. - b - a) / L) - - -def polyishift(px, a=-1, b=1): - """ - Inverse polynomial coefficient shift - - Polyishift does the inverse of Polyshift, - shift the polynomial coefficients by a variable shift: - - Y = 2*(X-.5*(b+a)/(b-a) - - i.e., the interval a <= X <= b is mapped to the interval -1 <= Y <= 1 - - Parameters - ---------- - px : array-like - polynomial coefficients for the variable x. - a,b : scalars - lower and upper limit. - - Returns - ------- - py : ndarray - polynomial coefficients for the variable y. - - See also - -------- - polyishift - - Example - ------- - >>> px = [1, 0] - >>> py = polyishift(px,0,5); - >>> polyval(px,[0, 2.5, 5]) #% This is the same as the line below - array([ 0. , 2.5, 5. ]) - >>> polyval(py,[-1, 0, 1]) - array([ 0. , 2.5, 5. ]) - """ - if (a == -1) & (b == 1): - return px - L = b - a - xscale = 2. / L - xloc = -float(a + b) / L - return polyreloc(polyrescl(px, xscale), xloc) - - -def map_from_interval(x, a, b): - """F(x), where F: [a,b] -> [-1,1].""" - return (x - (b + a) / 2.0) * (2.0 / (b - a)) - - -def map_to_interval(x, a, b): - """F(x), where F: [-1,1] -> [a,b].""" - return (x * (b - a) + (b + a)) / 2.0 - - -def poly2cheb(p, a=-1, b=1): - """ - Convert polynomial coefficients into Chebyshev coefficients - - Parameters - ---------- - p : array-like - polynomial coefficients - a,b : real scalars - lower and upper limits (Default -1,1) - - Returns - ------- - ck : ndarray - Chebychef coefficients - - POLY2CHEB do the inverse of CHEB2POLY: given a vector of polynomial - coefficients AK, returns an equivalent vector of Chebyshev - coefficients CK. - - This is useful for economization of power series. - The steps for doing so: - 1. Convert polynomial coefficients to Chebychev coefficients, CK. - 2. Truncate the CK series to a smaller number of terms, using the - coefficient of the first neglected Chebychev polynomial as an error - estimate. - 3 Convert back to a polynomial by CHEB2POLY - - See also - -------- - cheb2poly - chebval - chebfit - - Examples - -------- - >>> import numpy as np - >>> p = np.arange(5) - >>> ck = poly2cheb(p) - >>> cheb2poly(ck) - array([ 1., 2., 3., 4.]) - - Reference - --------- - William H. Press, Saul Teukolsky, - William T. Wetterling and Brian P. Flannery (1997) - "Numerical recipes in Fortran 77", Vol. 1, pp 184-194 - """ - f = poly1d(p) - n = len(f.coeffs) - return chebfit(f, n, a, b) - - -def cheb2poly(ck, a=-1, b=1): - """ - Converts Chebyshev coefficients to polynomial coefficients - - Parameters - ---------- - ck : array-like - Chebychef coefficients - a,b : real, scalars - lower and upper limits (Default -1,1) - - Returns - ------- - p : ndarray - polynomial coefficients - - It is not advised to do this for len(ck)>10 due to numerical cancellations. - - See also - -------- - chebval - chebfit - - Examples - -------- - >>> import numpy as np - >>> p = np.arange(5) - >>> ck = poly2cheb(p) - >>> cheb2poly(ck) - array([ 1., 2., 3., 4.]) - - - References - ---------- - http://en.wikipedia.org/wiki/Chebyshev_polynomials - http://en.wikipedia.org/wiki/Chebyshev_form - http://en.wikipedia.org/wiki/Clenshaw_algorithm - """ - - n = len(ck) - - b_Nmi = zeros(1) - b_Nmip1 = zeros(1) - y = np.r_[2 / (b - a), -(a + b) / (b - a)] - y2 = 2. * y - - # Clenshaw recurence - for ix in range(n - 1): - tmp = b_Nmi - b_Nmi = polymul(y2, b_Nmi) # polynomial multiplication - nb = len(b_Nmip1) - b_Nmip1[-1] = b_Nmip1[-1] - ck[ix] - b_Nmi[-nb::] = b_Nmi[-nb::] - b_Nmip1 - b_Nmip1 = tmp - - p = polymul(y, b_Nmi) # polynomial multiplication - nb = len(b_Nmip1) - b_Nmip1[-1] = b_Nmip1[-1] - ck[n - 1] - p[-nb::] = p[-nb::] - b_Nmip1 - return polytrim(p) - - -def chebextr(n): - """ - Return roots of derivative of Chebychev polynomial of the first kind. - - All local extreme values of the polynomial are either -1 or 1. So, - CHEBPOLY( N, CHEBEXTR(N) ) ) return the same as (-1).^(N:-1:0) - except for the numerical noise in the former. - - Because the extreme values of Chebychev polynomials of the first - kind are either -1 or 1, their roots are often used as starting - values for the nodes in minimax approximations. - - - Parameters - ---------- - n : scalar, integer - degree of Chebychev polynomial. - - Examples - -------- - >>> x = chebextr(4) - >>> chebpoly(4,x) - array([ 1., -1., 1., -1., 1.]) - - - Reference - --------- - http://en.wikipedia.org/wiki/Chebyshev_nodes - http://en.wikipedia.org/wiki/Chebyshev_polynomials - """ - return - cos((pi * arange(n + 1)) / n) - - -def chebroot(n, kind=1): - """ - Return roots of Chebychev polynomial of the first or second kind. - - The roots of the Chebychev polynomial of the first kind form a particularly - good set of nodes for polynomial interpolation because the resulting - interpolation polynomial minimizes the problem of Runge's phenomenon. - - Parameters - ---------- - n : scalar, integer - degree of Chebychev polynomial. - kind: 1 or 2, optional - kind of Chebychev polynomial (default 1) - - Examples - -------- - >>> import numpy as np - >>> x = chebroot(3) - >>> np.abs(chebpoly(3,x))<1e-15 - array([ True, True, True], dtype=bool) - >>> chebpoly(3) - array([ 4., 0., -3., 0.]) - >>> x2 = chebroot(4,kind=2) - >>> np.abs(chebpoly(4,x2,kind=2))<1e-15 - array([ True, True, True, True], dtype=bool) - >>> chebpoly(4,kind=2) - array([ 16., 0., -12., 0., 1.]) - - - Reference - --------- - http://en.wikipedia.org/wiki/Chebyshev_nodes - http://en.wikipedia.org/wiki/Chebyshev_polynomials - """ - if kind not in (1, 2): - raise ValueError('kind must be 1 or 2') - return - cos(pi * (arange(n) + 0.5 * kind) / (n + kind - 1)) - - -def chebpoly(n, x=None, kind=1): - """ - Return Chebyshev polynomial of the first or second kind. - - These polynomials are orthogonal on the interval [-1,1], with - respect to the weight function w(x) = (1-x**2)**(-1/2+kind-1). - - chebpoly(n) returns coefficients of the Chebychev polynomial of degree N. - chebpoly(n,x) returns the Chebychev polynomial of degree N evaluated at X. - - Parameters - ---------- - n : integer, scalar - degree of Chebychev polynomial. - x : array-like, optional - evaluation points - kind: 1 or 2, optional - kind of Chebychev polynomial (default 1) - - Returns - ------- - p : ndarray - polynomial coefficients if x is None. - Chebyshev polynomial evaluated at x otherwise - - Examples - -------- - >>> import numpy as np - >>> x = chebroot(3) - >>> np.abs(chebpoly(3,x))<1e-15 - array([ True, True, True], dtype=bool) - >>> chebpoly(3) - array([ 4., 0., -3., 0.]) - >>> x2 = chebroot(4,kind=2) - >>> np.abs(chebpoly(4,x2,kind=2))<1e-15 - array([ True, True, True, True], dtype=bool) - >>> chebpoly(4,kind=2) - array([ 16., 0., -12., 0., 1.]) - - - Reference - --------- - http://en.wikipedia.org/wiki/Chebyshev_polynomials - """ - if x is None: # Calculate coefficients. - if n == 0: - p = np.ones(1) - else: - p = round(pow(2, n - 2 + kind) * poly(chebroot(n, kind=kind))) - p[1::2] = 0 - return p - else: # Evaluate polynomial in chebychev form - ck = zeros(n + 1) - ck[0] = 1. - return _chebval(np.atleast_1d(x), ck, kind=kind) - - -def chebfit(fun, n=10, a=-1, b=1, trace=False): - """ - Computes the Chebyshevs coefficients - - so that f(x) can be approximated by: - - n-1 - f(x) = sum ck*Tk(x) - k=0 - - where Tk is the k'th Chebyshev polynomial of the first kind. - - Parameters - ---------- - fun : callable - function to approximate - n : integer, scalar, optional - number of base points (abscissas). Default n=10 (maximum 50) - a,b : real, scalars, optional - integration limits - - Returns - ------- - ck : ndarray - polynomial coefficients in Chebychev form. - - Examples - -------- - Fit exp(x) - - >>> import matplotlib.pyplot as plt - >>> a = 0; b = 2 - >>> ck = chebfit(np.exp,7,a,b); - >>> x = np.linspace(0,4); - >>> h=plt.plot(x, np.exp(x), 'r', x, chebval(x,ck,a,b), 'g.') - >>> x1 = chebroot(9)*(b-a)/2+(b+a)/2 - >>> ck1 = chebfit(np.exp(x1)) - >>> h = plt.plot(x,np.exp(x), 'r', x, chebval(x,ck1,a,b),'g.') - - >>> plt.close() - - See also - -------- - chebval - - Reference - --------- - http://en.wikipedia.org/wiki/Chebyshev_nodes - http://mathworld.wolfram.com/ChebyshevApproximationFormula.html - - W. Fraser (1965) - "A Survey of Methods of Computing Minimax and Near-Minimax Polynomial - Approximations for Functions of a Single Independent Variable" - Journal of the ACM (JACM), Vol. 12 , Issue 3, pp 295 - 314 - """ - - if (n > 50): - warnings.warn('CHEBFIT should only be used for n<50') - - if hasattr(fun, '__call__'): - x = map_to_interval(chebroot(n), a, b) - f = fun(x) - if trace: - plt.plot(x, f, '+') - else: - f = fun - n = len(f) - # N-1 - # c[k] = (2/N) sum w[n] f[n]*cos(pi*k*(2n+1)/(2N)), 0 <= k < N. - # n=0 - # - # w[0] = 0.5, w[n]=1 for n>0 - - ck = dct(f[::-1]) / n - ck[0] = ck[0] / 2. - return ck[::-1] - - -def chebfit_dct(f, n=(10, ), domain=None): - """ - Fit Chebyshev series to N-dimensional function - so that f(x1, x2,..., xn) can be approximated by: - - .. math:: f(x_1, x_2,...,x_n) = - \\sum_{i,j,...k} c_i T_i(x_1)*...*c_k T_k(x_n) , - - where Tk is the k'th Chebyshev polynomial of the first kind. - - Parameters - ---------- - f : callable - function to approximate - n : list of integers, optional - number of base points (abscissas) used for each dimension. - Default n=10 (maximum 50) - domain : vector [a1,b1,a2,b2 ,..., an, bn], optional - defining the rectangle [a1, b1] x [a2, b2] x ...x [an, bn]. - (default domain = (-1,1) * len(n)) - - Returns - ------- - ck : ndarray - polynomial coefficients in Chebychev form. - - Examples - -------- - Fit exponential function - - >>> import matplotlib.pyplot as plt - >>> domain = (0, 2) - >>> ck = chebfit_dct(np.exp, 7, domain) - >>> np.allclose(ck, [3.44152387e+00, 3.07252345e+00, 7.38000848e-01, - ... 1.20520053e-01, 1.48805268e-02, 1.47579673e-03, - ... 1.21719524e-04]) - True - >>> x1 = map_to_interval(chebroot(9), *domain) - >>> ck1 = chebfit(np.exp(x1)) - >>> np.allclose(ck1, [5.40019009e-07, 8.69418381e-06, 1.22261037e-04, - ... 1.47582673e-03, 1.48805283e-02, 1.20520053e-01, - ... 7.38000848e-01, 3.07252345e+00, 3.44152387e+00]) - True - - x = np.linspace(0,4) - h = plt.plot(x, np.exp(x), 'r', x, chebvalnd(ck, x,ck,a,b), 'g.') - h = plt.plot(x, np.exp(x), 'r', x, chebvalnd(ck1, x,ck1,a,b),'b.') - plt.close() - - See also - -------- - chebval, chebvalnd - - Reference - --------- - http://en.wikipedia.org/wiki/Chebyshev_nodes - http://mathworld.wolfram.com/ChebyshevApproximationFormula.html - - W. Fraser (1965) - "A Survey of Methods of Computing Minimax and Near-Minimax Polynomial - Approximations for Functions of a Single Independent Variable" - Journal of the ACM (JACM), Vol. 12 , Issue 3, pp 295 - 314 - """ - n = np.atleast_1d(n) - if np.any(n > 50): - warnings.warn('CHEBFIT should only be used for n<50') - - if hasattr(f, '__call__'): - if domain is None: - domain = (-1, 1) * len(n) - domain = np.atleast_2d(domain).reshape((-1, 2)) - xi = [map_to_interval(chebroot(ni), d[0], d[1]) - for ni, d in zip(n, domain)] - Xi = np.meshgrid(*xi) - ck = f(*Xi) - else: - ck = f - n = ck.shape - - ndim = len(n) - for i in range(ndim): - ck = dct(ck[..., ::-1]) - ck[..., 0] = ck[..., 0] / 2. - if i < ndim-1: - ck = np.rollaxis(ck, axis=-1) - return ck / np.product(n) - - -def idct(x, n=None): - """ - Inverse Discrete Cosine Transform - - N-1 - x[k] = 1/N sum w[n]*y[n]*cos(pi*k*(2n+1)/(2*N)), 0 <= k < N. - n=0 - - w(0) = 1/2 - w(n) = 1 for n>0 - - Examples - -------- - >>> import numpy as np - >>> x = np.arange(5) - >>> np.abs(x-idct(dct(x)))<1e-14 - array([ True, True, True, True, True], dtype=bool) - >>> np.abs(x-dct(idct(x)))<1e-14 - array([ True, True, True, True, True], dtype=bool) - - Reference - --------- - http://en.wikipedia.org/wiki/Discrete_cosine_transform - http://users.ece.utexas.edu/~bevans/courses/ee381k/lectures/ - """ - return _idct(x, n=n, norm=None)*0.5/len(x) - - -def _chebval(x, ck, kind=1): - """ - Evaluate polynomial in Chebyshev form. - - A polynomial of degree N in Chebyshev form is a polynomial p(x): - - N - p(x) = sum ck*Tk(x) - k=0 - or - N - p(x) = sum ck*Uk(x) - k=0 - - where Tk and Uk are the k'th Chebyshev polynomial of the first and second - kind, respectively. - - References - ---------- - http://en.wikipedia.org/wiki/Clenshaw_algorithm - http://mathworld.wolfram.com/ClenshawRecurrenceFormula.html - """ - n = len(ck) - b_Nmi = zeros(x.shape) # b_(N-i) - b_Nmip1 = b_Nmi.copy() # b_(N-i+1) - x2 = 2 * x - # Clenshaw reccurence - for ix in range(n - 1): - tmp = b_Nmi - b_Nmi = x2 * b_Nmi - b_Nmip1 + ck[ix] - b_Nmip1 = tmp - return kind * x * b_Nmi - b_Nmip1 + ck[n - 1] - - -def chebval(x, ck, a=-1, b=1, kind=1, fill=None): - """ - Evaluate polynomial in Chebyshev form at X - - A polynomial of degree N in Chebyshev form is a polynomial p(x) of the form - - N - p(x) = sum ck*Tk(x) - k=0 - - where Tk is the k'th Chebyshev polynomial of the first or second kind. - - Paramaters - ---------- - x : array-like - points to evaluate - ck : array-like - polynomial coefficients in Chebyshev form ordered from highest degree - to zero - a,b : real, scalars, optional - limits for polynomial (Default -1,1) - kind: 1 or 2, optional - kind of Chebychev polynomial (default 1) - fill : scalar, optional - If provided, define value to return for `x < a` or `b < x`. - - Examples - -------- - Plot Chebychev polynomial of the first kind and order 4: - >>> import matplotlib.pyplot as plt - >>> x = np.linspace(-1,1) - >>> ck = np.zeros(5); ck[-1]=1 - >>> h = plt.plot(x,chebval(x,ck),x,chebpoly(4,x),'.') - >>> plt.close() - - Fit exponential function: - >>> import matplotlib.pyplot as plt - >>> ck = chebfit(np.exp,7,0,2) - >>> x = np.linspace(0,4); - >>> h=plt.plot(x,chebval(x,ck,0,2),'g',x,np.exp(x)) - >>> plt.close() - - See also - -------- - chebfit - - References - ---------- - http://en.wikipedia.org/wiki/Clenshaw_algorithm - http://mathworld.wolfram.com/ClenshawRecurrenceFormula.html - """ - - y = map_from_interval(atleast_1d(x), a, b) - if fill is None: - f = _chebval(y, ck, kind=kind) - else: - cond = (abs(y) <= 1) - f = where(cond, 0, fill) - if any(cond): - yk = extract(cond, y) - f[cond] = _chebval(yk, ck, kind=kind) - return f - - -def chebder(ck, a=-1, b=1): - """ - Differentiate Chebyshev polynomial - - Parameters - ---------- - ck : array-like - polynomial coefficients in Chebyshev form of function to differentiate - a,b : real, scalars - limits for polynomial(Default -1,1) - - Return - ------ - cder : ndarray - polynomial coefficients in Chebyshev form of the derivative - - Examples - -------- - - Fit exponential function: - >>> import matplotlib.pyplot as plt - >>> ck = chebfit(np.exp,7,0,2) - >>> x = np.linspace(0,4) - >>> ck2 = chebder(ck,0,2); - >>> h = plt.plot(x,chebval(x,ck,0,2),'g',x,np.exp(x),'r') - >>> plt.close() - - See also - -------- - chebint - chebfit - - Reference - --------- - http://en.wikipedia.org/wiki/Chebyshev_polynomials - - W. Fraser (1965) - "A Survey of Methods of Computing Minimax and Near-Minimax Polynomial - Approximations for Functions of a Single Independent Variable" - Journal of the ACM (JACM), Vol. 12 , Issue 3, pp 295 - 314 - """ - - n = len(ck) - 1 - cder = zeros(n, dtype=asarray(ck).dtype) - cder[0] = 2 * n * ck[0] - cder[1] = 2 * (n - 1) * ck[1] - for j in range(2, n): - cder[j] = cder[j - 2] + 2 * (n - j) * ck[j] - - return cder * 2. / (b - a) # Normalize to the interval b-a. - - -def chebint(ck, a=-1, b=1): - """ - Integrate Chebyshev polynomial - - Parameters - ---------- - ck : array-like - polynomial coefficients in Chebyshev form of function to integrate. - a,b : real, scalars - limits for polynomial(Default -1,1) - - Return - ------ - cint : ndarray - polynomial coefficients in Chebyshev form of the integrated function - - Examples - -------- - Fit exponential function: - >>> import matplotlib.pyplot as plt - >>> ck = chebfit(np.exp,7,0,2) - >>> x = np.linspace(0,4) - >>> ck2 = chebint(ck,0,2); - >>> h=plt.plot(x,chebval(x,ck,0,2),'g',x,np.exp(x),'r.') - >>> plt.close() - - See also - -------- - chebder - chebfit - - Reference - --------- - http://en.wikipedia.org/wiki/Chebyshev_polynomials - - W. Fraser (1965) - "A Survey of Methods of Computing Minimax and Near-Minimax Polynomial - Approximations for Functions of a Single Independent Variable" - Journal of the ACM (JACM), Vol. 12 , Issue 3, pp 295 - 314 - """ - -# int T0(x) = T1(x)+1 -# int T1(x) = 0.5*(T2(x)/2-T0/2) -# int Tn(x) dx = 0.5*{Tn+1(x)/(n+1) - Tn-1(x)/(n-1)} -# N -# p(x) = sum cn*Tn(x) -# n=0 - -# int p(x) dx = sum cn * int(Tn(x)dx) = -# 0.5*sum cn *{Tn+1(x)/(n+1) - Tn-1(x)/(n-1)} = 0.5 sum (cn-1-cn+1)*Tn/n n>0 - - n = len(ck) - - cint = zeros(n) - con = 0.25 * (b - a) - - dif1 = diff(ck[-1::-2]) - ix1 = np.r_[1:n - 1:2] - cint[ix1] = -(con * dif1) / ix1 - if n > 3: - dif2 = diff(ck[-2::-2]) - ix2 = np.r_[2:n - 1:2] - cint[ix2] = -(con * dif2) / ix2 - cint = cint[::-1] - # cint(n) is a special case - cint[-1] = (con * ck[n - 2]) / (n - 1) - # Set integration constant - cint[0] = 2 * np.sum((-1) ** np.r_[0:n - 1] * cint[-2::-1]) - return cint - - -class Cheb1d(object): - coeffs = None - order = None - a = None - b = None - kind = None - - def __init__(self, ck, a=-1, b=1, kind=1): - if isinstance(ck, Cheb1d): - for key in ck.__dict__.keys(): - self.__dict__[key] = ck.__dict__[key] - return - cki = trim_zeros(atleast_1d(ck), 'b') - if len(cki.shape) > 1: - raise ValueError("Polynomial must be 1d only.") - self.__dict__['coeffs'] = cki - self.__dict__['order'] = len(cki) - 1 - self.__dict__['a'] = a - self.__dict__['b'] = b - self.__dict__['kind'] = kind - - def __call__(self, x): - return chebval(x, self.coeffs, self.a, self.b, self.kind) - - def __array__(self, t=None): - if t: - return asarray(self.coeffs, t) - else: - return asarray(self.coeffs) - - def __repr__(self): - vals = repr(self.coeffs) - vals = vals[6:-1] - return "Cheb1d(%s)" % vals - - def __len__(self): - return self.order - - def __str__(self): - pass - - def __neg__(self): - new = Cheb1d(self) - new.coeffs = -self.coeffs - return new - - def __pos__(self): - return self - - def __add__(self, other): - other = Cheb1d(other) - new = Cheb1d(self) - new.coeffs = polyadd(self.coeffs, other.coeffs) - return new - - def __radd__(self, other): - return self.__add__(other) - - def __sub__(self, other): - other = Cheb1d(other) - new = Cheb1d(self) - new.coeffs = polysub(self.coeffs, other.coeffs) - return new - - def __rsub__(self, other): - other = Cheb1d(other) - new = Cheb1d(self) - new.coeffs = polysub(other.coeffs, new.coeffs) - return new - - def __eq__(self, other): - other = Cheb1d(other) - return (all(self.coeffs == other.coeffs) and (self.a == other.a) and - (self.b == other.b) and (self.kind == other.kind)) - - def __ne__(self, other): - return any(self.coeffs != other.coeffs) or (self.a != other.a) or ( - self.b != other.b) or (self.kind != other.kind) - - def __setattr__(self, key, val): - raise ValueError("Attributes cannot be changed this way.") - - def __getattr__(self, key): - if key in ['c', 'coef', 'coefficients']: - return self.coeffs - elif key in ['o']: - return self.order - elif key in ['a']: - return self.a - elif key in ['b']: - return self.b - elif key in ['k']: - return self.kind - else: - try: - return self.__dict__[key] - except KeyError: - raise AttributeError( - "'%s' has no attribute '%s'" % - (self.__class__, key)) - - def __getitem__(self, val): - if val > self.order: - return 0 - if val < 0: - return 0 - return self.coeffs[val] - - def __setitem__(self, key, val): - # ind = self.order - key - if key < 0: - raise ValueError("Does not support negative powers.") - if key > self.order: - zr = zeros(key - self.order, self.coeffs.dtype) - self.__dict__['coeffs'] = concatenate((self.coeffs, zr)) - self.__dict__['order'] = key - self.__dict__['coeffs'][key] = val - return - - def __iter__(self): - return iter(self.coeffs) - - def integ(self, m=1): - """ - Return an antiderivative (indefinite integral) of this polynomial. - - Refer to `chebint` for full documentation. - - See Also - -------- - chebint : equivalent function - - """ - integ = Cheb1d(self) - integ.coeffs = chebint(self.coeffs, self.a, self.b) - return integ - - def deriv(self, m=1): - """ - Return a derivative of this polynomial. - - Refer to `chebder` for full documentation. - - See Also - -------- - chebder : equivalent function - - """ - der = Cheb1d(self) - der.coeffs = chebder(self.coeffs, self.a, self.b) - return der - - -def padefit(c, m=None): - """ - Rational polynomial fitting from polynomial coefficients - - Parameters - ---------- - c : array-like - coefficients of power series expansion from highest degree to zero. - m : scalar integer - order of denominator polynomial. (Default floor((len(c)-1)/2)) - - Returns - ------- - num, den : poly1d - numerator and denominator polynomials for the pade approximation - - If the function is well approximated by - M+N+1 - f(x) = sum c(2*n+2-k)*x^k - k=0 - - then the pade approximation is given by - M - sum c1(n-k+1)*x^k - k=0 - f(x) = ------------------------ - N - sum c2(n-k+1)*x^k - k=0 - - Note: c must be ordered for direct use with polyval - - Example - ------- - Pade approximation to exp(x) - >>> import scipy.special as sp - >>> import matplotlib.pyplot as plt - >>> c = poly1d(1./sp.gamma(np.r_[6+1:0:-1])) - >>> [p, q] = padefit(c) - >>> p; q - poly1d([ 0.00277778, 0.03333333, 0.2 , 0.66666667, 1. ]) - poly1d([ 0.03333333, -0.33333333, 1. ]) - - >>> x = np.linspace(0,4); - >>> h = plt.plot(x,c(x),x,p(x)/q(x),'g-', x,np.exp(x),'r.') - >>> plt.close() - - See also - -------- - scipy.misc.pade - - """ - if not m: - m = int(floor((len(c) - 1) * 0.5)) - c = asarray(c) - return pade(c[::-1], m) - - -def test_pade(): - cof = array(([1.0, 1.0, 1.0 / 2, 1. / 6, 1. / 24])) - p, q = pade(cof, 2) - t = arange(0, 2, 0.1) - assert(all(abs(p(t) / q(t) - exp(t)) < 0.3)) - - -def padefitlsq(fun, m, k, a=-1, b=1, trace=False, x=None, end_points=True): - """ - Rational polynomial fitting. A minimax solution by least squares. - - Parameters - ---------- - fun : callable or or a two column matrix - f=[x,f(x)] where length(x)>(m+k+1)*8. - m, k : integer - number of coefficients of the numerator and denominater, respectively. - a, b : real scalars - evaluation limits, (default a=-1,b=1) - - Returns - ------- - num, den : poly1d - numerator and denominator polynomials for the pade approximation - dev : ndarray - maximum absolute deviation of the approximation - - The pade approximation is given by - m - sum c1[m-i]*x**i - i=0 - f(x) = ------------------------ - k - sum c2[k-i]*x**i - i=0 - - If F is a two column matrix, [x f(x)], a good choice for x is: - - x = cos(pi/(N-1)*(N-1:-1:0))*(b-a)/2+ (a+b)/2, where N = (m+k+1)*8; - - Note: c1 and c2 are ordered for direct use with polyval - - Example - ------- - - Pade approximation to exp(x) between 0 and 2 - >>> import matplotlib.pyplot as plt - >>> [c1, c2] = padefitlsq(np.exp,3,3,0,2) - >>> c1; c2 - poly1d([ 0.01443847, 0.128842 , 0.55284547, 0.99999962]) - poly1d([-0.0049658 , 0.07610473, -0.44716929, 1. ]) - - >>> x = np.linspace(0,4) - >>> h = plt.plot(x, polyval(c1,x)/polyval(c2,x),'g') - >>> h = plt.plot(x, np.exp(x), 'r') - - See also - -------- - padefit - - Reference - --------- - William H. Press, Saul Teukolsky, - William T. Wetterling and Brian P. Flannery (1997) - "Numerical recipes in Fortran 77", Vol. 1, pp 197-20 - """ - - NFAC = 8 - BIG = 1e30 - MAXIT = 5 - - smallest_devmax = BIG - ncof = m + k + 1 - # Number of points where function is evaluated, i.e. fineness of mesh - npt = NFAC * ncof - - if x is None: - if end_points: - # Use the location of the local extreme values of - # the Chebychev polynomial of the first kind of degree NPT-1. - x = map_to_interval(chebextr(npt - 1), a, b) - else: - # Use the roots of the Chebychev polynomial of the first kind of - # degree NPT. Note this is useful if there are singularities close - # to the endpoints. - x = map_to_interval(chebroot(npt, kind=1), a, b) - - if hasattr(fun, '__call__'): - fs = fun(x) - else: - fs = fun - n = len(fs) - if n < npt: - warnings.warn( - 'Check the result! ' + - 'Number of function values should be at least: %d' % npt) - - if trace: - plt.plot(x, fs, '+') - - wt = np.ones((npt)) - ee = np.ones((npt)) - mad = 0 - - u = zeros((npt, ncof)) - for ix in range(MAXIT): - # Set up design matrix for least squares fit. - pow1 = wt - bb = pow1 * (fs + abs(mad) * sign(ee)) - - for jx in range(m + 1): - u[:, jx] = pow1 - pow1 = pow1 * x - - pow1 = -bb - for jx in range(m + 1, ncof): - pow1 = pow1 * x - u[:, jx] = pow1 - - [u1, w, v] = linalg.svd(u, full_matrices=False) - cof = where(w == 0, 0.0, np.dot(bb, u1) / w) - cof = np.dot(cof, v) - - # Tabulate the deviations and revise the weights - ee = polyval(cof[m::-1], x) / \ - polyval(cof[ncof:m:-1].tolist() + [1, ], x) - fs - - wt = np.abs(ee) - devmax = max(wt) - mad = wt.mean() # mean absolute deviation - - # Save only the best coefficients found - if (devmax <= smallest_devmax): - smallest_devmax = devmax - c1 = cof[m::-1] - c2 = cof[ncof:m:-1].tolist() + [1, ] - - if trace: - print('Iteration=%d, max error=%g' % (ix, devmax)) - plt.plot(x, fs, x, ee + fs) - return poly1d(c1), poly1d(c2) - - -def main(): - - [c1, c2] = padefitlsq(exp, 3, 3, 0, 2) - - x = linspace(0, 4) - plt.plot(x, polyval(c1, x) / polyval(c2, x), 'g') - plt.plot(x, exp(x), 'r') - - import scipy.special as sp - - p = [[1, 1, 1], [2, 2, 2]] - pi = polyint(p, 1) - _pr = polyreloc(p, 2) - _pd = polyder(p) - _st = poly2str(p) - c = poly1d( - 1. / - sp.gamma( - np.r_[ - 6 + - 1:0:- - 1])) # polynomial coeff exponential function - [p, q] = padefit(c) - x = linspace(0, 4) - plt.plot(x, c(x), x, p(x) / q(x), 'g-', x, exp(x), 'r.') - plt.close() - x = arange(4) - dx = dct(x) - _idx = idct(dx) - - a = 0 - b = 2 - ck = chebfit(exp, 6, a, b) - _t = chebval(0, ck, a, b) - x = linspace(0, 2, 6) - plt.plot(x, exp(x), 'r', x, chebval(x, ck, a, b), 'g.') - # x1 = chebroot(9).'*(b-a)/2+(b+a)/2 ; - # ck1 =chebfit([x1 exp(x1)],9,a,b); - # plot(x,exp(x),'r'), hold on - # plot(x,chebval(x,ck1,a,b),'g'), hold off - - _t = poly2hstr([1, 1, 2]) - py = [1, 0] - px = polyshift(py, 0, 5) - _t1 = polyval(px, [0, 2.5, 5]) # % This is the same as the line below - _t2 = polyval(py, [-1, 0, 1]) - - px = [1, 0] - py = polyishift(px, 0, 5) - t1 = polyval(px, [0, 2.5, 5]) # % This is the same as the line below - t2 = polyval(py, [-1, 0, 1]) - print(t1, t2) - - +""" + Extended functions to operate on polynomials +""" +# ------------------------------------------------------------------------ +# Name: polynomial +# Purpose: Functions to operate on polynomials. +# +# Author: pab +# polyXXX functions are based on functions found in the matlab toolbox polyutil +# written by +# Author: Peter J. Acklam +# E-mail: pjacklam@online.no +# WWW URL: http://home.online.no/~pjacklam +# +# Created: 30.12.2008 +# Copyright: (c) pab 2008 +# Licence: LGPL +# ------------------------------------------------------------------------ +# !/usr/bin/env python +from __future__ import absolute_import +import warnings # @UnusedImport +from numpy.polynomial import polyutils as pu +from .plotbackend import plotbackend as plt +import numpy as np +from numpy import (newaxis, arange, pi) +from scipy.fftpack import dct, idct as _idct +from numpy.lib.polynomial import * # @UnusedWildImport +from scipy.misc import pade # @UnresolvedImport +__all__ = np.lib.polynomial.__all__ +__all__ = __all__ + ['pade', 'padefit', 'polyreloc', 'polyrescl', 'polytrim', + 'poly2hstr', 'poly2str', 'polyshift', 'polyishift', + 'map_from_intervall', 'map_to_intervall', 'cheb2poly', + 'chebextr', 'chebroot', 'chebpoly', 'chebfit', 'chebval', + 'chebder', 'chebint', 'Cheb1d', 'dct', 'idct'] + + +def polyint(p, m=1, k=None): + """ + Return an antiderivative (indefinite integral) of a polynomial. + + The returned order `m` antiderivative `P` of polynomial `p` satisfies + :math:`\\frac{d^m}{dx^m}P(x) = p(x)` and is defined up to `m - 1` + integration constants `k`. The constants determine the low-order + polynomial part + + .. math:: \\frac{k_{m-1}}{0!} x^0 + \\ldots + \\frac{k_0}{(m-1)!}x^{m-1} + + of `P` so that :math:`P^{(j)}(0) = k_{m-j-1}`. + + Parameters + ---------- + p : {array_like, poly1d} + Polynomial to differentiate. + A sequence is interpreted as polynomial coefficients, see `poly1d`. + m : int, optional + Order of the antiderivative. (Default: 1) + k : {None, list of `m` scalars, scalar}, optional + Integration constants. They are given in the order of integration: + those corresponding to highest-order terms come first. + + If ``None`` (default), all constants are assumed to be zero. + If `m = 1`, a single scalar can be given instead of a list. + + See Also + -------- + polyder : derivative of a polynomial + poly1d.integ : equivalent method + + Examples + -------- + The defining property of the antiderivative: + + >>> p = np.poly1d([1,1,1]) + >>> P = np.polyint(p) + >>> P + poly1d([ 0.33333333, 0.5 , 1. , 0. ]) + >>> np.polyder(P) == p + True + + The integration constants default to zero, but can be specified: + + >>> P = np.polyint(p, 3) + >>> P(0) + 0.0 + >>> np.polyder(P)(0) + 0.0 + >>> np.polyder(P, 2)(0) + 0.0 + >>> P = np.polyint(p, 3, k=[6, 5, 3]) + >>> P + poly1d([ 0.01666667, 0.04166667, 0.16666667, 3. , 5. , 3. ]) + + Note that 3 = 6 / 2!, and that the constants are given in the order of + integrations. Constant of the highest-order polynomial term comes first: + + >>> np.polyder(P, 2)(0) + 6.0 + >>> np.polyder(P, 1)(0) + 5.0 + >>> P(0) + 3.0 + + """ + m = int(m) + if m < 0: + raise ValueError("Order of integral must be positive (see polyder)") + if k is None: + k = np.zeros(m, float) + k = np.atleast_1d(k) + if len(k) == 1 and m > 1: + k = k[0] * np.ones(m, float) + if len(k) < m: + raise ValueError( + "k must be a scalar or a rank-1 array of length 1 or >m.") + truepoly = isinstance(p, poly1d) + p = np.asarray(p) + if m == 0: + if truepoly: + return poly1d(p) + return p + else: + ix = arange(len(p), 0, -1) + if p.ndim > 1: + ix = ix[..., newaxis] + pieces = p.shape[-1] + k0 = k[0] * np.ones((1, pieces), dtype=int) + else: + k0 = [k[0]] + y = np.concatenate((p.__truediv__(ix), k0), axis=0) + + val = polyint(y, m - 1, k=k[1:]) + if truepoly: + return poly1d(val) + return val + + +def polyder(p, m=1): + """ + Return the derivative of the specified order of a polynomial. + + Parameters + ---------- + p : poly1d or sequence + Polynomial to differentiate. + A sequence is interpreted as polynomial coefficients, see `poly1d`. + m : int, optional + Order of differentiation (default: 1) + + Returns + ------- + der : poly1d + A new polynomial representing the derivative. + + See Also + -------- + polyint : Anti-derivative of a polynomial. + poly1d : Class for one-dimensional polynomials. + + Examples + -------- + The derivative of the polynomial :math:`x^3 + x^2 + x^1 + 1` is: + + >>> p = np.poly1d([1,1,1,1]) + >>> p2 = np.polyder(p) + >>> p2 + poly1d([3, 2, 1]) + + which evaluates to: + + >>> p2(2.) + 17.0 + + We can verify this, approximating the derivative with + ``(f(x + h) - f(x))/h``: + + >>> (p(2. + 0.001) - p(2.)) / 0.001 + 17.007000999997857 + + The fourth-order derivative of a 3rd-order polynomial is zero: + + >>> np.polyder(p, 2) + poly1d([6, 2]) + >>> np.polyder(p, 3) + poly1d([6]) + >>> np.polyder(p, 4) + poly1d([ 0.]) + + """ + m = int(m) + if m < 0: + raise ValueError("Order of derivative must be positive (see polyint)") + truepoly = isinstance(p, poly1d) + p = np.asarray(p) + if m == 0: + if truepoly: + return poly1d(p) + return p + else: + n = len(p) - 1 + ix = arange(n, 0, -1) + if p.ndim > 1: + ix = ix[..., newaxis] + y = ix * p[:-1] + val = polyder(y, m - 1) + if truepoly: + return poly1d(val) + return val + + +def polydeg(x, y): + ''' + Return optimal degree for polynomial fitting + + + N = POLYDEG(X,Y) finds the optimal degree for polynomial fitting, + according to the Akaike's information criterion. + + Assuming that you want to find the degree N of a polynomial that fits + the data Y(X) best in a least-squares sense, the Akaike's information + criterion is defined by: + 2*(N + 1) + n * (log(2 * pi * RSS / n) + 1) + where n is the number of points and RSS is the residual sum of squares. + The optimal degree N is defined here as that which minimizes AIC: + http://en.wikipedia.org/wiki/Akaike_Information_Criterion + + Notes: + ----- + If the number of data is small, POLYDEG may tend to return: + N = (number of points)-1. + + ORTHOFIT is more appropriate than POLYFIT for polynomial fitting with + relatively high degrees. + + Example: + ------- + >>> x = np.linspace(0,10,300) + >>> y = np.sin(x ** 3 / 100) ** 2 + 0.05 * np.random.randn(x.size) + >>> n = polydeg(x,y) + >>> n + 21 + + ys = orthofit(x,y,n); + plt.plot(x, y, '.', x, ys, 'k') + + See also + -------- + polyfit, orthofit + ''' + x, y = np.atleast_1d(x, y) + x = x.ravel() + y = y.ravel() + N = len(x) + + # Search the optimal degree minimizing the Akaike's information criterion + # y(x) are fitted in a least-squares sense using a polynomial of degree n + # developed in a series of orthogonal polynomials. + ys = np.ones((N,)) * y.mean() + # correction for small sample sizes + logsum2 = (np.log(2 * pi * ((ys - y) ** 2).sum() / N) + 1) + AIC = 2 + N * logsum2 + 4 / (N - 2) + + n = 1 + nit = 0 + + # While-loop is stopped when a minimum is detected. 3 more steps are + # required to take AIC noise into account and to ensure that this minimum + # is a (likely) global minimum. + + while nit < 6: + p = orthofit(x, y, n) + ys = orthoval(p, x) + # -- Akaike's Information Criterion + aic = (2 * (n + 1) * (1 + (n + 2) / (N - n - 2)) + + N * (np.log(2 * pi * np.sum((ys - y) ** 2) / N) + 1)) + + if aic >= AIC: + nit += 1 + else: + nit = 0 + AIC = aic + + n = n + 1 + + if n >= N: + break + n = n - nit - 1 + return n + + +def orthoval(p, x): + ''' + Evaluation of orthogonal polynomial + + Parameters + ---------- + p : array_like + 2D array of polynomial coefficients (including coefficients equal + to zero) from highest degree to the constant term. + x : array_like + A number or a 1D array of numbers "at" which to evaluate `p`. + + Returns + ------- + values : ndarray + + See Also + -------- + orthofit + ''' + p = np.atleast_2d(p) + n = p.shape[1] - 1 + xi = np.atleast_1d(x) + shape0 = xi.shape + if n == 0: + return np.ones(shape0) * p[0] + xi = xi.ravel() + xn = np.ones((n + 1, len(xi))) + xn[1] = xi - p[1, 1] + for i in range(2, n + 1): + xn[i, :] = (xi - p[1, i]) * xn[i - 1, :] - p[2, i] * xn[i - 2, :] + ys = np.dot(p[0], xn) + return ys.reshape(shape0) + + +def ortho2poly(p): + """ + Converts orthogonal polynomial to ordinary polynomial coefficients + + Parameters + ---------- + p : array-like + orthogonal polynomial coefficients + + Returns + ------- + p : ndarray + ordinary polynomial coefficients + + It is not advised to do this for p.shape[1]>10 due to numerical + cancellations. + + See also + -------- + orthoval + orthofit + + Examples + -------- + >>> import numpy as np + >>> x = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]) + >>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0]) + >>> p = orthofit(x, y, 3) + >>> p + array([[ 0. , -0.30285714, -0.16071429, 0.08703704], + [ 0. , 2.5 , 2.5 , 2.5 ], + [ 0. , 0. , 2.91666667, 2.13333333]]) + >>> ortho2poly(p) + array([ 0.08703704, -0.81349206, 1.69312169, -0.03968254]) + >>> np.polyfit(x, y, 3) + array([ 0.08703704, -0.81349206, 1.69312169, -0.03968254]) + + References + ---------- + """ + p = np.atleast_2d(p) + n = p.shape[1] - 1 + if n == 0: + return p[0] + x = [1, ] * (n + 1) + x[1] = np.array([1, - p[1, 1]]) + for i in range(2, n + 1): + x[i] = polyadd(polymul([1, - p[1, i]], x[i - 1]), - p[2, i] * x[i - 2]) + for i in range(n + 1): + x[i] *= p[0, i] + return reduce(polyadd, x) + + +def orthofit(x, y, n): + ''' + Fit orthogonal polynomial to data. + + Parameters + --------- + x, y : arrays + data Y(X) to fit to a polynomial. + n : integer + degree of fitted polynomial. + + Returns + ------- + p : array + orthogonal polynomial + + Notes: + ----- + Orthofit smooths/fits data using a polynomial of degree N developed in + a sequence of orthogonal polynomials. ORTHOFIT is more appropriate than + polyfit for polynomial fitting and smoothing since this method does not + involve any matrix linear system but a simple recursive procedure. + Degrees much higher than 30 could be used with orthogonal polynomials, + whereas badly conditioned matrices may appear with a classical + polynomial fitting of degree typically higher than 10. + + To avoid using unnecessarily high degrees, you may let the function + POLYDEG choose it for you. POLYDEG finds an optimal polynomial degree + according to the Akaike's information criterion. + + Example: + ------- + >>> x = np.linspace(0,10,300); + >>> y = np.sin(x**3/100)**2 + 0.05*np.random.randn(x.size) + >>> p = orthofit(x, y, 25) + >>> ys = orthoval(p, x) + + plot(x, y,'.',x, ys, 'k') + + See also + -------- + polydeg, polyfit, polyval + + Reference: + --------- + Methodes de calcul numerique 2. JP Nougier. Hermes Science + Publications, 2001. Section 4.7 pp 116-121 + ''' + x, y = np.atleast_1d(x, y) + x = x.ravel() + y = y.ravel() + # Particular case: n=0 + if n == 0: + return y.mean() + + # p = Coefficients of the orthogonal polynomials + p = np.zeros((3, n + 1)) + p[1, 1] = x.mean() + + N = len(x) + PL = np.ones((n + 1, N)) + PL[1] = x - p[1, 1] + + for i in range(2, n + 1): + p[1, i] = np.dot(x, PL[i - 1] ** 2) / np.sum(PL[i - 1] ** 2) + p[2, i] = np.dot(x, PL[i - 2] * PL[i - 1]) / np.sum(PL[i - 2] ** 2) + PL[i] = (x - p[1, i]) * PL[i - 1] - p[2, i] * PL[i - 2] + p[0, :] = np.dot(PL, y) / np.sum(PL ** 2, axis=1) + return p + # ys = np.dot(p[0, :], PL) # smoothed y + + +def polyreloc(p, x, y=0.0): + """ + Relocate polynomial + + The polynomial `p` is relocated by "moving" it `x` + units along the x-axis and `y` units along the y-axis. + So the polynomial `r` is relative to the point (x,y) as + the polynomial `p` is relative to the point (0,0). + + Parameters + ---------- + p : array-like, poly1d + vector or matrix of column vectors of polynomial coefficients to + relocate. (Polynomial coefficients are in decreasing order.) + x : scalar + distance to relocate P along x-axis + y : scalar + distance to relocate P along y-axis (default 0) + + Returns + ------- + r : ndarray, poly1d + vector/matrix/poly1d of relocated polynomial coefficients. + + See also + -------- + polyrescl + + Example + ------- + >>> import numpy as np + >>> p = np.arange(6); p.shape = (2,-1) + >>> np.polyval(p,0) + array([3, 4, 5]) + >>> np.polyval(p,1) + array([3, 5, 7]) + >>> r = polyreloc(p,-1) # move to the left along x-axis + >>> np.polyval(r,-1) # = polyval(p,0) + array([3, 4, 5]) + >>> np.polyval(r,0) # = polyval(p,1) + array([3, 5, 7]) + """ + + truepoly = isinstance(p, poly1d) + r = np.atleast_1d(p).copy() + n = r.shape[0] + + # Relocate polynomial using Horner's algorithm + for ii in range(n, 1, -1): + for i in range(1, ii): + r[i] = r[i] - x * r[i - 1] + r[-1] = r[-1] + y + if r.ndim > 1 and r.shape[-1] == 1: + r.shape = (r.size,) + if truepoly: + r = poly1d(r) + return r + + +def polyrescl(p, x, y=1.0): + """ + Rescale polynomial. + + Parameters + ---------- + p : array-like, poly1d + vector or matrix of column vectors of polynomial coefficients to + rescale. (Polynomial coefficients are in decreasing order.) + x,y : scalars + defining the factors to rescale the polynomial `p` in + x-direction and y-direction, respectively. + + Returns + ------- + r : ndarray, poly1d + vector/matrix/poly1d of rescaled polynomial coefficients. + + See also + -------- + polyreloc + + Example + ------- + >>> import numpy as np + >>> p = np.arange(6); p.shape = (2,-1) + >>> np.polyval(p,0) + array([3, 4, 5]) + >>> np.polyval(p,1) + array([3, 5, 7]) + >>> r = polyrescl(p,2) # scale by 2 along x-axis + >>> np.polyval(r,0) # = polyval(p,0) + array([ 3., 4., 5.]) + >>> np.polyval(r,2) # = polyval(p,1) + array([ 3., 5., 7.]) + """ + + truepoly = isinstance(p, poly1d) + r = np.atleast_1d(p) + n = r.shape[0] + + xscale = (float(x) ** arange(1 - n, 1)) + if r.ndim == 1: + q = y * r * xscale + else: + q = y * r * xscale[:, newaxis] + if truepoly: + q = poly1d(q) + return q + + +def polytrim(p): + """ + Trim polynomial by stripping off leading zeros. + + Parameters + ---------- + p : array-like, poly1d + vector or matrix of column vectors of polynomial coefficients in + decreasing order. + + Returns + ------- + r : ndarray, poly1d + vector/matrix/poly1d of trimmed polynomial coefficients. + + Example + ------- + >>> p = [0,1,2] + >>> polytrim(p) + array([1, 2]) + >>> p1 = [[0,0],[1,2],[3,4]] + >>> polytrim(p1) + array([[1, 2], + [3, 4]]) + """ + + truepoly = isinstance(p, poly1d) + if truepoly: + return p + else: + r = np.atleast_1d(p).copy() + # Remove leading zeros + is_not_lead_zeros = np.logical_or.accumulate(r != 0, axis=0) + if r.ndim == 1: + r = r[is_not_lead_zeros] + else: + is_not_lead_zeros = np.any(is_not_lead_zeros, axis=1) + r = r[is_not_lead_zeros, :] + return r + + +def poly2hstr(p, variable='x'): + """ + Return polynomial as a Horner represented string. + + Parameters + ---------- + p : array-like poly1d + vector of polynomial coefficients in decreasing order. + variable : string + display character for variable + + Returns + ------- + p_str : string + consisting of the polynomial coefficients in the vector P multiplied + by powers of the given `variable`. + + Examples + -------- + >>> poly2hstr([1, 1, 2], 's' ) + '(s + 1)*s + 2' + + See also + -------- + poly2str + """ + var = variable + + coefs = polytrim(np.atleast_1d(p)) + order = len(coefs) - 1 # Order of polynomial. + s = '' # Initialize output string. + ix = 1 + for expon in range(order, -1, -1): + coef = coefs[order - expon] + # There is no point in adding a zero term (except if it's the only + # term, but we'll take care of that later). + if coef == 0: + ix += 1 + else: + # Append exponent if necessary. + if ix > 1: + exponstr = '%.0f' % ix + s = '%s**%s' % (s, exponstr) + ix = 1 + # Is it the first term? + isfirst = s == '' + + # We need the coefficient only if it is different from 1 or -1 or + # when it is the constant term. + needcoef = ( + (abs(coef) != 1) | ( + expon == 0) & isfirst) | 1 - isfirst + + # We need the variable except in the constant term. + needvar = (expon != 0) + + # Add sign, but we don't need a leading plus-sign. + if isfirst: + if coef < 0: + s = '-' # % Unary minus. + else: + if coef < 0: + s = '%s - ' % s # % Binary minus (subtraction). + else: + s = '%s + ' % s # % Binary plus (addition). + + # Append the coefficient if it is different from one or when it is + # the constant term. + if needcoef: + coefstr = '%.20g' % abs(coef) + s = '%s%s' % (s, coefstr) + + # Append variable if necessary. + if needvar: + # Append a multiplication sign if necessary. + if needcoef: + if 1 - isfirst: + s = '(%s)' % s + s = '%s*' % s + s = '%s%s' % (s, var) + + # Now treat the special case where the polynomial is zero. + if s == '': + s = '0' + return s + + +def poly2str(p, variable='x'): + """ + Return polynomial as a string. + + Parameters + ---------- + p : array-like poly1d + vector of polynomial coefficients in decreasing order. + variable : string + display character for variable + + Returns + ------- + p_str : string + consisting of the polynomial coefficients in the vector P multiplied + by powers of the given `variable`. + + See also + -------- + poly2hstr + + Examples + -------- + >>> poly2str([1, 1, 2], 's' ) + 's**2 + s + 2' + """ + thestr = "0" + var = variable + + # Remove leading zeros + coeffs = polytrim(np.atleast_1d(p)) + + N = len(coeffs) - 1 + + for k in range(N+1): + coefstr = '%.4g' % abs(coeffs[k]) + if coefstr[-4:] == '0000': + coefstr = coefstr[:-5] + power = (N - k) + if power == 0: + if coefstr != '0': + newstr = '%s' % (coefstr,) + else: + newstr = '0' if k == 0 else '' + elif power == 1: + if coefstr == '0': + newstr = '' + elif coefstr in ['b', '1']: + newstr = var + else: + newstr = '%s*%s' % (coefstr, var) + else: + if coefstr == '0': + newstr = '' + elif coefstr in ['b', '1']: + newstr = '%s**%d' % (var, power,) + else: + newstr = '%s*%s**%d' % (coefstr, var, power) + + if k > 0: + if newstr != '': + if coeffs[k] < 0: + thestr = "%s - %s" % (thestr, newstr) + else: + thestr = "%s + %s" % (thestr, newstr) + elif (k == 0) and (newstr != '') and (coeffs[k] < 0): + thestr = "-%s" % (newstr,) + else: + thestr = newstr + return thestr + + +def polyshift(py, a=-1, b=1): + """ + Polynomial coefficient shift + + Polyshift shift the polynomial coefficients by a variable shift: + + Y = 2*(X-.5*(b+a))/(b-a) + + i.e., the interval -1 <= Y <= 1 is mapped to the interval a <= X <= b + + Parameters + ---------- + py : array-like + polynomial coefficients for the variable y. + a,b : scalars + lower and upper limit. + + Returns + ------- + px : ndarray + polynomial coefficients for the variable x. + + See also + -------- + polyishift + + Example + ------- + >>> py = [1, 0] + >>> px = polyshift(py,0,5) + >>> polyval(px,[0, 2.5, 5]) #% This is the same as the line below + array([-1., 0., 1.]) + >>> polyval(py,[-1, 0, 1 ]) + array([-1, 0, 1]) + """ + + if (a == -1) & (b == 1): + return py + L = b - a + return polyishift(py, -(2. + b + a) / L, (2. - b - a) / L) + + +def polyishift(px, a=-1, b=1): + """ + Inverse polynomial coefficient shift + + Polyishift does the inverse of Polyshift, + shift the polynomial coefficients by a variable shift: + + Y = 2*(X-.5*(b+a)/(b-a) + + i.e., the interval a <= X <= b is mapped to the interval -1 <= Y <= 1 + + Parameters + ---------- + px : array-like + polynomial coefficients for the variable x. + a,b : scalars + lower and upper limit. + + Returns + ------- + py : ndarray + polynomial coefficients for the variable y. + + See also + -------- + polyishift + + Example + ------- + >>> px = [1, 0] + >>> py = polyishift(px,0,5); + >>> polyval(px,[0, 2.5, 5]) #% This is the same as the line below + array([ 0. , 2.5, 5. ]) + >>> polyval(py,[-1, 0, 1]) + array([ 0. , 2.5, 5. ]) + """ + if (a == -1) & (b == 1): + return px + L = b - a + xscale = 2. / L + xloc = -float(a + b) / L + return polyreloc(polyrescl(px, xscale), xloc) + + +def map_from_interval(x, a, b): + """F(x), where F: [a,b] -> [-1,1].""" + return (x - (b + a) / 2.0) * (2.0 / (b - a)) + + +def map_to_interval(x, a, b): + """F(x), where F: [-1,1] -> [a,b].""" + return (x * (b - a) + (b + a)) / 2.0 + + +def poly2cheb(p, a=-1, b=1): + """ + Convert polynomial coefficients into Chebyshev coefficients + + Parameters + ---------- + p : array-like + polynomial coefficients + a,b : real scalars + lower and upper limits (Default -1,1) + + Returns + ------- + ck : ndarray + Chebychef coefficients + + POLY2CHEB do the inverse of CHEB2POLY: given a vector of polynomial + coefficients AK, returns an equivalent vector of Chebyshev + coefficients CK. + + This is useful for economization of power series. + The steps for doing so: + 1. Convert polynomial coefficients to Chebychev coefficients, CK. + 2. Truncate the CK series to a smaller number of terms, using the + coefficient of the first neglected Chebychev polynomial as an error + estimate. + 3 Convert back to a polynomial by CHEB2POLY + + See also + -------- + cheb2poly + chebval + chebfit + + Examples + -------- + >>> import numpy as np + >>> p = np.arange(5) + >>> ck = poly2cheb(p) + >>> cheb2poly(ck) + array([ 1., 2., 3., 4.]) + + Reference + --------- + William H. Press, Saul Teukolsky, + William T. Wetterling and Brian P. Flannery (1997) + "Numerical recipes in Fortran 77", Vol. 1, pp 184-194 + """ + f = poly1d(p) + n = len(f.coeffs) + return chebfit(f, n, a, b) + + +def cheb2poly(ck, a=-1, b=1): + """ + Converts Chebyshev coefficients to polynomial coefficients + + Parameters + ---------- + ck : array-like + Chebychef coefficients + a,b : real, scalars + lower and upper limits (Default -1,1) + + Returns + ------- + p : ndarray + polynomial coefficients + + It is not advised to do this for len(ck)>10 due to numerical cancellations. + + See also + -------- + chebval + chebfit + + Examples + -------- + >>> import numpy as np + >>> p = np.arange(5) + >>> ck = poly2cheb(p) + >>> cheb2poly(ck) + array([ 1., 2., 3., 4.]) + + + References + ---------- + http://en.wikipedia.org/wiki/Chebyshev_polynomials + http://en.wikipedia.org/wiki/Chebyshev_form + http://en.wikipedia.org/wiki/Clenshaw_algorithm + """ + + n = len(ck) + + b_Nmi = np.zeros(1) + b_Nmip1 = np.zeros(1) + y = np.r_[2 / (b - a), -(a + b) / (b - a)] + y2 = 2. * y + + # Clenshaw recurence + for ix in range(n - 1): + tmp = b_Nmi + b_Nmi = polymul(y2, b_Nmi) # polynomial multiplication + nb = len(b_Nmip1) + b_Nmip1[-1] = b_Nmip1[-1] - ck[ix] + b_Nmi[-nb::] = b_Nmi[-nb::] - b_Nmip1 + b_Nmip1 = tmp + + p = polymul(y, b_Nmi) # polynomial multiplication + nb = len(b_Nmip1) + b_Nmip1[-1] = b_Nmip1[-1] - ck[n - 1] + p[-nb::] = p[-nb::] - b_Nmip1 + return polytrim(p) + + +def chebextr(n): + """ + Return roots of derivative of Chebychev polynomial of the first kind. + + All local extreme values of the polynomial are either -1 or 1. So, + CHEBPOLY( N, CHEBEXTR(N) ) ) return the same as (-1).^(N:-1:0) + except for the numerical noise in the former. + + Because the extreme values of Chebychev polynomials of the first + kind are either -1 or 1, their roots are often used as starting + values for the nodes in minimax approximations. + + + Parameters + ---------- + n : scalar, integer + degree of Chebychev polynomial. + + Examples + -------- + >>> x = chebextr(4) + >>> chebpoly(4,x) + array([ 1., -1., 1., -1., 1.]) + + + Reference + --------- + http://en.wikipedia.org/wiki/Chebyshev_nodes + http://en.wikipedia.org/wiki/Chebyshev_polynomials + """ + return - np.cos((pi * arange(n + 1)) / n) + + +def chebroot(n, kind=1): + """ + Return roots of Chebychev polynomial of the first or second kind. + + The roots of the Chebychev polynomial of the first kind form a particularly + good set of nodes for polynomial interpolation because the resulting + interpolation polynomial minimizes the problem of Runge's phenomenon. + + Parameters + ---------- + n : scalar, integer + degree of Chebychev polynomial. + kind: 1 or 2, optional + kind of Chebychev polynomial (default 1) + + Examples + -------- + >>> import numpy as np + >>> x = chebroot(3) + >>> np.abs(chebpoly(3,x))<1e-15 + array([ True, True, True], dtype=bool) + >>> chebpoly(3) + array([ 4., 0., -3., 0.]) + >>> x2 = chebroot(4,kind=2) + >>> np.abs(chebpoly(4,x2,kind=2))<1e-15 + array([ True, True, True, True], dtype=bool) + >>> chebpoly(4,kind=2) + array([ 16., 0., -12., 0., 1.]) + + + Reference + --------- + http://en.wikipedia.org/wiki/Chebyshev_nodes + http://en.wikipedia.org/wiki/Chebyshev_polynomials + """ + if kind not in (1, 2): + raise ValueError('kind must be 1 or 2') + return - np.cos(pi * (arange(n) + 0.5 * kind) / (n + kind - 1)) + + +def chebpoly(n, x=None, kind=1): + """ + Return Chebyshev polynomial of the first or second kind. + + These polynomials are orthogonal on the interval [-1,1], with + respect to the weight function w(x) = (1-x**2)**(-1/2+kind-1). + + chebpoly(n) returns coefficients of the Chebychev polynomial of degree N. + chebpoly(n,x) returns the Chebychev polynomial of degree N evaluated at X. + + Parameters + ---------- + n : integer, scalar + degree of Chebychev polynomial. + x : array-like, optional + evaluation points + kind: 1 or 2, optional + kind of Chebychev polynomial (default 1) + + Returns + ------- + p : ndarray + polynomial coefficients if x is None. + Chebyshev polynomial evaluated at x otherwise + + Examples + -------- + >>> import numpy as np + >>> x = chebroot(3) + >>> np.abs(chebpoly(3,x))<1e-15 + array([ True, True, True], dtype=bool) + >>> chebpoly(3) + array([ 4., 0., -3., 0.]) + >>> x2 = chebroot(4,kind=2) + >>> np.abs(chebpoly(4,x2,kind=2))<1e-15 + array([ True, True, True, True], dtype=bool) + >>> chebpoly(4,kind=2) + array([ 16., 0., -12., 0., 1.]) + + + Reference + --------- + http://en.wikipedia.org/wiki/Chebyshev_polynomials + """ + if x is None: # Calculate coefficients. + if n == 0: + p = np.ones(1) + else: + p = np.round(pow(2, n - 2 + kind) * poly(chebroot(n, kind=kind))) + p[1::2] = 0 + return p + else: # Evaluate polynomial in chebychev form + ck = np.zeros(n + 1) + ck[0] = 1. + return _chebval(np.atleast_1d(x), ck, kind=kind) + + +def chebfit(fun, n=10, a=-1, b=1, trace=False): + """ + Computes the Chebyshevs coefficients + + so that f(x) can be approximated by: + + n-1 + f(x) = sum ck*Tk(x) + k=0 + + where Tk is the k'th Chebyshev polynomial of the first kind. + + Parameters + ---------- + fun : callable + function to approximate + n : integer, scalar, optional + number of base points (abscissas). Default n=10 (maximum 50) + a,b : real, scalars, optional + integration limits + + Returns + ------- + ck : ndarray + polynomial coefficients in Chebychev form. + + Examples + -------- + Fit exp(x) + + >>> import matplotlib.pyplot as plt + >>> a = 0; b = 2 + >>> ck = chebfit(np.exp,7,a,b); + >>> x = np.linspace(0,4); + >>> x1 = chebroot(9)*(b-a)/2+(b+a)/2 + >>> ck1 = chebfit(np.exp(x1)) + + h=plt.plot(x, np.exp(x), 'r', x, chebval(x,ck,a,b), 'g.') + h = plt.plot(x,np.exp(x), 'r', x, chebval(x,ck1,a,b),'g.') + plt.close() + + See also + -------- + chebval + + Reference + --------- + http://en.wikipedia.org/wiki/Chebyshev_nodes + http://mathworld.wolfram.com/ChebyshevApproximationFormula.html + + W. Fraser (1965) + "A Survey of Methods of Computing Minimax and Near-Minimax Polynomial + Approximations for Functions of a Single Independent Variable" + Journal of the ACM (JACM), Vol. 12 , Issue 3, pp 295 - 314 + """ + + if (n > 50): + warnings.warn('CHEBFIT should only be used for n<50') + + if hasattr(fun, '__call__'): + x = map_to_interval(chebroot(n), a, b) + f = fun(x) + if trace: + plt.plot(x, f, '+') + else: + f = fun + n = len(f) + # N-1 + # c[k] = (2/N) sum w[n] f[n]*cos(pi*k*(2n+1)/(2N)), 0 <= k < N. + # n=0 + # + # w[0] = 0.5, w[n]=1 for n>0 + + ck = dct(f[::-1]) / n + ck[0] = ck[0] / 2. + return ck[::-1] + + +def chebfit_dct(f, n=(10, ), domain=None): + """ + Fit Chebyshev series to N-dimensional function + so that f(x1, x2,..., xn) can be approximated by: + + .. math:: f(x_1, x_2,...,x_n) = + \\sum_{i,j,...k} c_i T_i(x_1)*...*c_k T_k(x_n) , + + where Tk is the k'th Chebyshev polynomial of the first kind. + + Parameters + ---------- + f : callable + function to approximate + n : list of integers, optional + number of base points (abscissas) used for each dimension. + Default n=10 (maximum 50) + domain : vector [a1,b1,a2,b2 ,..., an, bn], optional + defining the rectangle [a1, b1] x [a2, b2] x ...x [an, bn]. + (default domain = (-1,1) * len(n)) + + Returns + ------- + ck : ndarray + polynomial coefficients in Chebychev form. + + Examples + -------- + Fit exponential function + + >>> import matplotlib.pyplot as plt + >>> domain = (0, 2) + >>> ck = chebfit_dct(np.exp, 7, domain) + >>> np.allclose(ck, [3.44152387e+00, 3.07252345e+00, 7.38000848e-01, + ... 1.20520053e-01, 1.48805268e-02, 1.47579673e-03, + ... 1.21719524e-04]) + True + >>> x1 = map_to_interval(chebroot(9), *domain) + >>> ck1 = chebfit(np.exp(x1)) + >>> np.allclose(ck1, [5.40019009e-07, 8.69418381e-06, 1.22261037e-04, + ... 1.47582673e-03, 1.48805283e-02, 1.20520053e-01, + ... 7.38000848e-01, 3.07252345e+00, 3.44152387e+00]) + True + + x = np.linspace(0,4) + h = plt.plot(x, np.exp(x), 'r', x, chebvalnd(ck, x,ck,a,b), 'g.') + h = plt.plot(x, np.exp(x), 'r', x, chebvalnd(ck1, x,ck1,a,b),'b.') + plt.close() + + See also + -------- + chebval, chebvalnd + + Reference + --------- + http://en.wikipedia.org/wiki/Chebyshev_nodes + http://mathworld.wolfram.com/ChebyshevApproximationFormula.html + + W. Fraser (1965) + "A Survey of Methods of Computing Minimax and Near-Minimax Polynomial + Approximations for Functions of a Single Independent Variable" + Journal of the ACM (JACM), Vol. 12 , Issue 3, pp 295 - 314 + """ + n = np.atleast_1d(n) + if np.any(n > 50): + warnings.warn('CHEBFIT should only be used for n<50') + + if hasattr(f, '__call__'): + if domain is None: + domain = (-1, 1) * len(n) + domain = np.atleast_2d(domain).reshape((-1, 2)) + xi = [map_to_interval(chebroot(ni), d[0], d[1]) + for ni, d in zip(n, domain)] + Xi = np.meshgrid(*xi) + ck = f(*Xi) + else: + ck = f + n = ck.shape + + ndim = len(n) + for i in range(ndim): + ck = dct(ck[..., ::-1]) + ck[..., 0] = ck[..., 0] / 2. + if i < ndim-1: + ck = np.rollaxis(ck, axis=-1) + return ck / np.product(n) + + +def idct(x, n=None): + """ + Inverse Discrete Cosine Transform + + N-1 + x[k] = 1/N sum w[n]*y[n]*cos(pi*k*(2n+1)/(2*N)), 0 <= k < N. + n=0 + + w(0) = 1/2 + w(n) = 1 for n>0 + + Examples + -------- + >>> import numpy as np + >>> x = np.arange(5)*1.0 + >>> np.abs(x-idct(dct(x)))<1e-14 + array([ True, True, True, True, True], dtype=bool) + >>> np.abs(x-dct(idct(x)))<1e-14 + array([ True, True, True, True, True], dtype=bool) + + Reference + --------- + http://en.wikipedia.org/wiki/Discrete_cosine_transform + http://users.ece.utexas.edu/~bevans/courses/ee381k/lectures/ + """ + return _idct(x, n=n, norm=None)*0.5/len(x) + + +def _chebval(x, ck, kind=1): + """ + Evaluate polynomial in Chebyshev form. + + A polynomial of degree N in Chebyshev form is a polynomial p(x): + + N + p(x) = sum ck*Tk(x) + k=0 + or + N + p(x) = sum ck*Uk(x) + k=0 + + where Tk and Uk are the k'th Chebyshev polynomial of the first and second + kind, respectively. + + References + ---------- + http://en.wikipedia.org/wiki/Clenshaw_algorithm + http://mathworld.wolfram.com/ClenshawRecurrenceFormula.html + """ + n = len(ck) + b_Nmi = np.zeros(x.shape) # b_(N-i) + b_Nmip1 = b_Nmi.copy() # b_(N-i+1) + x2 = 2 * x + # Clenshaw reccurence + for ix in range(n - 1): + tmp = b_Nmi + b_Nmi = x2 * b_Nmi - b_Nmip1 + ck[ix] + b_Nmip1 = tmp + return kind * x * b_Nmi - b_Nmip1 + ck[n - 1] + + +def chebval(x, ck, a=-1, b=1, kind=1, fill=None): + """ + Evaluate polynomial in Chebyshev form at X + + A polynomial of degree N in Chebyshev form is a polynomial p(x) of the form + + N + p(x) = sum ck*Tk(x) + k=0 + + where Tk is the k'th Chebyshev polynomial of the first or second kind. + + Paramaters + ---------- + x : array-like + points to evaluate + ck : array-like + polynomial coefficients in Chebyshev form ordered from highest degree + to zero + a,b : real, scalars, optional + limits for polynomial (Default -1,1) + kind: 1 or 2, optional + kind of Chebychev polynomial (default 1) + fill : scalar, optional + If provided, define value to return for `x < a` or `b < x`. + + Examples + -------- + Plot Chebychev polynomial of the first kind and order 4: + >>> import matplotlib.pyplot as plt + >>> x = np.linspace(-1,1) + >>> ck = np.zeros(5); ck[-1]=1 + >>> y = chebval(x,ck) + + h = plt.plot(x, y, x, chebpoly(4,x),'.') + plt.close() + + Fit exponential function: + >>> import matplotlib.pyplot as plt + >>> ck = chebfit(np.exp,7,0,2) + >>> x = np.linspace(0,4); + >>> y2 = chebval(x,ck,0,2) + + h=plt.plot(x, y2, 'g', x, np.exp(x)) + plt.close() + + See also + -------- + chebfit + + References + ---------- + http://en.wikipedia.org/wiki/Clenshaw_algorithm + http://mathworld.wolfram.com/ClenshawRecurrenceFormula.html + """ + + y = map_from_interval(np.atleast_1d(x), a, b) + if fill is None: + f = _chebval(y, ck, kind=kind) + else: + cond = (abs(y) <= 1) + f = np.where(cond, 0, fill) + if np.any(cond): + yk = np.extract(cond, y) + f[cond] = _chebval(yk, ck, kind=kind) + return f + + +def chebder(ck, a=-1, b=1): + """ + Differentiate Chebyshev polynomial + + Parameters + ---------- + ck : array-like + polynomial coefficients in Chebyshev form of function to differentiate + a,b : real, scalars + limits for polynomial(Default -1,1) + + Return + ------ + cder : ndarray + polynomial coefficients in Chebyshev form of the derivative + + Examples + -------- + + Fit exponential function: + >>> import matplotlib.pyplot as plt + >>> ck = chebfit(np.exp,7,0,2) + >>> x = np.linspace(0,4) + >>> ck2 = chebder(ck,0,2) + >>> y = chebval(x,ck2,0,2) + + h = plt.plot(x, y, 'g', x, np.exp(x), 'r') + plt.close() + + See also + -------- + chebint + chebfit + + Reference + --------- + http://en.wikipedia.org/wiki/Chebyshev_polynomials + + W. Fraser (1965) + "A Survey of Methods of Computing Minimax and Near-Minimax Polynomial + Approximations for Functions of a Single Independent Variable" + Journal of the ACM (JACM), Vol. 12 , Issue 3, pp 295 - 314 + """ + + n = len(ck) - 1 + cder = np.zeros(n, dtype=np.asarray(ck).dtype) + cder[0] = 2 * n * ck[0] + cder[1] = 2 * (n - 1) * ck[1] + for j in range(2, n): + cder[j] = cder[j - 2] + 2 * (n - j) * ck[j] + + return cder * 2. / (b - a) # Normalize to the interval b-a. + + +def chebint(ck, a=-1, b=1): + """ + Integrate Chebyshev polynomial + + Parameters + ---------- + ck : array-like + polynomial coefficients in Chebyshev form of function to integrate. + a,b : real, scalars + limits for polynomial(Default -1,1) + + Return + ------ + cint : ndarray + polynomial coefficients in Chebyshev form of the integrated function + + Examples + -------- + Fit exponential function: + >>> import matplotlib.pyplot as plt + >>> ck = chebfit(np.exp,7,0,2) + >>> x = np.linspace(0,4) + >>> ck2 = chebint(ck,0,2); + >>> y =chebval(x,ck2,0,2) + + h=plt.plot(x,y,'g',x,np.exp(x),'r.') + plt.close() + + See also + -------- + chebder + chebfit + + Reference + --------- + http://en.wikipedia.org/wiki/Chebyshev_polynomials + + W. Fraser (1965) + "A Survey of Methods of Computing Minimax and Near-Minimax Polynomial + Approximations for Functions of a Single Independent Variable" + Journal of the ACM (JACM), Vol. 12 , Issue 3, pp 295 - 314 + """ + +# int T0(x) = T1(x)+1 +# int T1(x) = 0.5*(T2(x)/2-T0/2) +# int Tn(x) dx = 0.5*{Tn+1(x)/(n+1) - Tn-1(x)/(n-1)} +# N +# p(x) = sum cn*Tn(x) +# n=0 + +# int p(x) dx = sum cn * int(Tn(x)dx) = +# 0.5*sum cn *{Tn+1(x)/(n+1) - Tn-1(x)/(n-1)} = 0.5 sum (cn-1-cn+1)*Tn/n n>0 + + n = len(ck) + + cint = np.zeros(n) + con = 0.25 * (b - a) + + dif1 = np.diff(ck[-1::-2]) + ix1 = np.r_[1:n - 1:2] + cint[ix1] = -(con * dif1) / ix1 + if n > 3: + dif2 = np.diff(ck[-2::-2]) + ix2 = np.r_[2:n - 1:2] + cint[ix2] = -(con * dif2) / ix2 + cint = cint[::-1] + # cint(n) is a special case + cint[-1] = (con * ck[n - 2]) / (n - 1) + # Set integration constant + cint[0] = 2 * np.sum((-1) ** np.r_[0:n - 1] * cint[-2::-1]) + return cint + + +class Cheb1d(object): + coeffs = None + order = None + a = None + b = None + kind = None + + def __init__(self, ck, a=-1, b=1, kind=1): + if isinstance(ck, Cheb1d): + for key in ck.__dict__.keys(): + self.__dict__[key] = ck.__dict__[key] + return + cki = trim_zeros(np.atleast_1d(ck), 'b') + if len(cki.shape) > 1: + raise ValueError("Polynomial must be 1d only.") + self.__dict__['coeffs'] = cki + self.__dict__['order'] = len(cki) - 1 + self.__dict__['a'] = a + self.__dict__['b'] = b + self.__dict__['kind'] = kind + + def __call__(self, x): + return chebval(x, self.coeffs, self.a, self.b, self.kind) + + def __array__(self, t=None): + if t: + return np.asarray(self.coeffs, t) + else: + return np.asarray(self.coeffs) + + def __repr__(self): + vals = repr(self.coeffs) + vals = vals[6:-1] + return "Cheb1d(%s)" % vals + + def __len__(self): + return self.order + + def __str__(self): + pass + + def __neg__(self): + new = Cheb1d(self) + new.coeffs = -self.coeffs + return new + + def __pos__(self): + return self + + def __add__(self, other): + other = Cheb1d(other) + new = Cheb1d(self) + new.coeffs = polyadd(self.coeffs, other.coeffs) + return new + + def __radd__(self, other): + return self.__add__(other) + + def __sub__(self, other): + other = Cheb1d(other) + new = Cheb1d(self) + new.coeffs = polysub(self.coeffs, other.coeffs) + return new + + def __rsub__(self, other): + other = Cheb1d(other) + new = Cheb1d(self) + new.coeffs = polysub(other.coeffs, new.coeffs) + return new + + def __eq__(self, other): + other = Cheb1d(other) + return (np.all(self.coeffs == other.coeffs) and (self.a == other.a) and + (self.b == other.b) and (self.kind == other.kind)) + + def __ne__(self, other): + return np.any(self.coeffs != other.coeffs) or (self.a != other.a) or ( + self.b != other.b) or (self.kind != other.kind) + + def __setattr__(self, key, val): + raise ValueError("Attributes cannot be changed this way.") + + def __getattr__(self, key): + if key in ['c', 'coef', 'coefficients']: + return self.coeffs + elif key in ['o']: + return self.order + elif key in ['a']: + return self.a + elif key in ['b']: + return self.b + elif key in ['k']: + return self.kind + else: + try: + return self.__dict__[key] + except KeyError: + raise AttributeError( + "'%s' has no attribute '%s'" % + (self.__class__, key)) + + def __getitem__(self, val): + if val > self.order: + return 0 + if val < 0: + return 0 + return self.coeffs[val] + + def __setitem__(self, key, val): + # ind = self.order - key + if key < 0: + raise ValueError("Does not support negative powers.") + if key > self.order: + zr = np.zeros(key - self.order, self.coeffs.dtype) + self.__dict__['coeffs'] = np.concatenate((self.coeffs, zr)) + self.__dict__['order'] = key + self.__dict__['coeffs'][key] = val + return + + def __iter__(self): + return iter(self.coeffs) + + def integ(self, m=1): + """ + Return an antiderivative (indefinite integral) of this polynomial. + + Refer to `chebint` for full documentation. + + See Also + -------- + chebint : equivalent function + + """ + integ = Cheb1d(self) + integ.coeffs = chebint(self.coeffs, self.a, self.b) + return integ + + def deriv(self, m=1): + """ + Return a derivative of this polynomial. + + Refer to `chebder` for full documentation. + + See Also + -------- + chebder : equivalent function + + """ + der = Cheb1d(self) + der.coeffs = chebder(self.coeffs, self.a, self.b) + return der + + +def padefit(c, m=None): + """ + Rational polynomial fitting from polynomial coefficients + + Parameters + ---------- + c : array-like + coefficients of power series expansion from highest degree to zero. + m : scalar integer + order of denominator polynomial. (Default floor((len(c)-1)/2)) + + Returns + ------- + num, den : poly1d + numerator and denominator polynomials for the pade approximation + + If the function is well approximated by + M+N+1 + f(x) = sum c(2*n+2-k)*x^k + k=0 + + then the pade approximation is given by + M + sum c1(n-k+1)*x^k + k=0 + f(x) = ------------------------ + N + sum c2(n-k+1)*x^k + k=0 + + Note: c must be ordered for direct use with polyval + + Example + ------- + Pade approximation to exp(x) + >>> import scipy.special as sp + >>> import matplotlib.pyplot as plt + >>> c = poly1d(1./sp.gamma(np.r_[6+1:0:-1])) + >>> [p, q] = padefit(c) + >>> p; q + poly1d([ 0.00277778, 0.03333333, 0.2 , 0.66666667, 1. ]) + poly1d([ 0.03333333, -0.33333333, 1. ]) + + x = np.linspace(0,4) + h = plt.plot(x,c(x),x,p(x)/q(x),'g-', x,np.exp(x),'r.') + plt.close() + + See also + -------- + scipy.misc.pade + + """ + if not m: + m = int(np.floor((len(c) - 1) * 0.5)) + c = np.asarray(c) + return pade(c[::-1], m) + + +def test_pade(): + cof = np.array(([1.0, 1.0, 1.0 / 2, 1. / 6, 1. / 24])) + p, q = pade(cof, 2) + t = arange(0, 2, 0.1) + assert(np.all(abs(p(t) / q(t) - np.exp(t)) < 0.3)) + + +def padefitlsq(fun, m, k, a=-1, b=1, trace=False, x=None, end_points=True): + """ + Rational polynomial fitting. A minimax solution by least squares. + + Parameters + ---------- + fun : callable or or a two column matrix + f=[x,f(x)] where length(x)>(m+k+1)*8. + m, k : integer + number of coefficients of the numerator and denominater, respectively. + a, b : real scalars + evaluation limits, (default a=-1,b=1) + + Returns + ------- + num, den : poly1d + numerator and denominator polynomials for the pade approximation + dev : ndarray + maximum absolute deviation of the approximation + + The pade approximation is given by + m + sum c1[m-i]*x**i + i=0 + f(x) = ------------------------ + k + sum c2[k-i]*x**i + i=0 + + If F is a two column matrix, [x f(x)], a good choice for x is: + + x = cos(pi/(N-1)*(N-1:-1:0))*(b-a)/2+ (a+b)/2, where N = (m+k+1)*8; + + Note: c1 and c2 are ordered for direct use with polyval + + Example + ------- + + Pade approximation to exp(x) between 0 and 2 + >>> import matplotlib.pyplot as plt + >>> [c1, c2] = padefitlsq(np.exp,3,3,0,2) + >>> c1; c2 + poly1d([ 0.01443847, 0.128842 , 0.55284547, 0.99999962]) + poly1d([-0.0049658 , 0.07610473, -0.44716929, 1. ]) + + x = np.linspace(0,4) + h = plt.plot(x, polyval(c1,x)/polyval(c2,x),'g') + h = plt.plot(x, np.exp(x), 'r') + + See also + -------- + padefit + + Reference + --------- + William H. Press, Saul Teukolsky, + William T. Wetterling and Brian P. Flannery (1997) + "Numerical recipes in Fortran 77", Vol. 1, pp 197-20 + """ + + NFAC = 8 + BIG = 1e30 + MAXIT = 5 + + smallest_devmax = BIG + ncof = m + k + 1 + # Number of points where function is evaluated, i.e. fineness of mesh + npt = NFAC * ncof + + if x is None: + if end_points: + # Use the location of the local extreme values of + # the Chebychev polynomial of the first kind of degree NPT-1. + x = map_to_interval(chebextr(npt - 1), a, b) + else: + # Use the roots of the Chebychev polynomial of the first kind of + # degree NPT. Note this is useful if there are singularities close + # to the endpoints. + x = map_to_interval(chebroot(npt, kind=1), a, b) + + if hasattr(fun, '__call__'): + fs = fun(x) + else: + fs = fun + n = len(fs) + if n < npt: + warnings.warn('Check the result! Number of function values ' + + 'should be at least: %d' % npt) + + if trace: + plt.plot(x, fs, '+') + + wt = np.ones((npt)) + ee = np.ones((npt)) + mad = 0 + + u = np.zeros((npt, ncof)) + for ix in range(MAXIT): + # Set up design matrix for least squares fit. + pow1 = wt + bb = pow1 * (fs + abs(mad) * np.sign(ee)) + + for jx in range(m + 1): + u[:, jx] = pow1 + pow1 = pow1 * x + + pow1 = -bb + for jx in range(m + 1, ncof): + pow1 = pow1 * x + u[:, jx] = pow1 + + [u1, w, v] = np.linalg.svd(u, full_matrices=False) + cof = np.where(w == 0, 0.0, np.dot(bb, u1) / w) + cof = np.dot(cof, v) + + # Tabulate the deviations and revise the weights + ee = polyval(cof[m::-1], x) / \ + polyval(cof[ncof:m:-1].tolist() + [1, ], x) - fs + + wt = np.abs(ee) + devmax = max(wt) + mad = wt.mean() # mean absolute deviation + + # Save only the best coefficients found + if (devmax <= smallest_devmax): + smallest_devmax = devmax + c1 = cof[m::-1] + c2 = cof[ncof:m:-1].tolist() + [1, ] + + if trace: + print('Iteration=%d, max error=%g' % (ix, devmax)) + plt.plot(x, fs, x, ee + fs) + return poly1d(c1), poly1d(c2) + + +def main(): + exp = np.exp + [c1, c2] = padefitlsq(exp, 3, 3, 0, 2) + + x = np.linspace(0, 4) + plt.plot(x, polyval(c1, x) / polyval(c2, x), 'g') + plt.plot(x, exp(x), 'r') + + import scipy.special as sp + + p = [[1, 1, 1], [2, 2, 2]] + pi = polyint(p, 1) + _pr = polyreloc(p, 2) + _pd = polyder(p) + _st = poly2str(p) + # polynomial coeff exponential function: + c = poly1d(1. / sp.gamma(np.r_[6 + 1:0:-1])) + [p, q] = padefit(c) + x = np.linspace(0, 4) + plt.plot(x, c(x), x, p(x) / q(x), 'g-', x, exp(x), 'r.') + plt.close() + x = arange(4) + dx = dct(x) + _idx = idct(dx) + + a = 0 + b = 2 + ck = chebfit(exp, 6, a, b) + _t = chebval(0, ck, a, b) + x = np.linspace(0, 2, 6) + plt.plot(x, exp(x), 'r', x, chebval(x, ck, a, b), 'g.') + # x1 = chebroot(9).'*(b-a)/2+(b+a)/2 ; + # ck1 =chebfit([x1 exp(x1)],9,a,b); + # plot(x,exp(x),'r'), hold on + # plot(x,chebval(x,ck1,a,b),'g'), hold off + + _t = poly2hstr([1, 1, 2]) + py = [1, 0] + px = polyshift(py, 0, 5) + _t1 = polyval(px, [0, 2.5, 5]) # % This is the same as the line below + _t2 = polyval(py, [-1, 0, 1]) + + px = [1, 0] + py = polyishift(px, 0, 5) + t1 = polyval(px, [0, 2.5, 5]) # % This is the same as the line below + t2 = polyval(py, [-1, 0, 1]) + print(t1, t2) + + def test_polydeg(): x = np.linspace(0, 10, 300) y = np.sin(x ** 3 / 100) ** 2 + 0.05 * np.random.randn(x.size) n = polydeg(x, y) # n = 2 p = orthofit(x, y, n) - xi = linspace(x.min(), x.max()) + xi = np.linspace(x.min(), x.max()) ys0 = orthoval(p, x) ys = orthoval(p, xi) @@ -1942,378 +1939,379 @@ def test_polydeg(): p1 = polyfit(x, ys0, n) plt.plot(xi, polyval(p0, xi), 'g-.', xi, polyval(p1, xi), 'go') plt.show('hold') - - -def test_docstrings(): - import doctest - print('Testing docstrings in %s' % __file__) - doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE) - - -def chebvandernd(deg, *xi): - """Pseudo-Vandermonde matrix of given degrees. - - Returns the pseudo-Vandermonde matrix of degrees `deg` and sample - points `(x1, x2, ..., xn)`. If `l, m, n` are the given degrees in - `x1, x2, x3`, then The pseudo-Vandermonde matrix is defined by - - .. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = T_i(x1)*T_j(x2)*T_k(x3), - - where `0 <= i <= l`, `0 <= j <= m`, and `0 <= k <= n`. The leading - indices of `V` index the points `(x, y, z)` and the last index encodes - the degrees of the Chebyshev polynomials. - - If ``V = chebvandernd([xdeg, ydeg, zdeg], x, y, z)``, then the columns - of `V` correspond to the elements of a 3-D coefficient array `c` of - shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order - - .. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},... - - and ``np.dot(V, c.flat)`` and ``chebvalnd(c, x, y, z)`` will be the - same up to roundoff. This equivalence is useful both for least squares - fitting and for the evaluation of a large number of N-D Chebyshev - series of the same degrees and sample points. - - Parameters - ---------- - deg : list of ints - List of maximum degrees of the form [x1_deg, x2_deg, ...,xn_deg]. - x1, x2, ..., xn : array_like - Arrays of point coordinates, all of the same shape. The dtypes will - be converted to either float64 or complex128 depending on whether - any of the elements are complex. Scalars are converted to 1-D - arrays. - - Returns - ------- - vander : ndarray - The shape of the returned matrix is ``x1.shape + (order,)``, where - :math:`order = (deg[0]+1)*(deg([1]+1)*...*(deg[n-1]+1)`. The dtype - will be the same as the converted `x1`, `x2`, ... `xn`. - - See Also - -------- - chebvander, chebvalnd, chebfitnd - """ - ideg = [int(d) for d in deg] - is_valid = np.array([di == d and di >= 0 for di, d in zip(ideg, deg)]) - if np.any(is_valid != 1): - raise ValueError("degrees must be non-negative integers") - ndim = len(xi) - if len(ideg) != ndim: - msg = 'length of deg must be the same as number of dimensions' - raise ValueError(msg) - - xi = np.array(xi, copy=0) + 0.0 - chebvander = np.polynomial.chebyshev.chebvander - shape0 = xi[0].shape - s0 = (1,) * ndim - vxi = [chebvander(x, d).reshape(shape0 + s0[:i] + (-1,) + s0[i + 1::]) - for i, (d, x) in enumerate(zip(ideg, xi))] - - v = reduce(np.multiply, vxi) - - return v.reshape(v.shape[:-ndim] + (-1,)) - - -def chebfitnd(xi, f, deg, rcond=None, full=False, w=None): - """ - Least squares fit of Chebyshev series to N-dimensional data. - Return the coefficients of a Chebyshev series of degree `deg` that is the - least squares fit to the data values `f` given at points - `x1`, `x2`,..., `xn` - - The fitted polynomial(s) are in the form - .. math:: p(x,y) = c_00 + c_11 * T_1(x)*T_1(y) + ..c_ij * T_i(x)*T_j(y). - + c_nm * T_n(x)*T_m(y), - where `n`, `m` is `deg`. - - Parameters - ---------- - xi: tuple - x1-, x2-,....xn-coordinates of the sample points. - f : array_like - function values at the sample points ``(x1[i], x2[i], ..., xn[i])``. - deg : list - Degrees of the fitting series in the x1, x2, ..., xn directions, - respectively. - rcond : float, optional - Relative condition number of the fit. Singular values smaller than - this relative to the largest singular value will be ignored. The - default value is size(x1)*eps, where eps is the relative precision of - the float type, about 2e-16 in most cases. - full : bool, optional - Switch determining nature of return value. When it is False (the - default) just the coefficients are returned, when True diagnostic - information from the singular value decomposition is also returned. - w : array_like, optional - Weights. If not None, the contribution of each point - ``(x1[i], x2[i], ..., xn[i])`` to the fit is weighted by `w[i]`. - Ideally the weights are chosen so that the errors of the products - ``w[i]*f[i]`` all have the same variance. The default value is None. - - Returns - ------- - coef : ndarray, shape (M1, M2,..., Mn) - Chebyshev coefficients ordered from low to high. - [residuals, rank, singular_values, rcond] : list - These values are only returned if `full` = True - resid -- sum of squared residuals of the least squares fit - rank -- the numerical rank of the scaled Vandermonde matrix - sv -- singular values of the scaled Vandermonde matrix - rcond -- value of `rcond`. - For more details, see `linalg.lstsq`. - Warns - ----- - RankWarning - The rank of the coefficient matrix in the least-squares fit is - deficient. The warning is only raised if `full` = False. The - warnings can be turned off by - >>> import warnings - >>> warnings.simplefilter('ignore', RankWarning) - - See Also - -------- - chebvalnd, chebgridnd - - Notes - ----- - The solution is the coefficients of the Chebyshev series `p` that - minimizes the sum of the weighted squared errors - .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, - where :math:`w_j` are the weights. This problem is solved by setting up - as the (typically) overdetermined matrix equation - .. math:: V(x, y) * c = w * y, - where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the - coefficients to be solved for, `w` are the weights, and `y` are the - observed values. This equation is then solved using the singular value - decomposition of `V`. - If some of the singular values of `V` are so small that they are - neglected, then a `RankWarning` will be issued. This means that the - coefficient values may be poorly determined. Using a lower order fit - will usually get rid of the warning. The `rcond` parameter can also be - set to a value smaller than its default, but the resulting fit may be - spurious and have large contributions from roundoff error. - Fits using Chebyshev series are usually better conditioned than fits - using power series, but much can depend on the distribution of the - sample points and the smoothness of the data. If the quality of the fit - is inadequate splines may be a good alternative. - - References - ---------- - .. [1] Wikipedia, "Curve fitting", - http://en.wikipedia.org/wiki/Curve_fitting - Examples - -------- - """ - # xi = np.array(xi, copy=0) + 0.0 - z = np.array(f) - degrees = np.asarray(deg, dtype=int) - orders = degrees + 1 - order = np.product(orders) - - ndims = np.array([x.ndim for x in xi]) - ndim = len(ndims) - sizes = np.array([x.size for x in xi]) - if np.any(ndims != ndim) or z.ndim != ndim: - raise TypeError("expected %dD array for x1, x2,...,xn and f" % ndim) - if np.any(sizes == 0): - raise TypeError("expected non-empty vector for xi") - - lhs = chebvandernd(degrees, *xi).reshape((-1, order)) - rhs = z.ravel() - if w is not None: - w = np.asarray(w).ravel() + 0.0 - if len(lhs) != len(w): - raise TypeError("expected x and w to have same length") - lhs = lhs * w - rhs = rhs * w - - if rcond is None: - rcond = xi[0].size * np.finfo(x.dtype).eps - - if issubclass(lhs.dtype.type, np.complexfloating): - scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(axis=0)) - else: - scl = np.sqrt(np.square(lhs).sum(axis=0)) - scl[scl == 0] = 1 - - # Solve the least squares problem. - c, resids, rank, s = np.linalg.lstsq(lhs/scl, rhs, rcond) - c = (c/scl).reshape(orders) - - if rank != order and not full: - msg = "The fit may be poorly conditioned" - warnings.warn(msg, pu.RankWarning) - - if full: - return c, [resids, rank, s, rcond] - else: - return c - - -def chebvalnd(c, *xi): - """ - Evaluate a N-D Chebyshev series at points (x1, x2, ..., xn). - - This function returns the values: - - .. math:: p(x1,x2,...,xn) = - \\sum_{i,j,...,k} c_{i,j,...,k} * T_i(x1) * T_j(x2)*...* T_k(xn) - - The parameters `x1`, `x2`, ...., `xn` are converted to arrays only if - they are tuples or a lists, otherwise they are treated as a scalars and - they must have the same shape after conversion. In either case, either - `x1`, `x2`, ..., `xn` or their elements must support multiplication and - addition both with themselves and with the elements of `c`. - - If `c` has fewer than N dimensions, ones are implicitly appended to its - shape to make it N-D. The shape of the result will be c.shape[3:] + - x1.shape. - - Parameters - ---------- - c : array_like - Array of coefficients ordered so that the coefficient of the term of - multi-degree i,j,...,k is contained in ``c[i,j,...,k]``. If `c` has - dimension greater than N the remaining indices enumerate multiple sets - of coefficients. - x1, x2,..., xn : array_like, compatible object - The N dimensional series is evaluated at the points - `(x1, x2,...,xn)`, where `x1`, `x2`,..., `xn` must have the same shape. - If any of `x1`, `x2`, ..., `xn` is a list or tuple, it is first - converted to an ndarray, otherwise it is left unchanged and if it isn't - an ndarray it is treated as a scalar. - - Returns - ------- - values : ndarray, compatible object - The values of the multidimensional polynomial on points formed with - triples of corresponding values from `x`, `y`, and `z`. - - See Also - -------- - chebval, chebgridnd, chebfitnd - """ - try: - xi = np.array(xi, copy=0) - except: - raise ValueError('x, y, z are incompatible') - chebval = np.polynomial.chebyshev.chebval - c = chebval(xi[0], c) - for x in xi[1:]: - c = chebval(x, c, tensor=False) - return c - - -def chebgridnd(c, *xi): - """ - Evaluate a N-D Chebyshev series on the Cartesian product of x1, x2,..., xn. - - This function returns the values: - - .. math:: p(a,b,...) = \\sum_{i,j,...} c_{i,j,...} * T_i(a) * T_j(b) * ... - - where the points `(a, b, ...)` consist of all points formed by taking - `a` from `x1`, `b` from `x2`, and so on. The resulting points form - a grid with `x1` in the first dimension, `x2` in the second, and so on. - - The parameters `x1`, `x2`, ... and `xn` are converted to arrays only if - they are tuples or a lists, otherwise they are treated as a scalars. In - either case, either `x1`, `x2`,... and `xn` or their elements must support - multiplication and addition both with themselves and with the elements - of `c`. - - If `c` has fewer than N dimensions, ones are implicitly appended to - its shape to make it N-D. The shape of the result will be c.shape[3:] + - x1.shape + x2.shape + ... + xn.shape - - Parameters - ---------- - c : array_like - Array of coefficients ordered so that the coefficients for terms of - degree i,j are contained in ``c[i,j]``. If `c` has dimension - greater than two the remaining indices enumerate multiple sets of - coefficients. - x1, x2,..., xn : ndarray, compatible object - 1-D arrays representing the coordinates of a grid. - The N dimensional series is evaluated at the points in the - Cartesian product of `x1`, `x2`, ... and `xn`. If `xi`, is a - list or tuple, it is first converted to an ndarray, otherwise it is - left unchanged and, if it isn't an ndarray, it is treated as a - scalar. - - Returns - ------- - values : ndarray, compatible object - The values of the N dimensional polynomial at points in the Cartesian - product of `x1`, `x2`, ... and `xn`. - - See Also - -------- - chebval, chebvalnd, chebfitnd - """ - chebval = np.polynomial.chebyshev.chebval - for x in xi: - c = chebval(x, c) - return c - - -def test_chebfit1d(): - def f(x): - return np.exp(-x**2) - - # x = chebroot(n=64, kind=1) - # z = f(x) - - c = chebfit(f, n=64)[::-1] - - xi = np.linspace(-1, 1, 151) - zi = np.polynomial.chebyshev.chebval(xi, c) - - # plt.plot(xi, zi,'.', xi, f(xi)) - plt.semilogy(xi, np.abs(zi-f(xi))) - plt.show('hold') - - -def test_chebfit2d(): - n = 3 - xorder, yorder = n-1, n-1 - x = chebroot(n=n, kind=1) - xgrid, ygrid = meshgrid(x, x) - - def f(x, y): - return np.exp(-x**2-6*y**2) - zgrid = f(xgrid, ygrid) - - # v2d = np.polynomial.chebyshev.chebvander2d(xgrid, ygrid, - # [xorder,yorder]).reshape((-1, (xorder+1)*(yorder+1))) - # coeff, residuals, rank, s = np.linalg.lstsq(v2d, zgrid.ravel()) - # doeff = coeff.reshape(xorder+1,yorder+1) - _dcoeff2 = chebfitnd((xgrid, ygrid), zgrid, [xorder, yorder]) - dcoeff = chebfit_dct(f, n=(xorder+1, yorder+1)) - - xi = np.linspace(-1, 1, 151) - Xi, Yi = np.meshgrid(xi, xi) - Zi = f(Xi, Yi) - zzi = chebvalnd(dcoeff, Xi, Yi) - _devi = Zi - zzi - # plot residuals - # zz = np.polynomial.chebyshev.chebval2d(xgrid, ygrid, dcoeff) - zz = chebvalnd(dcoeff, xgrid, ygrid) - dev = zgrid - zz - # plt.spy(np.abs(dcoeff)>1e-13) - plt.contourf(xgrid, ygrid, np.abs(dev)) - # plt.contourf(Xi, Yi, np.abs(devi)) - plt.colorbar() - # plt.semilogy(np.abs(devi.ravel())) - plt.show('hold') - - -if __name__ == '__main__': - if False: # True: # - main() - else: - test_chebfit2d() - # test_docstrings() - # test_polydeg() + + +def test_docstrings(): + import doctest + print('Testing docstrings in %s' % __file__) + options = doctest.NORMALIZE_WHITESPACE | doctest.ELLIPSIS + doctest.testmod(optionflags=options, verbose=False) + + +def chebvandernd(deg, *xi): + """Pseudo-Vandermonde matrix of given degrees. + + Returns the pseudo-Vandermonde matrix of degrees `deg` and sample + points `(x1, x2, ..., xn)`. If `l, m, n` are the given degrees in + `x1, x2, x3`, then The pseudo-Vandermonde matrix is defined by + + .. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = T_i(x1)*T_j(x2)*T_k(x3), + + where `0 <= i <= l`, `0 <= j <= m`, and `0 <= k <= n`. The leading + indices of `V` index the points `(x, y, z)` and the last index encodes + the degrees of the Chebyshev polynomials. + + If ``V = chebvandernd([xdeg, ydeg, zdeg], x, y, z)``, then the columns + of `V` correspond to the elements of a 3-D coefficient array `c` of + shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order + + .. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},... + + and ``np.dot(V, c.flat)`` and ``chebvalnd(c, x, y, z)`` will be the + same up to roundoff. This equivalence is useful both for least squares + fitting and for the evaluation of a large number of N-D Chebyshev + series of the same degrees and sample points. + + Parameters + ---------- + deg : list of ints + List of maximum degrees of the form [x1_deg, x2_deg, ...,xn_deg]. + x1, x2, ..., xn : array_like + Arrays of point coordinates, all of the same shape. The dtypes will + be converted to either float64 or complex128 depending on whether + any of the elements are complex. Scalars are converted to 1-D + arrays. + + Returns + ------- + vander : ndarray + The shape of the returned matrix is ``x1.shape + (order,)``, where + :math:`order = (deg[0]+1)*(deg([1]+1)*...*(deg[n-1]+1)`. The dtype + will be the same as the converted `x1`, `x2`, ... `xn`. + + See Also + -------- + chebvander, chebvalnd, chebfitnd + """ + ideg = [int(d) for d in deg] + is_valid = np.array([di == d and di >= 0 for di, d in zip(ideg, deg)]) + if np.any(is_valid != 1): + raise ValueError("degrees must be non-negative integers") + ndim = len(xi) + if len(ideg) != ndim: + msg = 'length of deg must be the same as number of dimensions' + raise ValueError(msg) + + xi = np.array(xi, copy=0) + 0.0 + chebvander = np.polynomial.chebyshev.chebvander + shape0 = xi[0].shape + s0 = (1,) * ndim + vxi = [chebvander(x, d).reshape(shape0 + s0[:i] + (-1,) + s0[i + 1::]) + for i, (d, x) in enumerate(zip(ideg, xi))] + + v = reduce(np.multiply, vxi) + + return v.reshape(v.shape[:-ndim] + (-1,)) + + +def chebfitnd(xi, f, deg, rcond=None, full=False, w=None): + """ + Least squares fit of Chebyshev series to N-dimensional data. + Return the coefficients of a Chebyshev series of degree `deg` that is the + least squares fit to the data values `f` given at points + `x1`, `x2`,..., `xn` + + The fitted polynomial(s) are in the form + .. math:: p(x,y) = c_00 + c_11 * T_1(x)*T_1(y) + ..c_ij * T_i(x)*T_j(y). + + c_nm * T_n(x)*T_m(y), + where `n`, `m` is `deg`. + + Parameters + ---------- + xi: tuple + x1-, x2-,....xn-coordinates of the sample points. + f : array_like + function values at the sample points ``(x1[i], x2[i], ..., xn[i])``. + deg : list + Degrees of the fitting series in the x1, x2, ..., xn directions, + respectively. + rcond : float, optional + Relative condition number of the fit. Singular values smaller than + this relative to the largest singular value will be ignored. The + default value is size(x1)*eps, where eps is the relative precision of + the float type, about 2e-16 in most cases. + full : bool, optional + Switch determining nature of return value. When it is False (the + default) just the coefficients are returned, when True diagnostic + information from the singular value decomposition is also returned. + w : array_like, optional + Weights. If not None, the contribution of each point + ``(x1[i], x2[i], ..., xn[i])`` to the fit is weighted by `w[i]`. + Ideally the weights are chosen so that the errors of the products + ``w[i]*f[i]`` all have the same variance. The default value is None. + + Returns + ------- + coef : ndarray, shape (M1, M2,..., Mn) + Chebyshev coefficients ordered from low to high. + [residuals, rank, singular_values, rcond] : list + These values are only returned if `full` = True + resid -- sum of squared residuals of the least squares fit + rank -- the numerical rank of the scaled Vandermonde matrix + sv -- singular values of the scaled Vandermonde matrix + rcond -- value of `rcond`. + For more details, see `linalg.lstsq`. + Warns + ----- + RankWarning + The rank of the coefficient matrix in the least-squares fit is + deficient. The warning is only raised if `full` = False. The + warnings can be turned off by + >>> import warnings + >>> warnings.simplefilter('ignore', RankWarning) + + See Also + -------- + chebvalnd, chebgridnd + + Notes + ----- + The solution is the coefficients of the Chebyshev series `p` that + minimizes the sum of the weighted squared errors + .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, + where :math:`w_j` are the weights. This problem is solved by setting up + as the (typically) overdetermined matrix equation + .. math:: V(x, y) * c = w * y, + where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the + coefficients to be solved for, `w` are the weights, and `y` are the + observed values. This equation is then solved using the singular value + decomposition of `V`. + If some of the singular values of `V` are so small that they are + neglected, then a `RankWarning` will be issued. This means that the + coefficient values may be poorly determined. Using a lower order fit + will usually get rid of the warning. The `rcond` parameter can also be + set to a value smaller than its default, but the resulting fit may be + spurious and have large contributions from roundoff error. + Fits using Chebyshev series are usually better conditioned than fits + using power series, but much can depend on the distribution of the + sample points and the smoothness of the data. If the quality of the fit + is inadequate splines may be a good alternative. + + References + ---------- + .. [1] Wikipedia, "Curve fitting", + http://en.wikipedia.org/wiki/Curve_fitting + Examples + -------- + """ + # xi = np.array(xi, copy=0) + 0.0 + z = np.array(f) + degrees = np.asarray(deg, dtype=int) + orders = degrees + 1 + order = np.product(orders) + + ndims = np.array([x.ndim for x in xi]) + ndim = len(ndims) + sizes = np.array([x.size for x in xi]) + if np.any(ndims != ndim) or z.ndim != ndim: + raise TypeError("expected %dD array for x1, x2,...,xn and f" % ndim) + if np.any(sizes == 0): + raise TypeError("expected non-empty vector for xi") + + lhs = chebvandernd(degrees, *xi).reshape((-1, order)) + rhs = z.ravel() + if w is not None: + w = np.asarray(w).ravel() + 0.0 + if len(lhs) != len(w): + raise TypeError("expected x and w to have same length") + lhs = lhs * w + rhs = rhs * w + + if rcond is None: + rcond = xi[0].size * np.finfo(x.dtype).eps + + if issubclass(lhs.dtype.type, np.complexfloating): + scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(axis=0)) + else: + scl = np.sqrt(np.square(lhs).sum(axis=0)) + scl[scl == 0] = 1 + + # Solve the least squares problem. + c, resids, rank, s = np.linalg.lstsq(lhs/scl, rhs, rcond) + c = (c/scl).reshape(orders) + + if rank != order and not full: + msg = "The fit may be poorly conditioned" + warnings.warn(msg, pu.RankWarning) + + if full: + return c, [resids, rank, s, rcond] + else: + return c + + +def chebvalnd(c, *xi): + """ + Evaluate a N-D Chebyshev series at points (x1, x2, ..., xn). + + This function returns the values: + + .. math:: p(x1,x2,...,xn) = + \\sum_{i,j,...,k} c_{i,j,...,k} * T_i(x1) * T_j(x2)*...* T_k(xn) + + The parameters `x1`, `x2`, ...., `xn` are converted to arrays only if + they are tuples or a lists, otherwise they are treated as a scalars and + they must have the same shape after conversion. In either case, either + `x1`, `x2`, ..., `xn` or their elements must support multiplication and + addition both with themselves and with the elements of `c`. + + If `c` has fewer than N dimensions, ones are implicitly appended to its + shape to make it N-D. The shape of the result will be c.shape[3:] + + x1.shape. + + Parameters + ---------- + c : array_like + Array of coefficients ordered so that the coefficient of the term of + multi-degree i,j,...,k is contained in ``c[i,j,...,k]``. If `c` has + dimension greater than N the remaining indices enumerate multiple sets + of coefficients. + x1, x2,..., xn : array_like, compatible object + The N dimensional series is evaluated at the points + `(x1, x2,...,xn)`, where `x1`, `x2`,..., `xn` must have the same shape. + If any of `x1`, `x2`, ..., `xn` is a list or tuple, it is first + converted to an ndarray, otherwise it is left unchanged and if it isn't + an ndarray it is treated as a scalar. + + Returns + ------- + values : ndarray, compatible object + The values of the multidimensional polynomial on points formed with + triples of corresponding values from `x`, `y`, and `z`. + + See Also + -------- + chebval, chebgridnd, chebfitnd + """ + try: + xi = np.array(xi, copy=0) + except: + raise ValueError('x, y, z are incompatible') + chebval = np.polynomial.chebyshev.chebval + c = chebval(xi[0], c) + for x in xi[1:]: + c = chebval(x, c, tensor=False) + return c + + +def chebgridnd(c, *xi): + """ + Evaluate a N-D Chebyshev series on the Cartesian product of x1, x2,..., xn. + + This function returns the values: + + .. math:: p(a,b,...) = \\sum_{i,j,...} c_{i,j,...} * T_i(a) * T_j(b) * ... + + where the points `(a, b, ...)` consist of all points formed by taking + `a` from `x1`, `b` from `x2`, and so on. The resulting points form + a grid with `x1` in the first dimension, `x2` in the second, and so on. + + The parameters `x1`, `x2`, ... and `xn` are converted to arrays only if + they are tuples or a lists, otherwise they are treated as a scalars. In + either case, either `x1`, `x2`,... and `xn` or their elements must support + multiplication and addition both with themselves and with the elements + of `c`. + + If `c` has fewer than N dimensions, ones are implicitly appended to + its shape to make it N-D. The shape of the result will be c.shape[3:] + + x1.shape + x2.shape + ... + xn.shape + + Parameters + ---------- + c : array_like + Array of coefficients ordered so that the coefficients for terms of + degree i,j are contained in ``c[i,j]``. If `c` has dimension + greater than two the remaining indices enumerate multiple sets of + coefficients. + x1, x2,..., xn : ndarray, compatible object + 1-D arrays representing the coordinates of a grid. + The N dimensional series is evaluated at the points in the + Cartesian product of `x1`, `x2`, ... and `xn`. If `xi`, is a + list or tuple, it is first converted to an ndarray, otherwise it is + left unchanged and, if it isn't an ndarray, it is treated as a + scalar. + + Returns + ------- + values : ndarray, compatible object + The values of the N dimensional polynomial at points in the Cartesian + product of `x1`, `x2`, ... and `xn`. + + See Also + -------- + chebval, chebvalnd, chebfitnd + """ + chebval = np.polynomial.chebyshev.chebval + for x in xi: + c = chebval(x, c) + return c + + +def test_chebfit1d(): + def f(x): + return np.exp(-x**2) + + # x = chebroot(n=64, kind=1) + # z = f(x) + + c = chebfit(f, n=64)[::-1] + + xi = np.linspace(-1, 1, 151) + zi = np.polynomial.chebyshev.chebval(xi, c) + + # plt.plot(xi, zi,'.', xi, f(xi)) + plt.semilogy(xi, np.abs(zi-f(xi))) + plt.show('hold') + + +def test_chebfit2d(): + n = 3 + xorder, yorder = n-1, n-1 + x = chebroot(n=n, kind=1) + xgrid, ygrid = np.meshgrid(x, x) + + def f(x, y): + return np.exp(-x**2-6*y**2) + zgrid = f(xgrid, ygrid) + + # v2d = np.polynomial.chebyshev.chebvander2d(xgrid, ygrid, + # [xorder,yorder]).reshape((-1, (xorder+1)*(yorder+1))) + # coeff, residuals, rank, s = np.linalg.lstsq(v2d, zgrid.ravel()) + # doeff = coeff.reshape(xorder+1,yorder+1) + _dcoeff2 = chebfitnd((xgrid, ygrid), zgrid, [xorder, yorder]) + dcoeff = chebfit_dct(f, n=(xorder+1, yorder+1)) + + xi = np.linspace(-1, 1, 151) + Xi, Yi = np.meshgrid(xi, xi) + Zi = f(Xi, Yi) + zzi = chebvalnd(dcoeff, Xi, Yi) + _devi = Zi - zzi + # plot residuals + # zz = np.polynomial.chebyshev.chebval2d(xgrid, ygrid, dcoeff) + zz = chebvalnd(dcoeff, xgrid, ygrid) + dev = zgrid - zz + # plt.spy(np.abs(dcoeff)>1e-13) + plt.contourf(xgrid, ygrid, np.abs(dev)) + # plt.contourf(Xi, Yi, np.abs(devi)) + plt.colorbar() + # plt.semilogy(np.abs(devi.ravel())) + plt.show('hold') + + +if __name__ == '__main__': + if False: # True: # + main() + else: + # test_chebfit2d() + test_docstrings() + # test_polydeg()